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Preventive chemotherapy and transmission control ( PCT ) by mass drug administration is the cornerstone of the World Health Organization ( WHO ) ’s policy to control soil-transmitted helminthiases ( STHs ) caused by Ascaris lumbricoides ( roundworm ) , Trichuris trichiura ( whipworm ) and hookworm species ( Necator americanus and Ancylostama duodenale ) which affect over 1 billion people globally . Despite consensus that drug efficacies should be monitored for signs of decline that could jeopardise the effectiveness of PCT , systematic monitoring and evaluation is seldom implemented . Drug trials mostly report aggregate efficacies in groups of participants , but heterogeneities in design complicate classical meta-analyses of these data . Individual participant data ( IPD ) permit more detailed analysis of drug efficacies , offering increased sensitivity to identify atypical responses potentially caused by emerging drug resistance . We performed a systematic literature review to identify studies concluding after 2000 that collected IPD suitable for estimating drug efficacy against STH . We included studies that administered a variety of anthelmintics with follow ups less than 60 days after treatment . We estimated the number of IPD and extracted cohort- and study-level meta-data . We estimate that there exist individual data on approximately 35 , 000 participants from 129 studies conducted in 39 countries , including 34 out of 103 countries where PCT is recommended . We find significant heterogeneity in diagnostic methods , times of outcome assessment , and the reported measure of efficacy . We also quantify cohorts comprising pre-school age children , pregnant women , and co-infected participants , including with HIV . We argue that establishing a global IPD repository would improve the capacity to monitor and evaluate the efficacy of anthelmintic drugs , respond to changes and safeguard the ongoing effectiveness of PCT . Establishing a fair , transparent data governance policy will be key for the engagement of the global STH community .
Soil-transmitted helminthiases ( STHs ) are a group of intestinal nematode infections of humans most commonly , though not exclusively , caused by the roundworm Ascaris lumbricoides , the whipworm Trichuris trichiura and the hookworm species ( Necator americanus or Ancylostoma duodenale ) . These infections place a heavy burden of disease on endemic regions , mostly low and middle income countries ( LMICs ) . An estimated 3 . 2 million years lived with disability ( YLDs ) were caused by STHs in 2015 , with 1 . 8 million attributed to hookworm infection [1] which is associated with intestinal blood loss , iron deficiency anaemia and protein malnutrition [2] . Ascariasis is the most common STH with a global prevalence estimated at over 760 million . Altogether , it is estimated that about 1 . 45 billion people—about 20% of the world’s population—suffer from STHs [1] . Soil-transmitted helminthiases are treated and controlled predominantly using the benzimidazole drugs albendazole or mebendazole . The World Health Organization ( WHO ) recommends giving single ( 400 mg for albendazole , 500 mg for mebendazole ) doses of benzimidazoles to pre-school age children ( pre-SAC ) and school age children ( SAC ) in endemic communities ( the latter defined as having ≥20% overall prevalence ) at least annually as part of the preventive chemotherapy and transmission control ( PCT ) strategy delivered by mass drug administration ( MDA ) [3] . This strategy is aimed at controlling and preventing morbidity caused by STHs and , to meet this objective , the WHO has set 2020 treatment-coverage goals for at-risk populations of 75% in pre-SAC and SAC [4] . These goals were endorsed at the 2012 London Declaration on Neglected Tropical Diseases ( NTDs ) [5] and have since driven a scale-up in the global distribution of benzimidazoles . In 2015 alone , 572 . 7 million pre-SAC and SAC were treated for STH infection , approximately 60% of the globally at-risk population requiring preventive chemotherapy and a doubling in global treatment coverage since 2010 [6] . The scale-up of MDA towards the 2020 goals is projected to increase greatly the cost-effectiveness of STH control [7] . However , the continuing effectiveness of PCT as a control strategy depends on the enduring efficacy of the distributed drugs . There exist arguments both for and against STH resistance to benzimidazoles emerging [8] . Arguably , use of antimicrobial monotherapy has almost systematically led to emergence of resistance for other infectious disease pathogens , and , with few alternative treatments available or novel anthelmintics in the developmental pipeline [9] , the consequences of resistance are potentially severe . In some helminth infections of livestock ( particularly sheep ) , resistance is so widespread and arises so frequently that treatment-based control becomes ineffective [10 , 11] . It is essential that responses to treatment are monitored to identify signs of waning efficacy [8 , 12–15] . The WHO has issued technical guidance and standardised protocols on monitoring the efficacy of anthelmintic drugs using microscopy-based parasitological diagnostics [16] , including a requirement to follow up participants ( SAC ) 2–3 weeks after treatment to improve homogeneity of data collection . The recommended analytical techniques—largely adapted from methods applied in veterinary medicine where monitoring resistance in herds is commonplace [17 , 18]—are based on measuring the average efficacy in groups of children at a population or community level . However , it has been argued that this is an insensitive means of detecting the early warning signs of dwindling drug efficacy and alternative methods based on individual participant data ( IPD ) [19 , 20] have been proposed . Another important limitation of the current system is the added imprecision contributed by the diagnostic methods used ( detecting eggs in feces ) and the need for more sensitive molecular assays [21 , 22] . In practice , few control programmes routinely evaluate drug efficacy because of logistical complications and the additional burden on resources of returning to treated communities before the next round of MDA is due . Knowledge of how the anthelmintics used for MDA are performing is thus largely based on information from clinical trials or other research studies . The case for collating and sharing IPD has been argued recently in the context of monitoring the efficacy of antischistosomal drugs , particularly praziquantel , which is the mainstay of schistosomiasis control and elimination efforts [3 , 4] . A recent landscaping systematic review [23]—and companion to the work presented here—identified more than 20 , 000 IPD collected globally since 2000 . Without these IPD , it is virtually impossible to disentangle the effects of heterogeneous study designs from more meaningful temporal or spatial trends [24] . Hence , only the sharing , standardisation and analysis of IPD will make it possible to evaluate comprehensively global trends in the efficacy of anthelmintics , both against schistosomiasis and STHs . Here we present a systematic review that identifies studies that have collected IPD on the efficacy of benzimidazoles and other drugs used to treat STH . We evaluate heterogeneity in study designs , geographical location and other study features and we estimate the abundance of IPD , thus evaluating the feasibility and value of establishing a global IPD repository for STH .
We carried out a pilot search to estimate the volume of literature and the nature of the studies to be examined in the landscaping exercise . We searched the MEDLINE and Embase databases using a keyword-only search ( no MeSH or EMTREE terms ) and examined the references for potential inclusion . Based on the results , we decided to exclude search terms pertaining to trials , as there were some studies that were not drug trials but that did collect data from which drug efficacy could be calculated ( e . g . in studies testing diagnostic methods ) . We developed detailed search strategies for the databases MEDLINE , Embase , Web of Science , and the Cochrane Library and Cochrane Infectious Diseases Group register , compiling a list of disease and parasite-related terms , and a list of drugs . No limits on publication date or language were imposed within the search strategy . Where possible , we used controlled vocabulary terms to filter out non-human based research . In addition , relevant references were identified from bibliographies of other published secondary analyses , including the companion antischistosomal efficacy data landscaping review [23] . The drugs included in the search were: albendazole , mebendazole , levamisole , ivermectin , tribendimidine , nitazoxanide , pyrantel pamoate , and oxantel pamoate . Full details of the search strategy are given in S1 Text . The search was last conducted on 1st July 2016 . No contact with authors was attempted at this stage , or during data extraction . After automatic and manual de-duplication of the search results , we eliminated those published before 2001 ( as a first step to eliminating studies completed before 1st January 2000 ) . This cut-off was applied because older data are generally more difficult to retrieve [25] , lowering the likely availability for an of IPD database . Conference abstracts before 2014 were excluded due to increasing probability of secondary reporting on studies published elsewhere . These limits are consistent with the search conducted by Julé et al . [23] on studies generating IPD on antischistosomal efficacy . We screened the titles , keywords and abstracts and/or introductions of the remaining records for inclusion . We excluded: non-human or in vitro studies; case reports or series , and retrospective studies; reviews , secondary analyses and other non-primary research; studies in non-endemic settings; studies on costs only , coverage , perception or other aspects of MDA programmes . Results that were not excluded by this screening were retained for full-text reading and are itemized in S1 Dataset . Full text articles were assessed using a checklist ( see S1 Dataset ) . This checklist confirms whether the data from at least some participants in a study would be suitable for estimating drug efficacy . The checklist includes whether the study: involved diagnosis of STH infection in at least a subset of participants; administered at least one of the drugs in the search ( S1 Text ) to some infected participants , and carried out post-treatment parasitological diagnosis in some of those participants 60 days or less after the first treatment . This cut-off is longer than the 14- to 21-day timeframe recommended by the WHO [16] . Notwithstanding the potential diluting effect of reinfection on measurements made after 21 days , we adopted a more liberal cut-off to include data that may still be informative on the efficacy of anthelmintics ( with suitably adjusted statistical analysis ) , particularly against hookworm which can have a pre-patent period of 6 to 9 weeks [26 , 27] . When we identified multiple reports from one study , all relevant publications were noted , and one was chosen based on the amount of information found in each publication . Other reports were used for confirmation of unclear details or further data extraction if details were not found in the primary reference . Two researchers ( JBH , JB ) extracted data on study: i ) setting; ii ) design; iii ) participant ( cohort-level ) characteristics; iv ) type of outcomes measured and reported in the references; v ) the anthelmintic regimens used , and ( where reported ) vi ) the numbers of individual participants treated and followed up . An estimate of the number of IPD per study arm useable for efficacy estimation was made using the reported data ( where sufficient ) i . e . an estimate of the number of participants diagnosed and tested positive , treated , and followed up within 60 days after treatment . To facilitate standardised data extraction , we adapted a variable dictionary developed by Julé et al . [23] , modifying it to fit the requirements of this STH search ( S2 Text ) . Key terms from this dictionary are included in Table 1 . We obtained native language support for publications in Chinese , Spanish and Portuguese; data extraction from these publications was conducted in discussion with the researchers extracting the data from the English-language references to ensure consistency . We estimated the number of IPD in most instances where it was not clear how many participants’ data could be used to estimate efficacy . This often corresponded to cases where a positive diagnosis was not a criterion for treatment and where not enough data were reported to calculate ( rather than estimate ) how many of the followed participants were initially positive . The estimation method was adapted depending on the reported items in the publication . Common methods are detailed in S3 Text; full details of the methods used for each study arm are given in S3 dataset . The two most common examples are:
The literature search yielded 4 , 095 results after de-duplication . A total of 2 , 615 full-text articles published 2001 onwards ( i . e . , eliminating most studies completed before 1st January 2000 ) , or conference abstracts from 2014 onwards , were selected from these . We screened these 2 , 615 by reading the titles , abstracts/ introductions , and keywords , rejecting 2 , 111 clearly-ineligible articles . Articles which were not rejected at this initial screening progressed to a full-text elimination process . The 504 search results evaluated for inclusion at full-text level are detailed in S1 Dataset . Having applied the full-text eligibility criteria , we identified 129 studies comprising 167 cohorts for inclusion in this review . Of the 375 excluded search results , 91 were primarily excluded because they had a follow-up visit over 60 days after treatment ( but fulfilled other criteria ) ; another 91 did not report assessment of infection after treatment ( mainly prevalence surveys , reports of MDA , or on other aspects of treatment such as safety ) . Five search results had either an inaccessible full text or language support was not available; one of these apparently fulfilled the criteria for inclusion according to the abstract only , but was excluded because the full text could not be checked . The PRISMA flow chart summarising the identification , screening , eligibility and inclusion process is shown in Fig 1 . From the 129 included studies , 167 eligible cohorts were partitioned out and most data extracted at the by-cohort level; data on participant numbers and treatment regimens were extracted per arm . The number of IPD ( if given or calculable from the reported items ) that could be used for an estimate of efficacy was recorded . In 78 cohorts ( comprising 129 study arms ) , this number was estimated ( see methods and S3 Text ) , and in 15 cohorts , no calculation or estimate was made due to a lack of reported data ( usually either variable follow-up time , or no indication of prevalence of STH , especially in studies whose focus was on other parasites ) . The IPD estimates given within these results therefore represent an estimate using data from 152 of the 167 cohorts , in 114 of the 129 included studies . Included studies covered 34 of the 103 countries listed as requiring PCT by the WHO in 2015 [28] , with additional studies in 5 countries ( Argentina , Iran , Malaysia , Sri Lanka , Thailand ) not indicated as requiring PCT ( Fig 2 ) . In total , we identified 85 of the 129 included studies as having a drug efficacy assessment as a primary aim of the study . A total of 23 studies included a focus on a parasite other than those causing the three STHs of interest , including 6 with a focus on intestinal schistosomiasis . The treatment regimens applied in the studies covered a variety of drugs and dosages . Table 2 presents the number of studies and the associated IPD estimates for some common drugs and regimens; full details accompany each study arm’s entry in S3 Dataset . The most common drug administered is albendazole , with mebendazole second; of note , there are also some large trials of chemically-unrelated compounds such as tribendimidine ( an aminophenylamidine ) [29] , several smaller studies administering ivermectin ( a macrocyclic lactone ) [30] , and comparatively smaller trials of nitazoxanide ( a thiazolide ) [31] . Diethylcarbamazine , not included in the search strategy , was administered ( and its effect on STH estimated ) in one included study [32] and one excluded study ( due to there being no comparator in the list of drugs of interest ) . Many additional drugs were administered to participants , most notably praziquantel , given to at least 27 cohorts ( either as part of the regimens tested or when schistosomiasis was diagnosed ) . We identified 167 cohorts comprising an estimated 35 , 000 individual participants ( diagnosed and testing positive for STHs of interest before treatment and followed up 60 days or less after treatment; see S3 Text for estimation methods ) contributing data suitable for estimating drug efficacy for treating STHs . The majority of cohorts comprised fewer than 150 participants , with a heavy right skew to the distribution . 36 cohorts were estimated to comprise fewer than 50 participants eligible for efficacy estimation; WHO guidelines state a minimum of 50 participants should be initially positive ( per parasite ) if an assessment of efficacy is to be made . ( Fig 3 ) . Six cohorts comprised over 1 , 000 participants . The largest cohorts were found in studies conducted in India [29 , 30] , comprising 1 , 835 and 1 , 283 participants respectively . Generally , most cohorts ( 92 out of 166 , one further study had no information in the conference abstract in which it was reported ) focused on SAC , but other demographic groups are represented as shown in Table 3 . There were 9 cohorts that included pregnant women only; in the 111 cohorts recruiting any gender and reporting the breakdown , an estimate of 47% of participants were female . The age categories deemed to have been included were judged according to the report of the study . For example , where studies were school-based , the category assigned was SAC , reflecting the intention of the study . In some instances , school age participants were reported to be outside the limits defined by the WHO [6] . In these cases , the minimum and maximum ages reported were extracted but the category was defined as SAC . The majority of studies included a comparative aspect , defined here as either comparing different treatment regimens or comparing treatments in different settings or cohorts ( where the comparative aspect was defined a priori ) . For studies comprising more than one arm , not all studies indicated randomization between arms , and for those that did , not all reported the method used . Similarly , blinding was not mentioned ( or was not or could not have been carried out ) in some multi-arm studies , and for those studies in which it was , the details of who was blinded were not always given ( Fig 4 ) . Hence the risk of selection , performance , and detection biases may be difficult to quantify for many studies . Similarly , the number of patients who were lost to follow-up was not always explicitly stated , making risk of attrition bias occasionally troublesome to ascertain . The majority of cohorts were followed up at 2 , 3 or 4 weeks after treatment ( Fig 5 ) . Two small cohorts had follow-ups under a week only , and 4 cohorts were first followed up at our limit of 60 days . All studies used microscopy ( and occasionally also molecular ) methods to detect and count eggs in the feces of individual participants before and after treatment . The Kato-Katz method ( or its variations , see S1 Dataset ) [33] was the most commonly used diagnostic , albeit a significant number of studies used flotation methods such as the McMaster technique and FLOTAC [34] and a variety of gravity- and solvent-based sedimentation methods [35] . Many studies used multiple methods , sometimes for optimising detection of other parasites or for explicit comparison of diagnostic performance . The techniques used in each study are summarised in Table 4 with further details given in S1 Dataset . Participants in most cohorts were tested for infection using a single replicate from a single stool sample before and after treatment ( using the primary diagnostic technique as assigned following the list in Julé et al . [23] , with McMaster added to this list ) . The most tested number of stool samples tested was 3 ( before and after treatment ) and the most used number of replicates of any one technique per stool sample was 4 , albeit the number of sample and replica tests was unclear in a substantial number of cohorts . This heterogeneity is illustrated in Table 5 . Participants in 12 of the 167 cohorts were also tested for HIV , and in 20 cohorts for malaria . Reports of 37 cohorts specifically mentioned Schistosoma mansoni as a parasite being diagnosed alongside STHs ( S1 Fig ) . For 96 ( 83% ) of the 115 cohorts reporting an efficacy measure , a cure rate ( CR ) , the proportion of participants positive for parasites ( or parasite transmission stages ) before treatment who become parasitologically negative after treatment , was reported . Many also report an egg reduction rate ( ERR ) , the mean number of eggs per gram of feces ( EPG ) after treatment expressed as one minus the proportion of the mean EPG before treatment ( and the method recommended by the WHO [16] ) . Of the 89 cohorts for which an ERR was reported or calculable , 41 ( 46% ) used an arithmetic mean ( AM ) for the EPG ( before and after treatment ) , with the remainder mostly using a geometric mean ( GM ) , albeit with variations on how the latter was calculated . There were 2 cohorts for which a pre- and post-treatment median EPG was reported , and 3 for which either a log-transformed EPG or ERR calculated on log-transformed values was given . Heterogeneity in the reported efficacy measures is illustrated in Fig 6 . Within those calculating an ERR-type measure , there is further variety in methods; two studies reported an ERR calculated on uncured participants only ( while also reporting a CR ) . Occasionally an ERR-type measure is calculated using the mean differences between participants’ egg counts pre- and post- treatment , rather than the difference in the group mean .
We present a landscaping systematic literature review of clinical trials and related studies completed since 2000 , identifying those that have collected IPD on the efficacy of drugs used to treat the STHs ascariasis , trichuriasis and hookworm . We have estimated that there exist data on approximately 35 , 000 infected participants who took part in 129 studies across 39 countries trialling a variety of drugs and regimens , including those recommended by the WHO for the control of STHs by PCT . We have uncovered and documented substantial heterogeneities in the design , implementation and reporting of data from these studies . This would make a comprehensive evaluation of the status of anthelmintic efficacy challenging using standard meta-analytical approaches . Yet such evaluations and status updates are becoming increasingly important in the context of the current unprecedented scale-up of MDA for the treatment and control of STHs as endemic countries try to reach the impending 2020 milestones [6] . To this end , for these data to be most useful they would ideally be collated , standardised in a well-designed shared database and pooled for the purpose of conducting IPD meta-analyses . This study is a companion to a recent similar landscaping review of IPD on antischistosomal drug efficacy [23] . Just as with antischistosomal efficacy trials , heterogeneities in study design ( e . g . the use of control cohorts , the randomization of participants or communities to receive different treatments ) , implementation ( e . g . follow-up times for the assessment of efficacy and the demographic groups included in the study ) and the methods of measuring and reporting efficacy ( e . g . CRs or ERRs using arithmetic or geometric means themselves derived from different calculations ) can all influence the efficacy outcome . This makes it extremely difficult to use all the available aggregated information to undertake comprehensive evaluations on the global status of drug efficacy and on understanding potential geographical variation and temporal trends . Classical meta-analyses of antischistosomal and anti-STH efficacy generally adopt stringent inclusion criteria to ensure data quality and limit the influence of fundamental heterogeneities in study design . Often this means that only data from randomized controlled trials are considered for analysis [36–38] , severely restricting the availability of data and failing to circumvent problems associated with other heterogeneities ( such as variable follow-up times; diagnostic methods; approaches to efficacy evaluation and recruited demographic groups ) . More relaxed eligibility criteria combined with contemporary meta-analytical approaches [39 , 40] can increase the available evidence base and help to mitigate the influence of study heterogeneities . Nonetheless even these approaches remain fundamentally limited by the aggregate nature of data reporting . The benefit of IPD is that both individual- and study-level variables can be incorporated directly into statistical analyses while also accounting for unmeasured or unmeasurable random variation among studies , cohorts and individual participants . This permits not only detailed investigation of the influence of individual participant variables on drug responses ( e . g . age , sex , time of follow up and co-infection status ) but also residual ( unexplained ) variation among individual drug responses [20 , 41 , 42] . Such individual level meta-analyses offer greater sensitivity than their aggregate level counterparts to identify suspicious or atypical drug responses [19] that are potentially indicative of , for example , emerging drug resistance , suboptimal dosing in particular sub-populations , medicine quality issues or drug interactions and warrant further follow-up investigation . In particular , variation in parasite drug susceptibility ( possibly genetically mediated ) cannot be included in such individual patient ( host ) analyses and thus molecular [43] follow-up analyses of parasitological samples from individuals exhibiting atypical responses would be informative . Notwithstanding the potential benefits of IPD to identifying and responding to atypical drug responses , more research must be done to differentiate between truly suspicious responses and naturally expected levels of person-to-person variation . Such variation may be driven by both host [19 , 42] and parasite factors [44] ( including the more refractory nature of some STH species , particularly T . trichiura and to a lesser extent hookworm species [36] ) but may be dominated by error inherent to parasitological ( egg count-based ) diagnostics [45–47] . Underlying drivers may become somewhat unmasked as more accurate molecular-based diagnostics [21 , 22] become more commonplace . It will be important to define distributions of responses based on both parasitological and new molecular diagnostic approaches in populations infected with treatment-naïve parasites [42] to serve as a comparative reference to response distributions in populations exposed to multiple rounds of MDA under the PCT strategy [19] . Equally important , statistical approaches should properly integrate variability in diagnostics ( particularly the high variability associated with parasitological diagnostics ) into estimates of efficacies with robust associated uncertainty to avoid ‘false alarms’ from spuriously atypical point estimates [20 , 42] . Currently there is no incontrovertible evidence of benzimidazole resistance in humans [8 , 13 , 15] . Some studies have observed poor responses in communities under long-term MDA [48 , 49] although no genetic basis for these responses was found [50] . Nevertheless , it would be poor public health practice to ignore the possibility that resistance could emerge , especially since there are few alternatives available now and there remain significant challenges to incentivising commercial investment in anthelmintic drug discovery and development [51 , 52] . This means that the effectiveness and cost-effectiveness of PCT [7] is extremely vulnerable to reductions in drug efficacy and there is consensus on the importance of its robust monitoring and evaluation [8 , 12–15] . The WHO recommends that assessment of drug efficacy should be conducted in schoolchildren 14 to 21 days after treatment [16] . This presents logistical and resource challenges to programmes that must return to communities shortly after treatment rounds have been distributed and before the next round of MDA is due . Yet the risks posed by reduced efficacy to the sustainability and cost-effectiveness of PCT programs mean that efficacy assessment should be undertaken , at least in sentinel sites . The WHO guidelines [16] on standardizing the assessment of anthelmintic efficacy will hopefully increase the homogeneity of future efficacy studies . We suggest that guidance on conducting analyses using IPD could complement the existing recommended ( population-based ) protocols and that these should include detailed instruction on robust quantification and reporting of associated estimates of uncertainty . Maximizing the potential of IPD for global monitoring and evaluation requires broad stakeholder commitment to data sharing , and a framework that protects the rights of patients , data contributors and data users , and ultimately serves the purpose of increasing knowledge and improving health . The WHO has issued three guiding principles for the operation of data sharing during public health emergencies [53] , with adapted operating principles suggested for a global-health orientated approach [54]: an explicit ethical and legal framework governing data collection and use; the publication of results from additional analyses in a reasonable timeframe , and the development and publication of terms of data use by platform operators . Examples of data sharing platforms include Flu Informed Decisions ( FluID , https://extranet . who . int/fluid/ ) and the Worldwide Antimalarial Resistance Network ( WWARN , www . wwarn . org ) . We have focused here on studies generating IPD that could be used to estimate the efficacy of drugs used to treat STHs , following the general approach of the preceding companion paper that identified > 20 , 000 IPD suitable for quantifying antischistosomal efficacy [23] . The overlapping geographical epidemiology [55] and the closely related methods of quantitative diagnosis ( counting eggs in feces , or urine for urogenital schistosomiasis ) and methods for efficacy calculation ( e . g . ERRs [16] ) mean that schistosomiasis and STH would be natural companions in any future shared database . Indeed , many of the studies identified in our search diagnosed and administered treatment for both diseases ( S1 Fig , S1 Dataset ) . Moreover , four of the eligible studies identified by Julé et al . [23] which were also eligible for inclusion ( and included ) in this study were not identified by the STH-specific literature search because there were no relevant search terms in the title , abstract , keywords , or controlled vocabulary . This illustrates the possibility of retrieving STH-relevant data from studies on epidemiologically-related infections . Like Julé et al . [23] , we included all studies with a follow up within 60 days of drug administration . This includes a wide range of follow ups , many outside of the 2- to 3-week optimum window recommended by the WHO [16] ( Fig 5 ) . However , the inclusion of data collected at various follow up times , including less than 1 week when eggs will not yet have been completely cleared from the stool [56 , 57] and efficacy will be underestimated , would provide comprehensive information on the dynamics of the drug response , including initial clearance dynamics and longer-term reinfection or repopulation . The effect of follow up time ( and other covariates ) could be incorporated at the analysis stage offering a means to compare data collected by heterogeneous study designs [42] . Follow up time is a key variable in the interpretation and estimation of drug efficacy and while we wholly concord with WHO’s recommendation to standardize future study designs [16] , the reality of past studies is of heterogeneity ( in this and other important variables ) . Rather than discarding data from such studies , we suggest collation of IPD and suitable adjustment for study design at the analysis stage . The true availability of IPD on drug efficacy against STH is likely to be even greater than the 35 , 000 participants estimated here . We adopted a conservative approach to the estimation of the abundance of IPD in the 78 cohorts where it was not explicit , and did not calculate an estimate from a further 15 , so that we are likely to have underestimated the true value . We did not estimate the abundance of IPD from all studies on pregnant women because it was frequently unclear how many individuals were followed up before the 60-day cut-off; recruitment and treatment tended to be carried out during a wider temporal window ( second trimester onwards ) and the first follow-up stool sample was often taken at delivery or at an ante-natal visit yielding a variety of follow-up times that often exceeded our 60-day cut-off . A meaningful estimation of pregnant participants whose data could contribute to an efficacy calculation would require an indication of the distribution of follow-up time . Our estimated abundance of IPD is also likely to represent an underestimate because our search was limited to four databases in which the majority of the literature is published in English . We did not search the regional CNKI ( China National Knowledge Infrastructure ) or LILACS ( Latin American and Caribbean Health Sciences Literature ) databases [58] , which may contain relevant literature from endemic areas . Our search did yield a number of non-English language results; we noted that two of these studies originally published in Chinese were published 5 years later in English-language journals . Hence , although some studies may ultimately appear in English , there may be a substantial delay between the original publication and the translated version . One study conducted in Uzbekistan [59] , was not translated from Russian and its inclusion in the final analysis would have constituted the sole representative of the central Asian region . A further source of STH treatment efficacy data may be found in studies focused on other diseases; the key example is schistosomiasis , but studies treating other helminthiases ( especially strongyloidiasis , often treated with ivermectin ) or intestinal protozoan infections may also yield suitable data ( many studies identified here treated a range of parasitic diseases ) . Finally , we note that only published literature was searched , most of which was found through the search of online databases; grey literature and other sources of study information could not be retrieved with the methods used .
Published clinical trials on the efficacy of the drugs used to treat STH are highly variable in their design , implementation , and reporting of results . This heterogeneous landscape , which is common with antischistosomal drug trials , presents substantial challenges to conducting meta-analyses aiming to evaluate , in a comprehensive manner , the performance of anthelmintics drugs in the context of burgeoning MDA programmes in an attempt to meet the WHO 2020 goals of STH treatment coverage globally . Yet , together , these trials and other studies provide an abundance of IPD that , if extracted and appropriately analysed , could minimise the confounding associated with aggregate data and greatly improve the capacity of the global health community to understand naturally-occurring individual variation in responses and distinguish these from atypical or truly suspicious drug responses , potentially indicative of emerging drug resistance . We believe that this capability presents a compelling argument to embrace a data sharing philosophy within the STH , schistosomiasis and wider NTD communities , to develop a shared IPD database and to adopt rigorous individual-level meta-analysis approaches undertaken by conglomerates of stakeholders and for the benefit of public health end-users and health policy decision makers . | Soil-transmitted helminthiases ( STHs ) caused by roundworm , whipworm or hookworm affect over one billion of the world’s poorest people mostly living in low and middle income countries , exerting a major health and economic toll . These infections are controlled by regular mass drug distribution to affected populations . But with very few alternative medicines , the effectiveness of treatment programmes is vulnerable to the potential emergence of drug resistance . Despite a recent scale-up of mass drug distribution , systematic monitoring and evaluation of the efficacy of treatment is too rarely undertaken and our knowledge of how the drugs are performing is largely based on information from clinical trials . However , the design and reporting of information from these trials is very variable which makes it difficult to form a comprehensive picture of the status and trends in drug efficacy . Here , we present a systematic review of published studies completed since 2000 , characterise variation in their design , implementation and reporting and estimate the abundance of individual participant data . We argue that the co-ordinated sharing of these individual data would greatly increase the capacity of the global health community to monitor effectively drug efficacy , to respond accordingly to changes , and thereby to safeguard the effectiveness of STH control . | [
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]
| 2017 | Systematic review of studies generating individual participant data on the efficacy of drugs for treating soil-transmitted helminthiases and the case for data-sharing |
By genetic manipulations , we study the roles played by insulin-producing cells ( IPCs ) in the brain and their target , the corpora allata ( CA ) , for reproductive dormancy in female Drosophila melanogaster , which is induced by exposing them to a combination of low temperature ( 11°C ) , short-day photoperiod ( 10L:14D ) and starvation ( water only ) for 7 days immediately after eclosion ( dormancy-inducing conditions ) . Artificial inactivation of IPCs promotes , whereas artificial activation impedes , the induction of reproductive dormancy . A transcriptional reporter assay reveals that the IPC activity is reduced when the female flies are exposed to dormancy-inducing conditions . The photoperiod sensitivity of reproductive dormancy is lost in pigment-dispersing factor ( pdf ) , but not cry , mutants , suggesting that light input to IPCs is mediated by pdf-expressing neurons . Genetic manipulations to upregulate and downregulate insulin signaling in the CA , a pair of endocrine organs that synthesize the juvenile hormone ( JH ) , decrease and increase the incidence of reproductive dormancy , respectively . These results demonstrate that the IPC-CA axis constitutes a key regulatory pathway for reproductive dormancy .
To tolerate unfavorable environmental conditions such as extreme temperatures and desiccation , organisms , particularly insects , have evolved a powerful mechanism , in which the genetic program serves to arrest development and reproduction at a set developmental stage and confers resistance to environmental stresses on the animals [1–6] . In the mosquito Culex pipiens , for example , females respond to the short day-lengths of autumn by a cessation of ovary maturation , sugar ( but not blood ) feeding , suppressing metabolism , and migration to a hibernaculum where they can safely bridge the winter months [7 , 8] . In many insects , light ( photoperiod ) and temperature are key environmental factors for the initiation and termination of reproductive dormancy , which is under neuroendocrine control: the corpora allata , which are glandular organs that synthesize the juvenile hormone , and the brain insulin-producing cells ( IPCs ) are implicated as the two major neuroendocrine centers involved [1–3] . However , the molecular events that switch the neuroendocrine functions between the reproductively active and dormancy states remain poorly understood . A large body of evidence suggests that female adults of laboratory strains of the genetic model organism Drosophila melanogaster undergo reproductive arrest when exposed to shortened day-lengths and low temperatures [9–12] . In this study , we take advantage of the sophisticated genetic tools available in this organism to decipher the mechanism by which environmental factors induce adaptive changes in metabolic state that confer stress resistance to animals in a state of dormancy . We show that activities in the brain IPCs are lower in females under dormancy-inducing conditions than those under control conditions , and that artificial activation of IPCs decreases , whereas artificial inactivation of IPCs increases , the incidence of reproductive dormancy . We further demonstrate that manipulations to enhance and suppress insulin signaling in the corpora allata ( CA ) , a pair of endocrine organs targeted by the IPCs , result in a decrease and increase in the incidence of reproductive dormancy , respectively . We also present evidence that PDF is required for the photoperiod sensitivity of reproductive dormancy . Based on these findings , we propose that IPC activity levels determine the propensity for reproductive dormancy .
Reproductive dormancy in D . melanogaster females is defined by a lack of yolk accumulation in the entire ovary [9] . According to the standardized staging procedure that segments oogenesis into 14 stages [13] , yolk accumulation starts in the stage-8 egg chamber . Thus we judge that the female is in reproductive dormancy if none of the egg chambers in the entire ovary are stage-8 or beyond when examined 7 days after eclosion . Conversely , if a female has at least one egg chamber at or beyond stage-8 in the entire ovary , we judge that the female is not in reproductive dormancy . Under normal rearing conditions ( ad lib feeding at 25°C ) , all ovarioles in the ovary of our control w1118 strain had a fully developed stage-14 egg chamber ( Fig 1A ) . To induce reproductive dormancy , we first tested a protocol similar to one reported previously [9 , 14–16]; in our protocol , emerged flies were exposed to a combination of low temperature ( 11°C ) and short-day photoperiod ( 10 hr photophase and 14 hr scotophase: 10L:14D ) for a week . As controls , sib flies were exposed to long-day ( 16L:8D ) or constant darkness ( DD ) at 11°C . Our results showed that flies of all three groups exhibited only a low level of ovarian arrest ( 10 . 0–16 . 0% ) irrespective of the photoperiod applied ( Fig 1A and 1B ) . Our failure to induce ovarian arrest might have been partly due to the genetic background of the flies used , because w1118 has been reported to be less sensitive to the dormancy-inducing treatment than the Canton-special wild-type strain [14] . In an attempt to increase the proportion of flies in reproductive dormancy , we imposed starvation ( agar and water only ) on emerged flies for a week at 11°C ( Fig 1D ) . This treatment dramatically increased the proportion of flies with ovarian arrest , which was significantly higher in flies kept under the short-day photoperiod ( 10L:14D ) than those under the long-day photoperiod ( 16L:8D ) or constant darkness ( Fig 1A and 1C ) . As shown in Fig 1E , the proportion of egg chambers containing no vitellogenic eggs was gradually decreased with time , reaching a plateau in 10L:14D at 5–7 days after emergence in w1118 females . Therefore , in subsequent experiments to genetically manipulate reproductive dormancy , the flies were exposed to starvation at 11°C for 7 days under the long-day or short-day photoperiodic conditions . Once exposed to diapausing conditions immediately after eclosion , flies manifested another biological feature characteristic of animals in a dormant state in addition to arrested vitellogenesis . Namely , the flies that were consistently kept under the dormancy-inducing conditions survived significantly longer than those fed at 25°C for 2 days after eclosion before being placed under the dormancy-inducing conditions ( Fig 1F ) . It remains to be examined whether starvation in the first two days after eclosion subsequently reduces the meal size of the flies , when they are maintained under ad-lib feeding conditions on the third adult day and thereafter [12] . The IPCs in the brain are a major regulator of reproductive dormancy in Drosophila and other insects [1–3 , 14–16] . To test whether the IPC activity level is correlated with the incidence of reproductive dormancy , we employed a transcriptional reporter of intracellular Ca2+ ( TRIC ) assay [17] , which can monitor the cumulative activity levels of neurons . In this assay , GFP and RFP reporters ( as UAS-transgenes ) were driven by Gr28b . b-GAL4 ( Fig 2A , 2B–2B” , 2C and 2C’ ) , which yielded higher expression levels of both reporters in IPCs than Dilp2-GAL4 or Dilp3-GAL4 did . We used , as a reliable driver for TRIC assays , Gr28b . b-GAL4 instead of Dilp-GAL4s , because the latter reduced the activity of the flies to drive reporter expression , particularly under dormancy-inducing conditions , making it difficult to quantify TRIC signals . TRIC analysis revealed that the activity level of IPCs was significantly lower under short-day than long-day conditions in flies starved for 7 days at 11°C ( Fig 2E–2H ) . The IPCs in flies fed a normal diet showed high activity levels irrespective of the photoperiod ( Fig 2C” , 2D and 2H ) . It therefore appears that the IPC activity is inversely correlated with the incidence of reproductive dormancy . Because Gr28b . b-GAL4 also drove expression in some non-IPC neurons surrounding IPCs ( Fig 2B–2B” ) , we cannot exclude the possibility that these non-IPC neurons similarly respond in the manner of IPCs to the environmental stimuli that induce reproductive dormancy in flies . To evaluate the roles of brain IPCs in the control of reproductive dormancy , we overexpressed the cold-sensitive TRPM8 channel to excite these neurons [18] , and the constitutively active Kir2 . 1 channel to silence them [18] . The induced excitation and silencing of IPCs led to a marked reduction and increase in the proportion of flies with ovarian arrest , respectively , irrespective of photoperiod ( Fig 2I and 2J ) . We conclude that the activity level of brain IPCs has a strong impact on reproductive dormancy: when the IPC activity is low , the fly is more likely to undergo reproductive dormancy , and when the IPC activity is high , the fly is more likely not to undergo reproductive dormancy . This result is consistent with a recent report that IPC activation via a NaChBac channel markedly reduces the incidence of reproductive dormancy [16] . The loss of sensitivity to the photoperiodic conditions upon the activation and inactivation of IPCs would seem to indicate that the activities of IPCs encode photoperiodic information for controlling reproductive dormancy . Two groups of circadian clock pacemaker cells , morning cells ( M-cells , e . g . , s-LNv ) and evening cells ( E-cells , e . g . , LNd ) , have been suggested to be photoperiod sensors in the fly brain [19]; M-cells control locomotor activity in the morning while E-cells do so in the evening . PDF is a molecular marker that distinguishes these two pacemaker centers , i . e . , a defined subset of M-cells is PDF-positive whereas the entire population of E-cells is PDF-negative [19 , 20] . We therefore characterized reproductive dormancy in PDF mutants . Remarkably , PDF mutants lost the photoperiod sensitivity , exhibiting a very low dormancy rate in 10L:14D , which was indistinguishable from that in 16L:8D ( Fig 3A ) . We also examined ovaries in females that are mutant for cry , which encodes a photoreceptor protein expressed in both M-cells and E-cells to ensure the light entrainment of circadian rhythms [21 , 22] , and found that cry appeared to be dispensable for the photoperiod sensitivity of reproductive dormancy ( Fig 3B ) . A large array of literature on reproductive diapause in various insect species points to the critical role of JH synthesized by CA: a low titre of JH causes the individual animals to enter diapause , whereas a high JH titre causes them to terminate diapause [23 , 24] . In keeping with this notion , we found that CA-restricted overexpression of JH acid O-methyltransferase ( JHAMT ) , a rate-limiting enzyme of JH synthesis [25] , strikingly reduced the proportion of flies with ovarian arrest even under the low temperature/short-day/starved conditions ( Fig 4A ) . Conversely , JHAMT knockdown in the CA markedly increased the proportion of flies with ovarian arrest not only under the short-day but also under the long-day photoperiodic conditions ( Fig 4B ) . The substrates of JHAMT are synthesized by the mevalonate metabolic pathway , in which 3-hydroxy-3-methylglutaryl CoA reductase ( Hmgcr ) plays a key role [26] . As expected , Hmgcr mutant females exhibited markedly elevated dormancy rates not only in 10L:14D but also 16L:8D ( Fig 4C ) . We conclude that JH synthesis in the CA is causally related to the state of reproductive dormancy . We then asked whether insulin signaling in the CA contributes to the control of diapause-like state by elevating or suppressing the activity of insulin receptor ( InR ) as well as two downstream elements of insulin signaling , PI3K and mTOR [27] . Overexpression of constitutively active InR or PI3K significantly reduced the proportion of flies with ovarian arrest , with the reduction being especially pronounced under the short-day photoperiod ( Fig 4D and 4F ) ; conversely , InR RNAi or dominant negative PI3K or mTOR increased the proportion of flies with ovarian arrest under both the long-day and short-day photoperiod conditions ( Fig 4E , 4G , and 4H ) . One of the well-known output pathways of PI3K-mTOR signaling is mediated by the eukaryotic translation initiation factor 4E ( elF4E ) -binding protein ( 4E-BP ) , which represses translation by binding to elF4E , a positive regulator of cap-dependent translation [28] . We found that a hyperactive form of 4E-BP derived from the mutant transgene Thor . LL dramatically enhanced reproductive dormancy: nearly 100% of such females displayed ovarian arrest irrespective of whether they were kept under the 10L:14D or 16L:8D photoperiods ( Fig 4I ) . We propose that insulin signaling , initiated in the brain IPCs and relayed through the PI3K , mTOR and 4E-BP cascade in CA , ultimately leads to the promotion of yolk accumulation in the egg via the stimulation of JH synthesis .
The present results indicate that brain IPCs play a pivotal role in determining whether female flies continue to be reproductively active or cease their production of mature eggs , depending on the environmental conditions to which they are exposed . The arrest in egg maturation observed in this study was more pronounced under short-day compared to long-day conditions . Although it remains unknown exactly how animals measure the photoperiod , there exist compelling models for this process [29] . One such model , the internal coincidence model [29] , postulates two oscillators with distinct activity phases , one of which starts immediately at light-ON ( dawn ) , whereas the other starts with a delay ( at dusk , for example ) , and the difference between two oscillation phases is proportional to the difference between the scotophase and photophase , allowing the animal to measure the photoperiod . A current prevalent theory in regard to the fly circadian clock similarly postulates two pacemaker types , i . e . , M-cells and E-cells , which oscillate with a 10 hr phase difference from each other [22] . Interestingly , we found that loss of pdf normally expressed in M-cells and not E-cells abrogated the photoperiod sensitivity of reproductive dormancy ( Fig 3A ) . Intriguingly , it was reported that E-cells in pdf receptor mutants oscillate in the same phase as M-cells , indicating that PDF derived from M-cells imposes a 10 hr delay in the oscillatory phase on E-cells [22] . Thus , the loss of the photoperiod-dependence of reproductive dormancy we observed in this study is conceivably a result of synchronization of E-cells with M-cells in the oscillation phase , as the internal coincident model predicts . It was also reported that the manipulations of cry functions do not affect the 10 hr oscillation phase difference [22] . This observation is consistent with our result that cry mutants retain the photoperiod-dependence in ovarian arrest ( Fig 3B ) . A recent study identified Rhodopsin7 ( Rh7 ) as a photoreceptor protein involved in the entrainment of circadian rhythm [30] . Remarkably , Rh7 was expressed in an M-cell population that is positive for PDF but not expressed in E-cells [30] . PDF-positive M-cells extend neurites to the dorsal protocerebrum [31] , where IPCs , which are PDF-negative , also have dendritic arbors . M-cells carry large dense-core vesicles ( DCVs ) as well as small synaptic vesicles , both of which are immunoreactive to the anti-PDF antibody [31] . Because DCVs can be secreted from non-synaptic membranes [31] , PDF thus liberated from non-synaptic membrane might act on IPCs to modulate their activities . These possible modes of action of PDF on IPCs remain to be tested by neural activity recordings from IPCs . In this work , we imposed nutritional deficiency on flies to promote reproductive dormancy . Hormonal as well as neural inputs from the periphery , particularly those from the fat body , impinge onto IPCs to inform flies about the shortage in food resources [32–34] . In addition , bath-applied glucose has been shown to depolarize IPCs in an ex vivo brain preparation [35] , even though the primary circulating carbohydrate in insects is trehalose . Thus , the information crucial for the control of reproductive dormancy—photoperiod and nutritional state—converges onto IPCs . Our study provided evidence that JH synthesized in the CA promotes egg maturation and impedes reproductive dormancy: reduced functions of two key enzymes for JH synthesis , Hmgcr and JHAMT , led to a dramatic increase in the incidence of ovarian arrest ( Fig 4B and 4C ) . Manipulations to increase insulin signaling in the CA were found to reduce the incidence of reproductive dormancy , whereas those to decrease insulin signaling enhanced reproductive dormancy ( Fig 4D–4H ) . Our results also indicated that 4E-BP operates as an effector of insulin signaling in fly reproductive dormancy: almost all females with hyperactive 4E-BP underwent reproductive dormancy ( Fig 4I ) . This finding is consistent with the observation in mosquitoes that 4E-BP transcription is stimulated in the CA upon starvation , which concordantly reduces JH synthesis [36] . Transcriptional upregulation of 4E-BP has also been reported in overwintering Drosophila virilis females [37] . These findings suggest a conserved role of the IPC-CA axis for diapause in these insect species beyond its role for reproductive dormancy in D . melanogaster ( Fig 5 ) . IPCs and CA may also play important roles in development and behavior that are regulated by photoperiod . For example , seasonal migration in the monarch butterfly has been suggested to be under the control of the IPCs and CA [38] . Wing morphs in some insect species are specified by external factors , including photoperiod and ambient temperature , as well as internal factors , among which insulin signaling is pivotal [39] . Thus the functional analysis of the IPC-CA axis in the genetically tractable organism Drosophila melanogaster opens an avenue for exploring the molecular mechanisms underlying the environmental adaptation of creatures and the future development of novel technologies for its control .
Flies were raised until eclosion at 25°C under constant darkness ( DD ) with a diet consisting of the following: 400g cornmeal , 800g dry yeast , 1 , 000g glucose and 60g agar dissolved and mixed in 10 l of water supplemented with 4 ml propionic acid and 4 ml of 10% para-hydroxybenzonate . Approximately 500 individuals were raised in a plastic vial ( 3 mm in diameter and 120 mm in height ) , except when fly cultures yielded a smaller number of adults due to reduced viability of the flies . Newly emerged virgin females were collected within 6 hr of eclosion , then reared on a nutrient-deficient medium made of 100g agar and 10 l water at 11 ± 0 . 5°C under conditions of either short day length ( SD: 10L:14D ) , long day length ( LD:16L:8D ) , or DD for 7 days ( Fig 1D ) . Then the ovaries were dissected in PBS ( phosphate-buffered saline ) and scored for the maximum stage of egg chambers . Staging of oogenesis was done according to King [13] . For every vial , an average of 25–30 flies were subjected to the examination of ovaries . For each condition , a total of 100 flies were used to estimate the diapause rate , unless specifically indicated otherwise . We defined flies in diapause as those lacking egg chambers at stage 8 or later . The diapause rate was calculated as the percentage of flies in diapause over the percentage of all flies examined for each condition . w1118 served as a non-transgenic control line in this study . The following fly lines were gifts from the researchers indicated in parentheses: Dilp2-lacZ ( E . Hafen ) , Dilp3-lacZ ( E . Hafen ) , Dilp2-GAL4 ( N . Yamagata ) and Dilp3-GAL4 ( N . Yamagata ) . Other fly lines were obtained from the Bloomington Drosophila Stock Center , Drosophila Genome Research Center ( Kyoto ) and Vienna Drosophila RNAi Stock Center . UAS-PI3KCAAX is described in Dimitroff et al . [40] To determine the diapause rate of flies exposed to different environmental conditions as described above , the ovaries were dissected in 1× phosphate-buffered saline ( PBS ) under a Leica MZ8 binocular microscope . The dissected ovaries were observed under an MZ8 microscope without fixation and photographed by a Zeiss Axioplan 2 fluorescence microscope or a Leica M205FA fluorescence stereomicroscope . To observe the adult brains , the female brains at 4–7 days after eclosion were dissected in PBS and fixed in 4% paraformaldehyde for 1 hr on ice , and immunostaining was carried out using the following antibodies and dilutions: mouse anti-β-galactosidase ( Z378A; Promega ) at 1:1000 , rabbit anti-GFP at 1:500 ( 598; MBL ) , rat monoclonal anti-DN-cadherin at 1:20 ( DN-Ex#8; Developmental Studies Hybridoma Bank [DSHB] , University of Iowa , Iowa City , IA ) , and Alexa Fluor488 anti-rabbit IgG , Alexa Fluor546 anti-mouse IgG , and Alexa Fluor647 anti-rat IgG ( all at 1:200 and all from Invitrogen ) . Images were obtained with a Zeiss LSM 510 META confocal microscope using Zeiss LSM Image Browser software . Virgin females carrying Gr28b . b-Gal4 were crossed with males carrying transgenes for the TRIC system ( Bloomington #62827 ) , and newly emerged virgin G2-females were reared on a nutrient-deficient medium under either 10L:14D or 16L:8D conditions for 7 days . The females were anesthetized with CO2 gas , and the brains were dissected in 1× PBS , fixed in 4% paraformaldehyde for 1 hr on ice , and then stained with the following primary antibodies and dilutions: rabbit anti-GFP at 1:500 ( 598 , MBL ) , mouse anti-RFP at 1:400 ( M165-3 , MBL ) , and rat anti-DN-cadherin at 1:20 ( DN-Ex#8; DSHB ) . The brains were then washed three times with 1× PBS containing 0 . 2% Triton X-100 ( PBST ) and stained with the following secondary antibodies: Alexa Fluor488 anti-rabbit IgG , Alexa Fluor546 anti-mouse IgG , and Alexa Fluor647 anti-rat IgG ( all at 1:200 and all from Invitrogen ) . Finally , the brains were washed three times in PBST and mounted on slides using VECTASHIELD ( Vector Laboratories Inc . ) . In the fluorescence quantification for TRIC assays , the GFP signal intensities of IPCs in optical sections at 1 μm were obtained with a Zeiss LSM 510 META confocal microscope . With the software ImageJ , a maximum projection was made for the entire region that contained IPCs , resulting in a single Z-stack image . GFP intensities of three areas , “area a” containing IPCs ( the test area ) and “area b” and “area c” not containing IPCs ( the control areas ) , were quantified using ImageJ software ( Fig 2 ) . The GFP intensity of Dilp-PIs in flies exposed to different experimental conditions was determined by subtracting the average GFP intensity in areas b and c from the GFP intensity in area a . Statistical analysis was performed using the Excel Statcel 3 software package ( OMS Publishing , Ltd . , Tokyo ) and the js-STAR website ( http://www . kisnet . or . jp/nappa/software/star/index . htm ) . Statistical significances of diapause rate and TRIC-signal intensity were evaluated either by the two-tailed Fisher’s exact test or the one-way ANOVA post hoc Tukey’s multiple comparisons test . Statistical significance of the survival curves was evaluated by the log-rank test . Statistical parameters are reported in the Figure Legends . | In the temperate zone , winter is a challenging season for many animals because of the freezing temperature and shortage of foods , which are sustained over a period of several months . Wild mammals such as bears and squirrels have acquired the ability to hibernate , by reducing energy consumption to the minimum necessary for survival . To evade stressful winters , insects have evolved an even more elaborate system , in which shortening of the day length ( the short-day photoperiod ) triggers a genetic mechanism for switching the metabolic state from active to inactive , so as to develop resistances to environmental stresses , e . g . , extreme temperature and starvation , while reproduction is suspended ( reproductive dormancy ) . Although reproductive dormancy has long been a topic of research in entomology , a unified view on the mechanism underlying the entrance , maintenance and exit from reproductive dormancy has not emerged , partly because different insect species adopted mechanistically diversified means to ensure phenotypically similar states . To explore the molecular and cellular bases for reproductive dormancy , we employed the genetic model species Drosophila melanogaster , which , however , has been thought not to engage in reproductive dormancy in a predictable manner . In this study , we show that , upon the exposure of D . melanogaster female adults to a combination of short-day , low temperature and starvation , they consistently undergo reproductive dormancy , in which eggs in the ovary are arrested in development without accumulation of yolk . Energy resources are thus directed to survival rather than to reproduction in the female fly under environmental stress . We further show that insulin-producing neurons in the brain make the decision regarding whether or not to undergo reproductive dormancy by integrating information on the photoperiod , temperature and nutritional conditions . This decision is conveyed to the pair of endocrine organs known as the corpora allata , which increase their production of a hormone called the juvenile hormone to continue reproduction or decrease it to undergo reproductive dormancy . This hormone thus acts as a switch between the metabolically active and inactive state . Our results provide a solid foundation for elucidating the molecular and cellular mechanisms for environmental adaptation in animals . | [
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| 2018 | Genetic dissection of stress-induced reproductive arrest in Drosophila melanogaster females |
Heterogeneities in contact networks have a major effect in determining whether a pathogen can become epidemic or persist at endemic levels . Epidemic models that determine which interventions can successfully prevent an outbreak need to account for social structure and mixing patterns . Contact patterns vary across age and locations ( e . g . home , work , and school ) , and including them as predictors in transmission dynamic models of pathogens that spread socially will improve the models’ realism . Data from population-based contact diaries in eight European countries from the POLYMOD study were projected to 144 other countries using a Bayesian hierarchical model that estimated the proclivity of age-and-location-specific contact patterns for the countries , using Markov chain Monte Carlo simulation . Household level data from the Demographic and Health Surveys for nine lower-income countries and socio-demographic factors from several on-line databases for 152 countries were used to quantify similarity of countries to estimate contact patterns in the home , work , school and other locations for countries for which no contact data are available , accounting for demographic structure , household structure where known , and a variety of metrics including workforce participation and school enrolment . Contacts are highly assortative with age across all countries considered , but pronounced regional differences in the age-specific contacts at home were noticeable , with more inter-generational contacts in Asian countries than in other settings . Moreover , there were variations in contact patterns by location , with work-place contacts being least assortative . These variations led to differences in the effect of social distancing measures in an age structured epidemic model . Contacts have an important role in transmission dynamic models that use contact rates to characterize the spread of contact-transmissible diseases . This study provides estimates of mixing patterns for societies for which contact data such as POLYMOD are not yet available .
Events over the last decade have highlighted the threat posed internationally by contact-transmissible infectious diseases such as hand , foot and mouth disease [1 , 2] , MERS-CoV [3] , Ebola [4] , influenza [5] , and Tuberculosis [6] , putting pressure on governments and public health institutes [6] to ensure countries are pandemic-prepared . Research in social networks has shown that transmissibility , and hence the effectiveness of many interventions , is determined by the intensity of human-to-human interactions [12] . Heterogeneities in contact networks—in the sense of clustering of contacts within triadic structures and the existence of individuals or groups with many more contacts than average—has been shown in modelling studies ( i ) to have an effect on determining whether a pathogen can become epidemic [7–9] or can persist at endemic levels [10] , ( ii ) to exert selective pressure for low virulence [11] , and ( iii ) to determine which interventions can possibly mitigate an outbreak [8 , 12–14] or even eradicate a disease from the population [13] . While epidemic models that do not account for contact structure suffice for some research questions , such as prediction [15] or determining minimum vaccine coverage if there are no pockets of high transmission intensity or low coverage [16–19] , assessing the effectiveness of interventions that specifically target social networks , such as school closure [20–22] , requires models that explicitly account for such social structure . Determining contact patterns within and between different segments of the population is therefore vital to evidence-based prediction and planning with sufficient realism to inform good policy making . To this end , a seminal study by Mossong et al . in 2008 [23] measured the social structure of ~100 000 contacts across eight European countries using paper diaries as part of the POLYMOD project . The study illuminated the strong assortativity of social contacts with age , with children in particular driving the early period of an epidemic , a finding that has been reproduced in other studies [24] . Similar studies to measure the assortativity of contacts have been conducted in a few other locations: Viet Nam [25] , Taiwan [26] , southern China [27] , Peru [28] South Africa [29] , Kenya [30] , Russia [31] and Thailand [32] . However , the age and social structure of countries with different levels of socioeconomic development , family structure and at different stages of demographic transition differ substantially and contact patterns concomitantly vary across countries . The findings from the POLYMOD and limited number of other countries cannot therefore be directly applied to models of socially-transmitted infections , such as influenza , in other settings [32] . The lack of reliable contact studies in most of the world almost a decade after the original POLYMOD study highlights the logistical challenges of conducting such studies , particularly in low income countries which account for most of the infectious disease burden globally [33] . Although it is preferable for models in such a country to be based on empirical contact data from that country directly , in the absence of such data , modelers have to decide how to modify estimates from another country to fill the gap . One approach to tailor the POLYMOD estimates to another setting would be to adjust them based on the differences between the age-profiles of that country and the eight countries from the EU that contributed to POLYMOD , inflating the projected number of contacts with younger individuals , for instance , for a country with a young age-profile , and concurrently reducing the projected number of contacts with the elderly . However , because the POLYMOD survey collected information on where each contact occurred , a more refined projection can be obtained by incorporating data on household structures , labor-force participation rates , and school enrolment , and separately projecting contacts in different locations . This has the added benefit of providing a means to model location-specific interventions . The objective of this paper is to provide projected age-specific contact rates for countries in different stages of development and with different demographic structures to those studied in POLYMOD , which provide validated approximations to social contact patterns when directly measured data are not available . To this end , we combined data from POLYMOD , from the large scale Demographic Household Surveys ( DHS ) , from the UN population division and from various international indicators , to project household structures and school and labor force participation rates for most countries of the world , and thereby to provide baseline projections of age-specific contact patterns in settings where contact surveys have yet to be conducted , until empirical estimates become available .
The POLYMOD data collection was approved by national institutional review boards , as previously described [23] . As no identifying information was provided , institutional review was not required for reanalysis . The overall approach is to use data fusion to combine various data sources to project age- and location-specific contact rates for a spectrum of countries in different stages of development and with different demographic structures . Fig 1 gives an overview of the data sources discussed in the following section , and the major steps of the modelling framework described in the sections that follow that . The countries ( n = 152 , 95 . 9% of the world’s population ) are categorized into ( i ) POLYMOD , ( ii ) Demographic and Health Survey ( DHS ) , and ( iii ) Rest of the World ( ROW ) countries , which are illustrated on the world map . Data availability , as used in our study , varies across the country categories: with POLYMOD having the most data available and the ROW countries having the least . The quality of the data from the same source is consistent across countries . The modelling framework starts with a Bayesian hierarchical model ( A ) built for the POLYMOD contact data . This model estimates age- and location-specific contact rates for the POLYMOD countries collectively and independently . The ensuing subsections detail the methodology adopted to construct age-structured populations at home , work , and school ( B ) is combined with the population age structure and the POLYMOD aggregated estimates to get the global projections ( C ) . Internal validation using leave-one-out validation was conducted to verify that the household age matrices describing household structure could be reverse-engineered for countries ( POLYMOD and DHS ) for which empirical household age matrices were available . Not featured in this flowchart are the external validation and an example application ( i . e . age-specific Susceptible-Infected-Removed modelling ) that demonstrates the potential utility of these projections . The POLYMOD study design has been fully described elsewhere [23] . In brief , cross-sectional surveys were conducted across 8 countries then in the European Union ( Belgium ( BE ) , Germany ( DE ) , Finland ( FI ) , the United Kingdom ( UK ) , Italy ( IT ) , Luxembourg ( LU ) , the Netherlands ( NL ) , and Poland ( PL ) ) by commercial survey companies and public health institutes , between May 2005 and September 2006 . 7 , 290 participants documented their contacts as they occurred on a randomly assigned 24 hour period in a paper diary , including age and gender of contacted person , and the type ( physical or nonphysical ) , duration , location and frequency of the contact . Participant demographics were also recorded . Diaries of young children were completed by a parent or guardian . The study collected information of 97 , 904 contacts . In the analysis , 52 participants ( 0 . 7% ) and 148 contacts ( 0 . 2% ) were excluded for data quality reasons as detailed in the S1 Text . Because participants and contacts were mostly from the general community , the resulting contact patterns are suitable for models of diseases that are socially spread , such as influenza , but not directly usable for diseases with other routes of transmission , such as nosocomial infections . To extend the contact rate model to other countries of the world , we synthesized the POLYMOD data with four other data sources that either inform contact patterns in households , workplaces and schools , or provide a measure of the similarity of countries . ( i ) The Monitoring and Evaluation to Assess and Use Results Demographic and Health Surveys ( DHS ) provide data for lower-income countries . We extracted household structure data for nine countries—Bangladesh , Bolivia , Ghana , Indonesia , India , the Philippines , Sierra Leone , Uganda and Zambia—for which there were no usage or copyright restrictions . ( ii ) The population age compositions for all countries of the world were obtained from the United Nations Statistics Division . ( iii ) The labor force participation rate by sex and 5-year age groups for most countries of the world were obtained from the International Labor Organization on-line database . ( iv ) The pupil:teacher ratio in education and the enrolment rates of students at various level of education were obtained from United Nations Educational , Scientific and Cultural Organization Institute for Statistics ( UIS ) .
Fig 2 ( panel a ) shows the number of contacts at home made by individuals in the POLYMOD study stratified by household sizes . A near linear relationship is observed between the household size and the number of contacts , suggesting a frequency dependent relationship , rather than a density dependent one [41] . The age-specific contact patterns at home , including with visitors to the household , for the POLYMOD countries collectively are presented in Fig 2 , stratified by household size . A central diagonal is present for all household sizes , indicating assortativity of mixing with age , with secondary diagonals in households with at least 2 members . These secondary diagonals become more prominent with increasing household sizes . To illustrate some of the results , Germany , Bolivia , and South Africa are arbitrarily selected as country representatives of the POLYMOD , DHS and ROW , respectively . Results for the other 149 countries in the study can be found in the S1 Text . The population pyramids of Bolivia ( a DHS country , panel b ) and South Africa ( a ROW country , panels c ) in Fig 3 have the triangular shape common to countries still undergoing the demographic transition , while that of Germany ( in POLYMOD , panel a ) , with its narrow base , indicative of sub-replacement fertility , is similar to other aging populations . For all three countries , both the household age matrices ( panels d–f ) and age-specific contact patterns at home ( panels g–i ) have similar features: ( i ) a prominent central diagonal corresponding to interactions with siblings ( for younger individuals ) and partners ( for adults ) and ( ii ) two parallel secondary ridges about one generation distant from the main diagonal , which start around age 25 and reflect parent-child contacts . Together , these suggest that the contacts are dominated by two-generation familial structures for these three countries , although other countries such as India display evidence of three-generational structures . Results for India and 151 other countries are presented in the S1 Text . Average household sizes vary across countries , with some Asian and African countries having larger households than those in the POLYMOD study . We grouped all countries into 7 regions according to the World Bank’s definition ( East Asia & the Pacific , Europe & Central Asia , Latin America & the Caribbean , the Middle East & North Africa , North America , South Asia and Sub-Saharan Africa ) [42] . To obtain projected regional norms for contact patterns at home we computed the mean contact rates that were projected or inferred earlier within the region weighted by each country’s population size . Fig 4 shows the age-specific contact patterns at home of individuals aged 5–10 , 25–30 and 55–60 years by region . Proportionally more contacts are made by primary school aged individuals at home in Latin America & the Caribbean , South Asia and Sub-Saharan Africa are higher than of the other regions , a reflection of the larger households in these regions . The same age group also makes more contacts with those in the older age groups than their peers in other regions . For individuals in the age group 55–60 , the mean number of contacts made with younger individuals in South Asia is significantly higher than those in other regions . Contact matrices for India and Bangladesh have additional tertiary bands ( S1 Text ) , suggesting a greater preponderance of three-generation households . There were several countries , such as Sierra Leone and Burkina Faso , where we infer a deviation from that pattern as a result of skewed population structure ( S1 Text ) . Modelled projections of contact patterns in different locations—home , work , school , other and all—for Bolivia , Germany and South Africa , are plotted in Fig 5 . In Germany , for which it was explicitly measured , the age-specific contact patterns ( panels d–f ) at the workplace show wide clusters of contacts among working ages ( 20–60 ) , indicating relatively homogenous mixing in this setting . Adapting this finding to Bolivia and South Africa , accounting for the age structure of their labor forces , led to similar homogeneity in workforce contacts there . Unlike in households , the presence of more diverse age structures in workplaces could provide a ready channel for transmission between distinct age groups , separated in the main from each other within the home setting . Intense mixing , indicated by the pronounced central diagonal , is present in the age-specific school contact pattern ( panels g–i ) , suggesting the importance of this milieu for transmission potential within this age group . Within schools , the assortativity of mixing patterns is more pronounced in younger individuals ( below the age of 25 ) , while those of working-age ( teachers and support staff aged 30–60 ) have more moderate contact rates between themselves and younger individuals , the latter characterizing student-teacher interactions . This pattern was present across other countries ( Supporting Information ) . Similar to the home and school contact patterns , a strong central diagonal band and at times weak secondary diagonals can be observed in the projected contacts made at other ( non-home , non-work , non-school ) locations . This assortativity contrasts with the work environment . However , apart from the leading diagonal , the contact patterns within this other grouping vary across countries in a non-systematic way . Aggregating contact rates from the four locations ( home , work , school and others ) indicates that the strong central diagonal due to school and home contacts dominates overall contact rates . The distribution of age of infected cases under two pandemic scenarios is presented in Fig 6 for Germany , Bolivia and South Africa . Germany’s relatively older population leads to more infection among adults than the younger countries of Bolivia and South Africa , and also to a lower effectiveness of school closure ( which is modelled to reduce infection rates to ~80% for R0 = 1 . 2 and less than 20% for R0 = 1 . 5 ) . However , school closure and social distancing of younger individuals is expected to be effective in preventing an outbreak entirely in younger populations like Bolivia and South Africa , Workplace distancing has a greater impact on older populations like Germany ( preventing an outbreak entirely for R0 = 1 . 2 and reducing its size by ~50–70% for R0 = 1 . 5 ) than younger populations like Bolivia and South Africa ( ~30–70% for R0 = 1 . 2 and ~10–50% for R0 = 1 . 5 ) .
A decade after POLYMOD , age-structured contact matrices based on random population-based samples have been published for only a limited spectrum of countries: eight European Union countries [23] , a handful in Asia [25–27 , 31 , 32] , one in Latin America [28] and two in Sub-Saharan Africa [29 , 30] . The risk of emerging diseases spreading from other animals to humans is not restricted to these areas [43] ( as the recent MERS and Ebola events illustrate [3 , 4] ) , and there is a pressing need for contact matrices representing more diverse countries around the world for models of socially-spread diseases to be built . This study presents data-driven contact matrices for 152 countries of the world for the first time . Individuals’ interactions are non-random and , because they are contingent on the physical presence of the individual and contact , vary by location . As in the original analysis of these data [23] , we found that household , workplace and school structures across the world are consistent with age-specific contacts made by individuals , in that they are highly assortative , with much more frequent interactions with others of a similar age group . However , these precise age-dependent patterns differed by location and across the countries we studied . High assortativity of contacts is observed in schools but , at least in the POLYMOD countries , is less apparent in working-age individuals in the workplace . The former was expected , but the patterns of contacts in the workplace , with greater heterogeneity in the age of contacts reflecting a more diverse age structure , may provide a route for transmission to spread between families with school-age children and the rest of the population , in a similar way to the bridging role of bisexuals between hetero- and homosexual networks [44]: our estimates assume the same patterns apply in other countries , and future research should validate this . Glass and Glass [24] found similar assortativity among younger age groups , and proposed that this assortativity made those in younger age groups the transmission backbone of respiratory epidemics . Simulation studies using the POLYMOD data [23] and others [26 , 45 , 46] support this conclusion and explain why children and teenagers were the major channels for the initial transmission of infection during the 2009 influenza A ( H1N1 ) pandemic [47] , and why school closure is one of the main non-pharmaceutical interventions considered for pandemic mitigation [20 , 48] . Merler et al . found that spatiotemporal spread of the H1N1 2009 pandemic in Europe were influenced by the age-mixing patterns and social structures [49] . While age-mixing patterns shape the transmission of infectious disease [49] , analyses of the 2009 H1N1 influenza pandemic [50 , 51] suggest that it is crucial to account for age-specific susceptibility to infection . For most of the countries considered , three pronounced diagonal bands were observed in the contact matrices at home . This is partly a reflection of the structure found in the POLYMOD survey itself [23] , but is also consistent with the much larger DHS surveys which provide detailed data on household structures directly [52] . For the Asian countries in the DHS samples , noticeable tertiary diagonals , reflecting three generation households , were present , which highlight the limitations of using data from Europe to represent non-European societies without adjustments similar to those performed in this analysis . Interactions between school-going children and the elderly have important public health implications , as the former may have high infection rates while the latter are vulnerable to complications from infections , such as pneumonia [53 , 54] . Evidence for the effect of transmission from children to the elderly comes from Japan , where the cessation of the school influenza vaccination program led to a rise in mortality among the elderly [55] . Although we have used contact data only from the POLYMOD to build the global contact matrices , contact studies in Asia , Latin America and Sub-Saharan Africa ( from Viet Nam [25] , Taiwan [26] , Southern China [27] , Peru [28] , South Africa [29] and Kenya [30] , Russia [31] ) found similar evidence of tertiary diagonals in the contact matrices at home . The consistency of their survey-based findings with the emergent properties of our model provides some degree of empirical support to our findings . Synthetic contact matrices have been generated by individual-based model simulations [56] or derived from socio-demographic variables [57 , 58] and validated on serological data of H1N1 Influenza [56 , 58] , varicella and parvo-virus [57] using age-structured SIR models . Some of these synthetic contact matrices were created for only one country ( Hong Kong [56] and Italy [57] ) , while Ref . [58] estimated contact matrices for 26 European countries . The Fumanelli estimates [58] in particular did not reproduce the narrowness of the leading diagonal in the contact matrix observed in the POLYMOD study , in contrast to our approach ( more in the S1 Text ) . Despite the lack of social contact surveys , synthetic contact matrices have only developed for higher-incomed countries [56–59] , with large proportions lower-and-middle-income countries unrepresented . There are several assumptions underlying our study . We assumed the number of contacts in each location was Poisson , with over-dispersal accounted for using an individual-level random effect term that was assumed to govern contacts in all four locations , but in principle additional heterogeneities might be present leading to mischaracterization of correlations , dispersion [60] , and the number of zeros . As in the original POLYMOD study , we only quantified age-specific dyadic contacts , as eliciting higher order contacts in a survey is challenging both cognitively and practically . Triadic contacts—where A contacts B contacts C contacts A—are important for disease propagation in contact network models [12 , 61] and other methods to measure these , such as radio frequency tagging [61 , 62] , or more sophisticated structural models accounting temporal presence within the household [63] , may be needed to characterize higher degrees of contact . However , it remains to be demonstrated empirically that the differences in the patterns of transmission of an infection between two countries can be explained by differences in their contact patterns , although our simulation study in three countries suggested that the effect of interventions can vary substantially based solely on changes in contacts driven by age structure . The short simulation study in this paper made simplistic assumptions about how social distancing measures would translate into a reduction in contact rates , as well as assuming that heterogeneities in contact patterns could be well-described by an age-structured but otherwise mean-field model . In actual usage , the translation of a policy into a specific change in contact rates should be supported by evidence , while individual-based simulation models [64] provide a more flexible framework to capture shared contacts between individuals and allows policies such as reactive school closure to be assessed[65] . The primary limitation of the paper is that the projected contact matrices are derived from assumptions about the social structure in countries for which contact data are unavailable , so that the projected contact patterns cannot in most cases be directly validated . Our extension of POLYMOD to most countries of the world involved two main modelling steps with inherent assumptions , elaborated below . The first main modelling step was the creation of household structures and the demographics of workforce/school participation for countries for which this was not measured . For households , this involved identifying similarity to countries for which household data were available ( using POLYMOD or DHS ) , using a mixture of indicators on the economy and demography of each country , and applying a mapping from the age pyramids of countries to their household structure . The variables used gave more weight to pairs of countries that for the most part make intuitive sense ( for instance , Germany ( POLYMOD ) is assigned high weight in constructing Austria’s ( ROW ) contact matrices ) . This approach did , however , mean excluding several small countries with missing information , covering 4 . 1% of the world’s population . The variables were arbitrarily selected to span measures of health and social structure ( fertility , mortality , growth , and health expenditure ) , as well as development ( income , internet penetration and urbanization ) . The scree plot of the eigenvalue decomposition of these variables suggested that beyond 4 or 5 dimensions , additional variables did not add much new information , but alternative measures of distance would have resulted in some differences in the projected matrices . On validation , although there were some systematic discrepancies ( for instance , the slightly off-center leading diagonal for some Asian countries , which we suspect might be corrected by accounting for gender in future work ) , this approach reconstructed the empirical household structures with otherwise high accuracy for both the POLYMOD and DHS countries . We had substantially less data on potential school and work contacts , because direct samples from these environments were not available , even in POLYMOD , and so the approach extrapolated from indirect measures ( workforce and school participation rates , teacher to student ratios ) . This means that the number and age-profile of contacts outside the home are inherently less well projected by our approach than those within the home . It is not clear how this approach could be improved for these location types , or for ‘other’ locations , however , without collecting substantial amounts of new data . The original POLYMOD study found highly assortative mixing in ‘other’ locations , and while the study in Russia [31] found a similar pattern , it is not clear whether this would be observed in other , non-European or Eurasian settings . The second major modelling step was to project from POLYMOD to non-POLYMOD countries the relationship between ( i ) household structure or workforce/school participation and ( ii ) contact rates within those locations . This approach implicitly assumes that given a potential contact in a given location ( for instance , the existence of a cohabitant ) , the chance of an actual interaction is the same in POLYMOD and non-POLYMOD countries . The approximate validity of this assumption is supported by the broad similarities between our projections and estimates from the limited number of contact studies conducted outside Europe [25–31] , but some discrepancies were found in this validation . It is unclear to what extent these are due to violations of the assumptions made in this paper , due to differences in study design—such as interviewer- versus self-administered questionnaires , or translation effects—or due to differences between study populations and the country as a whole due to recruitment in a limited geographical area . Further empirical studies in other countries would provide additional validation . Those limitations notwithstanding , the projected contact matrices outlined in this paper provide a basis for model-based analyses to inform public health policy making around the world , until comprehensive studies can be carried out that cover a greater fraction of the world’s population . | The risk of infectious disease transmission varies in different settings , for instance at home , at work or in the community , as a result of the different social structures and mixing patterns in those locations . These social structures vary across countries in different stages of development and with different demographics . Social contact patterns have been measured in a small number of countries , but in large swathes of the world , contact patterns are unmeasured , which makes it challenging to build mathematical or computer models of disease spread and control . In this work , we developed a modelling framework to combine social contact data from the past studies of contact patterns within eight countries in the EU with data from multiple data sources including the Demographic Household Surveys , World Bank and UN Statistics Division , to provide validated approximations to age-and-location-specific contact rates for 152 countries covering 95 . 9% of the world’s population . | [
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| 2017 | Projecting social contact matrices in 152 countries using contact surveys and demographic data |
In mice , experimental infection with Trypanosoma brucei causes decreased bone marrow B-cell development , abolished splenic B-cell maturation and loss of antibody mediated protection including vaccine induced memory responses . Nothing is known about this phenomenon in human African trypanosomiasis ( HAT ) , but if occurring , it would imply the need of revaccination of HAT patients after therapy and abolish hope for a HAT vaccine . The effect of gambiense HAT on peripheral blood memory T- and B-cells and on innate and vaccine induced antibody levels was examined . The percentage of memory B- and T-cells was quantified in peripheral blood , prospectively collected in DR Congo from 117 Trypanosoma brucei gambiense infected HAT patients before and six months after treatment and 117 controls at the same time points . Antibodies against carbohydrate antigens on red blood cells and against measles were quantified . Before treatment , significantly higher percentages of memory B-cells , mainly T-independent memory B-cells , were observed in HAT patients compared to controls ( CD20+CD27+IgM+ , 13 . 0% versus 2 . 0% , p<0 . 001 ) . The percentage of memory T-cells , mainly early effector/memory T-cells , was higher in HAT ( CD3+CD45RO+CD27+ , 19 . 4% versus 16 . 7% , p = 0 . 003 ) . After treatment , the percentage of memory T-cells normalized , the percentage of memory B-cells did not . The median anti-red blood cell carbohydrate IgM level was one titer lower in HAT patients than in controls ( p<0 . 004 ) , and partially normalized after treatment . Anti-measles antibody concentrations were lower in HAT patients than in controls ( medians of 1500 versus 2250 mIU/ml , p = 0 . 02 ) , and remained so after treatment , but were above the cut-off level assumed to provide protection in 94 . 8% of HAT patients , before and after treatment ( versus 98 . 3% of controls , p = 0 . 3 ) . Although functionality of the B-cells was not verified , the results suggest that immunity was conserved in T . b . gambiense infected HAT patients and that B-cell dysfunction might not be that severe as in mouse models .
Human African Trypanosomiasis ( HAT ) or sleeping sickness , is a vector-borne parasitic disease occurring in sub-Saharan Africa . About 70 million persons are at risk for infection and 30 000 persons are estimated to be infected [1] . The parasites concerned belong to the Trypanosoma genus and are transmitted through the bites of tsetse flies ( Glossina genus ) . Two subspecies of Trypanosoma brucei ( T . b . ) , T . b . gambiense and T . b . rhodesiense , are responsible for human infection , which is usually fatal if left untreated . Infection with T . b . gambiense is responsible for chronic HAT in West- and Central-Africa , and characterized by low parasite numbers . In East-Africa , infection with T . b . rhodesiense leads to acute disease with relatively high parasite loads . Control of HAT relies on a combination of accurate diagnosis of cases , treatment of detected cases , and on control of the tsetse fly vector . No vaccine is available yet . The immunopathology of HAT remains poorly understood and most of our understanding comes from experimental T . b . brucei infections in mice , which also serve as a model for vaccine development . In T . b . brucei infected mice , host control over disease mainly relies on the T-cell independent IgM antibody response [2]–[4] . However , mice T . b . brucei infection results in decreased B-cell development in the bone marrow [5] . Lymphopoiesis , which is taken over by the spleen , is in turn abrogated by apoptosis of transitional B-cells , permanent loss of splenic marginal zone B-cells ( which are important for the early antibody response against T-cell independent antigens ) and depletion of follicular B-cells ( which normally develop into antibody producing plasma cells and memory B-cells ) . As a result of B-cell dysfunction , mice become susceptible to repetitive infections by previously encountered T . b . brucei variant antigenic types [6] . Furthermore , T . b . brucei infection equally affects the protective immune response towards unrelated pathogens , as observed in two experiments . First , in mice immunized against Trichinella spiralis , it was observed that upon subsequent infection with T . b . brucei and Trichinella spiralis , the effect of vaccination was lost in T . b . brucei infected mice only [7] . Similarly , in mice vaccinated against diphtheria , tetanus and Bordetella pertussis , the vaccine mediated protective effect was abrogated in mice that were infected with T . b . brucei prior to a Bordetella pertussis challenge , while vaccinated mice that had not been infected with T . b . brucei , remained protected upon challenge with Bordetella [6] . In vivo and in vitro correlates of cell-mediated immunity were observed to be depressed as well in rabbits infected with the African trypanosome T . congolense [8] . These results indicate that T . b . brucei infections can give rise to general memory B-cell destruction in animals , and point to the possibility that T . brucei infection may destruct memory B-cell and abrogate vaccine induced protection in humans as well . If confirmed , this would imply the need of revaccination of HAT patients after anti-trypanosomal therapy and development of a vaccine against the disease might be hard to achieve [9] . However , the relevance of the experimental models for humans remained unknown . Data about leukocyte phenotypes in HAT have remained limited to one study showing increased percentages of CD19+ B-cells and activated B-cells in blood of gambiense HAT patients , as well as a relative decrease in memory and effector CD8 T-cells [10] . Evidence for an increased occurrence of vaccine preventable diseases in cured HAT patients is missing , although such relationships may be easily overlooked due to weak surveillance systems in HAT endemic countries . The vaccine-induced memory response in HAT is difficult to assess . Firstly , one is limited to vaccines that provide life-long protection and have been administered to the majority of the population and prior to trypanosomiasis infection . Secondly , loss of protection cannot be tested by challenge with the pathogen . Moreover , HAT mainly occurs in remote rural settings where no standard laboratory infrastructure or electricity is available . Although in T . b . brucei animal models , immune depression may occur despite intact antibody levels [7] , we selected antibody quantification as an initial , though suboptimal , approach to assess immunological memory , taking into account that so far , nothing is known for the human situation . We opted for iso-agglutinins , which are innate antibodies against A and B carbohydrate antigens on red blood cells [11] , as well as for measles vaccine antibodies , as this vaccine is part of the standard vaccination programs [12] . We addressed the following questions: ( i ) does gambiense HAT eliminate peripheral blood memory B-cells; ( ii ) are peripheral blood memory T-cells affected in gambiense HAT ( iii ) does gambiense HAT influence iso-agglutinin levels and antibody levels against measles , and; ( iv ) are these effects reversible upon cure from gambiense HAT ?
Before enrolment into the study , written informed consent was obtained from adult participants . In the case of minors , an assent was asked for and parents/guardians provided written informed consent . Ethical clearance for the study was obtained from the institutional review board of ITM and the ethical committees of the University Hospital in Antwerp , Belgium ( study registration number B30020108363 ) and of the Ministry of Health of the Democratic Republic of the Congo ( DR Congo ) . Trypanosma brucei gambiense infected HAT patients and non-HAT endemic controls were prospectively enrolled ( T = 0 months ) in the study in DR Congo , Bandundu Province between July and December 2010 . Participants were identified during HAT screening activities of the dedicated HAT mobile team of Masi-Manimba , or included at the HAT treatment centres of Masi-Manimba and Bonga-Yasa . Inclusion criteria for HAT patients were the presence of trypanosomes in blood , lymph and/or cerebrospinal fluid ( irrespective of disease stage ) , and being 12 years or older . Exclusion criteria were pregnancy , being previously treated for HAT and being moribund . For each HAT patient , a control was included , fulfilling the following criteria: same gender and age and being and being resident in the same or a neighbouring village . Inclusion criteria for controls were absence of clinical evidence for HAT ( no swollen lymph nodes or neurological symptoms ) , absence of trypanosome specific antibodies in whole blood detected by card agglutination test for trypanosomiasis ( CATT ) [13]; no trypanosomes in blood detected by the mini anion exchange centrifugation technique [14] and being 12 years or older . Exclusion criteria were identical as for HAT patients . At enrolment , a crude assessment of the general condition ( normal , good , bad , pre-moribund or moribund ) was made , based on the participant's ability to eat , walk and take care of himself independently . Participants were questioned for their vaccination history ( measles , diphtheria-tetanus-whooping cough , polio , Bacillus Calmette-Guérin ( BCG ) ) and presence of a BCG scar was verified . Whole blood was collected by venipuncture and collected in 5 ml Cyto-Chex BCT blood collection tubes ( Streck , Omaha , NE , USA ) and shipped within one week to the Institute of Tropical Medicine ( ITM ) for phenotyping . From blood sampled on heparin , plasma was prepared that was snap frozen in liquid nitrogen and shipped to ITM where specimens were stored at −70°C until use . Blood taken on EDTA was preserved in an equal volume of GE buffer ( 6 M guanidine hydrochloride , 0 . 2 M EDTA , pH 8 . 0 ) at ambient temperature until DNA extraction . Thick and thin blood films were prepared and Giemsa stained for malaria diagnosis . The participants ABO blood group was determined using Eldoncard 2511 ( Eldon Biologicals , Gentofte , Denmark ) . The HIV status was determined using HIV 1/2 STAT-PAK Assay ( Chembio , Medford , NY , USA ) which , if positive , was followed by Uni-Gold HIV ( Trinity Biotech , Wicklow , Ireland ) , and if positive , by HIV 1/2 Oraquick ADVANCE ( Orasure Technologies , Bethlehem , PA , USA ) [15] . In participants positive for all 3 serological tests , HIV infection was confirmed a posteriori using PCR , following a nested method in an algorithm of three different primer sets in pol , env and LTR region [16] . CATT was performed on whole blood taken on heparin , and if positive , the plasma end-titre was determined . HAT was treated following the guidelines of the National HAT Control Program in DR Congo . HAT patients were revisited six months after treatment , controls at the corresponding time point ( T = 7 months ) . The participant's general condition was re-assessed . Blood taken on heparin and on Cyto-Chex BCT blood collection tubes was processed as described above . All participants were examined for absence of trypanosomes using the mini anion exchange centrifugation technique , and in controls , CATT was repeated . Whole blood , collected in Cyto-Chex BCT blood collection tubes ( Streck , Omaha , NE , USA ) , was used to study T and B cell subsets by flow cytometry . B-cells subsets were analysed using mouse anti-human monoclonal antibodies anti-CD45 PerCP ( leucocytes ) , anti-CD20-FITC ( B cells ) , anti-human CD27-APC ( IgG1 ) and anti-human IgM-PE ( IgG1 ) and appropriate IgG1 isotype controls ( BD Biosciences , Erembodegem , Belgium ) . These combinations were used to identify B-cells ( CD20 ) , naïve B-cells ( CD20+CD27− ) , memory B-cells ( CD20+CD27+ ) , T independent B-cells ( CD20+ IgM+ ) and T dependent B-cells ( CD20+IgM− ) [17] . T-cells subsets were stained with mouse anti-human monoclonal antibodies anti-CD45 PeCP , anti-CD3 ( IgG1 ) -PE , anti-CD45RO-FITC ( IgG2a ) , anti-CD27-APC ( IgG1 ) and appropriate IgG1 isotype controls ( BD Biosciences , Erembodegem , Belgium ) . These combinations were used to identify T-cells ( CD3 ) , naïve T-cells ( CD3+CD45RO−CD27+ ) , early effector/memory T-cells ( CD3+CD45RO+CD27+ ) and late effector/memory T-cells ( CD3+CD45RO+CD27− ) [18] . For the staining of B-cells , 50 µl of fixed blood was pipetted in two test tubes . Blood in both tubes was washed twice with 2 ml of phosphate buffered saline ( PBS ) containing 1% bovine serum albumin ( BSA ) to remove serum . Subsequently , a cocktail of anti-CD20/anti-CD27/anti-IgM was added to one tube and anti-CD20/isotype-control cocktail to the other . After 30 minutes of incubation , red blood cell lysing solution was added for 10 minutes , cells were washed and analysed on the flow cytometer ( FACSCalibur , BD Biosciences ) . For the staining of the T-cells the procedure was the same with exception of the washing step with PBS-BSA which was omitted . The cells subsets were analysed using FlowJo software ( Tree Star , US ) . Prior to the study , the antibody cocktails were tested using whole blood from 3 normal controls . Blood collected in Cyto-Chex BCT blood collection tubes was compared to fresh blood collected in EDTA tubes . Using the above described antibody cocktails , T- and B- cell subsets could be measured in blood collected in Cyto-Chex BCT blood collection stored for at least 14 days . Screening for irregular anti- erythrocyte antibodies ( antibodies causing agglutination but that are not A and B red blood cell carbohydrate antigen specific ) was performed with ID-Diacell I–II–III ( Bio-Rad , Cressier , Switzerland ) using undiluted plasma . For IgG , 25 µl of plasma and 50 µl of ID-Diacell I , ID-Diacell II or ID-Diacell III cell suspension were incubated for 15 minutes at 37°C on Coombs anti-IgG ID-cards ( Bio-Rad , Cressier , Switzerland ) . For IgM , 25 µl of plasma and 50 µl of each cell suspension were incubated for 15 minutes at 20°C on ID-cards NaCl , enzymetest and cold agglutinins ( Bio-Rad , Cressier , Switzerland ) . After incubation , gel cards were centrifuged ( ID-centrifuge , Bio-Rad , Cressier , Switzerland ) for 10 minutes and the agglutination reaction was scored . Samples positive for irregular anti-erythrocyte antibodies , implying a risk for false positive iso-agglutinin reactions , were excluded from further analysis . For assessment of antibody titres against A and B red blood cell carbohydrate antigens , plasma samples of patients with blood group O were tested with A1 and B cells ( ID-Diacell ABO , Bio-Rad , Cressier , Switzerland ) , those from blood group A or B were tested with respectively B or A1 cells only , those from blood group AB were not tested . Two-fold serial dilution series of plasma were prepared in phosphate buffered saline ( Yvsolab , Turnhout , Belgium ) . For IgG iso-agglutinin , 25 µl of diluted plasma and 50 µl of ID-Diacell A1 and/or B cell suspension were incubated for 15 minutes at 37°C on Coombs anti-IgG ID-cards ( Bio-Rad , Cressier , Switzerland ) . For IgM iso-agglutinin , 50 µl of diluted plasma and 50 µl of ID-Diacell A1 and/or B cell suspension were incubated for 15 minutes at 20°C on ID-cards NaCl , enzymetest and cold agglutinins ( Bio-Rad , Cressier , Switzerland ) . After incubation , gel cards were centrifuged ( ID-centrifuge , Bio-Rad , Cressier , Switzerland ) for 10 minutes and the agglutination reaction was scored . The end-titre was the highest plasma dilution still causing an agglutination reaction . Quantitative and qualitative determination of specific IgG antibodies to measles virus was performed using Enzygnost anti-measles Virus/IgG ELISA ( Siemens , Marburg , Germany ) , following the manufacturer instructions for the BEP III system ( Siemens , Marburg , Germany ) . Plasma of HAT patients at inclusion and 6 months post-treatment , and corresponding plasma from the respective control were analysed in the same ELISA plate . Based on the reference included in the kit , results were expressed as mIU/ml . Samples with OD<0 . 1 were negative , samples with OD>0 . 2 were positive , samples in the grey zone with 0 . 1<OD<0 . 2 were retested . A measles antibody level of ≥200 mIU/ml is assumed to provide protection against infection in a healthy population [12] , [19] . For analysis , only results for which the corresponding matched sample result at the same time point was available were taken into account . Comparisons of quantitative results between controls and HAT patients and between 0 and 7 months were performed with the Wilcoxon Signed Rank Test ( SigmaPlot 11 ) . Comparisons of quantitative results between first and second stage patients were performed with the Mann-Whitney Rank Sum test . Data are presented as medians with interquartile range ( IQR ) . Differences in proportions between controls and HAT patients were assessed with McNemar Chi square ( STATA 10 . 0 ) . A p-value of ≤0 . 05 was considered as significant .
In total , 117 controls and 117 gambiense HAT patients were included . Median age was 28 years , 45% of the participants were male . Respectively 9 . 9% of participants suffered from malaria ( 13 HAT patients and 9 controls positive/223 thick blood films ) and 1 control had HIV . Overall vaccination coverage reported by the study population ranged between 88 . 4% ( 183/207 ) for polio and 100% for BCG , and 80 . 6% ( 183/227 ) of participants had a BCG scar . The general condition for all study participants was judged good to normal . Among the participants , 51 . 3% had blood group O , 30 . 2% A , 15 . 5% B and 3 . 0% AB . For none of the above parameters , there were significant differences in proportions between HAT patients and controls , except for polio vaccination , reported by 83 . 3% of controls versus 93 . 3% of HAT patients ( p = 0 . 002 ) . Among the HAT patients , 97 . 4% ( 114/117 ) were positive in CATT on whole blood ( median plasma titre 16 , IQR 8–16 ) , 77 . 4% ( 48/62 ) had trypanosomes in the lymph node fluid after successful lymph node puncture , and respectively 43 . 4% ( 36/83 ) and 89 . 3% ( 100/112 ) had trypanosomes in blood detected by the micro-haematocrit centrifugation technique or in the mini-anion exchange centrifugation technique . Cerebrospinal fluid median white blood cell counts were 5/µl ( IQR 2–43 ) and trypanosomes were observed during the cell count in 15 . 7% ( 18/115 ) . About half ( 56/116 ) of the included HAT patients were in the meningo-encephalitic disease stage ( >5 white blood cells/µl or trypanosomes in cerebrospinal fluid ) . Respectively 111/117 HAT patients and 105/117 controls were revisited after a median of 211 ( IQR 197–241 days , T = 7 months ) and 204 days ( IQR 178–246 ) respectively . At revisit , all participants were in good general condition . Although one control had become CATT positive , no trypanosomes were detected in any of the study participants . An overview of the B-cell phenotyping results in HAT patients and controls is presented in Table 1 , and an example of a dot plot of CD27 and IgM expression on B-cell subsets ( CD20+ ) , in a HAT patient and a control is shown in Figure 1 . The percentage of CD20+ B-cells in HAT patients was significantly higher than in controls ( median 1 . 5 times higher , p<0 . 001 ) . Although the percentage of CD20+ cells had decreased 6 months after treatment of HAT , it still remained significantly higher than in controls ( p<0 . 001 ) . Within the CD20+ subset , the percentages of CD27+ memory B-cells and IgM+ B-cells were significantly higher in HAT than in controls ( increases of the median of respectively 2 . 3 and 3 . 6 times , p<0 . 001 ) . After HAT treatment the percentage of CD27+ cells within the B-cell ( CD20+ ) subset still remained significantly higher than in controls ( p = 0 . 001 ) , while no significant difference could be observed anymore for the percentage of IgM+ B-cells ( p = 0 . 7 ) . The most striking change within the B-cells subset was the more than 6-fold increase of the percentage CD27+IgM+ cells ( Q2 in Figure 1 ) in HAT patients compared to controls ( p<0 . 001 ) . After treatment , this subset returned to normal percentages . HAT was associated with only minor differences in the CD27+IgM− subset ( Q1 in Figure 1 , p = 0 . 004 ) of B cells . The relative decrease of naive ( CD27− ) B-cells in HAT was mainly due to a decrease of CD27−IgM− cells ( Q4 in Figure 1 , p<0 . 001 ) while the percentage of CD27−IgM+ cells within the B-cell subset had increased ( Q3 in Figure 1 , p<0 . 001 ) . For none of the B-cell phenotypes studied , significant differences between stage 1 and stage 2 HAT patients were observed ( 0 . 2<p<0 . 9 ) . A summary of the T-cell phenotypes is presented in Table 2 , and an example of a dot plot of the CD27 and CD45RO expression on T-cell subsets ( CD3+ ) , in a HAT patient and a healthy control subject is shown in Figure 2 . The percentage of CD3+ T-cells was significantly lower in HAT than in controls , and returned to normal 6 months after treatment . Within the T-cells subset , memory T-cells were significantly increased ( CD45RO+ , p = 0 . 002 ) , which was due to a relative increase in early effector/memory ( CD45RO+CD27+ ) T-cells in HAT ( 1 . 2 fold increase of the median , p = 0 . 003 , Figure 2 Q2 ) . After treatment , the observed differences in memory T-cell subsets between HAT and controls disappeared . No difference was observed in percentage of naïve ( CD45RO− CD27+ ) T-cells between HAT patients and controls ( p = 0 . 8 ) while the percentage of late effector ( CD45RO−CD27− ) T-cells was significantly lower in HAT than in controls ( p<0 . 001 , Figure 2 Q4 ) , but normalized after treatment . No differences were observed in function of the disease stage for any of the measured T-cell subsets ( p>0 . 08 ) . Screening for irregular anti-erythrocyte IgG with ID-Diacell I–II–III cells revealed respectively 9/116 and 12/104 reactive controls at T = 0 months and T = 7 months ( 9 at both time points ) , and 7/116 and 5/109 reactive HAT patients ( 4 at both time points ) . At T = 0 months or T = 7 months , respectively , 7/116 and 4/104 controls ( 4 at both time points ) , and 6/116 and 5/109 HAT patients ( 4 at both time points ) reacted for irregular anti-erythrocyte IgM . At inclusion , there was no difference in anti-A or anti-B IgG end titers between controls and HAT patients ( table 3 ) . For IgM , at time of inclusion median anti-A1 and anti-B iso-agglutinin end-titres were significantly lower in HAT patients than in controls ( p<0 . 004 ) . After treatment , at T = 7 months , the anti-A1 IgM iso-agglutinin end-titre had increased significantly in HAT patients ( p<0 . 01 ) , but remained lower for anti-B IgM . There was no difference in iso-agglutinin end-titres between stage 1 and stage 2 HAT patients ( p values>0 . 1 ) , except for anti-B IgM which was one titer lower in stage 2 ( p = 0 . 05 ) . Measles antibody concentrations in HAT patients at inclusion and after treatment and in controls at corresponding time points are summarized in Figure 3 . At inclusion , the median antibody concentration in HAT patients ( 1500 mIU/ml , IQR 643–3300 ) was significantly lower than in controls ( 2250 mIU/ml , IQR 940–4675 ) . Seven months later , the antibody concentration in the treated HAT patients ( 1700 mIU/ml , IQR 790–4300 ) remained significantly lower than in controls ( 2600 mIU/ml , IQR 1000–5500 ) although in both groups , the antibody level had increased significantly compared to inclusion ( p<0 . 001 and p = 0 . 006 respectively ) . There was no difference in measles antibody concentration between stage 1 and stage 2 HAT ( p = 0 . 7 ) . A measles antibody level superior to the cut-off assumed to provide protection against infection was present in 94 . 8% ( 110/116 ) of HAT patients and in 98 . 3% ( 114/116 ) of controls , at inclusion and 7 months later . There was no difference in proportions of HAT patients and controls exceeding this cut-off ( p = 0 . 3 ) . No relationship between high measles antibody levels and self-reported vaccination against measles , polio , diphtheria-tetanus-pertussis , BCG or presence of a BCG scar could be observed ( p = 0 . 6–1 ) .
Our results suggest that the issue of B-cell dysfunction that troubles mouse models for trypanosomiasis , might not be that severe in human African trypanosomiasis patients infected with T . b . gambiense . In gambiense HAT patients compared to controls , significantly higher percentages of memory B- and memory T-cells were present in peripheral blood . After treatment , the percentage of memory T-cells normalized and the percentage of memory B-cells did not yet normalize . Iso-agglutinin IgM end-titres were slightly lower in gambiense HAT , and normalized only partially after treatment . Although anti-measles antibody levels were , and remained , lower in gambiense HAT patients than in controls , no significant difference could be observed in the number of individuals with levels above the international cut-off for protection . Memory cell populations in experimental T . b . brucei infection have exclusively been studied in bone marrow and spleen [5] . For T . vivax experimental mice infections , peripheral blood data are available as well [20] . In the human host , only peripheral blood is readily accessible . Until now , data on peripheral blood lymphocyte subsets in HAT are rare , due to the important logistic challenges related to conducting research in settings like DR Congo . The observed relative B-cell increase is consistent with previous findings [10] and in line with polyclonal B-cell activation and proliferation of cells of the B lymphoid series previously described for HAT [21] . The upregulation of Fas ( CD95 ) expression in gambiense HAT , measured by Boda et al . , led these authors to suggest a poor conversion of B-cells into memory B-cells [10] . In our study , we observed a relative increase of CD27+IgM+ B-cells which are defined as T independent memory B-cells [17] in HAT . In T . vivax experimental mice infections , the fall in number of B-cells in the lymphoid organs is similar to experimental T . b . brucei infections . In peripheral blood it is accompanied by an increase in the number of transitional IgM+IgD− B-cells and switched IgM−IgD− plasma/memory cells and by a decrease in naive B-lymphocytes [20] . Although the marker combinations used to identify B-cells in these experimental T . vivax studies were different from ours , the results for peripheral blood are similar , if we assume that the memory B-cells defined by CD27+ in HAT , are similar to the IgD− population in T . vivax infected mice [17] . We confirm the moderate relative T-cell decrease in gambiense HAT observed previously by Boda et al . , associated to the relative expansion of B-cells . The present observation of a relative increase in early memory T-cells ( CD45RO+CD27+ ) seems to corroborate earlier findings of larger numbers of CD4+CD45RA−CD62L+ cells in HAT with CD8+CD45RA−CD62L+ cells remaining constant [10] . As previously suggested [10] , there were no differences in lymphocyte subsets according to the disease stage . HIV also causes a significant increase in the memory ( CD45RA−CD45RO+ ) phenotype CD8 subset [22] and reduces the CD27+ memory B-cell population [23] . The HIV prevalence in our study population was low and is not expected to affect the overall results . Malaria , which is associated with B- and T-cell exhaustion and an increase in an atypical CD19+CD27−CD21−CD10− memory B-cell population [24] , is not expected to account for differences between the control and HAT population since the frequency of occurrence of trophozoites in blood was similar in both groups . However , malaria , or other infections , might account for variation in some cell phenotypes in time , as was observed in the control group . This underlines the importance to sample controls at similar time points as HAT patients and to perform a matched statistical analysis , to maximally eliminate external variation . The loss of the host's capacity to recall vaccine-induced memory responses , as has been described for laboratory animals [6] , [7] , can in humans , for ethical reasons , not be tested by challenge with a pathogen . Therefore , the Multitest cell-mediated immunity ( Pasteur-Mérieux , Lyon , France ) , an intradermal skin test to measure delayed hypersensitivity as a marker for T-lymphocyte response , was considered but this test was no longer available anymore at time of the project . Neither was it feasible to set-up of facilities for cell culture or ELISPOT . We therefore had to rely on surrogate markers and opted for the quantification of iso-agglutinins and measles antibodies . Natural IgM antibodies against A and B carbohydrate antigens are T-cell independent , while a T-cell dependent antibody response results in higher affinity IgG1 and IgG3 antibodies [11] . In the presence of an intact immune system , iso-agglutinins to the missing A or B red blood cell carbohydrate antigens are always found , even if there has been no exposure to red blood cells carrying these antigens . These antibodies were therefore used to asses T-cell independent and T-cell dependent humoral immunity . The lower IgM iso-agglutinin titers observed in HAT patients , might indeed point to a moderate effect of gambiense HAT on the T-independent antibody response [6] , but seems reversible upon treatment . Measles were selected for antibody quantification for the following reasons . A high proportion of the population is expected to have antibodies against measles since the measles vaccine is part of the standard vaccination programs [12] . Half-life of measles IgG antibodies has been estimated at around 3000 years so they should be measurable in all subjects that have been infected or were successfully immunized [25] . Moreover , in healthy individuals , the absolute level of antibodies needed to fully protect against infection is known , as well as the concentration below which no protection is obtained anymore [26] , [27] . Vaccine coverage in the last 33 years in DR Congo has been estimated by WHO-UNICEF as 21–95% for BCG and 17–90% for the measles vaccine respectively [28] . Presence of a BCG scar in 80 . 6% of study participants indicated rather high vaccine coverage , and measles antibody levels above the cut-off were present in 96 . 6% . Measles antibody levels were comparable to levels observed in pregnant women in Belgium and in a Swedish volunteer group , in which concentrations were measured with the same ELISA kit [29] , [30] , or in an adult population in Addis-Abeba [31] . The increase in measles antibody concentrations in HAT patients and controls 7 months after inclusion , is unlikely due to a technical bias since samples taken at both time points were analyzed in the same ELISA plate . The rise might have been caused by a natural exposure to measles , as measles outbreaks regularly occur in DR Congo , also during the study period [32] . Although existing data are contradictory , presence of antibodies does not necessarily reflect presence of antibody secreting memory B-cells , as continuous antibody secretion might be due to long-lived plasma cells rather than on-going activation of memory B-cells [30] . Measles vaccine induces both humoral and cellular immune responses comparable to those following natural infection [33] . We withheld from revaccination , since live virus measles vaccination is not recommended for immune-suppressed patients [27] , a condition to be expected during HAT , and since revaccination was judged not to be in the patient's best interest , even after treatment for HAT . Overall , our results do not exclude an impairment of humoral and cellular immunity during gambiense HAT . Indeed , when given during gambiense HAT infection , a reduced response to typhoid vaccine has been observed , as well as diminished reactions to skin test antigens [34] . Similar observations have been made in domestic animals , where the antibody response to and/or efficacy of vaccination against e . g . contagious bovine pleuropneumonia [35] , foot and mouth disease [36] , swine fever [37] , antrax spore [38] and Brucella abortus [39] were affected when given during infection with various animal trypanosomes . Moreover , in the immunized T . b . brucei - Trichinella spiralis co-infection experimental model , the anti-Trichinella IgG1 response was not affected [7] although protection was partially lost . Due to polyclonal B-cell activation , characteristic for trypanosomiasis infection , specific functional antibodies may be replaced by non-protective , low affinity , cross-reactive antibodies [40] . Although for measles , antibody concentrations remained above the cut-off , we cannot exclude that they have become unfunctional in HAT and their protective capacity may have been lost . The lack of a functionality test is an important difference with previously published experimental mice studies [6] , [7] and represents the main limitation of the actual study . It might therefore be worth to further assess the protective capacity of the measles antibodies against infection , e . g . using a functional assay such as the plaque reduction neutralization test [41] . However , none of the study participants mentioned a measles episode while being questioned for their vaccination history , although as mentioned above , natural exposure might have occurred . Of interest , the agglutinating capacity of the iso-agglutinin antibodies was only moderately affected in our assay . As discussed above , other limitations inherent to our study are mainly related to research in humans instead of in laboratory animals , and to studying a disease that typically occurs in rural Africa , far from high-tech environments . In this context , blood specimens were collected on a blood stabilizer , intended to preserve peripheral blood samples' qualitative and quantitative leukocyte subset characteristics and allowing collection and storage of blood specimens for immunophenotyping by flow cytometry . Even using this stabiliser , preliminary experiments demonstrated that some lymphocyte subset cell markers ( e . g . the cell surface marker CCR7 , which we had originally selected to be used in combination with CD27 to better identify T memory subsets [18] were not optimally preserved , thus antibody cocktails had to be adapted accordingly . As we did not perform absolute counting of lymphocyte numbers , the observed changes in lymphocyte sub-populations are relative . For the iso-agglutinins , the participants blood group has to be taken into account , but the blood group was not used as a matching criterion at time of collection . In the settings we were working , in practice , it would have been difficult to identify a matched control for the patients with rarer B and AB blood groups . Data for HAT patients and controls that had different blood groups and were not tested against the same red blood cell carbohydrate antigen , were therefore lost . However , similarly lower IgM end-titers in HAT patients were also observed when statistical analysis was performed without matching the results for HAT patients and their corresponding controls ( data not shown ) . Overall , our results in gambiense HAT patients do not suggest trypanosomiasis associated massive memory cell destruction , or loss of antibody levels , although the antibody's protective capacity remains to be confirmed . So far there have never been epidemiological signals/reports that HAT patients , before or after treatment , were at increased risk of having vaccine-preventable diseases ( measles or others ) compared to the rest of the population . One should however acknowledge that epidemiological surveillance is generally weak in rural Africa and that such occurrences might have been missed . If some degree of immunity loss may exist in HAT patients infected with T . b gambiense , it does not seem of clinical relevance . At least for measles , our data indicate that antibody levels remain intact . Some open questions remain . Functionality of measles antibodies should be confirmed to completely ensure that revaccination after gambiense HAT , would not be necessary . It could also be interesting to assess activity of other vaccine-induced antibodies , as the decay of measles antibody concentrations is extremely slow and since we cannot exclude that other vaccines might depend more on memory cell dependent antibody production . Differences in immune-suppression and B-cell apoptosis observed between gambiense HAT and experimental infections may be linked to the differences in parasitemia between T . b . gambiense HAT and experimental infections [5] , [34] . As previously suggested [5] , [34] , it might therefore be worth to perform similar investigations in acute T . b . rhodesiense HAT , which is characterized by higher parasitemia , and for which no data on peripheral blood memory T- and B-cells or on acquired immunity are available . | African trypanosomes are parasites that cause sleeping sickness in humans . In mice models , trypanosomiasis causes loss of the spleen memory B-cell precursors , of the host memory response and of protection against certain pathogens , built up by vaccination . The phenomenon has never been studied in human sleeping sickness , but if occurring , revaccination after treatment would be required . We show that gambiense human sleeping sickness is associated with a relevant increase in memory T- and B- cells in peripheral blood , in particular T-independent memory B-cells . As measles vaccination is included in standard vaccination programs , we measured measles antibody concentrations , which , although slightly lower in sleeping sickness patients than in controls , exceeded in 95% of patients the minimum level considered protective . Anti-red blood cell IgM titres , measured to assess the T-cell independent antibody response , were one titre lower in patients than in controls , but normalized after treatment . Overall , our results in gambiense HAT patients do not suggest trypanosomiasis associated massive memory cell destruction , or loss of antibody levels , although the antibody's protective capacity remains to be confirmed . | [
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| 2014 | Gambiense Human African Trypanosomiasis and Immunological Memory: Effect on Phenotypic Lymphocyte Profiles and Humoral Immunity |
Contact structure is believed to have a large impact on epidemic spreading and consequently using networks to model such contact structure continues to gain interest in epidemiology . However , detailed knowledge of the exact contact structure underlying real epidemics is limited . Here we address the question whether the structure of the contact network leaves a detectable genetic fingerprint in the pathogen population . To this end we compare phylogenies generated by disease outbreaks in simulated populations with different types of contact networks . We find that the shape of these phylogenies strongly depends on contact structure . In particular , measures of tree imbalance allow us to quantify to what extent the contact structure underlying an epidemic deviates from a null model contact network and illustrate this in the case of random mixing . Using a phylogeny from the Swiss HIV epidemic , we show that this epidemic has a significantly more unbalanced tree than would be expected from random mixing .
Infectious disease epidemiology has a longstanding history of mathematical modeling . Simple population dynamical models assuming random-mixing between infected and susceptible individuals have yielded important insights into the dynamics and control of infectious diseases [1] . The assumption of random mixing has been made primarily for reasons of mathematical tractability , but it is unclear under which conditions this assumption is actually justified . To account for the effects of non-random mixing networks that describe the explicit contact structure are increasingly being incorporated into models of infectious disease dynamics [2]–[7] . These models showed that important epidemiological quantities such as the size of an epidemic or requirements for an epidemic to spread depend sensitively on the contact structure [8]–[11] . While this has led to important conceptual insights regarding disease dynamics and control , the applicability of these results to real world situations has been hampered by the paucity of data on actual contact networks . Great efforts are currently underway to infer contact structure from various sources of data [12]–[16] . However , most approaches for the reconstruction of contact networks are highly labor-intensive . These approaches are also all based on host contact structure which is not always easily translated into contacts that are relevant for transmission . While some studies have focused on differentiating contacts that may or may not result in transmission [15] , there are many factors that can cause discrepencies between interactions of hosts and interactations that are relevant for transmission . Considerable efforts have recently been made to link methods of phylogenetic analysis with epidemiological models resulting in a new research area termed phylodynamics [17]–[19] . If the evolutionary rate of a pathogen is sufficiently fast , then it is conceivable that also the contact structure underlying an epidemic leaves a traceable fingerprint in the genetic structure of the pathogen population [20] , [21] . This contact network will only contain those contacts that are relevant for disease transmission and therefore could circuimvent problems of translating host contacts into disease contacts . Using simulations of pathogen populations spreading on contact networks generated by various network models we compare the resulting phylogenetic trees . We find that quantitative measurements of tree shape such as the Sackin index contain information that can be used to differentiate between different classes of contact structures . Such descriptive measures of tree topology have previously been successfully used to infer evolutionary processes from phylogenetic trees [22]– . Most importantly , the Sackin index can be used to test whether the contact structure significantly deviates from what would be expected under random mixing .
To illustrate the effect of contact structure on the resulting phylogenetic tree , we perform simulations of epidemic outbreaks for three different network models: ( a ) the Erdös-Rényi ( ER ) random graph [29] , ( b ) the Barabási-Albert ( BA ) graph [30] and ( c ) the Watts-Strogatz ( WS ) graph [31] with a low rewiring probability , ( see Methods ) . Both the BA and the WS are representative for two important aspects of contact heterogeneity . Networks generated by the BA model have both a large variance in the degree distribution as well as short mean path lengths . Networks generated by the WS model have low degree distribution variance and long mean path lengths . We compare these network models to a full graph ( FG ) , which corresponds to a model with random mixing . Each of the three network models can be tuned with different parameters . The ER model generates networks with Poisson degree distributions where the mean can be varied . The BA model produces scale-free networks with a power-law degree distribution . The WS model produces networks that have a high degree of local clustering , but a degree distribution that lies between a Dirac distribution ( all nodes have the same degree ) and a Poisson distribution . We track the exact spreading pattern ( i . e . who infects whom ) of a susceptible-infected-removed ( SIR ) epidemic for different networks generated by each model to obtain the infection tree for each of these networks . The parameters for the network models are chosen such that all networks have the same mean degree , , yet different degree distributions , path length distributions and clustering coefficients . Figure 1A shows the imbalance measured by the Sackin index of the resulting infection trees for the three network models and the full graph at different values of , captured by the transmissibility ( see Methods for detailed definitions of the Sackin index and the transmissibility ) . For large values of ( large ) the whole network is infected , independent of the contact structure . Not surprisingly , the epidemic size is similar for all network types in this parameter range , since almost all individuals in the population eventually become infected before the epidemic dies out . The balancedness of the resulting trees , however , differ significantly for the three networks types . The ER model is virtually indistinguishable from the random mixing model ( FG ) . For sufficiently large the BA model has higher imbalance than the ER and FG . Finally , the most striking difference in imbalance is observed for the WS . For low ( low ) the imbalance vanishes for all networks for the simple reason that no epidemic outbreak occurs ( see Figure 1B ) . Interestingly , the imbalance is generally largest at , where the transmissibility is just large enough for an epidemic outbreak to occur . In this case each individual infects just one other individual on average , which results in an infection tree that continuously mostly branches off to one side and thus is maximally unbalanced ( Figure S2 ) . For all network types except the BA model , the imbalance of the transmission tree is maximal for values of right around , but then converges to a smaller value as approaches unity . This can be explained by the fact that the SIR infection process is equivalent to a birth-death process . When , the death rate is de facto zero ( birth rate death rate ) and thus all lineages survive to the end . If the death rate vanishes the expected imbalance of the resulting transmission trees is minimal and is given by the Yule model ( see Methods ) . The level of imbalance of the transmission trees for the different network types shown in Figure 1 obviously depends on the choice of the network model parameters . In the following we will investigate how imbalance depends on the average number of neighbors and on local connectivity . Moreover , we henceforth use the expected Sackin index given by the Yule model to define a normalized Sackin index ( see Methods ) , which has an expected value of zero for infection trees based on an SIR model with death rate zero . We focus on the ER graph because in the limit of a large number of neighbors this model is expected to converge to the random mixing model . Furthermore , to eliminate contributions to imbalance resulting from a non-zero death rate we show the results for . Figure 2 shows the imbalance for an ER model with nodes and an average number of neighbors . The effect of on networks generated by the WS and BA model are reported in the supporting text S2 . Increasing the mean number of neighbors essentially increases the number of infections caused by a single individual and therefore the imbalance is expected to decreases with increasing number of neighbors . This is confirmed by the results presented in Figure 2 . A small average number of neighbors results in more unbalanced transmission trees for a reason that is similar to why nonzero death rates increase imbalance . Once a node has infected all of its neighbors , it can no longer infect anyone else and is essentially removed from the system despite remaining infectious . In the WS model , the mean path length is directly related to the rewiring probability [31] . The WS model with rewiring probability essentially generates the same type of network as the ER model . Therefore the imbalance of the transmission trees resulting from epidemics spreading on such networks should converge with increasing rewiring probability to the same value as for ER random graphs . Figure 3 shows imbalance as a function of the rewiring probability and transmissibility . We identify two limiting cases for the imbalance of the epidemic . For values of there is essentially no epidemic outbreak and the imbalance remains small . For values of close to but larger than an epidemic can occur and the imbalance is maximal . As increases further , the number of shortcuts in the network increases and the mean path length decreases , as does the imbalance . For values substantially larger than the network converges to something similar to an ER graph and the hence normalized imbalance converges to zero ( for and ) or to a fixed value for finite populations and small mean degree . In the supporting text S1 we derive an analytical approximation for the normalized Sackin index given the transmission network ( see Figure S1 ) , ( 1 ) Here , is the average number of infections caused by an infected individual until that individual is removed ( i . e . the excess degree in the transmission network ) and is the mean shortest path length in the transmission network . This equation shows that assuming the transmission network were known , imbalance depends on one hand on the mean path length , , and on the other hand on the average excess degree . For networks generated by the configuration model , depends on the first and second moment of the degree distribution . BA networks are characterized by a large degree distribution variance , as well as a short mean path length . For low rewiring probabilities , WS networks have small degree distribution variances and large mean path lengths . These observations together with the analytical approximation in equation ( 1 ) can help explain why it is not always possible to distinguish between the BA and WS models when considering the Sackin index as a measure of tree topology ( see Figure 1 ) . This ambiguity is most pronounced when considering two idealized networks: a chain and a star . These two topologically very different networks would result in identical transmission trees ( see Figure S2 ) and therefore be indistinguishable using tree imbalance alone . Note that and in equation ( 1 ) refer to the transmission network rather than the actual contact network . The connection between contact networks and transmission networks has recently been studied in the context of epidemic percolation networks [32] . Unfortunately , the exact relationship between the quantities and in the transmission network and the contact network has not yet been described . However , since the transmission network is a subgraph of the contact network , it is feasible to assume that contact networks that display long or short mean shortest paths also result in transmission networks with long or short mean shortest paths , respectively , and contact networks that have large or small mean excess degrees result in transmission networks with large or small mean excesss degrees , respectively . Up to this point we have only considered the case where the full transmission network is known and we can thus infer the average phylogenetic tree of the disease outbreak . It is clear , however , that in the real world we only have access to a limited subset of leaves from a phylogenetic tree . It is thus necessary to study the robustness of the tree shape under random sampling of leaves . Figure 4 shows the imbalance of the tree as a function of the number of sampled lineages . All non sampled branches are pruned from the tree and the sampled branches are joined together at their last common ancestor to create the sample tree ( see Figure 5A ) . For small enough sampling sizes ( around 1% ) the ER and WS graphs become indistinguishable , indicating that the imbalance is driven by the finer structures of the tree , rather than the backbone . The imbalance of the BA network converges much slower to that of the ER network . Figures 5B and 5C show two schemes of time sampling for which we study the effect on tree imbalance . In the first scheme we truncate the tree at a time point before the end of the epidemic ( Figure 5B ) . This corresponds to the situation where samples of all individuals in an ongoing epidemic are available . In the second scheme , we use only those sequences from individuals that are infectious at a time point and exclude sequences from individuals who are no longer infectious or have died before ( Figure 5C ) . This corresponds to a snapshot of an epidemic . In Figure 6A , we observe that tree balancedness saturates at a certain value for ER and BA models , even before the epidemic has stopped . In the case of the WS model , tree imbalance continues to grow exponentially until the last individual has been infected . This indicates that in the ER and BA models , the early stages of the epidemic contribute more strongly to tree imbalance . In contrast , in the WS model the late stage infections contribute more strongly than the early stage infections . This is consistent with the observations made in the case of random sampling , since random sampling tends to destroy the tree structure towards the tips of the tree , while conserving the structure towards the root of the tree . This differentiation can no longer be observed when a snapshot of the epidemic is used to create the tree ( Figure 6B ) . The two schemes of time sampling are studied here because they are characteristic for data sampling in different biological contexts . The first scheme reflects the typical situation for real epidemics for which sequence information is sampled over a broad time window . The second scheme is more applicable to phylogenetic trees based on pathogen populations from within an individual host . While we have concentrated so far on the inference of epidemiological contact structure from phylogenetic trees , we note that our approach can also be used to study the imbalance of within-host trees , which may result from spatial structure or compartmentalization . Both these schemes are idealizations of available real data . In most situations the sample structure will in fact be a combination of one of the two time sampling schemes and random sampling as discussed in the previous section . Above we demonstrated that contact structure can result in strongly unbalanced trees . Here we investigate whether real epidemics also result in unbalanced trees . To this end we examine the imbalance of a phylogenetic tree constructed from 5961 patient sequences of the Swiss HIV cohort study [33] ( see Figure 7 ) . Since SIR dynamics with low ( i . e . small mean degree or transmissibility close to the critical value ) can potentially also generate strongly unbalanced trees , we compare the imbalance of the HIV tree to an SIR epidemic with random mixing and an , corresponding to the range of realistic that has been estimated for the HIV epidemic in Switzerland [34] . The sampled individuals cover 30–40% of all Swiss HIV infected individuals and we therefore restrict the total epidemic size to the range . It has been argued that the HIV epidemic is still in the exponential stage in developed countries [35] . However , because saturation of an epidemic also causes increased imbalance , we make the conservative assumption that the total population is finite and can be equal to the current epidemic size . We take the range of possible population sizes to be . As a null model , we use a likelihood-free test of departure from random mixing based on [36] . We repeatedly sample parameters uniformly from the intervals above and simulate an epidemic outbreak using these parameters under the assumption of random mixing . We then randomly sample between and individuals from the simulated tree and calculate the normalized Sackin index of the resulting subtree ( blue line and shaded areas in Figure 7 ) . We compare this to subtrees with identical number of sampled individuals from the HIV tree from [33] ( red line and shaded areas in Figure 7; see Figures S3 , S4 and S6 for an analysis using an alternative imbalance measure , as well as a more detailed view of the effect of individual parameters on tree imbalance ) . Comparing the HIV tree with an SIR epidemic with equal number of individuals connected by random mixing shows that the HIV tree exhibits strong imbalance . The normalized Sackin index of the HIV tree is with a minimum/maximum of / based on 100 bootstrap trees constructed from sequences with the amino acid positions resampled . The range of values of the normalized Sackin index of the HIV tree as well as the bootstrap trees is outside the 95% confidence interval for the SIR model , implying that the imbalance of the HIV tree is statistically highly significant . One important component of contact structure in the HIV epidemic is the preferential transmission within transmission groups ( such as heterosexuals , intravenous drug users , and men having sex with men ) [33] . Subepidemics occurring within these transmission groups are therefore expected to show decreased levels of imbalance . Indeed , calculating the Sackin index for the three largest transmission clusters [33] reveals much more balanced trees in these subepidemics ( see Figure 7 ) . However , the observed level of imbalance is still significant , suggesting that contact structure is present even within these transmission groups . As we pointed out above , the imbalance in the SIR model increases with approaching . Therefore , the significance of the imbalance of the subepidemics depends on the choice of and thus . In summary , our analysis of the HIV tree reveals substantial imbalance in the entire epidemic , possibly extending to the subepidemics , which is consistent with what would be expected from our knowledge of HIV transmission .
In this paper , we have studied the effect of different classes of contact networks to model SIR type epidemics . We show that simulations of epidemics on networks with non-random contact structure result in transmission trees with topologies that exhibit strong differences from tree topologies that would be obtained under the assumption of random mixing . Measures of tree imbalance such as the ( normalized ) Sackin index can be used to reveal such differences and to quantify the statistical significance of departure from models assuming random mixing . Epidemiological properties , such as rate of spread or probability of outbreak , are known to depend sensitively on contact structure . If appropriate genetic data are available , then the approach presented here allows testing whether an epidemic may be appropriately modeled by standard models assuming random mixing . Conversely , if one is interested in phylogenetic tree structure of infectious pathogens , then knowledge of the contact structure in the host population will be important for the correct interpretation of the tree topology . Such contact structure may be on a coarse grained level ( e . g . in between cities for human infectious diseases ) or on a fine grained level ( e . g . contact patterns between individuals ) . The level at which a phylogenetic tree is able to resolve any contact structure depends on the rate of evolution of the pathogen . In cases such as HIV , where the rates of evolution are high enough to result in substantial genetic differences between virus populations of individual hosts , a phylogenetic tree may reveal contact structure down to the individual level . Indeed our analysis of the phylogenetic tree underlying the Swiss HIV epidemic provides evidence for non-random contact structure on the population level as well as the individual level . A considerable part of the imbalance of the HIV tree is likely attributable to a high-level contact structure that arises from preferential transmission within transmission groups ( such as heterosexuals , intravenous drug users , and men having sex with men ) . An analysis of the imbalance of subtrees corresponding to individual transmission groups , however , also reveals a signal indicative of non-random contact structure within these transmission groups . The importance of contact structure for epidemiological processes has been clearly demonstrated by a large number of theoretical studies [2] , [4] , [5] , [8] , [10] , [11] , [37] . This has sparked considerable interests in determining the contact structure that underlies the transmission of different pathogens [12] , [13] , [16] . The determination of such contact networks is fraught with difficulties . Contact networks based on patient interviews may suffer from incompleteness , inaccuracy and in some cases also lack of reliability of patient information . Contact networks derived from devices that measure physical proximity often do not have sufficient spatial resolution or may neglect to account for important pathogen specific factors . Our method takes a first step to infer contact structure from genetic data . In comparison to the other methods mentioned above it has the advantage of being based on data that are readily available for many important pathogens . Moreover , our approach focuses only on those contacts that have led to transmission rather than other contacts between individuals that may be irrelevant for the spread of the epidemic . The method presented here allows testing for deviations from the assumption of random mixing . It is possible to extend the approach to test departure from BA or WS networks or other network models ( e . g . configuration model networks with specific degree distributions ) , equivalent to the Swiss HIV epidemic ( see Results ) . Thus our approach is able to distinguish between different types of contact networks in a statistical sense , but it does not yield the actual contact network that underlies the epidemic . The choice of null model will strongly depend on the epidemic studied . Different diseases have different transmission routes and thus a contact that is relevant for transmission for one disease may not be relevant for transmission of another disease . Our method is based on imbalance , which is only a crude measure of tree topology . We have shown that imbalance cannot distinguish between BA and WS networks in our case . We expect that using other independent measurements of tree topology can reveal further information on the structure of the underlying contact network . Branch lengths are one such measure [22] , [27] . In simulated epidemic outbreaks , where the exact waiting times between infection and recovery events are known , branch lengths can be used together with tree imbalance to distinguish between ER , BA and WS models ( Figure S7 ) . It is important to note that maximum likelihood analyses typically provide trees where branch lengths represent evolutionary time . The branch length statistic used in Figure S7 requires edge lengths in calender time . In order to obtain accurate branch length estimates in calender time rather than evolutionary time , we need to allow for the observed variation in evolutionary rates across branches , such as relaxed clock models [38] . Due to the model complexity , these analyses are typically done in a Bayesian MCMC framework which does not converge for datasets of our size using the current implementation [39] . Thus the reliability of branch length estimates in our reconstructed phylogenies is questionable and this measure should only be used when confidence in branch lengths of the reconstructed tree is very high . The imbalance of the reconstructed phylogenies depends on the the genetic data used . Sampling biases at the genetic level can result in a strongly unbalanced tree [27] , even if the underlying population is randomly mixed . This sampling bias will be reflected in the inferred contact structure . In this sense , if connected subsets of the population are more densely sampled than others , the resulting contact structure will show that these individuals are much more highly interconnected than the other individuals that belong to those sub-populations that are only sparsely sampled . This can be both advantageous as well as disadvantageous , depending on what the contact structure should reflect . If the sampling of genetic data is high in those sub-populations where we require high resolution , then the inferred contact structure will be representative of this sub-population , but not of those that are poorly sampled . Our method would therefore reject an epidemic model of contact structure where all individuals are equally likely to be interconnected for one where some individuals are highly connected ( i . e . those from the densely sampled sub-population ) and others are weakly connected ( i . e . those from the sparsely sampled sub-population ) . We also note that the approach presented here can be applied to the analysis of phylogenetic trees based on pathogen populations within an infected individual . The models for the dynamics of pathogen populations within an infected individual typically are also based on the assumption of random mixing and our approach would allow to test whether this assumption is fulfilled . For phylogenetic trees based on within-host data imbalance would likely reflect a compartmentalization of pathogen replication and could thus provide important insight into mechanisms of pathogenesis .
The Swiss HIV cohort study was approved by individual local institutional review boards of all participating centers ( www . shcs . ch ) . Written informed consent was obtained for each SHCS study participant . We consider a disease spreading amongst a susceptible population that displays susceptible-infected-removed ( SIR ) type dynamics [1] , [40] . In the limit of large population size and random mixing the model can be described by the simple system of differential equations ( 2 ) ( 3 ) ( 4 ) , and are the number of susceptible , infected , and removed individuals in each compartment at time . Here , is the rate of transmission per contact between a susceptible and infected individual and is the removal rate of infected individuals . In the context of a network the transmissibility is the probability that an individual will transmit the disease across a single contact over the whole duration of the epidemic . This can be calculated from and by averaging over the distribution of waiting times for transmission and recovery . For a given recovery time , the probability that transmission occurs before the individuals recovers is given by . Thus , if the recovery times are exponentially distributed [5] , ( 5 ) The basic reproductive ratio is the number of secondary infections caused by an infected individual placed into a wholly susceptible population ( ) [1] . In fully mixed populations , an epidemic can occur when . Here , . In non-homogeneous populations this threshold also depends on the contact structure . For networks generated by the configuration model [41] , [42] , i . e . random contact networks with a given degree distribution , the expected total number of second neighbors ( neighbors of my neighbors ) is given by , where and are the first and second moments of the degree distribution [43] . Then is the average number of nodes two steps away per neighbor . Thus the expected number of secondary infections per infected individual is . For an epidemic to occur must be greater than 1 or [2] , [5] . When the population additionally displays community structure ( such as clustering and modularity ) this threshold changes again . For example , the Watts-Strogatz model incorporates local connectedness by starting with a regular network where every node is connected to a fixed number of close neighbors . Then , each connection is rewired to a randomly chosen node with a certain probability , thus creating shortcuts in the contact network [31] . In this case the threshold for an epidemic outbreak also depends on this rewiring probability [37] . Since we are not interested in the exact values of the parameters , we can choose by rescaling without loss of generality . Furthermore , it should be noted that as approaches 1 , gets much larger than . Hence , the SIR model with large effectively reduces to an SI model . In order to simulate transmission trees of epidemics occurring in heterogeneously connected populations , a C++ implementation of Gillespie's Next-Reaction Method was used [44] . At the beginning of the simulation a single node is infected and a recovery time is sampled from the distribution of recovery times , . Each of the node's susceptible neighbors is then infected after a time chosen from the distribution of infections times , . If the infection time is shorter than the recovery time , the link is activated and the node is infected at time . The procedure is then repeated for each newly infected node . In case a node is scheduled to be infected by multiple neighbors , the earliest infection takes priority . By keeping track of who-infects-whom , each epidemic outbreak yields an infection tree . We study three different network models: ( a ) the Erdös-Rényi ( ER ) random graph [29] , ( b ) the Barabási-Albert ( BA ) graph [30] and ( c ) the Watts-Strogatz ( WS ) graph [31] . In the ER random graph every individual is connected to every other individual with a certain probability . This results in a graph with a Poissonian degree distribution with mean number of neighbors . The BA graph is constructed by preferential attachment . Each node is sequentially added to the graph and attached to neighbors , where nodes that already have many neighbors have a higher probability of being connected to the new node . This results in a degree distribution with a power-law tail . Such graphs are often referred to as scale-free [30] . Finally , WS graphs start out with a ring lattice , in which every node is connected to its nearest neighbors . Each link is then updated with probability in such a way that one end of the link is rewired to a randomly chosen node . Thus the node that loses the link decreases its degree by one and the node that the link is rewired to increases its degree by one . This process introduces shortcuts in the graph ( i . e . decreases the mean shortest path ) [31] . For the graph has strongly connected communities . For all links are randomly assigned and the graph is similar to the ER graph with the same mean number of neighbors ( equal number of edges ) [42] . For intermediate values of , the graphs often display both strong community structure and short path lengths , which are characteristics of small-world graphs [31] . The shape of a phylogenetic tree is described in part by its imbalance . Here , we use the Sackin index as a measure of imbalance [45] , because of its analogy to path lengths in graph theory . The Sackin index is defined as follows: Let the distance of a leaf be the number of internal nodes that need to be traversed when following the path from the root of the tree to a leaf . Then the Sackin index is the sum of all such paths , ( 6 ) When considering transmission trees , it is important to differentiate between two cases: The first case considers the complete transmission trees of an epidemic outbreak . This is essentially equivalent to a birth/death process . From the perspective of an individual , death corresponds to removal from the infectious class or the depletion of all its susceptible neighbors . In either case that individual can no longer infect anyone else . Thus the transmission trees have branches that do not all survive until the end of the epidemic . In the second type of tree , all lineages are extant at the end of the epidemic . Such a transmission tree could be generated by an SI-type epidemic in an infinite size population where each individual can infect every other individual . These trees are generated by the Yule model . The expected value of the Sackin index for a given number of leaves under the Yule model is given by [26] , ( 7 ) with Euler's constant . An exact expression for the expected value of the Sackin index is not known in the case where some lineages die before the end of the epidemic . However , it can be assumed that this will in general result in slightly more unbalanced trees . Since the expected value of the Sackin index increases with tree size , we introduce a normalized Sackin index defined by ( 8 ) measures the relative deviation of the tree imbalance from what would be expected for an SI epidemic ( or SIR with ) . | One of the recent key innovations in the epidemiology of infectious diseases was the incorporation of explicit contact structure ( i . e . who can infect whom ) into epidemiological models . Theoretical studies have generated a broad consensus in the field that knowledge of the contact network may help to greatly improve the control of the spread of epidemics . The key problem in the field , however , is that we lack knowledge regarding the actual contact structure underlying real epidemics . Much research is focused on trying to reconstruct actual contact networks in various ways ( mobile phone usage data , electronic devices that measure physical proximity , patient interviews , etc ) . All of these approaches are highly labour intensive and are fraught with many difficulties . Here , we present a new approach which is based on readily available sequence data . Using the Swiss HIV epidemic as an example , we show that it displays strong indications of a underlying contact structure that strongly differs from random interactions , thus undercutting the assumption of random mixing which is commonly made in epidemiological models . | [
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| 2012 | Inferring Epidemic Contact Structure from Phylogenetic Trees |
The exceptional toxicity of botulinum neurotoxins ( BoNTs ) is mediated by high avidity binding to complex polysialogangliosides and intraluminal segments of synaptic vesicle proteins embedded in the presynaptic membrane . One peculiarity is an exposed hydrophobic loop in the toxin’s cell binding domain HC , which is located between the ganglioside- and protein receptor-binding sites , and that is particularly pronounced in the serotypes BoNT/B , DC , and G sharing synaptotagmin as protein receptor . Here , we provide evidence that this HC loop is a critical component of their tripartite receptor recognition complex . Binding to nanodisc-embedded receptors and toxicity were virtually abolished in BoNT mutants lacking residues at the tip of the HC loop . Surface plasmon resonance experiments revealed that only insertion of the HC loop into the lipid-bilayer compensates for the entropic penalty inflicted by the dual-receptor binding . Our results represent a new paradigm of how BoNT/B , DC , and G employ ternary interactions with a protein , ganglioside , and lipids to mediate their extraordinary neurotoxicity .
Botulinum neurotoxins ( BoNTs ) are the most toxic bacterial toxins known and are produced e . g . in food by the anaerobic , spore-forming bacteria Clostridium ( C . ) botulinum , C . butyricum , and C . baratii . When contaminated food is ingested , BoNTs specifically inhibit acetylcholine release at the neuromuscular junctions . The resulting flaccid paralysis called botulism can lead to death by respiratory failure [1] . Due to their extraordinary toxicity ( intraperitoneal median lethal dose ( LD50 ) : 1 ng/kg [2] ) , BoNTs are regarded as a potential biothreat agent [3] . On the other hand , the BoNTs are successfully exploited as pharmacological agents for a broad range of medical and cosmetic applications [4] . Both their potency and specificity can be attributed to an elaborate and elegant mode of action , mediated by the different domains of the 150 kDa molecule [5] . First , the 50 kDa C-terminal domain HC of the 100 kDa heavy chain ( HC ) mediates high-affinity binding to specific receptors on the presynaptic membrane . Next , BoNT is taken up into recycling synaptic vesicles whereupon acidification causes the 50 kDa N-terminal domain ( HN ) to form a pore through which the 50 kDa light chain ( LC ) is translocated into the cytoplasm . Finally , LC specifically cleaves different members of the soluble N-ethylmaleimide-sensitive-factor attachment receptor ( SNARE ) protein complex which inhibits fusion of neurotransmitter-filled vesicles at cholinergic synapses . Until now , seven established BoNT serotypes ( BoNT/A-G ) and the newly pronounced BoNT/HA [aka BoNT/H or BoNT/FA] , BoNT/X , and eBoNT/J [aka BoNT/En] ) with more than 40 subtypes have been described which differ by the usage of their specific receptors , their substrate recognition , and/or specific cleavage site targeted [6–9] . According to the current dual-receptor binding paradigm , the simultaneous interaction with a protein and a carbohydrate receptor is needed for high-affinity binding of most serotypes [10–13] . While polysialogangliosides constitute the carbohydrate receptors , the luminal domains of different isoforms of the synaptic vesicle proteins SV2 ( SV2A , B , C ) and synaptotagmin ( Syt-I or II ) were identified as the protein receptors ( reviewed in [14] ) . Here , BoNT/A and E bind to SV2 [13 , 15–21] whereas BoNT/B , G , and the mosaic serotype BoNT/DC bind to Syt-I and II [11 , 12 , 22–28] . For BoNT/D and F , the functional details of the contribution of SV2 to receptor binding still need to be elucidated [29–31] . No protein receptor has been identified for BoNT/C [32 , 33] , BoNT/X , and eBoNT/J so far . Instead , binding of BoNT/C is mediated by two independent ganglioside-binding sites ( GBS ) and an interjacent WY-loop rich in aromatic residues [34–37] . A similar loop called ganglioside-binding loop ( GBL ) is present in BoNT/DC , and a role in binding to isolated ganglioside GM1a has been demonstrated [35] . The mutations of the three aromatic residues Y1251 , W1252 , and F1253 to alanine in the GBL region as well as GBL deletion equally abolished binding to GM1a , GD1a , and GQ1b embedded into a 2-oleoyl-1-palmitoyl-sn-glycero-3-phosphocholine ( POPC ) monolayer and binding and uptake into P19-derived neurons [38] . Lately , identical HCDC mutants were shown to lack binding to liposome-embedded ganglioside mix , but also to phosphatidylcholine-only liposomes , while HCDC wild-type exhibited weak binding [39] . Interestingly , the well-characterized Syt-binders BoNT/B and G also exhibit hydrophobic loops at analogous positions ( Fig 1A ) which we will call ‘HC loop’ in this work . Contributions of their HC loops to membrane binding were hypothesized but have never been shown experimentally [23 , 24 , 40] . In this work , we generated recombinant full-length BoNTs and isolated receptor-binding domains HC of BoNT/B , DC , and G devoid of key residues in their HC loops and analyzed the contribution of the HC loop to both binding and toxicity . Here , markedly reduced toxicities of BoNT ΔHC loop mutants indicate a key role for the HC loop in mediating the high toxicity . Systematic surface plasmon resonance ( SPR ) measurements revealed that binding of HCB , HCDC , and HCG ΔHC loop mutants to isolated Syt-II remains unaltered , while stable binding to gangliosides or Syt-II incorporated into micelles and/or nanodiscs is lost . The low-affinity binding of HCB , HCDC , and HCG ΔHC loop mutants towards dual-receptor nanodiscs containing both Syt-II protein and GT1b ganglioside receptors pinpoints the critical contribution of this structural feature in BoNT/B , DC , and G for the membrane binding . Thermodynamic binding analysis deciphers that the insertion of the HC loop into the lipid bilayer compensates for the large entropic penalty imposed by the dual-receptor binding . Our results show that the hydrophobic HC loop of BoNT/B , DC , and G is an integral component of the receptor binding and that ternary interactions between three different classes of molecules—proteins , gangliosides and lipids—are needed to mediate stable and high-affinity binding of BoNT/B , DC , and G to exert their exquisite toxicity .
Crystal structures of the cell-binding domains of BoNT/B , DC and G ( HCB , HCDC and HCG , respectively ) reveal the presence of a flexible , exposed peptide loop interjacent to the conserved ganglioside- and Syt protein receptor-binding sites in the C-terminal half of the HC domain ( HCC ) [22 , 24 , 40] . These HC loops comprise amino acids E1245-E1252 in HCB , F1245-H1255 in HCDC and K1250-D1257 in HCG and are rich in aliphatic and especially aromatic amino acids ( Fig 1A , S1 Fig ) . Interestingly , an analogous loop is absent in crystal structures of HCA , HCE and HCF which all employ SV2 as protein receptor [41–43] . To analyse the role of the HC loop of BoNT/B , DC and G in the binding mechanism three mutants lacking 3–5 mainly aliphatic and aromatic amino acid residues were constructed ( ΔHC loop mutants: HCB ΔG1247-F1250 , HCDC ΔY1251-F1253 and HCG ΔY1252-W1256 ) . Subtype BoNT/B4 , the most diverse BoNT/B subtype and major cause for food borne botulism e . g . in UK , displays a basic ( Arg ) instead of an aromatic residue ( Phe ) in the HC loop ( S1 Fig ) . Therefore , an additional HCB mutant was constructed comprising the exchanges I1248L/V1249L/F1250R based on the most diverse HC loop of the BoNT/B4 subtype ( S1 Fig ) produced by the non-proteolytic strain Templin [44] . All full-length BoNT and HC fragment ΔHC loop mutants were expressed and isolated in yields similar to the corresponding wild-type constructs indicating no major structural impairment due to the absence of the HC loop peptide . All ΔHC loop mutants displayed a slightly faster migration pattern than the respective wild-type proteins in sodium dodecyl sulfate polyacrylamide gel electrophoresis ( SDS-PAGE ) analysis due to their decreased molecular weight ( S1 Fig ) . Circular dichroism ( CD ) -spectroscopy and thermal denaturation experiments of the three ΔHC loop mutants vs . the three wild-type HC fragments indicated no change in secondary structure ( S1 Fig ) . First , the potency of the three full-length BoNT ΔHC loop mutants was assessed using the mouse phrenic nerve hemidiaphragm ( MPN ) assay . This ex vivo assay mimics the respiratory failure terminally induced in botulism by intoxicating an explanted hemidiaphragm [12 , 30 , 45] . The addition of BoNTs to the organ bath impairs nerve—muscle transmission and causes progressive muscle neuroparalysis . Dose—response calibration curves for BoNT/B , DC , and G wild-type toxins were generated and used to calculate the potency of the respective BoNT ΔHC loop mutants . While 2 nM BoNT/B wild-type caused 50% paralysis in 78 min , 20 nM BoNT/B ΔG1247-F1250 were required for a similar paralysis time of 81 min which constitutes a residual potency of 0 . 83% ( Fig 1B ) . Likewise , the potencies of BoNT/DC ΔY1251-F1253 and BoNT/G ΔY1252-W1256 were calculated as 0 . 40% and 0 . 16% of the respective BoNT wild-types . Hence , deletion of the HC loop drastically impairs the neurotoxicity of BoNT/B , DC and G , indicating an integral role of this structural feature in their mechanism of action . Glutathione S-transferase ( GST ) pull down assays were set out to determine the contribution of the hydrophobic HC loop to the toxin—receptor interactions . As previously demonstrated [12] , HCB and HCG wild-type showed robust binding to their protein receptor Syt-II lacking the transmembrane domain ( TMD; GST-rSyt-II 1–61 ) , but reduced binding when Syt-II comprises its TMD ( GST-rSyt-II 1–90 ) solvated in Triton X-100 micelles ( Fig 1C , S2 Fig ) . Upon addition of complex polysialogangliosides , HCB and HCG wild-type reached their maximum binding to GST-rSyt-II 1–90 due to the dual-receptor interaction . Also BoNT/DC recognizes Syt-I and Syt-II as protein receptor [22 , 26] . In GST pull down assays , HCDC wild-type shows a similar binding pattern to the diverse Syt-II configurations although at lower affinity , presumably due to the different Syt-binding site in the HCC ( Fig 1C ) . Gangliosides marginally contribute to HCDC-binding to Syt-II inserted into detergent micelles , indicating a lower affinity of ganglioside binding in BoNT/DC . Analysis of pure protein-protein interactions between ΔHC loop mutants ( HCB ΔG1247-F1250 , HCB I1248L/V1249L/F1250R , HCDC ΔY1251-F1253 , HCG ΔY1252-W1256 ) and their protein receptor GST-rSyt-II 1–61 revealed binding affinities virtually identical to those of HC wild-types clearly demonstrating that shortening the respective HC loop did not impair protein receptor recognition ( Fig 1C ) . Addition of the Syt-II TMD to insert GST-Syt-II 1–90 into Triton X-100 micelles almost abolished binding of HCB ΔG1247-F1250 as well as HCG ΔY1252-W1256 and drastically reduced binding of HCB I1248L/V1249L/F1250R while the already low affinity of HCDC was hardly reduced by the deletion ΔY1251-F1253 . Addition of gangliosides did not restore binding of HCDC ΔY1251-F1253 and HCG ΔY1252-W1256 , but partially rescued binding of HCB ΔG1247-F1250 to GST-rSyt-II 1–90 and fully restored the binding of HCB I1248L/V1249L/F1250R towards GST-rSyt-II 1–90 . Hence , the HC loop plays an integral role for BoNT/B , DC , and G in the recognition of membrane-embedded receptor structures . Furthermore , results of the B4-like HC loop mutant indicate that aromatic residues are not an absolute requirement for membrane interaction . To precisely decipher the contribution of the individual components of the receptor complex in a quasi-natural environment , we analyzed the binding of the recombinant HC domains to receptor molecules embedded in phospholipid-bilayer nanodiscs ( reviewed in [46] ) by SPR measurements . Here , we generated four different types of nanodiscs harboring specific BoNT receptor components: ( 1 ) empty nanodiscs consisting only of membrane scaffold proteins ( MSPs ) and POPC lipids , ( 2 ) nanodiscs additionally containing GT1b as ganglioside receptors , ( 3 ) nanodiscs alternatively containing GST-rSyt-II 1–90 as protein receptor , and finally ( 4 ) nanodiscs containing both GT1b and Syt-II to analyze the dual-receptor binding ( Fig 2 ) . We determined the binding kinetics and affinity of the recombinant receptor-binding domains HC of BoNT/B , DC , and G by SPR . First , we measured the binding of HC to the intraluminal domain 1–61 of Syt-II to exclude detrimental effects by ΔHC loop mutations on the Syt-binding site ( Fig 3A ) . No differences were observed between the binding affinities of the HC wild-types compared to the mutants tested ( Table 1 , S4 Fig ) , again demonstrating that the mutations did not impair binding to isolated protein receptor Syt-II . All binding kinetics show rapid association and immediate dissociation indicating that Syt-binding alone is insufficient to mediate high-affinity and stable receptor binding . Subsequently , we analyzed the binding of HC to receptor molecules embedded into phospholipid-bilayer nanodiscs by SPR . Empty nanodiscs were immobilized on the negative control flow cells while either GT1b- , Syt-II- or dual-receptor nanodiscs were immobilized on the measurement flow cells using the His-tag fused to MSP . Hereby , we ensured that any additional binding signals could only be caused by receptor molecules integrated into nanodiscs . HC wild-types showed low binding affinities in the μM-range to GT1b nanodiscs while the ΔHC loop mutants lacked any binding ( Fig 3B , Table 1 ) . This indicates that the HC loop is needed for the interaction with GT1b integrated in lipid membranes , but this set-up does not differentiate whether the HC loop mediates interactions with the ceramide portion of the gangliosides or the POPC lipids . However , when binding to Syt-II nanodiscs was tested ( Fig 3C ) , the complete lack of binding of all ΔHC-loop mutants indicates an indispensable role of the HC loop when BoNT/B , DC , and G bind to Syt-II embedded into a lipid bilayer . These results were also in good agreement with the pull-down data ( Spearman r = -0 . 96; Fig 1C , S4 Fig ) . Interestingly , the HCB I1248L/V1249L/F1250R mutant mimicking the HC loop of subtype BoNT/B4 showed binding to Syt-II nanodiscs albeit with ~15 times lower affinity than HCB wild-type . Of the eight residues ( E1245-E1252 ) comprising the BoNT/B HC loop , only three residues are strictly conserved and two aliphatic residues are similar in all eight BoNT/B subtypes ( S1 Fig ) . Here , exchange of F1250R might be the main cause for the reduced affinity of HCB I1248L/V1249L/F1250R . Overall , HC wild-type binding to Syt-II embedded into nanodiscs is more stable than the binding to Syt-II 1–61 only or to gangliosides embedded into nanodiscs , but not sufficiently stable to exert the exquisite potency observed . Finally , when binding to dual-receptor nanodiscs was analyzed , high-affinity and stable interactions were only observed for HCB wild-type as well as HCB I1248L/V1249L/F1250R ( Fig 3D ) , indicating that the HC loop binding is conserved across the BoNT/B subtypes despite differences in amino acid sequence . This demonstrates that three components , the ganglioside-binding site , the protein receptor-binding site , and the hydrophobic HC loop , are crucial for efficient membrane binding . Along this line , the mutant HCB ΔG1247-F1250 showed a 40-fold reduced affinity and unstable binding compared to HCB wild-type . An even more drastic situation accounts for BoNT/DC and G whose ΔHC loop mutants showed no or only very low binding , which is in good agreement with results obtained by pull-down assays ( Spearman r = -0 . 99; S4 Fig ) . The binding affinity of 5 . 9 ± 0 . 2 nM for HCB wild-type to dual-receptor nanodiscs corresponds well to previously reported KDs determined by pull down of BoNT/B by Syt-II and GT1b incorporated in Triton X-100 micelles of 7 . 0 ± 0 . 6 nM [23] . Binding of full-length BoNT/B to Syt-II and GT1b incorporated in lipid vesicles or exosomes resulted in slightly higher affinities of 0 . 23 nM when measured in a filtration assay [47] , or 0 . 6 nM by SPR [48] . Latter deviations could be explained by different read-out systems and the more physiological membrane composition of the exosomes , respectively . The affinity of HCDC wild-type to dual-receptor nanodiscs is similar to an apparent KD determined by pull-down assays using Triton X-100 micelles ( 160 nM vs . 172 ± 14 nM [26] ) . Altogether , the marked reduction in dissociation-rate constants due to the formation of a highly stable BoNT-receptors complex was clearly visible in our work . In conclusion , our data clearly show that the current dual-receptor model has to be extended by the HC loop-mediated interaction of BoNT/B , DC , and G with the lipid membrane , which ensures the high-affinity and stable receptor binding to a tripartite-receptor binding model . For a deeper understanding of the underlying mechanism governing the contribution of the hydrophobic HC loop to the high-affinity binding , we determined the thermodynamic binding parameters exemplarily for the HCB wild-type and ΔG1247-F1250 mutant to Syt-II or dual-receptor nanodiscs , respectively , by SPR . To this aim , the temperature-dependence of the binding affinity was determined by measuring the interaction at four different temperatures ( 11°C , 15°C , 25°C , and 37°C ) from which ΔG° , ΔH° , and -TΔS° were calculated by van’t Hoff plots ( Fig 4 , S5 Fig ) . The thermodynamic binding parameters determined for binding of HCB wild-type to isolated Syt-II by SPR were in close agreement with the binding parameters previously determined by isothermal titration calorimetry [24] and show that the interaction is favored by both entropy and enthalpy ( Fig 4 , Table 2 ) . Binding of HCB wild-type to nanodisc-incorporated Syt-II was largely driven by enthalpy ( -58 . 4 kJ/mol ) but opposed by entropy ( 18 . 99 kJ/mol ) , indicating a different mode of binding despite similar free Gibbs energy ( ΔG° ) . On the contrary , binding of HCB wild-type to dual-receptor nanodiscs was essentially driven by a gain in binding entropy ( -32 kJ/mol ) while the enthalpic contribution ( ΔH° = -14 kJ/mol ) was minor . The thermodynamic profile of the interaction of HCB ΔHC loop with dual-receptor nanodiscs is similar to that of HCB wild-type with Syt-II-only nanodisc , exhibiting an important contribution of the HC loop to the high gain in entropy . As a direct consequence , at physiological temperatures of 37°C , which reduces the contribution of entropy compared to 25°C ( –TΔS° ) , only HCB wild-type still displays a high-affinity interaction with dual-receptor nanodiscs while the affinity of HCB ΔHC loop is reduced ~50-fold ( S1 Table ) . In general , our thermodynamic analysis elucidates that the interplay of all three receptor components is indispensable for effective stabilization of the tripartite toxin—receptor complex under physiological temperatures , with the HC loop providing a significant gain in entropy .
In this manuscript , we analyzed for the first time the interaction of BoNT/B , DC , and G with their receptor components in the quasi-natural environment of phospholipid-bilayer nanodiscs which allowed new insights into the BoNT-receptor interaction and their kinetics at physiological conditions . We show how the hydrophobic HC loop located between the ganglioside- and protein receptor-binding sites of the toxin plays a crucial role in the mechanism of action of BoNT/B , DC , and G , all binding the protein receptors Syt-I and Syt-II . Figuratively speaking , the toxin provides its HC loop as anchor to hook up with the eukaryotic synaptic cell membrane ( Fig 5 ) . This HC loop stabilizes high-affinity binding of BoNT/B , DC , and G to the membrane-embedded receptors , thereby contributing to their exquisite neurotoxicity . Moreover , our thermodynamic binding analysis exhibits that the HC loop integrating into the membrane during receptor binding compensates for the loss of rotational freedom upon the dual-receptor binding . In essence , at least for BoNT/B , DC , and G , stable toxin—receptor interactions are based on ternary interactions involving proteins , gangliosides , and lipids ( Fig 5 ) and not just on binary interactions as previously predicted [10] . Crystal data of the BoNT/B-Syt-II binary toxin—receptor complex suggested a close proximity of the HC loop to the cell membrane upon protein—receptor binding , and a role of this hydrophobic loop in membrane interaction was hypothesized [23 , 24] . This hypothesis was revived but not proven upon crystallization of HCG , also comprising a prominent HC loop too flexible to be visible in the crystal structure [40] . Before structural data was available , site-directed mutagenesis of the W1258/Y1259 ( WY ) motif in BoNT/C HC showed a moderate effect on ganglioside binding and strong effects on synaptosomal membrane binding and neurotoxicity [30 , 49] . Consecutive structural data of HCC allocated this WY motif into an analogous HC loop and confirmed the previous binding and toxicity data [34 , 35] . In parallel , Strotmeier et al . could demonstrate experimentally the importance of an analogous hydrophobic HC loop ( F1240-Y1246 ) observed in the BoNT/D HC crystal structure for neuronal membrane binding and neurotoxicity [50] , which was essentially confirmed afterwards [51] . The HC fragment of BoNT/DC , 74% identical to BoNT/C HC , employs Syt-I and -II as protein receptor and displays an extended HC loop . The mutation W1252A in the loop region reduced binding of HCDC to coated ganglioside GM1 as well as to primary cortical neurons , which led the authors to term the region ganglioside-binding loop ( GBL ) [35] . Independent mutational analysis showed drastically reduced binding of HCDC HC loop mutants to P19 neurons as well as to ganglioside-containing POPC liposomes , irrespective of the carbohydrate moiety of gangliosides GM1a , GD1a , and GQ1b used in these SPR analyses [38] . Due to the absence of HCDC binding to POPC-only liposomes , Nuemket et al . excluded any lipid membrane interaction of the HC loop , but postulated instead that the hydrophilic portion of the ceramide would be targeted by the HC loop region [38] . However , the highest concentration of the HC-fragment tested was 500 nM; thus highly transient and low-affinity interactions frequently observed for lipid-binding proteins [52 , 53] remain undetected by the SPR method . Very recently the work by Zhang et al . added significantly to our understanding of the ganglioside recognition as well as the membrane interaction by the HC loop region for BoNT/DC . Their most intriguing finding was that the HC loop of BoNT/DC directly interacts with lipids only . In addition , the three single mutations Y1251A , W1252A , and F1253A designed by Nuemket et al . abolished HCDC interaction with lipids as well as liposome-embedded gangliosides [39] . These data agree well with our results showing that ΔHC loop mutants do not bind to GT1b embedded in nanodiscs ( Fig 3B ) . Altogether , the above data for BoNT/C , D , and DC cannot decipher whether the HC loop directly participates in ganglioside binding or membrane binding only or both , because isolated gangliosides already form micelles by themselves due to their ceramide portion , which enables insertion of the HC loop into the ganglioside micelle membrane . For BoNT/B , biochemical and structural studies definitely demonstrated that the carbohydrate moiety of only a single ganglioside exclusively interacts with residues of the conserved ganglioside-binding site in the neighborhood of the HC loop but not with HC loop residues themselves [11 , 54 , 55] . Analogous conclusions can be drawn for BoNT/G [12 , 28 , 40] . So far , no structural study has visualized or pointed towards direct interaction between an HC loop and the carbohydrate moiety of gangliosides . Our experimental approach , using only the luminal domain of the protein receptor Syt-II in the absence of any lipids and only Syt-II integrated either into Triton X-100 micelles or nanodiscs , unambiguously proves an interaction of the HC loop with the lipids . Here , deleting terminal residues at the tip of the HC loop did not impair the interaction of HCB , HCDC , and HCG with isolated Syt-II , but caused a strong reduction of the binding affinity towards nanodisc- or Triton X-100 micelle-embedded Syt-II . These findings can only be explained by interactions of the HC loop with membrane lipids . Along this line , Zhang et al . were able to demonstrate low-affinity binding of HCDC wild-type , but not HCC and HCD , to pure PC liposomes by immunoblot analysis which was abolished by above-mentioned single-residue mutations in the HC loop [39] . Remarkably , BoNT/B , DC , and G ΔHC loop mutants showed a strong reduction in binding and toxicity compared to the respective BoNT wild-type ( Fig 1B ) , clearly demonstrating an integral contribution of the HC loop to the binding , uptake , and putative membrane insertion of HN for LC translocation into the cytosol . A mechanistic explanation of how the HC loop mediates the high-affinity interaction was evolved by measuring the binding thermodynamics of the HCB interaction . Here , despite similar overall binding affinities of HCB to either isolated Syt-II or Syt-II integrated into nanodiscs , different contributions of the binding enthalpy and binding entropy were calculated . Although the much larger binding enthalpy for the binding of HCB to membrane integrated Syt-II leads to additional electrostatic interactions between positive protein surface charges and negatively charged phospholipid headgroups [56] , a large unfavorable contribution of the binding entropy indicates strong restrictions on the rotational and translational degrees of freedom upon binding [57] . The binding of HCB ΔHC loop mutant to dual-receptor nanodiscs revealed the same thermodynamic reaction profile . In contrast , a large and favorable binding entropy , which is typically associated with the burial of hydrophobic groups away from the solvent [58] , was measured for HCB wild-type bound to dual-receptor nanodiscs , which indicates an integration of the HC loop into the membrane bilayer . Indeed , the BoNT/B HC loop is solvated by up to 20 molecules of water [55] which are released upon membrane integration of the HC loop . It is noteworthy that this large gain in binding entropy is only observed for the dual-receptor nanodiscs , implying that the interaction with the carbohydrate portion of GT1b is needed to trigger the membrane insertion of the HC loop ( Fig 4B ) . This result is in agreement with recent findings that binding to the carbohydrate portion of gangliosides confers specificity , whereas binding strength is mediated by additional interactions of the toxins with the neuronal membrane itself [59] . Hence , the sequence of the tripartite interactions is likely to be i ) specific adherence of BoNT/B to a ganglioside , ii ) integration of BoNT/B HC loop into the membrane stabilizing the BoNT-receptor complex and iii ) binding of BoNT/B to synaptotagmin to direct its uptake into an SV to act locally in the synapse . However , the almost identical binding affinities between HCB wild-type bound to Syt-II nanodiscs and HCB ΔHC loop-mutant bound to dual-receptor nanodiscs imply that the contribution of the ganglioside to the binding strength equals that of the HC loop membrane insertion . Nevertheless , binding of HCB wild-type to empty nanodiscs was not reliably measurable by SPR , which points towards a very low intrinsic affinity of the HC loop for isolated lipid membranes , as previously observed also by Zhang et al . and Nuemket et al . [38 , 39] . Despite the ubiquitous abundance of lipid membranes in the organism , the very low intrinsic affinity of the HC loop prevents off-target binding which would effectively lower the neurotoxicity . Beside BoNT/B , DC , and G employing Syt as protein receptor , also BoNT/C , to which no protein receptor has been ascribed yet , displays an exposed HC loop [34] . This suggests that an HC loop—membrane insertion can occur also by BoNT serotypes not employing Syt as protein receptor . In contrast , in BoNT/A the HC loop is crippled and completely absent in the related BoNT/E and F . The absence of an analogous HC loop—membrane interaction of BoNT/A and E could also be compensated by the additional binding to the conserved N-glycan in SV2A-C [18 , 19 , 21] . Also other mechanisms can contribute to the high-affinity interaction with the membrane . For instance , BoNT/A interactions with the lipid membrane have been allocated to the N-terminal subdomain of the HC-fragment [61] . Finally , our results show that direct interactions between BoNT/B , DC , and G and the membrane already take place at neutral pH during the initial binding step . It has been shown before that interactions with GT1b are needed for the formation of a translocation pore at low pH [62] . The integration of the hydrophobic HC loop in the membrane potentially primes BoNT/B , DC , and G for subsequent conformational changes needed to insert the translocation domain HN into the membrane . The HC loop is always exposed and of sufficient length to constitute an epitope for a monoclonal antibody , thereby representing a novel target structure to neutralize BoNT/B , DC , and G in a clinical setting . MPN hemidiaphragm data clearly illustrate the loss of potency upon HC loop deletion , which is comparable to blockade by an antibody [63] . Interestingly , membrane interaction of a hydrophobic loop for high-affinity binding is a common feature that is shared between BoNT/B , DC , G , and the broadly neutralizing anti-HIV-1 antibody 4E10 [64 , 65] . 4E10 binds to contiguous epitopes within the membrane proximal external region ( MPER ) of the envelope transmembrane glycoprotein gp41 as well as host membrane lipids to exert its neutralizing activity . In conclusion , our data show how membrane interactions of BoNT/B , DC , and G via a hydrophobic HC loop contribute to the formation of a highly stable BoNT-receptors complex , involving a protein , gangliosides , and lipids , that is critical to confer the toxin’s exquisite toxicity ( Fig 5 ) .
All experiments were performed in accordance with the German Tierschutzgesetz ( TierSchG , 29th March 2017 ) and Tierschutz-Versuchstierverordnung ( TierSchVersV , 1st August 2013 ) and with the guidelines established by the European Community Council Directive n° 2010/63/EU and approved by the local authority veterinary services ( Veterinäramt Hannover , protocol file number §4/019 ) . Recombinant MSP 1E3D1 , sodium cholate hydrate , octyl β-D-glucopyranoside ( OG ) , POPC , anhydrous chloroform , trisialoganglioside GT1b from bovine brain , GD1a , and rat brain extract were obtained from Sigma Aldrich , GM1 and GD1b were obtained from Calbiochem while GM3 was from Avanti Polar Lipids . MSP was dissolved in phosphate buffered saline ( PBS , pH 7 . 3 ) containing 4 mM OG , the concentration was quantified photospectrometrically using a molecular extinction coefficient of ε280 = 29 , 400 M-1cm-1 and stored at -20°C . POPC was dissolved in chloroform while all gangliosides were dissolved in PBS and stored at -20°C . Bio-Beads SM-2 were obtained from Bio-Rad , activated in methanol ( Merck ) , and stored in distilled sterile water at 4°C until used . Protease-free bovine serum albumin ( BSA , fraction V ) , skimmed milk powder , and reduced glutathione were obtained from Carl Roth while a PageRuler prestained protein ladder was obtained from Thermo Fisher Scientific . Immobilon P PVDF-membranes ( 0 . 45 μm ) for western blotting were from Merck Millipore while BioTrace NT nitrocellulose membranes ( 0 . 2 μm ) for dot blots were from Pall corporation . A monoclonal mouse anti 6×His epitope tag antibody ( clone His . H8 ) was obtained from Thermo Fisher Scientific ( Pierce ) while an affinity-purified polyclonal rabbit antibody targeting amino acids 1 to 11 from mouse synaptotagmin 2 was obtained from Synaptic Systems . A mouse monoclonal antibody targeting GT1b ganglioside ( clone GT1b-2b ) was obtained from Merck Millipore . All primary antibodies were tested for specific detection of their respective target by western and dot blot and showed no cross-reactivity at the used concentrations ( S6 Fig ) . Horseradish peroxidase ( HRP ) -coupled goat anti-mouse IgG ( H+L ) or anti-rabbit IgG ( H+L ) -specific antibodies were obtained from Dianova or KPL . Plasmids encoding the HC-fragment of BoNT/B and G fused to a C-terminal Streptag ( HCBS , HCGS ) , full-length BoNT/B and G equipped with a C-terminal Streptag ( scBoNTBSL , scBoNTGS ) , as well as GST fusion proteins of rSyt-II 1–61 and rSyt-II 1–90 have been described previously [12 , 27 , 54] . The plasmids pHCDCS and pH6tBoNTDCS , encoding the HC-fragment of BoNT/DC fused to a C-terminal Streptag ( HCDCS; AA 863–1285 ) and full-length BoNT/DC equipped with an N-terminal His6tag and a C-terminal Streptag ( H6tBoNTDCS ) , respectively , were generated by amplifying the corresponding ORFs using genomic DNA of C . botulinum strain OFD05 ( Gen ID AB461915; kind gift from Keiji Nakamura , Osaka Prefecture University , JP ) as template and cloned into modified pQe3 vectors cut with BamH I/Xma I . The plasmid pHCBS I1248L/V1249L/F1250R contains the most diverse HC loop of the BoNT/B4 subtype produced by the non-proteolytic Group II strain Templin [44] which was isolated from a home-made sheep’s ham associated with food-borne botulism in Germany , 2006 . Sequence of the BoNT/B4 ( GenBank number: MG545727 ) showed 99 . 1% identity to the prototype BoNT/B4 sequence ( Genbank number: ABM73987 ) from strain Ecklund 17B [6] at amino acid level . The ΔHC loop ( deletion ) mutants pHCBS ΔG1247-F1250 , pHCBS I1248L/V1249L/F1250R , pBoNTBSL ΔG1247-F1250 , pHCDCS ΔY1251-F1253 , pH6tBoNTDCS ΔY1251-F1253 , pHCGS ΔY1252-W1256 , and pBoNTGS ΔY1252-W1256 were generated by PCR , applying the GeneTailor site-directed mutagenesis system ( Life Technologies , Darmstadt , Germany ) and suitable primers ( Eurofins , Ebersberg , Germany ) . Nucleotide sequences of all newly generated constructs were verified by DNA sequencing ( GATC Biotech , Konstanz , Germany ) . In general , expression and purification of active full-length BoNT and mutants thereof were conducted under biosafety level 2 containment ( project number GAA A/Z 40654/3/123 ) . The isolation of HA33 , GST-rSyt-II 1–61 , GST-rSyt-II 1–90 , wild-type HCBS , single-chain ( sc ) scBoNTBSL , HcGS , and scBoNTGS have been described previously [12 , 27 , 54 , 66] . HCDCS and H6tBoNTDCS variants were expressed analogously . After isolation via C-terminal StrepTag according to the manufacturer’s instruction ( StrepTactin resin; IBA GmbH , Göttingen , Germany ) , HCDCS wild-type and HCDCS ΔY1251-F1253 were purified further by size exclusion chromatography ( Superdex 75 , GE Healthcare , Freiburg , Germany ) in PBS , pH 7 . 4 . H6tBoNTDCS wild-type and H6tBoNTDCS ΔY1251-F1253 were first isolated by IMAC ( Co2+-Talon matrix; Takara Bio Europe S . A . S . , Saint-Germain-en-Laye , France ) and subsequently by affinity chromatography employing StrepTactin resin in 100 mM Tris-HCl , pH 8 . 0 . GST fusion proteins eluted by glutathione were dialyzed against PBS , pH 7 . 4 , two times with and two times without β-mercaptoethanol . Desired protein fractions were pooled , frozen in liquid nitrogen , and kept at −70°C . For CD analysis , desired volume of HC proteins was dialyzed against 100 mM Tris-HCl , pH 8 . 0 , 150 mM NaCl . Protein concentrations were determined subsequent to SDS-PAGE and Coomassie blue staining by using an LAS-3000 imaging system ( Fuji Photo Film ) , the AIDA 3 . 51 software ( Raytest , Straubenhardt , Germany ) , and BSA ( 100–1600 ng ) as reference protein . The MPN assay was performed as described previously [13 , 19] . Mice of strain RjHan:NMRI ( 18–25 g , Janvier , St Berthevin Cedex , France ) were sacrificed by trained personnel before dissection of organs . First , mice were euthanized by CO2 anesthesia and subsequently exsanguinated via an incision of the ventral aspect of the throat . Then the chest of the cadaver was opened . To limit the consumption of mice , the left and right phrenic nerve hemidiaphragms were excised from female mice and placed in an organ bath containing 4 ml of Earle’s Balanced Salt Solution . The pH was adjusted to 7 . 4 and oxygen saturation was achieved by gassing with 95% O2 and 5% CO2 . The phrenic nerve was continuously electro-stimulated at a frequency of 1 Hz with a pulse duration of 0 . 1 ms and a current of 25 mA to achieve maximal contraction amplitudes . Isometric contractions were recorded with a force transducer ( Scaime , Annemasse , France ) and the software VitroDat ( Föhr Medical Instruments GmbH ( FMI ) , Seeheim , Germany ) . The resting tension of the hemidiaphragm was approximately 10 mN . In each experiment , the preparation was first allowed to equilibrate for 15 min under control conditions . Then , the buffer was exchanged to 4 ml of Earle’s Balanced Salt Solution supplemented with 0 . 1% BSA and varying dilutions of wild-type BoNT/B , BoNT/DC , and BoNT/G . The times required to decrease the amplitude by 50% ( paralysis time t½ ≤ 180 min ) for three or four BoNT concentrations ( each n ≥ 3 ) were used to construct the calibration curves for scBoNT wild-type to which logarithmic or power functions were fitted ( y ( scBoNTBSL; 650/2000/6500 pM ) = -19 . 84ln ( x ) + 227 . 3 , R2 = 0 . 9999; y ( scH6tBoNTDCS; 100/300/1000 pM ) = -13 . 49ln ( x ) + 146 . 28 , R2 = 0 . 9978; ( y ( BoNT/G; 0 . 6 , 2 . 0 , 6 . 0 and 20 nM ) = 97 . 123x-0 . 271; R2 = 0 . 9967 ) . These functions were used to convert the mean paralysis times t½ determined for 200 nM scBoNTBSL ΔG1247-F1250 ( n = 4 ) , 30 nM H6tBoNTDCS ΔY1251-F1253 ( n = 4 ) , and 60 nM BoNTGS ΔY1252-W1256 ( n = 2 ) into the corresponding scBoNT wild-type concentrations and to express them as relative biological activity . Circular dichroism ( CD ) data was collected with a Jasco J-810 spectropolarimeter in a 1-mm path length cuvette with a concentration of 10 μM HCBS or 3 μM HCGS/HCDCS degassed . Spectra were recorded at 22°C from 195 to 250 nm with 100 nm/min , response of 1 s , standard sensitivity , bandwidth of 1 nm , and five accumulations . Spectra were analyzed , processed , and visualized using Spectra Manager II software ( JASCO International Co . Ltd . , Tokyo , Japan ) . Subsequent temperature-induced denaturation was performed by monitoring the CD signal at 210 nm from 25°C to 70°C with a stepwise temperature increase of 2 . 5°C every 6 min . The GST pull-down assays were similarly performed as previously described [13] with the addition of 125 μg of ganglioside mixture ( Matreya , State College , PA , USA ) in selected experiments as indicated . Briefly , GST and GST fusion proteins ( 150 pmol each ) were immobilized to 10 μL of glutathione-sepharose-4B matrix ( Qiagen , Hilden , Germany ) and subsequently incubated for 2 h at 4 °C with 100 pmol HC fragment in a total volume of 200 μL in binding buffer as stated in the respective figure legends . Beads were collected by centrifugation and washed two times each with the corresponding binding buffer . Washed pellet fractions were incubated at 37°C for 20 min in SDS sample buffer and analyzed by 12 . 5% SDS-PAGE . Protein bands were detected by Coomassie blue staining and subsequently quantified by densitometry using the software TINA ( version 2 . 09f , Raytest , Straubenhardt , Germany ) . Unspecific binding of ligand to immobilized GST matrix was subtracted from the specific binding signal of HC . Nanodisc assembly was performed as described before with the following modifications [67] . Recombinant GST-rSyt-II 1–90 in PBS containing 0 . 5% Triton X-100 was spin-concentrated at 4000 × g for 20 min through Amicon Ultra-4 centrifugal filter units ( Merck Millipore ) to a concentration of approximately 3 to 4 mg/mL . A 100 mM POPC stock solution was prepared by drying lipids dissolved in chloroform in 6 mL borosilicate glass tubes ( Fisher Scientific ) under a stream of nitrogen and subsequently under vacuum for 4 hours before solving the dried lipids in a 400 mM sodium cholate solution in PBS by vortexing rigorously and ultrasonic treatment until a clear solution was obtained . Assembly mixtures of a total volume of 170 μL were prepared in glass tubes by adding 100 mM POPC , GT1b ( 10 μg/mL in PBS ) , concentrated GST-rSyt-II 1–90 , and PBS to 100 μL of MSP 1E3D1 . Depending on the concentration of the MSP stock solution used and the kind of assembled nanodiscs , the volume of the reagents was adjusted to fulfill the following criteria: for empty nanodiscs , 130 POPC molecules were added per two molecules MSP ( 130 lipids per nanodisc ) ; for GT1b or dual-receptor nanodiscs the lipid content was reduced to 120 POPC molecules , while 10 molecules of GT1b were added per nanodiscs . Either 50 μL or 30 . 3 μL of concentrated GST-rSyt-II 1–90 was added for assembly into Syt-II nanodiscs or dual-receptor nanodiscs , respectively . The total volume was brought up to 170 μL by addition of PBS ( pH 7 . 3 ) so that the final cholate concentration was between 26 to 28 mM at lipid concentrations between 6 . 5 and 7 . 0 mM . After incubating the mixtures for 30 min at room temperature , the self-assembly process was initiated by transferring the mixtures to 170 μg of Bio-Beads that have been washed with PBS and degassed . After 2 hours of incubation on a shaker at 4°C the mixtures were transferred to a second batch of Bio-Beads to remove effectively the high concentrations of Triton X-100 contained within the concentrated GST-rSyt-II stock solutions and incubated over-night at 4°C on a vertical shaker at 150 rpm . The next day , the assembled nanodiscs were transferred to a new glass tube and further purified and characterized by size exclusion chromatography ( SEC ) . To this aim , the nanodiscs were fractioned using an ÄKTA Explorer 100 and a Superdex 200 Increase GL column ( both GE Healthcare ) at a flow rate of 0 . 75 mL/min in PBS . Beginning after 0 . 3 column volumes , 0 . 5-mL fractions were collected and further analyzed by indirect ELISA for the presence of receptor molecules integrated in nanodiscs . To identify SEC fractions containing both nanodiscs and receptor molecules , fractions were analyzed by indirect ELISA . To this aim , fractions were diluted 1:100 in PBS and 50 μL of this dilution were coated over-night to Nunc MaxiSorp microtiter plates ( Thermo Fisher Scientific ) . The next day , the plates were washed with 4 × 300 μL of washing buffer ( PBS containing 0 . 1% Tween 20 ) before being blocked for 2 hours at room temperature by adding 200 μL/well of a 3% ( w/v ) BSA solution in PBS . After washing , 50 μL of mouse anti-His ( 1:10 , 000 ) , rabbit anti-synaptotagmin 2 ( 1:2500 ) , or mouse anti-GT1b ( 1:2500 ) antibodies diluted in PBS containing 0 . 1% BSA were added for 1 hour at room temperature before bound antibodies were detected by incubation with 50 μL/well of HRP-coupled goat anti-mouse or rabbit IgG antibodies ( used at 1:5000 or 1:3000 dilutions , respectively ) after a further washing step . Signal development was initiated by adding 100 μL per well of Seramun Slow Blau TMB-substrate ( Diavita ) for 10 min before development was stopped by adding 0 . 25 M H2SO4 . Finally , signals were read at 450 nm referenced to 620 nm using an Infinite 200 ELISA reader ( Tecan ) . Fractions of the expected size for assembled nanodiscs were pooled and either used directly for kinetics measurements by SPR ( empty nanodiscs , GT1b nanodiscs ) or further purified using GST pull down ( GST-rSyt-II and dual-receptor nanodiscs ) . Purified nanodiscs were stored at 4°C until used . To separate nanodiscs containing recombinant GST-rSyt-II from nanodiscs without receptor proteins , we made use of the GST-tag contained on the Syt-II proteins for batch purification by GST pull down . To this aim , 334 μL of Protino glutathione agarose were added to glass tubes and washed once with 5 mL of PBS . After centrifugation for 5 min at 500 × g the supernatant was discarded and 1 . 75 mL of pooled nanodisc-containing fractions were added per tube . After a 1-hour incubation at room temperature under constant shaking ( 600 rpm ) the glutathione agarose was pelleted by centrifugation and washed once with 5 mL of PBS . Bound nanodiscs were eluted by incubation with elution buffer ( 10 mM reduced glutathione in 50 mM Tris-HCl , pH 8 . 0 ) for 10 min at room temperature under shaking . Fractions before the purification ( input ) , supernatant after binding ( SN ) , and eluted nanodiscs ( eluate ) were collected and analyzed for the presence of nanodiscs and receptor molecules by SDS-PAGE , Coomassie staining , western and dot blot , and electron microscopy as described below . For SDS-PAGE , fractions were mixed with 3 × Laemmli loading buffer containing dithiothreitol , heated for 5 min at 95°C , and cooled on ice before 10 μL were loaded on 12% polyacrylamide gels and separated according to standard procedures [68] . Gels were either stained using colloidal Coomassie staining [69] or electrophoretically transferred to methanol-activated PVDF membranes for subsequent western blotting [70] . To this aim , membranes were blocked for 1 hour at room temperature with 2% ( w/v ) skimmed milk powder in ELISA washing buffer before addition of either mouse anti-His ( 1:10 , 000 ) or rabbit anti-synaptotagmin 2 ( 1:5 , 000 ) antibodies . Detection was done by HRP-labelled goat anti-mouse IgG or anti-rabbit IgG ( both 1:10 , 000 ) antibodies for 1 h at room temperature . All antibodies were diluted in blocking buffer and the membranes were washed between incubation steps for 3 × 5 min with washing buffer . Detection was done with SuperSignal West Dura Extended Duration Substrate ( Thermo Fisher Scientific ) on a ChemiDoc imaging system ( Bio-Rad ) . Dot blots were performed accordingly except that 10 μL fractions of GT1b ( 100 μg/mL ) were dropped on a nitrocellulose membrane and air-dried before proceeding with blocking and incubation with an anti-GT1b antibody ( 1:2 , 500 ) and HRP-labeled goat anti-mouse IgG antibodies ( 1:10 , 000 ) . Suspensions of nanodiscs were prepared for negative staining electron microscopy using glow-discharged grids ( 400-mesh copper grid covered with carbon re-inforced plastic film ) . Uranylacetate ( 0 . 5% ) was used as negative stain . Examination of samples was performed with the Tecnai 12 BioTwin ( FEI , Thermo Fisher Scientific ) transmission electron microscope operated at 120 kV , and images were recorded with an Eagle CCD camera ( 4096 x 4096 pixels , 16 bit; FEI , Thermo Fisher Scientific ) . All SPR measurements were performed on a Biacore X100 or a T200 apparatus ( GE Healthcare ) using sensor chips CM5 and HBS-EP+ ( 10 mM HEPES , pH 7 . 4 , 150 mM NaCl , 3 mM EDTA , 0 . 05% Tween 20 ) of HBS-N ( 10 mM HEPES , pH 7 . 4 , 150 mM NaCl; nanodisc measurements ) as running buffers at 25°C unless otherwise noted . Binding kinetics and affinity of recombinant receptor-binding domains of BoNT/B , DC , and G to GST-rSyt-II 1–61 were determined on a Biacore X100 as described previously [60] . Briefly , recombinant GST or GST-tagged rSyt-II were captured on flow cells 1 or 2 to immobilization densities of 100 resonance units ( RUs ) or 220 RUs , respectively , using a GST Capture Kit modified sensor chip ( GE Healthcare ) before 1:3 dilution series of recombinant receptor-binding domains ( ~50 kDa ) were injected for 120 s at a flow rate of 30 μL/min , ranging from 1200 nM to 14 . 8 nM with duplicate injections at the highest concentration . The binding dissociation was monitored for 300 s before the sensor surface was regenerated using 10 mM glycine pH 2 . 1 for 120 s at a flow rate of 10 μL/min . All measurements were performed in duplicate . To determine the thermodynamics of the interaction between HCB and GST-rSyt-II 1–61 , both GST and GST-rSyt-II were immobilized covalently to a sensor chip CM5 each by dilution in 10 mM acetate buffer pH 4 . 5 ( GE Healthcare ) to 350 RUs ( GST-rSyt-II ) on flow cell 1 and 104 RUs ( GST ) on flow cell 2 , using standard amine coupling chemistry ( Amin coupling Kit; GE Healthcare ) on a Biacore X100 . Two-fold dilution series of HCB ranging from 800 nM to 6 . 25 nM with duplicate injections of the highest concentration were injected for 60 s before binding dissociation was monitored for 120 or 300 s . Regeneration was done by a 30 s injection of 10 mM glycine pH 1 . 7 at a flow rate of 10 μL/min . All measurements were replicated four times at 10°C , 15°C , 25°C , and 35°C . Nanodiscs containing no ( empty nanodiscs ) , GT1b only , Syt-II only , or both receptor molecules ( dual-receptor nanodiscs ) were immobilized to a series S sensor chip CM5 ( GE Healthcare ) via the His-tag incorporated on the MSP . To this aim , the sensor surface was modified using the His Capture Kit ( GE Healthcare ) according to manufacturer’s recommendations . Empty nanodiscs diluted 1:20 in HBS-N buffer were immobilized to the control flow cells 1 or 3 for 120 s at 10 μL/min , leading to immobilization levels of 281 ±38 RUs . GT1b-containing nanodiscs ( 1:20 ) , Syt-II-containing or dual-receptor nanodiscs ( both 1:5 ) were immobilized accordingly to flow cells 2 or 4 , leading to immobilization levels of 311 ±19 , 409 ±30 , and 275 ±25 RUs , respectively . After each capture step , the sensor chip was allowed to stabilize for 60 s before HC fragments were injected at 66 . 6 nM , 200 nM , and 600 nM in a kinetic titration series ( single cycle kinetics [71] ) at a flow rate of 30 μL/min for 120 s followed by a 600-s injection of running buffer to monitor binding dissociation . The sensor surface was regenerated by injection of 10 mM glycine pH 1 . 5 for 60 s at a flow rate of 30 μL/min . For thermodynamic measurements , binding of HCB and HCB ΔG1247-F1250 to dual-receptor nanodiscs as well as HCB wild-type to Syt-II-only nanodiscs was repeated at 11°C , 15°C , 25°C , and 37°C . All measurements were performed in duplicate . All binding curves were double referenced as described [72] . Additionally , directly before each measurement , binding of recombinant receptor-binding domains to either GST or empty nanodiscs on both the control and measurement flow cell was determined . Double-referenced binding curves from these measurements , which arose due to partially ineffective regeneration of the sensor surface especially during later injection cycles , were additionally subtracted to prevent artefacts . Unless otherwise stated , all binding curves were fit to 1:1 Langmuir interaction models using the BIAevaluation software ( 4 . 1 . 1 ) . Thermodynamic binding parameters ΔG° , ΔH° , and—TΔS° were derived from the temperature dependence of the binding affinity KD by using van’t Hoff plots ( S5 Fig ) . To this aim , ln ( KD ) was plotted over 1/T ( Kelvin ) and fitted using linear regression to determine the slope and Y-intercept using Prism 5 . 04 ( GraphPad ) from which ΔH° ( = slope × gas constant R ) and ΔS° ( = Y-intercept ×–gas constant R ) were calculated . ΔG° at 25°C was calculated from ΔH°–TΔS° . | Botulinum neurotoxins are Janus-faced molecules: due to their exquisite toxicity , botulinum neurotoxins are considered as biological weapons , but they are also highly effective medicines for numerous neurological indications . However , what mediates their exquisite toxicity ? The exclusive binding to neurons and the subsequent paralysis cuts off the host’s communication networks . The neurospecific binding is ensured by anchoring to two receptor molecules both embedded in the membrane: a complex ganglioside and a synaptic vesicle protein . Here , we reveal a third interaction between a hydrophobic so-called HC loop protruding from the surface of the serotypes BoNT/B , DC , and G into the lipid membrane . Only this HC loop ensures their high-affinity binding to the neuronal receptors also at physiological temperature ( 37°C ) . Hereby , BoNT/B , DC , and G prevent untimely dissociation prior to uptake into the neuron . Therefore , our study provides the mechanistic basis for the development of inhibitors to combat botulism , but it also has implications for engineering toxin—membrane interactions yielding optimized BoNT-based therapeutics to treat neuromuscular dysfunctions successfully . Intriguingly , a broadly neutralizing anti-HIV-1 antibody shares a similar strategy , emphasizing the general relevance of our results for host—pathogen interactions . | [
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| 2018 | A lipid-binding loop of botulinum neurotoxin serotypes B, DC and G is an essential feature to confer their exquisite potency |
Dengue fever ( DF ) is an emerging infectious disease in the tropics and subtropics . Determinants of DF epidemiology and factors involved in severe cases—dengue haemorrhagic fever ( DHF ) and dengue shock syndrome ( DSS ) —remain imperfectly characterized . Since 2000 , serotype 1 ( DENV-1 ) has predominated in the South Pacific . The aim of this study was ( i ) to determine the origin and ( ii ) to study the evolutionary relationships of DENV-1 viruses that have circulated in French Polynesia ( FP ) from the severe 2001 outbreak to the recent 2006 epidemic , and ( iii ) to analyse the viral intra-host genetic diversity according to clinical presentation . Sequences of 181 envelope gene and 12 complete polyproteins of DENV-1 viruses obtained from human sera in FP during the 2001–2006 period were generated . Phylogenetic analysis showed that all DENV-1 FP strains belonged to genotype IV–“South Pacific” and derived from a single introduction event from South-East Asia followed by a 6-year in situ evolution . Although the ratio of nonsynonymous/synonymous substitutions per site indicated strong negative selection , a mutation in the envelope glycoprotein ( S222T ) appeared in 2002 and was subsequently fixed . It was noted that genetic diversification was very significant during the 2002–2005 period of endemic DENV-1 circulation . For nine DF sera and eight DHF/DSS sera , approximately 40 clones/serum of partial envelope gene were sequenced . Importantly , analysis revealed that the intra-host genetic diversity was significantly lower in severe cases than in classical DF . First , this study showed that DENV-1 epidemiology in FP was different from that described in other South-Pacific islands , characterized by a long sustained viral circulation and the absence of new viral introduction over a 6-year period . Second , a significant part of DENV-1 evolution was observed during the endemic period characterized by the rapid fixation of S222T in the envelope protein that may reflect genetic drift or adaptation to the mosquito vector . Third , for the first time , it is suggested that clinical outcome may be correlated with intra-host genetic diversity .
Dengue fever is the most common vector-borne viral disease affecting humans and represents an archetypal emerging infectious disease whose epidemiological landscape has been substantially modified during the past century [1] , [2] . Each year , an estimated 100 million people contract dengue fever ( DF ) in the tropics and subtropics [3] with an increasing incidence of the severe forms , i . e . at least 500 , 000 cases annually of dengue haemorrhagic fever ( DHF ) or dengue shock syndrome ( DSS ) . Dengue virus ( DENV ) is a member of the genus Flavivirus in the family Flaviviridae , which includes single-stranded , positive-sense RNA viruses with a genome of approximately 11 kb that encodes three structural proteins ( capsid ( C ) , membrane ( M ) , envelope ( E ) ) and seven non structural proteins ( NS1 , NS2A , NS2B , NS3 , NS4A , NS4B , NS5 ) . Four serotypes exist ( denoted DENV-1 to DENV-4 ) , the infection by a given serotype conferring a specific and prolonged immunity to that serotype [4] . The factors that lead to severe infections are debated , and may include both viral factors ( e . g . , differences in strain virulence [5]–[7] ) , host immune factors such as antibody-dependent enhancement , cell-mediated immunity [8]–[10] and antigenic mimicry [11] . French Polynesia ( FP ) which comprises more than one hundred South Pacific islands , has experienced a large number of dengue fever epidemics involving all four serotypes ( DENV-1 in 1944 , 1975–76 , 1988–89 , 2001; DENV-2 in 1971 , 1996–97; DENV-3 in 1964 , 1969 , 1989–90; and DENV-4 in 1979 , 1985 ) [12]–[16] . Approximately 260 , 000 inhabitants live in FP , mostly in the Society Archipelago and particularly in Tahiti but importantly a large number of tourists from Asia , Central and South America , and other Pacific islands visit FP annually [17] possibly inadvertently introducing new DENV strains . Since 2000 , DENV-1 has been the predominant serotype in the Pacific region [18] causing successive outbreaks ( Palau in 2000; FP , Samoa , Hawaii and Easter island in 2001; Cook and Solomon islands in 2002; Wallis , Futuna and New Caledonia in 2002–2003 ) . After the severe DENV-1 outbreak which caused nearly 33 , 800 cases in 2001 [15] , FP experienced a period of low-level transmission from 2002 to 2005 , followed by a new epidemic in 2006 [19] . In this study , we performed an analysis of the E-gene sequence of 181 DENV-1 viruses and the nearly complete coding sequence of 12 DENV-1 viruses collected over a 6-year period from patients experiencing various clinical presentations in the five FP archipelagos . In addition , we performed a comprehensive comparative analysis of intra-host viral genetic diversity in 16 patients . This study enabled us to predict the precise geographic origin and evolutionary relationships , during both endemic and epidemic periods , of the DENV-1 isolates that circulated in FP from the severe 2001 outbreak to the recent 2006 epidemic . Original patterns of intra-host genetic diversity were also identified in association with the clinical severity of infection .
We analyzed serum samples from 181 DENV-1 infected patients from FP . Sampling was conducted in the five FP archipelagos ( Figure 1 ) : Society ( Windward and Leeward islands ) , Tuamotu , Gambier , Austral and Marquesas , from January 2001 to December 2006 ( Table 1 ) . The study period included the 2001 and 2006 DENV-1 outbreaks , separated by four years of low-level transmission ( 2002–2005 ) . From a total of 181 cases , 152 patients experienced DF , 19 DHF and ten DSS with one death . Dengue disease severity was graded according to the World Health Organization ( WHO ) classification guidelines [20] . The time of serum collection relative to infection ranged from one to six days in documented cases . All human sera analyzed in this study had been preserved at −80°C at the Institut Louis Malardé ( Tahiti , FP ) . All samples were obtained from sera initially sampled for diagnostic purpose , and archived at the Institut Louis Malardé ( Tahiti , FP ) . The use of biological samples and the collection of information were performed with the authorization of the “Direction des affaires juridiques et des droits des patients , Centre Hospitalier Territorial de Polynésie Française ( Tahiti ) ” and in accordance with French regulations . Virus RNA was extracted from acute-phase sera of DENV-1 infected patients using the QIAamp Viral RNA Mini Kit ( Qiagen ) according to manufacturer's instructions . DENV-1 sequences were retrieved from public databases and used to design oligonucleotide primers for reverse transcription-polymerase chain reaction ( RT-PCR ) amplification and sequencing of FP viruses . Genetic characterization of the E-gene was conducted using the Qiagen OneStep RT-PCR kit together with primers E1F-E4R , followed by a nested PCR using primers E2F-E3R ( Table S1 ) to produce a 1 , 759 nt fragment including the complete E-gene ( 1 , 485 nt ) which was subsequently sequenced directly using amplification primers . For characterization of full-length coding sequences , 12 overlapping cDNA fragments were generated by RT-PCR using 12 sets of oligonucleotide primers ( Table S1 ) . Fragments C1 , C3–C8 , C10 and C11 were obtained using the same one-step RT-PCR protocol as described above . Fragments C2 , C9 and C12 were synthesized using a two-step protocol: cDNA was generated using a mixture of random hexaprimers ( RT Taqman Applied Biosystems ) followed by PCR amplification using Taq Polymerase ( Invitrogen ) . Sequencing using amplification primers resulted in the characterization of a 10 , 075 nt sequence . For the analysis of intra-host genetic diversity , the Qiagen OneStep RT-PCR kit was used together with primers Q1F-Q1R ( Table S1 ) to produce a 758 nt fragment within the E-gene , which was subsequently purified using the QIAquick PCR Purification Kit , ligated into the cloning vector pCR 2 . 1 and transformed into TOP10 competent cells , according to the manufacturer's protocol ( TA Cloning , Invitrogen ) . Approximately 40 clones per serum were generated and sequenced using the T7 promoter primer ( 5′-CCCTATAGTGAGTCGTATTA-3′ ) . To estimate the error rates of our amplification system , we carried out a control experiment using a fully sequenced clone of the 758 nt fragment . Serial dilutions were produced and the last dilution providing a clear positive signal was used as a control . It was submitted to one-step RT-PCR amplification and clones ( n = 90 ) were characterized under identical conditions as viral RNA extracted directly from acute phase DENV-1 patient sera . In order to evaluate the influence of viral load in DENV-1 genetic diversity within patients , viral RNA was quantified by real-time RT-PCR , as described previously [21] in 11 of the 17 analyzed sera corresponding to five DHF/DSS cases and five DF cases ( including sequential blood samples for one patient: 47 . 2002 and 49 . 2002 ) . Sequence data from sequencing reactions were combined for analysis and edited using the Sequencher 4 . 7 software ( Gene Codes Corporation ) . Nucleotide sequences used for phylogenetic analyses were aligned using Clustal W [22] , and then imported into the MEGA 3 . 1 package [23] . Nucleotide genetic distances were calculated using the Kimura 2 algorithm [24] and Neighbor-Joining was used for phylogenetic reconstructions . Robustness of phylogenetic trees was assessed using bootstrap resampling analysis ( 1000 replications ) . Supplementary maximum likelihood phylogenetic analyses were performed using the Bayesian method available in MrBayes v3 . 1 . 2 [25] with a minimum of ten million generations and a burnin of 10% . Stationary was assessed at effective sample size ( ESS ) >400 using Tracer v1 . 4 . 1 ( part of the BEAST package [26] ) . ” Phylogenetic analysis of E-gene sequences was conducted using a sample of 240 DENV-1 sequences . This included 181 FP sequences generated in this study together with three sequences of viruses that were previously characterized during the 1988–89 and the 2001 DENV-1 outbreaks in FP [27]: D1 . French Polynesia/89 , GenBank accession number AY630408; D1 . French Polynesia/01 , GenBank accession numbers AY630407 and AB111070 . These FP sequences were combined with a sample of 56 viruses representing the global genetic variability of DENV-1 available from GenBank . In addition , we conducted a phylogenetic analysis based on the complete coding regions of 41 DENV-1 strains isolated worldwide ( available from GenBank ) and the corresponding sequences of 12 FP strains characterized in this study . Differences in nucleotide and protein sequences were analyzed and compared according to the geographical origin , the sampling period and the clinical presentation . The extent of sequence divergence was evaluated using the pairwise distance among the nucleotide sequences ( π nt ) and the amino acid sequences ( π aa ) . The mean ratio of nonsynonymous ( dN ) to synonymous ( dS ) substitutions per site was estimated using the pairwise method of Nei and Gojobori [28] as implemented in the MEGA 3 . 1 package . For the analysis of intra-host genetic diversity , the sequence of each clone was compared to all other clones for each human serum . The percentage of variable nucleotide sites ( number of variable nt sites/number of nt sites ) , of nucleotide mutations ( number of nt mutation/number of nt sequenced ) , and of mutant clones ( number of clones with mutation/total number of clones ) was calculated , as well as the π nt , π aa , dN , dS and dN/dS parameters . Results were then compared according to the clinical presentation of dengue infection . To explore the selection pressures acting on DENV-1 at different levels of viral evolution , distinct datasets were analyzed as follows: ( i ) “FP intra-host” dataset: this group included 17 series of cloned sequences obtained from 16 patients infected with DENV-1 ( eight DF , eight DHF/DSS ) in FP between 2001 and 2006 . For one patient ( DF - Moorea , Windward islands , Society archipelago - December 2002 ) two series of clones were produced from sequential blood samples obtained at day one and day four of the disease ; ( ii ) “FP inter-host” dataset: this group included the 181 sequences generated in FP between 2001 and 2006 ( this study ) ; ( iii ) “genotype IV inter-host” dataset: this included 26 sequences representing the genetic diversity of the “South Pacific” genotype; ( iv ) “serotype 1 inter-host” dataset: this included 59 sequences that reflect the worldwide diversity of DENV-1 isolates . For each dataset , the same parameters ( percentage of variable nucleotide sites , π nt , dS , dN , dN/dS ) were analyzed . All statistical analyses were performed using the R software package ( R development Core Team version 2 . 6 . 0 ) . Categorical and binary variables were compared using a Fisher's exact test . A Mann-Withney test was used for continuous variables ( p values below 0 . 05 were considered to indicate statistical significance ) . To evaluate differences between endemic and epidemic periods , a panel of 176 samples collected between March 2001 and December 2006 was analyzed ( five samples collected in February 2001 before the beginning of the 2001 outbreak were excluded ) . To assess differences in nucleotide sequences , we compared the matrix of pairwise distances obtained for 93 sequences of DENV-1 viruses sampled during the 2002–2005 endemic period and the matrix of pairwise distances obtained for 83 sequences of DENV-1 viruses sampled during epidemics ( i . e . a first matrix obtained from the 42 sequences related to the 2001 FP outbreak , combined with a second matrix from the 41 sequences related to the 2006 FP outbreak ) . For the analysis of intra-host genetic variability according to the clinical severity of dengue infection , we compared the percentage of variable nucleotide sites , the percentage of nucleotide mutation , the percentage of mutant clones , the average pairwise distance ( π nt ) and the mean dN , dS , dN/dS ratio obtained for each group of clones in nine DF sera ( including two sequential blood samples for one patient ) versus eight DHF or DSS sera .
Phylogenetic analysis of 240 E-gene nucleotide sequences ( including the 181 FP sequences generated in this study ) allowed the identification of DENV-1 genotypes I to V [29] previously defined “Asia” , “Thailand” , “sylvatic/Malaysia” , “South Pacific” , and “Americas/Africa” genotypes , respectively , according to their apparent geographic origin ( see Figure 2 ) [5] . The phylogenetic reconstruction based on E-gene nucleotide sequences showed that all DENV-1 strains that circulated in FP between 2001 and 2006 fall into genotype IV – “South Pacific” ( Figures 2 and S1 ) . This genotype also includes DENV-1 viruses originating from other locations in the Pacific ( Australia , Malaysia , Philippines , Palau , Yap , Nauru , Samoa , Hawaii ) , from South-East Asia ( Thailand , Myanmar , China , Indonesia , Timor ) , and from the Indian Ocean ( Seychelles , Reunion ) between 1974 and 2006 . According to a previous study [27] , the D1 . French Polynesia/89 strain isolated during the 1988–89 DENV-1 epidemic ( preceding the 2001 outbreak ) belonged to a different genotype ( genotype V “Americas/Africa” ) and was very close to D1 . French Guyana/89 . DENV-1 strains recovered in FP during the severe 2001 epidemic shared a common ancestor with D1 . Indonesia/98 , a strain isolated in 1998 in a patient from Indonesia ( Figures 2 and S1 ) . However , they were more distantly related to D1 . Palau/00 , a strain isolated in Palau ( Micronesia ) , the first island affected by DENV-1 in the Pacific Ocean in the 2000's [30] . The D1 . Palau/00 strain was found to be more closely related to strains isolated during DENV-1 epidemics in the Philippines or Samoa islands in 2001 and 2002 . Altogether , these results strongly suggest that DENV-1 that circulated in FP in 2001 originated from Indonesia rather than from Palau . This finding is further supported by phylogenetic analysis of 53 complete polyprotein sequences ( Figures 3 and S2 ) . In addition , phylogenetic analysis showed that most DENV-1 strains recovered during the 2001 outbreak in Hawaii clustered in the same lineage as FP 2001 strains ( Figures 3 and S2 ) , suggesting a Polynesian origin of the DENV-1 epidemic that occurred in Hawaii in 2001 [31] . A more detailed phylogenetic analysis of FP 2001–2006 sequences suggested that all FP viruses characterized in this study derived from a common ancestor , i . e . originated from a single introduction event in FP followed by a 6-year in situ evolution ( Figures 3 , S1 and S2 ) . Notably , the observed evolutionary pattern globally follows the chronology of viral spread rather than the geographical origin of viruses or the clinical severity of cases . However , a subgroup comprising seven 2006 strains ( 10-33-49-50-51-52-57 . 2006 ) was found to include viruses originating from Moorea , Raiatea , Tahaa , or the Austral archipelago , but did not include any of the 19 strains that infected patients in Tahiti in 2006 ( Figure S1 ) . The polyprotein sequences of 12 FP 2001–2006 DENV-1 viruses were studied . Nonsynonymous mutations were observed in all genes , except NS4A . Amongst them , four mutations located in the E , NS4B , and NS5 genes have been fixed during viral evolution ( Table 2 ) . Additional analyses were conducted on a 1 , 759 nt region that encompassed the complete E-gene for 181 FP 2001–2006 sequences ( Table 3 ) . A panel of 93 samples collected during the 2002–2005 endemic years and 83 samples collected during the 2001 and the 2006 outbreaks were analyzed . Synonymous and nonsynonymous mutations were observed both during epidemic and endemic periods . The number of variable sites was found to be significantly higher during endemic period than during epidemics . The nucleotide sequence divergence ( π nt ) was also higher during endemic than during epidemic periods ( p<0 . 001 ) . When focusing on the first appearance of amino acid changes , 56% occurred during the 2002–2005 endemic period ( most of them during the 2002 post-epidemic year ) whereas 24% and 20% occurred during the 2001 and 2006 epidemics , respectively ( Figure 4 ) . The number of new amino acid changes decreased from 2002 to 2004 and slightly increased in 2005 . The most frequent mutation ( nt T664A , E-gene numbering ) , observed in 88 of 181 strains , corresponded to a nonsynonymous substitution ( aa S222T ) . Figure 4 clearly shows that this mutation was not present in 2001; it appeared in August 2002 ( endemic period ) and was rapidly fixed ( 9% of sequenced strains in 2002 , 67% in 2003 , 100% in 2004 , 2005 and 2006 ) . Another interesting event was the occurrence of the K363R mutation in domain III of the envelope protein in a cluster of seven 2006 strains ( see above ) . All strains in this subgroup were isolated in patients with DF or DHF in Moorea , Raiatea , Tahaa , or the Austral archipelago , whereas this mutation was absent in all strains that infected patients in Tahiti in 2006 . To examine the extent of genetic diversity of DENV-1 in vivo at the intra-host level , we sequenced 662 clones corresponding to partial E-genes of DENV-1 populations from 16 human sera at a single time point during acute infection . For one DF patient , clones corresponding to sequential samples ( day one and day four of the symptoms ) were sequenced and compared . Approximately 40 clones from each sample were analyzed , and the results are summarized in Table 4 . We carried out a control experiment to evaluate the sequence variation due to in vitro polymerase errors ( see Methods ) . Among 90 clones of the 758 nt fragment studied within the E-gene , 55 nucleotide substitutions were found , corresponding to an error frequency of 0 . 10% or 24×10−6 changes/nt/PCR cycle . This result was significantly lower than the mean levels of intra-host diversity ( percentages of nucleotide mutations ) observed in our samples ( 0 . 25% or 60×10−6 changes/nt/PCR cycle , p<0 . 001 ) . In the 17 human sera studied , a high proportion of mutant clones was observed ( mean 69% ) with no significant difference in terms of clinical presentation , endemic or epidemic period , and time of sampling: analysis of sequential blood samples indicated that DENV-1 viraemia comprised a genetically heterogeneous mixture of variants that were present at the time of first appearance of the symptoms . Mutations occurred in 15 ( 2% ) to 81 ( 11% ) sites of the 758 nucleotides sequenced . The proportion of nonsynonymous mutations was very high in each group of clones ( 63% on average ) . Most mutations were observed only once . However , identical mutations were sometimes observed in several clones from the same serum and/or in different sera . For instance , E269K was observed in 16 clones ( strains 41 . 2002 , 47 . 2002 , 49 . 2002 , 10 . 2003 , and 10 . 2004 ) and E309K in 45 clones ( strains 37 . 2001 , 41 . 2002 , 42 . 2002 , 47 . 2002 , 49 . 2002 , 10 . 2003 , 10 . 2004 , and 32 . 2006 ) . They were present simultaneously in 14 clones ( strains 41 . 2002 , 47 . 2002 , 49 . 2002 , 10 . 2003 and 10 . 2004 ) . Of note , the mutation S222T was recovered in all clones of the 42 . 2002 strain , the first DENV-1 strain that expressed the mutation in August 2002 in Tahiti , and it was absent in all clones tested from sera of patients who were infected previously . Overall , clones with in-frame stop codons were identified in 9 of the 17 sera studied , with a frequency ranging from 0% to 12% ( strain 47 . 2002 ) . Over a total of 662 clones studied , 18 included stop codons ( 3% ) at aa positions 202 , 206 , 211 , 233 , 248 , 271 , 284 , 323 , 328 , 340 ( 2 clones/1 virus ) , 370 ( 3 clones/2 viruses ) , 420 , 426 and 434 ( 2 clones/1virus ) in the E-protein ( 495 aa ) . They occurred in DF , DHF and DSS , during outbreak and endemic periods . A comparative analysis of DENV-1 intra-host genetic diversity was conducted in 16 patients who had experienced infections of different severity ( eight DF versus eight DHF and DSS ) in FP between 2001 and 2006 ( Table 5 ) . The percentage of nucleotide mutations ( number of nt changes/number of nt sequenced ) was significantly lower in severe ( DHF and DSS ) clinical presentations ( mean 0 . 17% , range 0 . 10%–0 . 26% ) than in classical forms ( DF ) of dengue infection ( mean 0 . 32% , range 0 . 15%–0 . 57% , p = 0 . 015 ) . Moreover , the mean sequence divergence was found to be lower in severe cases than in DF cases ( p = 0 . 014 for π nt , p = 0 . 025 for π aa ) . Despite a similar proportion of mutant clones in DF and severe cases ( 69% ) , dN and dS were significantly lower in the latter cases ( p = 0 . 014 and p = 0 . 011 , respectively ) . When error frequencies calculated in our control experiment were subtracted from the results obtained for DF , DHF and DSS clones , differences between severe and classical cases remained significant ( data not shown ) . Altogether , these findings indicate that the level of intra-host genetic diversity is lower in severe presentations than in classical forms of DENV-1 infection . In order to evaluate the influence of viral load in DENV-1 genetic diversity within patients , viral RNA was quantified in five DHF/DSS sera and five DF sera ( including sequential serum samples for one patient: 47 . 2002 and 49 . 2002 ) . Ct ( cycle threshold ) levels indicated comparable viral loads in both DF and DHF/DSS serum samples ( mean Ct = 29 . 5 and 28 . 3 , respectively ) . No correlation was found between the level of intra-host genetic diversity and viral load ( range 0 . 12*105–4 . 5*105 , mean 1 . 10*105 RNA copies/µL ) : linear regression analysis showed that the percentage of nt mutations and π nt were not correlated with viral load ( p = 0 . 51 and 0 . 61 , respectively ) . Moreover , in these serum samples , viral loads were not significantly different in severe cases than in DF cases ( p = 0 . 31 , Mann-Withney test ) . Finally , the mode of evolution of DENV-1 in FP was investigated by analysing the mean ratio of nonsynonymous to synonymous substitutions per site ( dN/dS ) in our different dataset: dN/dS was 0 . 100 for complete genome sequences , and 0 . 091 for E gene sequences , indicating ( dN/dS<1 ) a strong negative ( purifying ) selection pressure [32] . This was confirmed by the study of the genetic variability at different levels of evolutionary divergence , i . e . in the four datasets: “FP intra-host” , “FP inter-host” , “genotype IV inter-host” , and “serotype 1 inter-host” ( Table 5 ) . Within the group of FP viruses , the genetic variability of DENV-1 was higher within hosts than between hosts , as indicated by π nt , and dN/dS values which were higher in the intra-host dataset than in the inter-host dataset . At the inter-host level , the genetic divergence increased with the scale of the population studied ( π nt “FP”<“genotype IV”<“serotype 1” ) whereas the proportion of nonsynonymous mutations decreased ( dN/dS “FP”>“genotype IV”>“serotype 1” ) , reflecting strong purifying selection pressures .
In this study , DENV-1 evolution was analyzed during two recent outbreaks in FP separated by a four-year period of low-level transmission . Original dynamics of epidemics were revealed in the FP ecosystem . Our results suggest that a significant part of DENV-1 evolution occurred during the 2002–2005 endemic years . Despite evidence for strong negative selection , we report mutations that could reflect viral adaptation , particularly S222T that has been fixed by viral evolution in the envelope glycoprotein . Importantly , we report for the first time a significant correlation between levels of intra-host DENV genetic variability and clinical outcome . Historically , FP has experienced successive dengue epidemics that involved the four DENV serotypes [12]–[16] , [19] . In contrast with most endemic countries and other islands such as those in the Caribbean , where different DENV serotypes circulate , prolonged co-circulation of several serotypes has never been detected in FP . Most Polynesian DENV epidemics were due to the introduction of a new serotype originating either from the Americas , South-East Asia or the Pacific region . Since 2000 , serotype 1 has predominated in the South Pacific region and a significant increase in the number of DENV-1 cases has been observed since spring 2006 in several Pacific islands , particularly in FP and in the neighbouring Cook islands [18] , [19] , [33] , [34] . Classically , dengue fever is not believed to be endemic in the Pacific region and outbreaks are usually linked with the importation of a new virus: it has been shown that multiple and repeated introductions of DENV-1 occurred in the Pacific between 2000 and 2003 from a variety of locations in Asia [35] . Accordingly , our first objective was to identify the origin of the DENV-1 strain responsible for the 2001 outbreak in FP . In accordance with a preliminary study [27] , phylogenetic analysis based on a large number of either complete polyprotein or E-gene sequences indicates that the most probable source of this epidemic was an Asian strain , as suggested by the close genetic relationship with a strain isolated in Indonesia in 1998 . This finding is in contradiction with the hypothesis that the first DENV-1 outbreak observed in the Pacific Ocean in 2000 in Palau ( Micronesia ) dispersed secondarily to Polynesia and Melanesia [30] , [33] , [34] and emphasizes the relation between DENV-1 viruses in Asia and those responsible for recent outbreaks in the Pacific [35] . Figure 2 shows that the strain implicated in the Palau outbreak is only distantly related to FP strains and cannot be implicated as the origin of DENV-1 circulation in FP . Our second objective was to determine whether or not the 2001 and 2006 FP outbreaks followed the model of iterative reintroductions evoked above . Our results indicate that the Polynesian dynamic of DENV-1 is different from that previously described in other Pacific islands such as New Caledonia [35] . Genetic analysis showed that no new introduction of DENV-1 strains occurred in FP after 2001 and that the virus responsible for the 2001 outbreak evolved in situ during the following six years . It circulated under a low level endemic mode until its re-emergence as an epidemic virus in 2006 . This phenomenon of re-emergence was previously observed in FP in 1964–1969 for DENV-3 ( genotype IV ) , and in 1979–1985 for DENV-4 ( genotype II ) [13] and thus may constitute an original epidemiological pattern characteristic for Dengue virus evolutionary dynamics in FP . The specific case of DENV-1 circulation between 2001 and 2006 constitutes a unique model of Dengue virus long term evolution in a given ecosystem which we further investigated through the detailed genetic characterization of 181 infected sera sampled during both endemic and epidemic periods . The complete polyprotein characterization ( obtained directly from serum samples ) of 12 DENV-1 viruses collected during the 2001–2006 period gave us the opportunity to analyze viral genetic evolution over this 6-year period . Twenty four nonsynonymous mutations were recorded , distributed all along the polyprotein with one third of mutations occurring in the NS2A and NS4B genes and the lowest rate of variation observed in the NS2B-NS3-NS4A region . Notably , the majority of nonsynonymous mutations appeared during the 2002–2005 endemic years , some of these mutations ( two that appeared in 2002 and two that appeared in 2005 ) being conserved in all subsequent sequences ( Table 2 ) . The observation that viral evolution also occurred during periods of endemic transmission was expected , but the extent of the phenomenon deserved further investigation . Accordingly , a detailed analysis of complete E-gene sequences was performed , which allowed to include a much higher number of sequences ( 93 FP sequences related to the 2002–2005 endemic period , and 83 FP sequences related to the 2001 and 2006 outbreaks ) . As previously noted in the case of complete polyprotein analysis , synonymous and nonsynonymous mutations were detected not only during the 2001 and 2006 outbreaks but also during the 2002–2005 endemic years . Statistical analysis showed that the number of variable sites ( nt and aa ) and the percentage of sequence divergence ( π nt ) were not higher during outbreaks than during endemic periods ( Table 3 ) -and even suggested the opposite . It may appear to be in conflict with conventional thinking since the total virus replicative turnover would be expected to be higher during epidemics , and thus it would be expected that viral genetic variability occurred mainly during the 2001 and 2006 outbreaks . However , the distribution of viral genetic variability between endemic and epidemic periods was considered carefully , since the delineation between endemic and epidemic periods may appear simplistic . For example , although the 2001 outbreak was considered to end in November , the number of confirmed DENV-1 cases reported monthly by the Institut Louis Malardé was still high until May 2002 ( data not shown ) and this transitional post-epidemic period may have specific characteristics , different from the actual endemic period . Altogether , it stands out from our analyses that a significant part of Dengue virus evolution occurred during periods of endemic transmission and not only during outbreaks . Moreover , the majority of amino acid changes were observed during the early stages of the endemic period ( Figure 4 ) , suggesting adaptation to new specific environmental conditions . This is notably the case for S222T , the most frequent substitution identified in 88 strains , which appeared in August 2002 and was subsequently fixed by viral evolution . This mutation concerns the envelope protein , a major component at the virion surface implicated in the interaction with host cells , membrane fusion and induction of a protective immune response . Residue 222 is localized in domain II which is implicated in the dimerization of the envelope protein at acidic pH preceding membrane fusion and viral entry into the host cell [36] . This mutation is not described in the literature and it is not present in DENV-1 sequences available on GenBank . The absence of relationship with clinical severity of human infection suggests that S222T is not a virulence factor . This mutation was observed in viruses collected in FP at different time points during the 2001–2006 period with an increasing frequency ( 9% of sequenced strains in 2002 , 67% in 2003 , 100% in 2004 , 2005 and 2006 ) . This mutation in the envelope glycoprotein of FP DENV-1 viruses may be the result of genetic drift but it may be explained by positive selection also . S222T appears to have been fixed rapidly ( 10 months ) which is not suggestive of a simple genetic drift . Its appearance during a period of endemic transmission ( August 2002 ) and its rapid stabilization through time suggest that S222T would confer a selective advantage to the virus and may possibly be associated with adaptation to the mosquito vector . Another event suggesting possible virus adaptation to the vector is the mutation K363R . This mutation was present in seven strains recovered in FP from March to December 2006 . Residue 363 is localized within the “immunoglobulin-like” domain III of the envelope protein which contains regions thought to be important for receptor binding [36] . This residue belongs to a B-cell epitope ( 293–402 ) identified in DENV-1 [37] . As DENV infection confers a prolonged type-specific protective immunity , the hypothesis of an immune selection of this variant in humans is unlikely [4] , [5] . Rather , K363R may be the consequence of adaptation to the mosquito vector . Importantly , this mutation occurred only in patients originating from Moorea , Raiatea , Tahaa or the Austral archipelago and was not observed in Tahiti where the majority of cases occurred . Since Aedes ( Stegomyia ) polynesiensis , an endemic mosquito specie widespread in most of islands from the Polynesian Triangle connecting Hawaii and Easter Island to New Zealand , is thought to be an important vector of Dengue virus in rural areas [38] , [39] , whereas Aedes ( Stegomyia ) aegypti is a major vector in urban and sub-urban zones , the K363R mutation may possibly reflect viral adaptation to Aedes polynesiensis in FP islands less urbanized than Tahiti . Although we provide tentative evidence for the existence of a few adaptive mutations during the 2001–2006 period , DENV-1 evolution over this period is globally characterized by strong negative selection , in accordance with previous studies on DENV-2 and DENV-3 evolution [40] , [41] . The low dN/dS values ( 0 . 100 for polyprotein sequences and 0 . 091 for E-gene sequences ) denote purifying selection and may reflect constraints imposed on Dengue virus evolution by the alternating replication of viruses in humans and mosquitoes . Further striking evidence for negative selection is provided by the analysis of genetic variability of DENV-1 at different levels of evolutionary divergence ( Table 5 ) : viral diffusion is associated with increasing purifying constraints as illustrated by the decrease in the dN/dS ratio measured in intra-host viral populations ( dN/dS “FP intra-host” = 0 . 620 ) , in a population of epidemiologically related viruses ( dN/dS “FP inter-host” = 0 . 333 ) , in viruses belonging to the same genotype ( dN/dS “genotype IV” = 0 . 058 ) or to the same serotype ( dN/dS “serotype 1” = 0 . 045 ) . These results indicate that only a small proportion of nonsynonymous mutations observed at a given level of evolution are likely to persist at a higher time- and space-scale . Dengue virus , like other RNA viruses , exhibits extensive intra-host genetic diversity [40]–[45] . We analyzed 662 clones from 16 patients infected with DENV-1 in the study period and observed that the structure of intra-host genetic diversity represents an extreme situation in which purifying selective constraints are lower than at higher levels of evolutionary divergence . As noted in a previous study on DENV-2 and DENV-3 [40] , [41] , [43] , [44] , most nonsynonymous mutations occurred in single cases ( not identified in more distantly related DENV-1 ) and genome-defective viruses ( with stop codons ) were identified ( 3% of clones ) in human sera . Similar results were previously reported in a study of 70 clones obtained from four mosquitoes and 220 clones obtained from 13 patients infected with DENV-1 in Myanmar [42] . Defective viruses may interfere with viral evolution but long term transmission of a stop-codon lineage has been described within humans and mosquitoes infected with DENV-1 [42]: complementation mechanisms may occur in host cells coinfected with both functional viruses and defective viruses . The large number of samples studied here allowed for the first time a comparative analysis of intra-host DENV-1 diversity according to the clinical presentation of the disease . We found that the extent of sequence diversity varied among infected patients . The composition of DENV-1 populations was different in classical ( DF ) and in severe infections ( DHF and DSS ) . Although intra-host sequence variability was probably overestimated due to in vitro artefacts [46] , genetic divergence was significantly lower in severe cases than in classical cases . In severe cases , dN and dS values were significantly lower than in classical presentations . In other words , DENV-1 populations were more genetically homogeneous in DHF or DSS cases than in DF cases . In our study , no correlation was found between the level of intra-host genetic diversity and viral load . Moreover , viral loads were not significantly different between the two groups , in a sample of five severe cases and five DF cases . It is therefore not likely that the lower intra-host genetic diversity observed in severe cases would have been influenced by larger amounts of template DNA in amplification reactions ( associated with a more rapid saturation of PCR reaction and thus lower error rates ) . The mechanisms that lead to different structures of DENV-1 intra-host genetic diversity according to the clinical severity remain undetermined . We do not know if the differences observed are the cause or the consequence of disease severity . Our findings suggest that further analysis of viral variation in both mosquitoes and human samples may in the future shed new light on dengue infection , pathogenesis and the existence of predictive factors of clinical outcome . | The molecular characterization of 181 serotype 1 Dengue fever ( DENV-1 ) viruses collected regularly during the 2001–2006 period in French Polynesia ( FP ) from patients experiencing various clinical presentations revealed that the virus responsible for the severe 2001 outbreak was introduced from South-East Asia , and evolved under an endemic mode until a new epidemic five years later . The dynamics of DENV-1 epidemics in FP did not follow the model of repeated virus introductions described in other South Pacific islands . They were characterized by a long sustained viral circulation and the absence of new viral introduction over a six-year period . Viral genetic variability was not observed only during outbreaks . In contrast with conventional thinking , a significant part of DENV-1 evolution may occur during endemic periods , and may reflect adaptation to the mosquito vector . However , DENV-1 evolution was globally characterized by strong purifying selection pressures leading to genome conservation , like other DENV serotypes and other arboviruses subject to constraints imposed by the host-vector alternating replication of viruses . Severe cases—dengue haemorrhagic fever ( DHF ) and dengue shock syndrome ( DSS ) —may be linked to both viral and host factors . For the first time , we report a significant correlation between intra-host viral genetic variability and clinical outcome . Severe cases were characterized by more homogeneous viral populations with lower intra-host genetic variability . | [
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| 2009 | Dengue 1 Diversity and Microevolution, French Polynesia 2001–2006: Connection with Epidemiology and Clinics |
HIV Dependency Factors ( HDFs ) are a class of human proteins that are essential for HIV replication , but are not lethal to the host cell when silenced . Three previous genome-wide RNAi experiments identified HDF sets with little overlap . We combine data from these three studies with a human protein interaction network to predict new HDFs , using an intuitive algorithm called SinkSource and four other algorithms published in the literature . Our algorithm achieves high precision and recall upon cross validation , as do the other methods . A number of HDFs that we predict are known to interact with HIV proteins . They belong to multiple protein complexes and biological processes that are known to be manipulated by HIV . We also demonstrate that many predicted HDF genes show significantly different programs of expression in early response to SIV infection in two non-human primate species that differ in AIDS progression . Our results suggest that many HDFs are yet to be discovered and that they have potential value as prognostic markers to determine pathological outcome and the likelihood of AIDS development . More generally , if multiple genome-wide gene-level studies have been performed at independent labs to study the same biological system or phenomenon , our methodology is applicable to interpret these studies simultaneously in the context of molecular interaction networks and to ask if they reinforce or contradict each other .
Conventional high-throughput antiviral discovery often targets the activities of specific viral enzymes . These approaches have been ineffective in stemming the emergence of drug-resistant variants , especially in the face of rapidly-mutating RNA viruses . One powerful yet under-explored avenue is the evolutionarily resilient nature of host proteins . Viral pathogens are parasitic in nature owing to their limited genomes . In principle , disruptions to host-pathogen interactions would impede the propagation of pathogens . The recent identification of HIV dependency factors ( HDFs ) or “host cellular factors” highlights this point [1] , [2] , [3] . HDFs represent a class of host proteins that are essential for HIV replication , but are not lethal to the host cell when silenced . By measuring levels of viral protein expression or production of infectious viral particles in human cells after knocking down individual genes using RNA interference ( RNAi ) , these studies search for human genes that are required by HIV . Such studies have also been performed for other viruses and bacteria pathogenic to humans [4] , [5] , [6] , [7] , [8] . HDFs not only provide critical insights into HIV pathogenesis by helping to identify potential mechanisms for manipulation of host pathways , but may also have the potential to serve as therapeutic targets . The studies conducted by Brass et al . [1] , Konig et al . [2] , and Zhou et al . [3] identified 275 , 296 , and 375 HDFs , respectively . The Brass and Konig sets had an overlap of 13 proteins , the Konig and Zhou sets had an overlap of 10 proteins , while the Brass and Zhou sets had 17 common proteins . One potential reason for the small overlap is that the experiments were performed in different cell lines; the Brass and Zhou studies used HeLa cells while the Konig study used HEK293T cells . The small overlaps could also arise from differences in the HIV strains used , the assay time post-infection , the procedures used to measure infection , and other approaches used to analyze experimental data [9] , [10] . Although the three siRNA screens showed little overlap at the level of individual genes , Bushman et al . [10] found that similar Gene Ontology ( GO ) terms were enriched in the three gene sets . Interestingly , Konig et al . noted that 64 HDFs reported by Brass et al . directly interacted ( via a physical interaction between proteins ) with a confirmed HDF in their study . In support of this observation , Bushman et al . constructed a network of protein-protein interactions among HIV proteins and 2 , 410 host cell genes identified in the three siRNA screens and six other HIV-related studies . Dense clusters within this network contained multiple proteins identified in two or more siRNA screens and were enriched in processes and complexes such as the proteasome and the mediator complex , which are known to be associated with HIV replication . In a related study , Wuchty et al . [11] found that HDFs and human proteins that interact with HIV also appeared in dense clusters . The proposed that such protein groups may serve as “infection gateways” that enable the virus to control specific human cellular processes . They also noted that transcription factors and protein kinases mediated indirect interactions between HDFs and viral proteins . Macpherson et al . [12] performed a complementary analysis . Starting from known human-HIV protein-protein interactions ( PPIs ) , they used biclustering to identify sets of human proteins that participated in the same types of interactions with HIV proteins . They evaluated the functional information in each bicluster and further grouped the human proteins in biclusters into higher-level subsystems . By overlapping these subsystems with HDFs , they characterized host systems that were perturbed by HIV-1 infection and identified patterns of human-HIV PPIs that correlated to these perturbations . We took these analyses as our starting point , since they suggested that the three siRNA genomic screens may be incomplete and that there are potentially many HDFs yet to be discovered . In particular , we hypothesized that the proximity of experimentally-detected HDFs within the human protein-protein interaction ( PPI ) network can be fruitfully exploited by machine-learning algorithms to predict novel HDFs . We treated the computational problem of predicting HDFs as an instance of semi-supervised learning: we combined HDFs identified by Brass et al . , Konig et al . , or Zhou et al . ( positive examples formed by the union of these three sets ) with non-HDFs ( negative examples , see “Data and Algorithms” for details ) in the context of a human PPI network . The other proteins in this network constituted the unknown examples . We used an intuitive graph-theoretic approach that we call SinkSource and other algorithms published in the literature [13] , [14] , [15] to predict undiscovered HDFs . Our results , along with those of other studies [10] , [11] , [12] , suggest that many HDFs are yet to be discovered and that they have potential value as prognostic markers to determine pathological outcome and the likelihood of AIDS development .
Figure 1 displays the results of two-fold cross validation for the six algorithms tested on four datasets . Two-fold cross validation involves splitting the positive and negative examples into two halves , and using each half to make predictions for the genes in the other half . We used two-fold cross validation since we felt it better mimics our state of knowledge of HDFs than the more commonly used five-fold or 10-fold cross validations . We averaged the results over 10 independent runs for each algorithm-dataset combination . For each algorithm , it is evident from Figure 1 ( a ) that the area under the precision-recall curve ( AUPRC ) value for the BKZ dataset is larger than the values for the B , K , or Z datasets . It is also clear that these results are robust to the randomization inherent in cross validation: the largest standard deviation in the AUPRC values is 0 . 033 ( as indicated by the error bars in Figure 1 ( a ) and data in Table S1 ) . Figure 1 ( b ) displays the precision-recall curve for SinkSource on the four datasets and Figure 1 ( c ) shows the results for SinkSource+ . The results for SinkSource+ were obtained with an internal parameter λ set to a value of 1 ( see “Other Algorithms” for the role played by this parameter in the SinkSource+ algorithm ) . In each figure , we observed that the curve for the BKZ dataset dominated the other three curves at most values of recall . This result is consistent with the expectation that the Brass , Konig , and Zhou studies did not discover all true HDFs , and that combining the three sets provides a better coverage of the true HDF universe . We also noted that the variation in precision ( indicated by the error bars in Figure 1 ( b ) and Figure 1 ( c ) ) decreases with increasing recall , suggesting that high confidence predictions are more subject to variation than low confidence predictions . Finally , Figure 1 ( d ) compares the performance of all seven algorithms on the BKZ dataset . Three of the algorithms that do not use negative examples ( Local+ , SinkSource+ , and Functional Flow with 1 and with 7 phases ) achieved higher precision values than the other algorithms for values of recall less than 20% . However , SinkSource has the best performance for values of recall greater than 20% . PRINCE , the fourth algorithm that did not use negative examples , had uniformly lower precision than SinkSource+ . Its precision was superior to that of SinkSource for values of recall less than 10% . To obtain the results for PRINCE , we used 0 . 8 for the value of an internal parameter α , since PRINCE achieved the highest precision values for this setting of α ( see “Other Algorithms” for the role played by this parameter in the SinkSource+ algorithm ) . Furthermore , the precisions of the algorithms that do not use negative examples dropped considerably beyond a recall of 20% ( beyond 10% in the case of PRINCE ) . We believe that this performance drop is caused by an undue influence of positive examples , resulting in many false positives . The performance of FunctionalFlow did not vary much with an increase in the number of phases ( see Figure S1 ) . The performance of SinkSource+ was independent of the parameter λ ( see Figure S2 ) , as was the performance of PRINCE with respect to the parameter α ( see Figure S3 ) . We also noted that the AUPRC values for the BKZ dataset were 0 . 67 for Local , Local+ , and for FunctionalFlow with 7 phases , 0 . 65 for PRINCE , 0 . 69 for SinkSource+ , 0 . 73 for SinkSource , and 0 . 74 for Hopfield . There is a difference of 11% between the AUPRCs of the worst performing algorithms ( 0 . 67 ) and the best performing algorithm ( 0 . 74 ) . The results for weighted versions of the network did not substantially differ from those for the unweighted network ( see Figure S4 and Table S2 ) . The SinkSource algorithm achieved a precision of 81% at 20% recall . The precision dropped only to 70% at a recall of 60% . The corresponding precisions for SinkSource+ were 85% and 60% . Although the Hopfield network algorithm achieved an AUPRC of 0 . 74 , we observed that the smallest recall value attained by the algorithm was 60% , since the algorithm assigned a confidence of either 1 or −1 to a large number of predictions . We concluded that the Hopfield network algorithm was not a good choice for prioritizing predictions for further experimental analysis . It is surprising that the very simple guilt-by-association algorithms ( Local+ and FunctionalFlow with one phase ) perform nearly as well as more sophisticated methods ( FunctionalFlow with 7 phases , Hopfield , PRINCE , and SinkSource ) that attempt to optimize predictions by taking into account constraints imposed by the entire protein interaction network . However , across 10 runs of cross validation , both Local+ and FunctionalFlow with one phase showed higher variation in precision and recall than the other algorithms ( see Figure S5 ) . Therefore , these two algorithms are likely to be more susceptible to missing or erroneous information . Based on these results , we concluded that SinkSource+ and SinkSource were the two best algorithms for predicting HDFs . When high precision is required , SinkSource+ is superior to SinkSource . Thus , the predictions made by SinkSource+ might be the most suitable as the basis for detailed experimental studies of candidate HDFs . In the rest of the paper , we focus on the results obtained by the SinkSource+ and SinkSource algorithms . We compared how many predictions SinkSource+ and SinkSource made at confidence values that correspond to approximately 80% precision after cross validation . SinkSource+ achieved a precision of 85% ( and a recall of 20% ) at a confidence of 0 . 5 . The corresponding numbers for SinkSource were a confidence of 0 . 71 at a precision of 81% ( and a recall of 20% ) . To further compare the two algorithms , we computed the overlaps in their predictions for different cutoffs on the confidence values . Specifically , we computed the k highest confidence genes predicted by SinkSource+ and the k highest-confidence genes predicted by SinkSource , and measured the Jaccard coefficient of the pair of gene sets , for different values of k in increments of 100 . Figure S6 demonstrates that the overlap between the predictions of the two algorithms is at least 0 . 34 up to the first 2000 predictions , with peaks at around 300 and 1000 predictions . These results are consistent with the relatively low recall ( 20–40% ) predicted for the two algorithms at this level of precision . The data suggest that approximately half of the predictions may be ranked differently by the two algorithms . Predictions made by SinkSource+ for different values of the parameter λ did not vary much in their ranking ( see Figures S7 and S8 ) . On the basis of these comparisons , we identified a set of high confidence predictions composed of the 1000 top-ranked predictions from SinkSource+ and from SinkSource respectively . These two sets contained 606 predictions in common and comprised a total of 1394 proteins in addition to the 908 BKZ HDFs . At the confidence levels of the 1000 SinkSource and SinkSource+ predictions , the precisions with two-fold cross validation are 88% and 81% respectively , suggesting that these predictions are relatively reliable . The corresponding recalls with two-fold validation are roughly 17% and 15% respectively , suggesting that these predictions are quite conservative . In the rest of the paper , we use the phrases “BKZ HDFs” , “SS+ predicted HDFs” , and “SS predicted HDFs” to distinguish between the HDFs identified by one or more of the three siRNA screens [1] , [2] , [3] , the HDFs predicted by SinkSource+ , and the HDFs predicted by SinkSource , respectively . We extensively evaluated the predicted HDFs by comparing them to each other and to BKZ HDFs in terms of their functional annotations , interactions with HIV proteins , clustering with the PPI network , and role in disease pathogenesis . We based these evaluations on additional datasets that we did not use for predicting HDFs . Specifically , the new datasets we used were ( i ) Gene Ontology ( GO ) annotations for human proteins , ( ii ) interactions between HIV and human proteins , and ( iii ) gene expression data from two non-human primate species following infection with SIV . Hence , the analyses described below constitute independent evaluation of the relevance of our predictions to HIV infection and disease progression . We summarized the functional roles of predicted HDFs by asking which GO terms were enriched in the HDFs , and whether any terms were considerably enriched in predicted HDFs but not in BKZ HDFs . We used the FuncAssociate software [23] for this purpose , since it can take ordered lists of genes as input , in which case it finds and utilizes the set of top-ranked genes displaying the greatest enrichment . FuncAssociate adjusts for multiple hypotheses testing by computing an experiment-wise p-value . Note that FuncAssociate operates solely on the ranked list of genes and the GO annotations . It does not utilize a network . ( See “Methods” for details . ) We invoked FuncAssociate with three inputs: ( a ) the unordered set of BKZ HDFs , ( b ) the SS+ predicted HDFs , ordered by confidence , and ( c ) the SS predicted HDFs , also ordered by confidence . We used default values of all other parameters used by FuncAssociate . FuncAssociate reported 52 GO terms as being enriched in BKZ HDFs with an adjusted p-value of 0 . 05 or less and 199 GO terms as enriched in SS+ predicted HDFs . We identified three classes of terms ( see Table S3 ) . We note that FuncAssociate may report many related terms as enriched , due to the hierarchical nature of GO . Therefore , we also manually inspected the directed acyclic graph connecting the enriched terms in order to make the observations below . The trends were similar for the HDFs predicted by SinkSource ( data not shown ) . Therefore , we compared the FuncAssociate results for SS+ predicted HDFs and for SS predicted HDFs in a similar manner . We only considered GO terms enriched with an adjusted p-value of 0 . 05 or less . As shown in Table S4 , 280 GO terms were enriched in both sets of predictions , 182 GO terms were enriched only in SinkSource+ predictions , and 25 GO terms were enriched only in SinkSource predictions . The 280 common terms were related to processes such as RNA splicing ( GO:0008380 ) , translation initiation ( GO:0003743 ) , and oxidative phosphorylation ( GO:0003743 ) and complexes such as the proteasome ( GO:0003743 ) , the kinetochore ( GO:0000776 ) , and the nuclear pore ( GO:0005643 ) ; we discuss their relevance to HIV when we discuss clusters in the PPI network below ( See “PPI Clusters Spanned by BKZ HDFs and Predicted HDFs Are Exploited by HIV” ) . The 182 GO terms enriched only in SinkSource+ predictions included the Ndc80 complex and MIS12/MIND type complex ( mentioned above ) , apoptosis ( including its induction and regulation ) ( GO:0006915 , GO:0006917 , and GO:0042981 ) , and specializations of terms enriched in both sets of predictions . Among the 25 GO terms enriched only in SinkSource predictions , there were 12 GO terms whose specializations or near neighbors ( in the GO directed acyclic graph ) were enriched in SinkSource+ predictions . Each of the remaining 13 GO terms enriched only in SinkSource predictions were closely related to the assembly of glycosylphosphatidylinositol ( GPI ) anchors ( GO:0006506 ) . Based on these results , we concluded that , for the most part , similar functions were enriched in HDFs predicted by SinkSource+ and by SinkSource . Bushman et al . observed that each of the Brass , Konig , and Zhou HDF sets were statistically significantly enriched with human proteins that interact with HIV proteins ( as reported in the NCBI HIV interaction database [25] ) . We hypothesized that predicted HDFs might be significantly enriched with HIV interactors . Accordingly , for each algorithm , we selected the k top ranking predictions made by that algorithm , for different values of k starting at 100 and in increments of 100 , computed the overlap of each set of predictions with the human proteins that interact with HIV , estimated the statistical significance of the overlap using the one-sided version of Fisher's exact test , and adjusted the p-values to account for testing multiple hypotheses [26] . The overlap fraction for SS+ predicted HDFs peaked at 26% ( 79 of the top 300 predicted HDFs interact with HIV proteins , p-value 2 . 1×10−7 ) , better than the BKZ HDFs of which 20% ( 109 proteins , p-value 9 . 11×10−6 ) interacted with HIV proteins . The trend for SS predicted HDFs was mixed: the overlap ratio was as high as 17 . 5% ( 70 of the top 400 predictions interact with HIV proteins ) , slightly less than the BKZ HDFs , but in no case was the enrichment statistically significant . These results suggest that SinkSource+ HDF predictions are dominated by proteins that lie close to BKZ and HIV proteins in the joint HIV-human PPI network , whereas the SinkSource predictions are dispersed further away . We discuss specific SS+ predicted HDFs that interact with HIV in the context of MCODE clusters below . The cross validation analysis suggested that HDFs are not randomly located in the human PPI network . Rather , HDFs are closer to each other within the PPI network than to the negative examples . Therefore , in order to better understand how BKZ HDFs and SS+ predicted HDFs are related to each other , we computed the subnetwork of PPIs spanned by these two sets of genes . We applied a modified version of the well-known MCODE [27] graph clustering algorithm to this sub-network ( see “Modifying MCODE to Compute PPI Clusters” ) . The network contained 1 , 562 proteins and 30 , 855 PPIs . MCODE identified 41 clusters of varying sizes containing a total of 829 proteins and 16 , 721 PPIs . Table 1 contains statistics on the 10 clusters with the largest number of PPIs computed by MCODE . Using the one-sided version of Fisher's exact test , we checked the overlap of each of the 42 clusters with BKZ HDFs . Only eight clusters had overlaps that were statistically significant , as shown in Table S5 . Table S6 contains a list of BKZ HDFs and HDFs predicted by SinkSource+ , annotated with MCODE cluster membership and information on interaction with HIV proteins . Table S7 lists the human PPIs in each MCODE cluster . We computed GO terms enriched in all clusters . Table 2 contains statistics on highly enriched GO terms in the 10 most highly-connected clusters discovered by MCODE . Among the top 10 clusters , only clusters #1 , #4 , #7 , #8 , and #9 have statistically significant overlaps with BKZ HDFs ( see Table S5 ) . The fraction of BKZ HDFs is small in clusters #1 , #4 , and #9 , so we reasoned that any functions enriched in these clusters would not be overly influenced by annotations of BKZ HDFs . In contrast , more than half the proteins in clusters #7 and #8 are BKZ HDFs; the functions enriched in these clusters are likely to annotate a number of BKZ HDFs . We now discuss the enriched functions in all clusters in Table 2 . We focus our discussion on selected predicted HDFs contained within these clusters and present the support in the literature for the relevance of these HDFs to HIV pathogenesis . Since HDFs play a critical role in HIV replication [1] , [2] , [3] , we hypothesized that some of them may have value as prognostic markers of HIV pathogenesis and of AIDS development and progression . We anticipated that both experimentally-detected ( BKZ ) and predicted HDFs would satisfy this hypothesis . To explore this question , we combined BKZ HDFs and predicted HDFs with DNA microarray data from a study detailing the host response to simian immunodeficiency virus ( SIV ) infection in African green monkeys ( AGMs ) and pigtailed macaques ( PTMs ) . AGMs are natural reservoirs of SIV that do not develop AIDS , while PTMs are non-natural hosts that develop AIDS when infected with SIV . The virus replicates to the same viral load in both of these hosts . Lederer et al . [56] performed a longitudinal transcriptomic analysis comparing AGMs to PTMs . They analyzed the host response in the setting of acute SIV infection with the same primary isolate ( SIVagm . sab92018 ) . They studied three different tissues: blood , colon , and lymph nodes . They collected samples at 10 days and 45 days post-viral inoculation and compared each sample to a sample from the same animal pre-inoculation . For each day-tissue combination , they performed an analysis of three AGMs and three PTMs using rhesus macaque ( Macaca mulatta ) oligonucleotide microarrays . The probes in this microarray were based on the human Reference Sequence ( RefSeq ) collection . Thus , there is a direct mapping from these probes to human gene identifiers . For each tissue ( blood , colon , lymph node ) and day ( 10 and 45 post infection ) combination , we performed a separate ANOVA analysis , using the host system as factor , to identify genes that are differentially expressed between AGMs and PTMs . Such differentially expressed genes could potentially serve as diagnostic markers of AIDS development and progression . We constructed six lists ( three tissues×two time points ) of genes that were differentially expressed between AGMs and PTMs to a statistically-significant extent ( p≤0 . 05 ) . We used the one-sided version of Fisher's exact test to determine if BKZ HDFs had a significant intersection with each of these six lists . We repeated this test with the top k predicted HDFs , for values of k starting at 100 and in increments of 100 . We used the method of Benjamini and Hochberg [26] to correct for testing multiple hypotheses . Figure 2 displays plots of the fraction of BKZ HDFs or of predicted HDFs that are also differentially-expressed to a significant extent in the AGM-PTM comparison; Figures S9 and S10 plot the corresponding p-values . Note that the plot for BKZ HDFs is a horizontal line since changing the score cutoff for predictions has no effect on BKZ HDFs . Three notable trends emerged from this analysis . First , for many tissue-day combinations , the overlap fraction for predicted HDFs was larger than the overlap fraction for BKZ HDFs . These trends were most noteworthy in day 10 lymph nodes , where the overlap ratio for predicted HDFs was larger than that for BKZ HDFs over the entire range of prediction confidence values . In particular , in day 10 lymph nodes , the overlap fraction of SS+ predicted HDFs peaked at 0 . 26 ( 53 of the top 203 predicted HDFs were also differentially-expressed in day 10 lymph nodes , p-value 0 . 01 ) . The largest overlap for SS predicted HDFs was also 0 . 26 ( 26 of the top 100 predicted HDFs , an insignificant p-value of 0 . 07 ) . In contrast , the overlap ratio for BKZ HDFs with genes differentially expressed in day 10 lymph nodes was 0 . 19 ( p-value , 0 . 59 ) . Second , none of the overlaps of BKZ HDFs with differentially-expressed genes were statistically significant , for any tissue-day combination . In contrast , p-values for HDFs predicted by each algorithm were statistically significant ( red points in Figure 2 and Figures S9 and S10 ) in day 10 lymph nodes , across a wide range of prediction confidences . Third , no statistically significant overlaps appeared for predicted HDFs in blood or colon samples at any time point or in day 45 samples from lymph nodes . We re-estimated the significance of these results after randomizing the gene expression data , by permuting each gene's p-values independently . This process retained the distribution of p-values for each gene , but randomized the associations between p-values and tissue-day combinations . We repeated the overlap analysis for predicted HDFs with each of 10 , 000 randomized gene expression data sets , for a total of 60 , 000 randomized tissue-day combinations . We observed only one randomized dataset for which any overlap ratio was at least as large as 0 . 26 , the largest overlap ratio between HDFs predicted by SinkSource+ and genes differentially expressed in day 10 lymph nodes . Thus , the p-value of the observed overlap ratio was 1 . 7×10−5 . For predictions made by SinkSource , we obtained a p-value of 8 . 3×10−5 , for the largest observed overlap of 0 . 26 . Thus , we concluded that the predicted HDFs have a significant overlap with genes that are differentially expressed between AGMs and PTMs in day 10 lymph nodes , indicating that many predicted HDFs show considerably different programs of expression in the two species in response to SIV infection , especially in early time points . These data suggest that the algorithms have identified a highly responsive subset of potential HDFs , and provide strong experimental support for the prediction that these proteins are in fact HDFs . This result further suggests that viral manipulation of these host factors in lymph nodes soon after infection may have an effect on long-term pathological outcome . We used FuncAssociate to perform GO enrichment analysis on predicted HDFs that were also differentially expressed between AGMs and PTMs in day 10 lymph nodes . The terms we found were almost identical to those reported in the PPI clusters ( data not shown ) . In summary , these results suggest that not only are HDFs critical for viral replication and infection , they may have potential value as prognostic markers to determine pathological outcome and the likelihood of AIDS development . We have used network-based approaches to predict HIV dependency factors ( HDFs ) . Upon two-fold cross-validation , we found that combining the three experimental data sets yielded much higher precision and recall than using each data set on its own . A number of the algorithms we compared achieved both high precision and recall on cross validation . Our results suggest that global optimization techniques such as SinkSource and SinkSource+ perform slightly better than the simple guilt-by-association rule [57] . Furthermore , SinkSource+ and SinkSource had the most consistent and reliable performance . Software implementing the function prediction algorithms is available at http://bioinformatics . cs . vt . edu/~murali/software/gain . We also observed that estimating the reliability of PPIs did not confer an advantage; in fact , the cross validation results worsened slightly with edge weights ( Table S2 ) . The decrease in performance is likely to be a combination of the close proximity of HDFs within the PPI network and the high reliability of PPIs that HDFs are involved in , since the corresponding biological processes are well studied . We found that the HDFs predicted by SinkSource+ were significantly enriched in proteins that interact with HIV proteins . On the other hand , SinkSource predicted a set of HDFs that were not significantly enriched in HIV-interacting proteins . We computed clusters within the subgraph of the PPI network that encompassed the BKZ HDFs and HDFs predicted by SinkSource+ . These clusters were enriched in host cellular complexes and pathways known to be that are known to be manipulated by HIV and perturbed during HIV infection such as the spliceosome , the microtubule network , the proteasome , the mitochondrion , and nuclear import and export . Finally , we integrated BKZ HDFs and predicted HDFs with gene expression data from a non-human primate study detailing the host response to SIV infection in non-human primates that do not develop AIDS ( African green monkeys ) and those that do ( pigtailed macaques ) [56] . We found that up to 26% of predicted HDFs are differentially expressed , when we compared their gene expression profiles in macaques to their profiles in African green monkeys . This differential expression of HDFs was time- and tissue-specific , being strongest in lymph nodes 10 days post-inoculation . These HDFs are excellent candidates for studying transcriptional programs relevant to AIDS progression in humans . Our results support three conclusions . First , existing genomic screens are incomplete and many HDFs are yet to be discovered . The HDFs predicted by SinkSource+ may include many proteins required for HIV replication that could not have been uncovered experimentally because the predictions were not constrained to non-essential human proteins . Second , HDFs are clustered in the human PPI network and belong to cellular pathways or protein complexes that play a critical role in HIV pathogenesis and AIDS progression . Third , many HDF genes show differential expression during AIDS development in non-human primates . Thus , HDFs may play an important role in the control of initial infection and eventual pathological outcome . It will be valuable to integrate other HIV-relevant functional genomic data with PPI networks to improve the quality and robustness of HDF prediction . Modeling the impact on off-target effects of siRNAs on false positive HDFs is also important . To date , experiments that have detected HDFs have been performed in cell lines . Approaches such as ours may help to prioritize HDFs for further experimental study in more disease-relevant models such as non-human primates . Ultimately , we anticipate that future extensions of our work may provide multiple new targets and strategies for combating HIV in humans . Our approach is general purpose and can be applied to interpret other genome wide gene-level studies . In particular , if independent labs have conducted multiple studies to study the same biological system or phenomenon , we provide a methodology to interpret them simultaneously within the context of molecular interaction networks . Our approach can be used to ask if the studies reinforce or contradict each other and to prioritize new genes for further experimental analysis .
We downloaded all the HDF and PPI data used in this study between August and December 2008 . We downloaded functional annotation data in December 2010 . We used Entrez Gene IDs in all analyses . We modeled the human protein interaction network as an undirected graph G = ( V , E ) , consisting of a set V of nodes ( i . e . , proteins ) and a set E of edges ( i . e . , interactions ) . We used wuv to denote the weight of the edge , computed as described earlier . We partitioned V into three subsets and V− as follows: V+ was the set of HDFs ( positive examples ) , V− was the set of human proteins orthologous to essential mouse proteins ( negative examples ) , and V0 was the remaining set of nodes ( unknown examples ) . For each node v∈V0 , our goal was to assess whether v should be a member of V+ or V− . We did so by computing a function that is “smooth” over G . Specifically , we set r ( v ) = 1 for every node v∈V+ , r ( v ) = 0 for every node v∈V− , and required that r minimize the functionMinimizing S ( G , r ) enforces the smoothness of r in the sense that the larger the weight of an edge ( u , v ) , the closer in value r ( u ) and r ( v ) must be . The function S ( G , r ) is minimized when , for each node v∈V0 , ( 1 ) where Nv is the set of neighbors of node v [64] . The right-hand side of this equation can be split into two parts: one corresponding to contributions to r ( v ) from neighbors in V0 and the second to a constant contribution from neighbors in V+ and V− . Let r0 denote the vector of values taken by the function r at the nodes in V0 . Let M denote the square matrix , where , for every . We see that r0 satisfies the equations r0 = Mr0+c , where c is a vector denoting contributions from V+ and V− . We computed r0 by initializing it to 0 for each node and repeatedly applying the operation r0 = M r0+c . This process is known to converge [64] , yielding a value of r0 = ( I−M ) −1c , where I is the identity matrix . The matrix M is sparse , being the adjacency matrix of a PPI network . Therefore , this iterative approach is efficient in practice . We implemented six other algorithms for the purpose of comparison . The first two algorithms use both positive and negative examples . The other four algorithms do not use negative examples for making predictions , avoiding the uncertainties associated with choosing an accurate set of negative examples . We used both types of algorithms in order to assess the impact of our choice of negative examples on the cross validation results . Table 5 summarizes these algorithms . Although SinkSource+ , Local+ , FunctionalFlow , and PRINCE do not use negative examples when making predictions , we used negative examples when computing the performance of these algorithms on cross validation in order to count the number of true negatives and false positives . A number of approaches are available for computing GO terms enriched in lists of genes [23] , [65] , [66] , [67] . Since BKZ HDFs are unordered while predicted HDFs can be ranked by confidence , we used the FuncAssociate software [23] , which can take both unordered and ordered lists of genes as input . For an ordered list of genes , FuncAssociate analyses each one of the list's prefixes , and reports results for the prefix with the smallest p-value . It asks if the genes annotated by each GO term have surprisingly low ranks in the ranked list . The final p-value computed by FuncAssociate can be informally interpreted as the probability that a given overlap between a GO term and a ranked list of genes could be observed if the genes were ranked randomly . Note that FuncAssociate operates solely on the ranked list of genes and the GO annotations . It does not utilize a network . Details on how FuncAssociate operates are provided at http://llama . mshri . on . ca/FuncAssociate_Methods . html . To determine enriched GO functions in each cluster computed by MCODE , we did not associate any weights with the proteins , since MCODE had already incorporated protein weights . We used an in-house implementation of the Ontologizer [68] to compute enriched GO terms . We chose the Ontologizer because it accounts for annotation dependencies that arise from GO's true path rule . We retained only those functions for which the p-value is at most 0 . 05 , after accounting for multiple hypothesis testing using the method of Benjamini and Hochberg [26] . We modified MCODE to multiply internally-computed node weights with externally-defined node weights . For our application , we supplied the SinkSource+-derived confidence as the weight of a predicted HDF . For every BKZ HDF , we defined its weight as 1 . By imposing these externally-defined weights , we aimed to bias MCODE towards finding dense subgraphs in the vicinity of BKZ and SS+ predicted HDFs . Therefore , we included all SS+ predictions together with their confidence levels in the network and used the ability of MCODE to utilize the confidence levels to identify high confidence clusters . | Medicines to cure infectious diseases usually target proteins in the pathogens . Since pathogens have short life cycles , the targeted proteins can rapidly evolve and make the medicines ineffective , especially in viruses such as HIV . However , since viruses have very small genomes , they must exploit the cellular machinery of the host to propagate . Therefore , disrupting the activity of selected host proteins may impede viruses . Three recent experiments have discovered hundreds of such proteins in human cells that HIV depends upon . Surprisingly , these three sets have very little overlap . In this work , we demonstrate that this discrepancy can be explained by considering physical interactions between the human proteins in these studies . Moreover , we exploit these interactions to predict new dependency factors for HIV . Our predictions show very significant overlaps with human proteins that are known to interact with HIV proteins and with human cellular processes that are known to be subverted by the virus . Most importantly , we show that proteins predicted by us may play a prominent role in affecting HIV-related disease progression in lymph nodes . Therefore , our predictions constitute a powerful resource for experimentalists who desire to discover new human proteins that can control the spread of HIV . | [
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| 2011 | Network-Based Prediction and Analysis of HIV Dependency Factors |
Neutrophils are classically defined as terminally differentiated , short-lived cells; however , neutrophils can be long-lived with phenotypic plasticity . During inflammation , a subset of neutrophils transdifferentiate into a population called neutrophil-DC hybrids ( PMN-DCs ) having properties of both neutrophils and dendritic cells . While these cells ubiquitously appear during inflammation , the role of PMN-DCs in disease remains poorly understood . We observed the differentiation of PMN-DCs in pre-clinical murine models of fungal infection: blastomycosis , aspergillosis and candidiasis . Using reporter strains of fungal viability , we found that PMN-DCs associate with fungal cells and kill them more efficiently than undifferentiated canonical neutrophils . During pulmonary blastomycosis , PMN-DCs comprised less than 1% of leukocytes yet contributed up to 15% of the fungal killing . PMN-DCs displayed higher expression of pattern recognition receptors , greater phagocytosis , and heightened production of reactive oxygen species compared to canonical neutrophils . PMN-DCs also displayed prominent NETosis . To further study PMN-DC function , we exploited a granulocyte/macrophage progenitor ( GMP ) cell line , generated PMN-DCs to over 90% purity , and used them for adoptive transfer and antigen presentation studies . Adoptively transferred PMN-DCs from the GMP line enhanced protection against systemic infection in vivo . PMN-DCs pulsed with antigen activated fungal calnexin-specific transgenic T cells in vitro and in vivo , promoting the production of interferon-γ and interleukin-17 in these CD4+ T cells . Through direct fungal killing and induction of adaptive immunity , PMN-DCs are potent effectors of antifungal immunity and thereby represent innovative cell therapeutic targets in treating life-threatening fungal infections .
In the waging battles between host immunity and microbial invaders , polymorphonuclear cells ( PMN ) or neutrophils are the most numerous cellular soldiers under the host banner . Neutrophils ward off and eliminate many infections , particularly those caused by fungi . As the infantry of an inflammatory immune response , neutrophils are often the first leukocytes to infiltrate infected tissue armed with an arsenal of antimicrobial agents and functions for direct combat against intruders [1] . Classically defined , neutrophils are terminally differentiated and short-lived at sites of infection; however , there is a growing appreciation of long-lived neutrophils often accompanied by morphological changes [2] . One of these long-lived neutrophil populations has the morphology of dendritic cells ( DCs ) and can present antigen to T cells [3] . These neutrophils , given a variety of names including antigen-presenting neutrophils , have been characterized as a bona fide neutrophil population under the name neutrophil-DC hybrids since they possess properties of both neutrophils and DCs [4] . We refer to them here as PMN-DCs . In a series of studies , highly purified murine neutrophils were shown to differentiate in vivo and in vitro into cells expressing major histocompatibility complex ( MHC ) class II , CD11c , and other DC markers while retaining neutrophil surface markers [4–6] . These PMN-DCs have the morphology of DCs , present antigen and promote T cell polarization , while remaining highly phagocytic cells that produce antimicrobials and release neutrophil extracellular traps ( NETs ) . PMN-DCs have been observed in humans and mice for decades under a variety of inflammatory conditions . In mice , PMN-DCs are characterized as being MHC class II+ and CD11c+ , and in humans PMN-DCs express HLA-DR ( MHC class II ) with differing reports of other DC markers such as CD40 , CD86 and CCR6 [3 , 4] . PMN-DCs arise under auto-inflammatory conditions such as Wegener’s granulomatous disease , rheumatoid arthritis and in a mouse model of inflammatory bowel disease [7–9] . In patients with bacterial infection and leishmaniasis , PMN-DCs have been found in the circulation [10–11] . HLA-DR+ neutrophils have also been found in tumors of lung cancer patients [12] . Adjunctive therapy with GM-CSF ( granulocyte-macrophage colony stimulating factor ) or interferon ( IFN ) -γ also induces the appearance of PMN-DCs in human patients [13 , 14] . While being observed under a variety of inflammatory circumstances , the role that PMN-DCs play–helpful or harmful—in disease is poorly understood . Because PMN-DCs retain neutrophil microbicidal functions and can polarize inflammatory T cell functions [4] , we hypothesize that PMN-DCs aid in immunity to infections where neutrophils and T helper ( Th ) type 1 and 17 responses are important e . g . fungal infections [15] . Neutrophils are essential in immunity to many fungal infections including the common causes of serious and lethal fungal diseases , Aspergillus and Candida species [16 , 17] . Candidemia is the 4th most common nosocomial bloodstream infection in the U . S . , with a mortality rate of 43% [18 , 19] . Mortality rates of invasive aspergillosis also exceed 40% [20] . Persons at greatest risk of serious fungal infections by these and other fungi are patients with cancer or solid organ transplant , often due to deficiencies in neutrophil immunity [16] . In heart and lung transplant recipients , incidence rates of invasive fungal infections are as high as 25% , with 90-day mortality rates of 23% [21 , 22] . In patients with hematological malignancies , rates of serious fungal infection reach 18% , and mortality rates , 20–30% [23–25] . Most serious fungal infections are treated with antifungal drugs , but antifungals can be ineffective due to fungal resistance , low bioavailability , toxicity , and limited spectrum of activity [26] . Due to these limits , immunotherapies are needed . No commercial vaccine against fungi is available , but some studies have investigated vaccine prevention against fungal disease [27 , 28] . Chimeric antigen receptor-modified T cells , a novel therapy for cancer patients , may be effective in treating fungal infections [29 , 30] . Neutrophil-directed therapies have been used to prevent and treat fungal infections , particularly in patients with neutropenia [31] . Ideally , immunotherapies against fungal infections should enhance innate killing mechanisms or promote long-term adaptive immunity . Thus , any therapies that target PMN-DCs would be promising because these cells enact both innate and adaptive immunity . To study the emergence and function of PMN-DCs during fungal infection , we initially analyzed neutrophils in a murine model of pulmonary blastomycosis , a fungal pneumonia . Blastomycosis is a pyogranulomatous disease eliciting exuberant , sustained recruitment of neutrophils [32 , 33] , permitting analysis of small neutrophil populations . Blastomycosis , like aspergillosis , is usually initiated by inhalation of spores from the environment [34] . Herein , we report that during pulmonary blastomycosis , PMN-DCs expanded and associated with Blastomyces dermatitidis more frequently than any other leukocyte . PMN-DCs also killed yeast better than undifferentiated canonical neutrophils . We extended our observations to other common human fungal infections , aspergillosis and candidiasis , finding that PMN-DCs emerge in murine models of these infections and kill these fungi better than canonical neutrophils . Additionally , we exploited a neutrophil progenitor cell line to generate a highly pure population of PMN-DCs without any taxing enrichment procedure . Adoptive transfer of PMN-DCs , derived from the cell line , reduced fungal burden during murine system candidiasis . We also used these cell line PMN-DCs to demonstrate that PMN-DCs efficiently present fungal antigen and prime protective Th1 and Th17 responses . In sum , PMN-DCs expand to a small proportion of neutrophils during infection , but they associate with and kill fungal cells far better than canonical neutrophils . Due to the longevity of PMN-DCs and their ability to prime adaptive responses , we believe that these cells contribute significantly to antifungal immunity and can be harnessed therapeutically as potent mediators of protection .
Of the copious neutrophils in the blastomycotic lung [32 , 33] , one population is phenotypically converted into PMN-DCs ( Fig 1A and 1B ) . PMN-DCs were absent from the lungs of naïve mice , but emerged after neutrophils extravasated across the lung capillaries ( Fig 1C and 1D , S1A Fig ) . The proportion of neutrophils that converted phenotypically to PMN-DCs rose rapidly after one day and steadily increased over 14 days ( Fig 1C ) until mice succumbed of pneumonia . The absolute number of PMN-DCs in the lung approached 105 after one week and 106 after two weeks ( Fig 1D ) making this population large enough to analyze in this pre-clinical model . PMN-DCs that appeared during pulmonary blastomycosis had DC morphology ( Fig 1B , S1B–S1D Fig ) and expressed DC markers ( MHC class II , CD11c , CD80 , CD86 , CD40 ) while retaining neutrophil levels of expression of Ly6G , Ly6C , CD11b , and CXCR2 ( Fig 1A , 1E and 1F , S2 Fig ) . The lack of Siglec F , NK1 . 1 , and F4/80 expression also indicated that the PMN-DCs observed were not contaminating populations of macrophages or natural killer cells ( Fig 1A , S2C Fig ) . Overall , these PMN-DCs resembled the characterized neutrophil-DC hybrids in mice [3–5] . Also , as previously characterized [3 , 4] , PMN-DCs appear to be neutrophil-derived and not monocyte-derived because they express Ly6G , a unique marker of murine neutrophils , and do not express CD115 ( macrophage-colony stimulating factor receptor ) found on monocytes and monocyte-derived cells ( S3a Fig ) . Additionally , equivalent numbers of PMN-DCs appeared in wild-type mice and ccr2-/- mice with severe defects in monocyte recruitment ( S3b Fig ) [35] . In mice , PMN-DCs express CD11c and MHC class II [4] . In humans , these neutrophils are defined by expression of HLA-DR ( MHC class II ) , although other DC markers are expressed [3] . We tracked all neutrophils that expressed either MHC class II or CD11c during pulmonary blastomycosis . CD11c+ only ( MHCII- CD11c+ ) neutrophils were virtually absent , decreasing in proportion from a minuscule population after day 1 ( Fig 1C ) . MHC class II+ only ( MHCII+CD11c- ) neutrophils and PMN-DCs ( MHCII+ CD11c+ ) rose in proportion through the first week . MHC class II+ only neutrophils showed lower expression of MHC class II than did PMN-DCs ( Fig 1A , S2A Fig ) , suggesting that this population may be intermediate between classical neutrophils and PMN-DCs . PMN-DCs expressed MHC class I and co-stimulatory ligands CD80 and CD86 similar to levels expressed by DCs ( Fig 1E and 1F ) . MHCII+ only neutrophils had low expression of DC markers , further indicating that they may be intermediately differentiated cells . Additionally , both PMN-DCs and MHC class II+ only neutrophils increased together through the first 7 days of infection and the relative proportions flipped at day 14 when the number of PMN-DCs surpassed MHC class II+ only neutrophils ( Fig 1C and 1D ) . One unanswered question about the biology of PMN-DCs concerns the type of DC that PMN-DCs most resemble . PMN-DCs , differentiated in vitro , are transcriptionally similar to DCs differentiated from bone marrow monocytes [4] . We examined the expression of surface markers that distinguish DC subsets [36] . Not surprisingly , PMN-DCs during early blastomycosis did not express CD8a or langerin found on DCs in lymphatic and skin tissue , respectively ( S4A Fig ) . PMN-DCs also did not express CD103 , which is found on a population of resident DCs ( S4A Fig ) . Nor did PMN-DCs express the plasmacytoid DC ( pDC ) markers B220 or Siglec H; however , a proportion of the MHCII+ only neutrophils did express B220 , but not Siglec H , indicating that some cells in this population may be a neutrophil that resembles pDCs ( S4C and S4D Fig ) . The monomeric Fc γ receptor CD64 , expressed highly on inflammatory monocyte-derived DCs ( moDCs ) [37] was expressed by a majority of PMN-DCs ( Fig 1G , S4B Fig ) . These data support the idea that PMN-DCs are most like moDCs . While the appearance of PMN-DCs during inflammation and the various functions of PMN-DCs have been documented [3] , the role of these cells during disease is poorly understood . Because PMN-DCs retain microbicidal properties of neutrophils , we investigated the role of PMN-DCs in killing fungi during infection . We previously created a DsRed strain of Blastomyces that reports yeast viability e . g . when yeasts are killed they lose DsRed fluorescence [35] . In tandem with Uvitex cell wall stain , we tracked live and killed yeast associated with leukocyte populations during infection ( Fig 2A ) . At 7 days post-infection ( dpi ) , when a relatively large PMN-DC population expanded , canonical neutrophils had low association rates with yeast ( <0 . 1% ) , whereas PMN-DCs had 30-fold higher association rates , showing up to 3% of PMN-DCs associated with yeast ( Fig 2A and 2B ) . PMN-DCs also killed yeast at twice the rate of canonical neutrophils ( Fig 2A and 2C ) . Overall , despite amounting to less than 1% of the lung leukocytes during early infection ( Fig 1 ) , PMN-DCs associated with ≈5% of the total yeast in the lung ( Fig 2D ) and accounted for up to 15% of the killed yeast in the lung ( Fig 2E ) . Previously , we observed that monocytes and moDCs had high rates of killing but a low association with B . dermatitidis in vivo [35] . Similarly , we saw that moDCs had a higher rate of killing compared to PMN-DCs , but much lower association with yeast ( Fig 2 ) . We tracked killing of yeast at an earlier time point , 2 dpi , when PMN-DCs were less established . Even earlier in infection PMN-DCs still had higher association rates with yeast and killed yeast better than did canonical neutrophils ( S5A–S5D Fig ) . Because uvitex staining persists on fungi for about 2 days in vivo , we stained yeast before infection allowing us to confirm that the high association rate of PMN-DCs with yeast was not a confounding effect of non-specific intracellular Uvitex staining . Separately , we tracked association of GFP yeast with PMN-DCs at 7 dpi and found comparable results ( Fig 3A ) . We considered the possibility that , in vivo , PMN-DCs engulf yeast already killed by neutrophils or other cells . To exclude this possibility , we FACS-sorted canonical neutrophils , PMN-DCs and moDCs from cultured bone marrow leukocytes and incubated these cells with Blastomyces yeast to investigate direct killing by PMN-DCs . In vitro , sorted PMN-DCs killed yeast better than sorted canonical neutrophils ( S5E Fig ) , but less well than moDCs , reproducing in vivo results . Taken together , these data indicate that PMN-DCs represent a significant leukocyte effector during lethal fungal pneumonia due to blastomycosis . Because yeasts were more frequently associated with PMN-DCs than classical neutrophils during infection , we asked whether PMN-DCs phagocytose yeast better than neutrophils . To test this , we challenged mice with GFP-expressing B . dermatitidis yeast and surfaced-stained samples with calcofluor white , a chitin cell-wall stain , to distinguish extracellular yeast ( Calcofluor+ ) from intracellular yeast ( Calcofluor- ) ( Calcofluor is not cell membrane permeable ) . PMN-DCs associated much more frequently with Calcofluor- yeast than did canonical neutrophils . About 15% of PMN-DCs associated with yeast had phagocytosed the yeast ( Fig 3A ) . PMN-DCs phagocytosed 8-fold more yeasts than canonical neutrophils or moDCs , and around 40% of all phagocytosed yeasts in the lung were engulfed by PMN-DCs ( Fig 3B ) . We quantified the expression of pattern recognition receptors ( PRR ) on the surface of PMN-DCs during early blastomycosis to discern what may explain the higher association and phagocytosis rates by PMN-DCs . Dectin-1 , Dectin-2 , Mincle , mannose receptor , TLR-2 , and TLR-4 are important surface receptors that recognize fungal pathogen-associated molecular patterns [15]; of these , Dectin-2 is essential for recognizing Blastomyces [38] . We found that PMN-DCs appearing in response to Blastomyces had higher expression of these PRRs on their surface than canonical neutrophils ( Fig 3C , S6 Fig ) . Surface expression of Dectin-2 , Mincle , mannose receptor , TLR-2 and TLR-4 on PMN-DCs was equivalent to that on moDCs; however , PMN-DCs had even greater surface expression of Dectin-1 and Galectin-3 than did moDCs . Reactive oxygen species ( ROS ) and nitric oxide ( NO ) are important killing mechanisms of B . dermatitidis and other fungi [16 , 35 , 39 , 40] . To identify the mechanism of fungal killing by PMN-DCs , we quantified ROS and NO production during blastomycosis . We stained leukocytes ex vivo with dihydrorhodamine-123 and DAF-FM , fluorescent indicators of ROS and NO respectively [41] . While similar proportions of PMN-DCs and canonical neutrophils produced ROS , PMN-DCs produced more ROS than canonical neutrophils ( Fig 3D ) . PMN-DCs also produced more ROS than canonical neutrophils when stimulated ex vivo with f-MLP , a potent inducer of neutrophil ROS ( S7A Fig ) . Neutrophils produced significantly more NO than moDCs , but PMN-DCs and canonical neutrophils produced equivalent NO with comparable proportions of NO+ cells ( Fig 3E ) . We also asked whether PMN-DCs could be induced to produce more NO by stimulating them ex vivo with LPS; we saw no enhancement of NO production by PMN-DCs versus canonical neutrophils under these conditions ( S7B Fig ) . These data indicate that PMN-DCs retain neutrophil functions of phagocytosis and production of ROS and NO necessary to kill fungal cells . PMN-DCs also display greater surface expression of PRRs than do canonical neutrophils , possibly contributing to the enhanced fungal killing . The high association rate of PMN-DCs with yeast , combined with antimicrobial defenses that are as good or better than canonical neutrophils , underscore that PMN-DCs are significant effectors of immunity to blastomycotic pneumonia . Because PMN-DCs contribute significantly to yeast killing during pulmonary blastomycosis , we asked whether they emerge during other fungal infections and contribute to killing . Neutrophil immunity is essential for clearing Aspergillus and Candida infections [16] . If the immunity enacted by PMN-DCs endures for these systemic fungal infections , PMN-DCs would be an important adjunct for cellular immunotherapy against these infections . We infected mice with A . fumigatus spores intratracheally ( IT ) to see if PMN-DCs expand during this infection . Aspergillus spores are rapidly cleared from healthy wild-type mice , so we looked for neutrophil differentiation at 48 hours when spores are still present in the lung . PMN-DCs ( MHCII+CD11c+ ) differentiated and comprised 0 . 7% of neutrophils ( Fig 4A ) . We challenged mice with DsRed A . fumigatus [42] to track association and killing by neutrophil populations ( Fig 4B ) . As in blastomycosis , A . fumigatus spores associated more frequently with PMN-DCs and were killed at a higher rate by PMN-DCs than canonical neutrophils ( Fig 4C , S8 Fig ) . PMN-DCs expand under inflammatory conditions in a variety of tissues [3 , 5] . We analyzed the numbers and differentiation state of neutrophils in a model of systemic candidiasis where C . albicans is administered intravenously ( IV ) [19] . The primary target organ of systemic candidiasis is the kidney [43] . We tracked neutrophils and their differentiation in the kidney , as well as the peripheral blood and spleen , where Candida appears early in infection . After challenge , CD11c expression was upregulated on neutrophils in the kidney , peripheral blood , and spleen; however , MHC class II was not greatly upregulated on neutrophils in the kidney or peripheral blood ( Fig 4D ) . Nevertheless , Candida infection induced significant increases in neutrophil numbers in these tissues . With the increase in recruitment and differentiation of neutrophils , the numbers of CD11c+ or MHCII+ neutrophils rose by orders of magnitude by 24 hours post infection ( Fig 4E ) . Thus , these data from pre-clinical models of systemic candidiasis , as well as pulmonary infection with Aspergillus or Blastomyces , identify PMN-DCs as important leukocyte effectors in several systemic fungal infections . To further investigate the role of PMN-DCs in antifungal immunity , we exploited a highly enriched population of PMN-DCs . PMN-DCs have previously been differentiated from primary neutrophils , but murine neutrophils require feeder cells for in vitro differentiation [4 , 6] . To obtain a sufficient number of pure or highly enriched PMN-DCs , extensive FACS sorting , time , and cost are required . These manipulations also jeopardize the quality of the cells . To circumvent these issues , we generated PMN-DCs from a neutrophil progenitor cell line . This line is a granulocyte/macrophage progenitor ( GMP ) that is maintained in progenitor status in the presence of estrogen by fusing the transcription factor HoxB8 to a truncated estrogen receptor ( ER ) , and , when removed from estrogen , ≈99% of these GMPs differentiate into neutrophils [44] . We differentiated GMPs into ≥95% neutrophils after 4–5 days of estrogen removal , and , importantly , GMPs did not differentiate down the monocyte/macrophage pathway because no cells in the neutrophil population expressed CD115 or F4/80 ( S9 Fig ) . To determine whether the ER-HoxB8 GMPs can be differentiated into PMN-DCs , we cultured GFP-expressing GMPs in the presence or absence of estrogen , then added GM-CSF and IL-4 with bone marrow feeder cells [6] . When GMPs matured into neutrophils in the absence of estrogen , a greater proportion and number of neutrophils became PMN-DCs ( Fig 5A ) . When estrogen was maintained , the cells had no increase in CD11c or MHC class II expression . Under differentiation conditions for 5 days , nearly 50% of GFP+ cells expressed CD11c with some increase in MHC class II expression . We assessed the function of GMP-derived PMN-DCs . Because primary PMN-DCs associated more frequently with fungal cells and killed them better than canonical neutrophils , we generated neutrophils and PMN-DCs and incubated them with Uvitex-stained DsRed A . fumigatus spores . Differentiated PMN-DCs associated with and killed spores at a higher rate than did undifferentiated neutrophils ( day 0 ) or neutrophils that remained undifferentiated despite culture with cytokines ( day 5 ) ( Fig 5B , lower panel ) . Notably , PMN-DCs upregulated MHC class II expression after culture with spores ( Fig 5B , upper panel ) . We also compared GMP-derived PMN-DCs and undifferentiated neutrophils in their ability to associate with or internalize fluorescent beads coated with β-1 , 3-glucan , a fungal cell wall glycan . After 1 hour , less than 1% of neutrophils were associated with β-glucan beads as compared to over 40% of PMN-DCs ( Fig 5C ) . We next optimized culture of GMP-derived neutrophils to obtain higher purities of PMN-DCs . Since removal of feeder cells is the greatest obstacle to obtaining pure murine PMN-DCs in vitro , we asked whether feeder cells are necessary in generating PMN-DCs from the cell line . After maturing GMP cells to neutrophils , we cultured neutrophils with or without feeder cells in the presence of GM-CSF and IL-4 for 5 days . Remarkably , we saw over 50% differentiation into PMN-DCs in the absence of feeder cells with only minor reductions in CD11c and MHC class II expression ( Fig 5D and 5E ) . Along with induction of DC markers , we observed morphological changes in the GMP-derived neutrophils from small , round cells with a segmented nucleus to large cells with membrane projections and mononuclear phenotype ( S10 Fig ) . To obtain a higher purity of PMN-DCs , we cultured GMP-derived neutrophils longer with GM-CSF and IL-4 . After 7 days , over 90% of cells showed increased CD11c expression and greater morphological changes ( Fig 5E , S10 Fig ) . In generating highly purified PMN-DCs from ER-HoxB8 GMPs , we addressed questions not testable in vivo or with mixed neutrophil populations . By incubating purified PMN-DCs with C . albicans or B . dermatitidis , we confirmed that PMN-DCs directly kill the fungi . This highly enriched population of PMN-DCs killed both C . albicans and B . dermatitidis significantly better than did undifferentiated neutrophils ( Fig 6A ) . PMN-DCs killed ≈70% of C . albicans after a 4-hour incubation . Because PMN-DCs kill fungi directly , we analyzed interactions between fungi and PMN-DCs or canonical neutrophils by scanning electron microscopy . As we noted above ( Fig 3A ) , PMN-DCs frequently phagocytosed B . dermatitidis , whereas canonical neutrophils did not successfully phagocytose this yeast ( Fig 6B , S11 Fig ) . We also observed that PMN-DCs released NETs against B . dermatitidis and C . albicans ( Fig 6B , S11 Fig ) . Interestingly , NETs released by PMN-DCs appeared to be thicker and associated with more protein-like material than NETs released by canonical neutrophils ( Fig 6C ) . To further investigate the role of NETs in killing by PMN-DCs , we induced C . albicans to filament ( S11 Fig ) before adding canonical neutrophils or PMN-DCs . NETs are known to kill Candida [45 , 46] , and filaments here induced greater NETosis . Although canonical neutrophils killed C . albicans better after it filamented , the PMN-DCs still killed C . albicans to a greater degree ( >80% ) than did canonical neutrophils ( Fig 6A and 6D ) . To determine the contribution of NETs to killing , we added DNase to killing assays to degrade NETs . DNase significantly decreased killing by both canonical neutrophils and PMN-DCs , suggesting that NETs contribute to PMN-DC-mediated killing of Candida ( Fig 6D ) . A major benefit of deriving PMN-DCs from ER-HoxB8 GMPs is that a large number of highly pure PMN-DCs are readily obtained without costly or detrimental enrichment procedures . Thus , adoptive transfer of pure PMN-DCs in numbers is feasible . Because PMN-DCs kill C . albicans well in vitro , we administered PMN-DCs ( or canonical neutrophils ) into wild-type mice with systemic candidiasis to test whether the cells augment antifungal immunity and have the potential as an adjunctive therapy . A single transfer of PMN-DCs , but not canonical neutrophils , significantly reduced the fungal burden in the kidneys of mice with systemic candidiasis ( Fig 6E ) . Thus , PMN-DCs may be a good candidate for cellular immunotherapy against invasive fungal infections . One key function that distinguishes PMN-DCs from canonical neutrophils is the capacity to process and present antigen and prime T cells . Much of the work investigating antigen presentation has used mixed neutrophil populations ( comprised largely of canonical neutrophils ) in in vitro assays with T cells [3] . Investigation of in vivo presentation by PMN-DCs has relied on presentation of model OVA peptide to OT-II cells [5] . To expand understanding of antigen presentation by PMN-DCs and investigate fungal antigen presentation by PMN-DCs , we employed 1807 TCR transgenic CD4+ cells ( Tg1807 ) that recognize fungal calnexin from multiple pathogenic fungal ascomycete species and confer vaccine immunity [28] . To investigate the capacity of PMN-DCs to process and present fungal antigen , we generated PMN-DCs from GMP cells ( as in Fig 5E ) and incubated them with recombinant fungal calnexin . Fungal calnexin induced IL-6 production by PMN-DCs ( Fig 7A ) . PBS vehicle control , unstimulated PMN-DCs , antigen-pulsed PMN-DCs , or antigen-pulsed bone marrow DCs ( BMDCs; a positive control ) were injected subcutaneously into mice that had received a transfer of naïve Tg1807 cells ( Fig 7B ) . After 7 days , we analyzed the activation of Tg1807 cells in skin draining lymph nodes . Activated ( CD44+CD62L- ) Tg1807 cells were increased in mice that had received calnexin-loaded PMN-DCs , but not in mice that had received vehicle or control PMN-DCs ( Fig 7C ) . Calnexin-pulsed PMN-DCs induced a 15-fold increase in the number of Tg1807 cells , and over a 100-fold increase in the number of activated Tg1807 cells in draining lymph nodes , when compared to recipients of control PMN-DCs ( Fig 7D ) . Delivery of calnexin-loaded PMN-DCs induced an antigen-specific response because the response among endogenous CD4+ and CD8+ T cells was undetectable after delivery of PMN-DCs pulsed or not with calnexin ( S12 Fig ) . Calnexin-pulsed PMN-DCs activated antigen specific T cells as well or better than calnexin-loaded BMDCs ( Fig 7C and 7D , S13 Fig ) . We also investigated the recall response of Tg1807 cells that were primed in vivo . Lymph node samples from the groups were incubated with recombinant calnexin ex vivo for 3 days to induce cytokine production in primed Tg1807 cells . We analyzed IFN-γ and IL-17 , which are important cytokines of CD4+ T cells that promote antifungal immunity [15] . Upon ex vivo stimulation , cells from mice that had received antigen-pulsed PMN-DCs produced significantly more IFN-γ and IL-17 than cells from animals that received vehicle control ( Fig 7E ) . We considered a caveat in our in vivo assay . When antigen-pulsed PMN-DCs were delivered into mice , other antigen presenting cells could have received the calnexin to present to T cells , for example through in vivo antigen transfer . To establish that PMN-DCs present calnexin , we mixed PMN-DCs with freshly enriched CD4+ T cells from naïve Tg1807 mice in in vitro antigen-presenting assays . Calnexin-loaded PMN-DCs induced robust T cell production of IFN-γ and IL-17 , and yeast-loaded PMN-DCs induced strong IL-17 production ( Fig 7F , S14 Fig ) . PMN-DCs in the absence of T cells did not produce IFN-γ or IL-17 ( S14 Fig ) . Several studies have linked T cell production of cytokines with expression of MHC class II on neutrophils [3] . One study showed that human memory T cells induced MHC class II expression on neutrophils ( with canonical phenotype ) in vitro and that this induction was dependent on presence of cognate antigen [47] . In our in vitro antigen presentation experiment , we asked whether Tg1807 cells induce expression of MHC class II on PMN-DCs , particularly because in vitro derived PMN-DCs tend to have low MHC class II expression ( Fig 5 ) . We found that PMN-DCs cultured in the presence of T cells had an increase in MHC class II surface expression , and this expression was slightly enhanced by the presence of calnexin or killed B . dermatitidis ( Fig 7G ) . In sum , PMN-DCs present fungal antigen and prime T cells . Antigen-pulsed PMN-DCs primed type 1 and type 17 responses known to be protective against fungal infections ( S15 Fig ) .
During pulmonary blastomycosis , PMN-DCs associated with yeast better than any other leukocyte . Yeast association rates were nearly 100-fold greater for PMN-DCs compared to canonical neutrophils . Association rates with A . fumigatus spores also were much higher for PMN-DCs than for canonical neutrophils both in vivo and in vitro . One explanation for this finding is the higher expression of surface PRRs on PMN-DCs than canonical neutrophils . PMN-DCs retain the neutrophil feature of high phagocytic capacity [3] . Bone marrow-derived PMN-DCs phagocytose bacteria at the same rate as canonical neutrophils , but engulf latex beads at much higher rates [4] . Murine PMN-DCs that emerge during peritonitis phagocytose E . coli better than other peritoneal neutrophils or DCs [5] . Also , HLA-DR+ neutrophils enriched from the peripheral blood of patients phagocytose Leishmania promastigotes better than HLA-DR- neutrophils [11] . We tracked phagocytosis in vivo and found that PMN-DCs phagocytosed many more yeasts than canonical neutrophils , accounting for ≈40% of the phagocytosed yeast in the lung . By using cell line-derived PMN-DCs , we confirmed that PMN-DCs phagocytosed yeast more efficiently than did canonical neutrophils . The increased size of PMN-DCs probably promotes enhanced phagocytosis , as particle size physically constrains phagocytosis [45] . At 8–10 μm , B . dermatitidis yeast are nearly as large as canonical neutrophils [49] , so that larger PMN-DCs ( ~20 μm ) are better able to phagocytose B . dermatitidis and other larger organisms such as Leishmania promastigotes . ROS and NO are essential neutrophil products for killing fungi [1 , 17 , 40] . PMN-DCs are known to produce ROS , but to our knowledge , we report the first instance of NO production by PMN-DCs . We found that NO production was similar for PMN-DCs and canonical neutrophils , whereas ROS production was greater in PMN-DCs than canonical neutrophils . Two studies of human HLA-DR+ neutrophils noted enhanced ROS production after in vitro stimulation [11 , 50] . We , too , saw that PMN-DCs from Blastomyces-infected lungs produced more ROS than canonical neutrophils when stimulated ex vivo with f-MLP . NETs are a neutrophil defense against large organisms such as filamentous fungi [45] . Candida and other fungi pathogens are susceptible to killing by NETs [46] . PMN-DCs release NETs in response to PMA [4] , and we show that fungi also induce NETosis by PMN-DCs . Because fungal killing was reduced in the presence of DNase , NETs from PMN-DCs appear to play a role in fungal killing . We also noted that NETs released by PMN-DCs appear thicker and have larger aggregates of protein-like material than NETs from canonical neutrophils; it is possible that NETs made by PMN-DCs could be laden with more antimicrobials and display greater killing potential . In sum , in the context of fungal infection , PMN-DCs have similar or enhanced antimicrobial functions compared to canonical neutrophils . Increased surface PRR expression , phagocytosis , ROS production , and NETosis after differentiation into PMN-DCs likely explain how PMN-DCs associate with and kill fungal cells so effectively . The most common , life-threatening fungal infections are caused by Asperigillus and Candida species [21] . We investigated the expansion of PMN-DCs in murine models of pulmonary aspergillosis and systemic candidiasis . Wild-type mice rapidly clear Aspergillus in the lung; however , wild-type mice quickly die with systemic candidiasis [43] . During pulmonary aspergillosis , we saw robust expansion of neutrophils that expressed both CD11c and MHC class II and killed spores with greater efficiency than did canonical neutrophils and moDCs . During systemic candidiasis , neutrophil differentiation was incomplete with few CD11c+MHCII+ appearing particularly in the primary target organ , the kidney . Because in vitro differentiated PMN-DCs kill C . albicans efficiently in vitro , the limited numbers and differentiation of PMN-DCs during systemic candidiasis may contribute to the eventual mortality . In this regard , it is noteworthy that adoptive transfer of fully differentiated PMN-DCs significantly decreased C . albicans in the kidneys . PMN-DCs may offer cell therapy strategies to be studied for improving outcomes during systemic candidiasis or pulmonary aspergillosis . Investigations of PMN-DCs have been greatly limited by the difficulty in obtaining a large source of pure PMN-DCs . Studies investigating PMN-DCs in murine models have either used mixed populations of neutrophils or relied on FACS sorting [4 , 5 , 9] . Work with murine PMN-DCs is also complicated by the fact that primary neutrophils require feeder cells to differentiate in vitro [4 , 6] . To circumvent these issues , we employed a GMP cell line to produce neutrophils that we then differentiated into PMN-DCs [44] . This cell line was a continual , consistent source of PMN-DCs that did not rely on an animal source . Additionally , we successfully generated PMN-DCs without the use of feeder cells , greatly simplifying their production . PMN-DCs generated from the GMP cell line resembled PMN-DCs differentiated from bone marrow neutrophils [4]: they displayed high amounts of CD11c , an increase in surface MHC class II , and morphological changes consistent with differentiation into a DC-like cell . The cell line-derived PMN-DCs were also functionally similar to in vivo PMN-DCs e . g . they associated with and killed fungal cells better than canonical neutrophils . Further , generating PMN-DCs from a cell line increases the feasibility of experiments probing the biology of PMN-DCs . For example , we verified that PMN-DCs directly kill fungal cells and produced NETs in response to fungi . We also used GMP-derived PMN-DCs to investigate presentation of fungal antigen and polarization of T cell responses . Finally , by using the cell line , we obtained enough pure PMN-DCs to show that adoptive transfer of PMN-DCs conferred immunity to systemic candidiasis . While human neutrophils have been shown to present disease-associated antigens to T cells in vitro , studies of murine neutrophils have been limited to presentation of OVA peptide to OT-I and OT-II cells [3 , 12 , 47] . We used Tg1807 CD4+ T cells , which recognize the pan-fungal antigen calnexin and confer protective immunity against various fungal infections [28] . Delivery of calnexin-pulsed PMN-DCs activated Tg1807 cells in vivo and promoted Th1 and Th17 recall responses . PMN-DCs given either recombinant calnexin or heat-killed B . dermatitidis also presented antigen to Tg1807 cells in vitro inducing production of IFN-γ and IL-17 . While this confirms prior studies that murine neutrophils polarize Th1 and Th17 in vitro [5 , 9 , 50] , type 1 and type 17 responses are essential for protective immunity against fungal infections including candidiasis , aspergillosis and blastomycosis [15] . By showing that antigen-pulsed PMN-DCs induce Th1 and Th17 in antigen-specific T cells , we unveil a role for PMN-DCs in arming adaptive immunity concomitantly while directly killing fungi ( S15 Fig ) . T cells , by producing cytokines , are implicated in inducing neutrophil differentiation into PMN-DCs . Human mucosal innate T cells induce PMN-DC differentiation in vitro and this differentiation is inhibited by neutralizing antibodies against T cell-produced cytokines [51] . Human memory T cells induce MHC class II expression on autologous neutrophils in vitro , but only in the presence of cognate antigen [47] . Murine T cells in the absence of antigen induce MHC class II expression on neutrophils in vitro [52] . We too looked at MHC class II expression in PMN-DCs differentiated from the GMP cell line . Like PMN-DCs generated from bone marrow neutrophils , GMP-derived PMN-DCs have low expression of MHC class II+ after differentiation . MHC class II expression increased significantly in the presence of T cells , although expression was only slightly increased after concomitant incubation with cognate antigen . We also noted increased MHC class II expression in GMP-derived PMN-DCs incubated overnight with A . fumigatus spores , but for this assay feeder cells were present , indicating that signals from these cells may influence MHC class II expression on neutrophils . Invasive fungal infections are a leading cause of death in cancer and transplant patients [20–25] , and in patients requiring intensive care [18] . Treatment of deadly fungal infections relies heavily on antifungal drugs , but antifungals have limitations in bioavailability , drug resistance and toxicity [26] . Because antifungals cannot fully control some fungal infections , immunotherapeutic approaches are being sought [31] . Neutrophils are essential for immunity to most fungal diseases , including aspergillosis and candidiasis [16] . Impaired neutrophil immunity is a major risk factor for invasive fungal infection and mortality , highlighting that neutrophils are crucial targets of immunotherapy . Our current work supports the targeting of this unique population of neutrophils , PMN-DCs , in treating invasive fungal infections . PMN-DCs are better cellular targets than therapies directed at canonical neutrophils because ( 1 ) PMN-DCs better engage and kill fungi; ( 2 ) PMN-DCs persist longer than canonical neutrophils , limiting the number or duration of treatments needed and saving cost and patient stress; ( 3 ) PMN-DCs arm adaptive immunity , promoting better defense and long-term protection , thereby reducing the need for continual intervention . Harnessing PMN-DCs to fight lethal fungal infections may only require modifications of therapies in practice that enhance neutrophil immunity . Adjunctive cytokine therapy increases circulating neutrophils in neutropenic patients and has helped in treating fungal infections [31] . Additionally , granulocyte transfusion , out of practice for some time , is now effective with appropriate donor neutrophil preparation [53] . Cytokine therapies could be modified to promote expansion of PMN-DCs in patients , or PMN-DCs could be differentiated from autologous or donor neutrophils ex vivo prior to transfusion . Although work is needed to determine how amplified PMN-DCs would affect patients , we show that these cells arise naturally during infection , and they likely increase in certain widely practiced immunotherapies . We also show that adoptive transfer of PMN-DCs confers protection during systemic infection . In patients at greatest risk from fungal infections , harnessing PMN-DCs could improve disease outcomes .
Wild-type C57BL/6 were purchased from Charles River Laboratories ( Wilmington , MA ) . Mice challenged with fungi or recipients of Tg1807 cells were 7–13 weeks old . Bone marrow used to culture PMN-DCs or BMDCs in vitro came from mice up to 16 weeks old . C57BL/6 Tg1807 CD90 . 1 mice [28] were bred in house; spleens and lymph nodes were harvested from Tg1807 mice between 8–18 weeks old . C57BL/6 ccr2-/- and ubiquitin C ( UBC ) -GFP mice were purchased from Jackson Laboratory ( Bar Harbor , ME ) . All procedures with mice were in accordance with a protocol approved by the University of Wisconsin Animal Care and Use Committee , and in line with accreditation by the American Association for Accreditation of Laboratory Animal Care and NIH guidelines . Blastomyces dermatitidis wild-type strain ATCC 26199 or engineered to express DsRed or GFP [35] were used in this study and maintained by passage on 7H10 slants . Anesthetized mice were challenged IT with 2 x104 yeast suspended in 20 μl of PBS unless otherwise noted . CFUs were quantified after experiments by spreading yeast on brain-heart infusion ( BHI ) agar plates after 6–7 days at 37°C . In some experiments , yeast were stained with 20 μg/ml Uvitex 2B ( PolySciences , Inc . , Warrington , PA ) , a fluorescent chitin stain . Yeast were heat-killed by incubation at 70° C for 40–60 minutes . Aspergillus fumigatus strain Af293 expressing DsRed , a kind gift from Tobias Hohl [42] , was grown on glucose minimal medium plates for 7 days at 37°C . Spores were harvested from plates with 0 . 01% TWEEN-80 water and strained to remove hyphae . Spores were stained with 10 μg/ml Uvitex for in vitro experiments or 40 μg/ml Uvitex before IT challenge . Before some in vitro experiments , spores were biotinylated and stained with streptavidin-Alexafluor633 ( Af633 ) [40 , 42] . For infection , mice were challenged with 4 x107 spores IT . Candida albicans strain SC5314 was grown from frozen stocks on yeast peptone dextrose ( YPD ) agar plates for 1–2 days at 30°C , and colonies were picked and grown in YPD medium overnight in a 30°C shaking incubator . After overnight culture , yeasts were prepared for challenge or in vitro assays . Anesthetized mice were challenged IV with 1 . 0–2 . 5 x 105 yeast in 0 . 5 mL PBS via the retro-orbital vein [43] . Lungs were harvested , processed , and digested with collagenase D and DNase I ( Sigma , St . Louis , MO ) , as described [35] . For experiments tracking fungal cells , to improve staining , mice were bled and hearts were perfused with 5–10 mL of PBS after the animals were euthanized . In some experiments where neutrophil populations were tracked , mice were injected IV with 2 μg/ml anti-CD45 ( 30-F11 ) ( Biolegend , San Diego , CA ) to stain leukocytes in the capillaries prior to euthanizing and harvesting lungs [33 , 54] . Lungs were harvested 2 days post infection ( dpi ) from mice challenged with A . fumigatus spores and between 1–14 dpi from mice challenged with B . dermatitidis . Peripheral blood , spleens and kidneys were collected from naïve mice or mice 1–3 dpi with C . albicans . Peripheral blood was collected from the retro-orbital vein into heparin and mixed with an equal volume of 2% dextran ( 450–650 kDa , Sigma ) to aggregate erythrocytes for 45–60 minutes at room temperature . The volume suspended over erythrocyte aggregates was collected and cells were washed before lysing the remaining erythrocytes with ACK ( ammonium-choride-potassium ) buffer [35] . Kidneys were minced , then crushed with a syringe plunger . Following mechanical disruption , kidneys were digested with 1 μg/ml collagenase I , 1 μg/ml collagenase II , 1 μg/ml collagenase IV ( Worthington Biochemical , Lakewood , NJ ) and 20 μg/ml DNase I ( Sigma ) in RPMI-1640 without inactivated fetal bovine serum ( FBS ) for 30 minutes at 37°C . After digest and erythrocyte lysis , cells were resuspended in 40% Percoll:60% RPMI ( with 1% FBS ) . Cells in 40% Percoll were underlaid with 80% Percoll and centrifuged for 20 minutes at 800 x g at room temperature [43] . The interface was collected , and cells were stained for flow cytometry . Spleens were dissociated with a syringe plunger through 40–70 μm mesh and splenic erythrocytes lysed in ACK buffer . Lymph nodes were also dissociated with a syringe plunger through 40–70 μm mesh . For collection of lymph nodes from Tg1807 mice , inguinal , axillary , brachial , and cervical lymph nodes were collected and pooled . For mice that received subcutaneous ( SC ) injections , skin-draining inguinal , axillary and brachial lymph nodes were collected and pooled . Leukocytes were stained in PBS containing 0 . 5% bovine serum albumin . For all experiments cells were incubated with Fc Block ( anti-mouse CD16/CD32 ) ( BD Biosciences , San Jose , CA ) to limit surface Fc receptors binding to staining antibodies . Staining cocktails contained fluorescent-conjugated antibodies from BioLegend , BD Biosciences , or eBiosciences ( ThermoFisher , Waltham , MA ) unless noted otherwise . Antibodies were directed against the following markers ( clones noted in parenthesis ) : CD4 ( RM4-5 or GK1 . 5 ) , CD8a ( 53–6 . 7 ) , CD11a ( M17/4 ) , CD11b ( M1/70 ) , CD11c ( N418 or HL3 ) , CD40 ( 3/23 ) , CD44 ( IM-7 ) , CD45/Ly5 ( 30-F11 ) , B220/CD45RA ( RA3-6B2 ) , CD62L ( MEL-14 ) , CD64 ( X54-5/7 . 1 ) , CD69 ( H1 . 2F3 ) , CD80 ( 16-10A1 ) , CD86 ( GL-1 ) , CD90 . 1/Th1 . 1 ( OX-7 ) , CD90 . 2/Th1 . 2 ( 53–2 . 1 ) , CD103 ( M290 ) , CXCR2/CD182 ( SA045E1 ) , Mannose Receptor/CD206 ( C068C2 ) , Langerin/CD207 ( RMUL . 2 ) , TLR-2/CD282 ( T2 . 5 ) , TLR-4/CD284 ( SA15-21 ) , Dectin-1 ( RH1 ) , Galectin-3/Mac-2 ( M3/38 ) , MHC class I H-2Kb/H-2Db ( 28-8-6 ) , MHC class II I-A/I-E ( M5/114 . 15 . 2 ) Ly6C ( HK1 . 4 or AL-21 ) , Ly6G ( 1A8 ) , F4/80 ( BM8 ) , NK1 . 1 ( PK136 ) , Siglec F ( E50-2440 ) , Siglec H ( 551 ) . Unconjugated rat anti-murine Dectin-2 ( D2 . 11E4 ) ( ThermoFisher , Waltham , MA ) and anti-murine Mincle ( 4A9 ) ( MBL , Woburn , MA ) were used as primary antibodies incubated alone with cells . After washing , PE or APC conjugated anti-rat IgG2a ( BD Biosciences ) was included in a cocktail containing the other fluorescent conjugated antibodies . FMO controls for Dectin-2 and Mincle did not receive primary antibody but were stained with anti-rat secondary antibody . To mark B . dermatitidis yeast ex vivo with Uvitex [35 , 55] , after lung processing , cells were stained for surface markers , washed , and fixed with BD cytofix/cytoperm . Cells were then stained with 1 μg/ml Uvitex 2B in BD perm/wash buffer for 30 minutes at room temperature before being washed with perm/wash buffer . Intracellular staining of fungi with Uvitex also stains phagocytic cells in the lungs of naïve mice; these stained cells have likely engulfed chitin or other polymers inhaled from food or bedding [56] . These cells are present in small number in the lungs of mice infected with B . dermatitidis; they have dim Uvitex staining and also are autofluorescent in many channels including DsRed . These false positive events were gated out from the cells determined to be Uvitex+ as shown by Wang et al [55] . To track phagocytosis of B . dermatitidis yeast in vivo , cells from processed lungs were surface stained with 1 μM calcofluor white M2R ( Sigma ) in stain cocktails for 20 minutes at 4° C . For all experiments , cells were stained with Invitrogen Live/Dead Fixable Yellow or Near Infared ( ThermoFisher ) to gate out dead cells . Forward and side scatter gates were also used to remove debris and , in experiments not investigating fungal cell association , to remove doublets ( FSC-A X FSC-H , SSC-A x SSC-H ) . Flow cytometry was performed on either a 3 or 5 laser BD LSRII or 5 laser BD Fortessa cytometers . Imaging cytometry was performed using an ImageStream MarkII ( Amnis ) . FACS sorting was completed on a BD FACS Aria II . FACS was performed on live cells cultured from bone marrow or on fixed cells from lungs infected B . dermatitidis . Most flow cytometry was performed at the University of Wisconsin Carbone Cancer Center Flow Cytometry Core . Flow cytometry data was analyzed and plots were designed using Flowjo10 ( Tree Star , Ashland , OR ) . Mean fluorescence intensities ( MFI ) were calculated in Flowjo using the geometric mean of fluorescence . Relative expression of surface proteins on cells was determined by subtracting the MFI of that marker’s fluorescence minus one ( FMO ) control on the population from the stained sample MFI of the population . Microscopy was completed with an Olympus BX60 microscope . Images were captured using QCapture Pro 6 . 0 and an Exi Aqua Camera ( QImaging , Surry , BC ) at room temperature . All images were captured under 40X or 100X power magnification . Raw images were cropped and resolution enhanced in Microsoft PowerPoint . To ascertain the morphology of PMN-DCs , lungs were harvested 7 dpi from mice challenged with B . dermatitidis as described above . Cells were fixed with 2% paraformaldehyde and then FACS sorted to obtain PMN-DCs ( CD11b+ , Ly6G+ , Ly6Cint , CD11c+ ) , canonical neutrophils ( CD11b+ , Ly6G+ , Ly6Cint , CD11c- ) , or moDCs ( CD11b+ , Ly6G- , Ly6C+ , CD11c+ ) . After sorting , cells were centrifuged onto a slide using a cytospin and stained with a Hema3 kit ( ThermoFisher , Waltham , MA ) . Morphology of ER-HoxB8 GMP derived cells ( GFP+ ) was tracked after maturation of cells to neutrophils . Sterilized coverslips were placed in dishes before the addition of neutrophils and differentiation medium . Coverslips were moved to slides through the course of differentiation to capture images of cells . GMP cells through the course of differentiation also were centrifuged on to a slide and stained with a Hema3 kit . To track interactions between fungal cells and neutrophils in vitro , canonical neutrophils or PMN-DCs , derived from GMP cells , were incubated for 3 hours at 37°C with C . albicans or B . dermatitidis yeast ( prestained with 20 μg/ml Uvitex ) in Lab-Tek chamber slides ( Thermo Fisher ) . Before visualizing cells , propidium iodide ( Sigma ) was added to cultures at a final concentration of 40 μM and incubated at 37° C for 15 minutes . To wells with C . albicans , Uvitex was also added at a final concentration of 20 μg/ml with propidium iodide . After processing lungs , a subset of cells was incubated with either ROS or NO fluorescent indicators dihydrorhodamine-123 ( DHR-123 ) ( Chemodex [Adipogen , San Diego , CA] ) or DAF-FM diacetate ( 4-amino-5-methylamino-2’ , 7’-difluorofluorescein diacetate ) ( Cayman , Ann Arbor , MI ) at 37° C [39] . To quantify ROS production , cells were incubated with 10 μg/ml DHR-123 in RPMI with 10% FBS for 3 hours if unstimulated or 30 minutes with 10nM N-formyl-L-methionyl-L-leucyl-phenylanalynine ( f-MLP ) [33] . To quantify NO production , unstimulated cells or cells pre-incubated 60 minutes with 2 μg/ml lipopolysaccharide ( LPS ) were incubated with 100 nM DAF-FM diacetate in PBS for 10 minutes . After incubation with indicator dyes , cells were stained for surface markers as described above . The marrow of wild-type mice was harvested from femurs and tibias as previously described [35] . Cells were resuspended in RPMI with 10% FBS and 10 ng/ml GM-CSF and 1 ng/ml IL-4 . Cells were cultured in 6-well plates at a density of 4–6 x 106 cells/well . Medium was refreshed every other day . Non-adherent cells were collected at day 6 and co-cultured with uvitex-stained fungal cells overnight in RMPI with 10% FBS at 37°C , then prepared for flow cytometry . Day 6 cells were also FACS sorted to purify canonical neutrophils and PMN-DCs . These sorted cells were resuspended in RMPI with 10% FBS and mixed with B . dermatitidis yeast at an effector-target ratio of 3:1 and incubated overnight at 37°C; then cells were lysed , and yeasts were plated on BHI agar to enumerate CFU . Bone marrow mononuclear cells isolated from a UBC-GFP mouse ( Jackson strain 004353 ) were transduced with retrovirus ( MSCVneo ) expressing the ER-HoxB8 ( estrogen receptor ) fusion protein as described by Wang et al [44] . Single-cell GMP clones were selected that uniformly matured into neutrophils as assessed by cell surface staining , function , and morphology . GMPs were maintained as progenitors in estrogen ( beta-estradiol , 0 . 5 μM ) and conditioned medium containing stem cell factor ( ~100 ng/ml ) and matured into neutrophils for 4–5 days in conditioned medium lacking estrogen . After cells were matured into neutrophils , they were centrifuged at low speed ( 150 x g ) to remove dead cells and cultured for 5–7 days under conditions that promote PMN-DC differentiation . Neutrophils were differentiated in RPMI with 10% FBS and 10 ng/ml GM-CSF and 1 ng/ml IL-4 [6] . For initial experiments , neutrophils were cultured with bone marrow feeder cells , prepared as described above , at a ratio of 3 feeder cells to 1 neutrophil; later experiments did not use feeder cells . GFP-expression was used to distinguish the PMN-DC from feeder cells . To assess functions of GMP-derived PMN-DCs , neutrophils before differentiation or PMN-DCs after differentiation were incubated with fungal cells or β-1 , 3-glucan coated beads . To track killing of Aspergillus , DsRed A . fumigatus spores , pre-stained with Uvitex , were co-cultured with neutrophils or PMN-DCs at an effector-to-target ratio of 3:1 for 12 hours , then stained for flow cytometry . To track association with fungal-like particles , AlexaFluor647 beads coated with the fungal cell wall component β-1 , 3-glucan [57] were incubated with neutrophils or PMN-DCs for 1 hour at 37°C and then assessed by flow cytometry . To track killing of B . dermatitidis , yeasts were co-cultured with neutrophils or PMN-DCs overnight at an effector-to-target ratio of 3:1; cells were then lysed and yeasts were spread on BHI plates to quantify viability . To track killing of C . albicans , yeasts were co-cultured with neutrophils or PMN-DCs , or C . albicans was incubated for 2 hours to promote filamentation before addition of neutrophils or PMN-DCs . Co-cultures of C . albicans and cells were incubated for 4 hours at 37°C; neutrophils were then lysed with water , and yeast viability was quantified by XTT , 2 , 3-Bis ( 2-methoxy-4-nitro-5-sulfophenyl ) -2H-tetrazolium-5-carboxanilide ( ThermoFisher ) , assay , as previously described [58] . To investigate the role of NETs , 50 μg/ml DNase I ( Sigma ) was added to co-cultures of neutrophils or PMN-DCs with C . albicans hyphae at the beginning of the 4-hour co-culture . Coverslips used for scanning electron microscopy ( SEM ) were prepared , as previously described [59–61] . Briefly , planktonic C . albicans or B . dermatitidis yeast were added to poly-L-lysine-treated plastic 13 mm diameter coverslips ( Thermonax , ThermoFisher ) and incubated for 1 hour at 30° C for C . albicans or 37° C for B . dermatitidis . Canonical neutrophils or PMN-DCs differentiated from the GMP cell line were added to wells containing coverslips to reach an effector-to-target ratio between 1:2 and 2:1 , and co-cultures were incubated for 4 hours at 37° C . After washing , coverslips were fixed overnight in 4% formaldehyde and 1% glutaraldehyde , followed by washing and treatment with 1% osmium tetroxide . Samples were then washed , dehydrated , dried , and coated with 14 nm platinum . Microscopy was completed on a LEO 1530 scanning electron microscope at 3 kV . Mice were challenged with 105 C . albicans IV , as described above , and 24 hours later 2 x 106 neutrophils or PMN-DCs , differentiated from ER-HoxB8 GMP cells as described above , were administered IV in 500 μl of PBS . For vehicle control , mice received 500 μl of PBS alone . At 3 dpi , kidneys were harvested and homogenized; CFU was quantified by plating kidney homogenates on YPD agar plates . Homogenates from both kidneys were combined and CFU/kidney was calculated by dividing CFU by 2 . PMN-DCs were generated from the GMP cell line , as described above , by culturing neutrophils for 7 days with GM-CSF and IL-4 . BMDCs were generated from bone marrow leukocytes with 20ng/ml GM-CSF for 7–10 days; for the first 2 days , the medium ( RPMI+10%FBS ) contained 5 ng/ml IL-4 . BMDCs were harvested by collecting non-adherent cells . For in vivo experiments , PMN-DCs or BMDCs were cultured alone or with recombinant calnexin ( from Paracoccidioides brasiliensis [28] ) at 50 ng/mL overnight . Supernatants were collected and cells were washed with PBS . PMN-DCs were resuspended to a concentration of 5 x 104 cells/mL in PBS; 1 mL of cells or vehicle control were then injected SC into mice . Before mice ( CD90 . 2 ) received calnexin-loaded cells or controls , they received an IV transfer of 2 x 106 pooled cells from spleens and kidneys of CD90 . 1 Tg1087 mice . We determined that about 10% of the transferred cells from Tg1807 mice were CD4+ T cells , which have calnexin-specific TCRs , so all mice received approximately 2 x105 Tg1807 cells IV . Seven days after antigen presenting cells were injected SC , skin-draining lymph nodes were collected and processed as described above before staining for flow cytometry . To investigate the recall responses of in vivo primed T cells , a subset of lymph node samples was cultured in RPMI with 10% FBS with 50 μg/ml calnexin for 3 days , and supernatant was collected for ELISA . For in vitro antigen presentation , PMN-DCs or BMDCs were generated as described above and cultured overnight alone or with 100 μg/ml recombinant calnexin or heat-killed B . dermatitidis yeast at a ratio of 1:1 . The next day CD4+ T cells from pooled spleens and lymph nodes of Tg1807 mice were positively enriched using anti-mouse CD4 magnetic particles-DM ( BD Biosciences ) . Enriched CD4+ Tg1807 cells were added in equal volume to PMN-DCs or BMDCs , which had been incubated with or without antigen , at a ratio of 5:1 T cells:APCs . As a control , Tg 1807 cells were added at equivalent numbers to wells without any APCs , containing medium alone , calnexin , or heat-killed B . dermatitidis . Tg1807 cells were incubated with or without APCs for 3 days and supernatants were collected for ELISA . Remaining cells were stained to track PMN-DC expression of MHC class II by flow cytometry . ELISA kits for murine IL-6 , IL-17 and IFN-γ ( R&D Systems , Minneapolis , MN ) were used to quantify cytokines in cell supernatants . Statistical analyses were performed using Graphpad Prism 5 . All experiments comparing populations of cells used statistical tests such as ANOVA with repeated measures , which grouped data coming from individual mice ( for in vivo experiments ) or cell culture wells ( for in vitro experiments ) , ANOVA with Tukey post-hoc test was used to compare groups in experiments , unless population distributions were not normal in which case the non-parametric Kruskal-Wallace test was used . If only two groups were compared , a two-tailed Student’s t-test or Mann-Whitney test were used . Some data was log-transformed to permit use of parametric tests , especially when means differed on a log-scale . P-values displayed in figures are *p<0 . 05 , **p<0 . 01 , ***p<0 . 001 . | Several patient populations including those with cancer or that receive organ-transplants are at risk of life-threatening invasive fungal infections , in part due to reduced function or numbers of white blood cells . Because of limitations in antifungal drug therapy , immune-based strategies to augment white blood cells are desired to treat fungal infections . Enhancing neutrophil immunity is one important therapeutic approach to treating deadly fungal diseases . We describe a role for a poorly understood neutrophil called the neutrophil-dendritic cell hybrid ( PMN-DCs ) in antifungal immunity . PMN-DCs retain the microbicidal function of neutrophils , while also acquiring the capacity of dendritic cells to stimulate adaptive immunity . We show that PMN-DCs trigger adaptive immunity against fungi and are potent killers of fungal pathogens . We investigated direct killing of medically relevant fungal pathogens by PMN-DCs in preclinical mouse models and by deriving PMN-DCs from a novel neutrophil cell line . We observed that PMN-DCs killed fungal cells better than typical neutrophils . We also demonstrated that administration of PMN-DCs during systemic infection reduced fungal burden . Because PMN-DCs are such potent killers of fungal cells and concomitantly induce long-term protective immunity , these cells are important targets for immunotherapies designed to treat life-threatening fungal infections . | [
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| 2018 | An unappreciated role for neutrophil-DC hybrids in immunity to invasive fungal infections |
Vertebrate development requires communication among cells of the embryo in order to define the body axis , and the Wnt-signaling network plays a key role in axis formation as well as in a vast array of other cellular processes . One arm of the Wnt-signaling network , the non-canonical Wnt pathway , mediates intracellular calcium release via activation of heterotrimeric G proteins . Regulator of G protein Signaling ( RGS ) proteins can accelerate inactivation of G proteins by acting as G protein GTPase-activating proteins ( GAPs ) , however , the possible role of RGS proteins in non-canonical Wnt signaling and development is not known . Here , we identify rgs3 as having an overlapping expression pattern with wnt5b in zebrafish and reveal that individual knockdown of either rgs3 or wnt5b gene function produces similar somite patterning defects . Additionally , we describe endogenous calcium release dynamics in developing zebrafish somites and determine that both rgs3 and wnt5b function are required for appropriate frequency and amplitude of calcium release activity . Using rescue of gene knockdown and in vivo calcium imaging assays , we demonstrate that the activity of Rgs3 requires its ability to interact with Gα subunits and function as a G protein GAP . Thus , Rgs3 function is necessary for appropriate frequency and amplitude of calcium release during somitogenesis and is downstream of Wnt5 activity . These results provide the first evidence for an essential developmental role of RGS proteins in modulating the duration of non-canonical Wnt signaling .
The Wnt signaling network is classified into β-catenin-dependent and β-catenin-independent pathways [1]–[3] . β-catenin-dependent Wnt signaling acts through the stabilization of β-catenin and subsequent transcriptional activation of β-catenin targets [4] , whereas β-catenin-independent Wnt signaling influences cell polarity ( known as Planar Cell Polarity or PCP in Drosophila ) . β-catenin-independent Wnt signaling has also been shown to lead to calcium ( Ca2+ ) release as well as activation of Rac , Rho and other cytoskeletal components in vertebrates [5] , [6] . In zebrafish , Wnt-5 and -11 function in Wnt/Ca2+ signaling [7] , [8] . Wnt11 is enriched in the anterior and mutants show anterior extension and eye fusion defects , while Wnt5b is enriched in the posterior and mutants show altered cell movements during gastrulation , often resulting in convergence extension and somite defects [9]–[11] . Zebrafish embryos demonstrate Ca2+ release dynamics during epiboly , gastrulation , convergent extension and organogenesis [12]–[21] . Two distinct types of Ca2+ release events , aperiodic transient fluxes found mainly in the enveloping layer and dorsal forerunner cells [17] , [18] , [22] , [23] and sustained increases in Ca2+ levels in the deep cell layer and yolk syncytial layer [24] , [25] , have been described . We have shown that early Ca2+ transients are , in part , modulated by Wnt5 [15] , [26] . The zebrafish wnt5b genetic mutant ( pipetail ) shows reduced Ca2+ release [24] and the ventralized maternal effect mutant hecate shows ectopic Ca2+ release [18] . Moreover , inhibition of Ca2+ release results in alteration of dorsal ventral patterning , cell movement and left-right patterning [17] , [26] . These observations suggest that the kinetics of Ca2+ release , both transient and sustained , translate into distinct developmental outputs [16] . Wnts interact with receptors of the Frizzled ( Fz ) family [27] and due to structural similarities to G protein coupled receptors ( GPCR ) , Fz receptors are hypothesized to stimulate heterotrimeric G protein activation [28]–[30] . We have shown previously that Wnt proteins work though specific Fz homologues to activate G proteins and to modulate Ca2+ release in zebrafish embryos [15] , [22] , [26] , [31] . Moreover , in Drosophila , Wnt target genes have been shown to be upregulated when Gαo is over-expressed and constitutively active Gαo is sufficient to restore Wnt signaling in the absence of Fz activity [32] . In addition , epistasis experiments support that G proteins function downstream of Fz and upstream of Disheveled ( Dvl ) [32] . G protein signaling is regulated by the lifetime of active Gα and βγ subunits . Activated Gα subunits have an intrinsic GTPase activity that converts the GTP-bound conformation to the Gα-GDP bound conformation allowing reassembly with Gβγ subunits to form the inactive Gαβγ heterotrimer [33] . Regulator of G protein Signaling ( RGS ) proteins have been shown to influence the duration of G protein signaling [34]–[37] . RGS proteins share a conserved RGS domain of 130 amino acids that binds to activated Gα subunits and accelerates their rates of GTP hydrolysis by up to 1000-fold [38]–[40] . By functioning as GTPase-activating proteins ( GAPs ) for G proteins , RGS proteins are uniquely situated to modulate the intensity and duration of Wnt signaling . However , no studies have ascertained the possible importance of RGS proteins in non-canonical Wnt signaling or whether changes in frequency and or amplitude of signaling result in biological defects . To investigate potential roles of RGS proteins in vertebrate development , we utilize gene knockdown in zebrafish . We focus on rgs3 , which was identified in an expression screen in zebrafish [41] . We find that rgs3 is expressed in overlapping and adjacent domains with wnt5b at multiple stages of zebrafish development . Morpholino knockdown of rgs3 in zebrafish embryos causes convergence and extension ( CE ) defects that resemble phenotypes observed in the wnt5b genetic mutant , pipetail [42] . To this end , we have identified a genetic interaction between rgs3 and wnt5b . Additionally , we describe endogenous Ca2+ release dynamics during somite stages and show that Rgs3 and Wnt5b impact the frequency of Ca2+ release . Moreover , we show that Rgs3 modulates the extent and duration of Wnt5b induced Ca2+ activity . Functional analyses show that both the rescue of the rgs3 knockdown defect and impact on Wnt5-induced Ca2+ release requires a key asparagine in the RGS domain of Rgs3 necessary for Gα binding and acceleration of its GTPase activity . This research identifies a link between Wnt5b signaling and Rgs3 activity relative to the frequency of Ca2+ release , thus revealing obligatory roles for RGS proteins in vertebrate development in the context of the whole animal . Our results also demonstrate that the biological outcome of Wnt signaling depends greatly upon regulating the duration of its signal , as shown here with Rgs3 .
Zebrafish rgs3 was identified in an expression screen during early somitogenesis stages [41] and is poised to interact with the Wnt signaling network . Utilizing Reverse Transcriptase Polymerase Chain Reaction ( RT-PCR ) , we determined that rgs3 expression begins during the blastula period shortly after zygotic transcription initiates ( 2 . 5–5 hours post fertilization , hpf ) , and persists through the segmentation period ( 10–24hpf ) ( Figure 1A ) . Whole Mount In Situ Hybridization ( WMISH ) demonstrated ubiquitous rgs3 expression during epiboly and gastrulation stages . During somite stages ( 10–20 hpf ) , rgs3 expression resolves in the somites , tailbud , and brain ( Figure 1B–1G ) , with discrete rgs3 expression in the midbrain/hindbrain boundary as demonstrated by overlap with the molecular marker engrailed 1 ( eng1 ) at 28 hpf ( Figure S1F ) , and enriched rgs3 expression in the posterior ( caudal ) portion of developing somites ( Figure 1D ) . rgs3 and wnt5b show both overlapping and adjacent expression domains in the somites and in the posterior tailbud ( Figure 1E–1G and Figure S1A , S1B , S1C , S1D ) . rgs3 expression is enriched around the Kupffer's vesicle ( Figure S1C ) , a ciliated organ in the zebrafish embryo that has been shown to influence left-right patterning , yet rgs3 does not appear to be required for organ laterality ( data not shown ) . As Wnt5b is a secreted ligand , the proximity of rgs3 to wnt5b producing cells suggests that Rgs3 may function in modulating Wnt5b signaling . In zebrafish , wnt5b induces increased Ca2+ release during the blastula stage in a G protein dependent manner [15] , [22] , [26] . To determine if rgs3 over-expression is sufficient to negatively regulate Wnt5b signaling ( Figure 2A ) , we tested the impact of rgs3 on wnt5b induced Ca2+ release . In vivo imaging in blastula stage embryos is accomplished with the Ca2+ sensor Fura-2 . Upon binding Ca2+ , Fura-2 exhibits an absorption shift that can be determined by collection at two wavelengths ( 340nm , Ca2+-saturated and 380nm , Ca2+-free ) . A ratio image is derived as the quotient of the 340-nm image divided by the 380-nm image on a pixel-by-pixel basis , and provides spatial and temporal measurement of Ca2+ dynamics . Ca2+ release activity was monitored over a 75 minute time course during the blastula stage . Sequential ratiometric images were analyzed by a subtractive algorithm to identify changes in Ca2+ release activity ( transients ) over time as well as the location of the activity as described previously [13] , [43]–[45] . Transients identified during the time course are presented as a composite image with location of Ca2+ release mapped on the embryo . The number of Ca2+ transients during the cellular blastoderm stage is represented by height of the peaks and color coded where purple is low and yellow/red is high number of events . The composite image of a wild-type ( wt ) embryo during the blastula stage indicates endogenous Ca2+ levels throughout the embryo ( Figure 2C ) compared to those observed during increased Ca2+ release in an embryo injected with wnt5b ( Figure 2B ) . Co-injection of rgs3 with dextran-conjugated Texas Red ( TxR ) lineage tracer into a subset of cells in embryos uniformly expressing wnt5b co-mixed with Fura-2 supports that rgs3 is sufficient to suppress wnt5b induced Ca2+ release as demonstrated by the reduction of Ca2+ levels ( Figure 2D ) in the rgs3/TxR positive region ( Figure 2F ) . We next investigated if Rgs3 suppression of wnt5b induced Ca2+ release requires GAP activity . A conserved asparagine within the RGS domain of RGS proteins is necessary for GAP activity for Gα subunits [46]–[48] . Substitution of this key asparagine ( N ) with Alanine ( A ) results in a loss of the GAP activity of RGS proteins towards Gα subunits in culture cells [46] , [48] . To elucidate the role of the GAP function of Rgs3 , we created an N to A mutation in zebrafish rgs3 ( rgs3N109A ) ( Figure 3A ) . We evaluated the impact of rgs3N109A expression on Wnt5b induced Ca2+ release . Unlike rgs3 , the rgs3N109A is unable to suppress wnt5b induced Ca2+ release ( Figure 2E ) as demonstrated by no change in the Ca2+ activity in the rgs3N109A /TxR positive region of embryos ( Figure 2G ) . To rule out the possibility that lack of suppression by Rgs3N109A was due to differences in its expression or localization compared to Rgs3 , we generated and expressed N-terminal myc-tagged rgs3 and rgs3N109A constructs in embryos . Western analysis reveals robust and comparable expression of Rgs3 and Rgs3N109A at the time of Ca2+ imaging as well as through 24hpf ( Figure 3B ) . Immunostaining for anti-myc in epiboly stage embryos also indicates that both proteins localize to the membrane and cytoplasm ( Data not shown ) . Together these data strongly indicate that rgs3 is sufficient to inhibit wnt5b-induced Ca2+ signaling and that this action requires the GAP activity of Rgs3 . Since Rgs3 is sufficient to modulate Wnt5 activity in an over-expression assay , we next evaluated the necessary role of rgs3 during development . To knockdown Rgs3 , we utilized antisense morpholino oligonucleotides ( MO ) [49] . Three separate MOs were designed to bind rgs3 5′UTR ( MO and MOb ) or splice junction ( SA ) ( Figure 3A ) . All MOs designed to knockdown Rgs3 produced similar defects . Control-injected embryos at 28 hpf are fully extended with a characteristic anterior-posterior ( A-P ) length ( Figure 3C ) . In contrast , rgs3 MO-injected embryos have shorter A-P extension and a kinked tail ( Figure 3D ) reminiscent of defects observed in the wnt5b ( pipetail ) genetic mutant [42] . Zebrafish somites develop sequentially anterior to posterior and form a distinct chevron shape [50] ( Figure 3E ) . rgs3 morphants display tighter packed and rounded somites ( Figure 3F ) . To evaluate anterior-posterior extension alterations at an earlier developmental stage ( 15 hpf ) , molecular markers were used . Control-injected embryos have a characteristic spacing of krox20 expression in the hindbrain rhombomeres 3 and 5 , as well as regular spaced blocks of myoD expression in the developing somites flanking the midline ( Figure 3G–3H and 3K–3L ) . In contrast , krox20 and myoD expression in rgs3 morphants reveal a failure of cells to converge on the midline resulting in a lateral expansion of the rhombomeres and somites ( Figure 3I and 3M ) . Additionally , rgs3 morphants fail to extend along the anterior-posterior ( A-P ) axis leading to closer spaced myoD ( Figure 3M , asterisks ) . The A-P extension defects were further confirmed with pax2 , a marker expressed in the anterior retina , midbrain/hindbrain , and otic vesicle of 18 hpf embryos ( Figure 3O ) . rgs3 morphants display compression of these regions along the A-P axis ( Figure 3P ) . Together these data strongly indicate that rgs3 is required for normal anterior-posterior axis extension . The specificity of the rgs3 knockdown as well as structural functional analyses were determined by RNA co-injection experiments . Injection of control 5bp mismatch MO resulted in negligible defects compared to rgs3 MO which induced morphological somite defects ( Figure 3R ) . Co-injection of rgs3 MO with rgs3 RNA suppressed the MO-induced defects evaluated by molecular markers krox20 ( Figure 3J ) , myoD ( Figure 3N , asterisks ) and pax2 ( Figure 3Q ) . Moreover , wild-type rgs3 RNA leads to significant suppression of MO-induced defects ( Figure 3R and Table S1 ) . In contrast , rgs3N109A mutant RNA does not suppress the MO-induced defect ( Figure 3R and Table S1 ) . These results demonstrate that Rgs3 GAP activity is required for its developmental functions . The functional requirement of rgs3 during anterior-posterior axis extension and the finding that over-expression of rgs3 is sufficient to inhibit wnt5b-induced Ca2+ signaling , raised the possibility that rgs3 may negatively modulate Ca2+ release dynamics during somitogenesis . In fact , Ca2+ signals along the trunk of zebrafish embryos during somitogenesis have been described using the bioluminescent Ca2+ reporter R-aequorin [12] , [51] , [52] . In order to compare changes in Ca2+ release dynamics upon rgs3 manipulation , we performed a detailed analysis of endogenous Ca2+ release in tissues that express both wnt5b and rgs3 . To this end , we utilized Fura-2 imaging to monitor Ca2+ activity with a focus on the developing somites and tailbud in either a dorsal ( Figure 4A ) or a lateral ( Figure S2A ) orientation . The pseudocolored ratio image provides a spatial and temporal measurement of Ca2+ dynamics with low Ca2+ represented by blue and high Ca2+ represented by yellow/red . Representative pseudocolored ratio images from a time-lapse series of Ca2+ measurements ( Video S1 ) , spanning the 3–13 somite stages are shown ( Figure 4B–4E ) . The notochord and forming somites can be identified in the grayscale fluorescence images ( Figure 4B′–4E′ ) . Overlay of grayscale and ratio images illustrate the regions of increased Ca2+ levels relative to morphology ( Figure 4B″–4E″ ) . Ca2+ release activity during somitogenesis is dynamic with sustained Ca2+ levels in the presomitic mesoderm , lateral to the somite forming region and flanking the midline/notochord ( Figure 4B″–4E″ ) . As somitogenesis proceeds , sustained Ca2+ levels appear distinctly between the somites ( Figure 4C″–4E″ , arrowheads ) . In addition , we observe localized short-lived increases in Ca2+ release ( referred to as transients ) . To demonstrate a transient , a region of interest ( ROI ) is noted by dashed circle ( Figure 5A–5C ) . In the ROI , an increase in Ca2+ is observed from time 0s to time 15s and the local increase subsides by time 30s . Since rgs3 may function to influence the frequency of Ca2+ release , we determined the number of transients as a function of developmental age ( Figure 5D ) . In wt embryos , we observe an average of 5 . 3 Ca2+ transients per hour ( n = 3 ) ( Figure 5E ) . A similar frequency is found when analyzing the data from a lateral view ( Figure S2B , S2C , S2D , and S2K ) . Having defined endogenous Ca2+ release dynamics during somitogenesis , we next determined the impact of rgs3 knockdown . From the development of somite 6 to somite 12 , rgs3 morphants have statistically more Ca2+ transients , with an average of 21 . 7 per hour ( n = 3 ) , when compared to wt embryos ( Figure 5D and 5E ) . rgs3 morphants have sustained Ca2+ levels in the lateral regions similar to wt . However the dynamics within the somite region frequently show initiating transients leading to responses in neighboring cells , resulting in larger areas of increased Ca2+ release ( Figure 5I–5K , Video S2 ) . These large and robust transients are not observed in wt embryos ( Figure 5F–5H , Video S1 ) or in morphant embryos co-injected with rgs3 RNA ( Video S3 ) . The same dramatic increase in both the frequency of release and amplitude is observed in lateral views as well ( Figure S2E , S2F , S2G , and S2K ) . The change in Ca2+ release dynamics in rgs3 morphants is consistent with a delayed shut-off of G protein signaling , i . e . normally mediated by the GAP activity of Rgs3 . These data indicate that during the segmentation period Rgs3 functions to limit the extent and duration of endogenous Ca2+ release activity . Previously , we reported reduced Ca2+ release in blastula stage Wnt5b ( pipetail ) genetic mutants [24] . When compared to wild-type embryos , wnt5b morphant embryos show a statistically reduced number of Ca2+ transients , averaging 1 . 3 per hour ( n = 2 ) during the segmentation period ( Figure 5D–5E , 5L , and 5M; Video S4 ) . A similar decrease in frequency is also observed in a lateral view ( Figure S2H , S2I , S2J , S2K ) . The size and duration of Ca2+ transients observed in wnt5b morphants are comparable to wt embryos ( Video S4 ) . In order to determine if the increased frequency of Ca2+ transients associated with rgs3 knockdown is dependent upon wnt5b signaling , we simultaneously knocked down wnt5b and rgs3 . Embryos co-injected with wnt5b MO and rgs3 MO and imaged during the segmentation period show a statistically reduced number of Ca2+ transients , 1 . 8 per hour ( n = 5 ) ( Figure 5D–5E ) . The reduced Ca2+ release in the double knockdown is not significantly different than wnt5b single knockdown , demonstrating that the rgs3 morphant phenotype is dependent upon Wnt signaling . Studies have shown that increased Wnt/Fz signaling leads to degradation of Dvl [53]–[55] . In addition Drosophila genetics places active G protein signaling upstream of Dvl [32] . Therefore , it seemed essential to determine whether Rgs3 plays a role in modulation of Dvl levels . In the absence of an antibody to evaluate Dvl levels , we generated a myc-tagged ( MT ) form of zebrafish Dvl2 that is readily detected by western blot after injection into embryos ( Figure 6A ) . We find that wnt5b co-expression reduced Dvl-MT levels ( Figure 6A ) . Reduction of Rgs3 function , via MO knockdown , also leads to decreased Dvl-MT levels . These data demonstrate that endogenous Rgs3 functions in the non-canonical Wnt pathway upstream of Dvl , thereby functioning to modulate the duration and robustness of Wnt5 signaling . To further explore interaction between Rgs3 and Wnt5b , we defined a low dose for wnt5b MO which results in a mild A-P extension phenotype and determined whether rgs3 enhances or suppresses the wnt5b gene knockdown defects . Phenotypes were evaluated by morphology ( Figure 6B , 6E , 6H , and 6K ) and molecular markers , krox20 and myoD ( Figure 6C–6D , 6F , 6G , 6I , 6J , 6L , and 6M ) . Compared to wt ( Figure 6B–6D ) , low dose wnt5b MO ( 2 ng ) results in a mild phenotype ( Figure 6E–6G ) . We next defined a sub-phenotypic dose for rgs3 MOsa ( 0 . 8 ng ) , which produced a phenotype ( Figure 6H–6J ) indistinguishable from wt ( Figure 6B–6D ) . Individual injection of low dose rgs3 MOsa or wnt5b MO did not induce any severe defects ( Figure 6N ) . However , wnt5b MO ( 2 ng ) combined with rgs3MOsa ( 0 . 8 ng ) resulted in a 92% penetrance of severe defects ( Figure 6K–6N ) . Our Ca2+ imaging implicated Rgs3 function in limiting the extent and duration of endogenous Ca2+ release activity and that this was dependent upon Wnt5 . However , in the presence of low level Wnt5 activity ( low-dose MO ) , partial knockdown of rgs3 could lead to discordant changes in the frequency and amplitude of Ca2+ release result in the dramatic phenotypic penetrance and severity .
These results provide new evidence for an essential role of Rgs3 in modulating the duration of Wnt5b signaling . We show that Rgs3 is necessary for proper gastrulation and somite patterning during zebrafish development . These actions of Rgs3 require its ability to interact with and accelerate the rate of GTP hydrolysis by G proteins , as revealed by studies employing an Rgs3 mutant defective in these activities . We describe endogenous Ca2+ release dynamics during somitogenesis . The frequency of Ca2+ transients as well as the observation of sustained Ca2+activity in the trunk and tail region are consistent with previous reports of Ca2+ activity during zebrafish somitogenesis [12] , [51] , [52] , [56] . Of particular significance is the dramatic change in frequency of endogenous Ca2+ release upon rgs3 knockdown . RGS proteins were identified as negative regulators of G protein signaling in the mid 1990s [57] , [58] and the role of G proteins in Wnt/Ca2+ signaling was first demonstrated in 1997 [22] . Subsequent reports further implicated G proteins in canonical Wnt signaling [31] , [59] , [60] . Heterotrimeric G protein activation and inactivation are tightly regulated to provide precise control of the amplitude and duration of G protein signaling . Receptor-stimulated GTP binding activates G proteins , while their intrinsic GTPase activity functions to terminate signaling . RGS proteins by definition accelerate this GTPase activity . Over-expression studies in cell culture [61] and in Xenopus embryos [62] have indicated that specific RGS proteins are sufficient to regulate canonical Wnt signaling . Although G protein signaling is required for normal cell movement during zebrafish gastrulation [11] , the role of RGS proteins in noncanonical Wnt signaling has not been investigated . Our current study identifies a member of the RGS protein family that has a direct impact on frequency and amplitude of Wnt5b signaling . We find that Rgs3 activity is sufficient to modulate wnt5b induced Ca2+ release and this ability requires GAP activity consistent with the known role of G proteins in the activation of Wnt signal transduction pathways [5] , [63] , [64] . We report the key novel finding that knockdown of Rgs3 results in increased frequency and amplitude of Ca2+ release that this dramatic impact on Ca2+ dynamics in the somites is dependent upon Wnt5 supporting that Wnt/Ca2+ signaling activity is an in vivo target of RGS proteins . Moreover , rgs3 demonstrates a complex genetic interaction with wnt5b . rgs3 is expressed in and near wnt5b expressing tissues and we find that combined low doses of wnt5b MO and rgs3 MOsa result in a large penetrance of severe somite defects which is not observed during their individual knockdown . Our data suggest that both the frequency and amplitude of wnt5b signaling must be tightly regulated to result in correct cell movement and somite patterning . Wnt5b modulates both transient Ca2+ release activity in small populations of cells , as well as , sustained activity in a large region of cells [16] . While the transient release correlates with limiting β-catenin activity [17] , [26] , we hypothesize that the sustained activity correlates with polarized cell movement , for example in convergence-extension movements during gastrulation or neural and vascular outgrowth [16] . This concept is supported by vascular outgrowth defects in pipetail genetic mutants [65] as well as the observation of a reduction in sustained Ca2+ activity at the somite boundaries ( data not shown ) . It is of interest to determine if interactions between rgs3 and wnt5b influence directed vascular outgrowth . Modulation of G protein signaling ( impacting frequency as well as duration ) is likely to influence directed cell migration , vascular development as well as numerous other developmental processes [66]–[68] . Our findings clearly justify the need for further investigations into the role of RGS proteins in this process and other interactions between Rgs3 and Wnt signaling to provide new insights into their mechanistic role in directed cell movement and disease . Our loss of function analysis coupled with rescue and in vivo physiological analysis in whole embryos has provided compelling functional insight into the developmental role of RGS proteins in the Wnt signaling network .
Adults were maintained in a 14-hour light / 10-hour dark cycle at 28°C . Embryos were collected from natural pairwise matings and staged by hours post fertilization ( hpf ) at 28 . 5°C and morphological criteria described in Kimmel et al . [50] , [69] . rgs3 ( clone IBD5096 ) was isolated in an expression screen in zebrafish [41] and generously provided by Dr . I . Dawid . MO-resistant rgs3 was generated by RT-PCR and directionally cloned ( 5′-3′ ) into the pCS2+ , pCS2+ myc or pCS2+ Flag expression vector . The Quick Change II site-directed mutagenesis kit ( Stratagene ) was used to generate an Asparagine ( N ) to Alanine ( A ) substitution at amino acid 109 which is located in the RGS domain of Rgs3 . Synthetic RNA was then in vitro transcribed using SP6 RNA polymerase and the mMessage mMachine system ( Ambion ) . Antisense morpholino oligonucleotides ( MO ) were designed to target the 5′-UTR/ATG ( rgs3 MO and rgs3 MOb ) to inhibit translation and an intron-exon junction in the RGS domain ( rgs3 MOsa ) to alter splicing . As a control rgs3 MOmm ( 5 bp mismatch in lowercase letters ) was designed ( Gene-Tools ) : rgs3 MO 5′-AGTCGGTTCTTCATGTCTTTGGCCC-3′ , rgs3 MOb 5′-TCTCCGAGAAATCCACCATAGTGTG-3′ , rgs3 MOsa 5′-CCAGTCCATCTGATGAGGGAGAGAG-3′ . rgs3 MOmm 5′-TCaCCcAGAAATCCtCCATtGTcTG-3′ . MOs ( 5–20ng ) were pressure-injected into one cell-stage embryos . For low-dose knockdown , 0 . 8ng rgs3 MOsa and/or 2 ng wnt5b MO [65] were injected into one cell zebrafish embryos . Control rgs3 MOmm did not produce any phenotype at 25 ng . For rescue , in vitro-transcribed MO-resistant rgs3 ( 500 pg ) was co-injected with 20 ng rgs3 MO . Injected embryos were characterized by morphological and molecular analysis . Embryos were fixed overnight in 4% paraformaldehyde and dechorionated . Single label WMISH was performed as previously described [24] , [70] , using digoxigenen ( Dig ) -labeled or dinitrophenyl ( DNP ) -labeled antisense and sense RNA probes . Detection was carried out using BM purple ( Roche Applied Science ) . Double label WMISH was performed as previously described [71] , using both Dig and DNP-labeled antisense probes . Purple color was developed with AP-conjugated anti-Dig and BM purple ( Roche Applied Science ) , and red color was developed with AP-conjugated anti-DNP and INT RED ( Roche Applied Science ) . Reactions were stopped in phosphate-buffered saline ( PBS ) . Embryos were mounted on bridged coverslips and photographed using a Zeiss Stemi M13 Stereoscope and an Axiocam digital camera . To compare levels of MT-Rgs3 and mutant MT-Rgs3 , embryos were injected with either myc-rgs3 or myc-rgs3 ( N109A ) ( 750 pg ) . To investigate Rgs3's impact on Dvl , C-terminal myc tagged zebrafish dvl2 ( 300 pg ) was injected alone , with rgs3 MOsa ( 5ng ) , with wnt5b ( 250pg ) , and with both rgs3 MOsa ( 5ng ) and wnt5b ( 250pg ) . Injected Embryos were allowed to develop to the appropriate stage ( 5 hpf and 24 hpf ) before incubating in lysis buffer ( 20 µM Tris , 100µM NaCl , 1µM EDTA , 5% Triton , . 5%SDS , 10% Leupeptin and 0 . 1µM PMSF ) at room temperature for 3 minutes . Embryos were then disrupted using a pestle , centrifuged at 13 , 000 rpm for 10 minutes at 4°C and the clear supernatant containing whole zebrafish protein was collected . Approximately 10µg of protein was loaded in each well and separated with SDS-PAGE gel electrophoresis . Proteins were transferred onto nitrocellulose membrane ( Li-Cor ) and incubated with the following primary antibodies: mouse anti-myc ( 1∶2 , 000; Cell Signaling Technology ) and rabbit anti-β actin ( 1∶2 , 000; Sigma ) , and then incubated with the following secondary antibodies: IRDye800 anti-mouse ( 1∶20 , 000; Li-Cor ) and IRDye680 anti-rabbit ( 1∶20 , 000; Li-Cor ) . The signal was visualized using the Odyssey Infrared Imaging System ( Li-Cor ) . Embryos injected with either myc-rgs3 or myc-rgs3 ( N109A ) ( 200 pg ) were fixed 1–3 hrs in 4% PFA/1× PBS at sphere/dome stage . Mouse anti–myc antibody ( 1∶1 , 500; Cell Signaling Technology ) , followed by goat-anti-mouse Alexa488 conjugated secondary antibody ( 1∶400; Molecular Probes ) was used to detect the rgs3 products . Nuclei were identified with 5 µM TO-PRO-3 ( Molecular Probes ) . Embryos were mounted in an animal pole orientation in bridged coverslips and optically sectioned using two-channel imaging on a scanning laser confocal microscope , Leica TCS SP2 . Wide-field fluorescence and bright–field images from a Zeiss Stemi M13 Bio Stereoscope were photographed using Axiovision ( Zeiss ) software and an Axiocam 5000 camera . Images were merged using Adobe Photoshop CS . The ratiometric Ca2+-sensing dye Fura-2 or Bis-Fura-2 ( Molecular Probes ) was injected into 1-cell zebrafish embryos . The excitation spectra are different between Ca2+ bound Fura-2 ( 340-nm ) and Ca2+ free ( 380-nm ) forms . By taking the ratio of the fluorescence intensity at these wavelengths an estimate of intracellular-free Ca2+ can be derived . To stimulate Wnt signaling , in vitro transcribed wnt5b RNA ( 400 pg ) was co-injected with Fura-2 at the one cell stage . rgs3 or rgs3N109A RNA ( 400 pg ) was unilaterally injected at the 16-cell stage mixed with dextran-conjugated Texas Red ( TxR ) lineage tracer . TxR distribution was determined by collecting a reference exposure at 540-nm excitation . For cellular blastoderm stage imaging , embryos were oriented in a lateral position in a glass-bottomed dish on a Zeiss axiovert epifluorescence microscope . Image pairs at 340 and 380-nm excitation wavelengths ( 510-nm emission ) were collected at 15-second intervals . Each imaging session collected 300 image pairs . The ratio image , a pixel by pixel match of both excitation wavelengths , is calculated by computer software ( RatioTool , Inovision ) . The sequence of ratio images was processed and the Ca2+ fluxes ( transients ) were determined by a subtractive analog patterned after [72] , [73] and described in [13] , [43] . The ratio image ( 340nm , Ca2+-saturated and 380nm , Ca2+-free ) imported for publication is encoded in 8 bits and converted to pseudocolor with low ratio ( low Ca2+ ) represented by blue and high ratio ( high Ca2+ ) represented by yellow/red . For somite imaging , 2–6 somite stage embryos were oriented in low melt agarose ( 0 . 4% ) in a dorsal or lateral orientation . Time courses collected images pairs until 12–15 somite stage at 15-second intervals ( Approximately 1000 images pairs ) . Image pairs were converted to ratio images as described above . Sequential ratio images were manually analyzed for changes in Ca2+ transients . Somite stage Ca2+ transients were defined as a localized increase in Ca2+ which persists no longer than thirty seconds . Calculations for MO rescue experiments were made using the Fisher's exact test and the two-tailed p-value was reported . Calculations for analysis of somite stage Ca2+ transients in morphant embryos were made using One-Way Analysis of Variance ( one-way ANOVA ) with Tukey HSD test p-values reported . | Vertebrate development requires communication among cells in order to define the body axis ( front/back , head/tail , or left/right ) . Secreted factors such as Wnts play key roles in a vast array of cellular processes , including patterning of the body axis . One arm of the Wnt-signaling network , the non-canonical pathway , mediates intracellular calcium release via activation of heterotrimeric G proteins . Regulator of G protein Signaling ( RGS ) proteins can accelerate inactivation of G proteins by acting as G protein GAPs and are uniquely situated to control the amplitude of a Wnt signal . Here , we combine cellular , molecular , and genetic analyses with high resolution calcium imaging to identify a role for RGS modulation of Wnt-mediated calcium release dynamics and developmental patterning events . We find that loss of rgs3 gene function produced body patterning defects like those observed with loss of wnt5b gene function . Analysis of endogenous calcium release dynamics in developing zebrafish revealed that both rgs3 and wnt5b are required for appropriate frequency and amplitude of calcium release . Our results provide new evidence that a member of the RGS protein family is essential for modulating the non-canonical Wnt network to assure normal tissue patterning during development . | [
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| 2010 | Regulator of G Protein Signaling 3 Modulates Wnt5b Calcium Dynamics and Somite Patterning |
Target identification is one of the most critical steps following cell-based phenotypic chemical screens aimed at identifying compounds with potential uses in cell biology and for developing novel disease therapies . Current in silico target identification methods , including chemical similarity database searches , are limited to single or sequential ligand analysis that have limited capabilities for accurate deconvolution of a large number of compounds with diverse chemical structures . Here , we present CSNAP ( Chemical Similarity Network Analysis Pulldown ) , a new computational target identification method that utilizes chemical similarity networks for large-scale chemotype ( consensus chemical pattern ) recognition and drug target profiling . Our benchmark study showed that CSNAP can achieve an overall higher accuracy ( >80% ) of target prediction with respect to representative chemotypes in large ( >200 ) compound sets , in comparison to the SEA approach ( 60–70% ) . Additionally , CSNAP is capable of integrating with biological knowledge-based databases ( Uniprot , GO ) and high-throughput biology platforms ( proteomic , genetic , etc ) for system-wise drug target validation . To demonstrate the utility of the CSNAP approach , we combined CSNAP's target prediction with experimental ligand evaluation to identify the major mitotic targets of hit compounds from a cell-based chemical screen and we highlight novel compounds targeting microtubules , an important cancer therapeutic target . The CSNAP method is freely available and can be accessed from the CSNAP web server ( http://services . mbi . ucla . edu/CSNAP/ ) .
The use of chemical screens to identify molecules for the treatment of proliferative diseases like cancer has relied on two major strategies , target-based screening and phenotypic screening [1 , 2] . Unbiased cell-based screens , including phenotypic screens , have successfully discovered numerous cytotoxic agents that inhibit cancer cell proliferation . By assaying structurally diverse compounds , cell-based phenotypic chemical screens have the potential to discover a multitude of druggable protein targets that modulate cell cycle progression through diverse mechanisms [2] . However , a major hurdle for cell-based phenotypic chemical screens has been the deconvolution of active compounds , i . e . target identification [2 , 3] . Classical methods for target identification like chemical proteomics rely on compound modification and immobilization to generate compound affinity matrixes that can be used to pull down associated proteins [4] . Without prior knowledge of compound structure-activity-relationship ( SAR ) , the modification of key functional groups can occlude compound activity and hamper protein-ligand interactions [5] . Additionally , these approaches are labor intensive , costly and have a low success rate . Computational approaches for predicting the targets , off-targets and poly-pharmacology of hit compounds have been used widely in recent years due to their speed , flexibility and ability to be easily coupled to experimental validation techniques [1 , 2] . In-silico target inference methods include ligand-based and structure-based approaches . Ligand-based approaches , such as similarity ensemble approach ( SEA ) , SuperPred , TargetHunter , HitPick , ChemMapper and others , compare hit compounds to a database of annotated compounds and drug targets of hit compounds are inferred from the targets of the most similar annotated compounds , based on their chemical structure similarity [6–9] . The premise of the 2D chemical similarity inference approach is the “chemical similarity principle” , which states that structurally similar compounds likely share similar biological activities [10–12] . The efficiency of 2D chemical search algorithms also led to the wide adoption of this target inference method in public bioactivity database searches including ChEMBL and PubChem [13 , 14] . Recently , similarity-based target inference has been extended to incorporate 3D chemical descriptors derived from the bioactive conformations of molecules [15] . For example , PharmMapper , ROCS and the Phase Shape programs use a reverse pharmacophore and shape matching strategy to identify putative targets [16–18] . Albeit computationally intensive , a major advantage of this approach is that “scaffold-hoppers” can be deorphanized , as these compounds often share low chemical similarity but bind similarly to known receptor sites [19] . On the other hand , structure-based target inference approaches , such a TarFisDock and INVDOCK , apply reverse panel docking and ranking of docking scores to predict protein targets from pre-annotated structures [10 , 20] . In comparison , ligand-based approaches are particularly advantageous due to their speed and algorithmic simplicity and they are not limited by structure availability . However , current ligand-based approaches analyze bioactive molecules in an independent sequential fashion , which has several disadvantages [2 , 8 , 21] . For example , target inference is based on finding a single most similar annotated compound for a given query ligand , which may not provide consistent target prediction for a group of structurally similar ligands . Additionally , subtle structural changes in the functional groups of active molecules can alter their potency and specificity toward drug targets; thus , analyzing each molecule independently may not offer a coherent SAR for a congeneric series . This suggests that a more global and systematic analysis of compound bioactivity is required to improve the current state of in-silico drug target prediction . Several global approaches to drug target profiling have been developed [2] . One approach is bioactivity profile matching , where model organisms are treated with compounds and compounds that induce similar phenotypic responses are clustered and inferred to have similar mechanisms of action [2 , 22 , 23] . However , bio-signature fingerprint comparisons do not infer direct protein-ligand interactions . Furthermore , large numbers of measurements are required to construct such fingerprints [22 , 24] . Alternatively , computational networks have been effectively utilized to mine the existing protein-ligand interaction data deposited in bioactivity databanks . One example is the drug-target network ( DTN ) , which utilizes a bipartite network encompassing interconnecting ligand and target vertex to capture complex poly-pharmacological interactions [25] . While this prediction model is useful for predicting drug side effects and identifying novel protein-ligand pairs , DTN demands statistical learning from prior protein-ligand interaction data using Beyesian analyses or Support Vector Machines . Thus , DTN’s predictability beyond the training space may not be accurate , limiting DTN’s applicability for large-scale drug target prediction [26–29] . To address the current challenges in computational drug target prediction , we developed a new drug target inference approach based on chemical similarity networks ( CSNs ) and implemented this approach as a computational program called CSNAP ( Chemical Similarity Network Analysis Pull-down ) . CSN is a promising computational framework that allows large-scale SAR analysis by clustering compounds based on their structural similarity [30] . This framework has recently been applied to investigate “bioactivity landscapes” from known drugs as well as for analyzing bioactivity correlations among secondary metabolites [30 , 31] . Furthermore , several network characteristics including degree of connectivity , centrality and cohesiveness offer critical information to study the global topology of large chemical networks and allow key compound members to be identified [32 , 33] . Although CSNs have been widely applied to SAR studies , their application to drug target inference has not been explored [30 , 32] . In our CSNAP approach , both query and annotated compounds are first clustered into CSNs , where nodes represent compounds and edges represent chemical similarity . The target annotations of the reference nodes are assigned to the connecting query nodes whenever two node types form a chemical similarity edge above a similarity threshold [13 , 34 , 35] . To determine the most probable target , a consensus statistics score is determined by the target annotation frequency shared among the immediate neighbors ( first-order neighbor ) of each query compound in the network . When multiple ligands were analyzed by the CSNAP approach , diverse compound structures were clustered into distinct chemical similarity sub-networks corresponding to a specific “chemotype” ( i . e . consensus chemical scaffold ) , which was associated with specific drug targets [36] . Within the context of drug design , “chemotype” has been widely used for drug repurposing . For example , a single scaffold can be diversified by combinatorial synthesis to modulate its specificity toward multiple secondary targets [36] . On the other hand , the CSNAP approach identifies consensus “chemotypes” from diverse chemical structures , which likely inhibit common targets capable of inducing similar phenotypes in cell culture . In contrast to current target prediction methods , CSNAP does not rely on absolute chemical similarity nor does it necessitate a training set to make target inferences . Additionally , CSNAP is capable of integrating with chemical and biological knowledge-based databases ( Uniprot , GO ) and high-throughput biology platforms ( proteomic , genetic , etc ) for system-wise drug target validation . Our benchmark study showed that CSNAP can achieve an overall higher accuracy ( >80% ) of target prediction with respect to representative chemotypes in large ( >200 ) compound sets , in comparison to the SEA approach ( 60–70% ) . To demonstrate the utility of the CSNAP approach , we combined CSNAP's target prediction with experimental ligand evaluation to identify the major mitotic targets of hit compounds from a cell-based chemical screen and we highlight novel compounds targeting microtubules , an important cancer therapeutic target . The CSNAP method is freely available and can be accessed from the CSNAP web server ( http://services . mbi . ucla . edu/CSNAP/ ) .
We have developed a new computational workflow for compound target deconvolution and prioritization of compounds based on chemical similarity networks that we have termed CSNAP ( Chemical Similarity Network Analysis Pull-down ) ( Fig . 1 ) . In CSNAP , the Obabel FP2 fingerprints , which characterize molecules by a series of structural motifs as binary numbers ( 0 and 1 ) , were utilized for structural comparison and compound retrieval from the ChEMBL database ( version 16 ) containing more than 1 million annotated molecules with reported bioactivities ( Fig . 1A , 1B and S1 Text ) [13 , 37] . In comparison to other available fingerprints ( FP3 , FP4 and MACCS ) , the FP2 fingerprint uses a path-based algorithm , which has high specificity , is generally applicable to any ligand size and is not limited to pre-defined substructure patterns [38] . To retrieve structurally similar ligands from the bioactivity database , two chemical similarity search functions were used: a threshold similarity search based on a Tanimoto coefficient ( Tc ) score and a Z-score ( S1 Text ) [39 , 40] . The Tc score is one of the most commonly used metrics for chemical similarity comparison in chemoinformatics , which compares two chemical fingerprints to determine the fraction of shared bits with values ranging from 0 to 1 . However , a fixed similarity threshold search may not detect compounds with statistical significant scores; thus , a Z-score was also used to search database compounds based on the overall similarity score distribution of the hits [40] . The target annotations of the selected ChEMBL compounds ( baits ) most similar to input ligands were subsequently retrieved from the ChEMBL and PubChem databases ( Fig . 1B and S1 Text ) . Based on the output of ligand similarity comparisons , a chemical similarity network was constructed by connecting pairs of ligands with similarity above a Tc threshold according to a weighted adjacency matrix ( Fig . 1C and S1 Text ) [41] . This resulted in weighted graphs ( networks ) in which nodes represent compounds and edges represent chemical similarity ( Fig . 1D ) . Target inference of the query compounds within the CSNAP-generated network , which contains both query and reference nodes , is similar to the protein functional assignment in protein-protein interaction ( PPI ) networks , where protein functional lineage between a characterized and an uncharacterized protein are used to assign shared protein functions [34 , 42] . Multiple scoring schemes have been developed to infer protein functions in PPI networks , including algorithms based on network connectivity , graph topology and modular recognition [43–45] . The most direct network-based scoring scheme is the neighbor counting method , where the annotation frequency in the immediate neighbors is ranked and assigned to the linked queries . Thus , the similarity between PPI networks and CSNs suggested that this approach could be effective for network-based drug target inference . As a proof-of-principle , we applied two neighbor-counting functions , Schwikowski score and Hishigaki score for drug target prediction in CSNAP networks [43 , 46] . Specifically , a target consensus statistics score , Schwikowski score ( S-score ) , was calculated by ranking the most common targets shared among the neighboring annotated ligands of each query compound within the network ( Fig . 1E and S1 Text ) [43] . Additionally , a Hishigaki score ( H-score ) , a chi-square like test based on the mean target annotation frequency distributed within the whole network , was also implemented to compute a significance value for each drug target assignment ( S1 Text ) [46] . The rationale for applying Schwikowski and Hishigaki scoring functions in CSNAP target inference , apart from their algorithmic efficiency and scalability for large-scale network computation , was their accuracy . For example , it was shown that a Schwikowski score correctly predicted >70% of proteins with at least one functional category in a large-scale S . cerevisiae PPI network [43] . Furthermore , a performance comparison in a S . cerevisiae network showed that these nearest neighbor approaches offer high specificity and prediction accuracy , making them competitive against more advanced statistical network models including Markov random field ( MRF ) and kernel logistic regression [33 , 34] . To validate CSNAP computationally , we tested CSNAP’s ability to correctly predict the assigned targets for annotated compounds as well as its ability to cluster compounds with similar target specificities using a diversity set retrieved from the directory of useful decoys ( DUD LIB VS 1 . 0 ) [47] . The diversity set contained 206 ligands from 6 target-specific drug classes with known target annotations ( including 46 angiotensin-converting enzyme ( ACE ) , 47 cyclin-dependent kinase 2 ( CDK2 ) , 23 heat-shock protein 90 ( HSP90 ) , 34 HIV reverse-transcriptase ( HIVRT ) , 25 HMG-CoA reductase ( HMGA ) and 31 Poly [ADP-ribose] polymerase ( PARP ) inhibitors ) ( S1 Table ) . Two chemical search criteria were initially tested for CSNAP drug target prediction including one search with a Z-score cutoff = 2 . 5 and Tc cutoff = 1 ( identical match ) and another search with a Z-score cutoff = 2 . 5 and Tc cutoff = 0 . 85 . In comparison , using an absolute Tc similarity cutoff = 0 . 85 substantially increased the network density ( number of nodes in each network cluster ) but did not significantly affect the number of network clusters generated ( 66 and 61 ) ( Figs 2A , S1 and S1 Text ) . In both cases , CSNAP was able to resolve 206 compounds into target specific chemical similarity sub-networks . Based on the chemical similarity network generated by the latter chemical search criteria , we then assessed the prediction accuracy ( percentage of correctly predicted ligands ) for each drug class by considering the top five consensus targets ranked by S-scores; meanwhile , we applied a set of S-score cutoffs for hit enrichment to reduce the target pool ( Fig . 2B , 2C and S1 Text ) . The results indicated that CSNAP’s overall prediction accuracy ( recall-like score ) for the benchmark compounds was 89% ( S-score = 0 ) and 80% ( S-score > = 4 ) respectively ( Fig . 2B and 2C ) . Of those compounds with a prediction , the precision-like score was 94% ( S-score = 0 ) and 85% ( S-score > = 4 ) respectively . To identify potential off-targets for these characterized drugs , we mapped the compound S-score for each drug class against the predicted targets using a ligand-target interaction fingerprint ( LTIF ) , which allowed us to differentiate primary targets from off-targets on a heatmap ( Fig . 2D and S1 Text ) [48] . To further rank the most common targets within the whole compound set , we generated a target spectrum by summing the target prediction score , S-score for each predicted target , by which the heights of the target spectrum can be correlated with the total S-score ( ∑ S-score ) . Next , we identified the most probable targets and off-targets from the top peaks above the average ∑ S-score . While we cannot exclude smaller peaks as false positives , as they may represent an experimentally verified interaction of the reference compounds in the ChEMBL database , the higher peaks nevertheless represent the most common targets and off-targets among the analyzed ligands . Within the context of a chemical screen , additional target selection can be aided by gene ontology ( GO ) analysis , where molecular functions , cellular processes and pathway information can be used to verify the functional role of the predicted targets ( see CSNAP website for additional details ) . We subjected the diversity set to two different LTIF analyses , first by analyzing each drug class independently and then all drug classes combined . Independent LTIF analysis of HIVRT , HMGA and PARP compound sets revealed specific target binding patterns in contrast to CDK2 and ACE , which showed multiple interactions , suggesting potential off-target bindings ( Fig . 2D ) . From the target spectrum , we identified ENP and CDK1 as the major off-targets for ACE and CDK2 inhibitors respectively , which had been previously reported ( Fig . 2D ) [49 , 50] . For the combined analysis , the targets and off-targets of the 206 benchmark compounds were likewise successfully identified from the target spectrum ( S2 Fig ) . Although these validated compounds were “drug-like” and had been optimized for target specificity and transport properties , CSNAP analysis nevertheless identified potential off-targets that were not originally intended for these ligands . This indicated that CSNAP could potentially be used for high-throughput target deorphanization and off-target prediction for bioactive compounds from any chemical screen . Next , we compared CSNAP’s target prediction accuracy with SEA ( Similarity Ensemble Approach ) , a widely used ligand-based target prediction approach based on sequential chemical similarity comparisons , to correctly identify the annotated targets of the benchmark sets ( S1 Table and S1 Text ) [51] . CSNAP showed an overall improvement in prediction accuracy ( 80–94% ) over SEA ( 63–75% ) at identifying the labeled targets of each of the six drug classes from the top 1 , top 5 and top 10 score rankings by each respective method . In particular , CSNAP provided substantially better target prediction for promiscuous ligands such as CDK2 and ACE inhibitors ( 92% and 96% ) than the SEA approach ( 30% and 65% ) ( Fig . 3A–3C and S1 Text ) . Recently , we performed a high-throughput cell-cycle modulator screen with a diverse , unbiased set of 90 , 000 drug-like compounds , which identified compounds arresting cancer cells in mitosis ( 212 compounds ) ( S2 , S3 Tables and S1 Text ) . We applied CSNAP to identify the potential targets of the 212 antimitotic compounds ( S3 Fig and Supporting File ) . CSNAP analysis generated 85 chemical similarity sub-networks representing diverse chemotypes and retrieved 116 UniProt target IDs from ChEMBL annotations ( Fig . 4A ) . These targets were analyzed using LTIF with a predefined cutoff ( ∑ S-score >10 ) from which we identified 4 broad categories of putative mitotic targets ( 20 UniProt target IDs ) ( Fig . 4B ) . These included 3 fatty acid desaturases ( SCD , SCD1 and FADS2 ) , 1 ABL1 kinase , 5 non-receptor type tyrosine phosphatases ( PTPN7 , PTPN12 , PTPN22 , PTPRC and ACP1 ) and 11 tubulin isoforms . Further compound deconvolution with respect to these targets identified 7 SCD inhibitors , 9 ABL1 inhibitors , 14 PTPN inhibitors and 7 TUBB inhibitors from 6 distinct clusters from the mitotic compound network ( including SCD/ABL1: cluster 6 , PTPN: cluster 3 and TUBB: clusters 1 , 2 , 4 and 5 ) and in which 4 compounds were shown to target both SCD and ABL1 ( Figs 4C , S4 and S1 Text ) . Meanwhile , by querying the PubChem target annotations with respect to these four target categories , we identified an additional 19 tubulin-associated clusters ( total 23 ) , including 51 compounds with unknown bioactivities , which were predicted to be tubulin binders that covered ~20% of our mitotic set ( S5A Fig ) . Among the predicted targets were the tubulins ( TUBB , including α and β-tubulin ) , which are the building blocks of microtubules that are essential for mitotic spindle assembly and are established anticancer drug targets [52 , 53] . Consistently , several well-known microtubule-targeting agents were identified in the TUBB clusters including mebendazole and nocodazole from cluster 5 ( Fig . 4A ) [52] . Although the compound chemotypes for ABL1 , SCD1 and PTPN were known , either identical or analogous to reference compounds deposited in the bioactivity databases , the assay context from which these compounds were retrieved was not related to mitosis [54–56] . Additionally , the function of ABL1 , SCD1 and PTPN in mitotic progression had not been explored [57–60] . Thus , this analysis linked these proteins to potentially important new roles during cell division . To further substantiate that these compounds were likely inhibiting these targets ( ABL1 , SCD , PTPN and TUBB ) , we compared the phenotypes induced by their siRNA knockdown ( which often correlates with inhibition of protein activity ) with the phenotypes induced upon treatment with compounds from each target category using immunofluorescence ( IF ) microscopy [61] . To determine the target siRNA phenotype , we queried the MitoCheck database , which maintains data on the mitotic phenotypes observed upon siRNA knockdown of gene expression for most human genes ( S1 Text ) . As expected , all four target categories ( SCD , ABL1 , PTPN and TUBB ) displayed diverse mitotic defects by siRNA treatment [62] . This included defects in spindle assembly , chromosome segregation and cytokinesis that led to mitotic delay , post-mitotic defects ( binuclear and polylobed nucleus ) and apoptosis ( cell death ) , suggesting that these targets were critical for cell division ( S6 and S7 Figs ) [62] . Next , five compounds from these target clusters were selected for phenotypic comparison including compound 1 from the SCD sub-cluster ( cluster 6 ) , compound 2 that overlapped with both SCD and ABL1 sub-clusters ( cluster 6 ) and compound 3 from the ABL1 sub-cluster ( cluster 6 ) . Additionally , compound 4 and compound 5 , were retrieved from the PTPN cluster ( cluster 3 ) and the TUBB cluster ( cluster 4 ) respectively ( Fig . 4A , 4C , and S4 Table ) . All five compounds showed consistent cell phenotypes between siRNA knockdown and drug treatment ( Figs 4D , 4E , and S8 ) . However , compound 1 ( SCD sub-cluster ) also displayed a “large nuclei” phenotype that was specific to ABL1 inhibitors , indicating that it may also target ABL1 based on chemical and phenotypic similarity ( Fig . 4D , 4E , and S8 ) . As expected , compound 2 ( SCD/ABL1 sub-clusters ) exhibited a “mixed” phenotype similar to compound 1 while compound 3 was ABL1 specific with very few mitotic delay and apoptotic cells that were specific to SCD inhibitors ( Figs 4D , 4E , and S8 ) . Based on target prediction , we selected microtubules ( α and β-tubulin ) as our target for in-vitro validation . To test CSNAP’s prediction that 51 of the 212 mitotic compounds were targeting microtubules , we re-acquired all 212 compounds and tested their ability to perturb microtubule polymerization ( stabilize or destabilize microtubules ) in an in-vitro microtubule polymerization assay at 50μM concentration ( Fig . 5A ) . The end-point absorbance ( dOD ) was used to quantify the degree of microtubule polymerization and was converted to percent fold change ( F ) relative to DMSO drug vehicle ( 0% ) , as previously described ( Fig . 5A and S1 Text ) [63] . Of the 51 compounds predicted to be targeting microtubules , 36 had more than 20% fold change in microtubule polymerization and 14 had no measurable effect ( S5B Fig ) . Thus CSNAP was able to predict the targets of this set with > 70% accuracy . In addition , in-vitro testing led to the discovery of 96 additional compounds for a total of 132 anti-tubulin agents , including structurally diverse compounds covering ~54 novel chemotypes not discovered in previous chemical screens ( S3 Table ) . Since CSNAP was able to cluster compounds into sub-networks with respect to target specificities , we asked if ligands within the same chemotypic cluster shared a consensus drug-target binding mechanism , as shape complementarity between receptor surface and ligand geometry is essential for inducing a specific cellular phenotype . To test this , we mapped the tubulin polymerization activity onto the mitotic chemical similarity network . Overall , compounds with similar drug mechanisms , e . g . tubulin polymerization or depolymerization were clustered in close proximity within the CSN ( S5A Fig ) . However , a few compounds with opposing mechanisms of action were clustered within the same sub-network . This was expected as chemical similarity may not always correlate with compound bioactivity [12] . Here , we investigated a chemical similarity sub-network consisting of 7 novel anti-tubulin ligands based on a phenyl-sulfanyl-thiazol-acetamide scaffold ( Fig . 5B and S9B ) . Notably , all the connected ligands within the sub-network shared a similar microtubule destabilization effect . By conducting SAR analysis on the network , we noticed that the addition of hydrophobic groups to the northern and eastern parts of the ligand enhanced microtubule depolymerization ( Fig . 5B and S1 Text ) . Consistently , a similar SAR trend was observed by evaluating each compound’s potency ( EC50 ) in HeLa cells with regards to their ability to arrest cells in G2/M-phase and induce cell death . This identified compound 8 ( EC50:G2/M = 33 nM; EC50: cell death = 60 nM ) as the most potent compound in the series ( S10 Fig and S1 Text ) . To provide a structural explanation for this SAR , we observed that compound 6 shared a common structural feature ( tri-methoxyphenyl ring ) with the microtubule depolymerizer colchicine , suggesting that compounds 6–12 , within the sub-network may share a common colchicine-like binding mechanism ( Fig . 5C ) [53] . To test this hypothesis , we performed a structural alignment of compound 6 with colchicine and docked the aligned conformations onto the ligand-bound tubulin crystal structure ( PDB: 1SA0 ) ( Fig . 5C ) . Surprisingly , the predicted binding modes of the two molecules were conserved despite low structural similarity . As further validation of this binding mode , the same binding conformation was also recovered from the top poses by re-docking compound 6 into the colchicine binding site of an apo beta tubulin structure ( chain B , PDB: 1FFX ) , giving a score of-10 . 82 ( London dG ) based on free energy binding of the ligand to the receptor site points . The docked structure revealed a consensus pharmacophore between the two aligned ligands including the 2 and 10-methoxy groups and a 9-keto group that interacted with Cys 241 of beta tubulin and Val 181 of alpha tubulin respectively , which had been previously reported ( Fig . 5D ) [52 , 64] . The docking of compounds 7–12 using the same approach also yielded similar binding interactions ( S11 Fig ) . The discovery of this consensus-binding model for compounds 6–12 allowed us to link specific protein-ligand recognition features to compound network association and their SAR . For example , the receptor hydrophobicity map showed that the increased potency of compounds 7 and 8 , compared to 6 , could be attributed to the additional interaction of N-propyl group of compound 7 and the N-phenyl group of compound 8 within a sub-pocket enclosed between Leu 248 and Lys 352 of the colchicine-binding site , thus enhancing the protein-ligand interaction ( Figs 5E and S11 ) . To validate the binding of these compounds to the colchicine site , we used a mass spectrometry-based competition assay where compound 8 competed with colchicine for tubulin binding , similar to the positive control podophyllotoxin ( colchicine site binder ) , and the negative control vincristine ( vinca site binder ) was unable to compete this interaction ( Fig . 5F and S1 Text ) [65] . To test if tubulin was the primary target , we treated HeLa cells with compounds 6–12 and analyzed their effects by IF microscopy . As expected , compounds 6–12 induced a microtubule depolymerization phenotype in HeLa cells ( Figs 5G and S12 ) . Thus , the structural binding analysis within a specific sub-network identified a relationship between network connectivity and consensus mechanism , likely due to shape complementarity between protein and ligands . Most importantly , this could be generalized as an effective strategy for structure-based target validation following CSNAP drug target prediction .
At the completion of cell-based chemical screening efforts researchers are faced with the daunting task of understanding drug mechanism of action and selecting lead compounds from a large number of structurally diverse hits to pursue further . To date , researchers have relied on experimental secondary screens , like multiparametric phenotypic profiling , to select a small number of compounds to validate , which is often costly to conduct and has reduced throughput [66] . On the other hand , computational approaches like simple chemical similarity searches do not capture the bioactivity correlation among the analyzed ligands , leading to prediction inconsistencies and low prediction accuracy . Our study demonstrated that CSNAP , a new computational target prediction methodology that uses chemical similarity networks coupled to a consensus-scoring scheme , improves the current state of the art in in-silico drug target identification . First , our benchmark study showed that CSNAP achieved a higher success rate than SEA , an approach based on sequential ligand similarity searches , at identifying pre-annotated drug targets from six major drug classes , especially for promiscuous ligands like CDK2 and ACE inhibitors . Since hit compounds from large chemical screens usually possess sub-optimal target specificity , CSNAP is particularly suitable for deconvolving these compounds compared to the existing approaches . Second , we applied CSNAP to predict and validate the drug targets of 212 mitotic compounds , whose drug binding mechanisms were previously unknown . Here , CSNAP was used in both a positive selection strategy to identify known compounds associated with three new categories of mitotic targets and in a negative selection strategy to identify novel chemotypes targeting microtubules , a major target in cancer drug discovery . Thus , we have demonstrated that CSNAP can achieve accurate large-scale drug target profiling of any compound set without relying on absolute chemical similarity or pre-conditioning from training sets . However , CSNAP has several limitations . For instance , our tubulin polymerization assays indicated that around 30% of the tubulin targeting compounds were not predicted by CSNAP . This highlights the general limitation of any ligand-based approach , in that target annotation of the intended chemotype has to be deposited in the bioactivity database a-priori . Nevertheless , our structural studies of the novel microtubule depolymerizer compound 6 , whose pharmacophore aligned with the known microtubule targeting agent colchicine , suggests that a chemical similarity measure based on the three-dimensional structure of the compounds could potentially improve CSNAP’s prediction power . Likewise , the similarity between CSNAP networks and PPI networks provides further opportunities to apply different PPI network scoring schemes to improve CSNAP prediction [34] . For instance , neighbor counting functions could be readily expanded to consider second-order network neighbors , which has been shown to improve the prediction accuracy of PPI networks [67] . Finally , we showed that incorporating multiple databases , for example PubChem in conjunction with ChEMBL , improved the prediction range of the mitotic compounds by CSNAP . Thus , the simultaneous integration of multiple chemogenomic and bioinformatic knowledge databases can potentially aid the ability of CSNAP to predict the targets of any compound set . In conclusion , we have developed a new network-based compound target identification method called CSNAP that can be used for large-scale profiling of hit compounds from chemical screens . To further extend the applicability of CSNAP for compound target prediction in a broad array of disciplines , we have made the CSNAP algorithm freely accessible as a CSNAP web server ( http://services . mbi . ucla . edu/CSNAP/ ) . The web server allows users to analyze up to 300 ligands in parallel , where each ligand can be processed in less than a minute on average ( S13 Fig ) . We envision that CSNAP will be instrumental for deconvolving bioactive compounds from past and future cell-based studies relating to the discovery of antiproliferative agents and other processes related to cell division . More broadly , the flexibility of CSNAP to incorporate a wide variety of databases enables it to analyze any active compound set identified from any cell-based high throughput screen , thus expanding its utility across disciplines . Finally , CSNAP should expedite target identification and validation , while limiting costs associated with conventional target identification approaches .
The benchmark validation sets were downloaded from the directory of useful decoys ( DUD ) VS 1 . 0 ( http://dud . docking . org/jahn/ ) . The mitotic compounds were retrieved from a vendor master compound SDfile . The ChEMBL reference compound databases were downloaded from the ChEMBL website ( http://www . ebi . ac . uk/chembl/ ) . A stock plate of the 212 mitotic compounds was prepared by transferring each drug in DMSO into a 384 well plate at a final concentration of 500 μM . Tubulin polymerization assays were conducted using HTS-Tubulin polymerization assay kit from Cytoskeleton Inc . To minimize pre-mature tubulin polymerization , 24 reactions were tested per run using multi-channel pipettes . Briefly , a 500 μM solution of each test compound and control compounds ( Nocodazole and Taxol ) were prepared in DMSO and subsequently diluted in ice-cold G-PEM buffer [80 mmol/L PIPES ( pH 6 . 9 ) , 2 . 0 mmol/L , MgCl2 , 0 . 5 mmol/L EGTA , 1 . 0 mmol/L GTP] to a final concentration of 50 μM . Lyophilized bovine brain tubulin was resuspended in ice-cold G-PEM buffer to a final concentration of 4 mg/ml . Test compounds were added to each well ( 2μl/well ) of a 384 well plate followed by the addition of tubulin ( 20μl/well ) . The reactions were assembled on ice to prevent tubulin pre-polymerization . The final concentration of test compounds was 50 μM in 0 . 5% DMSO . To measure tubulin polymerization kinetics , the plate was warmed to 37°C in a Tecan microplate reader ( Tecan Group Ltd . ) and read at 340 nm every minute for total of 1 hour . Colchicine ( 1 . 2 μM ) was incubated with porcine brain tubulin ( 1 . 0 mg/mL ) in incubation buffer [80 mM piperazine-N , N′-bis ( 2-ethanesulfonic acid ) ( PIPES ) , 2 . 0 mM magnesium chloride ( MgCl2 ) , 0 . 5 mM ethylene glycol tetra acetic acid ( EGTA ) , pH 6 . 9] at 37°C for 1 hour . Test compounds ( 100 μM ) were added to compete with the binding of colchicine to tubulin . After 1 h incubation , the filtrate was obtained using an ultrafiltration method ( microconcentrator ) ( Microcon , Bedford , MA ) with a molecular cut-off size of 30 kDa . The ability of the compounds of interest to inhibit the binding of colchicine was expressed as a percentage of control binding in the absence of any competitor . Each experiment was performed in triplicate . HeLa cells were grown in F12:DMEM 50:50 medium ( GIBCO ) with 10% FBS , 2 mM L-glutamine and antibiotics in 5% CO2 at 37°C . Immunofluorescence was carried out essentially as described previously [68] . HeLa cells were treated with indicated compounds at their respective EC90 for 20 hours , fixed with 4% paraformaldehyde , permeabilized with 0 . 2% Triton X-100/PBS and co-stained for DNA ( 0 . 5 μg/ml Hoechst 33342 ) and tubulin ( rat anti-tubulin primary antibodies and anti-rat Cy3 secondary antibodies ) . Images were captured with a Leica DMI6000 microscope at 63X magnification . The crystal structure of colchicine-bound tubulin was downloaded from the PDB database ( PDB code: 1SA0 ) and the beta tubulin monomer with bound colchicine ( chain D ) was extracted from the protein model [69] . Compounds 6–12 were flexible aligned with colchicine within the colchicine-binding site using the “flexible alignment” protocol and default parameters ( alignment mode: flexible , iteration limit: 200 , failure limit: 20 , energy cutoff: 15 , stochastic conformation search ) , which gave a score for each alignment by quantifying the quality of internal strain and overlap of molecular features . Additionally , we realigned the colchicine structure with its crystal-derived conformation to ensure accuracy of the protocol . The aligned conformation of each compound was subsequently energy minimized within the colchicine-binding pocket using the LigX protocol . The re-docking of compound 6 into the colchicine-binding site was performed using the Dock protocol with default parameters ( placement: triangle matcher , score: London dG , retained conformations: 30 ) . The molecular modeling was performed using the MOE software version 2009 . The mean and standard deviations of DMSO and Taxol controls for the in-vitro tubulin polymerization assays were calculated and used to scale the compound OD readout between different runs to normalize the heterogeneity of the reaction . All the statistical analysis for in-vitro tubulin polymerization assays was performed using Microsoft Excel . The CSNAP program is written in shell scripting language and Perl programming language on Ubuntu 12 . 10 Linux operating system . The program is dependent on the following external programs/scripts including OBABEL version 2 . 3 . 1 and NCI SDF toolkit version 1 . 2 . Additionally , the R statistical package and Cytoscape version 2 . 8 . 2 were applied for visualizing and analyzing heat maps and networks respectively . See Supporting Information for program description and tutorials . The CSNAP program is freely accessible from the CSNAP web server ( http://services . mbi . ucla . edu/CSNAP/ ) . Supporting Information includes Supporting Materials and Methods , thirteen figures , four tables , two supporting files , and supporting tutorials and can be found with this article online . | Determining the targets of compounds identified in cell-based high-throughput chemical screens is a critical step for downstream drug development and understanding of compound mechanism of action . However , current computational target prediction approaches like chemical similarity database searches are limited to single or sequential ligand analyses , which limits their ability to accurately deconvolve a large number of compounds that often have chemically diverse structures . Here , we have developed a new computational drug target prediction method , called CSNAP that is based on chemical similarity networks . By clustering diverse chemical structures into distinct sub-networks corresponding to chemotypes , we show that CSNAP improves target prediction accuracy and consistency over a board range of drug classes . We further coupled CSNAP to a mitotic database and successfully determined the major mitotic drug targets of a diverse compound set identified in a cell-based chemical screen . We demonstrate that CSNAP can easily integrate with diverse knowledge-based databases for on/off target prediction and post-target validation , thus broadening its applicability for identifying the targets of bioactive compounds from a wide range of chemical screens . | [
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| 2015 | Large-Scale Chemical Similarity Networks for Target Profiling of Compounds Identified in Cell-Based Chemical Screens |
Uropathogenic Escherichia coli ( UPEC ) represent the predominant cause of urinary tract infections ( UTIs ) . A key UPEC molecular virulence mechanism is type 1 fimbriae , whose expression is controlled by the orientation of an invertible chromosomal DNA element—the fim switch . Temperature has been shown to act as a major regulator of fim switching behavior and is overall an important indicator as well as functional feature of many urologic diseases , including UPEC host-pathogen interaction dynamics . Given this panoptic physiological role of temperature during UTI progression and notable empirical challenges to its direct in vivo studies , in silico modeling of corresponding biochemical and biophysical mechanisms essential to UPEC pathogenicity may significantly aid our understanding of the underlying disease processes . However , rigorous computational analysis of biological systems , such as fim switch temperature control circuit , has hereto presented a notoriously demanding problem due to both the substantial complexity of the gene regulatory networks involved as well as their often characteristically discrete and stochastic dynamics . To address these issues , we have developed an approach that enables automated multiscale abstraction of biological system descriptions based on reaction kinetics . Implemented as a computational tool , this method has allowed us to efficiently analyze the modular organization and behavior of the E . coli fimbriation switch circuit at different temperature settings , thus facilitating new insights into this mode of UPEC molecular virulence regulation . In particular , our results suggest that , with respect to its role in shutting down fimbriae expression , the primary function of FimB recombinase may be to effect a controlled down-regulation ( rather than increase ) of the ON-to-OFF fim switching rate via temperature-dependent suppression of competing dynamics mediated by recombinase FimE . Our computational analysis further implies that this down-regulation mechanism could be particularly significant inside the host environment , thus potentially contributing further understanding toward the development of novel therapeutic approaches to UPEC-caused UTIs .
Type 1 fimbriae ( pili ) represent the foremost virulence factor in lower urinary tract infections ( UTIs ) by uropathogenic Escherichia coli ( UPEC ) —the main causative agent that accounts for 80–90 percent of all community-acquired UTIs in the United States [1]–[4] . These adhesive surface organelles have been identified as both the UPEC virulence factor most frequently found in clinical isolates as well as the one that experiences the highest absolute and among the greatest relative increases of component gene expression in vivo during UTIs [5] , [6] . Type 1 fimbriae also have been shown to fulfill molecular Koch's postulates [2] , [7] and have been further reported as the only major uropathogenic virulence factor that is broadly significant for enteric E . coli strains as well [8] , [9] . The hair-like structures involved vary from a few fractions of a micrometer to more than 3 m in length and consist of 7nm-thick right-handed helical rods—largely made up of repeating subunits—with 3nm-wide tips containing the adhesin , which can bind to D-mannose-containing residues found on the surface of epithelial cells and mediate their invasion by UPEC [10]–[13] . Type 1 fimbriae are further thought to aid the UPEC infection process by enhancing the ability of bacteria to form biofilms and to develop intracellular bacterial communities ( IBCs ) with biofilm-like properties [13]–[18] . The latter allow E . coli to establish quiescent pathogen reservoirs shielded from native host defenses and antibiotic treatments as well as serve to seed subsequent UTIs in a type 1 fimbriae-dependent manner [2] , [13] , [19]–[21] . This may both contribute to the widespread emergence of multi-drug-resistant UPEC strains ( up to 20–50 percent of isolates ) as well as help account for the notably high rates of UTI incidence ( lifetime risk of over 50 percent for women and nearly 14 percent for men ) and recurrence ( 40 percent in women and 26 percent in men per annum ) – along with leading to a number of other significant public health implications ( e . g . , over 10 million estimated annual physician office visits in the United States alone ) [1] , [22] . However , while they provide a means for infection , type 1-fimbriated UPEC populations also have lower fitness due to phase-specific mechanisms that directly decrease growth rates through additional costs of fimbriae synthesis and contact-dependent inhibition as well as reduce motility , which allows competitors to more efficiently occupy advantageous colonization sites and take up resources [6] , [23]–[25] . Furthermore , type 1 fimbriae-mediated attachment can lead to preferential exfoliation of infected cells as part of the host immune response , which can result in rapid clearance of the infection [13] , [20] , [26]–[28] . Among other things , this apparent dichotomy between the essential role played by the piliated phase in the establishment of the infection and the noted fitness disadvantages conferred upon individual bacteria by type 1 fimbriae implies that their expression needs to be highly optimized and tightly controlled . As illustrated in Figure 1 , the expression of type 1 fimbriae in E . coli is randomly phase variable , whereby individual cells stochastically switch between fimbriate ( ON ) and afimbriate ( OFF ) states with rates regulated by various internal as well as environmental conditions [29]–[33] . With the ongoing advancements in high-resolution single-cell and single-molecule scale experimental methods , such bimodal and bistable mechanisms for generating phenotypic heterogeneity in clonal cell populations have been increasingly often identified and investigated across a broad range of prokaryotic and eukaryotic systems—where they have been shown to influence a diverse spectrum of processes—including organism development , behavior , disease , survival , and memory [34]–[44] . In the case of E . coli type 1 fimbriae , this phase variation is controlled by the fim circuit switch that functions based on the inversion of a 314bp chromosomal region , fimS , bounded by two 9bp inverted repeats left and right ( IRL and IRR ) [29] , [34] , [45] , [46] . The fimS element contains the promoter for fimA and other genes encoding structural subunits of type 1 fimbriae . As a result , an individual E . coli cell expresses type 1 fimbriae when the fim switch is in the ON position and rapidly becomes afimbriate when the switch flips into the OFF position [34] , [47] . This inversion of fimS requires either or site-specific recombinases binding at IRL and IRR [29] , [47] , [48] . However , whereas mediates recombination with little orientational bias , mediates recombination predominantly in the ON-to-OFF direction [30] , [49] . Empirical evidence has further revealed that the inversion of the fim switch is strongly controlled by temperature in a complex manner [30] , [31] . In particular , observations at , , and have indicated that wild-type ON-to-OFF switching frequency—dominated by —decreases in an exponential-like fashion as temperature increases , while -mediated switching frequency is higher at than either at or in both defined-rich and minimal media . Experimental results also show that the wild-type ON-to-OFF switching rate is much faster than -mediated switching rate alone , allowing E . coli to rapidly undergo afimbriation under appropriate conditions [30] , [50] . This work investigates the logic and behavior of the gene regulatory circuit , which controls the ON/OFF switching of type 1 fimbriae expression , by starting with the reaction-level description of its underlying biochemical and biophysical molecular interaction mechanisms . We are particularly interested in the role of environmental cues in this process and , specifically , of temperature as it is known to control many gene regulatory circuits in bacteria—often those responsible for virulence functions [51] . Temperature variations are also frequently characteristic of host-pathogen interaction dynamics—such as during cytokine response ( e . g . , through IL-6 as well as IL-8 and IL-1 ) and the ensuing inflammation that is indicative of the onset and progression of UPEC UTIs—as well as often generally representative of urinary tract pathology [52] , [53] . In this context , reaction-level modeling provides a framework for highly accurate description of the underlying biomolecular circuit behavior through application of the corresponding fundamental chemical and physical principles . However , the innate complexity of biological networks involved as well as the key role played by nonlinear , discrete , and stochastic kinetics in regulating the dynamics of cellular pathways driven by molecular-scale mechanisms result in profound computational challenges to their accurate quantitative analysis . The problem becomes particularly acute when dealing with biological systems , such as type 1 fimbriation circuit switch dynamics in UPEC , whose behavior is driven by internal or external discrete-stochastic processes to exhibit qualitative deviations from what might otherwise be expected on the bases of “classical” continuous-deterministic biochemical modeling via mass-action kinetics and reaction rate differential equations [39] , [54] . The resulting “deviant” dynamics lead such biological systems to behave in a distinctive but often quite unintuitive manner , which necessitates the use of differential-difference modeling based on the chemical master equation framework ( see [54]–[59] and Methods for details ) . However , while the latter approach is able to accurately account for both the stochastic occurrence as well as the discrete nature of individual molecular interactions that underlie the design , function , and control of most biological circuits—it also tends to produce dramatic increases in the associated analytical and computational demands [60]–[62] . Although these computational limitations may often render any direct implementations of the all-inclusive low-level quantitative models impractical , the use of entirely high-level qualitative representations frequently becomes inadequate as well , owing to the substantial multiscale dynamical and functional complexity that biological systems can manifest . In such cases , in silico analysis can greatly benefit from applications of appropriate intermediate-level system model abstractions—whereby multiple individual biological interactions are aggregated into significantly few ( er ) , but quantitatively analogous functional processes . An optimized model abstraction scheme then looks to accurately capture the target characteristics of biological system behavior , while trading off some tightly controlled degree of precision for significant computational gains . Additionally , the resulting abstracted model of the system may also be useful in helping to uncover any general high-level logical patterns embedded within the biological networks involved , which can otherwise be obscured by the low-level molecular interaction mechanics . Our method initiates the abstraction procedure with a detailed reaction-level representation of biological processes in question . This enables it to utilize basic biochemical and biophysical principles to rigorously implement many of the existing as well as potentially allow for the development and incorporation of novel abstraction techniques , Table 1 , in order to insure the desired degree of modeling accuracy versus computational efficiency for the abstracted representation at the system scale of interest [63] , [64] . However , such an approach to model complexity reduction could also lead to a further problem: while most abstractions used in the analysis of biomolecular networks have traditionally been implemented manually and on the mechanism-by-mechanism basis , doing so accurately in a general biological systems setting becomes tedious and time-consuming . The resulting model translation and transformation errors also tend to increase when progressively more intricate organism-scale physiological processes—from cell differentiation and tissue development to cancer , infection , host-pathogen interaction dynamics , etc . —are considered . The strategy used here is able to substantially overcome these issues by automating the abstraction process via a set of algorithms developed for and implemented in the reb2sac computational tool [63] , [64] . Its application has allowed us to generate abstracted representations of detailed reaction-level biological mechanisms—including genetic regulatory networks—which yield results in close correspondence with those obtained by using the underlying low-level models , while also significantly accelerating the required computations and often putting them on par with those of high-level descriptions . For instance , we were previously able to validate the overall robustness and utility of such an automated abstraction approach to biological systems analysis by using it to investigate the lysis/lysogeny developmental decision pathway in E . coli phage [63] , [64] . The ensuing abstracted model analysis not only yields results that substantially ( and in significantly less time ) reproduce those elicited through the examination of the detailed system description reported earlier [65] , but is further able to quantitatively investigate and similarly match experimental observations of system properties exhibited under environmental conditions that have been previously shown to cause the detailed model analysis to become so computationally demanding as to make it essentially infeasible [63] , [65] . Here , we use such computational analysis aided by automated model abstraction to examine the behavior of the basic genetic regulatory network responsible for the ON/OFF switching of type 1 fimbriae expression in uropathogenic E . coli , Figure 2 . We specifically focus on how different temperature settings quantitatively modulate the random switching of the UPEC fimbriation circuit into the transcriptionally silent fim mode through the corresponding ON-to-OFF inversion of fimS . Notably , while the behavior of most molecular processes depends on temperature , in this system global regulatory proteins and play a particularly important role in controlling switch inversion rates not only by directly effecting its internal molecular dynamics , but also by acting as sensors of certain environmental conditions that the fim circuit is subjected to in the physiological range—including those of a host . For instance , acts in a temperature-dependent manner when it binds to DNA regions containing fimB / fimE promoters and represses their expression [31] , [66] . Additionally , binds to three sites , which affects switching rates [50] , [67] , [68] . Since downregulates the expression of lrp [69] , [70] , also behaves in an effectively temperature-dependent manner . Finally , it has been shown that binds to / regulatory sites and is required for any observable phase variation , in part by playing a structural role in fim switching via its ability to introduce sharp bends into the target DNA [47] , [71] . The resulting molecular interactions that involve , , , as well as the fimS DNA element and associated regulatory sites are what largely serves to kinetically effect the ON-to-OFF fim switch circuit dynamics . As the latter physiologically initiates the transition of an individual bacterium from the virulent fimbriate to the largely benign afimbriate phase and given the wide-spread emergence of antibiotic-resistant UPEC , a better understanding of such processes could benefit the development of novel clinical UPEC UTI therapies by , among other things , providing deeper insights into mechanisms potentially able to medically abrogate UPEC virulence by exploiting its internal molecular circuitry responsible for regulating the state of fimS in order to inhibit type 1 fimbriae expression . Towards this end , the paper begins by considering a detailed reaction-level discrete and stochastic description of the biological molecular network controlling the fim switch . As discussed earlier , we then abstract this detailed representation by utilizing reb2sac , which enables us to successfully circumvent the otherwise significant computational issues involved . The accuracy of our abstracted model analysis with respect to the target system property—i . e . , the temperature dependence of the fim switch turn-off rate—is further validated by comparing its results with those computed via the unabstracted detailed model as well as with those derived from empirical observations ( where available ) . This , in turn , serves to explicitly demonstrate how automated model abstractions can be used to help substantially improve the speed and efficiency of biological molecular systems analysis , while also maintaining precision and improving interpretability of results . For instance , the abstracted representation has allowed us to better understand the general circuit-level organization of the regulatory logic behind the UPEC fimbriation switch and to identify the two key subnetworks— recombinase regulation and fim switch configuration—involved in its engineering design . Our conclusions also confirm that temperature has a major and non-trivial role in determining ON/OFF switching of fimbriae expression as well as suggest new insights into the role of in this process and offer novel clues toward its potential translational applications in the host environment . In particular , our results indicate that—when the control circuit behavior is analyzed quantitatively across different temperatures—the primary role of recombinase may not be to increase the total ON-to-OFF switching rate , but rather to reduce it by down-regulating the rate of switching mediated by the competing recombinase . That is , down-regulation of not only reduces the OFF-to-ON switching , but also serves to increase the ON-to-OFF rate in a temperature-sensitive manner , which indicates that this mechanism may provide a powerful regulatory tool for suppressing the fimbriate UPEC phase . Finally , as our analysis implies that the described effect is strongest and the switching rate is most sensitive to the corresponding mode of control in the physiological temperature range of the host environment , it may serve to potentially help identify new biomedical targets in the UPEC molecular virulence circuitry .
Based on the regulatory network diagrammed in Figure 2 , we have developed a molecular kinetic reaction-level description of E . coli fimbriation switch system , which has resulted in a detailed model of the fim circuit that comprises 52 reactions and 31 species ( Figures 3 and 4 ) . This model is then used to , among other things , quantitatively analyze the effects of temperature on both the total and -mediated ON-to-OFF fim switching probabilities over one cell generation . In particular , starting with the switch in the ON position at various temperature settings—i . e . , , , and —where the corresponding empirical observations were available ( see Methods and Text S1 ) , the detailed model was simulated 100 , 000 times by using our implementation of Gillespie's Stochastic Simulation Algorithm ( SSA ) . The ensuing switching behavior of the fim circuit was found to be both qualitatively and quantitatively consistent with that obtained via empirical observations [30] ( see Table 2 ) . However , computational demands presented by these detailed model simulations were significant , requiring over 30 hours on a 3GHz Pentium 4 with 1GB of memory ( Table 3 ) . After applying reb2sac automatic abstraction engine with the switch state as the target quantity of interest , the detailed model is transformed into an abstracted model with 10 reactions and 3 species ( , , and a conglomerate non-linear stochastic switch – see Figures 5 and 6 as well as Methods for further detail ) . In order to compare the abstracted and detailed models , we have performed numerical simulations to compute the wild-type and -mediated ON-to-OFF switching probabilities for one cell generation in minimal medium using the same simulator . The results of the abstracted analysis are found to be in close agreement with those obtained using the detailed model and substantially match the empirical observations ( see Table 2 ) . However , computational gains from the model abstraction are significant . The abstracted model simulation of 100 , 000 runs takes less than 2 hours on a 3GHz Pentium 4 with 1GB of memory , which is a speed-up of about 16 times compared with the runtime of detailed model simulations ( Table 3 ) . In addition to allowing for accurate kinetic simulation of circuit-level dynamics , the reaction-level description of biological networks is often useful in helping to reveal their broader structural and functional features , including the innate modular architecture of E . coli fimbriation switch design considered here . Specifically , graph-level analysis carried out as part of the detailed model abstraction process has naturally led us to separate out and identify its two major constitutive subnetworks . These turn out to correspond to the two principal functional units of the fim switch circuit: the module effecting production-degradation of and ; and the module responsible for the configuration dynamics of the fimS element itself ( e . g . , Figures 5 and 6 ) . Such a view of the internal fim switch circuit organization both makes its logic easier and more intuitive to understand as well as simplifies and provides further basis that serves to facilitate subsequent steps involved in the model abstraction process . By systematically refining our understanding of the underlying organization logic and improving required computational times , our approach further enhances the ability of in silico analysis to accurately explore various environmental as well as internal conditions and parameter regions of biological systems . This may be particularly useful when certain settings can be deemed physiologically important , yet are not easily amenable to or simply do not presently have sufficient number of experimental measurements available; and which lead to dynamics that are too complex or involve species too numerous to be productively investigated directly at the detailed molecular interaction network level . For example , in the case of the fimS inversion control circuit , probabilities of ON-to-OFF switching at various temperature points ( including those outside of the experimental range ) can be effectively and efficiently estimated by using the described model abstraction methods . Here , Figure 7 shows both wild-type and -only mediated ON-to-OFF switching probabilities computed via the abstracted fim switch model at – respectively – 7 and 15 additional temperature points , where experimental data are not available ( also see Table 2 ) . Notably , these results not only reaffirm earlier coarser-grained empirical observations of wild-type and -only mediated ON-to-OFF fim circuit switching frequency dependence on temperature [23] , [30] , but also offer the finer-grained resolution capable—as discussed below in more detail—of providing further insights into this relationship . In particular , while our analysis supports the prior suggestion that the wild-type fim ON-to-OFF rate is overall a decreasing function of temperature that varies by nearly two orders of magnitude in the physiological range , it also appears to indicate that this dependence has a supra-exponential component as well , Figure 7A . Furthermore , when the abstracted model is used to increase the resolution of FimB-mediated switching frequency dependence on temperature , it shows that UPEC may have evolved toward a tightly optimized type 1 fimbriae virulence factor expression control that is designed to sense and differentially respond based on whether the host temperature is within the normal physiological range of or if it is elevated/lowered instead . Whereas the circuit -mediated ON-to-OFF rate appears to be maintained at a relatively elevated but stable level across the entire normal temperature range—it looks to be significantly suppressed immediately outside of this characteristic band , Figure 7B , which may have notable implications for the persistence of the pathogenic UPEC phase and ensuing UTIs ( see Discussion ) . Since the -mediated switching probability can be orders of magnitude smaller than the wild type ON-to-OFF switching probability ( Table 2 ) , the effect of on the temperature control of the fimbriation circuit shutdown rate may also appear minimal . It is , furthermore , not immediately clear why -mediated switching needs to be exquisitely bidirectional rather than simply OFF-to-ON , given that essentially only promotes ON-to-OFF switching and completely dominates the rate in this direction . While various theories have been proposed to explain this feature of the fimbriation regulatory network design ( see Discussion ) , we wanted to generate a quantitative hypothesis regarding the role of in the temperature control of the fim ON-to-OFF circuit switching by using computational analysis methods to perturb the underlying molecular interaction-level network properties and to then explore the behavior of any resulting fimbriation mutants . To do this , we have modified the original fim switch inversion system in silico and generated several detailed mutant models—two of which proved to be of particular interest . One represents a mutant , where fimB has been placed under the control of a strong promoter that leads to overproduction by a factor of two relative to wild-type . The other describes a mutant , such as a knockout or an amino acid substitution , where protein has been rendered nonfunctional in the present context by losing its ON-to-OFF switch-mediating activity . Both mutant models were abstracted using reb2sac and simulated . Comparing the elucidated mutant and wild-type behaviors at the same 10 temperature points considered earlier ( e . g . , Figure 7A ) now allows us to quantitatively characterize the dependence of this fim switch circuit temperature control on the level of activity in the cell . As illustrated in Figure 8A , the total ON-to-OFF switching probability generally decreases inversely with levels across all temperatures . That is , in the physiological range , the total ON-to-OFF switching probabilities in the fimB− mutant are higher than those in the wild-type , which are—in turn—higher than those in the mutant where is overexpressed . Notably , this not only suggests that the -mediated shutdown of fimbriae expression is efficiently down-regulated by , but that—as shown in Figure 8B—this effect is strongest in the to temperature range , where the total ON-to-OFF switching probability of the fimB− mutant can be over two times higher than that of the wild-type and nearly three times that of the overexpressing mutant . Physiologically , this implies that the presence of at normal or elevated levels greatly enhances the persistence of type 1-fimbriated UPEC phase . Thus , although the -mediated fim switching probability is itself at least an order of magnitude lower than wild-type , may have a key role in regulating and enhancing the control of temperature-dependent functions in the E . coli fim switch circuit by—among other things—also reducing the effect of -mediated ON-to-OFF fim switching . This serves to regulate the type 1 fimbriae-based molecular virulence mechanism and , potentially , may help increase the life-time of the pathogenic fimbriate UPEC phase . The latter result is of particular interest because the effect appears to be most pronounced in the temperature range that corresponds to the intra-host bladder environment , opening up the possibility that it may be directly relevant to UPEC-caused UTIs .
In recent years , rapid advances of experimental biology made it practical to study both molecular- and network-scale organization of many biological and physiological processes in much greater detail than was previously feasible . This , in turn , has made computational analysis not only possible , but also essential to any efforts aimed at understanding the increasingly intricate structures and functions of multiscale biological systems that are being uncovered through empirical means . However , this growing wealth of knowledge about in situ biological processes has also led to the demand for progressively more sophisticated in silico system models . As a result , although accurate molecular-scale biochemical descriptions could be formulated for a large number of experimentally observed systems , their complexity is rapidly exceeding our present as well as near-future computational capabilities—the issue that has become more pronounced with the emerging understanding of the ubiquitous role played by nonlinear and discrete-stochastic ( “noisy” ) molecular dynamics in gene regulatory , signal transduction , and other biological systems [39] . That is , while their role may often be essential in defining the various design and functional characteristics of biomolecular circuits [72]–[78]—including temperature controls [79]–[82]—the resulting introduction of multiplicative noise and the possibility of ensuing deviant effects [54] , [83]–[89] can make computational analysis of such processes particularly demanding [62] . Going forward , these considerations appear to suggest that “model abstractions”—whereby , for instance , multiple biological network interactions comprising individual biomolecular mechanisms are rigorously and systematically aggregated into a few easily tractable , but functionally analogous components—will continue to become an increasingly useful tool in the general context of computational and systems biology . Importantly , model abstractions can serve not only to substantially reduce the computational requirements associated with the analysis of specific multiscale biological processes , but may also lead to identification of functional units that correspond to biologically meaningful modules or motifs ( exemplified here by the two functional subnetworks of the fim switch circuit ) . The latter helps contribute additional insights into the underlying system organization and physiology as well as make their often intricate logic easier to understand . Yet , given this growing scope and complexity of systems biological models , manual implementation of comprehensive abstractions with accuracy and efficiency becomes a challenge—creating the need for process automation . This work has demonstrated the utility of such an automated model abstraction approach by applying it to the investigation of the role of temperature in controlling the ON/OFF switch state of the fim genetic regulatory circuit that determines the expression of type 1 fimbriae ( Figure 1 ) , which is an essential virulence factor in uropathogenic E . coli—the leading cause of urinary tract infections and a major growing public health problem [1] . Insights into this fimbriation process—and , particularly , into the mechanisms that control its shutdown—may be especially useful as the widespread proliferation of antibiotic-resistant and biofilm-forming UPEC strains continues to increase the demands for novel treatment methods . In particular , a thorough understanding of their cellular network function under a range of conditions may allow us to manipulate UPEC's internal molecular virulence circuitry through external means , thus potentially opening up new approaches to modulating their pathogenicity . One such key external regulator is temperature , which not only often acts as an indicator of UTI progression and impacts its course , but may also be amenable to meaningful control in clinical settings . Furthermore , as experimental investigation of these processes in situ may offer a variety of practical challenges , in silico approaches could be very useful in helping to identify how internal molecular virulence machinery is influenced by external temperature variations . However , even in the case of the relatively small biological circuit controlling type 1 UPEC fimbriation switch considered here ( Figure 2 ) , its functions are qualitatively affected by the inherently discrete and stochastic as well as the largely nonlinear nature of the underlying biomolecular mechanisms . This necessitates the type of biological systems analysis that is capable of accurately accounting for contributions of molecular-scale reaction-level processes , which typically makes direct in silico studies of such systems highly taxing and investigations of detailed fimbriation circuit switch properties challenging . Here , we were able to substantially circumvent such issues through the use of systematic model abstractions , which allowed us to convert a highly computationally demanding problem of fim circuit switch response to temperature variations into a relatively accessible one by relying upon the automated model abstraction methodology we have developed and implemented in the reb2sac model abstraction tool [63] . We then used this abstracted model to gain deeper insights into the dynamics of this biomedically important system , including the role of in controlling the expression shutdown rates of type 1 fimbriae virulence factor . To do this , we have first constructed a molecular-scale reaction-based “detailed” model of the regulatory network that controls the orientation of fimS genomic element ( Figure 2 ) , which is responsible for ON/OFF switching of type 1 fimbriae expression . This model has allowed us to analyze—with high degree of fidelity , albeit at significant computational costs—the dynamic behavior of UPEC's discrete-stochastic genomic fimbriation circuit , including the ensuing effects of temperature on the wild-type and -mediated ON-to-OFF switching probabilities in minimal medium , which are shown to be quantitatively consistent with those observed empirically ( Table 2 ) . We then applied our reb2sac tool to the detailed model of the fim switch circuit . The resulting “abstracted” model substantially reduces the complexity of the problem , enabling us to significantly increase the throughput of our in silico analysis ( Table 3 ) , while still maintaining accuracy when compared with the detailed model predictions and available experimental observations ( Table 2 ) . This approach has further allowed us to compute the ON-to-OFF switching probabilities at additional temperature points and to investigate the behaviors of characteristic mutants in silico ( Figures 7 and 8 ) . As a result , we have been able to gain a number of insights into the internal dynamics of this clinically relevant system , including into the strong temperature dependence of putative UPEC afimbriation switching rates ( e . g . , Figure 7 ) , which characterize the intrinsic dynamics that may cause individual bacteria to autonomously transition from pathogenic to benign phase . In particular , while earlier theoretical studies [90] , [91] have discussed how the type 1-fimbriation level is regulated by the two recombinases , it has not been entirely clear what role ( if any ) has in turning off the fim switch , since the ON-to-OFF rate it mediates is at least an order of magnitude lower than that enabled by . This may also seem at odds with the evolutionary selection of the remarkably fair ON/OFF switching probabilities observed . Our analysis ( which—it should be emphasized—though based on primary empirical data , is done substantially in silico and so needs further experimental validation ) has been able to suggest a possible explanation for this ostensible contradiction by identifying a potentially key regulatory role of in directing UPEC afimbriation . Specifically , while the switching rate it can mediate directly remains low , may competitively modulate the dominant -dependent switching process in excess of three-fold—thus serving to significantly lower wild-type E . coli ON-to-OFF switching rates in the host environment . This process can help to further prolong or abridge the persistence of the fimbriate phase in individual bacteria , which may be crucial for UPEC survival when colonizing bladder and invading urothelium , while trying to escape immune system responses and effects of antibiotic treatments , Figure 8 . Furthermore , this -based regulation mechanism may be more robust against small perturbations in level than a simpler fim switch inversion control , which could be of importance in a highly variable and often rapidly fluctuating environment of the urinary tract . While the extent to which these innate mechanisms are able to curtail or enhance virulence of UPEC in situ could be affected by the various aspects of complex host-pathogen interactions noted previously , it may be worth considering that to date much of the discussion has been framed in the context of such immune response processes as cytokine production , resulting inflammation , and potential subsequent exfoliation of infected bladder epithelial cells that generally lead to the increase in local tissue temperature [27] , [52] , [92] , [93] . However , our results support a further understanding of UPEC adaptation to this aspect of host immune response . Although -mediated fimbriae expression shutdown rate appears elevated but largely insensitive to temperature in the normal range of a host , as temperature increases further—both and ON-to-OFF switching rates are lowered , while E . coli's ability to control this process through variations in becomes optimized . That is , as UTI triggers the onset of an inflammatory response , the resulting increase in temperature tends not only to lock this UPEC control circuit in the pathogenic fimbriate phase , but also to transiently maximize switch sensitivity towards regulation by at several degrees above normal—a range consistent with the corresponding host environment . The potential existence of such sensitized “pathogenic phase lock” ( PPL ) mechanism and its ensuing effects on UPEC virulence could have direct bearing on some of the clinical challenges in treating UTIs discussed earlier , since many of these characteristics are thought to be associated with type 1 fimbriae-dependent biofilm and IBC formation [15] , [16] . The latter structures have been shown to provide persistent pathogen reservoirs in bladder tissue and/or on abiotic surfaces ( e . g . , those of medical implants , such as catheters ) even in cases when antibiotic treatments can effectively sterilize urine [92] . Still , currently recommended treatment strategies include ongoing prophylactic daily or weekly antibiotic therapy in cases of recurrent UTIs ( defined as more than 2 episodes in 12 months ) , even though studies have shown no long-term reduction of UTI recurrence in such patients after prophylaxis cessation as compared with those in placebo groups [94] . Given further risks of various potential side effects—which can range from moderate to severe—and development of drug resistance as well as a number of other undesirable consequences , including growing epidemiological and public health implications [1] , [21] , [94] , presently available basic antibiotics-based UTI treatment strategies cannot be considered satisfactory . In fact , it has been strongly suggested that from a clinical perspective the use of traditional antibiotic therapies cannot be successful against biofilm/IBC-forming bacteria and that other treatment modes , particularly those that target biofilm/IBC/fimbriation-specific processes , need to be developed [95] , [96] . Thus , inference of type 1 fimbriae expression regulation circuit logic and elucidation of external intervention strategies able to influence or interfere with its internal dynamics , including via mechanisms that utilize controlled temperature variation to induce PPL relief and subsequent fim switch shutdown as discussed here , could offer promising potential for contributing further understanding towards the development of novel remedial approaches . Historically , many such original medicinal and other therapeutic methods have had their genesis in traditional or domestic practices [97]—a pattern that has been recently seen to accelerate because of , among other things , growing synergies between Western and Asian medical systems that have already resulted in such notable pharmacological and synthetic biological successes as ephedrine and artemisinin—with more on the way [98] , [99] . For instance , while a relatively prolonged exposure to cold has been generally associated with the increased incidence of UTIs [100] , [101] , a number of complementary therapies have been based around the practice of keeping genitourinary tract area cool or even briefly exposing it to low temperatures as beneficial for the prevention and treatment of various pathological processes , including microbial infections [102] , [103] . Yet , while the ongoing research into the effects of cold exposure on differential activation/repression of various adaptive and innate immune system components has now begun to suggest underlying cellular and molecular biological basis for these phenomena observed in clinical applications , their underlying modes of action on the whole remain poorly understood [104] , [105] . In this context , the results discussed here provide an example of the quantitative insight that multiscale reaction-based computational modeling brings to such complex processes . Specifically , based on the implications of our study for utilizing alternative temperature-driven approaches in targeting the dependence of UPEC virulence mechanisms on type 1 fimbriae expression—rather than relying solely on antibiotic or other biochemical means—two mechanisms may merit further attention . On the one hand , as host response to UTI includes tissue inflammation and a corresponding local or systemic increase in temperature , our analysis indicates that the adaptive feedback strategy evolved by UPEC tends to bring about PPL conditions , whereby ON-to-OFF type 1 fimbriation circuit switch may become maximally sensitized to . Combined with its central role in mediating the OFF-to-ON switching [47] , this implies that lowering activity may lead to a reciprocal decrease in the fraction of virulent fimbriate UPEC phase and subsequent reduction in the associated pathogen load—making the corresponding persistent UTIs more amenable to host immune mechanisms and , potentially , increasing the efficacy of existing medical treatments . However , given the challenges of developing and delivering the required inhibitors as well as further obstacles presented by IBC formation inside epithelial cells , it may not be immediately clear how direct variation of UPEC activity could be meaningfully achieved in vivo . On the other hand , our conclusions also support the notion that decreasing the temperature of UPEC environment may increase shutdown rates of type 1 fimbriation circuit switch ( including by indirectly lowering ) , thus potentially leading to the up-regulation of afimbriation rates in individual bacteria . This would tend to suppress UPEC pathogenicity by reducing their capability for attaching to and invading urothelial cells as well as by interfering with biofilm/IBC formation and maintenance , which may be expected to decrease their capacity for subsequent re-infection . As in this case only local temperature variations—including those directed by cool/warm intravesical media or such catheter and other device instillation—are principally required in order to elicit the indicated physiological response , the conditions necessary to influence UPEC fimbriation switching in this manner may be practically attainable in biomedical and clinical applications . It is important to note , however , that this merely suggests the possibility and does not engender any further assessment of potential efficacy such therapies may have in clinical UTI settings . The latter requires a more extensive follow on investigation—particularly in view of additional host-pathogen interaction dynamics , the multicellular nature of the system and commensurably greater complexity of intra-/inter-cellular networks it comprises , the epidemiology of autoinfection processes involved in promoting UTIs from and diversity of the endogenous bacterial flora , etc . as well as any associated difficulties in developing detailed models of the intra-host pathogen environment . Such challenges are often due to our understanding of biomolecular functions involved being insufficiently detailed and/or tissue-specific processes adding further layers of complexity to the overall infection dynamics . For instance , while this work has been able to use modeling and computational analysis in order to explore certain aspects of type 1 fimbriae switch control , the latter are primarily relevant to lower urinary tract infections . In contrast , upper UTIs are predominantly promulgated by P fimbriae—a distinct UPEC adhesive factor , which is regulated by significantly different biomolecular circuitry ( see [106] , [107] for detailed modeling of the corresponding pap switch ) that leads to its own mode of thermoregulation [108] . Still , recent experimental results—from those cited earlier with respect to UPEC and host immune system , to the discovery of TRP channel family of cold and hot sensors in human genitourinary tract [109]—have provided strong evidence that temperature and its variations can have major systemic influence on healthy functions as well as various pathological developments in the urinary tract and surrounding tissues . In fact , basic intravesical cooling or warming with media of desired temperature or via chemical agonists , such as menthol/icilin or capsaicin/resiniferatoxin – respectively , has had a long history of being used to induce nerve desensitization , bladder cooling reflex , and other physiological mechanisms in therapeutic applications ranging from treating patients with detrusor overactivity , bladder pain , and urothelium irritation to diagnosing various urinary tract and neurologic disorders [109]–[111] . This not only directly indicates that patient urinary tract temperature could be practically and therapeutically manipulated in clinical applications , but—as TRP sensors appear specific to animals and fungi [112]—also suggests that thermal regulation of human physiological response processes may be actively effected in a manner that by-and-large does not directly impinge upon prokaryotic pathogens . Conversely , with better empirical understanding and computational modeling of the underlying biological circuits , the same mechanism may allow us to substantively offset the effect on the host of moderate temperature changes by applying compensatory chemical stimuli to appropriate TRP channels and modulating their ensuing activity up to desensitization . This , in turn , opens up the possibility that externally controlled temperature variations may be guided by quantitative systems analysis to specifically target and manipulate the internal dynamics of bacterial or other pathogenic processes in sutu , causing them to either become innately less virulent—for example , as has been discussed here in the context of UPEC fimbriation circuit switching—or making them more susceptible to the immune response as well as antibiotic and other treatments , thus potentially contributing to the ongoing enhancement of existing and the development of novel therapeutic applications . Taken together , these results broadly serve to further demonstrate the potential utility of computational and systems biological approaches as we are beginning to understand and control many physiological processes in disease and development at the inter-/intra-cellular network and circuit levels [113]–[118] , thus enabling greater insights and providing more effective solutions to associated clinical and public health problems . They also highlight the benefits of model abstractions and the need for process automation as tools of in silico biological systems analysis , including their ability to significantly increase the efficiency with which practical multiscale biomolecular and biomedical problems may be addressed in situ . In fact—while this may be directly noted by considering just how much longer it takes to simulate a detailed network model , or how tedious a manual implementation of all constitutive abstractions can be , or significant simplifications in functional logic the corresponding process modularization may be able to achieve—what ultimately makes the automated model abstraction approach compelling is the eventual consideration of how relatively simple the E . coli type 1 fimbriation switch circuit and its temperature controls appear to be as compared to the complexity of many other biological and biomedical processes we may be expected to face in the context of systems and computational biology now or in the near future .
Unfortunately , solving the CME exactly for most biologically , physiologically , or clinically meaningful systems is typically not feasible either analytically or numerically due to the intrinsic complexity of its differential-difference form . To address this problem , a number of alternative methods—focusing on approximate analytical solutions , general computational techniques , and a range of specific applications—have been developed [62] , [121]–[126] . In practice , many of these methods either derive from or have their genesis in the Gillespie's Algorithm ( SSA ) , which enables one to gain insight into possible temporal behaviors of the system by specifying how its sample paths can be exactly drawn from the CME-described probability distribution [62] , [127] , [128] . Our numerical simulations approach is based on the SSA and , specifically , is implemented as a streamlined version of Gillespie's Direct Method [127] . This is a kinetic Monte Carlo simulation procedure , which—given the system in state at time —determines per iteration: ( i ) the waiting time to the next reaction , , based on an exponential random variable with mean ; and ( ii ) the index of the next reaction , , based on an integer random variable with probability . ( While the Next Reaction Method [129] is often considered to be the most efficient implementation of the SSA , recent study has discussed how the optimized version of the Direct Method generally performs better for many practical biochemical systems—largely owing to the high computational cost of maintaining extra data structures [130] . ) Our implementation is similar to other optimized versions of the Direct Method in the sense that it only evaluates propensity functions as necessary to minimize updates . The main difference is that our implementation does not create a dependency graph , but rather utilizes the bipartite graph structure of the reaction-based model to determine which propensity functions must be evaluated ( see FimB and FimE Regulation Subnetwork section below for additional detail ) . Using this implementation of the SSA in reb2sac , each simulation starts with the switch in the ON position and is run for up to one cell generation of 20 minutes as in [90] . If the switch moves to the OFF position within this time limit , the simulation is then counted as an ON-to-OFF switching event . The ON-to-OFF switching probability is calculated as the number of ON-to-OFF switching events divided by the total number of simulations with the same initial conditions . Alternatively , this could be viewed as computing the total ON-to-OFF switching probability by summing up switching events involved in all possible transition states , while the -mediated events only include transitions carried out due to the binding of —i . e . , those going through switch states S4 , S7 , and S8—see Figure 4 . Our detailed switch inversion model represents a molecular reaction-scale description of the fim circuit ( Figure 2 ) , which generally satisfies the Markovian requirement of the SSA . ( The discussion of how the individual reactions have been parameterized as well as generally identified from literature can be found below and in Text S1 . ) Such representations typically constitute the lowest-level ( highest-resolution ) description of biological systems used in most practical applications , which is one of the reasons why this model is correspondingly referred to as “detailed” . The reaction network graph examination carried out as part of the motif recognition , data flow , system organization , and abstraction analysis has led us to identify two major modules responsible for dynamically controlling the fimS inversion process as well as integrating external signals provided by global regulator proteins and environmental factors , such as temperature , thus entailing a number of significant analytical and computational simplifications . These subnetworks may be broadly labeled as: ( i ) the production-degradation processes of and ; and ( ii ) the processes regulating the configuration of the fim switch itself . While SSA offers a powerful method for numerically analyzing the behavior of discrete-stochastic biomolecular interaction networks , relying on just one or several simulation runs in order to gain a general understanding of a biological system subject to stochastic decision-making , such as UPEC fimbriation ON/OFF switching , could often be misleading because—similarly to the use of CCK—randomly-simulated individual sample trajectories of the underlying stochastic process are frequently insufficient to characterize its overall probabilistic dynamics [54] . In such settings , it typically requires thousands or more simulations in order to estimate the behavior of a system with reasonable statistical confidence . Yet , because SSA needs every single reaction event to be simulated one-at-a-time , it commonly leads to very high numbers of reaction events per given time step , particularly when the system has large characteristic time-scale separations . This makes computational requirements of exact numerical discrete-stochastic analysis exceedingly demanding for most practical biological and biomedical applications . In addition , the underlying complexity of biological chemical reaction and physical interaction networks as well as their innately differential response to varied environmental conditions generally impede qualitative interpretation of biological system organization and behavior . That is , though detailed reaction-level representations of biomolecular networks allow for very comprehensive descriptions of biological mechanisms , such low-level models can lead to substantial computational costs as well as may , potentially , obscure the overall system structure and dynamics . The problem could be further exacerbated by the particular choices of initial and environmental conditions that biological systems are embedded in . For example , while this paper discussed the behavior of the fim circuit in E . coli growing on minimal liquid medium , the in situ observed switching characteristics may be altered on rich liquid or solid medium [30] . Note that these adjustments in environmental conditions should not be expected to affect the underlying molecular reaction network structure of individual bacteria ( since such variations do not determine the presence or absence of constituent elementary biomolecular interactions—only their rates ) , but rather lead to changes in observations due to effects ranging from heterogeneity in population dynamics among cell colonies on solid medium to input-driven modulations of various process rates comprising the circuit when switching to rich medium . Accurate analysis of the system in the former case requires application of dedicated population modeling schemes that themselves can lead to non-trivial empirical effects [35] , [36] , [133] , thus creating further modeling complexity outside of the present scope . Similarly , in the latter case , changes in empirical settings—such as growing bacteria in a rich medium—tend to produce selective increases of some cellular process rates ( e . g . , those involved in metabolism/degradation or cell-division ) that nevertheless leave many others unchanged . This introduces further time-scale separations into the problem , thus potentially making exact numerical analysis of discrete-stochastic circuit dynamics accessible in a minimal medium , but infeasible in a rich one [63] , [64] . One approach toward addressing such challenges is the ongoing development of advanced analytical and numerical approximation methods—whether with respect to time ( e . g . , tau-leaping [60] , [134] ) , state space ( e . g . , finite state projection [135] , [136] ) , or other system variable—that are capable of significantly accelerating the analysis of master equation-type models to within a specified level of precision . This potentially makes feasible accurate computational analysis of molecular dynamics behind physiologically-meaningful biological networks that are otherwise too demanding for exact kinetic simulations ( as , for example , is the case with bacterial systems grown in rich media or other such initial/external conditions ) . Thus , derivation and use of quantitatively analogous , but qualitatively and computationally simpler higher-level abstracted representations—which could be efficiently accomplished through systematic and , given the complexity of most biological processes , automatic application of various model approximations and simplifications—becomes essential [60] , [62] , [63] , [134] , [135] , [137]–[142] . In practice , this could be done by utilizing a variety of techniques . For example , rapid-equilibrium and/or quasi-steady-state approximations [143]–[145] are often used to eliminate the various intermediates without significantly compromising our quantitative understanding of the overall system logic and functionality . Other methods may include: irrelevant node elimination , which removes species and reactions irrelevant with respect to the species of interest by statically analyzing the structure of the model; modifier constant propagation , which replaces a species-state variable in kinetic laws with the corresponding initial value and removes that species if that variable is statically known to be fixed; stoichiometry amplification , which amplifies stoichiometries and reduces the values of propensity functions—making the system and time advancement per reaction larger; and a number of additional approaches—many of which have been implemented in our reb2sac tool ( see Table 1 ) [63] , [64] , [138] . The key principle behind most of these techniques could be summarized as identifying and abstracting away various redundant or largely irrelevant variables , whose dynamics do not independently influence the behavior of the system under a particular set of conditions—or , equivalently , finding a reduced set of parameters containing sufficient information to indentify system states and transitions between them . Since in the probabilistic context all information about a system is contained within its PDF , this could be viewed as finding a minimal subset of variables or their combinations that span the range of most likely/relevant states and elucidating abstracted laws governing their dynamics from those of the detailed description . ( Various methods are available for quantifying the amount of probability distribution thus captured . For instance , information entropy and mutual information could be utilized for identifying the effective complexity of processes involved as well as further used to solve the inverse problem of elucidating system structure based on observations of state occupancies , such as inferring biomolecular network organization from individual species numbers [113] , [146]–[149] . ) Alternatively , having identified the region of state space where most of the system's probability is localized , one may seek to restrict the problem to this lower-dimensional subspace , so as to obtain the corresponding reductions in problem complexity or otherwise coarse-grain its resolution when away from most relevant states and timescales . These approaches can be particularly fruitful when applied to biological molecular systems , whose probability distributions can be described by the CME . The latter offers a well-defined analytical structure for rigorously developing such approximations—which has led to several novel methods being proposed and applied in recent years [136] , [137] , [150]–[154] . ( For example , it has been shown that master equations for switching systems can often be projected to much smaller dimensions with little loss in their accuracy [155] . ) Notably , since these methods are generally based on deep theoretical understanding of the underlying molecular chemical kinetics and reaction network graph analysis , the resulting abstracted models—such as those generated by reb2sac—on balance could be commensurably expected to accurately capture the overall biological system behaviors as well as to provide rigorous quantification of any potential divergences between the abstracted and detailed descriptions . Although many approximation and abstraction approaches have been in wide use individually , their traditionally manual implementation grows to be increasingly more tedious and demanding as multiple methods are collectively applied to progressively larger biological systems . This problem is becoming even more acute as advances in systems biology continue to drive rapid increases in the typical size of analyzed networks , eventually rendering them intractable to interaction-level investigation and potentially leading to significant errors in large model transformations required to generate accurate intermediate-level abstractions . Our approach alleviates these problems by using a set of novel and existing algorithms—implemented in the reb2sac abstraction and analysis tool—to automatically survey and test biological networks for patterns and characteristics amenable to various complexity reduction techniques at the given level of accuracy for some specified “target” system property of interest [63] , [64] . Among other things , this allows reb2sac to systematically scan through intermediate abstraction levels , to then automatically identify and implement appropriate approximation methods according to user preferences , and—by setting precision thresholds—to ultimately generate abstracted system models optimized for computational efficiency versus accuracy as desired . A high-level flow chart of our automated abstraction methodology is given in Figure 9 . Note that the outlined analysis framework is overall quite generic and so could be used not only to generate model abstractions of gene regulatory networks , but also of other biochemical/biophysical reaction systems—including signal transduction pathways , metabolic networks , and other epigenetic processes . Specifically , as shown in Figure 9 , our abstraction engine takes as input a detailed reaction-based model and a set of abstraction properties . The latter help determine which of and how individual abstraction methods should be applied to the input model . These properties can also specify parameters for the conditions used by individual methods , enabling users to control the level of abstraction . The abstraction engine then passes this information through three internal stages: ( i ) pre-processing; ( ii ) main abstraction loop; and ( iii ) post-processing . Pre-processing is used to modify the structure of the input model so that the appropriate abstraction methods in the main loop can be applied more effectively . For example , if a model initially contains irrelevant reactions with respect to a particular species or dynamical property that the user is interested in analyzing—these reactions are removed at the pre-processing step to help speed up the abstraction process . The main loop contains abstraction methods that are applied repeatedly until the structure of the model no longer changes . In the case of gene regulatory networks , abstraction methods such as operator site reduction are typically placed in the main loop . Post-processing is used to transform the model into a form suitable for subsequent application of follow-up analysis methods—e . g . , stochastic simulation , Markov chain analysis , etc . As discussed earlier , transforming a detailed biological system model into an abstracted one can substantially increase the efficiency of its computational analysis as well as potentially improve our understanding of its overall structure and function . In this work , we have used the reb2sac automated abstraction tool to simplify the detailed model by systematically going through the fim switch network and applying various qualifying simplifications and/or approximations as appropriate . The resulting abstracted model is indeed significantly simpler computationally and more understandable logically than the detailed one . For example , the production-degradation reaction scheme of and ( Figure 5A ) is reduced by first quantitatively identifying the transcriptional regulator binding/unbinding events at the fimB and fimE promoter sites as “rapid” and the corresponding number of the operator sites ( one ) as “low”—and by then applying the rapid-equilibrium and quasi-steady-state approximations to these processes . The tool then continues to examine the dynamics of other species and finds that the concentrations of and RNA polymerase ( ) do not change over time in our model . Thus , by applying modifier constant propagation , and are replaced with constants whose values are set to the corresponding initial concentrations and species and are removed from the model . This process continues until no further reductions are possible . Taken together with the constraints imparted by the rates involved and the set target of fim switching probability , these abstractions reduce the detailed subnetwork of and shown in Figure 5A to the one shown in Figure 5B . Similar computational and logical complexity reduction is also achieved for the fim element configuration subnetwork . For instance , the reaction process corresponding to the fim switch inversion through state 6 ( see Figure 4 ) is given in Figure 6A . The corresponding abstracted reaction scheme is shown in Figure 6B . Overall , after applying all of the available and appropriate abstraction techniques listed in Table 1 , the detailed model with 52 reactions and 31 species ( e . g . , two recombinases , global regulatory proteins , and various intermediate complexes given in Figures 3 and 4 ) is transformed by reb2sac into an abstracted model with 10 reactions and 3 species ( , , and switch given in Figures 5B and 6—the latter showing only reactions involved in ON-to-OFF switching events through circuit state 6 ) . | Urinary tract infections ( UTIs ) represent a major growing threat to global public health . With over 15 million cases a year in the United States alone , UTIs are characterized by very high recurrence/reinfection rates , particularly among women and minority groups [1] . The predominant cause of UTIs is uropathogenic Escherichia coli ( UPEC ) bacteria , whose wide-spread and increasing antibiotic-resistance has made the development of alternative anti-UPEC treatments progressively more important and urgent . UPEC's foremost virulence factor is hair-like surface structures called type 1 fimbriae . Thus , one such potentially promising therapeutic approach may be to manipulate bacteria's own cellular circuitry toward inducing UPEC to turn off their fimbriae expression—rendering individual microbes benign . This task requires detailed understanding of molecular mechanisms involved , which may be significantly aided by in silico modeling . However , for UPEC fimbriation control circuit and many other systems , low-level all-inclusive quantitative models inevitably become too computationally demanding to remain practical , while high-level qualitative representations frequently prove inadequate owing to the substantial organizational and behavioral complexity of biological processes involved . We have developed an automated multiscale model abstraction methodology that helps address these problems by systematically generating intermediate-level representations that rigorously balance computational efficiency and modeling accuracy . Here , we use such an approach to examine how different temperature settings quantitatively affect UPEC transitions between fimbriate and afimbriate phases , to gain new understanding of the underlying modular circuit switch control logic , and to suggest further insights into ways this knowledge could potentially be used in therapeutic applications . | [
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| 2010 | Temperature Control of Fimbriation Circuit Switch in Uropathogenic Escherichia coli: Quantitative Analysis via Automated Model Abstraction |
Estrogen-related receptor α ( ERRα ) is a member of the nuclear receptor superfamily controlling energy homeostasis; however , its precise role in regulating antiviral innate immunity remains to be clarified . Here , we showed that ERRα deficiency conferred resistance to viral infection both in vivo and in vitro . Mechanistically , ERRα inhibited the production of type-I interferon ( IFN-I ) and the expression of multiple interferon-stimulated genes ( ISGs ) . Furthermore , we found that viral infection induced TBK1-dependent ERRα stabilization , which in turn associated with TBK1 and IRF3 to impede the formation of TBK1-IRF3 , IRF3 phosphorylation , IRF3 dimerization , and the DNA binding affinity of IRF3 . The effect of ERRα on IFN-I production was independent of its transcriptional activity and PCG-1α . Notably , ERRα chemical inhibitor XCT790 has broad antiviral potency . This work not only identifies ERRα as a critical negative regulator of antiviral signaling , but also provides a potential target for future antiviral therapy .
The innate immune system plays important roles in the detection and elimination of invading pathogens . The host senses viral and bacterial pathogen invasion via the recognition of pathogen-associated molecular patterns ( PAMPs ) by pattern recognition receptors ( PRRs ) , including membrane-bound Toll-like receptors ( TLRs ) and cytosolic sensory molecules , such as RIG-like receptors ( RLRs ) and NOD-like receptors ( NLRs ) . These then activate a series of signal cascades , leading to the production of IFN-I and proinflammatory cytokines . Upon viral infection , TLRs detect pathogen nucleic acids in the lumen of endosomes , whereas RLRs , DAI , IFI16 , LRRFIP1 and cGAS sense pathogen nucleic acids in the cytoplasm [1–5] . TLRs-mediated signaling pathways associate with the adaptor protein MyD88 and TRIF , while RLRs recruit MAVS and STING . Both pathways ultimately converge on the activation of TBK1 upon adaptor recruitment . Activated TBK1 then phosphorylates IRF3 , IRF5 and IRF7 , triggers their dimerization and nuclear translocation , and activates IFN-I expression . Secreted IFN-α/β further activates downstream signaling pathways to induce a wide range of antiviral genes and elicit cellular antiviral responses . As a critical kinase involved in antiviral immunity , TBK1 activity must be tightly regulated to maintain immune homeostasis . Various mechanisms have been reported to positively or negatively regulate IFN-I production through effects on TBK1 . Nrdp1 [6] and GSK-β [7] enhance TBK1 activity by catalyzing Lys63-linked polyubiquitination or promoting TBK1 self-activation , respectively . TAX1BP1 , A20 and NLRP4 terminate antiviral signaling by promoting TBK1 degradation or disrupting the Lys63-linked polyubiquitination of TBK1 [8 , 9] . Affecting the formation of functional TBK1-containing complexes is another major mechanism that modulates antiviral immune responses . For example , HSP90 facilitates TBK1-IRF3 complex formation through TBK1 stabilization [10] . MIP-T3 and SIKE negatively regulate IFN-β production by inhibiting the formation of TRAF3-TBK1 and TBK1-IRF3 complexes [11 , 12] . ERRα is an orphan receptor that shares high sequence identity with nuclear receptors α/β ( ERα and ERβ ) . Nevertheless , a functional analysis has indicated that the majority of genes regulated by ERRα are distinct from those controlled by ERα [13] . ERRα possesses a central zinc finger DNA binding domain ( DBD ) , a conserved C-terminal domain with a putative ligand binding domain ( LBD ) and a less conserved N-terminal region [14] . Although the natural ligand of ERRα is unknown , ERRα activates the transcription of genes that are involved in mitochondrial function and energy metabolism [15–23] . However , the roles of ERRα may not be limited to the direct transcriptional regulation of metabolism . For instance , ERRα induces orientated cellular migration by promoting the transcriptional expression of TNFAIP1 , which subsequently destabilizes RHOA [24] . Under hypoxic conditions , ERRα acts as a co-activator to enhance HIF-mediated hypoxic responses by associating with HIF1α [25] . Mice lacking ERRα produce fewer reactive oxygen species ( ROS ) in macrophages and are susceptible to Listeria monocytogenes ( LM ) infection in response to IFN-γ treatment [22] . A recent study showed that ERRα negatively regulates TLR-induced inflammation by promoting the expression of A20 [26] . Hwang and colleagues proposed that ERRα is important for providing a favorable metabolic environment that supports optimal cytomegalovirus replication [27] . In the present study , we found that the inhibition of ERRα yielded broad anti-viral activities . ERRα deficiency induced significantly higher levels of IFN-β and increased the expression of a panel of ISGs in response to viral infection . Mechanistically , viral infection stabilized ERRα expression , which in turn associated with TBK1 to impede the formation of the TBK1-IRF3 complex , IRF3 phosphorylation , IRF3 dimerization and the DNA binding affinity of IRF3 . Therefore , ERRα is a feedback inhibitor of antiviral innate immunity .
An increasing amount of evidence has demonstrated the crosstalk between the innate immune response and metabolic pathways; however , the precise molecule that links the two systems remains to be clarified . As a member of the nuclear receptor superfamily involved in metabolic signaling , the precise role of ERRα in regulating antiviral innate immunity remains to be clarified . To evaluate the importance of ERRα in viral infection , we first infected wild type ( WT ) and ERRα-KO ( ERRα-KO ) mice with vesicular stomatitis virus ( VSV ) . As shown in Fig 1A , the ERRα-KO mice were more resistant to VSV infection in the overall survival assay . VSV titers in sera , liver and lung isolated from ERRα-KO mice were also significantly reduced , compared to WT mice on day 3 post-infection ( Fig 1B ) . We next infected WT type and ERRα-KO mice with herpes simplex virus type 1 ( HSV-1 ) , a DNA virus . As shown in Fig 1C , ERRα-KO mice were more resistant to lethal HSV-1 infection . To further determine the role of ERRα in viral infection , we examined the effects of ERRα deficiency on the replication of various viruses in isolated and cultured cells . Bone marrow-derived macrophages ( BMDMs ) from the ERRα-KO mice showed lower VSV production than the cells from the WT mice did ( Fig 1D and 1E and S1A Fig ) . Moreover , stable ERRα knockdown clone 2 ( shERRα-2 ) in human 293T ( S1B Fig ) resulted in decreased VSV titers ( Fig 1F ) and enhanced cell viability in response to VSV infection ( S1C Fig ) . Similarly , the expression of siRNA for ERRα ( siERRα ) in A549 cells also resulted in lower viral titers in the supernatant than transfection with control siRNA ( siCtrl; Fig 1G ) . Conversely , 293T cells with overexpressed ERRα showed significantly increased VSV titers in the supernatant ( S1D Fig ) . We next infected 293T cells with GFP-tagged Newcastle disease virus ( NDV-GFP ) and HSV . Based on quantification by fluorescence microscopy , flow cytometry and plaque assays , both NDV-GFP replication ( Fig 1H and S1E and S1F Fig ) and HSV-1 production ( Fig 1I ) was greatly reduced in the shERRα-2 cells . These data collectively suggest that ERRα deficiency confers resistance to viral infections both in vitro and in vivo . A recent study reported that ERRα was required for the efficient production of cytomegalovirus progeny by providing a favorable metabolic environment . Here , we used microarray analysis to determine the expression of genes altered by ERRα inhibition . ShCtrl and shERRα-2 clones in the 293T cell line ( S2A Fig ) were analyzed by microarray assay 12 h after VSV infection . First , we subjected genes that exhibited 1 . 5-fold changes to FunNet analysis to determine the several top pathways regulated by ERRα upon viral infection . Based on this analysis , the top eight most significant downregulated KEGG pathways following ERRα inhibition were associated with metabolic pathways , Wnt signaling , and adherens junctions , which have been reported previously [28–30] ( Fig 2A ) . Interestingly , the NLRs , TLRs and RLRs innate immune pathways were ranked as the top upregulated signaling pathways ( Fig 2A ) . Specially , IFNB1 and several interferon-responsive genes , including IFIT1 , IFIT2 , IFIT3 , IFIH1 and LILRB2 , were induced at greater levels in infected cells in which ERRα was knocked down ( Fig 2B ) . The increased expression of IFN-β and responsive genes by ERRα knockdown was validated by quantitative real-time PCR ( qRT-PCR ) on RNA samples prepared at various time points after VSV infection ( Fig 2C–2E ) . Consistent with a previous report , RNA corresponding to genes that encode triacylglycerol metabolism and glycolytic proteins were downregulated by at least a factor of two following knockdown of ERRα , including CRAT , ACO2 , LIPE , and BDH1 ( S2B Fig ) [22 , 24 , 31 , 32] . To further investigate the role of ERRα in innate immunity , we isolated primary BMDMs from WT or ERRα-KO mice and measured the expression of IFN-β expression in response to RLR- , cGAS- and DDX41-activating stimuli . We transfected 5’-triphosphate ( 5’-ppp ) dsRNA , poly ( I:C ) , poly ( dA:dT ) , and cyclic diguanosine monophosphate ( c-di-GMP ) in the WT and ERRα-KO BMDMs . IFN-β secretion was significantly increased in the ERRα-KO BMDMs ( Fig 3A ) . BMDMs from the ERRα-KO mice also produced significantly more IFN-β in response to poly ( I:C ) , lipopolysaccharide ( LPS ) or flagellin incubation , which activate TLR3 , TLR4 or TLR5 , respectively ( Fig 3A ) . Consistent with this result , ERRα-KO BMDMs showed upregulated IFNB1 mRNA expression in response to all the agonists tested ( Fig 3B ) . VSV is a negative-strand ssRNA rhabdovirus that activates IFN-α/β through RIG-I [33] . VSV-induced IFN-β secretion and IFNB1 mRNA expression was also greatly enhanced in ERRα-KO macrophages in a time-dependent manner ( Fig 3C and 3D ) . Therefore , ERRα is involved in negative regulation of the RLRs , DDX41 and TLRs signaling pathways . In reporter assays , shERRα-2 293T cells ( Fig 3E ) and siERRα A549 cells ( S3A Fig ) dramatically potentiated VSV-induced activation of the IFN-β promoter . To further determine the role of ERRα in type I interferon induction in vivo , we infected WT and ERRα-KO mice with VSV . In keeping with our in vitro data , the induction of IFNB1 mRNA expression was greatly enhanced in the organs of ERRα-KO mice compared to WT mice infected with VSV ( Fig 3F ) . Furthermore , we detected more circulating IFN-β in the blood of ERRα-KO mice on day 3 after VSV infection ( Fig 3G ) . The lungs of ERRα-KO mice demonstrated significantly less inflammation , with reduced epithelial damage , mononuclear cell infiltrates and alveolitis ( Fig 3H ) . Thus , ERRα functions as a negative regulator of type I interferon production upon viral infection . Various activators , such as RIG-I , MAVS , TBK1 , and IKKε , have been reported to be involved in the virus-triggered IRF3 activation pathway [34] . Overexpressed ERRα inhibited IFN-β ( Fig 4A and S4A Fig ) , IRF3 ( S4B Fig ) and ISRE activation ( S4C Fig ) induced by these activators in a luciferase reporter assay . Overexpression of IRF3 in 293T cells potently activated the IFN-β and ISRE promoters , while as little as 0 . 01 μg of ERRα was sufficient to cause potent repression ( >80% ) of IFN-β ( Fig 4B and S4D Fig ) and ISRE ( S4E Fig ) . The extent of the suppression increased with increasing amounts of ERRα , suggesting that ERRα inhibited the induction of IFN-β by IRF3 in a dose-dependent manner . The phosphorylation , dimerization and nuclear translocation of IRF3 necessary for the activation of IFNB1 transcription require IKKε and TBK1 . Knockdown of ERRα expression significantly enhanced IFN-β promoter activation by TBK1 or IKKε ( Fig 4C and S4F Fig ) . Our observation that ERRα inhibited the IFN-I production by targeting TBK1 and IRF3 raised the possibility that ERRα might physically interact with these targets . To test this possibility , lysates with ectopic expression of TBK1 or IRF3 from 293T cells were incubated with GST or the GST-ERRα fusion protein . Both TBK1 and IRF3 could bind to GST-ERRα but not to GST ( Fig 4D and 4E ) , demonstrating an in vitro interaction of ERRα with TBK1 and IRF3 . To test whether ERRα bound to TBK1 and IRF3 in mammalian cells , Flag-ERRα was transfected together with HA-TBK1 , HA-IRF3 or HA-IKKε . Immunoblotting analysis of anti-Flag immunoprecipitate with an anti-HA antibody showed a significant association between Flag-ERRα and HA-TBK1 , HA-IKKε and HA-IRF3 ( Fig 4F–4H ) . A far-western assay also revealed a direct interaction between ERRα and IRF3 ( Fig 4I ) . Importantly , we visualized endogenous ERRα-TBK1 complex formation using an in situ proximity ligation assay ( PLA ) . We observed few spots representing the ERRα-TBK1 complex in uninfected 293T cells , while the spots increased significantly at 9 hpi and began to reduce at 12 hpi ( Fig 4J and 4K ) . A domain mapping experiment indicated that the N-terminal domain of TBK1 [35] ( amino acids 1–510 ) was required for its interaction with the AF2 domain of ERRα ( Fig 4L and 4M ) . These data suggest that the interaction between TBK1 , IRF3 and ERRα is responsible for the ERRα-mediated inhibition of antiviral signaling . To further investigate the inhibitory mechanism underlying the role of ERRα in antiviral immune signaling , we explored the effect of ERRα on the TBK1-IRF3 interaction and IRF3 dimerization triggered by viral infection . Notably , the introduction of overexpressed ERRα destroyed the binding between TBK1 , IKKε and IRF3 ( Fig 5A ) . XCT790 is a specific inhibitor of ERRα , which reduces ERRα expression [36] ( S5A Fig ) . By using in situ PLA , we found that the number of spots representing the IRF3-TBK1 complex induced by stimulation with VSV was greatly increased in 293T cells treated with XCT790 ( Fig 5B and S5B Fig ) . In addition , we generated 293T cells lacking ERRα using the CRISPR/Cas9 system ( ERRαCRISPR-/- ) . As shown in Fig 5C , the TBK1-ERRα and TBK1-IRF3 interaction began to increase at 3 hpi . In the absence of ERRα , a significantly increased TBK1-IRF3 interaction was observed upon VSV infection ( Fig 5C and S5C Fig ) . Type I interferon gene transcription is mediated primarily through transcription factor IRF3 , which is localized inside the cytoplasm of resting cells . Upon stimulation , IRF3 is activated by serine/threonine phosphorylation , which leads to dimerization , nuclear translocation and binding to recognition sequences in the promoters and enhancers of type I interferon genes . We next attempted to dissect the effect of ERRα on the activity of IRF3 . Notably , the signals for VSV-induced IRF3 phosphorylation were significantly higher in BMDMs isolated from the ERRα-KO mice ( Fig 5D ) . Because IRF3 phosphorylation promotes its dimerization , we measured the dimerization of IRF3 using native PAGE gels . Flag-V or Flag-ERRα plasmids were transfected into ERRαCRISPR-/- cells . As expected , IRF3 dimerization and IRF3 phosphorylation in response to VSV infection occurred at much higher levels in the ERRαCRISPR-/- cells than in the WT cells or in the ERRαCRISPR-/- cells rescued with ERRα ( Fig 5E ) . Moreover , IRF3 dimerization was disrupted with the addition of ERRα ( Fig 5F ) . Because the IRF3 dimer binds more strongly to DNA than does the IRF3 monomer , the influence of ERRα on IRF3 binding to the IFN-β promoter was measured using a ChIP assay . As shown in Fig 5G , the increased binding to the IFN-β promoter region by VSV infection was significantly blocked by overexpressed ERRα . We then wanted to investigate whether the direct binding of ERRα to the N-terminal kinase domain of TBK1 has any functional relevance in ERRα-mediated type-I IFN inhibition . Consistent with the binding result , the AF2 domain of ERRα was required for the inhibition of VSV and TBK1-induced activation of the IFN-β promoter ( S5D Fig and Fig 5E ) . TBK1-induced ISRE activation was also inhibited by the AF2 domain ( S5F Fig ) . To further validate the role of the AF2 domain in regulating the production of IFN-β , we transfected HA-TBK1 along with Flag-vector , Flag-ERRα or Flag-ERRα deletion mutants into ERRαCRISPR-/- cells . TBK1-induced IFN-β activation was inhibited by WT ERRα , AF1 or DBD deletion mutant , but not by the AF2 deletion mutant ( Fig 5H and S5G Fig ) . Consistently , viral growth in ERRαCRISPR-/- cells transfected WT ERRα , AF1 or DBD deletion mutant was greater than that in ERRαCRISPR-/- cells transfected with the AF2 deletion mutant ( Fig 5I and S5H Fig ) . Based on these experiments , we concluded that the binding of ERRα inhibits antiviral signaling through direct physical binding with TBK1 . Our results indicated that ERRα prevented the formation of functional TBK1-IRF3 complex and inhibited the binding affinity of IRF3 to impede IFN-α/β activation . However , whether the transcriptional activity of ERRα is required for the inhibition of this process is unknown . Similar to other nuclear receptors , the DBD domain of ERRα consist of two zinc-finger motifs: the first zinc-finger is responsible for the recognition of specific DNA binding sites , and the second zinc-finger mediates homo-dimerization of the nuclear receptors . Because cysteine residues in the zinc-finger motifs are critical for zinc ion binding , an ERRα CA mutant was constructed by changing the cysteines at positions 79 , 96 , 115 , and 121 to alanines in order to abolish its transcriptional activity . We found that the ERRα CA mutant lost its ability to activate the ERRα promoters ( Fig 6A ) ; however , this mutant retained its ability to inhibit the activation of IFN-β and ISRE to levels as potent as the WT ( Fig 6B and 6C ) . TBK1-induced IFN-β activation was equally inhibited by the wild type ERRα and CA mutant ( Fig 6D ) . We then transfected Flag-ERRα or the Flag-ERRα CA mutant into ERRαCRISPR-/- cells . Viral growth in ERRαCRISPR-/- cells transfected with WT and CA mutant ERRα were greater than that in ERRαCRISPR-/- control cells ( Fig 6E ) . PGC-1α usually acts as a transcriptional cofactor for ERRα in the regulation of metabolic signaling . A previous report revealed that substitution of the ERRα H8–H9 loop ( amino acids 338–341 , ERRα H8/9 ) with ERα amino acids 457–468 abolished its interaction with PGC-1α [37] . The ERRα point mutations D338A and Q262E also significantly reduced its binding to PGC-1α [37] . Coimmunoprecipitation and reporter assays indicated that all three mutants could still interact with TBK1 ( Fig 6F ) and inhibited TBK1-induced IFN-I activation ( Fig 6D ) to a similar extent as WT ERRα . ERRα negatively regulates TLR4 induced inflammation partially mediated by transcriptional activation of A20 [26] . To assess the role of A20 in ERRα-mediated antiviral signaling , we transfected Flag-ERRα into A20 knockout 293T cells generated by the CRISPR-Cas9 system ( A20CRISPR-/- ) . We observed that the suppression abilities of ERRα on IFN-β activation and IRF3 phosphorylation in response to VSV infection were unchanged by A20 deletion ( Fig 6G and 6H ) . Taken together , these data suggest that the negative effect of ERRα on innate immune signaling is independent of its transcriptional activity and its cofactor PCG-1α . Because ERRα inhibited the production of interferons and regulated antiviral immunity , we examined whether ERRα was induced after viral infection . Protein levels of ERRα in 293T , BMDMs , THP-1 , mouse embryonic fibroblasts ( MEFs ) and HeLa cells were increased significantly and rapidly following VSV infection ( Fig 7A–7E ) . Moreover , ERRα expression was induced in THP-1 macrophages infected with HSV-1 ( Fig 7F ) or treated with LPS ( Fig 7G ) . ERRα protein levels were also induced at 12 and 24 hpi in the lung , liver , and spleen of mice infected by tail vein injection of VSV , with the highest induction observed in the spleen ( Fig 7H ) . Cytosolic redistribution of ERRα has been reported in response to HCMV infection [27] . Consistent with this result , nuclear-cytoplasmic fractionation of VSV-infected cells at different time points showed that ERRα was upregulated exclusively in the cytoplasm at 3 hpi , and this upregulation lasted until 24 hpi ( Fig 7I ) . VSV infection also caused a mild upregulation of nuclear ERRα . Interestingly , nuclear ERRα migrated slower than the cytoplasmic form . The expression of ERRα mRNA did not change by VSV infection ( Fig 7J ) . These results indicate that ERRα is stabilized by a post-transcriptional mechanism following viral infection . Although the natural ligand of ERRα is unknown , ERRα can be activated by several cytokines and by PGC-1α . To determine the contribution of PGC-1α to viral-induced ERRα activation , we evaluated the contribution of PGC-1α to ERRα activation in response to viral infection . As shown in Fig 8A , PGC-1α knockdown cells also exhibited an induction of ERRα expression after VSV infection , indicating that other factors are involved in viral-induced ERRα stabilization . The association between ERRα and TBK1 prompted us to analyze the effect of TBK1 on ERRα expression in response to viral infection . When expression plasmids encoding TBK1 or IKKε were transfected into 293T cells with Flag-ERRα , a significant enhancement in the cellular abundance of ERRα was found ( Fig 8B ) . Notably , the VSV-mediated expression of ERRα was completely inhibited by the TBK1 inhibitor BX795 ( Fig 8C ) . In addition , we found that the induction of ERRα triggered by VSV infection was severely impaired in TBK1 defective cells ( TBK1CRISPR-/-; Fig 8D ) . The half-life of ERRα was greatly reduced in the presence of BX795 ( Fig 8E ) . QRT-PCR analysis showed no significant difference in the transcriptional level of ERRα following TBK1 overexpression ( Fig 8F ) . We next wanted to explore the role of TBK1 kinase activity on ERRα stabilization . Overexpression of WT TBK1 , but not the TBK1 K38A kinase dead mutant ( in which the ATP binding residue Lys38 was mutated to alanine ) , caused increased stabilization of ERRα ( Fig 8G ) . ULD-mutated TBK1 ( TBK1 L352A , I353A ) which failed to activate the NF-κB , IFN-β and IRF3 promoter as shown previously [38] , retained its ability to induce ERRα expression ( Fig 8G ) . Therefore , TBK1 phosphorylation rather than the TBK1-mediated antiviral response is required for viral-mediated stabilization of ERRα . Consistent with the essential role of MAVS in TBK1 phosphorylation and activation , MAVS-KO MEFs failed to stabilize ERRα in response to VSV infection ( Fig 8H ) . TBK1 overexpression also increased the expression of the ERRα target gene ERRE [39] , as shown by the luciferase reporter assay ( Fig 8I ) . These results indicated that TBK1 is required for viral induced ERRα stabilization . To further delineate the mechanism of TBK1-mediated ERRα stabilization , Myc-ubiquitin was cotransfected with plasmids expressing Flag- ERRα together with HA-TBK1 or HA-TBK1 KD in the presence of proteasome inhibitors MG132 , ERRα was then immunoprecipitated by anti-Flag antibody and blotted with anti-Ubi-K48 antibody . Immunoprecipitation assay showed that overexpression of WT TBK1 , but not TBK1 KD mutant , led to a sharp reduction on the K48 ubiquitination level of ERRα ( Fig 8J ) , suggesting that TBK1 phosphorylation modification might contribute to the stabilization of ERRα by inhibiting its K48-linked polyubiquitylation . Next , we investigated the antiviral activity of XCT790 , a synthetic antagonist of ERRα . First , the effect of XCT790 on IFN-I induction was explored . As shown in Fig 9A , the mRNA expression levels of VSV-induced IFN-β and the IFN-regulated gene products IFIT1 and IP-10 were significantly upregulated in XCT790-treated cells . Accordingly , treatment of 293T cells with XCT790 inhibited VSV production ( Fig 9B ) and VSV-G expression ( with IFN-β and 25-HC as the positive control ) ( S6A Fig ) . XCT790 inhibited VSV-G protein expression in a dose-dependent manner ( Fig 9C ) . A similar antiviral effect was observed in several human cell lines , including HeLa , A549 , and primary isolated BMDMs ( S6B Fig ) . A cytoprotective effect of XCT790 in 293T , HeLa , and A549 cells upon VSV infection was also observed ( S6C Fig ) . To determine the breadth of the antiviral activity of XCT790 , we tested the effect of XCT790 on various viruses . By quantifying NDV-GFP using flow cytometry and fluorescence microscopy analysis , we found that XCT790 inhibited NDV-GFP replication in 293T cells by over 90% ( Fig 9D and S6D Fig ) . Treatment of A549 cells ( S6E Fig ) with XCT790 inhibited NDV-GFP expression by approximately 50% . XCT790 also inhibited HSV-1 production in 293T cells ( Fig 9E ) . Treatment with the indicated dose of XCT790 reduced HBV surface antigen ( HBsAg ) and e antigen ( HBeAg ) expression by 50% ( Fig 9F ) . To further evaluate the antiviral role of XCT790 in vivo , we intravenously administered VSV in mice treated with XCT790 or DMSO . As expected , the XCT790-treated mice had a much lower virus load in the serum , liver and lung ( Fig 9G ) . Taken together , our results demonstrate that ERRα chemical inhibitor XCT790 exhibits antiviral activity against several types of viruses .
Innate immunity and metabolism appear to be inextricably linked and are now known to regulate each other reciprocally [16 , 40–42] . Exciting new evidence is emerging with regard to the role of TLRs and NLRs in the regulation of metabolism and the activation of inflammatory pathways during the progression of metabolic disorders , such as type 2 diabetes [43] and Reye's syndrome [44] . Studies have also suggested that metabolites , such as 25-HC [45–47] , NAD [48] ( acting via deacetylases such as SIRT1 and SIRT2 ) and succinate [49] ( which regulates hypoxia-inducible factor 1 ) , regulate innate immunity . Additionally , extracellular overproduction of metabolites , such as uric acid and cholesterol crystals , is sensed by NLRP3 , leading to activation of the inflammasome complex and the production of IL-1β [50 , 51] . In turn , some nuclear receptors reported to regulate metabolism , such as the glucocorticoid receptor ( GR ) [52] , peroxisome proliferator-activated receptor γ ( PPAR-γ ) [53] and retinoid X receptor α ( RXRα ) [54] , have been implicated in type I interferon regulation . The interplay between immunity and metabolic changes is a growing field of research . This study investigated an unappreciated relationship between the host IFN-I response and ERRα , a member of the nuclear receptor superfamily involved in the transcriptional control of energy homeostasis . Several lines of evidence support this argument: ( 1 ) Viral infection led to increased ERRα expression both in vivo and in vitro . Further study showed that TBK1 was indispensable for viral-induced ERRα stabilization . ( 2 ) Overexpression of ERRα resulted in potent inhibition of virus-triggered IRF3 activation and IFN-β induction , while inhibition of ERRα by knockdown , chemical antagonist and knockout enhanced IFN-β production and increased resistance to VSV , NDV , HSV and HBV infections . ( 3 ) Mechanistically , ERRα disrupted the TBK1-IRF3 interaction and the homo-dimerization of IRF3 by interacting with TBK1 , IKKε and IRF3 , which are critical for virus-induced IRF3 activation and IFN-β induction . ( 4 ) The effect of ERRα on IFN-I production was independent of its transcriptional activity and PCG-1α . Therefore , our findings indicate that ERRα serves as a negative regulator downstream of TBK1 that attenuates the persistence of the antiviral state independently of its role in metabolic signaling . TBK1 is a key Ser-Thr kinase involved in innate immunity that is activated by adaptors , such as STING , TRIF and MAVS [55] . Activation of TBK1 leads to adaptor phosphorylation , IRF3 activation and expression of IRF3-dependent genes that are important for the response to viral infection; thus , their activities are tightly regulated . In addition to ERRα , MIP-T3 and SIKE have been identified as two other physiological suppressors that negatively regulate IFN-β production by inhibiting the formation of functional TBK1 complexes [11 , 12] . SIKE and ERRα disrupted the TBK1-IRF3 association by targeting TBK1 , while MIP-T3 disrupted the TRAF3-TBK1 association through its direct interaction with TRAF3 . Although both SIKE and ERRα are associated with TBK1 under physiological conditions , only the ERRα protein level was significantly increased in response to VSV infection . Hence , our work indicated that TBK1 activation not only activated IRF3 but also activated ERRα to affect both IFN-I induction and metabolic signaling , indicating the unique status of ERRα among the cellular inhibitors of innate immunity . Hwang reported that ERRα provides a metabolic environment to promote the production of cytomegalovirus [27] . In our study , we also showed that ERRα upregulation can be detected as early as 3 hpi . We speculated that the upregulation of ERRα at the early stage of viral infection may present a general strategy by which the host produces the energy to counteract the stress; however , the pathogen hijacks the host cell metabolic environment . Thus , pharmacological targeting of ERRα to uncouple pathogens from their nutritional dependencies and the host negative innate immune response may prove to be an effective strategy for controlling pathogen spread . ERRα functions downstream of PGC-1α and PGC-1β and controls the expression of genes involved in metabolism . The upregulation of ERRα and the unchanged expression of PGC-1α in response to viral infection imply that additional factors may be involved in the regulation of viral-induced ERRα upregulation . Here , we showed that ERRα was specifically stabilized in response to the virus infection downstream of TBK1 . Sequence profiles of ERRα across mammalian species revealed several putative consensus TBK1 phosphorylation motifs . We speculated that ERRα might be phosphorylated and activated by TBK1 . In support of this hypothesis , TBK1 kinase activity was required for ERRα activation . Recent reports have shown that MAVS phosphorylated by TBK1 relays its downstream signal to IRF3 for its phosphorylation and activation by TBK1 . Interestingly , MAVS knockout cells displayed both defective basal and activated ERRα in response to VSV infection . However , the phosphorylation of ERRα by TBK1 and the roles of adaptors in TBK1-mediated ERRα activation require further investigation . In a recent work , TBK1 was shown to be highly expressed in lung , breast and colon cancers , and subjects with tumors that highly express TBK1 have poor responsiveness to tamoxifen treatment and a high potential for relapse [35] . ERRα expression was markedly increased in neoplastic versus normal tissues , and ERRα-positive tumors were associated with more invasive disease and a higher risk of recurrence [56] . We established that ERRα associates directly with TBK1; thus , ERRα might affect cancer progression as a substrate of the TBK1 kinase in addition to cooperating with TBK1 in the regulation of innate immune signaling . Indeed , breast cancer patients with hypo-phosphorylated ERRα are more likely to respond to hormonal-blockade therapy and have a longer overall survival time than those with hyper-phosphorylated ERRα , which may be a direct consequence of TBK1-mediated ERRα phosphorylation [57] . Our studies also suggest the possibility that viral infection induced ERRα activation may be a tumor-promoting factor , especially in persistent infection , but further investigation is required . These findings influence our understanding of the complex relationship between innate immune effectors , metabolic regulators and the signaling events that drive tumor formation . Here , we provided direct evidence indicating the critical role of ERRα in virus replication by modulating IFN-I induction independent of its transcriptional activity . In line with this finding , the inhibition of ERRα effectively reduced the yield of VSV , NDV , HSV and HBV and showed a promising cytoprotective effect in response to viral infection in multiple cell lines . As ERRα is a potential target for the treatment of breast cancer and metabolic disorders , several selective ligands against ERRα are being developed . Our studies thus suggest the potential new application of ERRα antagonists in the treatment of viral infection .
ERRα-KO mice on a C57BL/6J background were purchased from the Jackson Laboratory and maintained in specific pathogen–free conditions . All animals were handled in strict accordance with the Guide for the Care and Use of Laboratory Animals and the principles for the utilization and care of vertebrate animals , and all animal work was approved by the Institutional Animal Care Committee of Beijing Institute of Biotechnology . Animal experiments were performed in accordance with the regulations in the Guide for the Care and Use of Laboratory Animals published by the Ministry of Science and echnology of the People’s Republic of China . The protocol was approved by the ethics committee of Beijing Institute of Biotechnology ( Permit Number: 2008–09 ) . Mammalian expression plasmids pCMV-Flag-ERRα and HA-STING were provided by Dr . Toren Finkel [58] and Hongbin Shu [59] . Expression plasmids for pEBB-HA-TBK1 and pEBB-HA-IKKε were gifts from Dr . Genhong Cheng [60] . Gal4-Luc and Gal4-IRF3 were obtained from Zhijian J . Chen [34] . The ERRE luciferase plasmid was a gift from Timothy F . Osborne . IRF3 and RIG-I cDNA were amplified from a human spleen library and subsequently cloned into CMV promoter-based vectors . Other tagged cDNA containing plasmids and mutants were constructed by PCR amplification based on these plasmids . IFN-β-Luciferase and ISRE-Luciferase reporter plasmids were purchased from Beyotime Corp . Other mammalian expression vectors encoding Flag- , Myc- , or HA-fusion proteins tagged at the amino terminus were constructed by inserting PCR-amplified fragments into pcDNA3 ( Invitrogen ) or pCMV ( Clontech ) . Plasmids encoding GST fusion proteins were generated by cloning PCR-amplified sequences into pGEX4T-1 ( Amersham Pharmacia Biotech ) . HuSH 29mer shRNA constructs against ERRα kit was purchased from OriGene Company . The sequence of effective shRNA that targeted ERRα is GCAAAGCCTTCTTCAAGAGGACCATCCAG . A non-effective 29-mer scrambled shRNA cassette in the same vector from the kit was used as a negative control . All plasmids were verified by restriction enzyme analysis and DNA sequencing . Total RNA from the cells with or without virus infection was quantified by the NanoDrop ND-2000 spectrophotometer ( Thermo Scientific ) , and the RNA integrity was assessed using the Agilent Bioanalyzer 2100 ( Agilent Technologies ) . The sample labeling , microarray hybridization and washing were performed based on the manufacturer’s standard protocols . Briefly , total RNA were transcribed to double stranded cDNA , then synthesized into cRNA and labeled with Cyanine-3-CTP . The labeled cRNAs were hybridized onto the Agilent Human Gene Expression ( 8*60K , Design ID: 039494 ) microarray . After washing , the arrays were scanned by the Agilent Scanner G2505C ( Agilent Technologies ) . Feature Extraction software ( version 10 . 7 . 1 . 1 , Agilent Technologies ) was used to analyze array images to obtain raw data . Genespring was employed to complete the basic analysis of the raw data . First , the raw data were normalized with the quantile algorithm . Then , GO analysis and KEGG analysis were applied to determine the roles of these differentially expressed mRNAs . BMDMs were isolated from WT and ERRα-KO C57BL/6 mice by culturing for 6 days in RPMI 1640 medium containing 10 ng/ml M-CSF ( PeproTech ) . Twenty-four hours prior to infection , 1 x 106 cells were seeded into 12-well plates with RPMI 1640 containing 10 ng/ml M-CSF and 10% fetal bovine serum ( FBS , HyClone ) . Human cell lines 293T , HeLa , A549 , mouse embryonic fibroblasts ( MEFs ) and HepG2 . 2 . 15 were routinely cultured in DMEM ( Invitrogen ) containing 10% FBS ( HyClone ) . 293T , HeLa and A549 cell lines were obtained from ATCC . MEFs and HepG2 . 2 . 15 cells were gifted from Dr . Cheng Cao . Cells were maintained as monolayers in a humidified atmosphere containing 5% CO2 at 37°C . Lipofectamine 2000 reagent was used for transfection following the manufacturer’s protocol ( Invitrogen ) . Stable cell lines were selected in 1 μg/ml puromycin for approximately 2 weeks . Individual clones were screened by standard immunoblotting protocols and produced similar results . The luciferase reporter assay was performed as described previously [61] . VSV and NDV were kindly provided by Dr . Cheng Cao . HSV-1 was donated by Dr . Wei Chen . Cells were infected with the virus at the indicted MOI for 1 h , and then the media was replaced with fresh media . For HSV-1 and VSV , supernatants were collected , and titers were measured by plaque assays using BHK21 cells [54 , 62] . Cell extracts were prepared , immunoprecipitated and analyzed as previously described [63] . An aliquot of the total lysate ( 5% , v/v ) was included as a control for the interaction assay . Immunoprecipitation was performed with an anti-Flag M2 Affinity Gel ( Sigma-Aldrich , A2220 ) and anti-ERRα ( Epitomics , 2131–1 ) . Western blotting was performed by HRP-labeled anti-Myc ( Sigma-Aldrich , A5598 ) , anti-HA ( Sigma-Aldrich , H9658 ) , anti-TBK1 ( Epitomics , 3296–1 ) , anti-ERRα ( Epitomics , 2131–1 ) , anti-IRF3 ( pSer386 ) ( Epitomics , 2346 ) , anti-IRF3 ( pSer396 ) ( Cell Signaling Technology , 4947s ) , anti-A20 ( ABclonal , A2127 ) , anti-IKKε ( ABclonal , A0244 ) or anti-α-Tubulin ( Sigma-Aldrich , T6074 ) antibodies . The antigen-antibody complexes were visualized by chemiluminescence . In a Far-Western assay , immunoprecipitates were separated by SDS-PAGE and then blotted onto nitrocellulose membranes . The membranes were subsequently incubated with purified GST-fusion proteins for 1 h at room temperature . The GST fusion proteins binding to nitrocellulose were probed with an anti-GST antibody . qRT-PCR was performed in the iQ5 Real-time PCR System ( Bio-Rad ) using iTaq universal SYBR Green supermix ( Bio-Rad ) . Each sample was analyzed in triplicate with GAPDH as the internal control . S1 Table lists the primer sequences used for different genes in this study . Cells were lysed in NP-40 lysis buffer as previously described [65] and mixed with native loading buffer ( 250 mM Tris-HCl ( pH 7 . 5 ) , 50% glycerol and 0 . 007% xylene cyanol ) . The 8% native gel was pre-run with 25 mM Tris and 192 mM glycine with 1% deoxycholate ( DOC ) in the inner chamber for 30 min at 40 mA . Then , the samples were resolved for 60 min at 40 mA at 4°C [64] . The proteins from the native gel were transferred to PVDF membranes for immunoblotting analysis , as described above . Fixed and permeabilized cells were incubated overnight at 4°C with the following pairs of primary antibodies: anti-ERRα ( Epitomics , 2131–1 ) , mouse mAb to IRF3 ( BioLegend , 655701 ) or mouse mAb to TBK1 ( Santa Cruz , sc-398366 ) . The cells were washed and allowed to react with a pair of proximity probes ( Olink Bioscience ) . The remainder of the in situ PLA protocol was performed according to the manufacturer’s instructions . The cells were examined by fluorescence microscopy ( UlthaView VOX , PerkinElmer ) , and the Duolink Image Tool ( Olink Bioscience ) was used for quantitative analysis . 293T cells cultured in 24-well plates were transfected using Lipofectamine 2000 with 0 . 1 μg of reporter , 0 . 002 μg of the pRL control vector , and various amounts of the indicated constructs . After incubation for 24 h , the cells were harvested , and luciferase activity was analyzed using the Dual Luciferase Reporter Assay System ( Promega ) . Total light production was measured with a TD-20/20 Single-Tube Luminometer ( Turner BioSystems ) . All experiments were repeated at least three times . Human and mouse IFN-β were quantified with IFN-β ELISA kits from Antigenix ( 4756 ) and Biolegend ( 3861 ) , respectively . The lungs from control or virus-infected mice were washed with PBS , and then fixed in 4% PBS-buffered paraformaldehyde for 12 h , embedded into paraffin , sectioned , stained with hematoxylin and eosin solution . 293T control cells or ERRα knockdown cell lines were infected by VSV for the indicated time and subjected to the ChIP assay using anti-IRF3 or control mouse IgG . The IFN-β enhancer region was amplified by PCR using specific primers as follows [65]: Sense , 5′-GAATCCACGGATACAGAACCT-3′ , Antisense , 5′-TTGACAACA-CGAACAGTGTCG-3′ . Amplification of the total input DNA was shown as an equal loading control . The experiment was performed as described in reference [65] . Significant differences were calculated using a paired Student’s t-test . *p < 0 . 05 , **p < 0 . 01 and ***p < 0 . 001 . Estimation of overall survival was performed using Kaplan–Meier analysis , and differences between curves were compared using log-rank tests . | As a member of the nuclear receptor superfamily involved in metabolism signaling , the precise role of ERRα in antiviral innate immunity remains to be clarified . Here , we showed that ERRα deficiency led to increased interferon production , resulting in enhanced resistance to viral infection both in vivo and in vitro . Mechanistically , viral infection induced TBK1-dependent ERRα stabilization , which in turn increased its binding to TBK1 and IRF3 to prevent the formation of a functional TBK1-IRF3 complex . The effect of ERRα on IFN-I production was independent of its transcriptional activity and PCG-1α . Notably , ERRα chemical inhibitor XCT790 has broad antiviral potency . Taken together , our results identified ERRα as an important negative downstream regulator of TBK1 in RLRs-TLRs signaling pathways and suggested a potential therapeutic target for viral infection . | [
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| 2017 | ERRα negatively regulates type I interferon induction by inhibiting TBK1-IRF3 interaction |
The bacterial cell wall , which is comprised of a mesh of polysaccharide strands crosslinked via peptide bridges ( peptidoglycan , PG ) , is critical for maintenance of cell shape and survival . PG assembly is mediated by a variety of Penicillin Binding Proteins ( PBP ) whose fundamental activities have been characterized in great detail; however , there is limited knowledge of the factors that modulate their activities in different environments or growth phases . In Vibrio cholerae , the cause of cholera , PG synthesis during the transition into stationary phase is primarily mediated by the bifunctional enzyme PBP1A . Here , we screened an ordered V . cholerae transposon library for mutants that are sensitive to growth inhibition by non-canonical D-amino acids ( DAA ) , which prevent growth and maintenance of cell shape in PBP1A-deficient V . cholerae . In addition to PBP1A and its lipoprotein activator LpoA , we found that CsiV , a small periplasmic protein with no previously described function , is essential for growth in the presence of DAA . Deletion of csiV , like deletion of lpoA or the PBP1A–encoding gene mrcA , causes cells to lose their rod shape in the presence of DAA or the beta-lactam antibiotic cefsulodin , and all three mutations are synthetically lethal with deletion of mrcB , which encodes PBP1B , V . cholerae's second key bifunctional PBP . CsiV interacts with LpoA and PG but apparently not with PBP1A , supporting the hypothesis that CsiV promotes LpoA's role as an activator of PBP1A , and thereby modulates V . cholerae PG biogenesis . Finally , the requirement for CsiV in PBP1A-mediated growth of V . cholerae can be overcome either by augmenting PG synthesis or by reducing PG degradation , thereby highlighting the importance of balancing these two processes for bacterial survival .
The bacterial cell wall is a remarkably sturdy , web-like structure composed mainly of peptidoglycan ( PG ) , a polysaccharide mesh whose approximately parallel strands are crosslinked via peptide sidechains [1]–[3] . It forms a relatively thin layer between the inner and outer membranes of gram-negative bacteria , and a thicker layer in gram-positive bacteria , for which it is often the outermost bacterial structure . PG serves as a bacterial exoskeleton and promotes maintenance of the shape and size of bacterial cells [4]–[6] . The presence of PG allows bacteria to remain viable in environments where the osmolarity of the extracellular millieu is markedly lower than intracellular turgor pressure . Owing to PG's importance for bacterial survival , PG synthesis pathways are the target of some of our most commonly used antibiotics , including the beta lactams , cephalosporins and glycopeptides [7] . Most analyses of gram-negative cell wall biogenesis have been performed in Escherichia coli . In this model organism , the first extracytoplasmic step of PG assembly is polymerization of disaccharide-pentapeptide precursors ( [N-acetylglucosamine – N-acetylmuramic acid]- pentapeptide ) into glycan strands ( transglycosylation ( TG ) ) [8] . Subsequently , the peptide residues of these new strands are crosslinked ( transpeptidation ( TP ) ) to the existing PG , enabling expansion of the PG mesh . These TG and TP reactions are mediated by inner membrane-bound Penicillin Binding Proteins ( PBPs ) [9] , of which two ( PBP1A and PBP1B ) are bifunctional , i . e . are able to catalyze both , TG and TP reactions . PBP1A and PBP1B are largely functionally redundant and conditionally essential , i . e . , in the absence of one , the other becomes strictly required for growth [10] , [11] . Recently , it was discovered that in vivo , the PG synthetic activities of E . coli PBP1A and PBP1B are dependent upon cognate outer membrane lipoproteins ( LpoA and LpoB , respectively ) [12] , [13] . Some analyses suggest that both Lpo proteins activate the TP activity of their partners; however , it has also been suggested that LpoB can promote glycan chain polymerization [14] . In either case , the Lpo proteins are thought to play an essential regulatory role in PG synthesis , rather than a catalytic role . Despite the distinct localization patterns of PBP1s ( inner membrane ) and Lpo proteins ( outer membrane ) , the PBP1/Lpo pairs were found to interact directly . It has been hypothesized that the activator proteins permit detection of gaps in the PG mesh , and thereby induce synthesis of new material where needed ( i . e . where the cell wall has thinned , allowing for interaction between Lpos and cognate PBP1s ) [8] . Enzymes that cleave PG also play an essential role in the survival and growth of gram-negative bacteria , and they are required for PG synthesis in vivo . Endopeptidases , which cleave the peptide bridges that link parallel glycan strands and thereby reverse the process of transpeptidation , are thought to create space into which new glycan strands can be inserted [15]–[17] . In E . coli , multiple conditionally essential enzymes mediate this process . One of 3 murein hydrolases must be present in order for incorporation of new material into the cell wall to occur , and cells lyse in the absence of all three [17] . V . cholerae , the gram-negative causative agent of the diarrheal disease cholera , contains a similar repertoire of PG synthetic enzymes as E . coli , including homologues of the PBP activators LpoA and LpoB [18] . As seen with the E . coli enzymes , V . cholerae PBP1A and PBP1B and their lipoprotein activators are conditionally essential . However , we have observed that V . cholerae lacking PBP1A or LpoA are more sensitive to a variety of stressors than are wt bacteria or those lacking PBP1B/LpoB , suggesting that PBP1A plays the dominant role in V . cholerae PG synthesis [18] . PBP1A-deficient cells appear to be particularly impaired in stationary phase , during which they lose their typical rod shape and adopt a spherical morphology . V . cholerae also produces functionally redundant endopeptidases that are required for cell elongation and survival , although their absence does not result in bacterial lysis [16] . Thus , V . cholerae and E . coli appear to rely on similar but not identical processes for cell wall synthesis , expansion , and maintenance . One notable difference between V . cholerae and E . coli PG results from V . cholerae's production of non-canonical D-amino acids ( DAA ) , i . e . , DAA other than D-Ala and D-Glu , which are typical components of PG peptide side chains [2] , [19] . As V . cholerae enters stationary phase , its periplasmic amino acid racemase BsrV enables it to produce additional DAA , predominantly D-Met and D-Leu , which are incorporated into PG [20] , [21] . Mutants unable to produce or incorporate non-canonical DAA into PG are hypersensitive to osmotic stress , suggesting that the strength of stationary phase PG is modulated by these DAA . Interestingly , DAA likely contribute to the altered shape and survival of stationary phase V . cholerae lacking PBP1A or LpoA , as these mutants cease growth and assume a spherical shape in the presence of ∼1 mM concentrations of D-Met . In contrast , the growth and morphology of wild type and PBP1B/LpoB-deficient cells is unperturbed by exposure to DAA . However , the precise role of DAA in stationary phase , and the means by which they modulate V . cholerae PG , remain to be identified . Here , with the aim of increasing our understanding of the processes modulated by DAA , we screened an ordered transposon library of V . cholerae for additional mutants that are sensitive to growth inhibition by DAA . Besides the expected insertions in the genes encoding PBP1A and LpoA , the screen was answered by an insertion in a gene of unknown function ( vc1887 ) , which we have subsequently renamed CsiV ( for cell shape integrity Vibrio ) . A mutant lacking CsiV shares numerous additional phenotypes with mutants lacking PBP1A or LpoA , although the three mutants are not identical in all assays . In particular , only the effect of csiV disruption could be moderated by deletion of shyA , which encodes an endopeptidase that hydrolyzes peptide crosslinks between PG strands . Biochemical analyses revealed that CsiV interacts both with PG and with LpoA . Collectively , our data suggests that PBP1A-mediated PG synthesis in V . cholerae is largely dependent upon the presence of CsiV , which likely modulates the activity of the PBP1A activator LpoA .
We analyzed the growth of an arrayed V . cholerae transposon library on agar containing 5 mM D-Methionine ( D-Met ) ( Fig . 1A ) . Of the 3 , 156 mutants in the library , only three were unable to grow under these conditions: strains with transposon insertions in mrcA ( which encodes PBP1A ) , in lpoA ( vc0581; which encodes a putative PBP1A activator [18] ) , and in vc1887 , whose putative product is annotated as a hypothetical protein . In-frame deletions of vc1887 and lpoA also prevented growth of V . cholerae in the presence of D-Met , as reported for deletion of mrcA ( Fig . 1B; [19] ) . Furthermore , growth of each mutant could be restored by ectopic expression of the deleted gene , thereby demonstrating that the mutations do not have polar effects and that the observed growth deficiency is due to the absence of the deleted genes ( Fig . 1B ) . Collectively , these results indicate that PBP1A , LpoA , and VC1887 are all required for survival of V . cholerae in the presence of DAA , and raise the possibility that VC1887 , like LpoA , makes a key contribution to PBP1A-mediated PG synthesis . Based on our subsequent analyses of VC1887 ( detailed below ) , we have renamed VC1887 as CsiV ( for cell-shape integrity in Vibrio ) . The amino terminus of CsiV is predicted to encode a signal sequence for export to the periplasm ( Fig . 2A ) , and , consistent with this prediction , a CsiV-mCherry-fusion ( Fig . S1 ) was targeted to the cell periphery , where it was diffusely distributed ( Fig . 2B ) . Based on a String Database search for CsiV homologues , CsiV contains no additional domains with a known function in any bacterial genome . CsiV is largely restricted to Vibrionaceae and certain Alteromonadales ( especially genus Shewanella ) as well as Pseudomonas sp , with the strongest homologues only present within the genus Vibrio ( Fig . S2 ) . Structural prediction analysis ( Phyre2; http://www . sbg . bio . ic . ac . uk/phyre2/html/page . cgi ? id=index ) did not identify high confidence structural homologues for any portion of the CsiV protein sequence , and the majority of the protein was predicted to be disordered . Thus , sequence analysis did not provide any clues regarding CsiV's function . Given the similar responses of the ΔcsiV , ΔmrcA , and ΔlpoA mutants to D-Met , we tested whether the ΔcsiV strain shared other characteristics with PBP1A pathway mutants , such as loss of rod shape after exposure to DAA , as well as sensitivity to detergents and beta-lactam antibiotics [19] . These analyses revealed numerous attributes that are shared among all 3 strains . The minimum inhibitory concentrations ( MIC ) of deoxycholate and cefsulodin were dramatically reduced ( >100 fold and ∼10 fold , respectively ) for all three strains compared with wild type V . cholerae ( Fig . 3A , B ) , and the MIC for DAA was reduced 5–10 fold ( Fig . 3C ) . Furthermore , all three strains turned spherical in the presence of D-Met ( Fig . 3D ) , and the process of sphere formation was comparable for the ΔcsiV , ΔmrcA , and ΔlpoA strains ( Fig . 3D ) . At first , small blebs were evident protruding from the cylindrical portion of the cell ( the site of cell elongation ) ; then , within the subsequent ∼5–10 min , DAA induced a catastrophic loss of cell shape . Thus , the three mutants appear to display similar sensitivities to a range of stresses thought to target the cell envelope . Finally , perhaps consistent with increased susceptibility to cell envelope stresses , ΔcsiV was also similarly defective in colonization of infant mice as the PBP1A pathway mutants ( Fig . S3 ) . Given PBP1A's prominent role in PG synthesis , we also compared PG content and composition for wt V . cholerae and the three mutants . Based on the abundance of diaminopimelic acid ( a PG constituent ) in cell wall material isolated from exponential phase cultures , 50–90% of wild type PG ( normalized to OD600 ) could be recovered from all three mutants , suggesting cells can compensate almost fully for the loss of the PBP1A pathway and CsiV during exponential phase . In contrast , for stationary phase cultures , PG recovery from both the ΔmrcA and ΔcsiV mutants was ∼90% less than from wild type V . cholerae ( Fig . 4A ) . The ΔlpoA mutant also contained markedly less PG than the wild type strain , albeit ∼5 fold more than the ΔmrcA and ΔcsiV mutants . These results suggest that PBP1A is responsible for a high proportion of V . cholerae PG synthesis during the transition into or in stationary phase , and suggest that this synthesis may be dependent upon CsiV . To gain insight into the regulation of PBP1A's transpeptidase ( TP ) and transglycosylase ( TG ) activities and their connection to CsiV , we also compared the degree of crosslinking and the length of glycan chains in PG from the wt and mutant strains . For these analyses , we included a mrcA mutant ( S481T ) predicted to produce PBP1A that lacks TP activity , due to disruption of the enzymatic active site . We found that crosslinking was slightly and insignificantly reduced , relative to the wt strain , in exponential phase-derived PG from all four mutants ( ΔcsiV , ΔmrcA , ΔlpoA , mrcAS481T ) ( Fig . 4C ) . In contrast , crosslinking was significantly reduced in stationary phase PG in all four mutants ( Fig . 4D ) , although deletion of csiV resulted in less reduction than did deletion of mrcA . These results , consistent with our analysis of PG content , suggest that while PBP1A is largely dispensable in exponential phase , it is linked to a significant fraction of PG crosslinking in stationary phase , and that CsiV may promote ( but not be required for ) PBP1A's TP activity . Unexpectedly , the average PG chain length ( number of GlcNac-MurNac subunits/chain ) differed markedly between mutants and growth phases ( Fig . 4E , F ) . While all four mutants had slightly longer PG chains ( though this difference was only significant for the mrcAS481T mutant ) than the wild type in exponential phase ( Fig . 4E ) , the ΔlpoA and S481T mutants had significantly longer glycan chains in stationary phase ( Fig . 4F ) , where in contrast chain length of ΔcsiV and ΔmrcA did not differ from the wild type . The similarity between the ΔlpoA and mrcAS481T strain in this assay suggests that LpoA may be particularly important for augmenting PBP1A's TP activity ( as has also been observed in E . coli ) during the transition into or in stationary phase . However , our results also suggest a possible interplay between PBP1A's two enzymatic activities , such that disruption of its TP activity may cause deregulation of its TG activity . Additionally , it is noteworthy that in our analyses of PG , unlike previously described assays , the absence of the putative non-enzymatic factors ( LpoA and CsiV ) does not always have the same consequences as the absence of PBP1A . In previous work , we have shown that mutations in V . cholerae mrcA and lpoA are synthetically lethal with mutations in mrcB and lpoB [18] , i . e . , that the PBP1B pathway is essential in the absence of either PBP1A or LpoA . Similarly , we were unable to generate an in-frame deletion of mrcB in the ΔcsiV background , strongly suggesting that PBP1B is also essential in this mutant , possibly because PBP1A activity in a ΔcsiV ΔmrcB mutant is insufficient to sustain growth . Unexpectedly , we were able to generate a ΔcsiV ΔlpoB mutant , although this strain did have a modest growth deficiency ( e . g . , a longer lag phase ) ( Fig . 5A ) . Presumably , either PBP1A or PBP1B ( or perhaps both ) has reduced activity , rather than a total loss of function , in this double mutant . This possibility is explored further below . By placing mrcB under the control of an arabinose-inducible promoter in the wt , ΔmrcA , ΔlpoA and ΔcsiV backgrounds , we were able to observe the consequences of PBP1B depletion in various genetic backgrounds . When grown in the presence of arabinose , the majority of cells displayed normal cell morphology in all 4 strains ( Fig . 5B , S4 and not shown ) . However , when arabinose was removed ( resulting in PBP1B depletion ) , almost all of the ΔmrcA , ΔlpoA and ΔcsiV cells became spherical , as observed following exposure of these strains to DAA . In contrast , PBP1B depletion had no effect on the morphology of otherwise wt cells ( data not shown ) . The similarity between changes in the mutants' cell shape in response to DAA and to the absence of PBP1B suggests that a key effect of DAA in V . cholerae may be to inhibit PBP1B . We also explored the requirement for CsiV , LpoA , and PBP1A in PBP1B-deficient cells by treating the panel of mutant strains with cefsulodin , which specifically inhibits PBP1B in V . cholerae [18] . When all proteins were expressed at endogenous levels , the strains failed to grow in 100 µg/ml cefulodin , and instead adopted a spherical morphology ( Fig . 6A ) , as seen in response to PBP1B depletion . However , when PBP1A was overproduced , CsiV-deficient cells were able to grow in the presence of cefsulodin ( albeit not as well as when CsiV was exogenously produced ) ( Fig . 6B ) , providing further evidence that PBP1A is not fully inactive in the absence of CsiV . Alternatively , the observed phenotype could indicate the rise of a resistant mutant or degradation of the antibiotic under these conditions; however , these alternative explanations seem unlikely since we never observed growth in the control ( ΔcsiV carrying empty plasmid+cefsulodin ) . In contrast to ΔcsiV , neither overexpression of PBP1A nor of CsiV ( data not shown ) enabled LpoA-deficient cells to grow in the presence of the antibiotic , indicating that , as in E . coli , LpoA is absolutely required for PBP1A function . To gain additional insight into the cellular role of CsiV , including its relationship to PBP1A and LpoA , we screened transposon insertion libraries generated in the ΔcsiV , ΔmrcA , and ΔlpoA strains for mutants that had regained the ability to replicate in the presence of DAA . No suppressor mutations were obtained for the ΔmrcA and ΔlpoA mutations; however , multiple independent insertions within shyA ( vca0079 ) were found to enable growth of the ΔcsiV mutant in the presence of DAA . The product of shyA , which is one of two functionally redundant periplasmic hydrolases required for cell elongation in V . cholerae ( [16] ) , can cleave the majority of peptide crosslinks in V . cholerae PG . Subsequent analyses revealed that an in-frame deletion in shyA was also necessary and sufficient to significantly mitigate the growth and morphology defects of the ΔcsiV strain in the presence of DAA ( not shown ) and cefsulodin ( Fig . 7 A , B , S5 ) and in response to PBP1B depletion ( Fig . 7C ) , while deletion of shyA did not enable growth or maintenance of normal cell shape for PBP1A or LpoA-deficient strains under these conditions ( Fig . 7A , C ) . A likely explanation for these data is that a small amount of PBP1A activity is still preserved in the ΔcsiV mutant ( but not the ΔmrcA or ΔlpoA strains ) , and that deletion of shyA reduces PG cleavage and thereby lessens the need for PG synthesis to a level that can be met by residual PBP1A activity . However , it is also possible that CsiV does not modulate PBP1A activity at all , but instead restrains the activity of ShyA , i . e . , that the PG-related phenotypes of the ΔcsiV strain are the consequences of elevated PG degradation rather than reduced synthesis . In theory , CsiV might modulate both synthetic and degradative processes . To explore whether ShyA deregulation in the absence of CsiV might account for some of the phenotypes of the ΔcsiV mutant , we assessed the effect of ShyA overexpression in the ΔcsiV , ΔmrcA , and ΔlpoA strains . Notably , ectopic expression of ShyA , which we have previously shown to be functional [16] , had no effect on the growth rate of the 3 mutants or of wt cells ( Fig . S6 ) , and all 4 strains maintained V . cholerae's normal rod morphology ( not shown ) . Thus , although ShyA activity is likely to be highly regulated in vivo ( [16] ) , to date we lack evidence that its activity is restrained by CsiV or components of the PBP1A pathway . Analyses of CsiV interaction partners also suggests that CsiV probably modulates V . cholerae cell shape and growth predominantly via an effect on PG synthesis rather than degradation . We performed affinity purification analyses , using His-antibody resin and 6× His-tagged CsiV expressed from its native chromosomal location , to identify proteins that interact with CsiV ( Fig . S7A ) . To stabilize protein complexes , some cells were treated with the crosslinker DSP ( dithiobis succinimidyl propionate , Lomant's reagent ) prior to lysis . Silver staining of column-purified proteins , followed by mass spectrometry analysis of bands of interest , revealed that LpoA copurified with CsiV even in the absence of crosslinker . Purification of an additional protein complex , which contained both LpoA and VC2168 , a small , predicted periplasmic protein of unknown function , was found to depend on crosslinking . Most additional protein bands were found to contain chaperones , ribosomal proteins , or CsiV . Comparable analyses , using lysates from +/− DSP-treated cells expressing LpoA-His6 , confirmed the crosslinker-independent co-purification of LpoA and CsiV and the crosslinker-dependent co-purification of LpoA and VC2168 ( Fig . S7B ) . Given the stringency of washing conditions used ( 500 mM NaCl ) , our data suggest that a high affinity interaction occurs between LpoA and CsiV , consistent with CsiV modulating PBP1A-mediated PG synthesis . The additional interaction partner , VC2168 , has a high degree of phylogenetic co-occurrence with CsiV ( string database , Fig . S2 ) , suggesting it may likewise play a role in cell envelope biogenesis . However , we have yet to identify any changes in cell growth or morphology associated with deletion of vc2168 , and its cellular role remains obscure . Somewhat unexpectedly , our mass spectrometry-based analyses did not detect any interaction between PBP1A and either CsiV or LpoA . The interaction between CsiV and LpoA , but not PBP1A was evident in a variety of additional assays as well . Western blotting of affinity-purified proteins expressed from chromosomal loci confirmed that purification of LpoA was mediated by an interaction with CsiV-His6 ( Fig . 8A ) . Furthermore , when His6-tagged purified LpoA61-433 or PBP1A ( as well as a control protein , MalE ) were loaded on a column containing truncated CsiV ( CsiV31-266 ) covalently linked to NHS-activated Sepharose , only LpoA was retained by the column ( Fig . 8B ) . Finally , using a split adenylate cyclase-based bacterial two-hybrid assay , CsiV was found to interact with LpoA but not PBP1A ( Fig . 8C ) or ShyA ( data not shown ) . Thus , although all our interaction assays do not rule out the possibility of interactions between CsiV and partners other than LpoA , our observations provide strong support for the hypothesis that CsiV modulates V . cholerae PG synthesis , and thereby affects cell shape and growth , via directly interacting with LpoA and promoting its function as an activator of PBP1A . Since periplasmic enzymes involved in cell wall biosynthesis are necessarily closely associated with the cell wall , we tested whether CsiV directly interacted with peptidoglycan . We incubated purified CsiV with purified PG and then pelleted PG using ultracentrifugation . CsiV exclusively associated with the pellet fraction in the presence of PG , but not when lysozyme was added to the reaction , suggesting a direct interaction between CsiV and the cell wall ( Fig . 9 ) . Interestingly , purified LpoA did not interact with PG by itself but was tethered to it by the simultaneous presence of CsiV . This interaction did not appear to be required for LpoA-PG interaction in vivo , as PG purified after treatment of cells with DSP ( which can mediate covalent attachment of proteins to the cell wall as well as crosslinking of protein complexes [22] ) retained natively expressed LpoA-His even in the absence of PBP1A and CsiV ( Fig . S8 ) .
Here , we discovered CsiV , a new player in cell wall biogenesis in V . cholerae and presumably in all vibrios as well as the other genera where strong homologues of this novel peptidoglycan-binding protein are found . CsiV plays a critical role in PBP1A-mediated cell wall biogenesis in V . cholerae and was identified by screening a mapped transposon library for mutants whose growth was inhibited by D-amino acids ( DAA ) . The screen was inspired by our work that revealed that D-amino acids are key modulators of cell wall synthesis , particularly as cells enter stationary phase [19] . D-amino acids inhibit growth of strains lacking PBP1A or its putative activator LpoA , and exposure of these mutants to DAA leads to loss of rod shape and sphere formation through an unknown mechanism [18] . CsiV-deficient cells , like PBP1A and LpoA-deficient cells , turn spherical in stationary phase , in the presence of DAA , and upon depletion of PBP1B . Additionally , V . cholerae lacking CsiV , LpoA , or PBP1A are hypersensitive to the bile acid deoxycholate and to cefsulodin , which inhibits V . cholerae PBP1B , and they all show marked changes in PG content in stationary phase . CsiV , LpoA and PBP1A are also all required for survival of V . cholerae lacking PBP1B , although the requirement for CsiV is diminished in the absence of the endopeptidase ShyA . CsiV interacts with LpoA as well as with PG , but does not appear to bind to PBP1A . Collectively , our data suggest that CsiV acts through LpoA ( and thereby through PBP1A ) to promote PG synthesis in V . cholerae , and that V . cholerae PBP1A is largely inactive in the absence of CsiV . Despite the extensive similarities of the phenotypes of V . cholerae csiV , lpoA , and mrcA mutants , there are also notable differences among these strains . A key difference is that overexpression of mrcA enables growth of the csiV mutant , but not the lpoA mutant , in the presence of cefsulodin . In the presence of this antibiotic , PBP1A-mediated PG synthesis is essential for growth; consequently , our results suggest that PBP1A retains a small amount of activity in the absence of csiV , but is completely inactive when LpoA is not present . Since CsiV interacts with LpoA , but does not appear to interact with PBP1A , a likely explanation for these results is that CsiV markedly enhances the ability of LpoA to activate PBP1A . Western blot analyses of the abundance of epitope tagged LpoA in the presence and absence of CsiV ( Fig . S8 and data not shown ) suggest that CsiV is not required simply to stabilize LpoA levels , nor is CsiV required to tether LpoA to PG ( Fig . S8 ) . Our path to elucidating the precise means by which CsiV modulates LpoA's activity will become clearer as the molecular bases for LpoA's enhancement of PBP1A activity are illuminated . In related analyses , we observed that disruption of V . cholerae lpoB is also possible in a csiV mutant , but not in an lpoA or mrcA mutant , and that disruption of mrcB is not possible in any of the three mutants . The former results are consistent with our previous conjecture that only the csiV mutant has residual PBP1A activity . Additionally , the differential requirement for lpoB and mrcB in the csiV mutant suggests that PBP1B may likewise have a small amount of residual activity in the absence of LpoB , and that PBP1B-mediated PG synthesis is critical for survival of the csiV lpoB mutant . Consistent with this supposition , we have found that growth of this strain is blocked by the PBP1B-specific antibiotic cefsulodin ( data not shown ) . Our studies have also revealed extensive similarities between the consequences of PBP1B depletion , exposure to cefsulodin , and exposure to DAA . All inhibit growth of PBP1A , LpoA , and CsiV-deficient cells and induce loss of rod shape and adoption of a spherical morphology . Although they do not provide conclusive evidence , these results suggest that one effect of DAA is to inhibit PBP1B activity . DAA might bind directly to PBP1B; alternatively , PG in which non-canonical DAA have been incorporated might be a poor substrate for crosslinking by PBP1B . Regardless , if effective reduction of PBP1B activity in the presence of DAA occurs , then PBP1A likely accounts for the majority of PG synthesis in stationary phase cultures , which accumulate high levels of DAA . Such a role for PBP1A would explain why the survival and PG content of PBP1A pathway mutants is particularly reduced during stationary phase . Since the detrimental effects of csiV deletion in the absence of PBP1B activity were mitigated by inactivation of the endopeptidase ShyA , it is formally possible that CsiV acts as a negative regulator of PG degradation rather than an indirect activator of PG synthesis . However , this scenario seems unlikely to account for all of CsiV's activity for a variety of reasons . First , CsiV interacted with LpoA , while we did not detect an interaction between CsiV and ShyA or any other endopeptidase . Second , overexpression of ShyA did not impede V . cholerae growth , even in the absence of CsiV , suggesting that restraint of detrimental PG digestion is not dependent upon CsiV ( Fig . S6 ) . Additionally , the deletion of shyA promoted but did not completely restore growth of the ΔcsiV mutant in cefsulodin , so , at minimum , the role of CsiV cannot be limited to regulation of ShyA activity . Thus , CsiV's most probable role is in promoting PG synthesis , via enhancing LpoA's activation of PBP1A . Finally , our observations have bearing on a central question in cell wall biogenesis - whether PG synthesis and degradation are co-ordinately regulated . These key processes may be tightly coupled , so that one “degradation unit” is always associated with one “synthesis unit” of cell wall material . It is also possible that degradation and synthesis are independently regulated in response to one or more cellular stimuli . For example , degradation might occur in response to elevated turgor pressure in the cell , while synthesis might be stimulated by detection of gaps within the PG structure . In either case , our observations suggest that cell survival depends on proper maintenance of a balance between these two processes . When CsiV and PBP1B are both absent , and consequently PG synthesis rests solely on residual PBP1A activity , cell growth is dependent upon compensatory measures: either removal of the endopeptidase ShyA or overexpression of PBP1A , which should reduce PG degradation or increase synthesis , respectively . In contrast , cell growth is not markedly impaired when only a single bifunctional PBP ( PBP1A or PBP1B ) was absent . While speculative , our findings are therefore consistent with a scenario in which PG synthesis and degradation are buffered , rather than precisely calibrated , processes , i . e . that overall PG synthetic activity can be lowered substantially until a threshold is reached , below which degradation outweighs synthesis to a degree that makes it impossible to maintain rod-shape . This is in agreement with recent studies of the activity of PBP2 , the transpeptidase essential for cell elongation , in E . coli [23] .
All strains were routinely grown at 37°C in LB medium supplemented with 200 µg/ml streptomycin . For growth curves , overnight cultures were diluted 1∶100 into fresh medium and incubated shaking until OD600∼0 . 1–0 . 3 . Cultures were then normalized to an OD600 of exactly 0 . 1 and transferred to 200 µL volume in 200-well honeycomb plates . Growth curves were then conducted at 37°C with continuous shaking in a Biotek growth curve machine . Primers are summarized in Table S1 . Complementation plasmids were constructed by cloning PCR fragments amplified with primers TDP166/167 ( csiV ) , TDP168/169 ( mrcA ) or TDP172/173 ( lpoA ) digested with Xma1/BamH1 ( NEB ) into likewise digested and Calf intestinal phosphatase ( CIP , NEB ) -treated pHL100 . pHL100shyA was constructed using isothermal assembly [24] of the product of primers TDP529/530 with Sma1 ( NEB ) -digested and CIP-treated pHL100 . pHL100csiV-mCherry was constructed using isothermal assembly of Sma1-digested/CIP-treated pHL100 and the products of TPD310/311 and TDP238/239 . All deletion plasmids are derivatives of the suicide-vector pCVD442 [25] . 300–500 bp long upstream and downstream homologies were amplified using primers TDP138/139+TDP140/141 ( lpoA ) , TDP205/206+TDP207/208 ( mrcA ) or TDP282/283+TDP284/285 ( lpoB ) , purified ( Qiagen PCR purification kit ) and fused using SOE PCR with the respective outside primers ( in bold ) . The resulting product was digested with Xba1 ( NEB ) and ligated into likewise digested pCVD442 . The csiV deletion plasmid was constructed using isothermal assembly of the products of TDP360/361+TDP362/362 into Sma1-digested pCVD442 . Overexpression plasmids for protein purification are derivatives of pET28b . Open reading frames encoding truncated CsiV and LpoA were cloned into the Nco1/Xho1 sites using the PCR products of TDP110/111 and TDP201/88 . HisPBP1A was amplified using primers TDP535/536 and cloned into Nco1-digested pET28b using isothermal assembly . Site-directed mutagenesis was performed using the QuikChange kit ( Agilent ) following the manufacturer's recommendations . Primers TDP160/161 were used to amplify mutated mrcA from pHL100mrcA . Mutated mrcA was then amplified using primers TDP212/214 and used as template together with the products of TDP196/212+213/199 in a SOE PCR reaction with primers TDP196/199 . The resulting product , containing mutated mrcA+upstream and downstream homology regions was then digested with Xba1 and ligated into likewise digested pCVD442 . Strains and plasmids are summarized in Table S2 . All Vibrio cholerae strains are derivatives of El Tor N16961 . Deletion and replacement mutants were generated using the suicide plasmid pCVD442 or the lacZ integration plasmid pJL1 [26] in the donor strain SM10 using published methodology ( [25] ) . An ordered transposon library in 96 well format [27] was transferred to 200 µL LB medium using a 96-Pin Tool , incubated overnight at 37°C and then spotted on LB agar plates with either no addition or 5 mM D-methionine ( Sigma ) . After another overnight incubation , agar plates were visually inspected for growth . Colonies that grew neither on LB nor on D-Met were recultured from the library and retested for growth on D-Met individually . Colonies that grew on LB agar but not on LB were scored as hits . To isolate murein sacculi , either 1L ( stationary phase culture , OD600∼2 ) or 2L ( exponential phase culture , OD600∼0 . 2 ) of culture was pelleted , resuspended in 5 ml PBS and slowly added to 10 ml of boiling 10% SDS while stirring . Samples were boiled for 4 h , then stirred overnight at 37°C . Cell wall material was then pelleted by ultracentrifugation ( 110 . 000 rpm , 1 h ) and washed 3× in MQ water . Peptidoglycan ( PG ) samples were analyzed as described previously [28] . After washing with MQ water , samples were digested with pronase E ( 100 µg/ml ) in a TrisHCl 10 mM pH 7 . 5 buffer for 1 hour at 60°C to remove Braun's lipoprotein . After heat-inactivation and washing , the samples were treated with muramidase ( 100 µg/ml ) for 16 hours at 37°C , in 50 mM phosphate buffer , pH 4 . 9 . Muramidase digestion was stopped by boiling , coagulated proteins were removed by centrifugation ( 10 min , 14000 rpm ) and the supernatants were reduced with 150 µl 0 . 5 M sodium borate pH 9 . 5 and sodium borohydride ( 10 mg/ml final concentration , 30 min at RT ) . Finally , samples ( 100 µl ) were adjusted to pH 3 . 5 with phosphoric acid . UPLC analyses of muropeptides were performed on an ACQUITY UPLC BEH C18 Column , 130Å , 1 . 7 µm , 2 . 1 mm×150 mm ( Water , USA ) and detected at Abs . 204 nm . Muropeptides were separated using a linear gradient from buffer A ( phosphate buffer 50 mM pH 4 . 35 ) to buffer B ( phosphate buffer 50 mM pH 4 . 95 methanol 15% ( v/v ) ) in a 20 minutes run . Identity of the peaks was assigned by comparison of the retention times and profiles to other chromatograms in which mass spectrometry data has been collected . The relative amount of each muropeptide was calculated by comparison of the relative area of the peak compared to the total area of the chromatogram . Representative chromatograms are shown in Fig . S9 . The degree of crosslinking is expressed as the relative amount of peptide bonds that connect two peptide stems ( [dimers+trimers/2] ) . The average length is indirectly proportional to the relative amount of anhydro-muropeptides . Isolated PG sacculi were hydrolysed for 15 hours with HCl 6M at 100°C , followed by water removal using a centrifugal concentrator ( Speed Vac ) . Completely dried samples were resuspended in water and treated with ninhydrin ( 250 mg of ninhydrin in 4 ml of phosphoric acid 0 . 6 M and 6 ml of pure acetic acid ) for 5 minutes at 100°C . Absorbance was measured at 434 nm and concentration of muropeptides was calculated by comparison to a mDAP standard curve [29] . All proteins were overproduced in E . coli strain RosettaGami ( Invitrogen ) as 6×His-tagged constructs from a pET28b+ vector . Overnight cultures of overexpression strains carrying either truncated CsiV31-266-His , truncated LpoA61-653 or full-length His-PBP1A were diluted into 1 L LB broth , grown until OD600 = 0 . 5 . Flasks were then cooled down at 4°C for 30 min , followed by addition of 1 mM IPTG and slow shaking at room temperature for 16 h . Cells were pelleted , washed 1× in PBS and resuspended in Buffer A ( 20 mM Tris , pH 7 . 2 , 150 mM NaCl , 1 mM DTT+protease inhibitor cocktail ( Roche ) and stored at −80°C . Following thawing on ice , cells were disrupted by passaging through a French press twice . Salt was then adjusted to 300 mM NaCl and 0 . 1% triton x-100 ( reduced , Sigma ) as well as 1% CHAPS ( Sigma ) and 40 mM Imidazole added . Lysates were then incubated for 1 hour rotating at 4°C , followed by centrifugation for 1 h ( 25 . 000 rpm , Beckman Coulter Avanti J26-XP centrifuge , JL-25 . 50 rotor ) at 4°C . Nickel NTA resin ( 0 . 5 ml , equilibrated in buffer A ) was then added to the supernatant , followed by incubation at 4°C , rotating . The lysate was then allowed to drain from the Ni-resin by flow-through in a filter cartridge and the resin washed ( 5×10 ml ) with Wash buffer ( Buffer A adjusted to 500 mM NaCl , 50 mM imidazole and 0 . 1% triton x-100 ) and eluted with wash buffer containing an imidazole gradient ( 60–300 mM ) . Fractions were subjected to SDS PAGE and Coomassie Brilliant Blue staining and the cleanest fractions pooled . Proteins were quantified using Nanodrop . For co-affinity purification , strains carrying chromosomal C-terminal His fusions of the proteins of interest were grown to OD600∼0 . 5 in LB , pelleted , washed twice in PBS and then resuspended in PBS and crosslinked for 30 min with 5 mM DSP ( Thermo Scientific ) . The pellet was then washed 3× in PBS and resuspended in buffer B ( 20 mM Tris , 300 mM NaCl , 1 mM DTT , 1% Triton X-100 , Roche complete protease inhibitor ) and cells were lysed by passaging three times through a French Press . Then , 1% CHAPS was added and the lysates stirred 2 h – overnight at 4°C . Lysates were then cleared by centrifugation ( 25 . 000 rpm , 1 h ) and incubated for 2 h with His-antibody resin ( R&D systems ) equilibrated in buffer B . The resin was washed 3× with Buffer B adjusted to 500 mM NaCl and protein complexes eluted with Buffer C ( 100 mM glycine , pH 2 . 5 , 300 mM NaCl , 1 mM DTT , 1% Triton X-100 ) . Proteins were then concentrated ∼10fold using Amicon centrifuge filter units with 10 kDa MW cutoff and subjected to SDS PAGE followed by silver staining . Bands of interest were cut out from the gel and proteins identified via Mass Spectrometry . CsiV-his was covalently linked to NHS-activated sepharose resin ( Thermo Scientific ) using the manufacturer's protocol . Purified LpoA-his and His-PBP1A were added to CsiV-Sepharose equilibrated in 20 mM Tris-HCl ( pH 7 . 2 ) , 150 mM NaCl , 1 mM DTT and 0 . 1% reduced Triton X-100 and incubated at 4°C rotating for 2 h . The resin was then washed 3× with buffer and proteins eluted by boiling the resin in buffer containing 1% SDS ( 95°C , 5 min ) . Proteins were then visualized via Western Blot . BACTH was conducted using a split adenylate cyclase system as described previously [30] . PG binding was assayed as described previously [31] . In short , purified PG sacculi were incubated with purified proteins in pulldown buffer ( 20 mM Tris/maleate pH 6 . 8 , 50 mM NaCl , 10 mM MgCl2 0 . 1% Triton X-100 ) for 30 min on ice , then ultracentrifuged ( 110 . 000 rpm ) . Pellets were washed once in pulldown buffer and proteins in pellets and supernatant fractions visualized by Western Blot . | Bacteria surround themselves with a mesh-like peptidoglycan ( PG ) cell wall , which is essential for maintenance of cell shape and survival . While the enzymes that catalyze the assembly of the cell wall ( aka penicillin-binding proteins ( PBPs ) ) have been extensively characterized , our understanding of the factors that modulate the activities of these enzymes is less developed . Here , using a genetic screen , we identified a gene of unknown function that plays a crucial role in PBP1A-mediated cell wall synthesis in Vibrio cholerae , the bacterium causing cholera . V . cholerae mutants lacking this gene , whose protein product was re-named CsiV ( for Cell shape integrity Vibrio ) , share many phenotypes with PBP1A mutants , including becoming spherical during stationary phase . We show that CsiV interacts with LpoA , a lipoprotein activator of PBP1A , as well as with PG . CsiV , LpoA , or PBP1A are all required for survival of V . cholerae lacking PBP1B , and mutants lacking any of these factors show marked changes in PG content in stationary phase . Collectively , our data suggest that CsiV acts through LpoA to promote PG biogenesis in V . cholerae and other vibrio species as well as in the other genera where this protein is found . | [
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| 2014 | A Novel Peptidoglycan Binding Protein Crucial for PBP1A-Mediated Cell Wall Biogenesis in Vibrio cholerae |
Epidermal stratification of the mammalian skin requires proliferative basal progenitors to generate intermediate cells that separate from the basal layer and are replaced by post-mitotic cells . Although Wnt signaling has been implicated in this developmental process , the mechanism underlying Wnt-mediated regulation of basal progenitors remains elusive . Here we show that Wnt secreted from proliferative basal cells is not required for their differentiation . However , epidermal production of Wnts is essential for the formation of the spinous layer through modulation of a BMP-FGF signaling cascade in the dermis . The spinous layer defects caused by disruption of Wnt secretion can be restored by transgenically expressed Bmp4 . Non-cell autonomous BMP4 promotes activation of FGF7 and FGF10 signaling , leading to an increase in proliferative basal cell population . Our findings identify an essential BMP-FGF signaling axis in the dermis that responds to the epidermal Wnts and feedbacks to regulate basal progenitors during epidermal stratification .
Vertebrate epidermis , the outermost layer of skin , functions as a barrier for protection against environmental insult and dehydration . At approximately embryonic day 8 . 5 ( E8 . 5 ) during mouse embryogenesis , the single-layered surface ectoderm adopts an epidermal developmental fate by turning off the expression of keratins 8 and 18 ( K8/K18 ) and switching on the expression of K5/K14 , leading to the replacement of the unspecified ectoderm by the embryonic basal layer [1] , [2] . Subsequently , the change of cell proliferation from symmetric to asymmetric division becomes evident at E12 . 5 to 14 . 5 [3] . The proliferative basal layer periodically produces intermediate suprabasal cells positive for K1/K10 , programmed for terminal differentiation of keratinocytes [2] . The transient intermediate keratinocytes then exit the cell cycle , followed by detachment from the basal layer and migration outward to form the spinous layer , characterized by the expression of K1 and K10 . Subsequent developmental events engage the expression of differentiation genes , including loricrin and filaggrin , as spinous keratinocytes further develop into the granular and cornified layers contributing to barrier establishment at late embryonic stages ( [2] . The tumor-suppressor p53-related transcription factor , p63 , encodes regulators required for initiating epithelial stratification during development and maintaining proliferative potential of the basal layer keratinocytes [4] , [5] , [6] , [7] . Two different classes of protein are encoded by p63: the first contains the amino terminal transactivation domain ( TA isoforms ) and the second lacks this domain ( ΔN isoforms ) [8] . ΔNp63 is expressed predominantly in the basal layer keratinocytes but its expression is down-regulated in the post-mitotic suprabasal keratinocytes , suggesting that p63 plays a crucial role in proliferative capacity of the epidermal progenitors [9] , [10] . Several families of secreted signaling molecules , including bone morphogenetic protein ( BMP ) , fibroblast growth factor ( FGF ) , Hedgehog ( Hh ) , and Wnt , have been implicated in embryonic epidermal morphogenesis . Among them , Wnt appears to be the earliest signal known to promote epidermal development [11] , [12] , [13] . Our previous studies have demonstrated that embryonic epidermis is the source of Wnts essential for establishing and orchestrating signaling communication between the epidermis and the dermis in hair follicle initiation [14] . Overexpression of Dkk1 , a Wnt antagonist , in the epidermis also results in the absence of hair follicles [11] , whereas expression of a constitutively active form of β-catenin in the epithelium leads to premature development of the hair follicle placode [15] . In chicks , high levels of Wnt are able to activate BMP signaling through repression of FGF signaling , leading to a switch of neural cell fate into epidermal cell fate [16] , [17] . In addition , BMP signals have also been suggested to control p63 expression during ectodermal development [18] . In an embryonic stem cell ( ESC ) model recapitulating the stepwise appearance of the epidermal stratification in vitro , BMP4 treatment activates the expression of ΔNp63 isoforms , promoting an induction of the proliferative basal keratinocyte makers , K5 and K14 , and a progressive enhancement of the terminal differentiation markers , K1 , K10 , involucrin and filaggrins [19] . In addition , BMP signals have also been suggested to control p63 expression during ectodermal development . Moreover , BMP signaling is also active in the interfollicular epidermis where it may act as a morphogen by promoting epidermal development through inhibition of the hair follicle fate during skin morphogenesis [1] , [11] , [20] , [21] . It has been suggested that FGF7 ( KGF ) and FGF10 function in concert via FGFR-2 ( IIIb ) to stimulate keratinocyte proliferation in the epidermis [22] , [23] , [24] , [25] , [26] , despite the fact that targeted loss of Fgf7 has no effect on skin development in the mouse [27] . Interestingly , FGF ligands appear to be expressed in the dermis while the receptor is present in the epidermis during skin development [22] , [24] , [28] . However , how these developmental signals are integrated and interplayed across the epithelium and mesenchyme to control epidermal stratification remains to be elucidated . In this study , we investigated the genetic regulation of these signaling pathways during epidermal stratification and elucidated the mechanism underlying this developmental process orchestrated by the Wnt , BMP , and FGF signaling pathways . Using a mouse model with epithelial ablation of Gpr177 ( also known as Wls/Evi/Srt in Drosophila ) , a regulator essential for intracellular Wnt trafficking , to disrupt Wnt secretion in skin development [29] , [30] , [31] , [32] , we identified a crucial role of Wnt signaling in orchestrating epidermal stratification . We demonstrate that signaling of epidermal Wnt to the dermis initiates mesenchymal responses by activating a BMP-FGF signaling cascade . This activation is required for feedback regulations in the epidermis to control the stratification process . Our findings thus decipher a hierarchy of signaling loop essential for epithelial-mesenchymal interactions in the mammalian skin development .
Gpr177 is expressed in the skin of the developing limb bud as early as E11 . 5 ( Figure S1A , B ) . Similar to our previous observations in dorsal body skin [14] , Gpr177 protein can be found predominantly in the epidermis and weakly in the underlying dermis ( Figure 1A–C ) at E11 . 5–13 . 5 . To assess the requirement of epidermal Wnts in the development of skin , we generated Gpr177K14 mice in which Gpr177 is inactivated by the K14-Cre transgenic allele to disrupt the secretion of Wnt proteins [32] . Using a R26R reporter line , we examined the Cre-mediated deletion , which occurs only in the epidermis ( Figure S1C , D ) . The loss of Gpr177 was clearly evident in the epidermis but not the dermis of Gpr177K14 ( Figure 1A′–C′ ) , indicating a targeted removal of Gpr177 in the mutants . We noted that the Cre recombination is uniformly detected in the limb skin ( Figure S1C , D ) but not in the dorsal body skin ( Figure S1 E , F–G , F′–G′ ) using the K14-Cre line . Compared to the Gpr177K5 mice that exhibited a uniform expression pattern of Cre and consistent phenotypes associated the Gpr177 deletion described previously [14] , the Gpr177K14 mice are not suitable for the study of the body skin due to inconsistent results on skin thickness ( Figure S1 F–H , F′–H′ ) . However , the Gpr177K14 model is ideally suited for studies on epidermal development of the limb . The Gpr177K14 autopods displayed severe deformities including loss of nail formation ( Figure 1D , D′ ) . The interdigital and dorsal soft tissues appeared to be edematous ( Figure 1E , E′ ) , but skeletal staining revealed comparable structures between controls and mutants ( Figure 1F , F′ ) , suggesting that the dysmorphic features of the Gpr177K14 autopods is likely due to impairments in the skin tissue . Histological analysis of autopods showed a reduction in skin thickness as well as in cell proliferation rate , indicating the ablation of skin stratification in Gpr177K14 ( Figure 1G–H′ and Figure S2A , B and E ) . To further investigate the edematous skin abnormalities , we characterized epidermal stratification of the limb skin using markers specific for basal , spinous , and granular epidermal layers . The deletion of Gpr177 diminished the number of basal cells expressing KRT5 ( Figure 1I , I′ ) . Significant reduction of the spinous layer positive for KRT1 and KRT10 was also identified in the longitudinal sections along the dorsal skin of the mutant autopods ( Figure 1J–K , J′–K′ ) . However , the granular layer positive for loricrin and the basal membrane protein , laminin 1 , did not show significant alterations ( Figure 1L–M , L′–M′ ) . The results were consistent with alterations of the limb skin thickness caused by the Cre-mediated deletion of Gpr177 ( Figure S2 ) . Besides , an uneven decrease in skin thickness also occurred in the dorsal body of Gpr177K14 , as shown by histology ( Figure S1H , H′ ) and immunohistochemistry specific for the spinous and basal layers ( Figure S1I–J , I′–J′ ) . TUNEL assay did not reveal significant changes in apoptosis , indicating that defects in the spinous layer were not caused by abnormal cell death ( Figure S3 ) . Thus , the spinous hypoplasia is likely attributed to defects in the epithelial vertical expansion of Gpr177K14 mice . The deletion of Gpr177 has been shown to affect Wnt signaling during the development of other organs [14] , [32] , [33] . This is also true during the morphogenesis of the limb skin , as the the expression of several downstream mediator critical for Wnt signal transduction including Axin2 , Dkk1 , Fzd1 , Lef1 , and TCF4 was significantly reduced in the skin of Gpr177K14 autopods ( Figure 2A ) , and the activity of Wnt/β-catenin signaling in the mesenchyme underlying the interfollicular epithelium was almost completely eliminated , evidenced by the lack of TopGal reporter activity ( Figure 2B–C , B′–C′ ) . In situ hybridization analysis further confirmed that epidermal ablation of Gpr177 affects the expression of Lef1 and Axin2 in both the epithelium and mesenchyme ( Figure 2D–G , D′–G′ ) . These observations are consistent with our observations in dorsal body skin ( Figure S4A–D , E–F , E′–F′ ) [14] , indicating a requirement of epidermal Wnt for signaling activation in both epidermal and dermal layers . Consistent with this finding , Dermo1-Cre mediated deletion of Gpr177 in the dermis did not alter the skin thickness ( Figure S5 ) , suggesting a dispensable role of dermal Wnt in epidermal stratification . To decipher the effects of the alteration in Wnt signaling during autopod skin morphogenesis , we performed RNA expression profiling analysis using microarray to identify genes that are differentially expressed in the E15 . 5 distal limbs ( Figure S6 and Table S1 and Table S2 ) . Among those altered genes , members of BMP family were significantly affected in Gpr177K14 . In response to β-catenin/Wnt signaling , BMP signaling in the dermal mesenchyme plays critical role in hair follicle induction [14] . Thus , we hypothesized that BMPs are downstream targets of Wnt signaling and regulate epidermal stratification . Real time RT-PCR analysis validated that Bmp2 , 4 , and 7 expression was decreased in the mutants ( Figure 3A ) . During normal development of the autopod skin , Bmp2 and Bmp7 were found in both the epidermis and dermis while Bmp4 appeared to be exclusively expressed in the dermis ( Figure 3B–G ) . However , epidermal deletion of Gpr177 caused profound reduction of Bmp2 , Bmp4 , and Bmp7 in the developing skin ( Figure 3B′–G′ and Figure S7A–G , A′–G′ ) , suggesting that BMP signaling , regulated by Wnt signaling , is likely to be involved in epidermal stratification . To test the functional requirement of BMP signaling in the Gpr177-mediated skin morphogenesis , we used a conditional Bmp4 transgenic allele . The Tg-pmes-Bmp4 transgenic mouse was crossed onto the Gpr177K14 background to generate Gpr177K14/Tg-pmes-Bmp4 mice . The transgenic Bmp4 expression from this transgenic allele was tightly controlled by a transcription and translation STOP cassette flanked by two loxP sites , permitting the Cre-mediated activation ( Figure 3H–I ) [34] , [35] . The transgenic expression of Bmp4 was able to alleviate the dysmorphic phenotype caused by the deletion of Gpr177 ( Figure 3J–L ) . The Gpr177K14/Tg-pmes-Bmp4 autopods displayed five separated digits without skin edema ( Figure 3L ) , suggesting that BMP4 acts downstream of Wnt signaling in skin stratification . To determine if this epidermal expression of transgenic Bmp4 could substitute for mesenchymal Bmp4 to rescue spinous layer defect , we examined the spinous layer of Gpr177K14/Tg-pmes-Bmp4 autopods . Immunostaining of KRT5 and KRT1/10 revealed a significant enhancement in their expression ( Figure 3N–P , R–T and V–X ) . Histological ( Figure S2 C–F ) and ultrastructural analyses ( Figure S2 F–H ) further showed that the thickness of the spinous layer was obviously increased in the E18 . 5 Gpr177K14/Tg-pmes-Bmp4 epidermis , as compared to that in Gpr177K14 epidermis . The transgenic expression of Bmp4 in the epidermis ( Figure 3H–I ) may exert its signaling effects in a cell autonomous or non-cell autonomous manner . For non-cell autonomous signaling , it requires the diffusion of BMP4 through an inter-tissue signal transduction mechanism . It has been shown that BMPR1A is responsible for mediating BMP signaling in epidermal development [20] , [36] , [37] . If the transgenic Bmp4 indeed acts in a cell autonomous manner , we assumed that activation of BMPR1A-mediated signaling in the epidermis would also alleviate the stratification defects in Gpr177K14 autopods . Accordingly , we compounded a conditional transgenic allele that expresses a constitutively active form of BMPR1A receptor ( caBMPR1A ) with Gpr177K14 mice ( Figure 3H–I ) [34] . However , ectopic activation of BMPR1A signaling neither rescued the autopod defects at the morphological ( Figure 3M ) and histological ( Figure S2D ) levels nor restored the expression of the basal and spinous layer makers , KRT5 ( Figure 3Q ) , KRT1 ( Figure 3U ) , and KRT10 ( Figure 3V ) , as compared to that in Gpr177K14 mice ( Figure 3O , S , W ) . These results thus suggest a non-cell autonomous BMP signaling across tissue layers to alleviate the epidermal defects of Gpr177K14 , and the BMP4 activity in the dermal mesenchyme , but not in the epidermis , is required for proper stratification of the mammalian skin . Maturation of the spinous layer first requires the mitotic suprabasal intermediate cells to be replaced by the post-mitotic cells [2] . The hypoplasia developed in the Gpr177K14 spinous layer might be attributed to failure in this replacement . To test this possibility , we performed a BrdU labeling experiment to identify the KRT1 positive keratinocytes undergoing active proliferation between E13 . 5 and 16 . 5 . Double labeling was able to detect cells positive for BrdU and KRT1 in the E13 . 5 and 14 . 5 wild type epidermis ( Figure 4A , B ) . No double positive cells were found at E15 . 5 and 16 . 5 ( Figure 4C , D ) . In addition , this replacement process did not seem to be affected by Gpr177 deletion or transgenic Bmp4 expression ( Figure 4E–L and Y ) . Thus , the initial programming of intermediate cells to become spinous keratinocytes is independent of the Gpr177 mediated regulation and BMP signaling . As skin stratification requires proper proliferation of the basal cells [9] , [10] , we further examined if defects in basal cell division contribute to the epidermal abnormalities caused by Gpr177 deficiency . Double labeling of BrdU and KRT5 permits quantification of the ratio of basal cells proliferation . Closer examinations revealed that the number of KRT5-positive basal cells labeled with BrdU ( Figure 4M–P ) is significantly reduced by Gpr177 ablation ( Figure 4Q–T ) . However , this hypoplastic feature was alleviated in the Gpr177K14/Tg-pmes-Bmp4 mutants ( Figure 4U–X ) , where the ratio of BrdU labelled basal cells arises between E14 . 5 and 16 . 5 to the levels close to controls ( Figure 4Z ) . These observations suggest that the Gpr177-mediated regulation of BMP signaling maintains the high proliferative potential of the basal cells essential for epidermal stratification . It has been shown that p63 transcription factor is critical for the proliferative potential of epidermal stem cells in the stratified epithelium [9] , [10] , [18] , [38] . We therefore tested if p63 is involved in the epidermal stratification mediated by the Wnt/BMP regulatory axis . In situ hybridization analysis showed that the expression of p63 in the epidermis was affected by Gpr177 deletion at E13 . 5 and 14 . 5 ( Figure 5A–B , A′–B′ ) . The loss of p63 transcripts in the mutants suggests a role of Wnt signaling in the maintenance of its expression in the basal cells ( Figure 5A′–B′ and Figure S8A ) . We next examined the alteration of p63 at the protein level using antibodies against total p63 and its specific isoforms , TA-p63 and ΔNp63 . The percentage of the total p63 and ΔNp63 positive basal cells was significantly decreased in Gpr177K14 mutants ( Figure 5C–D , F–G , I–J and Figure S8B–J ) . Consistent with the previous reports [4] , [5] . TA-p63 was not involved in epidermal development at these stages ( Figure S8 K–P ) . In addition , transgenic BMP4 was able to elevate the percentage of the total p63 and ΔNp63 positive cells in the basal layer similar to that of wild type control at E13 . 5 , 14 . 5 and 16 . 5 ( Figure 5C–K and U ) . To further determine the role of p63 in basal cell proliferation , we performed double labeling of BrdU and p63 . The number of the p63-expressing mitotic keratinocytes was reduced in the Gpr177K14 basal layer ( Figure 5L–M , O–P and R–S and V ) , but this reduction was restored by transgenically expressed BMP4 ( Figure 5N , Q , T and V ) , suggesting an involvement of p63 in maintaining the high proliferative potential of basal cells mediated by the Wnt/BMP regulatory axis during epidermal stratification . To further elucidate the mechanism underlying epidermal stratification mediated by BMP signaling , we examined the activation of Smad1/5/8 mediators that transduce the BMP canonical pathway . Immunostaining of phosphorylated Smad1/5/8 revealed that their activations were significantly affected in the dermis , but not in the epidermis of Gpr177K14 mice ( Figure 6A , B , G and D , E , H ) . The dermal-specific effect was restored by transgenically expressed Bmp4 in Gpr177K14/Tg-pmes-Bmp4 mutants ( Figure 3H–I , Figure 6A , B , C , G and Figure S9 ) . In contrast , activation of BMPR1A-mediated signaling failed to restore dermal activation of Smad1/5/8 in the Gpr177K14/Tg-pmes-caBmpr1a mutants ( Figure 6D , E , F , H and Figure S9 ) , consistent with non-cell autonomous effects of BMP signaling on the spinous layer ( Figure 3 ) . These findings strongly suggest that BMP signaling functions primarily in the dermis , through the canonical pathway , to regulate downstream signaling molecules that act back on the epidermis to control epidermal stratification . We next sought to identify the downstream mediators of BMP signaling on epidermal stratification . FGF signaling came to our attention because several FGF ligands are known to be expressed exclusively in the dermal cells [22] , [39] , and knockout of Fgf10 or its receptor FGFR2-IIIb leads to epidermal hypoplastic defects [23] , similar to that seen in Gpr177K14 mutants ( Figure 1 ) . Using real time RT-PCR analysis , we found that Gpr177 deficiency significantly diminishes the expression of Fgf7 ( KGF ) and Fgf10 ( Figure 7A ) , both working in concert to activate downstream signaling via FGFR2-IIIb [24] , [26] , [28] , [40] . Furthermore , the reduced expression of Fgf7 and Fgf10 in Gpr177K14 mutants was restored by transgenic Bmp4 expression ( Figure 7A and Figure S10A–C ) . Interestingly , a decrease in the expression of epidermal-specific Fgfr2IIIb was not significantly detected in the Gpr177K14 mutant at the early stage , but was observed at E14 . 5 ( Figure 7A ) , suggesting an indirect consequence of activation . This reduction of Fgfr2IIIb expression was restored in Gpr177K14Tg-pmes-Bmp4 mice ( Figure 7A ) . In vitro beads implantation assays further demonstrated that exogenously applied BMP2 or BMP4 was able to induce the expression of Fgf7 ( 17/20 in BMP2 implants and 15/21 in BMP4 implants ) and Fgf10 ( 15/19 in BMP2 implants and 22/25 in BMP4 implants ) in the dermal explants of Gpr177K14 mice ( Figure 7B ) , supporting our hypothesis that FGF signaling acts downstream of the Wnt/BMP regulatory axis . To further determine if both Fgf7 and Fgf10 are transcription targets of pSmad1/5/8 signaling , we tested potential binding of pSmad1/5/8 to the regulatory region of Fgf7 and Fgf10 by in vivo chromatin immunoprecipitation ( ChIP ) assays using embryonic limb skin samples . We utilized five sets of oligos pairs ( see Methods and Materials ) that amplify five potential binding sites of Smad1/5/8 [41] , [42] in the regulatory regions of Fgf7 ( Figure 7C ) and two sets of oligo pairs for the binding sites in that of Fgf10 ( Figure 7C ) . Quantitative PCR showed that after immunoprecipitation of linked chromatin there was specific enrichment of Smad to a DNA fragment that corresponds to one of potential sites with antibodies against either pSmad1/5/8 or Smad1/5/8 compared to IgG controls ( Figure 7D ) . Thus , ChIP results strongly support the notion that in embryonic limb skin of mouse in vivo , activated Smad1/5/8 is present in the regulatory regions of Fgf7 and Fgf10 loci . To further demonstrate the involvement of FGF signaling in epidermal stratification , organ culture analysis was performed . The wild type and Gpr177K14 skin explants were supplemented with BSA as controls , or with exogenous FGF7 and FGF10 . Immunostaining of keratinocyte markers was carried out 48 hours in organ culture . Although the wild type explants exhibited minimal response to the exogenous FGF7 and FGF10 , the mutant explants exhibited increased thickness of the spinous layer , elevated number of KRT5-expressing mitotic cells , as well as enhanced expression of p63 in the presence of FGF7 and FGF10 ( Figure 8A and Figure S10 ) . Our results thus uncover a functional requirement of the Wnt/BMP/FGF signaling axis as well as their signaling interplay across the epidermis and dermis to orchestrate epidermis stratification .
The Wnt , BMP , and FGF signaling pathways play critical roles in the embryonic development of the skin [11] , [23] , [24] , [25] , [43] , [44] . Recent studies using mouse models with Wls/Gpr177 deletion have shown that Wnt secreted from the epidermis is essential for the dermal activation of the canonical Wnt pathway and activation of BMP signaling during hair follicle induction [14] , [33] . However , how Wnt , BMP , and FGF pathways interact in the genetic networking that regulates the epidermal stratification during embryogenesis remains unclear . Here we used a transgenic Bmp4 mouse line to successfully rescue the defective epidermal stratification of Gpr177K14 mice . We dissect the sequential relationship and signaling crosstalk by which these key pathways interact and mediate epidermal stratification . Based on our results , we propose a genetic hierarchy model that integrates Wnt , BMP , and FGF signaling in the regulation of epidermal stratification ( Figure 8B ) . In this model , a BMP/Smad1/5/8/FGF7/10 signaling cascade in the dermis is activated by epidermal Wnts and feedbacks to regulate basal cell proliferation and the subsequent epidermal stratification . Although the specificity of the Cre mouse line used in this study allows us to present this molecular circuit based on data from the limb skin , our observations from the dorsal skin suggest that the molecular responses involved in this model do not bias the body regions ( Figure S1 , S4 , S6 , S8A ) . Our in vivo results showed that the proliferating basal cells expressing ΔNp63 were targets of the epidermal Wnt signal , and failed expression of ΔNp63 accounts for the hypoproliferation of these basal cells in the absence of epidermal Wnt . It is consistent with the functional importance of p63 in controlling basal cell proliferation of epidermal development and homeostasis [5] , [10] , [18] , [45] , suggesting that sustained expression of Wnt pathway regulated ΔNp63 is critical in maintaining the capability of basal keratinocytes to form the stratified epidermis in the developing mouse embryo . ΔNp63 has been implicated in the developmental program of epidermal stratification through several mechanisms , including aymmetric division of basal cells and cell cycle exit of intermediate suprabasal cells [3] , [5] , [46] , [47] . Although the basal layer lacking epidermal Wnt failed to maintain the proliferative capability of ΔNp63-expressing cells to form a normal spinous layer , the developmental events of epidermal stratification do take place normally , as evidenced by the occurrence of the asymmetric basal cell division to form intermediate mitotic keratinocytes and the replacement of these cells by post-mitotic keratinocytes in spite of a thinned spinous layer . Hence , our studies suggest that the mechanism by which epidermal production of Wnt affects the vertical expansion of the epidermis underlying the ΔNp63-governed basal keratinocytes is independent of both initiation of the intermediate keratinocytes and cell cycle exit for epidermal differentiation . Notably and interestingly , unlike the effects of autocrine Wnt signaling on the interfollicular epidermal stem cells ( IFESCs ) of adult skin [48] , loss of epidermal Wnt production in the embryonic skin in our study is not associated with premature differentiation of basal cells . Given the evidence of the embryonic epidermis as a tissue source for activation of β-catenin/Wnt signaling in the dermis of the developing skin [14] , [33] , there appears to exist a functional requirement for paracrine Wnt signaling in the maintenance of proliferative basal cells in epidermal stratification of embryonic skin . Epidermal deletion of Gpr177 disrupts the canonical Wnt signaling in the dermis [14] , [33] at E13 . 5 , prior to the formation of the intermediate keratinocytic layer and maturation of the spinous layer [3] , [5] , [9] . Subsequently , expression of Bmp2 , Bmp4 , Bmp7 , Fgf7 , and Fgf10 , critical for epidermal development [21] , [22] , [39] , [44] , [49] , is specifically disrupted in the dermis [14] , indicating that Wnt signaling functions upstream of these signals . BMP signaling appears to act downstream of Wnt signaling to mediate Wnt function , because activation of BMP signaling ( Smad1/5/8 signaling ) in the dermis of Gpr177K14 mutants successfully rescues the development of epidermal stratification and underlying molecular events . Irrespective of the contribution of BMPR1A and BMPR1B [36] , [50] , canonical BMP signaling is activated both in the epithelium and in the dermal mesenchyme of developing skin [51] , [52] , [53] . Our findings show that while the expression of transgenic Bmp4 is activated in the epidermis of Gpr177K14 mice , the activation of canonical BMP signaling in the dermis enable it to rescue epidermal stratification , suggesting that BMP/Smad1/5/8 signaling in the dermis mediates Wnt signaling to control basal cell proliferation , consistent with the recognized role of balanced BMP signaling in the maintenance of epidermal stem cells , progenitor cell differentiation , and hair follicle induction [1] , [21] , [36] , [44] , [54] . Based on the specific activation of Smad1/5/8 pathway by non-cell autonomous transgenic BMP4 seen in the dermis of Gpr177K14 mutants , we suggest that the downstream signaling feedback mechanism is required for the regulation of epidermal basal cells . Given that loss of epidermal Wnt production at least partially phenocopies the epidermal defects in mice lacking Fgfr2-IIIb [23] , the expression of Fgf7 and Fgf10 in the dermis is directly dependent on the presence of BMP/Smad1/5/8 signaling in the dermis in response to Wnt signaling . This implicates FGF7/10 as the downstream mediator for canonical BMP signaling in the dermis for the maintenance of basal cell proliferation . This hypothesis is supported by our skin organ culture experiments where exogenously applied FGF7/FGF10 are sufficient to functionally attenuate the reduction of proliferative basal cells and to rescue the hypoplastic spinous layer of the Gpr177K14 skin , consistent with the function of FGF7 and FGF10 in epidermal development [25] , [28] , [55] . It would be interesting to see if other keratinocyte mitogens such as EGF can exert similar rescue functions as the FGFs in future investigations . Nevertheless , we propose that in normal stratification of embryonic epidermis , FGF7 and FGF10 secreted from the dermis diffuse to the epidermis to mediate feedback regulation of Wnt and BMP/Smad1/5/8 signaling , which is required for the maintenance of proliferative keratinocytes in the basal layer through modulation of ΔNp63 [56] , [57] . Consistent with previous studies that showed FGFR2 is a transcription target of p63 in the epidermis [56] , [58] , our quantitative RT-PCR results showing the down-regulation of Fgfr2-IIIb at the late stages of epidermal development further support a role of Fgfr2 signaling acting downstream of p63 in epidermal development . Nonetheless , our data suggest that the FGF7/FGF10 function as feedback factors to epidermis , but cannot rule out the possibility of involvement of additional feedback mechanisms [58] , [59] between FGF7/10 , Fgfr2 , and p63 in the epidermis . However , the mechanism of how FGF7/10 signaling feedbacks to the epidermis and positively regulates ΔNp63 to maintain the proliferative basal cells remains unknown and warrants future studies . In the adult skin , interfollicular epidermal basal cells , unlike hair follicles , proliferate throughout animal life . Recent studies on subtle genetic deletions by Millar and colleagues [60] have distinguished that Wnt/β-catenin signaling contribute to the mechanism controlling interfollicular epidermal cell ( IFE ) proliferation in the postnatal skin rather than the long-term maintenance of IFE stem cells . In embryonic skin development , our current study supports the notion that the epidermal Wnt initiates mesenchymal responses in the dermis by activating a BMP-FGF signaling cascade . This activation is crucial for the feedback regulations that control the stratification processes in the interfolliclular epidermis , indicating a profound effect of Wnt on signaling interplays across the epithelium and the mesenchyme in orchestrating the basal cell proliferation during epidermal stratification .
Mice carrying Gpr177 floxed allele [30] was crossed with K14-Cre transgenic mice [61] to generate mice with epidermal loss-of-function of Gpr177 ( Gpr177K14 ) . A Dermo1-Cre mouse was crossed to Gpr177 floxed allele to delete Gpr177 in dermal compartment of the skin [14] . TOPOGAL reporter [62] , BATGAL reporter [63] , R26R reporter , Dermo1-Cre mice , and transgenic K14-Cre mice were purchased from The Jackson Laboratory , Maine . Generation of transgenic Tg-pmes-Bmp4 and Tg-pmes-caBmpr1a mice has been described previously , in which the transgenic allele expresses Bmp4 ( or caBmpr1a ) and Gfp ( Green fluorescent protein ) simultaneously via an IRES ( Internal Ribosome Entry Site ) [34] , [35] . Animal experimental protocols were approved by The Animal Committee of Hangzhou Normal University , China . Embryo collection , histology , and in situ hybridization for whole-mount and on sections were performed as previously described [32] . For real-time RT-PCR , embryonic autopods were dissected and treated with 0 . 1% collegenase to separate the dermal and epidermal compartments . RNA extraction using RNA isolation kit ( ambion , RNAqueous-4RNA ) and real-time RT-PCR analysis for RNA expression were performed as previously described [32] . The primers: QAxin2: 5′-ACGCAC- TGACCGACGATT-3′ and 5-AAGGCAGCA- GGTTCCACA-3′; QFzd1: 5′-GAGTTCTGGACCAGTAATCCGC-3′ and 5′- ATGAGCCCGT- AAACCTTGGTG-3′; QLef1: 5′- AACGAGTCCGAAATCATCCCA-3′ and 5′- GCCAGAGTA- ACTGGAGTAGGA-3′; QTcf4: 5′-GATGGGACTCCCTATGACCAC-3′ and 5′- GAAAGGGTT- CCTGGATTGCCC-3′; QBmp2: 5′- TCTTCCGGGAACAGATACAGG-3′ and 5′- TGGTGTCC- AATAGTCTGGTCA-3′; QBmp4: 5′-GACTTCGAGGCGACACTTCTA-3′ and 5′- GAATGA- CGGCGCTCTTGCTA-3′; QBmp7: 5′-AGGGCTTCTCCTACCCCTAC-3′ and 5′- GGTGGTAT- CGAGGGTGGAAGA-3′; Q18S: 5′- GAAACGGCTACCACATCC-3′ and 5′- ACCAGAC- TTGCCCTCCA-3′; QDkk1: 5′- GACCTGCTACGAGACCTGGA-3′ and 5′- CTGGAGAGGG- TATGGTTGCC-3′; QFgf7: 5′-CAGAACAAAAGTCAAGGAGCAACCG-3′ and 5′- GTCGCTCGGGGCTGGAACAG-3′; QFgf10: 5′- TCAGCGGGACCAAGAATGAAG-3′ and 5′-CGGCA- ACAACTCCGATTTCC-3′; QFgfr-IIIb: 5′- CCTCGATGTCGTTGAACGGTC-3′ and 5′- CAGCATCCATCTCCGTCACA-3′ . QTg-Bmp4: 5′- GGGCTGGCCATTGAGGTGAC-3′ and 5′-ATGGCGACGGCAGTTCTTATTCTT-3′ . QTg-caBmpr1a: 5′- TAATAACACATGCATAACTAAT-3′ and 5′-GCTTTTGGTGAATCCTTGCA -3′ . Cell proliferation rate was measured by BrdU incorporation as previously described [32] . Briefly , timed pregnant mice were injected intraperitoneally with BrdU solution at a dosage of 3 mg/100 g of body weight using BrdU Labeling and Detection Kit ( Roch Applied Science ) 30 minutes prior to embryo collection . Cell apoptosis was detected with TUNEL assay kit ( Roche Applied Science ) . At least 4 embryonic limbs for each genotype were fixed in 4% paraformaldehyde and processed for at 5–7 µm paraffin sections for immunofluorescence analysis according to manufacturer's instructions . Embryonic limb were fixed in 4% PFA for 30 minutes , washed several times in PBS , and then processed for either paraffin sections or cryostat sections . For cryostat sections , samples were treated for in 5% sucrose and 15% sucrose , 2 hours each , in 30% sucrose . For 2–3 days . Samples were embedded in OCT and sectioned at 20 µm . To conduct immunohistochemical staining , sections were washed 3 times in PBST ( 0 . 1%Triton X-100/PBS ) , then blocked in 5% BSA for 30 minutes , and incubated with primary antibodies diluted with 5% BSA at 4°C overnight in a humid chamber . Sections were subsequently washed in PBST , 3 times for 10 minutes each . Secondary antibodies ( 1∶1000 ) and DAPI ( 1∶500 ) diluted in 5% BSA were applied for 30 minutes in the dark . Following application of secondary antibodies , the sections were washed several times with PBST , for 10 minutes for each , mounted with Mowiol ( Sigma ) and stored at 4°C . Primary antibodies used in this study were commercially purchased from Abcam , as detailed below: Cytokeratin 5 ( ab24647 ) , Cytokeratin 10 ( ab9025 ) , Cytokeratin 1 ( ab24643 ) , Filaggrin ( ab24584 ) , Loricrin ( Ab24722 ) , Anti-laminin ( ab14055 ) , p63 ( ab53039 ) . Antibody against ΔN-p63 was purchased from Santa Cruz ( sc-8609 ) , Antibody against BrdU was purchased from Roche ( 19691800 ) and antibody against pSmad1/5/8 purchased from Cell Signaling . Antibody against FGF10 was purchased from Santa Cruz ( sc-7375 ) . For quantification of proliferation , BrdU-positive cells were counted ( n = 3–7 limb samples , ≥15 consecutive fields at 40× magnification ) and calculated as a percentage of antibody labeled cells and total nuclear stained cells ( DAPI positive ) otherwise within a defined arbitrary area . For quantification of pSmd1/5/8-positive cells in either the epidermis or the underlying dermis in Figure 6G–H , the numbers of pSmad1/5/8 positive cells in every 300 DAPI positives were counted and calculated as a percentage ( n = 3–5 limb samples , ≥15 fields at 40× magnification for each genotype ) . For quantification of epidermal p63-positive cells in Figure 5 , p63-posive cells were counted and calculated in similar way as described above ( n = 3 limb samples for each geneotype ) . Statistical significance was determined using Student's t-test . Embryonic limbs were dissected from embryos at E13 . 5 and dorsal skin was separated manually using fined forceps and placed dorsal upward onto a Nucleopore membrane in a culture plate with a central well . Protein beads were soaked with BMP2 ( 100 ng/µl , R&D ) , BMP4 ( 100 ng/µl , R&D ) , BSA ( l00 ng/µl ) . Explants were cultured at 37°C for 24 hours after implantation of beads onto explants . Skin organ culture of the dorsal-autopod was conducted using a modification of a previously published procedure [24] . Briefly , dorsal skin portions were dissected from embryonic autopods ( hands/feet ) at late E13 . 5 with the assistance of 0 . 1% collagenase treatment . Skin explants were placed epidermal side up onto a Nucleopore filter ( Whitman , pore-size 0 . 7 µm ) that was coated with rat tail collagen type 1 ( Sigma ) in an organ culture plate with a central well , and cultured in DMEM without serum in 5% CO2 for 72 hours . Protein mixtures of recombinant FGF7 ( R&D ) and FGF10 ( R&D ) were applied onto DMEM medium at a final concentration of 250 ng/µl each , and the protein-containing media were replaced every 12 hours . In parallel experiments , BSA was applied onto DEME medium at the same concentration of proteins as control . Organ-cultured skin samples were fixed with 4% PFA and processed for paraffin sections for either immunohistochemistry or H&E staining . β-Gal staining for both whole-mount and cryostat sections were performed with commercial purchased Kit ( Roche ) according to manufacturer's instructions . For electronic microscopic analyses , embryonic limbs were fixed in 2 . 5% glutaraldehyde and dehydrate through graded ethanol and acetone . Samples were processed according to standard protocols Limb skin tissues from E13 . 5 mouse embryos were cut into small pieces , and then rinsed in 1% formaldehyde/PBS for 30 min on ice for cross-linking . The cross-linking reaction was stopped by adding glycine to a final concentration of 0 . 125 M and rotating for 5 min . The crosslinked tissues were ground by Dounce tissue grinder in tissue lysis buffer from Magna ChIP G Tissue Kit . Lysed cells were collected by spin at 10 , 000× g for 5 min . The pelleted cells were resuspended in 200 µl of Micrococcal nuclease buffer per 30 mg of the pelleted cells . The resuspended cells were digested with 1 µl of Micrococcal nuclease ( New England Biolabs ) at 37°C for 20 min . Then the reaction was stopped by adding EDTA to a final concentration of 50 mM and followed by sonication on ice at 30 W for 12 pulses of 1 second on , 3 seconds off to further disrupt and release chromatins . Chromatin immunoprecipitation was performed with antibody against Smad1/5/8 ( Santa Cruz , sc-6031 ) , pSmad1/5/8 ( Cell signaling technology , 9511 ) or normal rabbit IgG ( Beyotime , A7016 ) using Magna ChIP G Tissue Kit ( Millipore ) according to the user manual . For the detection of the immunoprecipitated Fgf7 and Fgf10 promoter region , eluted DNA was used as template for quantitative real time PCR analysis with primers specific for Smad-binding sites [41] , [42] . Real-time PCR was performed in triplicate using SsoFast EvaGreen Supermix with CFX96 Real-Time PCR Detection System ( Bio-Rad Laboratories ) . Primers: Fgf7-L1:5′-CTCCATCCTGGTTTTCCTCC-3′ and 5′-GAATAGGACACAGGAAGACAG-3′; Fgf7-L2:5′-AACCTGCTCAGTGACATTCC-3′ and 5′-ACTACAGAATGCCCAGTCTC-3′; Fgf7-L3:5′-TTAGGGTGGTGATACGATGG-3′ and 5′-CTTTCCAGCCTGAGCTTGTG-3′; Fgf7-L4:5′-AGCTGAGCCATGGGGAAGTA-3′ and 5′-GGCTGAGAAGACCTAGTTTC-3′; Fgf7-L5:5′-TTGCTTCCAATGAGGTCAGC-3′ and 5′-GATTTTCTCCGTGTGTGAGC-3′; Fgf10-L1:5′-GGCCATAGAAACAGAGCATG-3′ and 5′-GCTTCAGATTAGAATGGTACC-3′; Fgf10-L2 , 3:5′-GCAATTAGCAGGAGCTGCAG-3′ and 5′-GATGCCTTTG- CTCTGAGCTG-3′ . | Epidermis , a thin layer of stratified epithelium forming the outmost surface of the skin , provides the crucial function to protect animals from environmental insults , such as bacterial pathogens and water loss . This barrier function is established in embryogenesis , during which single layered epithelial cells differentiate into distinct layers of keratinocytes . Many human genetic diseases are featured with epidermal disruption , affecting at least one in five patients . Skin regeneration and future therapeutics require a thorough understanding of the molecular mechanisms underlying epidermal stratification . Wnt ligands have been implicated in hair follicle induction during skin development and self-renewal of stem cells in the interfollicular epidermis of adult skin; however , little is known about the mechanism of how Wnt signaling controls epidermal stratification during embryogenesis . In this study , by using a genetic mouse model to disrupt Wnt production in skin development , we found that signaling of epidermal Wnt in the dermis initiate mesenchymal responses by activating a Bone Morphogenetic Protein ( BMP ) and Fibroblast growth factor ( FGF ) signaling cascade , and this activation is required for feedback regulations in the embryonic epidermis to control stratification . Our findings identify a genetic hierarchy of signaling essential for epidermal-mesenchymal interactions , and promote our understanding of mammalian skin development . | [
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| 2014 | BMP-FGF Signaling Axis Mediates Wnt-Induced Epidermal Stratification in Developing Mammalian Skin |
The prevalence of previously undiagnosed leprosy ( PPUL ) in the general population was determined to estimate the background level of leprosy in the population and to compare this with registered prevalence and the known PPUL in different levels of contacts of leprosy patients . Multistage cluster sampling including 20 clusters of 1 , 000 persons each in two districts with over 4 million population . Physical examination was performed on all individuals . The number of newly found leprosy cases among 17 , 862 people above 5 years of age from the cluster sample was 27 ( 19 SLPB , 8 PB2-5 ) , giving a PPUL rate of 15 . 1 per 10 , 000 . PPUL in the general population is six times higher than the registered prevalence , but three times lower than that in the most distant subgroup of contacts ( neighbour of neighbour and social contacts ) of leprosy patients in the same area . Full village or neighbourhood surveys may be preferable to contact surveys where leprosy is highly endemic .
For over 60 years it is known that contacts of leprosy patients have a higher risk of developing leprosy than people in the general population . [1] Besides the type of leprosy of the index patient , i . e . multibacillary ( MB ) leprosy , the physical distance is also an important factor determining this risk . [2] It is likely that , as the distance increases , the relative risk for having leprosy as compared to the general population gradually comes down to one . Contact examination is an important intervention strategy to find early leprosy cases among close contacts of recently diagnosed leprosy patients , but it is unclear to what level of contact this is effective in terms of preventing new cases of leprosy and transmission of M . leprae in the population . Therefore it is important to know the background prevalence of leprosy in the population . As part of a larger study into transmission of M . leprae and the possibility to target contacts with preventive interventions such as chemoprophylaxis[3] , we estimated the background prevalence of leprosy in an endemic community through a random sample of the general population . The aim of the study was to establish the prevalence of previously undiagnosed leprosy ( PPUL ) in the general population and compare this with the registered prevalence of leprosy , and with the prevalence of PPUL among different levels of contacts of leprosy patients in the same population .
A total of 20 clusters of 1000 people each were randomly sampled from the 13 sub-districts ( thana's ) . One to three clusters were allocated to each sub-district proportionally to the size of its population . A list of unions ( in rural areas ) and wards ( in urban areas ) per sub-district was drawn up . A union or ward has an average population of around 23 , 500 . In case the population of a large union was more than three times the size of that of the smallest union , the largest union was split . Then one to three unions ( the number of clusters allocated to that sub-district ) were selected from the list by means of computerised randomisation . Per selected union a list of all sub-unions ( mostly equivalent to villages ) was prepared in such a way that the population of the largest village was maximally three times the population of the smallest . These sub-unions have an average population of 5300 . Grouping of small villages was sometimes needed , as the accepted minimum size was a population of 1600 ( estimation based on census 1991 ) . The computer then randomly selected one sub-union per union . Three out of the twenty clusters were thus allocated to urban areas , which is a proper reflection of the population figures . The surveys of all clusters were performed between November 2002 and February 2003 . The population of the village/area was informed in advance about the purpose and time the team would perform the survey . During the survey the people were asked about symptoms of leprosy and a body check was performed . Genital areas , and for females also the buttocks and the breasts , were not examined . The survey included all people present , whereby female health workers examined the adult females . It started at the northern border of the selected area and stopped when about 1000 people were examined . The criteria used for diagnosis and classification were those of the local leprosy control programme , which follows the WHO guidelines [5] , but those patients with a single lesion with a satellite were recorded as single lesion paucibacillary ( SLPB ) and not as paucibacillary with 2–5 lesions ( PB2-5 ) . [6] All persons suspected of having leprosy were referred to an experienced medical doctor for confirmation . If the disease was confirmed , people were offered regular treatment . All data were entered on registration cards , whereby partly filled cards were used for the next household . Data were analysed by means of descriptive statistics and logistic regression with the Statistical Package for the Social Sciences ( SPSS for Windows , release 11 . 0 . 1 , SPSS Inc . , Chicago , Illinois ) . We obtained ethical clearance from the Ethical Review Committee of the Bangladesh Medical Research Council in Dhaka ( ref . no . BMRC/ERC/2001-2004/799 ) . All subjects were informed verbally in their own language ( Bangla ) about the study and invited to participate . Written consent was requested from each adult . For children consent from a parent or guardian was given .
The total number of people enumerated on the registration cards was 20 , 299 of whom 100 were excluded because there were missing data in the records . Of 52 people it was known that they were released from leprosy treatment ( RFT ) before the survey . As cured leprosy patients presumably can become infected again , these known RFT cases were not excluded . There were 2337 children ( 1208 male and 1129 female ) below the age of five years . As we used the figures in comparison to the figures from the COLEP chemoprophylaxis trial from which under-fives were excluded[3] , the children below the age of five were also excluded from the analysis in this study . This left 17 , 862 persons for this analysis . Table 1 shows the sex and age distribution by cluster . Among these people , 27 previously undiagnosed cases of leprosy were found . The PPUL is thus 15 . 1 per 10 , 000 ( 95% CI = 9 . 4–20 . 8 ) . All newly found cases had PB leprosy ( 19 SLPB , 8 PB2-5 ) . None of the children younger than 5 years of age had leprosy , so when they are included , the PPUL comes down to 13 . 4 per 10 , 000 . Table 2 shows the PPUL per age group and by sex . There is no difference in risk between the sexes , but there is a trend that people of higher age are more at risk . When the subjects are divided into two age groups ( under 30 years of age and 30 years and above ) , age is a statistically significant risk factor . The OR for those 30 years of age or older is 2 . 55 ( 95% CI = 1 . 17–5 . 57 , p = 0 . 019 ) .
The PPUL in northwest Bangladesh in the population of 5 years and older , as found by means of a random cluster survey , is 15 . 1 per 10 , 000 . This study , which included about 0 . 5% of the total population of the area , was based on established multistage cluster sampling techniques . We believe that the results give a reliable picture of the leprosy situation in northwest Bangladesh , in an area where an extensive leprosy control programme has been implemented for more than 10 years . Potential sources for selection and information bias were considered , especially as only those present during the survey were included . Selection bias on cluster level is not likely , but on individual level selection bias is possible as the survey is announced in advance and those afraid of the diagnosis may go into hiding . Males are less likely to be at home during the day and indeed only 42% of those examined are males . In our data , however , the PPUL among males and females is the same . It is possible that , due to stigma , those with leprosy have a higher chance of being unemployed or rejected at school , so they could be over-represented at the survey , but as all patients found were in the early stage of the disease , this does not seem to be a likely reason for the high number of cases found in our study . We conclude that the possible sources of bias probably have had no effect . In the past , over-diagnosis has not been a problem in this particular field programme , as was confirmed by an independent evaluator in 2001[7] , but to avoid possible over-diagnosis in this study , all suspected cases were seen by senior leprosy control officers with more than 5 years experience in the diagnosis of leprosy at referral centre level , and confirmed by a medical doctor . We found that the PPUL ( including children under five ) found by active screening was nearly 6 times higher than the registered prevalence ( 13 . 4 vs . 2 . 31/10 , 000 ) . Registered prevalence is largely based on passive case detection . A large difference between the official new case detection ( NCD ) or prevalence , based on passive case detection , and the NCD or prevalence found by door-to-door surveys has been described before . For example , Schreuder et al . found by a rapid village survey in Java , Indonesia , two and a half times the number of known cases[8] , and Bakker et al . found during a survey on a few small Indonesian islands 96 cases of leprosy of whom only 11 were previously known . [9] Different sample surveys in India have also revealed sample prevalences 4–5 times the recorded prevalence . [10] Self-healing of leprosy contributes to the difference between active and passive case-finding . In South India Ekambaram et al . found that the percentage of self-healing among non-lepromatous patients was around 74% . [11] In Africa Browne found that 34% of non-treated patients healed spontaneously . [12] Table 3 shows the PPUL in the general population sample as described in this paper , together with the PPUL in the subgroups of contacts of leprosy patients as found during the intake of the COLEP trial . [13] These subgroups were defined by their physical distance to the index patient . The age distribution in the general population examined is similar to the distribution in the contact group , so this is not a major cause for bias . In the contact group of the COLEP study as a whole , the PPUL rate was 73/10 , 000 , compared to 15 . 1/10 , 000 in the population sample . [3] , [13] With regard to the different categories in the contact group , we conclude that even in the most distant category ( the neighbours of the neighbours and social contacts ) the PPUL rate ( 49/10 , 000 ) does not come down to the same level as that of the general population . It may therefore be preferable under such high-endemic circumstances to conduct full village or neighbourhood surveys instead of ( close ) contact surveys . There is a marked variance in PPUL among the different clusters . A gradient along geographical lines was not found . The clusters with a low number of newly found cases are scattered over both districts , as are the clusters with the highest numbers . In the three urban clusters however , relative high numbers of cases were found . This is in contrast to the findings of Kumar et al . in Agra , India , where the prevalence of leprosy in the urban areas was about one third lower than in the rural areas . [14] Sterne et al . observed a lower incidence of leprosy in the semi-urban district capital of the Karonga District in Malawi[15] , while Lapa et al . report that in the State of Pernambuco , Brazil , leprosy is mainly an urban disease . [16] In conclusion , our data show that the PPUL in the general population is six times higher than the registered prevalence , but three times lower than that in the most distant subgroup of contacts of leprosy patients in the same area . It has to be kept in mind however , that most new cases in populations where leprosy is relatively highly endemic come from the non-close contact group . Hence full village or neighbourhood surveys might be preferable to contact surveys under such circumstances . [17] There are indications that in lower endemic areas the incidence of leprosy among contacts declines faster as the physical distance to the patient increases . [18] If that is indeed the case , screening of contacts further removed from the patient might not be as useful in lower endemic areas . | In order to estimate the level of leprosy in an area with many leprosy patients , we determined the prevalence of previously undiagnosed leprosy in the general population and compared this with the registered ( or known ) number of leprosy patients . We also compared it with the known prevalence of leprosy in contacts of leprosy patients . We examined 20 randomly selected geographical clusters of 1 , 000 persons each in two districts of Bangladesh , with over 4 million population . Physical examination was performed on all individuals . The number of newly found leprosy cases among 17 , 862 people above 5 years of age from the clusters was 27 , giving a rate of previously undiagnosed leprosy of 15 . 1 per 10 , 000 . This rate is six times higher than the registered prevalence , but three times lower than the rate in the most distant subgroup of contacts ( neighbour of neighbour and social contacts ) of leprosy patients in the same area . We conclude that in areas where leprosy is common , it may be preferable to do full village or neighbourhood surveys when a new leprosy patient is found , rather than to limit contact surveys to close contacts only , such as household members . | [
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| 2008 | The Prevalence of Previously Undiagnosed Leprosy in the General Population of Northwest Bangladesh |
One Health addresses complex challenges to promote the health of all species and the environment by integrating relevant sciences at systems level . Its application to zoonotic diseases is recommended , but few coherent frameworks exist that combine approaches from multiple disciplines . Rabies requires an interdisciplinary approach for effective and efficient management . A framework is proposed to assess the value of rabies interventions holistically . The economic assessment compares additional monetary and non-monetary costs and benefits of an intervention taking into account epidemiological , animal welfare , societal impact and cost data . It is complemented by an ethical assessment . The framework is applied to Colombo City , Sri Lanka , where modified dog rabies intervention measures were implemented in 2007 . The two options included for analysis were the control measures in place until 2006 ( “baseline scenario” ) and the new comprehensive intervention measures ( “intervention” ) for a four-year duration . Differences in control cost; monetary human health costs after exposure; Disability-Adjusted Life Years ( DALYs ) lost due to human rabies deaths and the psychological burden following a bite; negative impact on animal welfare; epidemiological indicators; social acceptance of dogs; and ethical considerations were estimated using a mixed method approach including primary and secondary data . Over the four years analysed , the intervention cost US $1 . 03 million more than the baseline scenario in 2011 prices ( adjusted for inflation ) and caused a reduction in dog rabies cases; 738 DALYs averted; an increase in acceptability among non-dog owners; a perception of positive changes in society including a decrease in the number of roaming dogs; and a net reduction in the impact on animal welfare from intermediate-high to low-intermediate . The findings illustrate the multiple outcomes relevant to stakeholders and allow greater understanding of the value of the implemented rabies control measures , thereby providing a solid foundation for informed decision-making and sustainable control .
The One Health paradigm aims to effectively manage complex risks affecting human , animal , and environmental health by forging new interdisciplinary partnerships and collaborations . Rabies , an acute progressive encephalomyelitis with almost 100% case fatality rate caused by viruses in the genus Lyssavirus , is a zoonotic disease that is responsible for an estimated 55 , 000 human deaths , tens of millions of human exposures , and substantial animal losses annually [1] . It requires a generalised approach if it is to be managed effectively and efficiently [2] . While One Health thinking has come into vogue , systematic integration of various disciplines such as biological , environmental , social , and health sciences to manage health more holistically is often complicated by interdisciplinary and intersectoral barriers to effective collaboration [3] . One major challenge is the paradigm debate caused by the philosophical assumptions that guide the collection and analysis of quantitative ( post-positivist ) and qualitative ( constructivist ) data which may be viewed differently by disciplines . It has been suggested that using both approaches in the same study provides , in combination , a superior understanding of research problems than either approach alone [4] . Another important barrier is the current institutional architecture in which public funds are allocated to specific ministries thereby hindering development of joint public health programmes , which in the case of zoonotic diseases can result in a fragmented approach to control . The most important vector for maintenance of rabies virus and transmission to humans is the domestic dog , with over 90% of human cases attributable to dog bites . The tools to eliminate rabies from animal populations exist , yet relatively few countries are currently rabies-free placing a major strain on public health budgets . Nearly all human rabies deaths occur in developing countries because they are lacking the resources and capacity to provide both adequate pre-exposure prophylaxis and post-exposure prophylaxis ( PEP ) in humans and effective management of the virus in animal populations . The World Health Organisation estimates that the annual cost of rabies may be in excess of US $6 billion per year including an estimated US $1 . 6 billion for PEP [5] . Where rabies control has been successful , efforts have been based on quarantine in an advantageous geographical location ( e . g . United Kingdom ) or the systematic mass vaccination of domestic and wild host populations ( e . g . mainland Europe ) . In the long term , controlling rabies in the dog population through mass dog vaccination has been shown to be more cost-effective than human PEP alone [6] . The World Health Organisation , the World Organisation for Animal Health , and the Food and Agriculture Organisation of the United Nations acknowledge the need for intersectoral collaboration to manage rabies [5] . However , the systematic control of rabies in animal populations requires financial resources , and the technical capacity to plan , implement and evaluate the vaccination campaign; aspects that are often lacking in affected countries . Sustaining control demands political , societal and financial backing to maintain the campaign as well as the logistic and human resource capacity to deliver vaccine , and knowledge of , and access to , target populations . On-going collection of data through surveillance systems to monitor and evaluate the economic and technical efficiency of campaigns is necessary to ensure objectives are being achieved , and surveillance must be continuous following eradication to detect re-emergence of the virus promptly . Many of these components need the active support of the public in affected areas . In many countries where rabies is endemic these requisite criteria are not met , and interventions against other diseases are given a higher priority . As a result rabies is considered a neglected disease . Modern science tends to abstract phenomena and reduce reality into smaller portions that can be easily understood and , as much as possible , be expressed in mathematical terms . While these mathematical abstractions are critical in modelling the dynamics of disease in a population and to assess the effectiveness of interventions , they do not provide an understanding of the support for rabies control measures in society nor do they shed any light on wider-reaching issues such as ethical concerns or animal welfare , in short , they oversimplify reality . For example , anecdotal evidence suggests that some people are not supportive of rabies control measures such as dog culling and actually jeopardise the process by hiding or moving their dogs . Thus , both reductionist in-depth studies , as well as collaboration with other disciplines are needed to understand and plan sustainable and publicly acceptable control programmes . Many projects have focused on individual components of rabies impact , for example the use of pre-exposure prophylaxis and PEP in humans [7]–[10] , the effectiveness of different strategies for dog vaccination [11] , [12] , willingness-to-pay for dog vaccination [13] and the indirect costs of rabies exposure [14] . However , they have all been assessed independently . Assessed in conjunction , they provide important insights into the positive and negative consequences of rabies management and build a robust basis for informed decision-making . This paper proposes a generic framework for the assessment of rabies interventions encompassing a wide range of positive and negative consequences and local conditions in order to assess economic efficiency and illustrates its use by applying it to the rabies control programme in Colombo City , Sri Lanka .
In Colombo City , canine rabies has been endemic for several decades . The national anti-rabies strategy aims to protect people who are exposed and those at risk of contracting the disease , establish dog population immunity and to control the dog population . A well regulated system of PEP is in place , limiting the average number of human rabies cases between 1995 and 2011 to 0 . 65 per year in a city of 650 , 000 ( unpublished data , Veterinary Department of Colombo Municipal Council ) . The Veterinary Department of Colombo Municipal Council used to combat rabies through culling of roaming dogs via carbon monoxide and carbon dioxide poisoning in a gas chamber and vaccination of owned dogs , but canine rabies cases continued to persist in the city . From 2007 to 2012 , following cessation of culling by Presidential decree in 2006 , a modified comprehensive intervention to control rabies was implemented , which included mass vaccination of dogs , targeted sterilisation of both owned and unowned dogs , education of children and adults in bite prevention and rabies awareness , and development of dog managed zones in public areas . The stakeholders involved in the intervention hypothesised that the new measures would lead to a decrease in the number of dog rabies cases , an associated reduction in the administration of PEP to people , an increased acceptance of dogs in society , and overall a positive net value of the intervention in Colombo City . The aim of this case study was to assess the economic value of the intervention explicitly taking into account monetary and non-monetary consequences resulting from the change in rabies prevalence , animal welfare and social acceptance .
The survey in 2007 found 23 dog bites in 1 , 063 household members or an annual incidence rate of 0 . 0216 . The survey in 2010 found 8 dog bites in 559 household members or an annual incidence rate of 0 . 0143 . The difference in incidence rate in 2007 and 2010 was not significant ( p = 0 . 3105 , significance level set at 5% ) . Extrapolating these dog bite incidence rates to the total population of Colombo City of 642 , 163 in 2007 and 644 , 450 in 2010 , respectively , resulted in the following inputs for the economic assessment: 13 , 871 annual dog bites for the baseline scenario and 9 , 216 annual dog bites for the intervention . These figures were multiplied by four to estimate the total number of dog bites for a four year period , which resulted in 55 , 484 and 36 , 864 dog bites for the baseline scenario and the intervention , respectively . The average number of human deaths for the four year duration of the intervention and the baseline scenario , respectively , was three human deaths each for the four year period . The national hospital reported that in May 2006 , 131 people sought care following dog bites and in May 2011 , 160 people were recorded . These monthly figures were multiplied by 48 to estimate proxies for the number of people seeking medical attention for dog bites in Colombo City for the baseline scenario ( n = 6 , 288 ) and the intervention ( n = 7 , 680 ) , respectively . The estimated rate of reporting was 0 . 11 for the baseline scenario and 0 . 21 for the intervention , respectively . The number of dog rabies cases was 19 for 2007 ( proportionally estimated from annual figure for the period June to December ) , 17 in 2008 , 20 in 2009 , 10 in 2010 , and 2 in 2011 ( until June ) . For the baseline scenario , the estimated average number of dog rabies cases per year was 43 , i . e . 172 for the four year duration . The number of dogs culled with a mixture of carbon monoxide and dioxide in the exhaust fumes produced by a freestanding combustion engine was zero in the intervention due to the presidential decree in 2006 that stopped the elimination of dogs and an estimated 9 , 384 in the baseline scenario for the four years . Field data from Colombo City collected by the BPT from 5 July to 13 August 2011 during 24 vaccination sessions in 12 different wards ( total dogs vaccinated = 658 ) showed that a mean 28% ( SD = 21 . 9% ) of the total dogs vaccinated were held by people from the community ( owner or other people ) and the remaining dogs were caught in a net for vaccination . Using this proportion to estimate the number of dogs in the situation ‘dogs held by owner and vaccinated’ resulted in 36 , 300 dogs for the intervention and 25 , 013 dogs for the baseline scenario for the four years . The number of dogs in the situation ‘catch in net and vaccinate’ was estimated at 10 , 740 for the four years of intervention . The number of dogs sterilised in the intervention during the four years was 5 , 323 in total based on records from the Blue Paw Trust . Table 4 summarises the additional investment and the additional outcomes in monetary and non-monetary terms resulting from the intervention when compared with the baseline scenario over a time period of four years . The overall costs of the intervention were US $1 . 03 million , which was the sum of the additional investment of US $818 , 851 for the control measures in the animal health sector and the additional US $215 , 064 spent on monetary human health costs . The net benefits from the intervention were 738 DALYs averted resulting from the reduction in dog bites , increased acceptance of roaming dogs in society and improved animal welfare . The detailed findings are presented below . Table 5 illustrates the total costs incurred for dog rabies control activities for the intervention from different organisations involved ( Sri Lankan government , Blue Paw Trust ) . Table 6 lists the total costs incurred by the Sri Lankan government for dog rabies control in the years 2002 to 2006 which reflect the control costs in the baseline scenario . In the intervention , the largest proportion of the total costs was staff costs ( 33% ) , followed by implementation costs ( 21% ) , other costs ( 19% ) , and planning and preparation costs ( 11% ) . In the baseline scenario , the costs for implementation activities contributed most ( about 92% ) to the total annual costs in all years . The difference in costs between the baseline scenario and the intervention over a time period of four years was US $818 , 851 . The total human health cost per dog bite was estimated at US $159 without using immunoglobulin , US $163 with equine immunoglobulin and US $39 for the people who only needed medical care , but not vaccination . The total human health costs for the four years of intervention and the baseline scenario were US $1 , 179 , 925 and US $964 , 861 , respectively ( Table 4 ) . The difference between the two was US $215 , 064 . The total DALYs lost for the four years related to psychological distress were 1 , 461 for a total 36 , 864 dog bites in the intervention and 2 , 199 for a total 55 , 484 dog bites in the baseline scenario , respectively . The total DALYs lost for a four year period related to human deaths were 83 . 97 for both the intervention and the baseline scenario with three human deaths each . The total number of DALYs averted in the intervention period as compared to the baseline scenario for the four year period was 738 . The sensitivity analyses on the input variables that determined the outcomes “difference in monetary human health costs” and “DALYs averted” over the four years are illustrated in Figures 2 and 3 . For the outcome “difference in monetary human health costs” the most influential variables were the number of people bitten and seeking treatment in the intervention ( outcome changed by 82% ) and the baseline scenario ( outcome changed by 67% ) , respectively , followed by the overhead cost per hospital visit ( outcome changed by 13% ) and the proportion of people presented with dog bites receiving PEP ( outcome changed by 11% ) . All other input variables caused changes in outcome of 1% or less ( Figure 2 ) . The difference in monetary human health costs when varying the two most influential inputs number of people bitten and seeking treatment in the intervention and baseline scenario , respectively , between −30% and +30% from the base is shown in Table 7 . The results demonstrate by how much the inputs need to change for the intervention to create a benefit in terms of monetary human health costs . When keeping the base value for the baseline scenario constant , a reduction of the intervention input by at least 20% would lead to a monetary benefit in the human health sector . The additional expenditures for the intervention spent by the animal health sector could be recovered by monetary human health benefits if , ceteris paribus , the input people seeking treatment in the intervention was 950 ( 12% of the base value ) or the input people seeking treatment in the baseline scenario was 13 , 026 ( 207% of the base value ) . For the outcome “DALYs averted” the most influential variables were the number of dog bites in the baseline scenario ( outcome changed by 45% ) and in the intervention ( outcome changed by 30% ) , respectively , followed by the DALYs lost per dog bite due to psychological distress ( outcome changed by 15% ) . The DALYs lost per human rabies death did not influence the outcome ( Figure 3 ) . Table 8 and Table 9 illustrate the score per situation without taking into account dog numbers and the score per situation taking into account dog numbers . For the intervention , the qualitative estimates ranged between very low and high . For the baseline scenario , the estimates ranged between very low and very high . The overall score was estimated as low-intermediate for the intervention and intermediate-high for the baseline scenario . Table 10 summarises the overall acceptance scores for the baseline scenario and the intervention among dog owners and non-dog owners derived from the two surveys . The Kruskal-Wallis rank test to compare different groups showed that the differences between the four groups of dog owners and non-dog owners were statistically significant ( p = 0 . 001 ) . The post-hoc Wilcoxon rank-sum tests yielded a significant difference between dog owners and non-dog owners in 2007 ( z = 8 . 22 , p<0 . 0001 ) , dog owners and non-dog owners in 2010 ( z = 3 . 836 , p = 0 . 0001 ) , and non-dog owners in 2007 and 2010 ( z = −2 . 71 , p = 0 . 0068 ) . There was no significant difference between all participants in the baseline scenario and the intervention ( z = −0 . 938 , p = 0 . 35 ) . Of the 61 focus group participants , 53 were women and 8 were men . There were 17 housewives and 28 who did not indicate their professions . The rest of the occupations included salesmen , students , nursery teachers , garment makers , an architect and business people . When asked about dog-related issues in the past , the groups described significantly more problems for the past than the present , specifically past problems 7 . 8±1 . 5 and present problems 3 . 3±1 . 2 ( Wilcoxon test , p<0 . 01 ) . Figure 4 illustrates the number of dog related problems reported by the nine focus groups . Significantly fewer groups mentioned rabies and breeding or puppies as problems at present than in the past ( Mc Nemar's test , p<0 . 05 ) . The stark decrease in the perception of rabies as a problem was explained by workshop participants as being due to possession of knowledge about the disease and knowing what to do when bitten by a dog . The population control measures mentioned by participants were sterilisation , vaccination , shelter , re-homing , treatments , birth control injection , dumping , education , and awareness campaigns . The highest preference across all groups was given to sterilisation , vaccination and education . None of the groups mentioned culling as a means of population control . All focus groups indicated that their behaviour following a dog bite had changed . Many groups reported the application of Murunga ( a local plant ) in the past , but would nowadays wash the wound with soap and running water and go to a hospital to seek treatment . The mean acceptable total number of roaming dogs reported in the vicinity ( i . e . street ) was 2 ( SD 2 , range 0 to 10 ) . There was a significant difference in levels of roaming dogs reported for the past and the present across all focus groups ( p<0 . 001 ) ( Table 11 ) . There was no significant difference in the total number of roaming dogs reported by income levels ( p = 0 . 184 ) , whether the household reared dogs ( p = 0 . 708 ) , gender ( p = 0 . 535 ) , and occupation of participants ( p = 0 . 696 ) . The economic analysis showed that the use of an additional US $818 , 851 in the animal health sector to combat rabies and manage the dog population in Colombo City had both negative and positive consequences in society when contrasting the intervention and the baseline scenario . Non-monetary benefits included an increase in the acceptance of roaming dogs among non-dog owners and dog owners , a reduction in animal suffering , and 738 DALYs averted . The increased acceptance of roaming dogs and the DALYs averted increased well-being of society . While reducing animal suffering overall , the intervention strategy at the same time compromised animal welfare ( e . g . due to sterilisation or catching in a net ) . Negative consequences included an increase of US $215 , 064 in human health costs related to seeking health care following dog bites . Hence , there was a net cost to society in monetary terms of US $1 . 03 m and a net benefit in non-monetary terms . The lower number of estimated dog bites and the improvement in reporting of bites and treatment of people indicated that the risk to people of contracting rabies was decreasing . The intervention was shown to be effective , as the official number of dog rabies cases decreased from an average of 43 cases per year ( 2001 to 2005 ) to just two cases in the first six months of 2011 . Ethical aspects relating to the rights and fairness approach in dogs and humans as well as the virtue approach in people included the following: In people: In dogs: The judgement if the good of the intervention outweighed the harm ( the utilitarian approach ) and if it best served the community as whole and not just some members ( the common good approach ) depends on how decision-makers prioritise ethical issues . It might be argued that the avoidance of animal suffering and the increased well-being of people justified the net monetary cost of the strategy . Others might attribute more weight to monetary values resulting from the control activities .
The article proposes a comprehensive framework for assessing multiple aspects of rabies control and combining them in an economic analysis . It is composed of five components ( epidemiological , economic , social , animal welfare and ethical assessments ) that are all interlinked to guide decision-making and the allocation of resources . While almost all parts were covered individually in previous studies , to the authors' knowledge there are no publications on rabies control that cover all these aspects in the spirit of One Health and link them in an economic analysis . The advantage of the framework is its comprehensive nature that provides decision-makers with a wide array of information that they need to be able to take informed decisions on disease management . However , it requires capacity in multiple disciplines , extensive data collection and an acknowledgment of the multi-factorial processes of decision-making . Similar elements essential for One Health decision making have also been identified by others . For example , a framework published after this study was conducted for the estimation of the economic costs of zoonoses [31] conceptually linked epidemiological and economic models and placed them in the context of wider risk management strategies including assessment of the context , hazard identification , risk assessment , capacity building and communication . The approach proposed here can be considered as an expansion of the risk assessment and risk management steps described in the other framework , whilst providing more detail on a specific disease ( i . e . rabies ) and the associated effects . The comparison of additional costs with both monetary and non-monetary outcomes required presenting the results in an unconventional way . On the one hand , this presentation allowed reflecting the complexity of the real world and the various economic consequences related to a decision . On the other hand , the combination of negative monetary and positive non-monetary outcomes made the interpretation more challenging than a conventional net present value or cost-benefit ratio . Cost-benefit analysis is an approach that is intuitively appealing , because it assesses the positive and negative consequences of a strategy in a common unit , generally money . Cost-effectiveness analysis uses the same basic approach , but presents the outcome of a strategy in non-monetary units . The selection of an appropriate measure of effectiveness is critical , and must be in accordance with the control objective . A “CEA is only as valid as its underlying measures of effectiveness and cost” [32] , but unlike in health economics , where attempts have been made to harmonise CEA methodologies and encourage comparability of studies [33] , there are no specific guidelines available yet for its application in animal health . Currently , due to variability of interests , approaches , designs , capacity and resource availability of organisations involved in rabies control , any incremental cost-effectiveness analyses going beyond human health will vary depending on the outcome measures defined . If the scientific community was to find an agreement on a standardised approach to measure outcomes of rabies control in an integrated way , the economic efficiency of such control measures could be compared internationally and the best approach chosen . As long as there is no standardisation of effectiveness measures for rabies or disease control in general , the variety in outcomes will make a meta-analysis difficult or even impossible . The presented framework is a starting point that may help to create awareness and stimulate discussion . A range of approaches were used in the case study to cover the multifaceted control measures implemented which were expected to decrease the number of dog rabies cases , to reduce the number of PEP applied to people , to increase acceptance of dogs in society , and to generate a positive net value overall . The case study illustrates the various components of the proposed framework in a developing country context . Because of the limited availability of resources for the case study , secondary data were used whenever possible and where primary data collection was necessary , low-cost approaches were considered for data collection . While the case study is subject to various limitations as described below , it provides information for Sri Lankan stakeholders involved in rabies control on the profitability and cost-effectiveness of the implemented intervention and demonstrates the advantages and challenges of the proposed framework . Importantly , the number of dog rabies cases was drastically reduced during the time of the intervention to only two in the last six months of the study period compared to a previous high number of dog rabies cases ( an average of 43 per year in the period of 2001 to 2005 ) . This indicated that high enough vaccination coverage was achieved and that good progress was being made towards the elimination of rabies in the years 2014–2015 , the specified long term target . Given that rabies is still prevalent in other parts of the island , it is important to continue intervention and surveillance efforts in Colombo City to maintain the favourable situation until rabies can be eliminated island-wide . One critical variable in the estimation of monetary and non-monetary human health consequences was the number of dog bites . While the number of people seeking health care following a dog bite derived from data from the national hospital showed an increase from 2006 to 2011 , the numbers derived from the two surveys in 2007 and 2010 showed a decrease in the number of dog bites . There are four possible explanations for this increase: 1 ) people were more aware of rabies prophylaxis and went to the hospitals more often , 2 ) there was a better system in place to record dog bites in hospitals , 3 ) there were effectively more dog bites , and 4 ) unknown factors related to the two months of data provided caused a fluctuation in numbers ( a comprehensive data set for the entire period of 2006 to 2011 was not available ) . Given the fact that the intervention substantially decreased the number of dog rabies cases in the population , an increase in the number of dog bites seems highly unlikely . This hypothesis is corroborated by the survey and focus group data . Because the survey data showed a decrease in the number of dog bites and the focus groups an increase in disease awareness , it is most likely that the increase in the number of registered dog bites was due to a higher number of people seeking medical advice in case of dog bites . The analysis of the focus groups demonstrated that people's reaction following a dog bite had changed . All focus groups reported that they would now wash the wound with soap and water and go to the hospital to receive PEP . Also , the development of a better system to record bites in hospitals in recent years was expected to have had a positive impact on the number of registered cases ( personal communication Dr Obeyesekere ) . The difference between the number of dog bites collected from the national hospital and the number estimated from the surveys provided an indication of the rate of under-reporting . The estimated reporting rates indicated an improvement in dog bite reporting in the intervention compared to the baseline scenario . This observation further confirmed the increased rabies awareness of people in the community . However , it also showed that a considerable part of the population did not seek medical attention after being bitten by a dog . As long as rabies is not eradicated from the dog population , people should constantly be informed about the appropriate behaviour in case of a dog bite . The increase of registered dog bite cases in health centres caused an increase in human health costs . For the savings in monetary human health costs to cover the additional investment made in the animal health sector , the number of people seeking treatment following dog bites would have to be reduced drastically as shown in the sensitivity analysis . It is expected that the number of people seeking medical advice will remain high or increase despite a reduction in dog bites , because the on-going intervention activities constantly promote disease awareness . Only elimination of rabies from the dog population will allow reducing the provision of PEP after dog bites . As long as rabies is endemic in the dog population , people bitten by rabies-suspect animals should get a thorough assessment by health professionals and PEP , as recommended by World Health Organisation guidelines . The only way to reduce public health costs in a rabies endemic situation is to find cheaper and equally effective methods of PEP . The public health sector has already initiated such cost savings by using intradermal vaccines and only administering immunoglobulin in priority cases following a sound history taking and assessment . Remarkably , there was a considerable reduction in the number of problems listed in all focus groups . Nearly all groups reported that there had been a reduction in rabies , barking , puppies and breeding behaviour and dog fights since the implementation of the intervention . Thus , dogs were perceived more favourably by people , because they looked healthier and showed reduced breeding and nuisance behaviour . Moreover , some focus group participants indicated that their fear of rabies had decreased drastically , because of their improved knowledge of the disease . The selection of participants was performed independently by the community liaison officers in collaboration with community leaders and therefore not influenced by the staff of the BPT . Because the community liaison officers did not receive fixed criteria about socio-economic status of participants , it is likely that ‘high’ socioeconomic groups represented more the middle level , as those at the truly high end did not have the time or interest to participate and were not known well to the community leaders . To promote open sharing of thoughts and concerns , the facilitator made sure to create a comfortable atmosphere and assured participants that the data would be handled anonymously and that their answers did not have any negative consequences for them . However , it is still possible that a few participants may have felt that a less than positive evaluation would result in discontinuation of the project . While such behaviour introduces bias into the results , it also reflects the social desirability of the project , i . e . a community wanting the project to continue is in itself an indication of the degree of perceived success . A source of bias that could not be controlled was the imbalance in gender representation in the focus groups . Only a few men were able to join the focus groups , which was due to the fact that all groups met during the day when the men were at work . While a variety of approaches are available to assess animal welfare ( e . g . welfare assessment protocols for commercial livestock ) , there are no guidelines in place for the systematic assessment of the impact of rabies and its control on animal welfare . Therefore , we developed a qualitative approach to assess defined situations related to rabies and its control that may negatively affect animal welfare . The assessment was a combination of field data , scientific literature , logical reasoning and professional judgment . Importantly , the scores attributed to the different situations were relative and not absolute . The development of an absolute scoring system would require systematic measurement of physiological and behavioural parameters , which was not within the scope of this project . Taking into account the numbers of dogs in the situation , the highest score ( ‘very high’ ) was attributed to the situation culling dogs via carbon monoxide and carbon dioxide poisoning using the exhaust fumes of a combustion engine , and the lowest scores to the situation of holding dogs by the owner or people from the community , and vaccination . Thus , replacing the culling of dogs by other intervention strategies reduced animal suffering . Because none of the focus groups mentioned culling of dogs as an intervention strategy for rabies or population control , it is most likely that the avoidance of culling dogs not only promotes animal welfare , but also the well-being of people in society who care for the dogs . The ethical assessment helped guide the interpretation of the results . However , it did not attribute weights to the different criteria analysed . Such weights were expected to differ among decision-makers depending on the political agenda , local norms and customs , available resources , experience and personal preferences . Further benefits that were not quantified in the analysis and remain open to further research include a potential reduction of rabies cases in other animals , promotion of responsible dog ownership and thus better animal welfare , and the decrease of fear in the human population . This case study explicitly took into account a range of factors that impact on the value of rabies control measures . By combining different monetary and non-monetary aspects , it not only provided information about the impact of rabies control on monetary public health costs , but also important insights about non-monetary effects , particularly animal welfare and social acceptability that were not only valuable outcomes in themselves , but also helped to explain and support some of the other findings . For example , the epidemiological data on the number of dog rabies cases as well as the information from the surveys on dog bites and the focus groups on disease awareness provide an explanation for the increase in human health costs . Linkages between the individual components could be more formalised by for example making social assessments an integral part of epidemiological analysis . The proposed framework provides a first proposal for looking at rabies control in a holistic way and covers multiple facets that inform decision-making . The framework is expected to help planning impact evaluations of rabies control so that future data collection protocols can take into account not only the health costs , but also consider factors like social acceptance and animal welfare . It thereby helps to conduct integrated assessments for zoonotic disease control and can be further developed to address more complex One Health challenges . | Successful rabies control generates benefits in terms of improved human and animal health and well-being and safer environments . A key requirement of successful and sustainable rabies control is empowering policy makers to make decisions in an efficient manner; essential to this is the availability of evidence supporting the design and implementation of the most cost-effective strategies . Because there are many , at times differing , stakeholder interests and priorities in the control of zoonotic diseases , it is important to assess intervention strategies in a holistic way . This paper describes how different methods and data from multiple disciplines can be integrated in a One Health framework to provide decision-makers with relevant information , and applies it to a case study of rabies control in Colombo City , Sri Lanka . In Colombo City , a new comprehensive intervention was initiated in 2007 based on vaccination , sterilisation , education , and dog managed zones . Results showed that for the four year time period considered , the new measures overall cost approximately US $ 1 million more than the previous programme , but achieved a reduction in dog rabies cases and human distress due to dog bites , reduced animal suffering and stimulated a perception of positive changes in society . All these achievements have a value that can be compared against the monetary cost of the programme to judge its overall worth . | [
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| 2014 | A One Health Framework for the Evaluation of Rabies Control Programmes: A Case Study from Colombo City, Sri Lanka |
The malarial life cycle involves repeated rounds of intraerythrocytic replication interspersed by host cell rupture which releases merozoites that rapidly invade fresh erythrocytes . Apical membrane antigen-1 ( AMA1 ) is a merozoite protein that plays a critical role in invasion . Antibodies against AMA1 prevent invasion and can protect against malaria in vivo , so AMA1 is of interest as a malaria vaccine candidate . AMA1 is efficiently shed from the invading parasite surface , predominantly through juxtamembrane cleavage by a membrane-bound protease called SUB2 , but also by limited intramembrane cleavage . We have investigated the structural requirements for shedding of Plasmodium falciparum AMA1 ( PfAMA1 ) , and the consequences of its inhibition . Mutagenesis of the intramembrane cleavage site by targeted homologous recombination abolished intramembrane cleavage with no effect on parasite viability in vitro . Examination of PfSUB2-mediated shedding of episomally-expressed PfAMA1 revealed that the position of cleavage is determined primarily by its distance from the parasite membrane . Certain mutations at the PfSUB2 cleavage site block shedding , and parasites expressing these non-cleavable forms of PfAMA1 on a background of expression of the wild type gene invade and replicate normally in vitro . The non-cleavable PfAMA1 is also functional in invasion . However – in contrast to the intramembrane cleavage site - mutations that block PfSUB2-mediated shedding could not be stably introduced into the genomic pfama1 locus , indicating that some shedding of PfAMA1 by PfSUB2 is essential . Remarkably , parasites expressing shedding-resistant forms of PfAMA1 exhibit enhanced sensitivity to antibody-mediated inhibition of invasion . Drugs that inhibit PfSUB2 activity should block parasite replication and may also enhance the efficacy of vaccines based on AMA1 and other merozoite surface proteins .
The phylum Apicomplexa contains several pathogens of major clinical and veterinary importance . These include the aetiological agents of coccidiosis in poultry , theileriosis and babesiosis in cattle , and toxoplasmosis , cryptosporidiosis and malaria in humans . In all cases , the causative agents are parasitic protozoa that share the feature of possessing several life-cycle stages that switch sequentially between replicative intracellular forms and transiently extracellular zoite stages equipped to seek out and invade suitable host cells . Invasion by apicomplexan zoites has been a subject of great attention for two major reasons: first , because it represents a step at which the parasite is exposed to host antibodies and other effector molecules capable of preventing invasion [1] , [2]; and second , because it involves parasite-specific biochemical processes – including a number of protease-dependent modifications [3] and a specialised actinomyosin motor that drives motility and invasion ( reviewed in [4] ) - that are potential targets for the development of new antiparasite drugs . The clinical manifestations of malaria stem from replication of asexual blood stages of Plasmodium spp . in circulating erythrocytes . In the case of the most dangerous malarial species , Plasmodium falciparum , the parasite develops intracellularly over the course of∼48 hours to produce a mature schizont containing around 16 daughter merozoites . The schizont eventually ruptures , releasing the merozoites which rapidly invade fresh erythrocytes , thus perpetuating the cycle . Erythrocyte invasion comprises several discrete steps [5]–[7] , and is facilitated by the discharge of adhesive ligands and other proteins onto the parasite surface from specialised apical organelles called rhoptries and micronemes . Invasion is also accompanied by efficient proteolytic shedding of certain parasite surface and micronemal proteins . These include a micronemal type I integral membrane protein called AMA1 and an abundant glycosyl phosphatidylinositol-anchored merozoite surface protein called MSP1 ( which forms a complex with two additional proteins called MSP6 and MSP7 , collectively referred to as the MSP1/6/7 complex ) . Shedding of these proteins goes virtually to completion upon invasion [8]–[10] . Previous work from our group and others has demonstrated that shedding of P . falciparum AMA1 ( PfAMA1 ) and the MSP1/6/7 complex , as well as another micronemal protein called PTRAMP , is all mediated by the same membrane-associated , calcium-dependent parasite subtilisin-like serine protease called PfSUB2 , which is itself released from micronemes onto the merozoite surface during invasion [11]–[13] . Precise mapping of the single PfSUB2 cleavage sites in P . falciparum MSP1 and PfAMA1 has shown that the sequences that flank the scissile bond lack obvious similarity [12] , [14] , leading us to suggest that PfSUB2 may share characteristics with a number of vertebrate membrane-bound 'sheddases' that cleave relatively unstructured juxtamembrane regions of their protein substrates in a non sequence-dependent manner [15]–[20] . However , the requirements for substrate recognition by PfSUB2 have not been experimentally addressed . In addition to PfSUB2-mediated shedding , under normal conditions of in vitro culture a small fraction of PfAMA1 is also shed by intramembrane cleavage [8] , [12] , likely mediated by either a parasite plasma membrane-localised rhomboid-like protease called PfROM4 [21] or a micronemal rhomboid called PfROM1 [22] . Given the relatively low levels of this intramembrane cleavage , we have previously postulated that it is physiologically unimportant in P . falciparum . Intriguingly , however , shedding of the Toxoplasma gondii AMA1 orthologue , TgAMA1 , takes place exclusively through intramembrane cleavage [8] , mediated predominantly through the action of the Toxoplasma orthologue of ROM4 [23] , and recent findings suggests that this shedding plays a critical role in triggering parasite replication following invasion [24] . There is no known functional homologue of PfSUB2 in Toxoplasma , so the relative importance of PfSUB2-mediated and intramembrane shedding of PfAMA1 remains unclear . Attempts to disrupt the single-copy P . falciparum PfSUB2 gene ( pfsub2 ) or its orthologue in the rodent malaria species P . berghei have been unsuccessful , suggesting that SUB2 plays an indispensable role in parasite asexual blood-stages [11] , [25] . Consistent with this , small molecules and monoclonal antibodies that bind structures close to the PfSUB2 cleavage site in MSP1 efficiently block shedding and also prevent invasion [26]–[29] , suggesting that shedding of the MSP1/6/7 complex is important for invasion . An association between antibody-mediated inhibition of shedding and invasion has also been noted for antibodies against PfAMA1 [29]–[31] , but other studies have suggested that certain antibodies to PfAMA1 block invasion primarily by preventing its interactions with a set of rhoptry neck-derived parasite partner proteins ( RON proteins ) that are thought to associate with PfAMA1 at the moving junction during invasion [32]–[36] . There is to date no firm evidence that shedding of PfAMA1 is important for parasite viability . Like SUB2 , both MSP1 and AMA1 are also indispensable in the asexual blood stage life cycle , indicating that they play critical roles [37]–[40] . Whilst MSP1 has orthologues only in other Plasmodium species , AMA1 is conserved in most other apicomplexan genomes examined [41]–[43] . In addition to its putative role in the transition to replicative growth referred to above , AMA1 has also been implicated in signalling events during invasion [44] , [45] . Both AMA1 and MSP1 are widely considered potential malaria vaccine candidates , since they can induce antibody responses that block invasion in vitro and protect against blood-stage malaria in vivo ( see [46] and [47] for recent reviews ) . Many of the in vitro studies have found that substantial concentrations of specific antibody ( usually>0 . 1 mg ml−1 ) are required to obtain a significant effect on invasion ( e . g . [9] , [48]–[50] . Perhaps in part as a result of this , clinical trials of vaccines based on these two proteins have thus far proved disappointing [51]–[54] . Shedding of apicomplexan zoite surface proteins during invasion is frequently observed across the phylum ( reviewed in [55] ) , provoking speculation regarding its general role . One proposal - that shedding may be required to disengage adhesive interactions between parasite ligands and host cell receptors - has received substantial experimental support [21] , [23] , [56] . In addition , shedding may allow evasion of invasion-inhibitory antibodies during invasion , as the shed molecules could bind and deplete serum antibodies in the microenvironment of the parasite-host cell interaction , as well as minimizing accumulation of immune complexes on the parasite surface [57] . Though intuitively attractive , there is no evidence supporting the latter hypothesis . Here we have investigated the structural requirements for shedding of PfAMA1 , and the consequences of its inhibition . We first show that intramembrane cleavage of PfAMA1 can be reduced to virtually undetectable levels by mutagenesis , with no discernible phenotypic consequences . We then show that the position of cleavage by PfSUB2 is determined primarily not by the sequence at that site but by its distance from the transmembrane domain ( TMD ) . Despite this , certain radical mutations at the PfSUB2 cleavage site can block shedding . Parasites expressing these non-cleavable forms of PfAMA1 on a background of expression of the wild type protein invade and replicate normally in vitro . However , the same mutations that block PfSUB2-mediated shedding could not be stably introduced into the parasite genome , suggesting that some shedding of PfAMA1 by PfSUB2 is essential . Furthermore , parasites expressing shedding-resistant mutants of PfAMA1 are markedly more sensitive to antibody-mediated inhibition of invasion than those expressing wild-type forms of PfAMA1 , suggesting that shedding of surface proteins during invasion by apicomplexan zoites may also serve as a mechanism to evade potentially protective invasion-inhibitory serum antibodies .
Precise mapping by us and others of the intramembrane cleavage sites in PfAMA1 and TgAMA1 [8] , as well as in the P . falciparum microneme protein EBA-175 [21] and the other Toxoplasma microneme proteins MIC2 [58] and MIC6 [59] , has shown in all cases that cleavage takes place on the C-terminal side of an Ala residue ( the P1 position in Schechter and Berger terminology [60] ) . No detailed information is available about the specific substrate requirements of PfROM4 or PfROM1 , the rhomboid-like enzymes potentially responsible for intramembrane cleavage of PfAMA1 . However , studies of several other rhomboids from evolutionarily diverse organisms have indicated that only residues with a small side-chain , such as Ala , Cys , Ser and Gly , can be accommodated at P1 , and that cleavage can often be prevented by substitution of the P1 residue with bulky residues such as Tyr or Phe [61] , [62] . To investigate the importance of intramembrane shedding of PfAMA1 , we therefore produced a construct designed to modify the endogenous pfama1 gene by targeted homologous recombination , replacing the P1 Ala550 with a Tyr residue ( Figure 1 ) . Parasites transfected either with this construct ( called intAMA_R-TKmod ) or with control construct intAMA_C-TKmod ( which is identical to intAMA_R-TKmod aside from the Ala550Tyr substitution , and is thus designed to simply reconstitute the authentic PfAMA1 TMD sequence ) were maintained in the presence of WR99210 . After 2 cycles of drug selection and ganciclovir treatment to enrich for integration events , both parasite lines were cloned by limiting dilution . Southern blot analysis of the obtained clones ( Figure S1 in Supporting Information Text S1 ) showed that integration of both constructs had occurred in the expected manner , resulting in transgenic parasites that should express PfAMA1 with either the Ala550Tyr substitution or an unmodified TMD . Growth assays showed no discernible growth defect in any of the clones , and immunofluorescence analysis ( IFA ) and Western blot with antibodies to PfAMA1 indicated no alteration in the sub-cellular pattern or levels of PfAMA1 expression in schizonts ( not shown ) . To assess the effect of the mutation on shedding of PfAMA1 , mature schizonts enriched from synchronised cultures of clones 3D7_AMA_C_E9 ( control ) and 3D7_AMA_R_D4 ( Ala550Tyr ) were allowed to undergo rupture in either normal medium , or medium supplemented with the calcium chelator EGTA . This compound has no effect on schizont rupture under the conditions used , but efficiently inhibits both invasion and PfSUB2-mediated shedding ( which is calcium-dependent ) whilst simultaneously enhancing shedding by intramembrane cleavage , presumably due to accumulation of PfAMA1 on the surface of released merozoites [8] , [12] . As shown in Figure 2 , the usual products of PfSUB2 cleavage , PfAMA144 and PfAMA148 , were equally abundant in normal culture supernatants from both clones . However , the 3D7_AMA_R_D4 supernatants were clearly deficient in the PfAMA152 fragment which derives from intramembrane cleavage . Furthermore , whereas in the case of control clone 3D7_AMA_C_E9 the addition of EGTA resulted in a relative increase in levels of shed PfAMA152 , as expected , the same treatment severely ablated all PfAMA1 shedding in the mutant 3D7_AMA_R_D4 clone . Titration of the culture supernatants by Western blot showed that the Ala550Tyr substitution reduced production of the PfAMA152 fragment by a factor of at least 8-fold ( Figure S2 in Text S1 ) . These results convincingly demonstrate that the Ala550Tyr substitution in the PfAMA1 TMD efficiently blocks shedding of PfAMA1 by intramembrane cleavage . The fact that the mutant clones show no growth phenotype in vitro strongly suggests that intramembrane shedding of PfAMA1 does not play a critical role in the asexual blood-stage cycle of P . falciparum , or at least that only very low levels of intramembrane cleavage ( close to or below those detectable by Western blot ) are sufficient for normal parasite growth . Having shown to our satisfaction that normal levels of intramembrane shedding of PfAMA1 are not essential in asexual blood-stages , we turned our attention to the importance and mechanism of juxtamembrane shedding . In previous work [32] , [63] , [64] we have described episomal expression of a synthetic recodonised FVO pfama1 transgene in P . falciparum . Expressed under the control of the pfama1 promoter , the transgene product ( called PfAMA1/DIII-HA; Figure 1 ) is correctly trafficked to micronemes and eventually to the merozoite surface , where it is correctly cleaved and shed at erythrocyte invasion . The presence of a single hemagglutinin ( HA ) epitope tag in the domain III loop of the transgene product allows it to be discriminated from endogenous PfAMA1 , which continues to be expressed in the transgenic lines . For the present study , we decided to use this same episomal expression system to examine the sequence requirements for cleavage by PfSUB2 . To do this , we expressed a set of mutant forms of PfAMA1/DIII-HA in P . falciparum and examined their expression and proteolytic shedding . Most proteases recognise specific amino acid residues immediately flanking the scissile bond , so in initial experiments we examined the importance of these residues in processing by PfSUB2 . Constructs for episomal expression of three mutants of PfAMA1/DIII-HA were first generated , incorporating Ala substitutions of all three residues just upstream of the cleavage site ( positions P3–P1; mutant AMA_M1 ) , or the three residues downstream of the processing site ( P1′–P3′; mutant AMA_M2 ) , or all six residues ( P3–P3′; mutant AMA_M3 ) ( Figure 1 ) . As shown in Figure 3 panels A and B , examination by IFA and Western blot using an HA-specific antibody showed that all three mutant transgene products were expressed in transfected parasites at levels equivalent to those of the parental construct ( AMA_C ) , and exhibited a similar pattern of sub-cellular localisation and intracellular proteolytic prosequence removal as is usually observed for PfAMA1 [65] , [66] . Most notably , examination of culture supernatants harvested following schizont rupture and merozoite release revealed , in all cases , the presence of the PfAMA148 species diagnostic of correct ectodomain shedding by PfSUB2 ( Figure 3C ) . Under carefully standardised culture conditions there were no reproducible differences in the degree of shedding or the apparent molecular mass of the shed PfAMA148 protein on SDS-PAGE . These results strongly suggest that cleavage of PfAMA1 by PfSUB2 at the bond between residues 517–518 can occur efficiently irrespective of the precise amino acid sequence immediately flanking the scissile bond . A number of reports examining proteolytic cleavage of membrane-bound proteins by membrane-associated sheddases have shown that the site selected for cleavage , rather than being dependent on its precise flanking sequence , is instead determined by its distance from the membrane and its location within a relatively unstructured juxtamembrane segment [15] , [16]–[20] . In the light of our above results , we decided to exploit further mutants of PfAMA1/DIII-HA to investigate whether a similar rule holds true for PfSUB2 cleavage of PfAMA1 . Mutant construct AMA_ins ( Figure 1 ) was designed to contain a perfect duplication of 21 residues ( Glu526 – Lys546 inclusive ) that lie within the residual 'stub' that is normally left on the merozoite surface after PfSUB2-mediated shedding of the bulk of the PfAMA1 ectodomain . As a result , in AMA_ins the normal Thr517-Ser518 PfSUB2 cleavage site within the motif AEVT↓SNNE is shifted substantially further away from the predicted TMD than its position in the wild-type protein . We hypothesised that , should this mutant remain sensitive to PfSUB2 , cleavage would occur in one of two manners dependent upon the mechanism of substrate recognition . If cleavage was strictly sequence-specific , cleavage would occur as usual at the Thr517–Ser518 bond , resulting in the release of the normal PfAMA148 product . If , on the other hand , the site of cleavage was dependent on its distance from the membrane , cleavage would now take place at a new site , probably at or around the position indicated in Figure 1 , since this would now lie 29 residues from the TMD; importantly , the latter scenario would result in the shedding of a∼2 . 5 kDa larger form of PfAMA148 . As shown in Figure 4A and 4B , episomal expression of AMA_ins in transfected P . falciparum resulted in the usual pattern of microneme localisation , and the expression as expected of larger forms of intracellular anti-HA-reactive PfAMA1 , likely due to the presence of the 21 residue ( ∼2 . 6 kDa ) insertion in AMA_ins . Examination of culture supernatants from this line ( Figure 4C ) , showed that the shed form of AMA_ins migrated on SDS-PAGE substantially more slowly than the AMA_C-derived 'wild-type' PfAMA148 species . These results are consistent with the notion that PfSUB2-mediated shedding of PfAMA1 is not primarily dependent on the flanking sequence but takes place at a constant distance from the TMD , irrespective of the amino acid sequence . To seek a more precise estimate of the position of PfSUB2-mediated cleavage in the AMA_ins transgene product , we decided to isolate the shed product from culture supernatants for more detailed structural examination . Attempts to affinity-purify it using anti-HA antibodies failed , possibly due to poor solvent accessibility of the domain III-located single HA epitope tag in the folded protein . However , using a previously described method [66] , we successfully affinity-purified the shed protein using the anti-PfAMA1 monoclonal antibody ( mAb ) 4G2 . This mAb recognises both the transgene product and endogenous PfAMA1 , so the purified preparations contained both forms of the protein; however , staining and Western blot analysis with anti-HA antibodies clearly discriminated the AMA_ins product , which migrates slightly more slowly than the endogenous PfAMA152 species ( Figure S3 in Text S1 ) . Examination by LC/MS/MS of in-gel tryptic digests of the AMA_ins fragment unambiguously identified peptides spanning most of the predicted ectodomain downstream of the known position of prosequence cleavage at Ile97 [66] ( Figure S4 in Text S1 ) . Importantly , the most C-terminal tryptic peptide identified , AEVTSNNEVVVK ( predicted monoisotopic m/z 1 , 288 . 67 ) , has never been observed in digests of wild-type PfAMA148 [12] , confirming that cleavage of AMA_ins takes place at a site downstream of the normal AEVT↓SNNE site ( see Figure 1 ) . We were unable to identify any peptide species resembling DEYADIPEH , the expected C-terminal product of tryptic digestion if cleavage of AMA_ins takes place as predicted in Figure 1 . Nonetheless , the presence of the AEVTSNNEVVVK peptide , and absence of additional tryptic peptides C-terminal to that sequence supports our prediction that cleavage of AMA_ins occurs at or around the IPEH↓KPTY motif . This in turn supports the notion that PfSUB2-mediated shedding of PfAMA1 is primarily dependent on its distance from the TMD and is not constrained by primary sequence determinants flanking the normal AEVT↓SNNE site . Expression of transgenes from episomal constructs in P . falciparum is complicated by the fact that , even under continuous selective drug pressure , episomes generally segregate poorly in the parasite [67] . The result of this is that , at any one time , only a fraction of the parasites in a drug-selected population express the transgene of interest . Estimation of this fraction is possible using antibodies specific for the transgene . IFA analysis of the various lines described above using anti-HA and anti-PfAMA1 antibodies showed reproducibly that ∼30% of schizonts expressing PfAMA1 also exhibited expression of the transgenic PfAMA1/DIII-HA ( data not shown ) . On the other hand , accurate quantification of relative abundance of the transgene product and endogenous gene product in these parasites can be difficult , especially when ( as in the case of most of the mutants described above ) the transgene product migrates on SDS-PAGE similarly to the endogenous gene product . Expression of the AMA_ins mutant , however , allowed us to take advantage of its distinctly slower migration characteristics to estimate its expression levels relative to that of endogenous PfAMA1 . As shown in Figure S5 in Text S1 , titration by Western blot using polyclonal antibodies to PfAMA1 revealed that ∼15% of the total PfAMA1 expressed by the AMA_ins line was in the form of the larger AMA_ins products . This is also in approximate concordance with the stained SDS PAGE analysis of the affinity-purified protein shown in Figure S3 in Text S1 . Taken together with the IFA data this allowed us to estimate that , in those individual parasites harbouring the episome , ∼50% ( 15/30% ) of the total PfAMA1 expressed was derived from the transgene . Since all PfAMA1/DIII-HA-expressing constructs used in this study shared a common plasmid backbone and regulatory sequence , we considered it reasonable to assume that this value holds for all mutants examined . This was important for the interpretation of subsequent experiments , as described below . As described in the Introduction , it has been suggested that efficient proteolytic shedding of PfAMA1 from the merozoite surface is a prerequisite for successful erythrocyte invasion . In view of our findings that straightforward Ala substitution of residues immediately flanking the PfSUB2 cleavage site in PfAMA1 has no effect on PfSUB2-mediated shedding , we decided to produce a further series of mutant PfAMA1/DIII-HA expression constructs designed to identify more radical substitutions around this site that might prevent shedding . To assess the sensitivity of cleavage to charged or large residues at the P1 and P1′ positions , constructs AMA_R1 , AMA_Y1 and AMA_Y1-Y1′ were first designed , in which either the P1 position only was replaced with Arg or Tyr , or both P1 and P1′ positions were replaced with Tyr , respectively . In addition , mutant AMA_F2 was produced , in which the P2 Val residue was substituted with a Phe residue . Many proteases are unable to cleave adjacent to Pro residues in a peptide chain , due to the cyclic nature and conformational rigidity of the associated imido bond; indeed , two adjacent Pro residues provide a high degree of resistance to most proteases [68] . Accordingly , mutant constructs AMA_P1 and AMA_P1-P1′ were also produced , respectively encoding Pro residues in positions P1 only , or in both P1 and P1′ relative to the PfSUB2 cleavage site ( Figure 1 ) . The final mutant produced was AMA_del , in which 14 residues ( Glu531–Asn544 inclusive ) that lie between the PfSUB2 cleavage site and the TMD were deleted ( Figure 1 ) . This was predicted to have the effect of bringing the globular domains I–III of PfAMA1/DIII-HA closer to the membrane , rendering the cleavage site less accessible to PfSUB2 . It was also expected to result in a gene product ∼1 . 7 kDa smaller than the non-mutated PfAMA1/DIII-HA expressed from construct AMA_C . Transfection of these constructs into P . falciparum resulted in expression of all 7 transgene products at levels approximately equivalent to those of the parental construct AMA_C , with the same pattern of sub-cellular localisation ( not shown ) and intracellular proteolytic prosequence cleavage ( Figure 5A–B ) . Examination of culture supernatants from the parasite lines showed that shedding of the AMA_Y1 , AMA_Y1-Y1′ and AMA_F2 transgene products was as efficient as that of the control protein , supporting the conclusions of the alanine mutagenesis studies that PfSUB2 shedding is not dependent on the precise sequence at the cleavage site . Shedding of the AMA_R1 mutant was also reproducibly efficient , although in this case enhanced levels of the intramembrane cleavage product PfAMA152 were also evident ( Figure 5A ) . However , shedding of the other mutant transgenes was substantially less efficient , with decreased shedding of the AMA_del mutant and virtually complete absence of shedding in the case of the AMA_P1 and AMA_P1–P1′ mutants ( Figure 5B ) . Further analysis of these latter two lines by IFA showed a substantially increased proportion of anti-HA reactive newly-invaded rings relative to the AMA_C control , consistent with a severe defect in shedding of the PfAMA1 ectodomain at invasion ( Figure 5C and 5D ) . The anti-HA signal in these rings partially co-localised with that of a monoclonal antibody against MSP119 , the GPI-anchored MSP1-derived fragment that remains on the merozoite surface following PfSUB2-mediated cleavage of this protein at invasion ( see Figure S6 in Text S1 ) . This shows that at least some of the transgenic PfAMA1 carried into the rings was at the parasite plasma membrane and not still resident in micronemes . Despite this , there were no significant differences in growth rates between the control transgenic line and any of the mutant lines over>20 growth cycles ( data not shown ) . Collectively , these results demonstrate that certain major modifications of the PfSUB2 cleavage site within PfAMA1 can effectively modify or block its shedding by the protease , but that – under conditions of episomal PfAMA1 expression on a background of expression of the genomic wild-type gene – this is not detectably deleterious to parasite growth in vitro . In all the transgenic lines described above , expression of PfAMA1/DIII-HA or mutants thereof takes place on a background of expression of the authentic wild-type pfama1 gene from its genomic locus . Encouraged by our identification of mutations capable of efficiently blocking shedding of the PfAMA1/DIII-HA transgene products , we sought to determine whether these same mutations could be introduced into the genomic pfama1 gene , as previously achieved with the Ala550Tyr that blocks intramembrane cleavage . This would be predicted to block all PfSUB2-mediated shedding of endogenous PfAMA1 , enabling us to determine whether any shedding of the protein is required for asexual blood-stage parasite viability . To do this , three new constructs were produced , each designed to integrate into the pfama1 locus by single-crossover homologous recombination . Whereas integration of construct intAMA_C would effectively reconstitute the sequence encoding the wild-type gene product ( thus acting as a control ) , integration of constructs intAMA_P1 and intAMA_P1-P1′ was predicted to introduce Pro substitutions of residues at either the P1 position or both the P1 and P1′ positions respectively relative to the PfSUB2 cleavage site ( Figure 6A ) . The three constructs were independently introduced into 3D7 parasites by transfection , and integrants selected for using a standard drug cycling protocol . As shown in Figure 6B , in 2 independent experiments , PCR analysis of drug-resistant parasite populations after 2 cycles of drug selection clearly detected the predicted integration events in all cases , suggesting that parasites in which integration had occurred were at least transiently viable . However , examination of drug-selected lines by Southern blot after 3 drug cycles ( Figure 6C ) showed integration only in the case of construct intAMA_C , strongly suggesting that introduction of either the P1 or P1-P1′ Pro substitutions was deleterious to parasite replication . Identical results were obtained using another set of constructs , called intAMA_C_TK , intAMA_P1_TK and intAMA_P1-P1'_TK , based on a pHHT-TK vector backbone ( not shown ) . Our findings suggest that complete blockade of PfSUB2-mediated shedding of PfAMA1 impacts on parasite viability and so cannot be tolerated by the parasite . Having failed to obtain viable parasites in which the endogenous pfama1 gene was modified so as to block PfAMA1 shedding , we turned our attention back to the transgenic parasite lines episomally expressing shedding-resistant mutants of PfAMA1 . PfAMA1 is a remarkably polymorphic molecule , with documented naturally-occurring amino acid substitutions in at least 52 positions within the ectodomain [69] . Previous work from Foley and colleagues has identified a 20-residue peptide called R1 which binds to PfAMA1 in an allele-specific manner [70] , [71] . R1 potently inhibits erythrocyte invasion by those parasite strains that express forms of PfAMA1 bound by the peptide , probably by preventing interactions between PfAMA1 and its rhoptry neck-derived RON partner proteins at invasion [72] . In two previous studies of PfAMA1 function this strain-specific invasion-inhibitory activity of R1 was elegantly exploited to evaluate functional complementation by a series of episomally-expressed PfAMA1 mutants containing modifications within the cytoplasmic domain [44] , [45] . We decided to take a similar approach to study the functional competence of our episomally-expressed , shedding-resistant PfAMA1 mutants . These are all based on the FVO PfAMA1 , which is not bound by R1 , whereas the parental 3D7 parasite clone used throughout this work is fully sensitive to R1 [70] . Lines harbouring AMA_C , AMA_P1 and AMA_P1-P1′ were synchronised in parallel with the parental 3D7 clone , then allowed to undergo erythrocyte invasion in the presence or absence of 100 µg ml−1 peptide R1 . As shown in Figure 7 , invasion by the parental 3D7 was efficiently ( ∼95% ) blocked by R1 , as expected . In contrast , all three of the PfAMA1/DIII-HA-expressing lines exhibited substantially higher levels of invasion , indicating partial functional complementation by the transgene . Importantly , the degree of complementation in each case ( ∼20% ) was similar , indicating that the presence of the cleavage site mutations has no appreciable impact on PfAMA1 function at invasion . As a positive control in these assays we also included a 3D7-derived transgenic clone called 3D7-sgPfA1/HA . F5 [63] in which the endogenous 3D7-type pfama1 gene has been entirely replaced by targeted integration of a full-length copy of the same synthetic FVO gene used in the PfAMA1/DIII-HA-expressing lines; clone 3D7-sgPfA1/HA . F5 therefore expresses only the FVO form of PfAMA1 , but is otherwise isogenic with 3D7 . As expected , 3D7-sgPfA1/HA . F5 was completely unaffected by the R1 peptide ( data not shown ) , unambiguously demonstrating that the effect of R1 on invasion is solely a result of its capacity to bind PfAMA1 . Inhibition of erythrocyte invasion by antibodies against AMA1 is widely documented , and has been an important contributory factor to the continued interest in the molecule as a malaria vaccine candidate . Although the mechanistic basis of antibody-mediated invasion-inhibition remains controversial , it has been suggested that shedding of AMA1 and other parasite surface proteins may facilitate host cell entry by sequestering antibodies in the microenvironment of the invading parasite , and/or by preventing the accumulation of immune complexes on the parasite surface that might interfere with invasion [55] , [57] . Having produced parasite lines expressing shedding-resistant forms of PfAMA1 , we decided to take advantage of them to test this hypothesis . Lines harbouring episomal constructs AMA_C , AMA_P1 and AMA_P1-P1′ were examined for their susceptibility to two invasion-inhibitory anti-PfAMA1 antibody preparations: the rat mAb 4G2 [32] , [63] , [73]; and rabbit polyclonal antibodies raised against correctly folded recombinant PfAMA1 [64] ( see legend to Figure 1 ) . The invasion-inhibition experiments were performed using concentrations of antibodies that inhibit invasion by the parental 3D7 clone by no more than ∼20% , in order to best enable us to distinguish differences in susceptibility to the antibodies; preliminary experiments also demonstrated that the parental clone and AMA_C line were equally sensitive to the antibodies ( Figure S7 in Text S1 ) . In order to functionally inactivate the endogenous , normally-shed PfAMA1 expressed by the three parasite lines under investigation , thus focusing on the effects of the antibodies on the episomally-expressed transgenic PfAMA1 , all assays comparing the three transgenic lines also included R1 peptide at a concentration ( 100 µg ml−1 ) that effectively blocks invasion by the parental 3F7 clone ( see Figure 7 ) . As shown in Figure 8 , under these conditions mutant lines AMA_P1 and AMA_P1-P1′ were reproducibly more sensitive than the control AMA_C line to the invasion-inhibitory activity of the antibodies . Since these mutants differ from AMA_C only by the substitution of one ( AMA_P1 ) or two ( AMA_P1-P1′ ) amino acid residues that alter susceptibility to PfSUB2-mediated shedding , these results strongly suggest that shedding of PfAMA1 indeed enables the parasite to evade invasion-inhibitory antibodies .
This study had three primary aims: to establish the importance of intramembrane shedding of PfAMA1; to interrogate the primary sequence requirements for cleavage of PfAMA1 by PfSUB2; and to examine the functional consequences of modifications that block that shedding . An intriguing aspect of AMA1 is that , despite its conservation across the apicomplexan phylum , it is not shed in a conserved manner . Shedding of TgAMA1 is mediated exclusively by cleavage within the TMD by a rhomboid-like protease [8] , and recent elegant work from Buguliskis and colleagues has implicated a Toxoplasma tachyzoite surface rhomboid called TgROM4 in this [23] . Like TgROM4 , the P . falciparum orthologue of ROM4 is similarly expressed at the merozoite surface [21] , and this protease may be responsible for the small fraction of PfAMA1 shedding that is mediated by intramembrane cleavage [8] . In conflict with this is evidence from another study using a cell-based in vitro assay of intramembrane cleavage activity , which suggested that another parasite rhomboid called PfROM1 , but not PfROM4 , has the capacity to shed PfAMA1 [22] . Whichever enzyme is responsible , prior to this study the biological relevance of this low-level rhomboid-mediated cleavage of PfAMA1 was unknown , and indeed we have previously speculated that it is artefactual [8] . Our ability here to severely ablate intramembrane shedding ( in the absence of EGTA ) by appropriate mutagenesis of the intra-TMD cleavage site , with no effect on parasite growth , supports that . We cannot formally rule out that very low level intramembrane shedding , below the level of detection by Western blot , is important . This possibility is highlighted by the observation that low-level production of the PfAMA152 form could be detected under the very artificial conditions of merozoite release in the presence of EGTA , which enhances the products of intramembrane cleavage presumably due to its blockade of both invasion and PfSUB2 activity [8] , [12] . This residual intramembrane shedding could be due to incomplete blockade of cleavage by the Ala550Tyr mutation , or even low-level phenotypic reversion of in culture of the mutant , given the single-crossover nature of its genomic modification . Collectively , however , we consider that the weight of evidence argues against an essential role for intramembrane cleavage of AMA1 in P . falciparum , a view in accord with the fact that the Ala550 residue at which intramembrane cleavage of PfAMA1 occurs is not conserved across predicted Plasmodium AMA1 TMD sequences; indeed several species lack an Ala residue or any of the known 'helix-destabilising' residues within the luminal 8–10 residues of their TMD ( Figure S8 in Text S1 ) that are considered to be a signature of rhomboid substrates [61] ( see [74] for a recent review of this subject ) . It might therefore be predicted that intramembrane cleavage of AMA1 is not a common feature in Plasmodium . In previous work we had noted that the sequences flanking the PfSUB2 cleavage sites in MSP1 and PfAMA1 show no obvious similarity , and so PfSUB2 was tentatively likened to the secretases or 'sheddases' , a group of metalloproteases responsible for shedding of ectodomains of membrane-associated proteins in metazoa . A common characteristic of this group of proteases is that they do not recognise specific sequence motifs but instead are thought able to cleave within any relatively disordered sequence at positions a certain distance from the membrane or a membrane distal globular domain . Using episomal expression in the parasite of epitope-tagged PfAMA1 transgenes , it was therefore not completely surprising to us that substitution with Ala of all six residues ( P3-P3' ) flanking the PfAMA1 cleavage site , or mutation of the P1 Thr to Arg or Tyr , or the P2 Val to a Phe residue , or even substitution of both P1 and P1' residues with Tyr , had no detectable effect on the efficiency of shedding . The shed cleavage products from the AMA_M1 , AMA_M2 , AMA_M3 , AMA_R1 , AMA_Y1 , AMA_Y1-Y1′ and AMA_F2 mutants co-migrated precisely on SDS PAGE with those of the parental AMA_C transgene product , leading us to conclude that cleavage of the mutants likely takes place at the same position relative to the TMD . The significance of the enhanced levels of the intramembrane cleavage product , PfAMA152 , present in the AMA_R1 culture supernatants is presently unclear , but the site-specificity of cleavage appeared unchanged . These conclusions were supported by our subsequent observation that duplication of a 21-residue sequence that lies between the canonical PfSUB2 cleavage site and the TMD resulted in the shedding of an appropriately larger form of the PfAMA1/DIII-HA ectodomain . Mass spectrometric mapping of affinity-purified shed AMA_ins protein – although failing to definitively assign the new site – confirmed cleavage downstream of the normal position . This finding strongly suggests that the position of cleavage is determined primarily by its distance from the parasite membrane , fully supporting the sheddase hypothesis . Our results are consistent with previous observations regarding the lack of sequence conservation at the PfSUB2 processing site in AMA1 across Plasmodium species [8] , and also the observation that PTRAMP , a third PfSUB2 substrate [13] , contains no sequence motifs similar to either the PfAMA1 or MSP1 cleavage sites . Presumably , the broad specificity of PfSUB2 enables it to cleave a range of membrane-bound substrates , largely irrespective of their primary sequence . However , the fact that not all merozoite surface proteins are shed by PfSUB2 [21] indicates some degree of discrimination . Further work will be required to establish the full repertoire of merozoite proteins that are substrates for the protease , as well as the higher order structural or other features that render a protein susceptible to PfSUB2 . The fact that the AMA_del mutant , in which part of the juxtamembrane 'stalk' region was deleted , exhibited only poor cleavage may indicate that a reasonably long such region is a minimal requirement for targeting by PfSUB2 . The third part of our study addressed the effects of mutations at the processing site that interfere with cleavage . As previously observed for several other proteases , we found that the insertion of a single Pro residue at the P1 position , or two Pro residues flanking the scissile bond , very efficiently blocked shedding of the PfAMA1/DIII-HA transgene product . We had anticipated that , given the normally highly efficient shedding of PfAMA1 at invasion , expression of such episomal constructs in the parasite might be deleterious to parasite growth . In fact , we found that the cleavage-resistant AMA_del , AMA_P1 , or AMA_P1-P1′ transgenes were expressed just as efficiently as the other transgene mutants , and that the lines carrying them showed no detectable growth defect . Focusing on the AMA_P1 , or AMA_P1-P1′ mutants , we used antibodies to the ectodomain-located HA epitope tag to show that , consistent with the virtually complete absence of shed ectodomain released into culture supernatants , the mutant gene products were efficiently carried into host erythrocytes on the surface of invading merozoites . This proves unambiguously that complete shedding is not a prerequisite for invasion . Furthermore , using the R1 peptide , which blocks the invasion-related function of 3D7 PfAMA1 in an allele-specific manner , we showed that the FVO-based cleavage-resistant mutants complemented the function of R1-inactivated endogenous PfAMA1 as efficiently as the AMA_C-derived transgene product , convincingly demonstrating that PfSUB2-mediated shedding is not required for the function of PfAMA1 at invasion . Given these findings , what is the explanation for our inability to introduce the P1 and P1-P1' mutations into the endogenous pfama1 gene ? One interpretation of this result is that although PfSUB2-mediated cleavage of PfAMA1 is not essential for the function of PfAMA1 during invasion , some shedding is essential at some point in the asexual blood-stage life cycle . This notion is supported by a recent study in Toxoplasma indicating that intramembrane cleavage of TgAMA1 is required not for invasion , but to trigger parasite replication following invasion [24] . Is it possible that PfSUB2-mediated cleavage in P . falciparum performs an analogous role to that of TgROM4 in Toxoplasma ? Work is underway to address this question . Unlike many other microneme proteins ( e . g . the erythrocyte binding protein EBA-175 [21] ) PfAMA1 is abundantly detected on the surface of free merozoites , and indeed its discharge onto the merozoite surface has been documented to occasionally occur even prior to schizont rupture [65] , [75] . This , combined with the established importance of AMA1 in invasion and its apparent requirement to form interactions with the RON proteins , may account for its relative sensitivity to antibodies compared with that of other microneme proteins that have been examined , several of which are predominantly discharged only upon contact with the host cell ( e . g . [76] ) . We took advantage of the episomal parasite lines expressing shedding-resistant PfAMA1/DIII-HA to examine their susceptibility to invasion-inhibition by anti-PfAMA1 antibodies . Surprisingly , we found that the transgenic parasites expressing the AMA_P1 or AMA_P1-P1′ mutants were significantly more sensitive to antibody-mediated invasion inhibition that the control AMA_C line , indicating that shedding decreases sensitivity to these antibodies . These assays focused on the transgene products only , because they were all performed in the presence of concentrations of the R1 peptide that effectively block the function of the endogenous 3D7 PfAMA1 . We can only speculate on the mechanistic basis of this enhanced sensitivity . Previous knockdown studies in Toxoplasma using a tetracycline-regulated conditional system have indicated that TgAMA1 is normally expressed at levels well in excess of those necessary to sustain its role at invasion , since reduction of endogenous protein levels by ∼90% ( upon replacement of the endogenous gene with a regulated copy ) had no effect on invasion efficiency [41] . We hypothesise that if this is also the case in Plasmodium , and if only those PfAMA1 molecules involved in interactions with the RON complex play a productive role in invasion , it may be important for the parasite to rapidly discard excess surface PfAMA1 molecules in the presence of anti-PfAMA1 antibodies in order to prevent accumulation of antibody complexes on the parasite surface that interfere with the parasite's normally rapid passage into the nascent parasitophorous vacuole . Thus , even though our results clearly demonstrate that complete shedding from the merozoite surface is not mechanistically essential for invasion to go to completion , it may be evolutionarily advantageous for shedding of PfAMA1 to occur in an efficient manner in order to cope with the threat of host-derived anti-PfAMA1 antibodies . In conclusion , our results suggest that shedding of PfAMA1 by PfSUB2 may perform dual functions , one of which is essential and the other of which enables the parasite to more efficiently evade the host humoral response . Shedding of apicomplexan zoite surface proteins is a widespread phenomenon [55] , and our observations have obvious implications for shedding of other immunogenic parasite proteins too . Collectively , our findings imply that efficient inhibition of PfSUB2 activity by suitable drug-like inhibitors would prevent parasite growth , and – importantly – that even partial inhibition of PfSUB2 activity may enhance the efficacy of invasion-inhibitory anti-merozoite antibody responses in vivo .
Constructs AMA_M1 , AMA_M2 , AMA_M3 , AMA_R1 , AMA_Y1 , AMA_Y1-Y1′ , AMA_F2 , AMA_P1 , AMA_P1-P1' , and AMA_A550Y were produced by introducing mutations by Quikchange site-directed mutagenesis ( Stratagene ) into plasmid pST2A-sgPfa1 [32] , then ( in all cases except AMA_A550Y ) subcloning DNA fragments containing the mutations into plasmid pHAM-sgA1/DIII-HA [32] ( here renamed AMA_C for clarity ) , using flanking AvrII and StuI restriction sites . To obtain construct AMA_ins , AMA_C was digested with BsiWI and StuI and the excised fragment replaced by a PCR product containing an additional 65 bp encoding Glu526–Lys546 inclusive , in order to duplicate this sequence in AMA_ins . The insert was PCR-amplified from AMA_C using forward primer AMA1ins_F and reverse primer AMA1ins_R ( see Table S1 in Text S1 for all primers used ) . Construct AMA_del was obtained by PCR-amplification of sequence encoding residues Asn519-Asp530 using primers AMA1del_F and AMA1ins_R , then subcloning the resulting PCR product back into SpeI/StuI-digested AMA_C . To produce integration constructs intAMA_R-TKmod and intAMA_C-TKmod , one of the multiple cloning sites was first removed from pHH-TK with restriction enzymes HpaI and KspI , followed by blunting and religation , producing pHH-TKΔMCS2 . The P . berghei dhfr 3′ UTR ( PbDT3′ ) was then PCR-amplified from pHH1 using primers PbDT3′_ClaI_MCS_F and PbDT3′_R and subcloned into pHH-TKΔMCS2 , giving rise to plasmid pHH-TKmod . Sequence including the mutation at the rhomboid cleavage site in PfAMA1 was subcloned from AMA_A550Y into intermediate plasmid PSL1180_AMA_C ( see below ) with restriction enzymes SnaBI and StuI . The entire chimeric gene from this plasmid , as well as the control chimera from PSL1180_AMA_C was excised by restriction with XhoI and blunting the cut ends followed by digestion with BglII . This was cloned into pHH-TKmod pre-digested with BglII and HindII , producing intAMA_R-TKmod and intAMA_C-TKmod . Integration constructs intAMA_C , intAMA_P1 and intAMA_P1-P1′ were generated using AMA_C , AMA_P1 and AMA_P1-P1′ respectively . A 1071 bp 5′ targeting region of the pfama1 gene , excluding sequence encoding the first 4 Met residues , was amplified from 3D7 genomic DNA using primers AMAint_F and AMAint_R . This was cloned into plasmids AMA_C , AMA_P1 and AMA_P1-P1′ using restriction enzymes HpaI and AvrII , replacing the pfama1 promoter sequence and 3′ end of the synthetic FVO ama1 gene and resulting in a partial in-frame chimera between the targeting and synthetic coding sequences . To obtain the derivatives intAMA_C_TK , intAMA_P1_TK and intAMA_P1-P1′_TK the entire chimeric coding sequence of each construct plus its downstream P . berghei dhfr 3′ UTR ( PbDT3′ ) was excised with restriction enzymes HpaI and NotI , cloned into plasmid PSL1180 ( GE Healthcare ) pre-digested with EcoRV and NotI ( to produce plasmids PSL1180_AMA_C , PSL1180_AMA_P1 and PSL1180_AMA_P1-P1′ ) then the inserts excised with restriction enzymes BglII and KspI and cloned into plasmid pHHT-TK digested with the same enzymes . All final constructs were confirmed by sequencing on both strands . P . falciparum clone 3D7 was maintained and synchronized as described previously [77] . For transfection , ring-stage parasites ( 5–10% parasitaemia ) were electroporated with 100 µg of plasmid DNA using standard procedures [11] . Episomal lines were obtained by continuous selection with 10 nM WR99210 ( Jacobus Pharmaceuticals , New Jersey , USA ) , whereas to select for integration in parasites transfected with plasmids intAMA_P1 , intAMA_C and intAMA_P1-P1′ , cultures were subjected to repeated cycles of WR99210 treatment and removal as described [11] . Parasites transfected with plasmids intAMA_R-TKmod , intAMA_C-TKmod , intAMA_P1_TK , intAMA_C_TK and intAMA_P1-P1′_TK were expanded under selection with 2 . 5 nM WR99210 and then treated with increasing concentrations of gancyclovir ( 4 µM , 8 µM and 20 µM , for ∼7 days at each concentration ) to select for parasites containing single copies of the TK gene resulting from single-crossover integration of the input plasmid into the pfama1 locus . For invasion assays , schizonts were enriched by centrifugation over cushions of 70% Percoll ( GE Healthcare ) , added to fresh erythrocytes to obtained a parasitaemia of ∼4% and 1% haematocrit , and cultured overnight in the presence or absence of 100 µg ml-1 R1 peptide , with or without the addition of 0 . 2 mg ml-1 mAb 4G2 or Protein G-purified IgG from the rabbit anti-PfAMA1 antiserum Rb1 , described previously [32] . Giemsa stained thin blood films were prepared 16-18 h later and parasitaemia values determined microscopically . Normalised relative invasion efficiencies were calculated by dividing the parasitaemia values of the R1-treated cultures with those of the untreated samples , or by dividing the parasitaemia values of the R1 plus antibody-treated samples with those of the R1 only-treated cultures . For purified ring preparations , schizonts enriched by centrifugation over cushions of 70% Percoll were cultured with fresh erythrocytes for 3 hours to allow rupture and reinvasion , then residual schizonts removed by repeated passage over 70% Percoll cushions as described previously [9] . For analysis of proteins released into Albumax-free culture supernatants , samples were prepared as previously described [32] . Genomic DNA from wild type and integration lines was extracted using a QIAamp DNA blood mini kit ( QIAGEN ) , digested to completion with XbaI ( for parasites transfected with intAMA_P1 , intAMA_C , intAMA_P1-P1′ , intAMA_C_TK , intAMA_P1_TK and intAMA_P1-P1′_TK ) or NdeI ( for lines transfected with intAMA_R-TKmod and intAMA_C-TKmod ) , electrophoresed on a 0 . 7% agarose gel , transferred to Hybond N+ nylon membrane ( GE Healthcare ) using standard procedures , and probed with [32P]-labelled DNA fragments , produced using the Prime-It II random primer labelling kit ( Stratagene ) . The 3′ region of the endogenous pfama1 gene ( amplified from genomic DNA with primers AMA1Sprobe_F and AMA1Sprobe_R , Table S1 in Text S1 ) was used as a probe in Southern blot hybridisations . Primers used for diagnostic PCR reactions , called verAMAwt_R , verAMAint_F , and verAMAint_R are also shown in Table S1 in Text S1 . The latter two are schematically represented in Figure 6A . PCR reactions were performed using high fidelity Taq polymerase ( Invitrogen ) according to the manufacturers' instructions , with 30 amplification cycles using an annealing temperature of 55°C and an extension time of 1 minute . As a negative control a mixture of genomic DNA from 3D7 parasites and plasmid intAMA_C was used as a template . Rabbit antibodies raised to correctly-folded recombinant FVO PfAMA1 , and the polyclonal mouse anti-PfAMA1 antibody R5 have been described previously [32] , [64] , as has a polyclonal antibody against EBA-175 [21] , mAb 4G2 [63] , the anti-SERA5 mAb 24C6 . 1F1 [78] and the anti-MSP1 mAb X509 [79] . A polyclonal antibody against the parasitophorous vacuole protein Exp1 was a kind gift of Klaus Lingelbach , University of Marburg , Germany . For Western blot analysis , culture supernatants and pellets of synchronous highly mature schizonts were solubilised in SDS sample buffer and subjected to SDS-PAGE under reducing conditions , followed by transfer to Hybond-C extra nitrocellulose membrane ( GE Healthcare ) . Membranes were probed with mAbs or polyclonal antibodies as described previously [32] . For IFA , air dried thin blood films were fixed in 4% ( w/v ) formaldehyde ( Agar Scientific Ltd . ) for 30 min , permeabilized for 10 min with 0 . 1% ( w/v ) Triton X100 , then blocked for 1 h with 3% ( w/v ) bovine serum albumin . After incubation for 30 min with the HA-specific mAb 3F10 ( Roche ) diluted 1:200 and/or a rabbit polyclonal antiserum raised against recombinant region VI of EBA175 [21] diluted 1:500 , films were washed for 5 min in PBS . Thin films were then incubated with a biotinylated goat anti-rat IgG ( Chemicon ) diluted 1:500 , followed by incubation with FITC streptavidin ( Vector Laboratories ) diluted 1:500 and with Alexa Fluor 594 conjugated anti-rabbit antibody ( Invitrogen ) diluted 1:500 . Nuclei were stained by 5 min immersion in 2 µg ml−1 DAPI in PBS , then washed in PBS . Samples were mounted in Citifluor ( Citifluor Ltd . , Canterbury , U . K . ) , and images collected using AxioVision 3 . 1 software on an Axioplan 2 Imaging system ( Zeiss ) using a Plan-APOCHROMAT 1006/1 . 4 oil immersion objective . PfAMA1 fragments shed into culture supernatants were affinity-purified on a column of immobilised mAb 4G2 as previously described [12] , [66] . Following SDS PAGE and staining with InstantBlue ( Generon ) , bands corresponding to proteins of interest were excised and the proteins analysed by LC/MS/MS as previously described [80] . LC/MS/MS data were searched against the UniProt KB ( release 15 . 5 ) protein database using the Mascot search engine programme ( Matrix Science , UK ) , as well as against the predicted primary sequence of the AMA_ins transgene . | The malaria parasite invades red blood cells . During invasion several parasite proteins , including a vaccine candidate called PfAMA1 , are clipped from the parasite surface . Most of this clipping is performed by an enzyme called PfSUB2 , but some also occurs through intramembrane cleavage . The function of this shedding is unknown . We have examined the requirements for shedding of PfAMA1 , and the effects of mutations that block shedding . Mutations that block intramembrane cleavage have no effect on the parasite . We then show that PfSUB2 does not recognise a specific amino acid sequence in PfAMA1 , but rather cleaves it at a position determined primarily by its distance from the parasite membrane . Certain mutations at the PfSUB2 cleavage site prevent shedding , and parasites expressing non-cleavable PfAMA1 along with unmodified PfAMA1 grow normally . However , these mutations cannot be introduced into the parasite's genome , showing that some shedding by PfSUB2 is essential for parasite survival . Parasites expressing shedding-resistant mutants of PfAMA1 show enhanced sensitivity to invasion-inhibitory antibodies , suggesting that shedding of surface proteins during invasion helps the parasite to evade potentially protective antibodies . Drugs that inhibit PfSUB2 may prevent disease and enhance the efficacy of vaccines based on PfAMA1 . | [
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| 2011 | Juxtamembrane Shedding of Plasmodium falciparum AMA1 Is Sequence Independent and Essential, and Helps Evade Invasion-Inhibitory Antibodies |
RNA-seq technologies have provided significant insight into the transcription networks of mycobacteria . However , such studies provide no definitive information on the translational landscape . Here , we use a combination of high-throughput transcriptome and proteome-profiling approaches to more rigorously understand protein expression in two mycobacterial species . RNA-seq and ribosome profiling in Mycobacterium smegmatis , and transcription start site ( TSS ) mapping and N-terminal peptide mass spectrometry in Mycobacterium tuberculosis , provide complementary , empirical datasets to examine the congruence of transcription and translation in the Mycobacterium genus . We find that nearly one-quarter of mycobacterial transcripts are leaderless , lacking a 5’ untranslated region ( UTR ) and Shine-Dalgarno ribosome-binding site . Our data indicate that leaderless translation is a major feature of mycobacterial genomes and is comparably robust to leadered initiation . Using translational reporters to systematically probe the cis-sequence requirements of leaderless translation initiation in mycobacteria , we find that an ATG or GTG at the mRNA 5’ end is both necessary and sufficient . This criterion , together with our ribosome occupancy data , suggests that mycobacteria encode hundreds of small , unannotated proteins at the 5’ ends of transcripts . The conservation of small proteins in both mycobacterial species tested suggests that some play important roles in mycobacterial physiology . Our translational-reporter system further indicates that mycobacterial leadered translation initiation requires a Shine Dalgarno site in the 5’ UTR and that ATG , GTG , TTG , and ATT codons can robustly initiate translation . Our combined approaches provide the first comprehensive view of mycobacterial gene structures and their non-canonical mechanisms of protein expression .
The mechanism of bacterial translation initiation has been the subject of intensive study in the model organism , Escherichia coli . Canonical translation initiation in bacteria is a multistep process that begins with the binding of a small ( 30S ) ribosomal subunit to a Shine-Dalgarno element in the 5’ UTR of an mRNA . The Shine-Dalgarno sequence is generally centered 8–9 nt upstream of the start codon and interacts with a complementary sequence in the 16S rRNA of the 30S ribosomal subunit to help position this ribosomal subunit [1] . A complex , comprising the mRNA-bound 30S subunit , fMet-tRNA , and initiation factors , recruits the large ( 50S ) ribosomal subunit , forming a complete 70S ribosome for initiating translation of the open reading frame ( ORF ) . Although almost all studies of bacterial translation initiation have focused on mRNAs that contain a Shine-Dalgarno , non-canonical mechanisms of translation initiation have also been described , including re-initiation and leaderless translation . Translation of genes embedded in polycistronic mRNAs can be coupled to translation of the respective upstream gene in a process known as re-initiation , if the downstream start and upstream stop codons are positioned close to one another . Once translation of the upstream gene has terminated , the ribosome scans until it finds a suitable start codon further downstream [2] . Low levels of ribosome recycling factor ( RRF ) impair post-termination disassembly , allowing intact ribosomes to re-initiate translation [3] . Closely spaced or overlapping stop/start codons lead to the most efficient coupling [4] . The precise mechanism of re-initiation is unclear . “Leaderless” transcripts lack a 5’ UTR and Shine-Dalgarno sequence . In such cases , 70S ribosomes must bind directly to the first nucleotide of the mRNAs to initiate translation . Few leaderless genes have been identified and analyzed in E . coli , and those that have are mostly of mobile DNA origin , including from λ and P2 phage , and from Tn1721 [5–8] . More recently , the expansion of genome sequence information , gene expression data , and computational analyses into bacterial species other than E . coli , suggest that leaderless gene structures may not be so unusual [9–17] . Leaderless genes also appear to be more common in some archaeal species [18–22] and in mitochondria [23–25] . These isolated reports hint at a very broad distribution of leaderless genes , suggesting that it is an ancient—and possibly the original—mode of translation initiation [18 , 21 , 26] . Paradoxically , almost all mechanistic studies of bacterial leaderless translation have used E . coli as a model system , despite the relative paucity of leaderless genes in this species . It is likely that fundamental differences in translation underlie robust leaderless translation in those species with pervasive leaderless gene structures . Leaderless translation is poorly understood , compared with canonical leadered translation . Early studies established the absolute requirement for a 5’ ATG ( AUG in the mRNA ) for translation [27–29] . Leaderless translation is less robust in E . coli than leadered and early studies focused on identifying factors that contributed to this difference . Leaderless transcripts are likely recognized by 70S ribosomes , rather than the 30S subunit [30–32] . 70S binding is stimulated by initiation-factor 2 [33] , whereas initiation-factor 3 inhibits leaderless translation likely by destabilizing codon-anticodon interactions [33 , 34] , an effect enhanced by the S1 protein [32 , 35–37] . More recent studies suggest that at least some leaderless translation is mediated by a distinct sub-population of 70S ribosomes that have been modified by the stress-induced endoribonuclease , MazF [37 , 38] . MazF cleaves the 3’ end of the 16S rRNA , thereby removing the anti-Shine-Dalgarno sequence . Hence , these so-called “stress-ribosomes” fail to initiate translation of canonical leadered mRNAs but their translation initiation of leaderless transcripts is unimpaired . Thus , based on E . coli studies , leadered and leaderless translation processes are functionally distinct . In contrast to bacteria , eukaryotes utilize ribosome-scanning translation initiation mechanisms . Small open reading frames are frequently found in the 5’ UTRs of eukaryotic genes , and these upstream ORFs ( uORFs ) can attenuate translation of the annotated downstream ORF [39–41] . Less clear is whether similar upstream ORFs meaningfully regulate expression of the downstream proteins in prokaryotes . In bacteria , short ORFs have been identified upstream of some genes . However , this phenomenon has been described far less frequently than uORFs in eukaryotes [42] . Independent of putative roles in cis-regulating downstream ORFs , short ORFs may encode small proteins with trans activities . Regulatory functions have been attributed to encoded small proteins themselves [43] in response to environmental cues such as amino acid availability [44] , global translation levels [45] , and ATP abundance [46] . The genus Mycobacterium includes major pathogens as well as non-pathogens . There have been very few studies of translation initiation mechanisms in mycobacteria , although a recent study inferred widespread leaderless translation in Mycobacterium tuberculosis by mapping transcription start sites ( TSS ) onto an annotated reference genome[9] . Here , we use RNA-seq coupled with ribosome profiling to map RNAs and translated RNAs , respectively , in the model mycobacterial species , Mycobacterium smegmatis . In parallel , we use TSS mapping coupled with mass spectrometry detection of protein N-termini to map the sites of initiation of the transcriptome and proteome of M . tuberculosis . These complementary approaches in related species reveal widespread non-canonical translation initiation , including leaderless translation and the use of alternative start codons . Lastly , we developed a next-generation sequencing-based translational reporter system , which allows a controlled assessment of the requirements of translation initiation in mycobacteria . This assay demonstrated that the requirements for leaderless and leadered translation are distinct , and supports the use of alternative start codons for leadered translation . Thus , our collective data provide the first systems-level description of key translational parameters in mycobacteria , a genus that exhibits significant endogenous leaderless translation .
The initial report of leaderless translation in M . tuberculosis relied on annotation pipeline predictions [9] , which in turn are predicated on rules derived from canonical leadered translation systems . To generate a dataset that did not inherently rely on annotation predictions , we sought to empirically determine the genomic placement of active transcripts as well as their translated ORFs . The coordinate application of RNA-seq and ribosome profiling allowed simultaneous assessment of the relative levels of all transcripts and their translated regions genome-wide in M . smegmatis without reliance on annotations . As each genome-wide approach has strengths and weaknesses , combining independent criteria or datasets provides higher confidence results . In this study , we used standard growth conditions including rich media and harvesting RNA from cells at mid-exponential phase to profile those genes most relevant to common experimental conditions . Mapped read depth tended to be greatest near the 5’ ends of the genes , facilitating our search for TSSs and translation initiation sites as the leading edge of stacked sequence reads from RNA-seq and ribosome profiling , respectively . Focusing on the upstream boundaries of regions of sequencing signal in either the total RNA or ribosome profiling datasets , we identified putative TSSs and translation initiation sites for genes in M . smegmatis that were sufficiently expressed . We noted that many ( 206 ) upstream boundaries of mapped RNA-seq reads aligned perfectly with a ribosome profiling boundary , and also coincided with a putative ATG or GTG ( collectively , RTG ) initiation codon ( S1 Table ) . Of these RTGs , 130 ( 63 . 1% ) matched the 5’ start of an annotated gene . This convergence of transcription and translation initiation is consistent with leaderless mRNAs ( Fig 1A ) . The alignments of RNA-seq and ribosome footprinting boundaries that also align with an RTG codon in the M . smegmatis genome are collectively very stringent criteria . We observed 1412 RNA-seq boundaries , 2709 ribosome profiling boundaries , and 444 , 642 RTG occurrences in a genome of 13 , 976 , 418 bases ( total for both strands ) , combining for a chance alignment genome-wide at 0 . 0087—far below the 206 we observed . Therefore , RTG codon occurrences are enriched at the 5’ boundaries of transcripts . Our identification of abundant leaderless mRNAs in M . smegmatis lends empirical support for their presence in M . tuberculosis [9] , as well as validating our experimental approach . A putative initiating RTG triplet at the other 76 RNA-seq and ribosome profiling 5’ boundaries identified unannotated start codons . Most corresponded to a new in-frame upstream ( 26 ) or downstream ( 28 ) start codon for an annotated gene , thereby extending or reducing the length of the annotated ORF , respectively ( S1 Fig and S1 Table ) . The remaining 22 RTGs initiated new ORFs that had not been predicted by genome annotation pipelines ( see below ) . Leaderless transcripts generate distinctive 5’ read boundary profiles; their ribosomal footprints characteristically begin abruptly at the initiation codon . In addition , mRNAs with high ribosome occupancy can be enriched relative to their corresponding RNA-seq read depth in paired libraries that are otherwise comparable . Therefore , while some transcripts were not expressed highly enough to meet our read depth-mapping threshold in RNA-seq , ribosome isolation enriched those mRNAs enough to surpass the mapping threshold . Omitting the criterion of a coincident RNA-seq boundary from our analysis ( still requiring a 5’ ribosome footprint aligning with an RTG ) identified 762 candidate leaderless transcript boundaries . While we expect more false positives in this list of 762 transcripts , 457 ( 60 . 0% ) matched a predicted annotated start , which was very similar to the 63% observed for the most stringent list , conferring confidence to those candidate initiation sites generated by this approach . Of the remaining 305 candidate leaderless RTGs , 175 ( 23% ) were in-frame with annotated genes , predicting new upstream ( 99 instances ) or downstream ( 76 instances ) start codons for those ORFs ( S2 Table ) . The 130 ( 17% ) RTG codons that did not correspond to an annotated ORF represent potential initiation sites for new , unannotated , ORFs ( S2 Table , column I ) . These metrics parallel our most stringent list above and suggest that 206 leaderless transcripts is an underestimate . Our M . smegmatis and M . tuberculosis transcript mapping data in its entirety can be interactively viewed by links found at <http://www . wadsworth . org/research/scientific-resources/interactive-genomics/> , and run directly through common internet browsers . Leadered transcripts , however , generate a range of ribosomal footprints that begin in the 5’ UTR , spanning a putative Shine-Dalgarno site and the initiation codon ( Fig 1B ) . The range of footprint created by the ribosome on the 5’ UTR creates ambiguity in confidently assigning the start codon . We reasoned that leaderless and leadered mRNAs would fundamentally differ in their RTG codon placement relative to the onset of the ribosome profiling footprint: leaderless RTGs would map to the 5’ ribosome profiling boundary , whereas ribosome occupancy would protect the 5’ UTR of leadered transcripts most proximal to the initiating RTG codon . We therefore plotted the distance between the 5’ ribosome profiling boundary and the next downstream RTG triplet as a candidate initiation codon . This generated two peaks: the major leaderless peak with no separation ( 762 candidates , as described above ) , and a second broad peak with a mode ( 103 ) at 24 nucleotides of separation ( S2 Fig and S2 Table ) . Conservatively bracketing the second broad peak to include spacing of 20 to 30 nt of separation between the footprint boundary and the next RTG , identified 731 candidate leadered initiation RTG codons . Of these , 423 ( 58% ) mapped exactly to the start of an annotated gene , validating the predictive value of the leadered transcript dataset . Other RTG codons within this 20 to 30 nt footprint window were in-frame with the annotated ORFs , but were either upstream ( 45 ) or downstream ( 133 ) of the predicted start codon , indicating probable mis-assigned start codons . The remaining 130 RTG triplets within this ribosome footprint range could represent either initiation codons for new unannotated ORFs ( as for the leaderless RTGs ) , or background internal RNA fragments that fortuitously include an RTG sequence . We also took an independent approach in the related pathogenic species , M . tuberculosis , to determine whether non-canonical translation initiation mechanisms are conserved throughout the genus . We used a modified RNA-seq approach to map TSSs genome-wide , identifying 4 , 978 TSSs representing 2254 genes ( S3 Table ) . We found that 1 , 098 TSSs ( 22% ) initiated with an RTG triplet at the +1 position , and 497 ( 45%; S3 Table ) of these corresponded to the annotated start codon of a gene , consistent with Cortes et . al . and our findings in M . smegmatis , that leaderless mRNAs are common in mycobacteria . Also consistent with our findings in M . smegmatis , an additional 76 ( 7% ) and 208 ( 19% ) extended or shortened the annotated reading frames of same-frame leaderless RTGs that were upstream or downstream of the predicted annotated initiation codon , respectively . These novel leaderless TSSs would be missed in studies that rely on the accuracy of annotated genomes . Sites of translation initiation have been assumed from genome annotation predictions in M . tuberculosis [9] , or determined by our ribosome-associated mRNA profiling ( ribosome profiling ) in M . smegmatis . We independently mapped sites of translation initiation in M . tuberculosis by a direct mass spectrometry ( MS ) approach focused on identifying the spectral signatures of those peptides with N-termini created by translation initiation , rather than tryptic digestion . We performed LC-MS/MS on tryptic digests of total protein from M . tuberculosis lysates and specifically searched for N-terminal peptides within annotated ORFs . ORFs that could be extended by conceptual translation to candidate initiation codons upstream were included in the search . To be confident of our N-terminal assignments , we then stringently limited our list to a very high-confidence subset of N-terminal peptides: those with acetylated N-terminal residues and those with intact methionines present at non-ATG codons . 211 protein N-termini met at least one of these criteria , 144 of which matched annotated starts ( S4 Table ) . For leaderless genes , we predict that mapped N-termini should correspond to the +1 nucleotide of the TSS . Comparison of mapped N-termini and TSS addresses in M . tuberculosis confirmed translation initiation for 51 peptides ( 24% ) that coincided with an RTG at the +1 position of the transcript ( S4 Table ) . Thirty-two ( 62% ) of these 51 leaderless M . tuberculosis N-terminus/mRNA pairs matched JCVI predicted genome annotations; a similar proportion to that predicted in M . smegmatis by ribosome-profiling ( 63% ) . Even though the reduced sensitivity ( compared to our transcriptomic methods ) of our N-terminal proteomic approach prevented a fully comprehensive survey of translation initiation sites , it provided independent , empirical evidence that leaderless mRNAs in M . tuberculosis are translated from the +1 nucleotide . We sought to identify genomic features that could distinguish the start sites for predicted leadered and leaderless genes . TSSs that did not have a candidate RTG initiation codon at the 5’ end were considered to represent classical leadered genes . An alignment of the 20 nucleotides upstream of the +1 transcription start site for the putative leadered transcripts ( TSSs that began with something other than RTG ) in M . tuberculosis showed the expected discernable enrichment for A and T nucleotides that mark the -10 hexamer for sigma factor binding ( Fig 2A ) . Looking from these TSSs downstream into the putative 5’ UTRs showed no position-dependent enrichment of nucleotide sequences . Ribosome profiling in M . smegmatis allowed features around translation initiation codons to be examined . Viewing the 5’ UTR upstream from the translation initiation codon of the 731 transcripts with an RTG 20–30 nt from the 5’ ribosome profiling boundary ( S2 Table ) showed that these potentially leadered mRNAs were enriched for purines in the proximal upstream region where a Shine-Dalgarno element would be expected ( Fig 2B ) . Slight displacement within the phasing tolerances separating the Shine-Dalgarno and initiation codon produced the mound of enriched purines , similar to the profile observed in E . coli [47] . Subsequent MEME analysis identified a Shine-Dalgarno-like core element in 318 of the transcripts ( S3 Fig ) . The ORF initiated by the identified start codon was evident in downstream alignments by an enrichment of periodic C and G nucleotides , reflecting a selective pressure for the sequence of the encoded protein while conforming to the wobble codon bias of a G/C-rich organism . Therefore , the classic leadered transcripts in mycobacteria appear to have all of the hallmarks of bacterial genes: a -10 hexamer in the promoter , a Shine-Dalgarno sequence in the 5’ UTR proximal to the initiating RTG , and codon bias of the encoded open reading frame downstream of the RTG . We reasoned that the features of leaderless genes should be very similar to the leadered mRNAs , though lacking the features found in 5’ UTRs . Alignment and Logo projection of the sequences upstream of M . tuberculosis RTG-initiated TSSs showed a -10 hexamer that was indistinguishable from that of leadered genes ( Fig 2C ) . This observation underscores the presence of a promoter upstream of the mRNA’s +1 site , and the lack of a Shine-Dalgarno element upstream of the RTG translation initiation codon . These genomic features provide independent evidence that leaderless transcripts are generated de novo by transcription , and not by post-transcriptional processing . As observed for the classic leadered ORFs , the synchronous wobble of the leaderless ORFs suggests that the encoded proteins have experienced similar selective pressures as leadered genes . Our comparative genomic analyses between leaderless and leadered genes did not identify any new motifs specifically associated with leaderless gene expression . However , the convergence of canonical promoter elements juxtaposed with protein coding sequences without intervening untranslated regions again supports our conclusion that there is widespread leaderless translation . We compared orthologous M . smegmatis and M . tuberculosis genes to assess the conservation of leaderless gene structures . Of the 206 M . smegmatis leaderless transcripts , 184 corresponded to annotated genes , with the leaderless RTG representing the predicted start ( 130 instances ) or a new start either upstream or downstream of the predicted start ( 54 instances ) . Of these , 114 of had orthologs in M . tuberculosis ( S1 Table ) , most ( 97 ) of which had TSSs within 200 nt of the annotated start , suggesting that they were sufficiently expressed for analysis . Of these 97 expressed M . tuberculosis genes , 71 were also leaderless in this species . Therefore , ~73% ( 71/97 ) of M . smegmatis leaderless genes have expressed leaderless orthologs in M . tuberculosis . Orthologs with conserved leaderless initiation codons highlight the need for empirical reannotation of these genomes ( see example , S4 Fig and see below ) . Genomic analyses provide a broad overview of translation initiation sites , but encompass too many variables that obscure mechanistic insight . To control for some of the variables associated with leaderless translation , we developed reporter constructs that allowed β-galactosidase quantification of translation efficiency directed by candidate leaderless and leadered mRNAs ( Fig 3 ) . N-terminal fusions have negligible effects on β-galactosidase activity [48]; therefore , translation of the 5’ leader should be well tolerated . In a fixed reporter context , translational features were independently tested , including the presence of an initiation-competent start codon ( e . g . , ATG ) , or a putative initiation-incompetent codon ( e . g . , ATC ) at either the leadered or leaderless positions . In E . coli , β-galactosidase activity supported through leaderless translation initiation was ~10% of that observed through leadered ATG initiation ( Fig 3B ) ; none of the candidate NTG initiation codons were able to support substantial translation from the leaderless position . When introduced into M . smegmatis , these same leaderless reporters were translated as efficiently as leadered constructs ( Fig 3A ) ; transcripts with an ATG or GTG beginning at +1 produced β-galactosidase activity levels similar to an ATG at the leadered position . The CTG and TTG candidate codons were poor initiators at the leaderless position , consistent with our genomic data that showed no enrichment of these triplets in M . tuberculosis transcription start sites , or ribosome occupancy by ribosome profiling in M . smegmatis . Our TSS mapping for M . tuberculosis indicated that transcription initiation favors purines at the +1 position ( 95% of TSSs ) , and the +1 nucleotide identity could influence promoter strength as it does for T7 RNA polymerase [49] . Thus , there was a possibility that β-galactosidase activity differences could arise from the effect of different +1 nucleotides on the robustness of transcription , rather than translation . To ensure that changing the +1 nucleotide did not affect transcript abundance , each nucleotide was tested at the +1 position for its effect on β-galactosidase activity produced by a conventional leadered ATG codon . The levels of β-galactosidase activity indicated only a slight effect of a +1 pyrimidine on mRNA levels ( Fig 3A ) . We conclude that ATG or GTG , but not CTG or TTG , codons directed leaderless translation in mycobacteria at levels comparable to a leadered ATG codon in this context . We then took an unbiased approach to identify sequences that can initiate , or influence the initiation of , translation . We developed a viability reporter predicated on sufficient translation of a zeocin antibiotic resistance gene ( zeor ) to allow selection of sequences that could successfully initiate translation , which could then be identified by PCR amplification followed by next generation sequencing ( Fig 4A ) . Clusters of randomized nucleotides were embedded in the 5’ leader to address specific hypotheses . The pre-selection total library was maintained by hygromycin selection , encoded elsewhere in the plasmid . Growth in culture with zeocin antibiotic required translation of the zeocin reporter , directed by sequences present in the 5’ leader . A library was generated with randomized nucleotides comprising the leaderless codon ( 5’ end of transcript ) and at the annotated leadered start codon of the zeor ORF in order to create constructs that could potentially support translation initiation from either of these two codons . The Shine-Dalgarno sequence was omitted . Sequencing amplicon pools of the zeocin resistant M . smegmatis showed that an ATG or GTG beginning at the +1 position of the transcript was necessary to initiate translation of the downstream zeor gene ( Fig 4B ) . Leaderless RTG reads accounted for all but 547 of the 343 , 242 total reads ( S5 Table ) . Importantly , initiation by RTG codons at the leaderless position was independent of codon identity at the leadered position , with the notable exceptions of the three possible stop codons . No codon at the leadered initiation position was consistently enriched , indicating an absolute requirement for an appropriately placed Shine-Dalgarno sequence for efficient leadered translation initiation in our reporter constructs . We then repeated the assay in the context of a consensus Shine-Dalgarno sequence . The zeocin resistant pool now showed strong enrichment for specific sequences at both the leaderless and leadered start codons ( Fig 4C ) . ATG and GTG were again enriched at the leaderless start codon position , accounting for 17 . 0% of the 299 , 098 total reads ( S5 Table ) . In contrast to reporters lacking a Shine-Dalgarno , ATG ( 20 . 5% ) , GTG ( 22 . 2% ) and TTG ( 28 . 7% ) were now enriched at the leadered start codon position ( net leadered total 245 , 065 ) , even more robustly than at the RTG leaderless initiation codons . Notably , CTG ( 0 . 03% ) was not enriched , consistent with the paucity of CTG start codons in mycobacteria . Surprisingly , ATT ( 9 . 8% ) codons were substantially enriched at the leadered position , indicating that this codon can act as a translation initiation codon in this context in mycobacteria . We note that our presumed negative control initiation codon , ATC , persisted through zeocin selection ( 0 . 2% ) , indicating low , but above background , translation initiation activity when accompanied by a Shine-Dalgarno sequence . We modified the unbiased zeocin resistance selection approach to define sequences suitable for ribosome binding in mycobacteria . A contiguous block of 6 nt was randomized upstream of a leadered ATG initiation codon , and constructs providing zeocin resistance were collected and sequenced ( S5 Table ) . We found sequences enriched for adenines and guanines in the randomized region , typical for a canonical Shine-Dalgarno site ( Fig 5A ) . This is consistent with the conservation in mycobacteria of the complementary anti-Shine-Dalgarno element on the 16S rRNA . We further leveraged our viability reporter system to determine whether any additional sequences support translation of leaderless transcripts near the ATG or GTG codon at the +1 position . Six contiguous random nucleotides were embedded immediately downstream ( positions +4 through +9 ) of a leaderless ATG triplet ( S5 Table ) . All transcripts had an ATC triplet in the leadered position , and lacked the necessary Shine-Dalgarno sequence , thereby precluding leadered initiation and requiring leaderless translation initiation for zeocin resistance . The selected library pools showed no discernable enrichment of motifs or nucleotide composition ( Fig 5B ) . This finding was consistent with the lack of enrichment for any specific downstream sequence associated with naturally occurring leaderless transcripts ( Fig 2C ) . Our ribosome profiling in M . smegmatis , and our viability reporter assays , indicated that an RTG triplet at the TSS is necessary and sufficient to direct leaderless translation initiation . Therefore , leaderless +1 RTGs not initiating translation of annotated reading frames are initiating translation of novel ORFs ( S1 Fig ) . Our most conservative leaderless dataset from M . smegmatis requiring the convergence of three parameters ( RNA-seq boundary , ribosome profiling boundary , and an RTG triplet ) , identified 22 unannotated ORFs ( S1 Table ) . We also identified 130 RTG codons ( of 762 ) at the 5’ boundary of the ribosome profile consistent with their initiation of unannotated ORFs ( see the leaderless peak in S2 Fig and S2 Table , column H ) . Ribosome profiling of these novel ORFs showed association with 70S ribosomes , indicating that they are very likely to be translated . These novel ORFs often encode small proteins ( defined here as 5–50 amino acids ) that map immediately upstream of an annotated ORF ( S6 Table ) . While the size limits applied to this definition have not been functionally determined , the range highlights a collection of proteins often excluded in annotation prediction algorithms . These encoded small proteins could function as diffusible products in trans , as do larger sized proteins; alternatively , they could act in cis , influencing the expression of downstream ORF ( s ) . Our data suggest two cis mechanisms by which the ORF , rather than the encoded small protein , may regulate the downstream genes of the polycistronic mRNA ( see below ) . The ORFs encoding small proteins can be categorized with respect to the position of their stop codon relative to the initiation codon of the adjacent annotated gene ( Fig 6A and S1B Fig ) . The first class comprises proteins whose ORFs stopped upstream of the neighboring annotated gene . We refer to these as upstream ORFs ( uORFs ) , a nomenclature applied extensively to equivalent ORFs in eukaryotes . The second class comprises proteins whose ORFs extend into the coding region of the annotated ORF downstream , utilizing an alternative reading frame ( overlapping ORFs ) . A subset of this class comprises proteins for which the TGA stop codon of the upstream peptide also formed the second two nucleotides of the RTG start codon of the downstream ORF; we refer to these small proteins as “coupled” . We find similar numbers of predicted small proteins are encoded from leaderless transcripts in M . tuberculosis using an entirely different experimental approach . Our TSS data indicated that 317 ( 28 . 8% ) of the leaderless RTG codons initiated novel ORFs , and 197 of these are between 5–50 amino acids suggesting that M . tuberculosis encodes hundreds of undocumented small proteins ( S3 Table , see “Leaderless” sheet , column K ) . Forty-nine of these small ORFs appear to be encoded on transcripts separate from annotated genes under the conditions analyzed , while others appear to be encoded polycistronically and followed by annotated genes . Collectively , our data are consistent with mycobacterial small proteomes comprising hundreds of unannotated small proteins . Coupled small proteins require a precise overlap of two codons ( one Opal stop , one start ) . Our data indicate that these structures are significantly overrepresented in mycobacteria ( Fig 6A , Fisher’s Exact Test p < 0 . 0003 ) . The RTGA sequence links the two ORFs physically , and likely has functional consequences resulting from coupled translation . Sometimes only the genomic placement and coupled architecture of divergent small proteins are conserved between M . tuberculosis and M . smegmatis , suggesting that the coupled linkage is under greater selection than the encoded small protein sequence ( see example Fig 6B ) . The relative contribution of coupling to the total translation initiation of the downstream ORF may augment overall gene expression by maximizing ribosome occupancy . We identified orthologous uORFs in M . smegmatis and M . tuberculosis ( Fig 7 ) . These small proteins shared not only their genomic context , but also significant amino acid identity . The juxtaposition of the encoded small proteins and downstream genes suggests an operonic structure , with possible regulatory effects . Small uORFs in other bacterial species are often found at the 5’ end of polycistronic mRNAs dedicated to biosynthetic pathways for amino acids , wherein the amino acid product of the biosynthetic operon is overrepresented in the encoded peptide . An abundance of that amino acid transcriptionally attenuates the downstream biosynthetic genes in the operon . Inspection of the encoded peptides for enrichment of amino acids identified several candidates for attenuating peptides . One predicted small ORF contained a cluster of codons for cysteine at its C-terminus , and was located upstream of the cysA2 gene , Rv0815 ( Fig 7A ) . The relative location and sequence of this encoded protein is conserved in mycobacteria , strongly supporting its identification as a functional small ORF . Our ribosome profiling of the orthologous locus in M . smegmatis showed robust 70S ribosome occupancy of both the small ORF and the downstream genes ( Fig 7A ) . We speculate that cysteine homeostasis or redox status may influence ribosome processivity through the small ORF , affecting the transcription or translation of the linked operon genes downstream . We found other upstream-encoded small proteins that were conserved in mycobacteria , and enriched in cysteine residues upstream of putative hypothetical or cysteine-metabolic genes ( Fig 7B and 7C , respectively ) . We note that in each of the examples shown , the JCVI annotation predicted a gene encoded on the opposite ( negative ) strand to these uORFs; our data find no negative-strand transcripts to support those predicted annotations . These examples illustrate the functional potential of a few of the members of the mycobacterial small proteome , providing testable hypotheses for these candidates ( e . g . , through cysteine or redox modulation ) , while others may have less obvious—but no less important functional roles . The location of many unannotated uORFs as the first genes in their operons , suggests new possibilities for regulatory and functional interactions of the small proteins with the downstream operon and encoded proteins .
We combined independent assays from two species to profile the transcriptional and translational landscapes of mycobacteria . Our findings collectively indicate that translational mechanisms and the gene architectures they process differ from the canonical models derived from extensive studies in E . coli . A prominent non-canonical feature is the frequency , translational robustness , and conservation of leaderless mRNAs in mycobacteria . Experimentally determining translation initiation sites of actively translated ORFs provides empirical evidence for the prevalence of mycobacterial leaderless transcripts , and significantly refines and extends leaderless gene predictions derived from genome annotations [9] . We find 2166 TSSs also identified by Cortes et al . in addition to many novel initiation sites , while our independent M . smegmatis approach provides evolutionary depth in a tractable model bacterium . There does not appear to be a unifying criterion for leadered and leaderless gene structures . They have comparable GC content , initiate genes of similar length , begin operons of similar composition , and—under standard growth conditions—are not enriched for functionally related proteins . We developed novel reporters to begin to address mechanistic hypotheses about cis-sequences that affect leaderless and leadered translation initiation in mycobacteria . Our data show that any mRNA beginning with ATG or GTG ( AUG or GUG in mRNA ) will be translated as a leaderless mRNA in mycobacteria . Strikingly , no other codon initiates leaderless translation , in contrast to leadered translation , which can initiate with a wide variety of codons , including rare start codons such as TTG and ATT . In addition to Shine-Dalgarno dependence , differing start codon requirements indicate fundamental mechanistic differences between leadered and leaderless translation initiation . However , there is currently insufficient understanding of leaderless translation to determine why this difference exists . The combination of multiple , independent observations strongly indicate that a +1 RTG codon is both necessary and sufficient for leaderless translation initiation . We did not identify any sequences beyond the start codon that were enriched in leaderless genes , either in mycobacterial genomes or by our viability reporter assays , suggesting that the RTG start codon is not supported by an enhancing downstream box [7] . Leaderless genes in M . smegmatis are frequently leaderless in M . tuberculosis , in spite of sequence variation ( indicating an absence of a functionally important element ) near the initiating RTG codon . We observe robust leaderless initiation regardless of the reporter context , none of which have mycobacterial origins that could contain leaderless support elements . Varied contexts and disparate assays consistently produce robust leaderless translation efficiencies in mycobacteria . Thus , an ATG or GTG at the TSS is the only discernable criterion for translation initiation at that same site . Previous studies in E . coli have indicated that ATG was optimal for leaderless initiation , with <10% activity for GTG [27] . Moreover , leaderless expression from even an optimal ATG initiation codon in E . coli is weak ( 10% ) compared to initiation from a leadered transcript [41 and Fig 3] , again contrasting to the relatively similar levels of expression from leadered and leaderless mRNAs in M . smegmatis . Together , these observations indicate that recognition and translational initiation of leaderless mRNAs in M . smegmatis occurs by a distinctly different mechanism than that described for E . coli . Overall leaderless gene expression appears to be controlled by a balance of promoter activity , mRNA stability , and protein turnover; leadered gene expression has an additional level of control provided by features of the 5’ UTR , including the Shine-Dalgarno sequence and RNA secondary structure . We speculate that this additional level of expression modulation may have provided the evolutionary impetus for leadered gene structures . Analyses of TSS mapping data indicate that RNA polymerase exhibits context bias in selecting a TSS . For example , a cytosine at the -1 is preferred , followed by a pyrimidine at the +2 [50] . Could RNA polymerase sequence preferences be responsible for driving the enrichment of RTG ( 1098/4979 , 22% ) triplets at the transcription start sites of M . tuberculosis ? Substituting chemically similar but translationally inert nucleotides into the second and third positions of the RTG triplet , we see that they are found at TSSs much less often: RCG is found at 270 ( 5 . 4% ) of M . tuberculosis TSSs , and RTA at 121 ( 2 . 4% ) . Clearly , the over-representation of RTG at TSSs ( Fisher’s Exact Test , p <0 . 0001 ) indicates that there is a strong evolutionary selection for RTGs at the TSS in mycobacteria . We conservatively defined leaderless genes as those that have an RTG beginning at the first ( +1 ) nucleotide of their corresponding transcript . Leaderless genes have also been more broadly defined as those having a potential initiation codon from +1 through +5 [9] , with the rationale that a miniscule 5’ UTR could not include a Shine-Dalgarno element . Convergence mapping of our protein N-termini and M . tuberculosis transcriptional start sites provides some experimental assessment of the accuracy of this broader definition . While N-termini were identified for 51 of 1098 ( 4 . 7% ) leaderless +1 TSSs , only 4 of 558 ( 0 . 7% ) candidate RTG codons from +2 to +5 had associated N-termini ( S4 Table and S5 Fig ) . Many factors affect peptide identification in mass spectrometry analyses; therefore the relative paucity of N-terminal peptides initiated by +2 to +5 leaderless codons is not definitive . Nevertheless , our data suggest that translation initiation efficiency is greater for RTG codons at +1 than from +2 through +5 . Independent of the N-terminal mapping , the non-random distribution of RTG triplets in the first five positions of M . tuberculosis transcripts supports RTG enrichment specifically at the +1 nucleotide . If leaderless translation initiation utilizes any near-TSS RTG equivalently , RTGs should be evenly distributed throughout this window . However , the 1098 RTG triplets at the +1 are greater than the next four positions combined ( 558 ) ( S5 Fig ) . Including RTG codons downstream of the +1 position in the definition of leaderless mRNAs should therefore carry the caveat that their initiation efficiencies are likely to be lower than an absolute leaderless codon . The enrichment of RTGs at the TSS in mycobacteria clearly indicates a positive selection for leaderless translation in the genus , and not simply a tolerated inefficiency of a promiscuous initiation mechanism . Leaderless translation is far more efficient in M . smegmatis , and presumably M . tuberculosis , than in E . coli . The reason for this disparity is unclear , although there are many differences in ribosomal proteins between mycobacteria and E . coli [51] . Importantly , almost all studies of leaderless translation have used E . coli as a model system . As illustrated in our genomic landscape profiles , the mutually exclusive presence of a -10 hexamer or a Shine-Dalgarno ribosome-binding site upstream of the initiation codon can distinguish leaderless from leadered genes . Our data suggest that other species with a high frequency of predicted leaderless gene structures [21 , 22] are also likely to initiate leaderless translation efficiently and by a different initiation mechanism than that for E . coli . The unbiased sequence-panning approach we developed to define cis elements that support translation initiation clearly show that a Shine-Dalgarno sequence is required for efficient leadered initiation . The purine-rich consensus of the recovered zeocin-resistant constructs agreed well with the canonical motif defined in E . coli [1 , 47] . The functionally selected ribosome binding sites also agreed well with our genomic sequence analyses of the 5’ UTRs of leadered transcripts in M . smegmatis ( DNA Logo in Fig 2B and S3 Fig ) . The conservation of canonical Shine-Dalgarno elements in 5’ UTRs and cognate anti-Shine-Dalgarno sequences in 16S rRNA tails , suggests that the mechanism aligning the ribosomal subunits with a leadered translation initiation codon in these two species is conserved . Our focus on structurally and functionally defining the 5’ ends of genes led us to identify large numbers of small ORFs that are likely to be translated in mycobacteria . The prevalence and efficiency of leaderless translation initiation that we see in mycobacteria support the prediction that leaderless ORFs will be translated . It is possible that upstream small ORFs enhance translation initiation of the annotated genes downstream , particularly in cases of RTGA coupling . This may be through non-canonical re-initiation of 70S ribosomes that remain intact while tracking into the downstream ORF . Alternatively , traditional Shine-Dalgarno mediated initiation of the downstream ORF may be augmented by an increased local concentration of ribosomal subunits at the junction of the two ORFs . Previous studies of re-initiation have focused exclusively on pairs of ORFs within operons . Operons comprise annotated ORFs that are frequently coupled at their junctions as well , which may offer efficiency advantages through ribosome loading of the downstream gene . Alignments of annotated operon gene junctions clearly show that wobble positions of the codons are guanine/adenine enriched for dual use as a Shine-Dalgarno sequence embedded in coding sequence ( S6 Fig ) . This suggests that translation initiation of the downstream gene can occur by two distinct mechanisms ( non-canonical re-initiation , and canonical Shine-Dalgarno directed ) , or that a Shine-Dalgarno sequence can improve the efficiency of re-initiation . Similar mechanisms may apply to the couplings joining small protein ORFs with their downstream genes as described here . The delivery of ribosomes to the downstream ORF via a coupling tetramer does not depend on the amino acid sequence of the small protein sequence itself , only that , once loaded , the ribosomes make it through that ORF to be near the initiation codon of the downstream ORF . It is possible , however , that the sequence of the encoded peptide might modulate ribosome delivery . The conserved candidate cysteine-rich uORFs that we identified would be the first examples of attenuation identified in mycobacteria , and for cysteine-directed attenuation in bacteria . While our hypothesized cis regulatory roles for the small protein ORFs are still speculative , they may offer an alternative regulatory mechanism compensating for the loss of the 5’ UTR in leaderless genes . These possible mechanisms could help modulate protein production beyond promoter-controlled transcription initiation . The precedent and the presence of potential cis regulatory ORFs are clearest for small proteins encoded at the beginning of an operon[43 , 52–54] . Nevertheless , precedent also exists for trans activities inherent to the small proteins themselves[54] . It is tempting to speculate that upstream leaders are fertile incubators poised for the nascent evolution of expressed sequences as the first gene of an expanding operon . Moreover , some of the predicted small proteins are not apparently affiliated with another nearby ORF ( at least 49 in M . tuberculosis ) , suggesting that these small protein singletons are not cis-regulators . It is unlikely that every member of the mycobacterial small proteome has an individual function , but clearly some of them bear hallmarks of functional features . Small protein ORFs that are part of an operon may function in the pathway encoded by the trailing genes of that operon . Expanded study of the emerging small proteome will help to identify the likely candidates , determine their functional roles , and evaluate their collective influence on the phenotype or phenotypic variation of the bacteria . We focus on small proteins encoded by leaderless transcripts because of the abundant support for their presence in both mycobacterial species analyzed here . However , as leadered initiation of annotated genes in mycobacteria still outnumber leaderless annotated genes by at least two to one , it is reasonable to speculate that leadered codons will initiate translation of small proteins at comparable rates . Therefore , the small proteome estimates derived solely from leaderless mRNAs under standard rich media growth conditions , as presented here , are conservative underestimates . In summary , we have identified widespread , conserved , non-canonical translation initiation in mycobacteria . Abundant leaderless translation initiation , encoded small proteins , and coupled linkages suggest translation mechanisms in mycobacteria are likely to fundamentally differ from those modeled in E . coli . Further , our data may help improve current genome annotation pipelines to more accurately predict leaderless gene structures , transcription and translation isoforms , and translation initiation sites in bacteria . Moreover , we anticipate that genome-scale approaches combining ribosome profiling , TSS , and LC-MS/MS will uncover similar phenomena in diverse bacterial species , challenging long-held paradigms of translation initiation in bacteria .
The mc2155 strain of M . smegmatis was grown in Middlebrook 7H9 broth supplemented with ADC and 0 . 05% Tween 80 with shaking at 230 RPM at 37°C to an OD600 of ~1 . 0 . Mycobacterium tuberculosis ( strain H37Rv ) was cultured in Middlebrook 7H9 supplemented with OADC , 0 . 05% Tween 80 and 0 . 2% glycerol to an OD600 of 0 . 8–1 . 0 for transcriptomics experiments , and the same strain was cultured as described in [55] for mass spectrometry . Extracts were prepared as described [56] with minor modifications . 200 ml Middlebrook 7H9 medium was inoculated with 2 . 0 ml of an overnight M . smegmatis culture , and grown at 37°C to an OD600 of ~1 . 0 . Chloramphenicol was added to 100 μg/ml 2 minutes before harvesting cells to stabilize the mRNA-associated ribosomes . Cells were harvested by rapid filtration using a 500 ml 0 . 45 μm PES filter system ( Celltreat ) and flash frozen in liquid nitrogen together with 0 . 7 ml lysis buffer [56] . The frozen cells were pulverized 6 times at 15 Hz for 3 min in a mixer mill ( Retsch MM400 ) . The grinding jars were re-chilled in liquid nitrogen to keep the cells frozen between each cycle . After the pulverized cells were recovered , a small aliquot was saved to assess bacterial transcript enrichment . The pulverized cell powder was extracted with acid phenol and chloroform followed by isopropanol precipitation [56] . 16S and 23S ribosomal RNAs were removed by subtractive hybridization using a Ribo-Zero Magnetic kit ( Epicentre ) following the manufacturer’s protocol . The enriched mRNAs were randomly fragmented as described [56] . Ribosome profiling was performed as described [56] . Briefly , the pulverized cells were thawed and the soluble cytoplasmic fraction isolated by centrifugation of insoluble material . The clarified lysates were treated with micrococcal nuclease to degrade DNA and reduce polysomes to monosomes ( MNase , Worthington Biochemical Corp ) . Monosomes were isolated by sucrose-gradient fractionation . mRNA was extracted from the monosome fraction by treatment with acid phenol and chloroform extraction , and isopropanol precipitation . Both ribosomal footprints and the enriched mRNAs were converted into cDNA libraries as described [56 , 57] with minor modifications . The RNA molecules were dephosphorylated by treatment with T4 polynucleotide kinase ( New England Biolabs ) . Then polyacrylamide gel purification was performed for size selection of ~28 nt RNA fragments . Approximately 25–30 nt poly-A tails were added to recovered RNA fragments with an Ambion poly ( A ) tailing kit ( Life Technologies ) following the manufacturer’s protocol . The polyadenylated RNA samples were reverse transcribed using SuperScript III ( Life Technologies ) , and primer JW2364 ( /5Phos/GATCGTCGGACTGTAGAACTCTGAACCTGTCGGTGGTCGCCGTATCATT/iSp18/CACTCA/iSp18/CAAGCAGAAGACGGCATACGATTTTTTTTTTTTTTTTTTTTVN ) [57] . The reverse transcription products were circularized by CircLigase ssDNA ligase ( Epicentre ) . For the footprint libraries , ribosomal RNAs were subtracted from circularized ssDNA using biotinylated sense-strand oligonucleotides JW4267 ( /5Biosg/TATCCTGAGAGGTGATGCATAGCCG ) , JW4268 ( /5Biosg/TAAACGGTGGGTACTAGGTGTGGGTTTC ) , JW4269 ( /5Biosg/CTTGGGATCCGTGCCGTAGCTAACGCAT ) , JW4270 ( /5Biosg/AGGAAGGTAGCCGTACCGGTCAGTG ) , and JW4271 ( /5Biosg/CACACCGCCGAAGCCGCGGCAGCCAAC ) . PCR amplification was performed using the circularized cDNA as template , JW2365 AATGATACGGCGACCACCGA ) and JW2366 ( CAAGCAGAAGACGGCATACGA ) as primers[57] , and Phusion High-Fidelity DNA Polymerase ( New England Biolabs ) . The PCR amplified DNA libraries were deep sequenced by Illumina HiSeq 2500 ( University at Buffalo Next-Generation Sequencing and Analysis Expression Core Facility ) . Sequence reads were mapped to the mc2155 reference sequence ( GenBank CP000480 . 1 ) . Raw read counts for both RNAseq and RiboSeq replicates ( S7 Fig ) were added and merged together prior to prediction of TSS . TSS were predicted using adjacent sliding windows of multiple sizes and by calculating the ratio of total-read counts between the windows ( Downstream window counts / Upstream window counts ) . The windows were moved one nucleotide at a time along the reference genomes . The window sizes used for ratio calculations were of 10 nt , 15 nt , 25 nt , 35 nt , 50 nt , 75 nt , 100 nt , 125 nt , 150 nt , 200 nt , 250 nt , 300 nt , and 350 nt . Smaller window sizes allow detection of sharp increases of reads counts along the data , while larger windows are more suitable for detecting shallower but constant rate increases . The window ratios at each position along the genome were assessed to determine the presence of a boundary ( or peaks ) marking the onset of transcription ( Bioinformatics & Statistics Core , Wadsworth Center ) . A potential transcription start site was defined as the position in a genome of the maximum height of a peak equal >50 for the RNA-seq data and >100 for the ribosome profiling data . Leaderless TSS were determined by looking at RNAseq or ribosome profiling boundaries that fell directly at RTG positions in the genome . The list of boundaries were compared to the annotations provided by the J . Craig Venter Institute ( JCVI ) to identify genes that were correctly or incorrectly annotated using traditional annotation tools . To reduce the false-positive rate with any single dataset , independent datasets were often combined to generate a working list of high-confidence calls . The high-confidence leaderless transcript dataset in M . smegmatis was generated from the overlap of RTGs at RNA-seq boundaries ( 633 ) and ribosome profiling boundaries ( 1362 ) for 498 total . Mapped reads from RNA-seq and ribosome profiling were also visualized in a SignalMap browser ( Nimblegen ) , with annotations provided by the J . Craig Venter Institute ( JCVI ) and PathoSystems Resource Integration Center ( PATRIC ) . Biological replicate cultures were grown to an optical density of 1 in roller bottles and inactivated with RNAlater ( Ambion ) prior to pelleting and RNA extraction . Pellets from 18 ml of culture each disrupted in 1 mL Trizol ( Life Technologies ) in Lysing Matrix B tubes ( MP ) in a FastPrep-24 ( MP ) using two cycles of 30–45 seconds at 6 . 5 m/sec . 300 μL chloroform was added , samples were centrifuged for 15 minutes at 4°C , supernatants were mixed with equal volumes of isopropanol , and the resulting samples were incubated for 1 hour at -20°C before centrifugation for 10 minutes at 4°C . Pellets were washed with 75% ethanol , resuspended in water , treated with DNase Turbo ( Ambion ) and purified by RNeasy ( Qiagen ) as directed by the manufacturer . RNA samples were subject to two consecutive rounds of ribosomal RNA depletion using a MICROBExpress kit ( Ambion ) as directed by the manufacturer . Transcription start sites were mapped as described in [58] with modifications . Briefly: RNA samples from two biological replicates were each processed to create two parallel libraries: a “converted” library , which captured RNA 5’ ends bearing 5’ triphosphates and 5’ monophosphates , and a “non-converted” library , which captured only RNA 5’ ends bearing 5’ monophosphates . The resulting libraries bore Illumina TruSeq adapter sequences , with the first base of Read 1 corresponding to the exact 5’ end of an RNA molecule . To convert triphosphates to monophosphates in the “converted” library , 1 . 7 μg rRNA-depleted RNA was incubated with 1 μL 5’ Polyphosphatase ( Epicentre ) in a 20 μL reaction for 1 hour at 37°C . For the “non-converted” library , 1 . 7 μg rRNA-depleted RNA was incubated in parallel in a mock reaction lacking enzyme . Samples were purified by RNeasy ( Qiagen ) and eluted in 100 μL water containing RNaseOUT ( Life Technologies ) and DTT . One μL 100 mM Tris pH 7 . 5 was added and samples were concentrated to 8 μL by vacuum centrifugation . Concentrated samples were mixed with 1 μL of 5 μg/μL oligo SSS392 ( TCCCTACACGACGCTCTTCCGAUCU; normal font indicates deoxyribonucleotides and italics indicate ribonucleotides ) , denatured at 65°C . for 10 minutes , and cooled in an ice-water bath . Three μL 10X T4 RNA ligase buffer ( New England Biolabs ) , 1 μL RNaseOUT , 10 μL 50% PEG8000 , 3 μL RNaseOUT , 10 μL of 10 mM ATP , 3 μL DMSO , and 1 μL T4 RNA ligase I ( New England Biolabs ) were added and reactions incubated 18 hours at 20°C . Seventy μL water was added and samples were purified by RNeasy ( Qiagen ) and eluted in 126 μL water . Samples were sheared in a Covaris sonicator in AFA MicroTubes ( Covaris ) as follows: exposure time , 180 seconds; duty cycle , 10%; intensity , 5; cycles/burst , 200 . One μL each Tris pH 7 . 5 , 100 mM DTT , and RNaseOUT ( Life Technologies ) were added and samples concentrated to 11 . 25 μL . One μL of 2 . 2 μg/μL SSS397 ( CTGGAGTTCAGACGTGTGCTCTTCCGATCTNNNNNN , where “N” represents a degenerate base ) was added , samples were incubated at 65°C for 10 minutes and cooled in an ice-water bath . Four μL 5X first-strand buffer ( Life Technologies ) , 1 μL dNTP mix containing 10 mM each dNTP , 0 . 5 μL RNaseOUT ( Life Technologies ) , 0 . 25 μL of 1 mg/mL actinomycin D , 1 μL 100 mM DTT , and 1 μL Superscript III ( Life Technologies ) were added and reactions incubated 18 hours at 42°C . RNA was degraded by treatment with 10 μL each 1 N NaOH and 500 mM EDTA at 65°C . for 15 minutes , 25 μL 1 M Tris pH 7 . 5 was added , and cDNA was purified by MinElute kit ( Qiagen ) and eluted in 60 μL water . To add the remaining adapter sequences , samples were subject to PCR as follows: 10 μL 5X HF buffer ( Finnzymes ) , 2 . 5 μL of 10 μM oligo SSS398 ( AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTC ) , 2 . 5 μL of 10 μM reverse oligo ( CAAGCAGAAGACGGCATACGAGATXXXXXXGTGACTGGAGTTCAGACGTGTGCT , where “XXXXXX” is a 6 nt Illumina index sequence ) , 20 μL 5 M betaine , 0 . 5 μL Phusion polymerase ( Finnzymes ) , 0 . 4 μL dNTP mix containing 25 mM each dNTP , and 14 . 1 μL purified cDNA from the previous step . Thermal cycler settings: 1 cycle of 98°C for 3 minutes , 8 cycles of 98°C for 80 seconds , 60°C for 30 seconds , and 72°C for 30 seconds , 1 cycle of 72°C for 5 minutes . Reaction volumes were set to 100 μL on the thermal cycler to increase ramp time [59] . Three reactions were performed for each sample . Products between 150 and 500 nt were size-selected on a 1 . 5% agarose gel , purified by a Qiagen gel extraction kit , eluted in 40 μL water , and replicates were pooled and concentrated by vacuum centrifugation to a final volume of 50 μL per library . Samples were further purified by Ampure XP beads ( Agencourt ) using a beads:sample ratio of 1 . 8 and an elution of 50 μL water . To enrich for molecules with full-length adatpers , products were subject to an additional PCR as follows: 10 μL 5X HF buffer ( Finnzymes ) , 2 . 5 μL of 10 μM oligo SSS401 ( AATGATACGGCGACCACCGAGATC ) , 2 . 5 μL of 10 μM oligo SSS402 ( CAAGCAGAAGACGGCATACGAGAT ) , 20 μL 5 M betaine , 0 . 5 μL Phusion polymerase ( Finnzymes ) , 0 . 4 μL dNTP mix containing 25 mM each dNTP , and 14 . 1 μL purified PCR product from the previous step . Thermal cycler settings: 1 cycle of 98°C for 3 minutes , 4 cycles of 98°C for 80 seconds , 60°C for 30 seconds , and 72°C for 30 seconds , 1 cycle of 72°C for 5 minutes . Reaction volumes were set to 100 μL on the thermal cycler to increase ramp time [59] . Three reactions were performed for each sample . Products were purified twice using Ampure XP beads ( Agencourt ) with a beads:sample ratio of 1 . 8 and an elution of 60 μL water . RNA-seq expression libraries were made from the same samples used for 5’-end mapping . 1 μg of MICROBExpress-treated RNA from each biological replicate was sheared by a Covaris sonicator as follows: exposure time , 180 seconds; duty cycle , 10%; intensity , 5; cycles/burst , 200 . Samples were concentrated by vacuum centrifugation and cDNA was synthesized by adding 3 μg of random primers ( Life Technologies ) , incubating at 70°C for 10 minutes and snap-cooling in an ice water bath , adding 4 μL 5X first-strand buffer ( Life Technologies ) , 1 μL dNTP mix containing 10 mM each dNTP , 0 . 5 μL RNaseOUT ( Life Technologies ) , 0 . 5 μL of 1 mg/mL actinomycin D , 1 μL 100 mM DTT , and 1 μL Superscript III ( Life Technologies ) and incubating overnight at 42°C . RNA was degraded and cDNA purified as described above for 5’ end mapping libraries . Each first-strand cDNA sample was divided into 8 replicate second-strand synthesis reactions . Approximately 100 ng cDNA was mixed with 3 μg of random primers ( Life Technologies ) and 2 μL 100 mM Tris pH 7 . 5 , incubated at 95°C for 3 minutes , cooled rapidly to 50°C , and then cooled from 50°C to 4°C at a rate of -0 . 4°C/second . The following were added before an overnight incubation at 16°C: 8 μL NEBNext Second Strand Synthesis ( dNTP-free ) Reaction Buffer , 1 . 6 μL dNTP mix containing 10 mM each dATP , dCTP , dGTP , and dUTP , 4 μL NEBNext Second Strand Synthesis enzyme mix , and water to a final volume of 80 μL . Reactions were purified with MinElute columns ( Qiagen ) . 1 μg of purified second-strand synthesis reaction was then subject to end repair , A-tailing , and adapter ligation using a TruSeq DNA Sample Preparation Kit ( Illumina ) according to the manufacturer’s instructions . Following the ligation and subsequent clean-up steps , the entire product was run on a 2% agarose gel and the region between approximately 250 and 600 bp was excised and extracted with a Qiagen gel extraction kit . Each sample was divided into 4 reactions containing approximately 80 ng DNA , 1 μL 100 mM Tris pH 7 . 5 , and 2 μL USER enzyme mix ( NEB ) and incubated at 37°C for 30 minutes . The following were added for PCR: 10 μL 5X HF buffer ( Finnzymes ) , 5 μL primer mix from Illumina TruSeq kit , 0 . 4 μL dNTP mix that contains 25 mM each dNTP , 20 μL 5 M betaine , and 0 . 5 μL Phusion polymerase ( Finnzymes ) . Thermal cycler conditions were as follows: 1 cycle of 98°C . for 3 minutes , 10 cycles of 98°C for 1 minute , 60°C for 30 seconds , and 72°C for 30 seconds , 1 cycle of 72°C for 5 minutes . Reaction volumes were set to 100 μL on the thermal cycler to increase ramp time [59] . Duplicate reactions were combined and purified twice with Ampure XP beads ( Agencourt ) according to the manufacturer’s instructions . Libraries were sequenced on an Illumina Genome Analyzer in a paired-end run producing 56 nt reads . Ssaha2 was used to map reads to M . tuberculosis H37Rv genome version NC_000962 . The 1st base of “Read 1” ( hereafter called “1st nt” ) was extracted to determine the number of RNA 5’ ends mapped to each genome coordinate . Zero values were replaced by 0 . 5 . To identify above-background peaks in 5’ end coverage , the mean coverage at each coordinate in the two biological replicate “converted” libraries was averaged and the ratios of “1st nt” coverage at each coordinate relative to the positions 10 nt upstream and 10 nt downstream were determined . Approximately 22 , 000 peaks were identified where at least one of the two ratios was over 25 . These peaks in 5’ end coverage were subject to the following filters: ( 1 ) peaks with a mean “converted” library coverage under 20 were removed; and ( 2 ) the ratio of mean “converted” library “1st nt” coverage to mean RNA-seq expression library coverage in the 200 nt region upstream of the “1st nt” peak was determined , and only peaks with ratios of at least 0 . 7 were retained . For each filtered RNA 5’ end , the “1st nt” coverage in the replicate libraries was summed and the ratio in the converted/non-converted libraries was determined . The distribution of ratios was bimodal , and Gaussian mixture modeling was used to estimate the means and standard deviations for two skewed normal distributions ( one comprised of processed 5’ ends , and one comprised of unprocessed 5’ ends corresponding to transcription start sites ) . RNA 5' ends with ratios greater than 1 . 74 had a cumulative probability of ≤0 . 01 of belonging to the processed 5’ end population ( after adjusting for multiple comparisons by the Benjamini-Hochberg procedure ) and were therefore designated transcription start sites ( TSSs ) . Because transcriptional initiation is imprecise , the 6 , 900 statistically significant TSSs were filtered to remove all but the single TSS with the highest converted-library coverage in each 11 nt window . 4 , 978 TSSs passed this filter and are included in S3 Table . Whole-cell protein lysates were prepared as described in [55] . Pelleted lysates were resuspended in 6 M urea/50 mM ammonium bicarbonate . Protein content of each sample was measured using Pierce BCA protein assay . 20 mM DTT was added to 500 μg of protein and samples were incubated for 30 minutes at 37°C . Iodoacetamide was added at a final concentration of 50 mM and samples were incubated for 30 minutes in the dark at room temperature . Prior to trypsin digestion , urea concentration was diluted to less than 1 M by adding water and pH adjusted to 8 with a 1 M Tris solution . 10 μg sequencing grade trypsin ( Cat . No . V5280 , Promega , Madison , WI ) was added ( 1:50 enzyme to substrate ratio ) and samples were incubated at 37°C with shaking for 16 hours . The reaction was stopped by addition of formic acid ( FA ) to a final concentration of 1% and the solution was desalted with a 1 cc ( 30 mg ) Oasis HLB reverse phase cartridge ( Cat . No . WAT054955 , Waters , Milford , USA ) conditioned with 3 x 500 μL acetonitrile ( ACN ) , followed by 4 x 500 μL 0 . 1% FA . Samples were loaded onto the cartridges and washed with 3 x 500 μL 0 . 1% FA . Desalted peptides were eluted by 2 applications of 500 μL of 80% ACN/0 . 1% FA . Eluates were frozen , dried via vacuum centrifugation prior to peptide fractionation . The sample was then fractionated by reverse phase chromatography into 24 fractions and analyzed by liquid chromatography—tandem mass spectrometry . Briefly , each of the 24 fractions were resuspended in 20 μL of 3% ACN/ 0 . 5% FA and 2 μL of the peptide mixture was injected and separated by a 100 min gradient ( ~0 . 7%B/min . ) of increasing acetonitrile from 5–60%B . A PicoFrit column ( New Objective , Woburn , MA ) , with an inner diameter of 75 μm packed with 12–14 cm of ReproSil-Pur C18 3 μm particles , was directly interfaced to an Agilent 1100 HPLC coupled Orbitrap Velos mass spectrometer ( Thermo ) equipped with a custom nano-electrospray ionization source . MS analysis settings for protein identification were as follows . One precursor MS scan at 60 , 000 resolution in profile mode was followed by data-dependent scans of the top 15 most abundant ions at low-resolution in centroid mode . Dynamic exclusion was enabled with a repeat count of 2 , repeat duration of 20 seconds , exclusion duration of 30 seconds and an exclusion list size of 500 . MS/MS spectra were collected with normalized collision energy of 28 and an isolation width of 3 amu . The extensive peptide fractionation coupled with in depth MS analysis allowed detection and identification of very low levels of peptides . All MS data were processed using Agilent Spectrum Mill MS Proteomics Workbench ( Agilent Technologies , Palo Alto , USA ) . Specific codons were tested for their ability to initiate translation of the lacZ gene by generating translational fusions . Briefly , oligonucleotides were synthesized with candidate initiation codons ( ATG , CTG , GTG , or TTG = NTG ) at either the leadered or leaderless positions . A well-characterized E . coli derived promoter with a mapped +1 transcription start site was used to drive expression [60] . Putative negative control codons ( NTC ) were also substituted to evaluate specificity and robustness of translation initiation at that codon . Since altering the +1 nucleotide could conceivably alter transcriptional robustness—as reported for T7 RNA polymerase [49]—substituting this nucleotide in the conventional leadered reporters controlled for this potential variable . Overlapping oligonucleotides were paired to create the desired leaderless- and leadered-codon combinations , extended to duplex DNAs by self-templating PCR , and cloned into the lacZ reporter using 15-nt recombination arms in the promoter region and lacZ ORF by InFusion ( Clontech ) . E . coli cultures were grown in LB supplemented with hygromycin , and M . smegmatis in 7H9 supplemented with Tween 80 and hygromycin . Cultures were expanded , their density determined by OD600 , and lysed crude extracts were prepared and β-galactosidase activities measured by ONPG conversion and reading at OD420 . Activities were calculated in Miller Units , and displayed as percentages relative to a leadered ATG control construct . A zeocin-resistance gene reporter was created to allow expression by translational fusion , as for the lacZ reporter . The same basic oligonucleotide design described for lacZ was modified to accommodate the zeor gene overlap for InFusion cloning . Clusters of nucleotides of each oligo were specified for functionality ( e . g . , ATG or ATC codons , Shine-Dalgarno sequence ) or unspecified with random nucleotides to allow subsequent selection . Duplex DNAs were created by a single round of bidirectional primer extension and the library was created by InFusion cloning . Hygromycin-resistant E . coli colonies ( >10 , 000 per library pool ) were collected and a plasmid library was created for electroporation into M . smegmatis . Hygromycin-resistant M . smegmatis colonies ( >8 , 000 colonies ) were scraped and grown under zeocin ( 100 μg/ml ) selection in TSA + 0 . 05% Tween 80 . Plasmids were purified with aliquots from the Hyg-resistant and Zeo-resistant pools , and these were used as templates for PCR amplification of the cloned leader segments for amplicon-based next generation sequencing ( Ion Torrent ) . Equivalent amounts of the barcoded amplicons were pooled and sequenced on an Ion 318 chip ( Applied Genomic Technologies Core , Wadsworth Center ) . Sequence reads were aligned , trimmed , and the sequences at the randomized positions were compiled . Recovered sequences are shown as log10 converted heat maps of every possible codon , or as sequence logos for supporting elements . The enrichment of all codon combinations that contain at least one active initiation codon , with the notable exception of stop codons at the leadered position , indicates that clonal jackpot artifacts are not a major problem . Two hundred and twenty leaderless ( begin with an RTG ) M . tuberculosis transcripts were identified that initiate small ORFs of 5–50 amino acids . The stop codons of these small ORFs were mapped relative to the annotated start of the next downstream ORF , and 23 were found to have coupled architectures ( RTGA ) at the junction of the two ORFs . To determine whether 23 of 220 represented an enriched population , 1000 random sites in the genome were considered arbitrary start sites for ORFs that were similarly analyzed . Of the 1000 randomly identified in silico ORFs , 32 were coupled to a downstream ORF . The enrichment of 23/220 to 32/1000 was determined to be significant ( p < 0 . 0003 , one-tailed Fisher’s Exact test ) . The primary sequence data have been deposited in public repositories . The M . smegmatis RNA-seq and Ribo-seq data can be found at the European Nucleotide Archive <https://www . ebi . ac . uk/arrayexpress/experiments/E-MTAB-2929/> . The M . tuberculosis RNA-seq and the TSS data have been submitted to GEO under the accession number GSE62152 . The Mass Spec data used for N-terminal peptide identification have been submitted to MassIVE , ID number MSV000079012 , password 1tuberculosis . The data are also posted in the form of mapped sequence reads ( Applied Genomic Technologies Core , Wadsworth Center ) , and viewed at http://www . wadsworth . org/research/scientific-resources/interactive-genomics/ . | The current paradigm for bacterial translation is based on an mRNA that includes an untranslated leader sequence containing the ribosome-binding site upstream of the initiation codon . We applied genome-scale approaches to map the protein-coding regions in the genomes of Mycobacterium smegmatis and Mycobacterium tuberculosis . We found that nearly one-quarter of mycobacterial transcripts are leaderless in mycobacterial species , thus indicating that ribosomes must recognize these mRNAs by a novel mechanism and suggesting that there are alternative modes of bacterial translation beyond the Escherichia coli paradigm . Our translational profiling showed that many mycobacterial proteins are mis-annotated , and also found many new genes encoding small proteins that had been previously overlooked , which are likely to play novel roles in diverse cellular processes . We also developed a new reporter system that provides mechanistic insights into translation initiation through deep sequencing . Our data show that leaderless translation is a robust process that is conserved in mycobacteria , that leaderless translation only requires that the mRNA begin with a start codon , and predict that mycobacteria encode hundreds of small proteins . This work will help us understand gene structure , genome organization and protein expression in bacteria , and how the translational machinery differs in different organisms . | [
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| 2015 | Leaderless Transcripts and Small Proteins Are Common Features of the Mycobacterial Translational Landscape |
Mammalian common fragile sites are loci of frequent chromosome breakage and putative recombination hotspots . Here , we utilized Replication Slow Zones ( RSZs ) , a budding yeast homolog of the mammalian common fragile sites , to examine recombination activities at these loci . We found that rates of URA3 inactivation of a hisG-URA3-hisG reporter at RSZ and non-RSZ loci were comparable under all conditions tested , including those that specifically promote chromosome breakage at RSZs ( hydroxyurea [HU] , mec1Δ sml1Δ , and high temperature ) , and those that suppress it ( sml1Δ and rrm3Δ ) . These observations indicate that RSZs are not recombination hotspots and that chromosome fragility and recombination activity can be uncoupled . Results confirmed recombinogenic effects of HU , mec1Δ sml1Δ , and rrm3Δ and identified temperature as a regulator of mitotic recombination . We also found that these conditions altered the nature of recombination outcomes , leading to a significant increase in the frequency of URA3 inactivation via loss of heterozygosity ( LOH ) , the type of genetic alteration involved in cancer development . Further analyses revealed that the increase was likely due to down regulation of intrachromatid and intersister ( IC/IS ) bias in mitotic recombination , and that RSZs exhibited greater sensitivity to HU dependent loss of IC/IS bias than non RSZ loci . These observations suggest that recombinogenic conditions contribute to genome rearrangements not only by increasing the overall recombination activity , but also by altering the nature of recombination outcomes by their effects on recombination partner choice . Similarly , fragile sites may contribute to cancer more frequently than non-fragile loci due their enhanced sensitivity to certain conditions that down-regulate the IC/IS bias rather than intrinsically higher rates of recombination .
Accidental DNA double strand breaks ( DSBs ) arise during unperturbed proliferation . Such “endogenous” or “spontaneous” chromosome breakage does not occur randomly throughout the genome , but at specific loci , often referred to as fragile sites . Fragile sites have been observed in organisms ranging from bacteria to mammals , suggesting that they might be a ubiquitous feature of the genome [1] , [2] , [3] , [4] . Evidence points to the existence of multiple types of fragile sites that are distinguishable from one another based on its structure , function , and/or genetic requirement ( s ) for its stability [5] , [6] , [7] . Mammalian fragile sites are one of the most extensively studied naturally occurring breakage prone regions of the genome . They are classified as either “rare” or “common” , depending on their incidence among general population [4] . Rare fragile sites are found in less than 5% of the population . In most cases , rare fragile sites are tri-nucleotide repeats , expansion of which has been linked to conditions such as Fragile X-syndrome and Huntington disease [8] . In contrast , common fragile sites are present in all individuals and can be found on every chromosome , indicating that they are a normal component of the chromosome . Common fragile sites extend over large regions of the genome , from several hundred kilobases ( kb ) to over 1 megabase ( Mb ) with breaks or gaps occurring throughout these regions . There is no sequence determinant that defines common fragile sites [4] , [9] . Studies have implicated a link between common fragile sites , genome instability , and cancer [9] . For instance , some fragile sites have been shown to be loci of frequent chromosome deletions , translocations , and/or viral genome integration [10] , [11] as well as oncogenic chromosomal rearrangements ( e . g . [12] ) . Combining these with the observations that some fragile sites in model organisms exhibit elevated rates of recombination , it has been proposed that mammalian common fragile sites are recombination hotspots and that increased recombination activities at these loci contribute to cancer . Replication Slow Zone ( RSZ ) is a type of fragile site in budding yeast and a putative homolog of the mammalian common fragile sites . It was identified as loci of preferred chromosome breakage following inactivation of Mec1 , the budding yeast homolog of ATR , where high levels of replication dependent single stranded DNA ( ssDNA ) , a precursor to DSBs , accumulate [3] , [13] . Like its mammalian counterpart , RSZs are relatively large ( ∼10 kb ) , and appear to be a normal component of the chromosome . Other similarities between the two include; ( i ) timing of their replication during normal S-phase , which occurs late , ( ii ) their sensitivity to mild replication stress and inactivation of the ATR/ATM family kinases , and ( iii ) the lack of a defining sequence determinant ( s ) [3] , [14] , [15] , [16] . The mammalian ATR/ATM and their budding yeast homologs Mec1/Tel1 are conserved signal transduction proteins best known for their roles in S-phase and DNA damage checkpoint responses [17] . In addition , they also play essential roles in a number of fundamental DNA and chromosomal processes including genome duplication , meiotic recombination , and DNA repair ( e . g . [3] , [18] , [19] ) . Here , we utilized RSZ as a model to test the proposal that mammalian common fragile sites are recombination hotspots . Unexpectedly , we found that recombination rates at RSZ and non RSZ loci were comparable under all conditions tested , indicating that RSZs are not recombination hotspots . Based on these and other observations , we propose a model whereby regulation of the nature of the recombination outcome ( s ) , irrespective of the overall recombination activity , may play a key role in controlling genome rearrangements .
The observations that stalled- and collapsed- replication forks can promote recombination [6] , [7] , [25] , [26] and that RSZs are loci of slowed replication fork progression during normal S-phase [3] , suggest that RSZs might be recombination hotspots during normal proliferation . ( NB: In the current context , an arrested fork that can ultimately resume replication without intervention is referred to as a “stalled fork” whereas that requires an active fork-restart process is referred to as a “collapsed fork” ) . To test this , we compared the rate of URA3 inactivation at the RSZ and non RSZ loci under a standard yeast growth condition ( 2% glucose at 30°C , hereupon referred to as “YPD” ) . For each locus , the rate was estimated by the method of the median from two independently derived strains , and the average of the two was used for the locus-to-locus comparison ( Figure S2 ) [24] . In haploids , the locus specific recombination rates varied very little , from 1 . 8×10−5 per cell generation at ORI to 2 . 1×10−5 at RSZ1 ( Figure 2Ai ) . In diploids , the variation was greater , about 3 fold , and ranged from 1 . 1×10−5 per cell generation at TER to 3 . 3×10−5 at NON ( Figure 2Aii ) . Importantly , the rate of URA3 inactivation at the two RSZs was not higher than the three non RSZ loci in either haploids or diploids . We conclude that RSZs are not spontaneous recombination hotspots , defined as loci of intrinsically higher recombination activities . Chromosome breakage at RSZs does not occur during normal proliferation , but is promoted by a modest level of HU , high temperature , and/or inactivation of Mec1 [3] , [21] . Thus , it is possible that the putative recombination hotspot activity associated with RSZs might also require these conditions . To test this , we examined the effect of HU . The same ten haploid and ten diploid strains analyzed above were subjected to a transient ( 18 hour ) exposure to 10 mM HU before 5FOA selection . We found that the exposure lead to a statistically significant increase in rate of URA3 inactivation at every locus ( Figure 2D ) . Importantly , the rates at the two RSZs were not any higher than the three non RSZ loci in either haploids or diploids ( Figure 2B ) . We conclude that RSZs are not recombination hotspots even under a condition that promotes RSZ specific chromosome breakage . Next , we examined the nature of genetic alternations associated with URA3 inactivation in diploids . To this end , we utilized Southern Blot analysis that enabled us to monitor the presence of the following three alleles ( Figure 3A , Figure S3 ) ; ( i ) WT , ( ii ) the allele containing the hisG-URA3-hisG reporter integrated , hereupon referred to as “INT” for “integrant” , and ( iii ) the “pop-out” or “PO” allele , indicative of an intrachromatid or intersister ( IC/IS ) recombination or recombination related event ( Figure 1C ) . As expected , the parent ura3 diploid strain exhibited a single band diagnostic of the WT allele at each of the five loci examined; in contrast , each of the URA3 heterozygotes derived from the parent exhibited an additional band ( s ) , corresponding to the INT allele ( Figure 3B , Figure S3 ) . We found that all 5FOAR colonies examined had lost the INT fragment , suggesting that URA3 inactivation in every case involved a relatively large structural change ( s ) at the integration locus ( Figure 3C; data not shown ) . Overall , ∼80% ( 117/146 ) of the samples exhibited the diagnostic PO band , indicating that on average , URA3 inactivation was four times more likely to occur via an IC/IS-mediated event than all other mechanisms combined ( Figure 3E “Total” ) . The latter is consistent with previous reports on strong IC/IS-bias in mitotic recombination ( e . g . [27] ) . Locus specific PO fraction ranged from 70% ( 14/20 ) at TER to 100% ( 26/26 ) at RSZ2 , suggesting that the extent of IC/IS bias might be influenced by local environment ( Figure 3E ) . The average PO fraction for the two RSZs was higher than the three non-RSZ loci ( 90% versus 73%; Figure 3E ) ; however the difference was not statistically significant ( p = 0 . 1209 ) . We conclude that recombination activity at RSZs during standard growth condition is comparable to non RSZ loci with regard to both the rate of recombination and the extent of the IC/IS bias . The same Southern Blot analysis was performed on 5FOAR colonies that arose in the presence of HU . Similarly to the YPD samples , all 148 HU 5FOAR samples had lost the diagnostic INT fragment ( Figure 3D; data not shown ) . Overall , 53% ( 79/148 ) of the HU 5FOAR colonies carried the PO allele , a significant reduction from the 80% ( 117/148 ) observed in YPD ( Figure 3F “Total”; Chi square test , p<0 . 0001 ) . The negative effect of HU on IC/IS bias was observed at every locus; however , the only statistically significant reduction was at the two RSZs ( Fisher's exact test; Figure 3F ) . At RSZ1 , the PO fraction was reduced from 80%in YPD to 33% in HU , indicating that the 4∶1 bias toward IC/IS mediated URA3 inactivation in YPD was completely lost in HU , where the majority of URA3 inactivation occurred via non IC/IS mediated events . RSZ2 was notable because the extent of IC/IS bias in YPD appeared to be unusually strong ( 26/26; Figure 3E ) . In HU , we found that six of 29 had undergone a non IC/IS mechanism of URA3 loss , suggesting that the negative effect of HU might be irrespective of the intrinsic robustness of the IC/IS-bias . In the current analysis , a 5FOAR colony exhibiting only the WT band ( e . g . Figure 3CD , samples denoted by an “*” ) was inferred to have undergone URA3 inactivation via a non IC/IS mediated event , such as an IH gene conversion/crossover , ectopic recombination ( ECT ) , or chromosome loss ( Figure 1C ) . As mentioned above , it is possible to distinguish an IH event from the others by the virtue of the fact that a 5FOAR colony that arose via an IH recombination event would carry two copies of WT allele , while the rest carries just one ( Figure 1C ) . Indeed , quantitative analysis of the WT fragments in Southern Blot images revealed that some of the non IC/IS samples had a two-fold greater signal associated with the WT band relative to an IC/IS sample that carried a copy of the WT and the PO allele each ( Figure 3G , compare WT band intensity in “IH” and “IC/IS” lanes ) ; these samples were inferred to have undergone URA3 inactivation via IH-recombination . We were also able to confirm the occurrence of an ectopic ( ECT ) event by the presence of a novel chromosome sized fragment containing the hisG sequence on a Southern Blot of a pulse field gel ( PFG ) ( e . g . Figure 3F ) , suggestive of a chromosome translocation event . Applying these criteria , we were able to infer that the majority of 5FOAR colonies that arose in YPD ( 85/86 ) or in HU ( 78/89 ) had undergone URA3 inactivation via one of these three ( IC/IS , IH , or ECT ) mechanisms ( Figure 3I and J ) . The rest , 1/86 in YPD and 11/89 in HU , was classified as “Other” , which would include mechanisms such as chromosome loss . Of the 86 YPD 5FOAR colonies analyzed , about 25% , or 15 , had undergone URA3 loss via a non IC/IS event ( s ) . Among them , all but two were mediated by IH recombination . The remaining two corresponded to an ECT and an Other event each ( Figure 3I , “Total” ) . In HU , a reduction in the fraction of IC/IS event was accompanied by a significant increase in the fractions of ECT and Other , where they rose to 8% ( 7/89 ) and 12% ( 11/89 ) from about 1% in YPD ( Figure 3J , “Total” ) . The only significant increase in IH fraction conferred by HU was at RSZ2 , where it rose from 0/26 in YPD to 6/29 in HU ( Figure 3I and J ) . To test whether the effect of HU on the IC/IS bias might have been due to reduced dNTP levels , we examined the effect of sml1Δ . Sml1 is a negative regulator of RNR and its deletion leads to a ∼2 . 5 fold increase in dNTP levels [22] . We found that the mutation conferred a modest , but statistically significant increase in the overall PO fraction from 80% ( 117/146 ) in YPD to 89% ( 89/100 ) in sml1Δ ( Chi-square analysis , p<0 . 05; Figure 4F , “Total” ) . The observed effects of HU and sml1Δ suggest a positive correlation between dNTP levels and the extent of IC/IS bias , and implicate dNTP availability in regulation of recombination outcomes and genome rearrangements . The results also revealed that the effect of sml1Δ on IC/IS bias might be locus dependent; while the overall effect was an increase , the mutation actually lead to a modest decrease in the bias at RSZ2 from 100% ( 26/26 ) in WT to 90% ( 18/20 ) in sml1Δ ( Figure 3E; Figure 4F ) . Similarly , we found that the effect of sml1Δ on recombination rate was locus specific; while the mutation lead to an increase at RSZ1 , RSZ2 , and TER , but it reduced the rate at ORI ( Figure 4D , E ) . Overall , the effect of sml1Δ on ORI differed from the rest in that it was the only locus where the mutation conferred a statistically significant reduction in recombination rate and a significant increase in the IC/IS-bias . It would be require analysis of additional origin sequences to confirm whether the observed effect might be origin specific . No significant effect of sml1Δ was observed on the rate of URA3 inactivation in haploids ( Figure 4A , C ) . If chromosome breakage is mechanistically linked to recombination activity , then HU , a condition that promotes RSZ specific chromosome breakage , should also confer an RSZ specific increase in the rate of URA3 inactivation . Unexpectedly however , we found that HU increased the rate at both RSZ and non RSZ loci , indicating that the breakage was not linked to recombination activity . To confirm this further , we examined the effects of additional conditions shown to regulate chromosome breakage at RSZs . Specifically , we chose high temperature and mec1Δ sml1Δ , the two conditions shown to promote the breakage , and rrm3Δ , a mutation shown to suppress it [21] . Rrm3 encodes a DNA helicase involved in replication fork progression through ∼1000 discrete fork pause sites in the budding yeast genome , whose inactivation leads to fork stalling at these loci [28] , [29] . The rrm3Δ mediated fork stalling triggers Mec1/Tel1 dependent S phase checkpoint activation and Sml1 removal . The latter in turn , promotes fork progression through RSZ and prevents RSZ breakage even in the absence of Mec1 function [21] , [30] . We limited our analyses to diploids only , where the effect on IC/IS bias can be assessed in addition to the rate of URA3 inactivation . Results showed that high temperature ( 37°C ) , rrm3Δ , and mec1Δ sml1Δ increased the average rate of URA3 inactivation by ∼2 . 5 fold over WT at 30°C ( Figure 5A , “Average” ) . Low temperature ( 23°C ) , on the other hand , lead to ∼30% reduction . These observations confirmed previous reports on recombinogenic effects of rrm3Δ and mec1Δ sml1Δ . Furthermore , they revealed a positive correlation between temperature and recombination rate , suggesting that temperature itself might regulate endogenous recombination activity . Importantly , the observed effect was not specific to RSZs , providing further support for the notion that chromosome fragility and recombination activity at RSZs are not mechanistically linked . Similarly to HU , the three recombinogenic conditions , 37°C , rrm3Δ , and mec1Δ sml1Δ lead to a reduction in the overall PO fraction ( Figure 5B , “Average” ) . In contrast , low temperature ( 23°C ) , the only condition that decreased the rate of recombination , lead to a modest increase in the fraction . Together , these observations suggest an association between increased recombination activity and loss of IC/IS bias . Finally , results also showed that the effects of different conditions on IC/IS bias can be either RSZ specific , as in the case of HU , or general , as in the case of temperature , rrm3Δ , and mec1Δ sml1Δ ( Figure 5B legend ) .
We found that rates of URA3 inactivation at RSZs were comparable to non-RSZ loci under all tested conditions . Based on this , we conclude that RSZs are not recombination hotspots , defined as loci of increased recombination activity . A key assumption is that the URA3 inactivation event monitored in the current study is a readout for recombination activities at the locus , and not for an indirect effect ( s ) of a more distant element ( s ) . For example , studies of LOH in diploids indicate that most LOH events occur by a crossover between the heterozygous loci , and that the frequency of these events increases as a function of distance from centromere ( e . g . [31] ) . In addition , Ty elements are recombination hotspots that might affect recombination activities downstream of the loci ( e . g . [32] ) . Notably however , the frequency of the LOH events monitored in the current study was independent of the distance from CENIII ( Figure 3I , J ) . Similarly , the rates of overall URA3 inactivation at the loci downstream ( the three non RSZ loci ) and upstream ( the two RSZ loci ) of a Ty element were comparable ( Figure 2B , 4B , and 5A ) . Furthermore , the approach employed in the current study – i . e . integration of a reporter construct at a specific locus - is a widely utilized means of assessing local recombination activities ( e . g . [33] ) . Together , these considerations strongly suggest that the assay utilized in the current study monitors local recombination activity . It was surprising that RSZs did not have intrinsically higher rates of recombination than non RSZs . However , the current observation is actually consistent with the fact that RSZ breakage occurs during prometaphase in the context of topoisomerase II- and condensin- dependent chromosome condensation [3] , [20] , while most spontaneous mitotic recombination occurs during S phase in the context of stalled- and collapsed-replication forks [5] , [7] , [26] . The apparent temporal separation and differential genetic requirements suggest that chromosome breakage and recombination at RSZs might each entail a process that is independently regulated . However , it is also possible that the lack of correlation is a feature specific to RSZ ( and mammalian fragile sites , by extension ) and not a general feature of a fragile site . Our results confirmed an earlier observation that the locus-to-locus variation in spontaneous mitotic recombination rate is relatively modest , in contrast to the variation in meiotic recombination rates , which can be several orders of magnitude [34] , [35] . This difference is likely due , at least in part , to the nature of DNA structure that leads to recombination in each case: stalled- or collapsed-replication forks for mitotic recombination and developmentally programmed DSBs for meiotic recombination [26] , [36] . Formation of meiotic DSBs are regulated at multiple levels , including targeted localization of Spo11 , the enzyme that catalyzes the breakage , to the hotspot regions of the genome [37] . This in turn ensures that meiotic DSBs do not occur uniformly throughout the genome , but preferentially at DSB hotspots . In contrast , the occurrence of stalled- or collapsed-forks leading to spontaneous mitotic recombination does not appear to be regulated per se; rather , it appears to be a unintended consequence of a replication fork encountering a locus that is difficult to replicate , for example , due to either unusual DNA or chromatin structures or damaged DNA [6] , [7] , [25] , [26] , [38] . Previous studies have shown that genes encoding for proteins involved in processes such as replication fork progression , stalled fork integrity , and fork restart impact endogenous mitotic recombination rates ( e . g . [39] , [40] ) . The recombinogenic effects of HU or rrm3Δ observed in current study are likely due to increased incidences ( either in the frequency and/or the duration ) of fork stalling stemming from depletion of dNTP pools or loss of a replisome associated helicase activity , respectively [28] , [41] . The effects of mec1Δ sml1Δ , on the other hand , is unlikely due to increased fork stalling because replication forks proceed faster in an sml1Δ background compared to WT [3] . Given that Mec1 is required for stability of stalled forks and is a key regulator of homologous recombination and recombination related processes involved in replication fork restart [18] , [42] , the recomginogenic effects of mec1Δ sml1Δ are likely to stem from a defect ( s ) in processes that occur after fork stalling . The mechanism ( s ) by which temperature might affect endogenous recombination activity remains unknown . Notably however , there has been a precedent in meiotic recombination , where temperature appears to play an important regulatory role ( s ) [43] . On average , URA3 inactivation under the standard growth condition was four times more likely to occur via an IC/IS mediated event than all other mechanisms combined . We found that HU , high temperature , rrm3Δ , and mec1Δ sml1Δ abolished this bias while low temperature and sml1Δ enhanced it . These observations suggest that partner choice in mitotic recombination might be subjected to regulation , for example by factors like dNTP availability and temperature . During meiotic recombiantion , a significant fraction of meiotic DSBs is developmentally programmed to be repaired with an IH bias , using an intact homolog as a repair template , rather than a sister chromatid . Evidence indicates that such IH bias in meiotic recombination is mediated , at least in part , by expression of several meiosis specific chromosomal proteins that fundamentally alter meiotic chromatin structure; this in turn , favours physical interaction between the homologs while minimizing that between sister chromatids , overcoming the IC/IS bias intrinsic during mitotic recombination [44] , [45] , [46] . The latter implicates chromatin structure , notably the status of sister chromatid cohesion , in mitotic IC/IS bias . We utilized RSZ , a model for mammalian common fragile sites , to address the proposal that the fragile sites contribute to cancer due to increased recombination activity at the loci . The evidence presented here , however , suggests an alternative mechanism , which implicates the nature of recombination outcomes , rather than the overall recombination rate . The only RSZ specific recombination activity revealed in the current study was its greater sensitivity to HU induced loss of IC/IS bias . Although the sample size is limiting ( i . e . one insertion in each of two RSZs ) , an implication would be that , depending on the nature of stress , recombination events at RSZs ( and at mammalian common fragile sites , by extension ) , might be more likely to lead to a LOH and translocation , the type of alterations shown to contribute to cancer . Taken together , current observations provide a fresh insight into the ways in which fragile sites and other recombinogenic conditions may contribute to genome rearrangements .
Relevant genotypes of the strains utilized in current study are summarized in Table S1 . All URA3 strains were generated by standard yeast genetics procedures including transformation , mating , sporulation , and specific phenotype selections . A wild type haploid strain NKY291 , was transformed with each of the five DNA fragments containing the hisG-URA3-hisG cassette flanked by ∼500 bp upstream ( L ) and ∼500 bp downstream ( R ) genomic sequences of the targeted loci . The fragments were prepared from integration plasmids ( Figure S1 ) by NotI digestion and gel purification . Correct integration of hisG-URA3-hisG at each locus among randomly selected URA transformants was confirmed by Southern Blot analysis ( Figure S3 ) . Two independent transformants of each locus were mated with NKY292 , a WT haploid strain of the opposite mating type to generate heterozygous diploid strains , from which URA3 haploids were re-derived . These haploid strains ( JDCY 463 , 465 , 230 , 233 , 239 , 243 , 232 , 233 , 235 , 237 ) were used for all subsequent strain construction . The rate of URA3 inactivation was determined by the method of median [24] . For each measurement , 15 colonies of comparable size ( 1 . 5–2 mm in diameter ) freshly grown on YPD plates , were individually suspended in 5 mls of YPD or YPD+10 mM HU and cultured for 18 hours at 30°C ( Figure S2A ) . Samples were then diluted in water and plated on YPD or 5FOA plates to measure the number of total viable cells or those that had undergone a URA3 inactivation event , respectively . To ensure that only the URA3 inactivation events that occurred during the 18 hour of unselected growth in liquid medium were included for the analyses , only the 5FOARcolonies of comparable size ( 1 . 5–2 mm ) were counted following a three day incubation at 30°C . Statistical analyses on the rates of URA3 inactivation were assessed as described [47] . For each condition examined , a total of 20 or more independent 5FOAR colonies from each locus were subjected to molecular analysis ( Figure S2B ) . Genomic DNA from each colony was restricted using the appropriate restriction enzyme ( Figure S3; Figure 3 ) and subjected to Southern Blot analysis . For each locus , the L and R fragments used for plasmid construction ( Figure S1 ) were used as probes . As a control for quantifying the relative signals associated with different diagnostic fragments ( e . g . Figure 3E ) , a PCR amplified Mec1 fragment corresponding to nucleotide numbers 5539 to 7027 in the OFR was included as a probe ( Table S2 ) . Each restriction enzyme used for Southern analysis cleaves endogenous MEC1 sequence to generate a novel sized fragment that hybridizes to the MEC1 probe . To confirm the occurrence of ectopic recombination events , the candidate 5FOAR colonies were analyzed by Pulse Field Gel electrophoresis ( PFGE ) and Southern Blot analyses using hisG as a probe . For PFGE , Chromosome sized genomic DNA samples were prepared in low melting point agarous plugs as previously described [48] . Electrophoresis condition optimized for resolution around ChrIII was performed as described [49] . | Chromosome rearrangements are frequently associated with human cancers . Such rearrangement can result from a DNA break followed by an erroneous repair . Mammalian common fragile sites are one of the most extensively studied naturally occurring breakage prone regions of the genome . It has been proposed that fragile sites are recombination hotspots and that increased recombination activity at these loci contribute to cancer . We examined this hypothesis using a model organism , budding yeast Saccharomyces cerevisiae , where a homolog of the mammalian common fragile sites has been identified . Unexpectedly , our results showed that the rate of recombination at the fragile sites was not any higher than non fragile sites , even under the conditions that promoted chromosome breakage at the fragile sites . However , we found that the frequency of loss of heterozygosity ( LOH ) and translocation , the type of recombination outcomes known to contribute to cancer , to be significantly elevated at fragile sites under certain conditions . These findings suggest that the fragile sites might indeed contribute to cancer more frequently than non-fragile loci , but the reason for this is likely to be due the nature of the recombination outcome ( s ) rather than higher rates of recombination . | [
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| 2013 | Recombinogenic Conditions Influence Partner Choice in Spontaneous Mitotic Recombination |
For almost 50 years sub-Saharan Africa , including Uganda , has experienced several outbreaks due to Vibrio cholerae . Our aim was to determine the genetic relatedness and spread of strains responsible for cholera outbreaks in Uganda . Sixty-three V . cholerae isolates collected from outbreaks in Uganda between 2014 and 2016 were tested using multiplex polymerase chain reaction ( PCR ) , multi-locus variable number of tandem repeat analysis ( MLVA ) and whole genome sequencing ( WGS ) . Three closely related MLVA clonal complexes ( CC ) were identified: CC1 , 32% ( 20/63 ) ; CC2 , 40% ( 25/63 ) and CC3 , 28% ( 18/63 ) . Each CC contained isolates from a different WGS clade . These clades were contained in the third wave of the 7th cholera pandemic strain , two clades were contained in the transmission event ( T ) 10 lineage and other in T13 . Analysing the dates and genetic relatedness revealed that V . cholerae genetic lineages spread between districts within Uganda and across national borders . The V . cholerae strains showed local and regional transmission within Uganda and the East African region . To prevent , control and eliminate cholera , these countries should implement strong cross-border collaboration and regional coordination of preventive activities .
Vibrio cholerae remains a major cause of morbidity and mortality globally [1] . There have been seven cholera pandemics since the disease was recognized as a global threat [2] . The English record of pandemics of cholera started in 1816 , but cholera as a disease goes back centuries in Indian literature [3] . The organism responsible for cholera outbreaks , V . cholerae , was cultured over 130 years ago by Robert Koch ( 1884 ) in India [4] and its epidemiology in England was described by John Snow in 1886 [5] . Over time , considerable knowledge and skills in the management of this deadly infectious disease have accumulated leading to better prevention and control of epidemics [6–8] . Industrialized countries essentially have eliminated cholera as a public health problem through improved water and sanitation [9] . Nonetheless , this enteric bacterium continues to cause deaths and suffering in many countries [10–12] . Sub-Saharan Africa bears the highest reported cholera disease burden [13] . The ongoing outbreaks in Africa and elsewhere in the world are part of the seventh pandemic caused by the V . cholerae O1 , El Tor lineage [14 , 15] . Genetic differences among isolates allow for a greater understanding of the transmission of the bacteria within and between geographic regions and time periods [16] . Two methods , multilocus variable-number tandem-repeat analysis ( MLVA ) [17 , 18] and whole genome sequencing ( WGS ) [19] , provide sufficient genetic differentiation to distinguish between the isolates across different places and times . Less complex methods such as culture , biochemical and serological tests to detect , confirm and describe V . cholerae [20] , do not permit accurate tracking of the spread of specific genetic lineages . Yet these are the only methods available in most African countries including Uganda [21] . The goal of this study was to analyze V . cholerae isolates responsible for cholera outbreaks that occurred between 2014–2016 in Uganda using multiplex PCR , MLVA and WGS to determine the genetic relatedness and spread of V . cholerae isolates from different outbreaks in Uganda .
A cross-sectional study was conducted using all available viable V . cholerae isolates collected during cholera outbreaks in Uganda between 2014 and 2016 and kept frozen ( -80°C ) at the Central Public Health Laboratory ( CPHL ) in Kampala . In addition , aggregated epidemiological cholera surveillance data for the years 2014–2016 were reviewed and used to generate Epi-maps that contextualized the epidemic spread and transmission of cholera .
A total of 63 V . cholerae isolates for the years 2014–2016 were tested . The isolates were from 9 locations: 8 districts in Uganda and a ninth from patients who acquired their illness in Juba , South Sudan , and were treated in Uganda . All 63 isolates tested positive for ompW , toxR and ctxA indicating the presence of V . cholerae virulence genes . The isolates included both V . cholerae Inaba ( 63% ) and Ogawa ( 34% ) serotypes as shown in Table 1 . All 63 V . cholerae isolates were genotyped using MLVA . Three clonal complexes ( CC ) were identified circulating in Uganda . MLVA CC1 contained 32% ( 20/63 ) ; MLVA CC2 , 40% ( 25/63 ) ; and MLVA CC3 , 28% ( 18/63 ) of the isolates . The three MLVA CCs are shown in Fig 1 . The spatial distribution of MLVA CCs in Uganda reveals the presence of multiple genetic lineages within outbreaks and genetically defined connections between outbreaks ( Fig 2 ) . Two lineages were observed in 2014 , when CCs 1 & 3 were isolated in Arua and Moyo districts in northwest Uganda . In 2015 , CCs 1 & 3 were observed in Hoima and CCs 1 & 2 were isolated in Kasese district in southwest Uganda . Each separate CC identified one of three genetically related series of outbreaks . First , isolates from CC3 were observed in June 2015 in individuals from Juba , South Sudan , and later in July 2015 in nearby Arua district , Uganda . Additional isolates were seen further south in September 2015 in Hoima on Lake Albert in Uganda . A second outbreak , defined by CC2 , was initially identified in April 2015 in Kasese district in western Uganda , and subsequently in November 2015 in Wakiso district in central Uganda , in December 2015 in Kampala district in central Uganda and in December 2015 in Moroto district in northeastern Uganda . This outbreak persisted into January 2016 when it was found in Kampala and Mityana in central Uganda and in Mbale district in eastern Uganda . A third outbreak , defined by CC1 , contained isolates collected in May and July 2015 in Kasese district , Uganda , and in June 2015 in individuals from Juba , South Sudan . WGS genotyping of ten isolates indicated that the DNA was typical of the third wave of the seventh pandemic containing the classical allele of ctxA ( S2 Table ) . The Ugandan DNA sequences belonged to three distinct clades . Within these distinct clades , the Ugandan sequences differed by five or fewer nucleotides ( Fig 3 ) . Two clades were contained in the transmission event ( T ) 10 lineage and the other was contained in T13; no Ugandan isolate sequences were contained in a third African lineage T12 . The Ugandan clades were closely related to each other and to sequences from Democratic Republic of Congo and Tanzania ( Fig 3 ) . Clade 2 sequences from Kasese district in April 2015 were related most closely to sequences from Mbale district in January 2016 and secondarily to sequences from i ) the Democratic Republic of Congo and ii ) epidemic isolates from Dar es Salaam , Tanzania in August 2015 which spread across Tanzania during 2015 . Clade 3 sequences from Arua and Moyo districts , Uganda in April and May 2014 and Clade 1 sequences from Kasese district , Uganda in April and May 2015 were related closely to sequences from an outbreak in January 2015 in Kigoma , Tanzania . The distance between the Ugandan and Tanzanian clades was nine or fewer nucleotides .
No V . cholerae isolates were collected and tested from a cholera outbreak in 2016 in northwestern Uganda that started with the influx of South Sudan refugees and was restricted to districts where the refugees settled and their immediate neighborhoods . However , since this outbreak was restricted to a few districts in northwestern Uganda with refugees , it is unlikely that this had an effect on the findings of this study . The cholera outbreaks in Uganda were due to genetically diverse V . cholerae O1 isolates from two introductions from wave 3 of the seventh pandemic carrying the classical El Tor toxin gene . The V . cholerae strains showed local and regional transmission within Uganda and East Africa . Interventions to prevent , control , and eliminate cholera in Uganda and throughout East Africa should be strengthened with a focus on regional collaboration . | Cholera , an acute diarrheal disease , essentially was eliminated in the western world many decades ago , but has continued to cause many deaths in sub-Saharan Africa , South America and Asia . Cholera diagnosis in most countries in sub-Saharan Africa , including Uganda , is by stool culture , serology and biochemical methods . These testing methods are unable to establish the relatedness , virulence and spread of Vibrio cholerae in region . To determine the spread , relatedness and virulence of V . cholerae responsible for the various cholera outbreaks in Uganda , we used DNA-based testing methods . We tested 63 V . cholerae isolates from samples collected in Uganda from 2014–2016 . Our results showed three distinct lineages of genetically related cholera-causing bacteria . These organisms showed internal spread in Uganda and cross-border spread to neighboring countries in East Africa . These findings provide a valuable baseline and help define the context for directing control measures and technologies for cholera prevention in East Africa . | [
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| 2018 | Molecular characterization of Vibrio cholerae responsible for cholera epidemics in Uganda by PCR, MLVA and WGS |
Understanding of scabies immunopathology has been hampered by the inability to undertake longitudinal studies in humans . Pigs are a useful animal model for scabies , and show clinical and immunologic changes similar to those in humans . Crusted scabies can be readily established in pigs by treatment with the glucocorticoid dexamethasone ( Dex ) . Prospective study of 24 pigs in four groups: a ) Scabies+/Dex+ , b ) Scabies+/Dex- , c ) Scabies-/Dex+ and d ) Scabies-/Dex- . Clinical symptoms were monitored . Histological profiling and transcriptional analysis of skin biopsies was undertaken to compare changes in cell infiltrates and representative cytokines . A range of clinical responses to Sarcoptes scabiei were observed in Dex treated and non-immunosuppressed pigs . An association was confirmed between disease severity and transcription of the Th2 cytokines IL-4 and IL-13 , and up-regulation of the Th17 cytokines IL-17 and IL-23 in pigs with crusted scabies . Immunohistochemistry revealed marked infiltration of lymphocytes and mast cells , and strong staining for IL-17 . While an allergic Th2 type response to scabies has been previously described , these results suggest that IL-17 related pathways may also contribute to immunopathology of crusted scabies . This may lead to new strategies to protect vulnerable subjects from contracting recurrent crusted scabies .
Sarcoptes scabiei infestation is associated with considerable global morbidity [1] . The disease is prevalent in overcrowded living conditions , with the highest disease burdens seen in young children [2] . The link between scabies , secondary bacterial infection and sequleae such as post-streptococcal glomerulonephritis [3] has resulted in efforts to reduce the prevalence of scabies in endemic communities . Ordinary scabies manifests as a localised or general rash with low mite burden ( <20 mites ) . Crusted ( Norwegian ) scabies is a less common but debilitating form , with proliferation of mites , hyperkeratosis , and risk of serious secondary infection . Crusted scabies requires aggressive treatment , and recrudescence and reinfestation are common [4] . Factors underlying the development of crusted scabies include iatrogenic immunosuppression and other immunosuppressive conditions such as HIV , HTLV-I and systemic lupus erythematosus [5–7] . The disease has also been described in those with no immune deficit [7–9] , and reasons for crusted scabies development in this cohort are unknown . Limited humoral and cellular studies conducted to date suggest that crusted scabies is associated with a non-protective allergic T helper ( Th ) 2 response [10–12] , but these are confounded by difficulties in assessing clinical severity [13] , and the fact that patients present at an advanced stage of infestation . Prospective studies are necessary to gain meaningful insights into immune responses driving crusted scabies . Scabies is associated with delayed onset of symptoms ( 4–6 weeks ) in primary infestation , and several studies show that S . scabiei is capable of down regulating cytokine expression , likely suppressing early immune responses to allow mites to establish [14–18] . However , these studies were mostly in-vitro , utilising mite extracts and cultured cells or skin equivalents . We have recently developed a porcine model to investigate aspects of scabies immunology [19–21] . Pigs are a natural host of S . scabiei var suis , developing similar clinical manifestations to humans , including crusted and ordinary scabies . In this study we conducted transcriptional analysis of representative Th1 , Th2 , and Th17 pathway cytokines in the skin of infected pigs at several time points post infestation , and assessed skin biopsies with different clinical phenotypes by immunohistochemistry for inflammatory markers .
Animal ethics approval was obtained from the QIMR Berghofer Medical Research Institute ( Approval 1266 ) and the Queensland Department of Agriculture , Forestry and Fisheries ( Approval SA 2009/07/294 ) . Animals were handled in accordance with good animal practice as defined by the Australian code of practice for the care and use of animals for scientific purposes and the Australian National Health and Medical Research Council’s Animal Code of Practice . Details regarding trial design have been described elsewhere [20] . The study involved 24 female piglets in four treatment groups ( n = 6 per group ) . Group A: treated daily with 0 . 25mg/kg oral Dexamethasone ( Dex ) and ears infested with approximately 2 , 000 S . scabiei var suis mites . Group B: infested with approximately 2 , 000 mites . Group C: treated daily with 0 . 25mg/kg Dex ( Dex only control ) . Group D: No Dex or mite infestation ( negative control ) . While the infested and non-infested groups were kept isolated from each other , the allocation of individual pigs to pens was random , meaning that Dex and non-Dex pigs were housed together . Skin lesions were scored weekly on a 1–8 scale ( 1 = minimal change , >4 = development of crusts , 8 = extensive crusting . Skin scrapings were collected from a 2cm2 ear region of each pig fortnightly to approximate mite burden , as described previously [19] . Mite burden was graded as follows: – = no mites , + <20 mites/scrape , ++ = >20–100 mites/scrape , +++ = >100 mites/scrape . Two adjacent 3mm skin punch biopsies were collected from the ears of all pigs at week 0 , 4 , 8 , and 12 post-infestation . At this size biopsies healed rapidly with minimal scarring . For infested pigs , biopsies were taken directly from lesional areas where scabies infestation was apparent . Biopsies were full skin thickness , including hyperkeratotic areas ( if apparent ) , epidermis , dermis , and underlying ear cartilage . One biopsy was stored in RNA Later reagent ( Life Technologies ) and kept at-80°C . The second was collected into 10% neutral buffered formalin , fixed for 24 hours , transferred to 70% ethanol , and kept at 4°C . Ten pigs were selected for analysis- two from groups A , C and D , and four from Group B . These represented different clinical phenotypes—crusted scabies , ordinary scabies , and non-infested , based on clinical presentation and mite burden . The four pigs in Group B included two pigs with crusted scabies ( designated Group B+ in subsequent results ) . Serial sections ( 4–7μM ) were cut from paraffin embedded biopsies , dewaxed and stained with hemotoxylin and eosin . Slides were examined for cellular , structural and vascular changes ( Table 1 ) and each parameter allocated a score of 0–5 where 0 = minimal change and 5 = extensive change . The inspecting pathologist was blinded to the allocated group . Immunohistochemistry was undertaken to characterise the cellular infiltrate , as well as for IL17 cytokine staining . T cell numbers were assessed by staining with anti-CD3 antibody . Dewaxed sections were incubated with high pH antigen retrieval solution ( Dako pH 9 . 0 ) and blocked with purified casein ( Medical background sniper , Biocare ) . Rabbit anti-human CD3 antibody ( Biocare ) , previously established to cross-react with pig tissue , was diluted 1:275 and incubated overnight at room temperature . Sections were washed 3 times for 5 minutes in phosphate buffered saline . Anti-rabbit HRP secondary antibody ( Vector labs ) was applied for 30 minutes , washed as above , and the HRP substrate Novared ( Vector labs ) applied and developed for 5 minutes . For mast cell visualisation , sections were stained with toluidine blue . Analysis was performed on CD3 and toluidine blue stained sections by scanning slides with an Aperio XT scanner . Positively labelled cells were counted in 10 fields at 20 X magnification . Cell concentration ( cell/mm2 ) was calculated for each field by dividing the count total by the area of the field ( 0 . 234mm2 ) and the value for each of the 10 fields averaged . For IL-17 detection , dewaxed sections were blocked for endogenous peroxidases with 1 . 0% H2O2 , 0 . 1% sodium azide for 10 minutes . Sections were incubated in citrate pH 6 . 0 antigen retrieval buffer at 97°C , and blocked in 4% skim milk powder , followed by purified casein ( Medical background sniper , Biocare ) plus 10% normal goat serum and 1 . 0% BSA . Polyclonal rabbit anti-human IL-17 ( Abcam , 1mg/mL ) , diluted 1:100 , was applied for 2 hours at room temperature . This antibody was derived from a synthetic 19 amino acid peptide of human IL-17A , and based on sequence conservation was predicted to be cross reactive with its pig homologue . Sections were washed 3 times for 5 minutes in tris buffered saline . Anti-rabbit-HRP secondary antibody ( Mach2 , Biocare ) was applied for 45 minutes , sections washed as above and developed with 3 , 3'-diaminobenzidine ( DAB ) with H2O2 as substrate for 5 to 10 minutes . Staining intensity was assessed qualitatively on a scale of 0–4 by a dermatologist blinded to the allocated group . Biopsies stored in RNA Later were thawed and homogenised in 600μL TRIzol reagent ( Life Technologies ) using the Tissue Lyser II homogeniser ( Qiagen ) . Phase separation with TRIzol was undertaken according to the manufacturers’ protocol . The aqueous phase was column purified as per the manufacturers’ protocol ( PureLink RNA mini-kit , Life Technologies ) , including DNAse digestion . RNA was eluted in RNAse free dH20 and stored at-80°C . RNA quantity and integrity was assessed using the Nanodrop ND2000 spectrophotometer ( Nanodrop Technologies ) and Agilent Bioanalyzer RNA 6000 nano-kit ( Agilent Technologies ) . One μg of purified total RNA was reverse transcribed to cDNA using the QuantiTect reverse transcription kit ( Qiagen ) . The cDNA was diluted 1:4 in dH20 and stored at-20°C . Primers were designed using Primer3 software ( http://frodo . wi . mit . edu/ ) ( Table 2 ) . Hypoxanthine phosphoribosyl transferase 1 ( HPRT1 ) was selected as a reference gene , as this gene is proposed to be stable under different environmental conditions [22] . Gradient PCR to test optimal annealing temperatures was performed using control skin cDNA . PCR products were purified ( Roche ) , cloned ( pGEM-T , Promega ) and sequenced ( Big Dye 3 . 1 , Applied Biosystems ) . Sequence identity was confirmed using BLASTx ( http://blast . ncbi . nlm . nih . gov/ ) . To assess amplification efficiency , plasmids containing the gene of interest were linearised and serially diluted . qPCR was done using the QuantiTect SYBR green PCR kit ( Qiagen ) . Reactions contained 1 X SYBR green master mix , 0 . 4μM primers , 1μL diluted plasmid DNA and dH20 to total volume of 10μL . Reactions were cycled in the Rotor Gene 6000 real-time cycler ( Qiagen ) . Cycling conditions were: initial denaturation 95°C , 15 min , followed by 40 cycles of 94°C , 15 s; 56°C , 30 s; 72°C , 30 s; with data acquisition at 76°C , 20 s . Standard curves , melting temperature and efficiency calculations were produced using the Rotor Gene software . qPCR was run on the cDNA samples for the gene of interest in parallel with HPRT1 , allowing for normalisation . A no-RT control containing RNA as template was used to confirm that co-amplification of genomic DNA was not occurring . Each PCR also included a no template control . Reactions were performed in duplicate . Individual reaction mixtures were as above , except that 2μL cDNA was used as template . To measure transcriptional differences between treatment groups relative to the untreated , uninfested control group ( Group D ) , the ΔΔCt formula was used , corrected for PCR efficiency [23] . Significance of differences between groups was assessed using unpaired T tests at each time-point using GraphPad Prism version 5 . 0 ( GraphPad Software , Inc . ) .
Details of the clinical phenotypes observed in the trial are presented elsewhere [20] . All pigs in Group A developed crusted mange ( skin score >4 ) from weeks 8–24 post-infestation ( Fig 1A ) . Two pigs in group B also developed crusted mange in the absence of Dex immunosuppression ( designated as group B+ in subsequent results ) . The remaining pigs in group B developed an acute reaction , with lesion severity 1–4 , peaking at weeks 8–12 before declining ( Fig 1B ) . Mite counts were associated with lesion scores , with positive scrapings obtained from 4/6 pigs in group A , and 3/6 pigs in group B in week 4 . From week 8 , differences between the groups became more apparent , with most pigs in group A having heavy mite infestations . In group B , 2 pigs developed heavy infestations ( Group B+ ) while 4 pigs had low-moderate infestations . Pigs in the non-infested groups did not develop skin lesions nor have detectable mites at any time ( Table 3 ) . General histopathology . Major epidermal changes characteristic of severe crusted S . scabiei infestation included acanthosis , rete peg hypertrophy and para-hyperkeratosis ( Fig 2A ) . Other changes included apoptosis / necrosis /erosion , microabscesses and transudation ( Table 4 ) . At the dermal level pathology included edema , vasculitis , and infiltrates of granulocytes and monocytes . The level of pathology was associated with clinical severity , with the greatest changes observed in Groups A & B+ . Group A had fewer histological changes at 4 weeks , but more dramatic change at 8 and 12 weeks . Histological changes in group B pigs that clinically had self-limiting infestation peaked at week 4 and were reduced at week 8 and 12 . Minor changes were observed in one pig in group C ( thickening , ortho-hyperkeratosis , minor mononuclear infiltrate ) . No histological changes were apparent in the group D , Table 4 ) . CD3 immunolabeling . Pigs in Groups A & B had increased T cell infiltrates relative to non-infested pigs in groups C & D as ascertained by CD3 immunolabeling ( Fig 2C , 3A ) . Positive cells aggregated in a perivascular pattern in the papillary and reticular dermis and in the stratum basale and stratum spinosum of the epidermis . This increase was most marked in pigs in Group A and B+ . Maximal infiltration was observed in pigs in group A at weeks 8 and 12 , in Group B+ at weeks 4 and 8 , and in Group B at week 4 ( Fig 3A ) . Mast cell staining . Mast cell numbers in crusted pigs were increased relative to non-infested controls at weeks 8 and 12 ( Fig 2D , 3B ) . Positively stained cells were perivascular in the papillary and reticular dermis . No stained cells were present in the epidermis . Mast cell numbers in pigs with ordinary scabies did not change dramatically over the course of infestation , but were slightly elevated relative to other groups at week 4 . IL-17 immunolabeling . IL-17 staining l was moderate to intense in pigs with crusted scabies at weeks 8 and 12 ( Group A , B+ ) , while low to moderate in pigs with ordinary scabies ( Group B ) and minimal in non-infested pigs ( Table 4 , Fig 4 ) . Where positive , IL-17 labeling was widespread and generally dispersed and located in dermal cells , stratum basale and stratum spinosum , as well as within vessels . There was also a strong signal in keratinocytes ( Fig 4 ) . No signal was observed with the isotype control antibody ( Fig 4D ) . Scabies was associated with significant changes to several cytokines measured , including transforming growth factor β ( TGF-β ) , interleukin ( IL ) -2 , IL-4 , IL-13 , IL-17 and IL-23 ( Fig 5 ) . No significant changes to interferon γ ( IFNγ ) , IL-5 , IL-6 or IL-10 were detected . In the following section , while fold changes in transcription are noted , the Group B+ P-values are not reported due to the low numbers of pigs in this group limiting meaningful statistical interpretation . When comparing pigs with crusted scabies ( Groups A and B+ ) to those with ordinary scabies ( Group B ) an increased magnitude of IL-13 , IL-17 and IL-23 responses was observed from 4 weeks . IL-13 was increased both in Group A and B+ by 13-fold at 4 weeks ( Group A p = 0 . 05 ) , in Group A ( 15-fold , p = 0 . 002 ) and B+ ( 4-fold ) at 8 weeks , and in Group A ( 16-fold , p = 0 . 009 ) and B+ ( 8-fold ) at 12 weeks . By contrast in Group B pigs with ordinary scabies elevation of IL-13 was only observed at week 12 ( 8-fold , p = 0 . 03 ) . We saw upregulation in the Th17 cytokines , IL-17 and IL-23 only in pigs with crusted scabies . IL-17 was significantly upregulated at all time points , most strongly at week 8 ( Group A 41-fold , p = 0 . 009 , Group B+ 37-fold ) . Upregulation of IL-23 was observed at all time points , with a 30-fold increase observed in Group A pigs from week 4 ( p = 0 . 03 ) . Transcription of IL-4 was increased in all infected pigs at all time points , with the exception of group B+ at 4 weeks . The greatest elevation of IL-4 was in Group A pigs at 8 weeks ( 24-fold , p = 0 . 002 ) . IL-2 levels also increased in all infected pigs from week 4 , but the change only became significant after week 8 . Similarlary TGB-β was modestly but signficantly upregulated in infected pigs at all time points , with the exception of Group A at week 4 . There were no significant differences between non-infected Dex +ve and Dex—ve pigs ( Groups C & D ) , suggesting that the Dex had little impact on baseline levels of these cytokines in the skin .
Comparison of immune responses in scabies been confounded by the limited availability of clinical samples and standardisation problems related to differences in disease presentation and the existence of co-morbidities [24] . Animal models offer the ability to correlate clinical phenotype with immune parameters and to report the temporal development of immune responses . We observed that phenotypic differences between crusted and ordinary scabies in a porcine model were associated with differences in both the timing and magnitude of cytokine responses and histological changes . Scabies became clinically apparent in infested pigs from week 4 , with mite numbers correlated with the appearance of clinical lesions . As skin scrapings can have poor diagnostic sensitivity due to low mite numbers , and are difficult to perform in large herds or community studies , clinical appearance is more useful as a proxy measure of infestation level [25] . For example , while several pigs in Group B had negative skin scrapings at weeks 4 and 8 , clinical scores indicated they were still infested . While most infested pigs had similar mite counts at week 4 , pigs that developed crusted scabies had substantially increased mite numbers from week 8 , while those with ordinary scabies maintained low or moderate numbers of mites . Notably , two pigs from group B developed crusted scabies in the absence of immunosuppression . While acknowledging the small number and consequent limited interpretation of results for Group B+ , we elected to compare these pigs as a separate “subgroup” , as the development of crusted scabies in the absence of Dex immunosuppression is of interest . These clinical observations reflect what is well documented in the literature- while the majority of pigs with sarcoptic mange develop an ‘acute’ manifestation with clinical peak of around 8 weeks before a decline in skin lesions and mite numbers , indicative of a self limiting infestation , some pigs develop chronic hyperkeratotic mange akin to crusted scabies in humans . This reinforces the value of the porcine model to explore protective versus pathologic immune responses in scabies , and further studies by our group have focused on the further study of different clinical phenotypes in pigs not receiving Dex treatment [21] . As histological analysis of scabies lesions has been reported in the literature previously for both pigs and humans , we did not intend to undertake comprehensive histological comparisons in this study , but rather obtain a representative “snapshot” to link our clinical and molecular observations in different clinical phenotypes of scabies . Being mindful of the limited numbers of pigs examined , histopathology generally mirrored clinical observations . An exception was that pigs in Group A had delayed inflammatory responses at week 4 relative to Group B . As the pigs in Group B+ with crusted scabies also had inflammatory changes at week 4 , these differences may be more attributable to Dex supressing early inflammatory responses rather than differences between crusted and ordinary scabies . From week 8 pathologic changes between the clinical phenotypes were more apparent , which was also reflected in CD3+ T cell numbers . Increased T lymphocytes in scabies lesions have also been reported in humans [26] and other animals [26–29] . Ongoing work has shown that the CD3+ T cell infiltrate in pigs with crusted scabies is comprised largely of γδ T cells and CD8+ T cells [21] . Although γδ T cells have not yet been examined in human scabies , CD8+ tropism has been observed in crusted scabies [30] , while increased CD4+ cell infiltrates were associated with protective immunity in canine mange [28] . Crusted scabies was associated with increased mast cell numbers , most notably at week 12 post infestation . Mast cell numbers remained steady throughout the study in pigs with ordinary scabies . The presence of mast cells is consistent with previous findings [27 , 29 , 31 , 32] . The presence of mast cells , often with accompanying eosinophilia , is reflective of the allergic and immediate hypersensitivity component of the scabies immune response , particularly upon secondary exposure [31] . The role of mast cells and related high IgE levels in protective versus pathologic responses to scabies is yet to be resolved [33] . As well as general T cell proliferation and inflammatory markers such as IL-2 and TGF-β , crusted scabies was associated with a pronounced Th2 response . This was most evident with IL-13 , and to a lesser extent , IL-4 , whereas IL-5 was not signifigantly elevated . These findings are in accordance with cross-sectional studies on human patients [10] , where peripheral blood mononuclear cells ( PBMCs ) from crusted scabies patients secreted more IL-5 and IL-13 , and reduced IFNγ in response to stimulation with S . scabiei antigens [10] . While we did not see any transcriptional changes in IFNγ in the present study , this may be related to the timing of infestation , local versus peripheral responses , or primary versus secondary infestation . For example Lalli et al [34] found that while primary exposure to S . scabiei in mice was associated with an IL-4 response , secondary exposure following immunization was IFNγ oriented . Other studies by our group have shown increased CD4+ IFNγ+ T cells at one week post infestation in PBMCs from mange infested pigs [21] . This is the first study to measure temporal changes in cytokine levels in scabies infested skin . In studies undertaken on clinical patients , little information was available regarding duration of current infection and a key question was if elevated Th2 responses precede , or are simply a consequence of , the extreme antigen burden in crusted scabies [35] . Our studies show that Th2 elevation , particulary of IL-13 , occured prior to the development of high mite burdens and before major clinical or histological differences between groups became evident . The observation of increased IL-17 in the skin by immunohistochemistry and qPCR supports our recent findings of increased CD3+ IL-17+ cells in crusted scabies as determined by intracellular cytokine staining [21] . In this study , increased IL-17 was observed at week 15 post-infestation . Here , we show that transcriptional increases of IL-17 begin from as early as week 4 post-infestation . Again , this was prior to the development of strong clinical or inflammatory changes in the skin , suggesting that the IL-17 increase is associated with a dysregulated response rather than just a consequence of a changed inflammatory skin milieu . IL-17 is a proinflammatory cytokine implicated with a number of allergic and inflammatory diseases . Traditionally associated with CD4+ T cells ( Th17 ) , IL-17 is also secreted by other innate and adaptive immune cells in the skin , including CD8+ T cells , γδ T cells , and mast cells [36] . While γδ cells are likely a major source of IL-17 in crusted scabies [21] , the contribution of CD8+ and mast cells to local IL-17 production is still to be investigated . Regardless of the cell type , it is accepted that functional maturation and IL-17 secretion is promoted by increases in IL-23 , secreted by dendritic cells , macrophages and keratinocytes , in the presence of TGB-β and IL-6 [36] . These are all present in scabies infested skin , supporting an IL-17 environment . Importantly , IL-23 was only increased in crusted scabies , potentially promoting the subsequent high levels of IL-17 . Using human skin equivalents , Morgan and colleagues [15] demonstrated that S . scabiei promotes up-regulation of IL-23 from 48 hours post infestation . It is suggested that increases in IL-17 could be the result of a dysregulated regulatory T ( Treg ) /Th17 balance , or due to a deficit in IL-10 [21] . While mite extracts are capable of inducing IL-10 secretion in human PBMCs [37] , reduced IL-10 was observed in PBMCs isolated from crusted scabies patients relative to ordinary scabies [10] . In the current study there were no observable differences in IL-10 between crusted and ordinary scabies . A limitation was that other markers of Treg function were not examined . A role for IL-10 regulation of IL-17 is supported by studies in leishmaniasis , where blockade of IL-10 resulted in increased IL-17 and exacerbation of skin pathology [38] . Increased IL-4 is also reported to suppress IL-10 , exacerbating syptoms of Th2 mediated atopic dermatitis [39] . An important consideration is the potential impact of Dex on the immune parameters investigated . It is accepted that the effects of Dex are pleotropic , with dose , timing and experimental system appearing to play a role . These preliminary findings need to be supported by larger studies with non-immunosupressed pigs with crusted scabies . While the utilisation of Dex to induce the clinical phenotype of crusted scabies somewhat confounds interpretation of the immunologic parameters measured in this study , the data obtained is still informative . Firstly , comparing immune responses in the crusted scabies phenotype in the presence and absnece of immunosupression assists in refining a common immunopathology , regardless of causation . Secondly , crusted scabies in humans frequently arises from corticosteroid use , so an understanding of immune responses and potential implications for immunotherapy under these conditions are of interest . Thirdly , the effects of Dex on specific aspects of the immune system remain poorly defined in both humans and animals , so this study adds value at a general level . In our study , pigs were maintained on a relatively low dose of Dex ( 0 . 25mg/kg ) , with others reporting that porcine immunne funtion was resistant to higher doses ( 2mg/kg ) [40] . Despite the low dose , there are several factors whereby Dex may be conducive to the development of crusted scabies . Dex may promote Th2 bias , with increased IL-4 and decreased IFNγ [41–43] . Other studies report Dex inhibition of Th2 responses [44 , 45] but again , these differences may be in part explained by the concentration used , with low doses stimulating , and high doses inhibiting IL-4 [46] . Of particular relevance , populations of double positive Th2/Th17 cells secreting IL-4 and IL-17 have been identified in severe asthma , and these cells were insensitive to Dex [47] . Dex treatment may decrease FoxP3+ CD4+ T cells [48] , possibly causing further amplification of Th2 and Th17 pathways and promoting the development of crusted scabies . This study contributes to the limited knowledge regarding the immunopathogenesis of crusted scabies , with a theme for involvement of Th2 and Th17 related cytokines now emerging , although numbers of pigs and human patients studied remains small . It is now important to gain more detailed insights into pathways of immune dysregulation in crusted scabies , particularly the contribution of regulatory T cells . Longitudinal studies are also needed earlier in infestation , prior to the development of clinical symptoms . Finally , studies where pigs are treated , then reinfested , would be of value to compare primary versus secondary immune responses to S . scabiei . | Scabies is a neglected tropical skin disease caused by the tiny parasitic mite Sarcoptes scabiei . Scabies is common in developing countries , and scabies outbreaks also occur in institutional settings worldwide . Scabies often underlies secondary bacterial skin infection and resulting complications , and is thus associated with considerable morbidity . Crusted scabies is a an extremely severe and debilitating clinical form of the disease , but host immune responses leading to the development of crusted or ordinary scabies are poorly understood . This is largely due to limited access to clinical samples , and the difficulty in monitoring the progression of infestation in human patients . We have overcome this challenge by using a pig model of scabies infestation , since pigs and humans with scabies display clinical and immunological similarities . In this study , we undertook longitudinal analysis of clinical , histological and molecular immunological changes in pigs experimentally infected with scabies . We confirmed that disease severity was associated with a pronounced allergic , Th2 immune response , as previously reported . In a novel finding , we showed that the Th17 associated cytokines interleukin-17 and interleukin-23 were also associated with the development of crusted scabies . This may lead to new immunotherapeutic strategies to protect vulnerable subjects from contracting recurrent crusted scabies . | [
"Abstract",
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| 2015 | Prospective Study in a Porcine Model of Sarcoptes scabiei Indicates the Association of Th2 and Th17 Pathways with the Clinical Severity of Scabies |
The nuclear genomes of vertebrates show a highly organized program of DNA replication where GC-rich isochores are replicated early in S-phase , while AT-rich isochores are late replicating . GC-rich regions are gene dense and are enriched for active transcription , suggesting a connection between gene regulation and replication timing . Insulator elements can organize independent domains of gene transcription and are suitable candidates for being key regulators of replication timing . We have tested the impact of inserting a strong replication origin flanked by the β-globin HS4 insulator on the replication timing of naturally late replicating regions in two different avian cell types , DT40 ( lymphoid ) and 6C2 ( erythroid ) . We find that the HS4 insulator has the capacity to impose a shift to earlier replication . This shift requires the presence of HS4 on both sides of the replication origin and results in an advance of replication timing of the target locus from the second half of S-phase to the first half when a transcribed gene is positioned nearby . Moreover , we find that the USF transcription factor binding site is the key cis-element inside the HS4 insulator that controls replication timing . Taken together , our data identify a combination of cis-elements that might constitute the basic unit of multi-replicon megabase-sized early domains of DNA replication .
The nuclear genome of higher eukaryotes is replicated according to an established temporal program . Although replication timing has been precisely analyzed in many cell types from several organisms , the molecular mechanisms involved in its regulation are still poorly characterized [1] . It is now well established that there is an important correlation between replication timing and gene regulation with GC-rich and gene-rich regions replicating early and AT-rich and gene-poor regions replicating late [2]–[4] . However , two recent genome-wide studies in mouse and Drosophila show that this relationship is indirect , since a large fraction of late-replicating genes ( about 20% ) are expressed and some genes change transcription without change in replication timing and vice versa [5] , [6] . The simplest explanation is that replication timing is related to chromosomal organization rather than transcription . Several studies suggest that post-translational histone modifications directly regulate replication timing , but the effects of altering particular modifications are relatively minor in vertebrates [7] , [8] . The cis tethering of histone acetyltransferase activity adjacent to the human β-globin origin of replication induced only a weak shift in replication timing from late to mid-late S-phase in lymphocytes , suggesting that this signal is insufficient to organize the early domain of replication that exists in erythroid cells [7] . The disruption of histone modifications in trans following the genetic knockout of histone modifying enzymes also results in minor changes in replication timing . Only a minority ( 5/23 ) of 23 single copy loci displayed a weak shift of replication timing in a panel of mutant mouse ES cell lines that were disrupted for histone deacetylation; H3K4 , H3K9 , and H3K27 methylation; or DNA methylation activities [9] . Depletion of H3K9me2 has no effect on replication timing genome-wide [10] . A direct role for histone modifications in the regulation of early replication timing remains to be demonstrated in vertebrates . One hypothesis is that they may represent a secondary , fine-tuning role [11] . In contrast , origin efficiency is not dependent on canonical histone marks such as H3 or H4 acetylation or di- or trimethylation of H3K4 in vertebrates [12]–[17] . However , it was shown that histone acetylation is critical for replication origin activation at the amplified Drosophila chorion locus and that insulator elements protect amplification of this origin from position effect , suggesting that some specific origins might be controlled by histone tails modifications [18] , [19] . In general , euchromatic domains reside in the interior of the nucleus and replicate in early S-phase , whereas heterochromatic domains localize to the nuclear periphery or near nucleoli and replicate late [20] . In every case examined , the dynamic changes in replication timing observed during development are accompanied by sub-nuclear repositioning [5] , [21] . Moreover , replication timing is re-established during early G1-phase at the timing decision point ( TDP ) , coincident with the repositioning of chromosomal domains in the nucleus after mitosis [22]–[24] . Therefore , the molecular events that direct the organization of discrete chromosomal domains are also predicted to play an important role in regulating replication timing . The cis-acting DNA elements that organize such genomic domains , particularly those that are replicated in the first half of S-phase , remain to be determined . Insulator elements have been found to set the boundaries of transcriptionally active and repressive chromatin domains and represent candidate regulators of early DNA replication domain organization [25]–[27] . Insulators can be used in a transgenic context to shield transgenes from chromosomal position effect , where the chromatin environment around an integration site can dominantly silence or enhance transgene expression . The majority of random integration sites in vertebrates result in pervasive chromosomal silencing , but a minor fraction of loci are permissive to transgene expression , with a slight portion under the control of an endogenous enhancer . The biochemical activities that underlie the protection from these two types of regulatory interference are different , and the elements that are involved have distinct names: those that can block activation by enhancers are known as enhancer-blocking insulators , whereas those that protect against chromosomal silencing are known as barrier insulators . The first insulator element to be identified in vertebrates is HS4 , which lies at the 5′ boundary of the chicken β-globin domain [28] . A 275 bp compound HS4 element harbors separable enhancer-blocking and barrier activities [29]–[31] . HS4's enhancer-blocking activity is mediated by a single binding site ( FII ) for the ubiquitously expressed zinc finger protein CTCF [29] . HS4's barrier function depends on four transcription factor binding sites ( FI , FIII , FIV , and FV ) [31] . The FIV site is bound by the ubiquitous transcription factors USF1 and USF2 , which recruit a panel of active histone modifications that interrupt the spread of repressive histone modifications [32]–[34] . The remaining barrier sites FI , FIII , and FV , each bound by the broadly expressed zinc finger protein VEZF1 , mediate protection from DNA methylation [35] . Replication origins have been precisely mapped along the chicken β-globin locus in the early erythroid cell line 6C2 [17] . This region is replicated by a group of four origins located at the 5′ HS4 insulator , 5′ and 3′ of the ρ-globin gene , and inside the promoter of the βA-globin gene . The globin genes are not expressed in 6C2 cells , and only the replication origin overlapping the HS4 insulator is marked by active histone modifications [36] . Moreover , the chicken β-globin locus is replicated early in S-phase independently of globin gene expression in contrast to the paralogue human β-globin locus [17] , [37] . In this study , we explored the possibility that the HS4 insulator may establish early DNA replication timing when inserted into a locus that is normally replicated in late or mid-late S-phase . To do so , we studied the effect of targeting insulator constructs on the timing of a mid-late replicating locus in DT40 lymphoid cells . We also studied the replication timing of insulator containing transgenes that are randomly integrated into erythroid cells . Our data show that the introduction of the strong βA-globin replication origin flanked by HS4 insulator elements can significantly advance the replication timing of a mid-late replicating region . Surprisingly , the insulator activities of the HS4 element are not required for the imposition of earlier replication . We find that the USF protein binding site from the HS4 insulator is sufficient to control replication timing . Moreover , we demonstrate that USF binding sites need to flank the origin in order to drive a replication timing shift . The shift to earlier replication becomes dramatic when a transcriptionally active promoter is combined with HS4 insulators .
Our aim was to identify combinations of cis-elements capable of imposing early replication timing in a naturally late replicating region . To do so , we employed the efficient homologous recombination capacity of the chicken lymphoid DT40 cell line as a model system [38] . We decided to target an isogenic locus that is replicated in the second part of S-phase and is devoid of replication origins . Firstly , we determined the genome-wide replication timing profiles of DT40 cells following pulse labeling with BrdU and cell sorting into three fractions ( early , mid , and late S-phase ) . BrdU labeled nascent DNA from early and late fractions were immunoprecipitated with anti-BrdU antibody , amplified , differentially labeled , and co-hybridized onto a whole chicken genome oligonucleotide microarray . The log2-ratio of the abundance of each genomic probe in the early and late S-phase fractions generates a replication-timing profile that reveals early and late replicated domains ( Figure 1A ) . Exclusion of the mid-replicated fraction increases the intensity of the differences between early and late replicating domains without changing the global shape of the pattern ( compare Figure 1A and 1B ) . We made replicate experiments with nascent strands extracted from cells sorted into early and late-replicating DNA fractions , which were reciprocally labeled ( “dye-switch” ) prior to hybridization . The mirror image of the timing profiles reflects a high degree of correlation ( Figure 1B ) . We also constructed a whole genome map of replication origins in DT40 cells to allow the identification of origin free regions . We prepared four independent biological samples of short nascent strands ( SNS ) from 108 cells as described previously [17] . SNS , by contrast to NS , which are synthesized along the whole genome , are only enriched at replication starting points . SNS were pooled and made double stranded by random-priming and ligation . DNA was then fragmented and two different libraries were constructed and subject to high throughput sequencing . A total of 60 million uniquely mapped reads were generated . We arbitrarily selected a mid-late replicating region on chicken chromosome 1 that is devoid of replication origins ( Figures 1A and 2 ) . The chromosomal landscape of the chosen integration site in DT40 cells is AT-rich and lacks transcriptionally active genes ( Figure 2A ) . The closest origins , detected by deep sequencing and validated by qPCR , are located 58 kb upstream and 80 kb downstream of the site of insertion ( Figure 2A ) . We analyzed replication timing more precisely by sorting BrdU pulse-labeled cells into four S-phase fractions , from early to late ( S1–S4 ) . We quantified nascent strands ( NS ) across the chromosomal region surrounding the site of integration . The region from 140 kb upstream to 150 kb downstream of the site of integration was found to be mid-late replicating ( Figure 2B ) , in agreement with our genome-wide profiling of replication timing ( Figure 1A ) . In conclusion , we identified and selected a 100 kb intergenic region devoid of replication origins that is replicated in mid-late S-phase . The capacity to specifically target this region by homologous recombination gave us a model system in which we can test the effect of cis-regulatory elements on replication dynamics in a very controlled manner . We used homologous recombination in DT40 cells to target transgenes carrying combinations of regulatory elements into one allele at chr1: 74 , 813 , 240 . We isolated and quantified NS produced at the site of integration on either the allele containing the transgene ( With ) , the wild type allele ( Without ) , or both alleles ( Both ) during early to late S-phase ( Figure 3A and 3B ) . We present analyses from single independent cell sorts , where each data point is the average of at least two independent PCR quantifications . Analyses of both alleles should display an average picture of the profiles obtained on each allele , thus providing a validation of the PCR quantification ( Figure 3B ) . We have analyzed two independent clones or two cell sorts to confirm the robust reproducibility of the timing profiles shown . Finally , in order to compare precisely the extent of the replication timing shift between constructs , we calculate for each cell line the changes occurring on late fractions ( ΔL ) and on the earliest fraction ( ΔE ) as follows: ΔL = [% ( S3+S4 ) With−% ( S3+S4 ) Without] and ΔE = [%S1 With]−[%S1 Without] ( Figure 3B ) . The addition of S2 to S1 would lead to a number equal in absolute value to the sum of change in S3 and S4 . We therefore decided to calculate only the change in S1 that provides complementary information on the extent of the replication timing shift . We consider that we have a significant replication timing shift when ΔL≤−10% and ΔE≥+5% . In the first targeting experiment , we introduced a blasticidin resistance transgene under the control of the strong chicken β-actin promoter . Cells are maintained in the presence of blasticidin and must therefore continue to transcribe the resistance gene . When comparing the allele carrying the blasticidin resistance gene with the wild type allele , we observe a faint shift in replication timing ( Figure 4A ) ( ΔL = −10% and ΔE = +5% ) . Analysis of both alleles shows an intermediate profile . Therefore , the introduction of an actively transcribed gene has little impact on replication timing at this chromosomal region . We then added the strong βA-globin origin ( the βA-globin promoter ) to this transgene , which results in a more pronounced shift in replication timing ( Figure 4B ) ( ΔL = −16% and ΔE = +9% ) . We confirmed that the βA-globin promoter has strong origin activity at this chromosomal site as it is highly enriched in short nascent strands ( SNS ) ( Figure 4C , primer pair 2 ) . We also detected origin activity at the β-actin promoter , although lower than that at the βA-globin promoter ( Figure 4C , primer pair 3 ) . This is in agreement with the observation that the blasticidin resistance transgene under the control of the strong chicken β-actin promoter induces a faint replication timing shift ( Figure 4A ) . We also performed chromatin immunoprecipitation assays for histone H3 acetylation ( H3K9acK14ac ) and histone H4 density . We found that the adjacent βA-globin promoter replicator and β-actin promoter are highly enriched for H3 acetylation and are locally depleted of nucleosomes ( Figure 4D ) . Taken together , these results show that transcriptionally active , open chromatin is not sufficient to trigger a significant shift to early replication , despite the proximity of a strong origin . This replication timing shift is similar to the one previously obtained when the human β-globin origin is located near a site of histone acetylation [7] ( ΔL = −14% and ΔE = +3% ) and therefore suggests that H3 acetylation only acts to fine tune the regulation of replication timing [11] . We next tested whether additional cis-regulatory elements might cause a more dramatic replication timing shift toward the first half of S-phase . To this aim , we made use of transgenes flanked by HS4 insulator elements , which are protected from chromosomal silencing and also contain the βA-globin promoter replicator [31] , [34] , [35] . The HS4 insulator may assist the early firing of a replicator as it recruits active histone modifications , protects against DNA methylation , and can form the boundaries of chromatin domains [32]–[35] . We reasoned that such protected chromatin environment could be important for the formation of an early replicated domain . The IL-2R reporter transgene with its erythroid-specific regulatory elements and two flanking copies of the HS4 insulator were integrated with the selection marker gene ( Figure 5A ) . We confirmed by flow cytometric analysis of IL-2R expression that the transgene is weakly transcribed or inactive in the lymphoid DT40 cell lines ( Figure 5A ) . Despite the lack of transcriptional activity from the transgene , this construct induces a substantial shift in replication timing from the second half to the first half of S-phase ( ΔL = −37% and ΔE = +12% for clone 1 , ΔL = −31% and ΔE = +9% for clone 2 ) . This result suggests that the HS4 insulator contains cis-elements that control replication timing . We next assessed whether the addition of only two copies of the 275 bp core HS4 insulator element next to the βA replicator was sufficient to increase a shift toward mid-early replication ( Figure 5B ) . We observed only a slight increase in the replication shift compared to the same construct devoid of 2× HS4 ( ΔL = −20% and ΔE = +9% ) ( compare Figures 5B and 4B ) . This suggests that the HS4 insulator has to flank the βA-globin promoter replicator in order to control replication timing , although we cannot exclude the contribution of other cis-elements found in our construct such as the β/ε enhancer . Altogether these results suggest that HS4 contains an activity that can control replication timing when flanking a replicator and that cooperation with an increasing number of cis-regulatory elements intensifies the extent of the replication timing shift . We next examined which components of the HS4 element contribute to the control of replication . The HS4 insulator is a composite element containing five binding sites ( FI-FV ) for three different insulator proteins: CTCF ( FII ) , VEZF1 ( FI , FIII , and FV ) , and USF1/USF2 ( FIV ) . The USF binding site is required for the recruitment of active histone modifications to the HS4 insulator and USF1 interacts with histone modifying enzymes [32]–[34] . Given that histone acetylation was shown to control replication timing in S . cerevisiae and mildly influence replication timing in mammals [7] , [8] , we therefore considered that the USF binding element FIV might be the key regulatory element providing timing information in our system . To address this issue , we assembled a construct where the HS4 insulators were substituted with two copies of 23 bp HS4 fragments containing the USF site FIV ( Figure 6A ) . We analyzed two independent clones and found that this construct imposes a shift to earlier replication timing which is comparable to the one observed using the whole insulator ( compare Figure 6A with Figure 5A ) ( ΔL = −26% and ΔE = +14% for clone 1 , ΔL = −32% and ΔE = +8% for clone 2 ) . Therefore , the USF binding site and the activities it recruits is the key component of HS4 that controls replication timing . We also wanted to address whether active transcription was required to provide a significant replication shift . Thus far , every construct that induces a significant shift contains the very efficient β-actin promoter and the blasticidin resistance gene . Every construct in our targeting experiments was integrated into a DT40 cell line that constitutively expresses an inactivated Cre recombinase fused to the mutated estrogen receptor [39] . The blasticidin resistance marker gene , which is flanked by two mutant loxP sites , is readily excised by transient induction of Cre recombinase activity with 4-hydroxy tamoxifen . We validated the correct excision by PCR and tested replication timing in a new isolated clone . We generated clones in which the blasticidin gene cassette was excised following Cre induction , leaving the IL-2R transgene flanked by FIV sites ( Figure 6B ) . Replication timing analyses of two independent clones showed that although the IL-2R transgene is transcriptionally inactive , this construct has the capacity to induce a significant replication timing shift ( Figure 6B ) ( ΔL = −16% and ΔE = +4% for clone 1 , ΔL = −19% and ΔE = +4% for clone 2 ) . This effect is weaker than the construct linked to the β-actin promoter , consistent with a cooperation between cis-elements in the control of the extent of the replication timing shift ( compare Figure 6A with Figure 6B ) . The FIV site of the HS4 insulator contains a divergent E-box that is bound by the transcription factors USF1 and USF2 [34] . In order to confirm that USF binding is required for replication timing control , we mutated the E box of FIV ( Figure 7A ) , which abrogates the binding of USF1 and USF2 [34] . The FIV mutation almost abolishes the replication timing shift observed with IL-2R transgenes flanked by wild type USF sites ( compare Figure 7A with Figure 6B ) ( ΔL = −5% and ΔE = +3% for clone 1 , ΔL = −8% and ΔE = +3% for clone 2 ) . This finding shows that USF binding motifs are critical regulators of replication timing control . It has been shown previously that this USF binding site is responsible for the recruitment of several histone modifications associated with open chromatin to the endogenous HS4 insulator [34] . We used native chromatin immunoprecipitation ( ChIP ) assays to monitor histone modifications in DT40 cells following the integration of transgenes flanked by FIV USF sites . Prior to transgene integration , the target locus is highly enriched in the repressive mark H3K27me3 , and devoid of active chromatin marks such as acetylated histones H3 and H2A . Z , or methylated H3K4 ( Figure S1A ) . We find that the FIV USF site is sufficient to recruit the active chromatin marks H3K4me2 and acetylated H3 , and incorporate acetylated H2A . Z at the transgene ( Figure S1C–E ) . The level of these marks at the single copy transgene are 40%–50% of the levels observed at the endogenous HS4 insulator , consistent with a similar performance of the transgenic and endogenous USF sites in DT40 cells . The presence of the FIV USF sites also results in a depletion of H3K27me3 at the target site ( Figure S1F ) . Mutation of the FIV USF sites considerably reduces the recruitment of active marks and there is no depletion of H3K27me3 at the transgene site ( Figure S1C–F ) . Altogether , these data support the hypothesis that active histone modifications may play a role in advancing replication timing . Our experiments have shown that the integration of an origin flanked by HS4 or FIV insulator sequences can induce a shift toward early replication . However , the integration of the βA promoter/origin with HS4 insulator sequences only on one side does not significantly increase the replication timing shift ( Figure 5B ) , suggesting that it is necessary to surround an origin with insulator sequences to achieve a significant replication timing shift . In order to confirm this , we analyzed a new construct where four copies of FIV are located just upstream of the βA promoter/origin . This 4× FIV ( one side ) construct has the same genetic content as that containing pairs of flanking FIV sites ( compare Figure 7B with Figure 6B ) . We generated two independent clones that carry the 4× FIV construct , both of which showed a very weak replication timing shift ( ΔL = −8% and ΔE = −2% for clone 1 , ΔL = +5% and ΔE = +1% for clone 2 ) . The significant differences observed in the replication timing shift between the 2× FIV ( flanking ) and 4× FIV ( one side ) clones demonstrate that the flanking of an origin with sequences that recruit USF proteins is critical for replication timing advancement . Finally , it remains plausible that USF binding sites can induce a replication timing shift in the absence of a proximal replicator . In order to prove that USF binding sites need to cooperate with a functional replicator , we generated two independent clones containing a construct similar to the one described in Figure 6B but deleted for the βA promoter/origin ( Figure 8 ) . Both showed no replication timing shift ( ΔL = 0% and ΔE = 0% for clone 1 , ΔL = +5% and ΔE = 0% for clone 2 ) , thus demonstrating the necessity of strong proximal replicator . We next asked whether the early replication timing we observe at the transgene integration site extends into flanking chromosomal regions . This should depend on the presence of active origins nearby , the capacity of transgenic cis-elements to influence the replication timing of remote origins , and the speed of replication forks coming from the transgene . We targeted a DT40 cell line containing the 2× FIV ( flanking ) construct linked to the blasticidin gene on one allele with a similar construct carrying the puromycin resistance gene ( Figure 9A ) . The targeting of both alleles with a construct that resulted in the largest timing shift at the integration site allows us to study the advancement of replication timing at remote sites . We then analyzed replication timing using primer pairs located inside the transgene ( With ) , near the site of integration ( Both ) , at the flanking endogenous origins ( Ori L and Ori R ) and distantly from the site of integration ( ∼150 kb ) ( Figure 9A ) . We determined earlier that the replication timing for 150 kb around the integration site is consistently mid-late replicating in non-transgenic DT40 cells ( Figure 2B ) . Consistent with our earlier analysis of targeting one allele , integration of the 2× FIV transgene construct at both alleles results in a shift to earlier replication at the integration site ( Figure 9B ) . The replication timing patterns for the With and Both primer pairs are indistinguishable , confirming that both alleles are correctly targeted ( Figure 9B ) . Replication timing at the nearest endogenous origins Ori L ( −58 kb ) and Ori R ( +80 kb ) is comparable to the integration site in clone 2 , but slightly delayed in clone 1 . However , the transgene-induced shift to early replication diminishes significantly at positions −140 kb and +150 kb in both clones ( Figure 9B ) . DNA replication in chicken DT40 cells is mostly bi-directional , where the average rate of fork progression is ∼1 . 5 kb/min [40] . We would anticipate that forks should advance ∼90 kb from the site of replication initiation during the 1 h BrdU pulse labeling . Our observations indicate that only the integrated transgenes contain mid-early firing origins . The advanced replication timing of their chromosomal neighborhood appears to be due to the uni-directional progression of a replication fork initiated at the βA-globin replicator , showing that replication timing is locally controlled at one individual replicon . We have shown that the HS4 insulator in cooperation with other cis-regulatory elements can induce the early replication of a specific mid-late replicating locus in DT40 cells . Next , we wanted to address whether similar constructs could induce earlier replication at other chromosomal loci and in another cell type . We therefore studied transgenic 6C2 chicken erythroid cell lines that contain exactly the same IL-2R reporter transgene cassette flanked by mutant insulator elements [31] . These transgenic cells contain a co-integrated hygromycin resistance gene under the control of the HSV-thymidine kinase promoter . We analyzed cell lines that contain a single copy of the IL-2R transgene , flanked by HS4 elements that are devoid of either its enhancer blocking ( ΔFII ) or barrier ( ΔFIII ) activities ( Figure 10A ) [31] . These cell lines have been extensively characterized for transgene expression during prolonged culture , histone and DNA modifications , and insulator protein binding [31] , [34] , [35] . We cultured early passage cells for 40 d to allow for chromosomal position effect silencing to occur . Using flow cytometry , we confirmed previous findings that IL-2R expression remains stable when the transgene is flanked by intact HS4 barrier elements ( ΔFII ) but succumbs to chromosomal silencing without barrier activity ( ΔFIII ) ( Figure S2B ) [31] . We then mapped replication origins across the IL-2R transgene by quantifying the abundance of short nascent strands ( SNS ) . We find that the βA-globin promoter performs as an autonomous replicator as it displays strong replication activity at various exogenous loci . For the two cell lines , SNS enrichment at the transgenic βA-globin promoter ( position 2 , Figure S2A and C ) is greater or equal to that at the endogenous ρ-globin origin of replication ( P ) , and at least 20 times higher than an endogenous negative control locus ( N ) that is devoid of origin activity ( Figure S2C ) . We also find that , like inside the endogenous locus , transgenic HS4 elements ( positions 1 and 4 , Figure S2A and C ) tend to be enriched in SNS , although never as highly as the βA-globin promoter . We mapped the genomic locations and replication timing of the ΔFII and ΔFIII transgene integration sites . The transgene flanked by ΔFII insulators is located within an AT-rich region lacking CpG islands ( CGI ) and the nearest known gene is over a megabase away ( Figure 10B ) . The integration site of the ΔFII transgene is a naturally late replicating region and insertion of the transcriptionally active transgene induces a dramatic shift of replication timing to mid-early S-phase ( Figure 10C and Figure S2D ) ( ΔL = −63% and ΔE = +18% ) . The transgene flanked by ΔFIII insulators is also located in an intergenic region , but within a more GC-rich context ( Figure 10B ) . We found that the integration site of the ΔFIII transgene is naturally a mid-late replicating region and insertion of the transgene induces a dramatic shift of replication timing to early S-phase ( Figure 10C and Figure S2D ) ( ΔL = −53% and ΔE = +39% ) . This replication shift occurs despite the transcriptional silencing and DNA methylation of this transgene ( Figure S2B ) [35] . We note that the replication timing shifts observed in these two erythroid 6C2 cell lines are greater than those seen with similar constructs in DT40 cells . This may be due to inherent differences between the chromosomal loci studied or may be due to an additional contribution of the β/ε enhancer which is functional only in erythroid cells . In conclusion , we demonstrate that the insertion of a transgene containing a strong replicator , flanked by HS4 insulators in proximity to a transcribed gene , shifts the timing of two heterologous late replicating loci to early S-phase in erythroid cells . Moreover , neither the enhancer blocking nor barrier activities of the HS4 insulator are required to induce this important shift . We note that despite the loss of barrier activity and transgene silencing , the ΔFIII mutant insulators in the same cell line we studied here remain bound by USF proteins and recruit active histone modifications [34] , [35] . The ΔFIII transgene in erythroid cells ( Figure 10 ) therefore functions in the same way as the transgene flanked by FIV USF sites in lymphoid cells ( Figure 6A ) . Taken together , our results show that USF binding sites can induce the early firing of a proximal replicator in an otherwise late replicating chromosomal environment .
This study identifies the chicken β-globin HS4 insulator element as a potential regulator of origin firing . The shift to early replication of the βA replicator was achieved only when the origin was flanked on both sides by insulator sequences ( Figure 11B and C and Table S1 ) . The HS4 elements may be shielding the βA replicator from processes in its chromosomal environment that suppress early firing . A recent genome-wide study in Drosophila showed that H4K16 acetylation ( a chromatin modification characteristic of active domains ) is more closely correlated with early replication than it is with transcription [6] , suggesting that active histone marks such as this might be involved in replication timing control . Our study pinpoints the binding site for USF proteins as the key cis-element of the HS4 insulator responsible for the induction of early replication timing control . The USF site is sufficient to direct active H3K4 methylation and histone acetylation , supporting the view that active histone modifications at an origin might favor earlier replication timing ( Figure 11C ) . In eukaryotic genomes , DNA replication timing and the positioning of chromosomal domains within the nucleus are linked . During early G1 , the replication timing program is re-established at the timing decision point ( TDP ) , which is coincident with the repositioning of chromosomal domains within the nucleus [22] . Early replicating domains tend to be positioned toward the center of the nucleus , whereas late replicating domains are typically near the periphery . Furthermore , the Hi-C method of studying chromosomal interactions reveals a close correlation between replication timing and the spatial separation of chromosomal compartments [42] . It is possible that the histone modifications or other activities recruited to USF binding sites may influence their 3-dimensional nuclear localization , which in turn controls the replication timing of origins within their proximity ( Figure 11C ) . Moreover , the observation that replication origins located 58 kb upstream and 80 kb downstream do not fire at the same time as the transgene origin suggests that the advanced timing is restricted to a single replicon and does not involve broader reorganization of a large chromosomal region ( Figure 11D ) . It would be of great interest to study the nuclear positioning of late replicating regions before and after the targeting of cis-regulatory elements that control replication timing . It would be important to determine whether changes in replication timing are always accompanied by sub-nuclear repositioning . Our study shows a correlation between the extent of the replication timing shift and the number of cis-regulatory elements surrounding the βA replicator ( Figure 11C and D ) . In agreement with previous studies , the presence of a replicator is necessary to induce a replication timing shift [43] and the proximity of a transcriptionally active gene can further advance replication timing [44] . This suggests that these elements act to increase the probability of the molecular events that govern the early firing of a proximal replication origin . We speculate that USF elements promote chromatin opening and/or position proximal origins into nuclear compartments that increase the accessibility of pre-RCs to replication factors needed for origin firing during S-phase [45]–[47] . It is also possible that the recruitment and activity of factors like DDK ( Dfp1/Dbf4-dependent kinase Cdc7 ) and/or CDK ( Cyclin dependent kinase ) may be regulated by USF-directed post-translation modifications of either the histones and/or replication factors . Recent genome-wide studies in both Drosophila and mouse find that replication timing and gene activity are not tightly coupled . Genes replicated in the first half of S-phase have an equal probability of being active and 10%–20% of late replicating genes are expressed [5] , [6] . In agreement with these studies , we also find that transcription is neither necessary nor sufficient for the induction of early replication ( Figures 4A and 6B ) and moreover that maintaining a transgene in an early replicating domain is not sufficient to prevent its silencing ( Figure S2B and D , ΔFIII transgene ) . However , analyses of a large subset of genes highlight classes of genes showing a link between their expression and their timing of replication , implying a potential role for replication timing in both transcriptional activation and repression . In Drosophila , genes related to wing development are replicated earlier in a cell line derived from imaginal discs than in embryonic cells even though these genes are transcriptionally inactive in both cell types [6] . This might reflect an open chromatin state poised for subsequent activation . In mouse , analyses of embryonic stem cell differentiation into neuronal cells revealed that high and low CpG-density promoters showed distinct behaviors upon switching to a late-replicating environment: only CpG-poor promoters showed a higher tendency toward transcriptional down-regulation [5] . Overall , these results favor a model in which only a specific class of genes is affected by replication timing . Furthermore , a reporter plasmid micro-injected into early or late S-phase mammalian nuclei assembled into active hyper-acetylated and inactive hypo-acetylated plasmids , respectively [48] . Taken together , these studies indicate that cis-regulatory elements involved in the control of replication timing may also help to organize chromosomal domains that assist the regulation of gene expression . This study shows that USF binding sites can regulate the formation of early replication domains in avian cells . The ubiquitous expression and high sequence conservation of USF transcription factors suggest that this regulation would occur in most vertebrate cell types [34] , [49] . This newly proposed function of USF might be an important means to establish chromosomal domains favoring gene expression along vertebrate genomes . The mammalian and chicken genomes are partitioned into isochores with different GC content and gene density [50] , and isochores that are high in both GC and genes tend to be replicated early in S-phase . In this study , we show that combining several GC-rich cis-elements leads to the formation of an early domain of DNA replication in naturally late or mid-late replicating regions . This minimal combination , although artificial , contains only one GC-rich replicator flanked by two copies of a USF binding site , which is enhanced when linked to a constitutive CpG island ( CGI ) promoter . These elements are bound by transcription factors that are broadly expressed and are known to be part of many transcriptional regulatory elements . GC-rich isochores have a high probability of containing an efficient combination of strong origins ( mostly found near CGI ) [12] , [51] , cis-regulatory elements bound by USF , and transcriptionally active genes every 50–100 kb . This combination of elements that we find to be capable of determining an early independent replicon could be the basic unit of most early replicated domains . In agreement with this model , we have found that human USF1 is mostly bound within early replicated regions that are also dense in replication origins ( Picard F , Cadoret J-C , Audit B; Arnéodo A , and Prioleau M-N , unpublished data ) .
Short nascent strands were purified as described previously [17] . The quality of the preparation was tested by real-time quantitative PCR using primer pairs corresponding to the detection of the endogenous 3′ ρ-globin origin ( positive control , set arbitrarily at 100% ) and to a region devoid of replication origin located 21 kb upstream of HS4 ( negative control ) . The endogenous βA-origin has a relative enrichment of ∼130% in most preparations . For the genome-wide mapping of origins , four preparations of SNS were made independently from 108 cells and then pooled . SNS were made double stranded by random priming and ligation . Two libraries were constructed using illumina protocols , and four deep sequencing were performed using the GA-IIx sequencer ( Illumina ) generating 76 bp length reads . TheSOAP v2 software was used to map reads to reference chicken genome ( UCSC , Gallus gallus Build May 2006 ) with the r:0 , I:30 , and v:5 command-line . The enrichment of short nascent strand sequencing reads was detected using the Sole search program with the following parameters: Ref genome = generic , permit = 5 , fragment = 200 , alpha value = 0 . 001 , and FDR = 1 . 10–4 [52] . Detected peaks were tested on another SNS preparation by qPCR; out of four peaks identified along the region and shown in Figure 2A , only two had a significant enrichment . Timing analyses were made as previously described [17] except that S-phase was divided into four fractions from early to late S-phase named S1 to S4 for qPCR analyses and into two or three fractions for DNA microarrays studies . For DNA microarrays , in order to obtain sufficient DNA , immunoprecipitated nascent strands were amplified by whole-genome amplification ( WGA ) ( Sigma ) . After amplification , early and late nascent strands were labeled with Cy3 and Cy5 ULS molecules ( Genomic DNA labeling Kit , Agilent ) as recommended by the manufacturer . The hybridization was performed according to the manufacturer instructions on 4× 180K Chicken microarrays ( Chicken Genome CGH Microarray 4× 180K , custom microarray design , genome reference Gallus gallus V3 May 2006 ) that cover the whole genome with one probe every 5 . 6 Kb . Microarrays were scanned with an Agilent's High-Resolution C Scanner using a resolution of 2 µm and the autofocus option . Feature extraction was done with Feature Extraction 9 . 1 software ( Agilent technologies ) . For each experiment , the raw data sets were automatically normalized by the Feature extraction software . Analysis was performed with Agilent Genomic Workbench 5 . 0 software . The log2-ratio timing profiles were smoothed using the Moving Average option of the Agilent Genomic Workbench 5 . 0 software with the linear algorithm and 200 kb windows . 500 ng of genomic DNA extracted from different 6C2 cell lines was digested with AluI , HaeIII , or HpaII restriction enzymes . 100 ng of digested DNA was used for ligation in a 10 µl volume in order to favor intra-molecular events . 3 µl of the ligation was used for nested PCR with two primer pairs located at the border of the transgene . Specific products were cloned using the TopoBlunt cloning kit ( Invitrogen ) , and recombinant plasmids were sequenced . Sequences were aligned to the chicken genome using the UCSC genome browser ( http://genome . ucsc . edu/cgi-bin/hgBlat ) and the correct mapping of insertion sites were validated by the amplification of a fragment covering the end of the inserted plasmid and the chicken genomic sequence identified . For karyotype analysis , metaphase chromosome spreads were prepared as previously described [53] . Briefly , ∼107 exponentially growing DT40 cells were treated with 0 . 1 µg/ml KaryoMAX Colcemid Solution ( GIBCO ) for 2 h . Harvested cells were treated with 1 ml of 0 . 9% sodium citrate for 20 min at room temperature . Cells were fixed by washing with 5 ml of freshly prepared 3∶1 mixture of methanol/acetic acid and incubating in the same solution for 20 min at room temperature . Fixed cells were finally dissolved in the 200 µl of methanol/acetic acid and dropped onto ice-cold glass slides pre-wet with 50% ethanol from about 50 cm high . Slides were air dried and applied glycerol with DAPI ( 1 µg/ml ) . 6C2 cells were maintained in α-MEM containing 10% FBS , 2% chicken serum , 1 mM Hepes , 0 . 05 mM β-mercaptoethanol , and penicillin plus streptomycin at 37°C in 5% CO2 . DT40 cells were grown in RPMI containing 10% FBS , 1% chicken serum , 2 mM L-glutamin , 0 . 1 mM β-mercaptoethanol , and penicillin plus streptomycin at 37°C in 5% CO2 . The karyotype of the wild type DT40 cell line used for every construct was verified and displayed a typical DT40 karyotype nearly diploid with a trisomy of chromosome 2 ( Figure S4A ) . For electroporation 107 exponentially growing cells were resuspended into 800 µl of DT40 medium . The cell suspension was transferred to an electroporation cuvette with 35 µg of the linearized plasmid DNA ( final concentration 1 µg/µl ) and maintained for 10 min at 4°C . Electroporation was made using a Biorad electroporator set at 25 µF and 750 V . After a further 10-min incubation on ice , cells were transferred to a plate containing 10 ml of DT40 medium . The following day , cells were diluted six times in DT40 medium containing blasticidin at a final concentration of 20 µg/ml . 200 µl of cell suspension were distributed into three 96-well flat-bottom microtiter plates . Plates were left for about 7 to 10 d in the incubator without changing the medium . Isolated colonies were then progressively transferred to larger plates until they reached a 10 ml volume . Genomic DNA was extracted from 5 ml of cultured cells with the DNeasy Blood & Tissue kit ( Qiagen ) , and 100 ng of genomic DNA was analyzed by PCR with a primer pair containing one oligonucleotide inside the construct and one just upstream of the arm used for homologous recombination . At least two positive clones were randomly selected and amplified for further studies . A qPCR on 4 ng of genomic DNA of each selected clone with one primer pair inside the transgene ( With ) , one overlapping the site of insertion ( Without ) and one near the site of insertion ( Both ) , was performed in parallel with genomic DNA extracted from wild type DT40 cells to confirm that each clone only contains one copy of the transgene and that the transgene has been correctly inserted at the target site following homologous recombination ( Figure S4B ) . For excision of floxed cassettes , cells were cultured for 2 d in chicken medium containing 0 . 01 mM hydroxyl-Tamoxifen . After serial dilutions of 50 , 100 , and 300 viable cells per 10 ml , 200 µl of the cell suspension were distributed into three 96-well flat-bottom microtiter plates . DNA from isolated colonies were analyzed by PCR with two primer pairs , one containing one oligonucleotide inside the blasticidin gene and one in the 3′ arm ( negative control ) and one positive PCR with one oligonucleotide inside the construct and one in the 3′ arm . At least two positive clones were randomly selected and amplified for replication studies . As previously described , primers were designed such that one primer is located in the test construct ( βA origin , 5′ GTGCAGCATCAGTGGATAAAGT 3′ ) and one primer is just upstream of the 5′ arm used in the construct ( 5′ TCTGCCTTCTCCCTGATAACG 3′ ) ( Figure S5 ) . Thus only the genomic DNA from clones integrated by homologous recombination will be amplified by PCR . The specificity of the PCR products was analyzed by EcoRI digestion ( Figure S5 ) . To screen cell lines after excision of the Blasticidin cassette , PCR was performed with forward primer in the IL-2R gene ( 5′ CAAAGCCATGGCCTACAAGG 3′ ) and reverse primer in the 3′ targeting arm ( 5′ TCATTGTTCTCCAGGCTGTACTC 3′ ) ( Figure S5 ) . Only genomic DNA from cell lines excised for the Blasticidin is amplified by PCR . To screen for the cell lines with homologous recombination on both alleles , PCR was performed with primers located on each side of the integration point ( 5′ GTGCAGCATCAGTGGATAAAGT 3′ and 5′ GGCCTGAACACTGTGTCAAT 3′ ) such that the double insertion lines do not produce the corresponding PCR product . To perform all the above stated PCR , we used the Herculase II Fusion DNA Polymerase PCR system ( Stratagene ) with the condition as follows , the initial denaturation at 95°C for 2 min , and 35 cycles of 95°C for 30 s , 57°C for 30 s , and 72°C for 2 min and a final extension of 72°C for 5 min . PCR products were analyzed on 1% agarose gel ( Figure S5 ) . Homologous recombination in DT40 cells was done with plasmids constructed with the multisite gateway pro kit ( Invitrogen ) . Entry clones were constructed by PCR amplification with the Herculase II fusion DNA polymerase ( Stratagene ) with primers flanked by appropriate attB sites . 5′ and 3′ target arms were amplified from DT40 genomic DNA with primer pairs ( 5′ arm forward: AGTTTCAGCTGTAAGCCTACA; 5′ arm reverse: CTCTTGTGAATACCTGCTGTC ) and ( 3′ arm forward: CGACTCAACTCTGATGCATTGA; 3′ arm reverse: GGGAAGCAATCTGAATCAGAT ) and give 2 , 058 bp and 2 , 034 bp amplicons , respectively . The blasticidin resistance gene under the control of the β-actin promoter and flanked by loxP sites was amplified from the pLoxBsr vector with the primer pair ( forward: GTCGACGGTATCGATAAGCT and reverse: CGACGGCCAGTGAATTGT ) [39] . PCR amplified fragments were used for BP recombination reaction; each final entry vector was verified by sequencing . For every construct the same three entry vectors 5′ arm , Blasticidin resistance , and 3′ arm were used , respectively , as fragments 1 , 3 , and 4 for a MutliSite Gateway pro LR 4 fragments recombination reaction . The last entry vectors ( fragment 2 ) were constructed by PCR amplifications with the Herculase II Fusion DNA Polymerase ( Stratagene ) of the construct designed in Gary Felsenfeld's laboratory for testing barrier activity with appropriate oligonucleotides . For fragments containing FIV and FIVmut binding sites , a 3 kb DNA fragment containing βA promoter , the IL2R gene linked to the β/ε enhancer , was amplified with two primers containing two copies of the chicken HS4 insulator FIV region at their 5′ end: 2XFIV5′-F: GGGGACAACTTTGTATACAAAAGTTGAGGTGGCACGGGATCGCTTTCCTAGGTGGCACGGGATCGCTTTCCTCTGCCCACACCCTCCTG 3′; and 2XFIV-R: 5′GGGGACAACTTTGTATAGAAAAGTTGGGTGAGGAAAGCGATCCCGTGCCACCTAGGAAAGCGATCCCGTGCCACCTGATGATCCGTCATCCAGACATG3′; 2XFIV mut5′-F: 5′GGGGACAACTTTGTATACAAAAGTTGAGGTGGCcatGGATCGCTTTCCTAGGTGGCcatGGATCGCTTTCCTCTGCCCACACCCTCCTG3′; 2XFIVmut3′-R: 5′GGGGACAACTTTGTATAGAAAAGTTGGGTGAGGAAAGCGATCCatgGCCACCTAGGAAAGCGATCCatgGCCACCTGATGATCCGTCATCCAGACATG3′ . To generate the 4× FIV ( one side ) construct , we used PCR amplification of the 2× FIV ( flanking ) vector . We designed the forward primer such that it anneals to the 5′ 2× FIV region and carries two extra FIV at its 5′ end . Whereas the reverse primer anneals upstream of the 3′ 2XFIV , the PCR product was sub-cloned into a TOPO-TA vector . Selection of a correct clone was made after checking the presence of proper 4× FIV by sequencing and then put into an MutliSite Gateway entry vector . Oligo sequences used for amplification of 4× FIV: 4× FIV_F: 5′GGGGACAACTTTGTATACAAAAGTTGAGGTGGCACGGGATCGCTTTCCTAGGTGGCACGGGATCGCTTTCCTAAGTTGAGGTGGCACGGG 3′; 4× FIV_R: 5′GCCGCGAGCTGCGGGGACAACTTTGTATAGAAAAGTTGGGTGGATGATCCGTCATCCAGACATGATAAGATACATTGATG3′ . Each entry vector was controlled by sequencing . After LR reaction , final vectors were verified by restriction enzyme digestion with several restriction enzymes . For electroporation , final vectors were linearized by ScaI . The IL-2R construct deleted for the origin and flanked by 2XFIV was made by a PCR method . The 1 . 6 kb IL-2R fragment was amplified by forward and reverse primers containing 2XFIV site along with AttB site to use in Gateway cloning . We used Herculase II Fusion DNA Polymerase PCR system ( Stratagene ) for the amplification with the following conditions: the initial denaturation at 95°C for 2 min , and 35 cycles of 95°C for 30 s , 57°C for 30 s , and 72°C for 3 min and a final extension of 72°C for 5 min . At first , the PCR product was cloned with Gateway BP Clonase system ( Invitrogen ) to generate the entry vector . The positive IL-2R entry vectors were checked for no mutations by sequencing . The final targeting vector was prepared by a four fragment multisite Gateway system ( Invitrogen ) : Forward: 5′GGGGACAACTTTGTATACAAAAGTTGAGGTGGCACGGGATCGCTTTCCTAGGTGGCACGGGATCGCTTTCCTCAAAGCCATGGCCTACAAGG 3′; Reverse: 5′GGGGACAACTTTGTATAGAAAAGTTGGGTGAGGAAAGCGATCCCGTGCCACCTAGGAAAGCGATCCCGTGCCACCTGATGATCCGTCATCCAGACATG 3′ . Crosslinking ChIP was performed as described previously [13] with anti-diacetylated K9 and K14 histone H3 and anti histone H4 pan antibodies ( Millipore , ref# 06-599 and 05-858 ) . Low salt native ChIP was performed on DT40 cell nucleosomes . Nuclei were prepared and treated with lysis buffer ( 10 mM NaCl , 3 mM MgCl2 , 0 . 4% NP-40 , and 10 mM Tris pH 7 . 5 ) for MNase ( Sigma ) digestion in the presence of 1 mM CaCl2 . The MNase concentration required to yield mostly di- and tri-nucleosomes was firstly determined . For ChIP experiments , three equal aliquots of nuclei were incubated with ½× , 1× , and 2× MNase at 37°C for 17 min to obtain representative di- and tri-nucleosomes . Digestion was stopped with 10 mM EDTA . Soluble chromatin was collected by centrifugation at 2 , 500 g for 5 min . The three supernatants were combined ( S1 ) . The remaining pellets were combined and resuspended in lysis buffer supplemented with 10 mM EDTA and left on ice for 15 min . Chromatin was released by passing through 20 then 25 gauge needles , and collected by centrifugation at 10 , 000 g for 10 min . The supernatant ( S2 ) was combined with S1 for sucrose gradient fractionation . ∼1 . 5 mg of S1–S2 chromatin was fractionated on 13 . 5 ml 5%–25% linear sucrose gradients ( Biocomp gradient master ) in a SW40Ti rotor at 31 , 000 rpm for 14 h at 4°C . Fractions containing di- and tri-nucleosomes were pooled and fixed with 0 . 1% formaldehyde at room temperature for 10 min . The crosslinking reaction was stopped with 0 . 125 M glycine . Nucleosomes were exchanged into N-ChIP buffer ( 50 mM NaCl , 5 mM EDTA , 10 mM Tris pH 7 . 5 ) using P-6DG Bio-Gel ( BioRad ) . 50 µg of nucleosomes were pre-cleared with 5 µg of non-immune IgG and 100 µl ( 50% slurry in N-ChIP buffer ) of protein A/G agarose at 4°C for 3 h . 10 µg of specific antibody were incubated with pre-cleared chromatin at 4°C with agitation overnight . H3K4me2 ( 07-030 ) , H3K27me3 ( 07-449 ) , and H3K9acK14ac ( 06-599 ) antibodies and H2A . ZK4acK7acK11ac ( ab18262 ) antibodies were obtained from Millipore and Abcam . Binding of protein A/G agarose was carried out at 4°C for 2 h . Immunoprecipitated chromatin was collected and washed 5 times with 1 ml N-ChIP wash buffer ( 150 mM NaCl , 0 . 2 mM EDTA , 0 . 1% Tween-20 , and 20 mM Tris pH 7 . 4 ) . Chromatin was eluted with N-ChIP buffer supplemented with 1% SDS followed by 0 . 5% SDS . Eluates were digested with Proteinase K at 45°C for 2 h and DNA extracted by phenol/chloroform and precipitated for qPCR analysis . Quantitative real-time PCR was performed by using the LightCycler 2 . 0 Roche detection system with an Absolute QPCR-SYBR Green mix ( ABgene ) . For each reaction , amplification of the purified short nascent strands , BrdUTP-labeled nascent strands , and ChIP DNA were performed in duplicate . Four 4-fold dilutions of total genomic DNA and a reaction mixture without template DNA were used as controls . Subsequent to amplification , a melting curve analysis was performed to analyze the products and to control the specificity of the reaction . The second derivative maximum method was used to quantify sequences , as described in the LightCycler Software . Primer pairs used are listed in Table S2 . For short nascent strands analyses , primer pairs overlapping a region with an origin ( P ) and a primer in a region devoid of origin ( N ) were used as controls in each reaction . For timing analyses , the abundance of mitochondrial DNA in each fraction was measured by using a specific primer pair ( MIT ) alongside late and early controls and studied regions . Primer pairs overlapping the site of insertion and next to the site of insertion were used to detect the timing of the wild type allele and both alleles , respectively . A primer pair specific to the transgene was used to analyze the timing of the allele containing the transgene . | All eukaryotic organisms must duplicate their genome precisely once before cell division . This occurs according to an established temporal program during S-phase ( when DNA synthesis takes place ) of the cell cycle . In vertebrates , this program is regulated at the level of large chromosomal domains ranging from 200 kb to 2 Mb , but the molecular mechanisms that control the temporal firing order of animal replication origins are not clearly understood . Using the genetically tractable chicken DT40 cell system , we identified a minimal combination of cis-regulatory DNA elements that is able to shift the timing of a naturally “mid-late” replicated region to “mid-early . ” This critical group of elements is composed of one strong replication origin flanked by binding sequences for the upstream stimulatory factor ( USF ) protein . The additional presence of a strongly transcribed gene shifted the region towards an even earlier replication time , suggesting cooperation between cis-elements when establishing temporal programs of replication . We speculate that USF binding sequences cooperate with sites of replication initiation and transcribed genes to promote the establishment of early replicating domains along vertebrate genomes . | [
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| 2012 | USF Binding Sequences from the HS4 Insulator Element Impose Early Replication Timing on a Vertebrate Replicator |
The first line treatment for Chagas disease , a neglected tropical disease caused by the protozoan parasite Trypanosoma cruzi , involves administration of benznidazole ( Bzn ) . Bzn is a 2-nitroimidazole pro-drug which requires nitroreduction to become active , although its mode of action is not fully understood . In the present work we used a non-targeted MS-based metabolomics approach to study the metabolic response of T . cruzi to Bzn . Parasites treated with Bzn were minimally altered compared to untreated trypanosomes , although the redox active thiols trypanothione , homotrypanothione and cysteine were significantly diminished in abundance post-treatment . In addition , multiple Bzn-derived metabolites were detected after treatment . These metabolites included reduction products , fragments and covalent adducts of reduced Bzn linked to each of the major low molecular weight thiols: trypanothione , glutathione , γ-glutamylcysteine , glutathionylspermidine , cysteine and ovothiol A . Bzn products known to be generated in vitro by the unusual trypanosomal nitroreductase , TcNTRI , were found within the parasites , but low molecular weight adducts of glyoxal , a proposed toxic end-product of NTRI Bzn metabolism , were not detected . Our data is indicative of a major role of the thiol binding capacity of Bzn reduction products in the mechanism of Bzn toxicity against T . cruzi .
Ten million people worldwide are infected with Trypanosoma cruzi , the causative agent of Chagas disease , and 40 million are at risk of infection [1] , [2] . In spite of a substantial reduction in prevalence over the last few decades , the disease is considered among the world's 17 most neglected tropical diseases and is responsible for 13 , 000 annual deaths according to the World Health Organization ( WHO ) . T . cruzi is naturally transmitted to humans and other mammals by reduviid insects of the subfamily Triatominae , and may also be transmitted by blood transfusions , organ transplants , orally through contaminated food , and vertically from mother to child . The disease progresses with an initial acute phase , usually asymptomatic , that can subsequently develop into a chronic form with cardiac and digestive pathologies that can lead to death [3] . Benznidazole ( Bzn ) , formerly commercialized as Rochagan and Radanil ( Roche ) , and nifurtimox ( Nfx ) , marketed as Lampit ( Bayer ) , are the only drugs proven effective against Chagas disease . Both contain a nitro group linked , respectively , to an imidazole or furan ring , and unwanted side effects are common , leading to treatment discontinuation in some cases . Bzn has the best safety and efficacy profile , and is therefore used as first line treatment . A major limitation is the low potency of these drugs against parasites in the established chronic disease , which is the form most commonly encountered clinically [4] , [5] . The therapeutic benefit of Bzn in established mild to moderate Chagas disease is currently under scrutiny in the Benznidazole Evaluation for Interrupting Trypanosomiasis ( BENEFIT ) trial [6] . In spite of the limitations of Bzn and Nfx in treatment , only a few compounds are undergoing clinical trials against chronic Chagas disease , and there are no immediate prospects of a vaccine . Interest in nitro-heterocyclic compounds has recently been reinvigorated given the advancement of several members of the class into clinical trials [7] , [8] , [9] . Bzn was discovered as an anti-trypanosomal agent through screening against parasites without understanding its mechanism of action . Other nitroimidazoles , including the 5-nitroimidazole metronidazole , are well-established in the treatment of anaerobic protozoal and bacterial infections [10] . The mode of action of nitroheterocyclic compounds appears to involve metabolic activation of the compounds initiated through reduction of the compounds' nitro group . Subsequent metabolism of the compounds can be divergent . The anti-mycobacterial agent PA-824 , for example , is reduced by a deazaflavin ( F420 ) -dependent nitroreductase ( Ddn ) in M . tuberculosis and eventually decomposes to various reactive nitrogen species including nitric oxide [11] . Bzn activity has been proposed to be mediated via reduced intermediates that covalently modify macromolecules in vivo , including lipids , DNA and proteins , rather than by formation of radical intermediates producing reactive oxygen species [12] , [13] . An unusual prokaryotic type I nitroreductase ( NTRI ) was identified in trypanosomatid protozoa which is primarily responsible for the reductive activation of some trypanocidal nitroheterocycles including Bzn , Nfx and also fexinidazole [14] , [15] , [16] . In vitro TcNTR can catalyse the consecutive two electron reduction of Bzn , leading to formation of a dihydroxy-dihydroimidazole derivative which may decompose to give glyoxal , a well-known toxic metabolite . Glyoxal was postulated to contribute to the pleiotropic effects of Bzn on trypanosomes [17] , although roles for each of the metabolic products of Bzn have not been investigated in situ . Here we report , for the first time , an untargeted metabolomics analysis of T . cruzi to investigate changes in the parasite associated with exposure to the drug . Using a platform involving HILIC chromatography to separate low molecular weight metabolites coupled to high resolution mass spectrometry [18] we detected in the order of a thousand T . cruzi metabolites and were able to identify metabolic perturbations associated with Bzn exposure , as well as the production of Bzn metabolites and their in situ reaction products .
T . cruzi epimastigotes of the DM28c strain [19] were grown in LIT medium supplemented with yeast extract and 10% foetal bovine serum at 28°C [20] . For metabolite extraction a protocol was adapted from that used for other trypanosomatid protozoa [18] , [21] . Cultures were initiated by inoculating exponentially growing epimastigotes to a final concentration of 2 . 5×107 parasites per mL . Two days after inoculation , parasites were counted in a Neubauer chamber and 1×108 parasites per sample were taken for treatment ( approximately 1 mL ) . After adding 20 or 50 µM Bzn , parasites were incubated at 28°C for 6 h . Tubes containing parasites in suspension were quenched on ice for 3 min , after which Bzn was added to control cells when necessary ( Table S1 ) . Cells were collected immediately by centrifugation ( 2000× g , 4°C , 3 min ) . Supernatants were carefully removed from cell pellets . Samples from each supernatant were separated for analysis as medium samples ( 5 µL ) . Cell disruption and metabolite extraction was performed using 200 µl chloroform/methanol/water 20/60/20 ( v/v/v ) during 1 hour in a Thermomixer ( 1000 rpm , 4°C –Eppendorf AG , Hamburg , Germany ) . Metabolite extracts were separated from cell debris by centrifugation ( 13 , 000× g , 4°C , 3 min ) . Extracts were stored at −70°C under nitrogen gas until analysis . Biological replicates were grown , incubated and extracted on different days . A short drug incubation period was chosen to impede changes in the number of parasites during treatment . For viability determinations , treated parasites were washed , incubated in fresh medium for 72 h and counted in Neubauer chamber . Samples were analysed on an Exactive Orbitrap mass spectrometer ( Thermo Fisher Scientific ) in both positive and negative modes ( rapid switching ) , coupled to a HPLC separation with a ZIC-HILIC column ( Sequant ) as has previously been described [18] . All samples from each experiment were analysed in the same analytical batch in randomised order and the quality of chromatography and signal reproducibility were checked by analysis of quality control samples , internal standards and total ion chromatograms . A standard mix containing approximately 160 authentic metabolite standards was run at the start of each analysis batch to aid metabolite identification . MSMS analysis was performed on an LTQ Orbitrap Velos mass spectrometer ( Thermo Fisher Scientific ) in positive mode with ZIC-HILIC chromatography as described above . High resolution ( 15 , 000 ) MSMS spectra were obtained with HCD induced fragmentation at normalised collision energy of 35 eV . MSMS spectra for the follow-up 50 µM Bzn experiment were collected at unit resolution . Untargeted metabolite analysis was conducted with the freely available software packages mzMatch [22] and Ideom ( http://mzmatch . sourceforge . net/ideom . php ) [23] . Raw LC-MS data was converted to mzXML format and peak detection was performed with XCMS [24] and saved in peakML format . Mzmatch . R was used for sample alignment , peak filtering ( based on reproducibility , peak shape and an intensity threshold of 3000 ) , gap filling and annotation of related peaks . Ideom was used to remove contaminants and LC-MS artefact peaks and to perform metabolite identification . Metabolite identities were confirmed by exact mass ( within 3 ppm after correction for loss or gain of a proton in negative mode or positive mode ESI respectively ) and retention time where authentic standards were available for analysis . Putative identification of all other metabolites was made on the basis of exact mass and predicted retention time from all metabolites from the KEGG , MetaCyc and Lipidmaps databases [18] . The m/z values corrected for proton gain or loss are referred as m/zc in the text . In cases where identification was putative , the most likely metabolite was chosen based on available chemical and biological knowledge [23] . However , LC-MS data alone is often insufficient for accurate isomer identification and lists of alternative identifications with meta-data for each identified formula are accessible in the macro-enabled Ideom files ( Files S1 and S2; help documentation available at mzmatch . sourceforge . net/ideom . php ) . Quantification is based on raw peak heights , and expressed relative to the average peak height observed in untreated cells from the same experiment . Unidentified peaks in the LC-MS data were also investigated for drug-induced changes . After removal of LC-MS artefacts and known contaminants , measured exact masses were compared with theoretical exact masses of Bzn derived metabolites contained in Bznmet database ( “targeted sheet” , Files S1 and S2 ) . This database included reported and putative Bzn reduction products and covalent adducts of Bzn with small cellular metabolites , all retrieved from existing reports on in vivo and in vitro modification processes for Bzn and similar nitroimidazoles , covering enzymatic and non-enzymatic conversions [17] , [25] , [26] , [27] . Accurate mass and relative isotope abundance was used to determine the chemical formulae for the remaining unidentified metabolites detected specifically in the 50 µM Bzn treated samples . In most cases these formulae contain the subset C12H14N4O , suggesting that they are adducts of reduced Bzn with a broad range of unexpected metabolites . In addition , a targeted analysis of potential metabolites in the Bznmet database was performed on the raw data using accurate mass within a 3 ppm mass range , to allow detection of metabolites that may have been excluded by the automated data processing due to peak shape , intensity or reproducibility filters . Manually retrieved intensity and RT values from all samples are included in File S3 . TcNTRI: GenBank AHD24669 . 1 TbNTRI: GenBank AAX69576 . 1 Dnd Mycobacterium tuberculosis: UniProtKB/Swiss-Prot P71854 . 1 TcCPR-B: GenBank ABI15738 . 1 TcOYE: GenBank AAX54861 . 1 TcAKR: GenBank ACD93222 . 1
Metabolites were extracted from T . cruzi epimastigote cell pellets with a monophasic solvent mixture of chloroform , methanol and water ( 1∶3∶1 ) ; separated and analysed by ZIC-HILIC chromatography coupled to high accuracy MS using an Orbitrap mass spectrometer . Five biological replicates were analysed for each condition . Signal extraction and initial filtering of the LC-MS data yielded 3 , 117 peaks for negative mode and 5 , 528 peaks for positive ESI mode . Additional artefact filtering and polarity merging in Ideom reduced the list of features to 1 , 477 candidate molecules , of which over 70% ( 1 , 069 ) matched compounds in the metabolite databases based on accurate mass and retention time information [18] . A complete list of putatively identified metabolites , with the detected peak heights for each sample and confidence values for their identifications is supplied in File S1 ( see “Comparison” sheet ) . Only putatively identified metabolites were considered for intensity comparisons among groups of samples . Based on the Ideom software's automated metabolite calling we can divide metabolites into several classes , although additional confirmation of the identity of each individual metabolite would be required to impose certainty on these classifications . The largest class of metabolites was peptides , representing 42 . 1% of the total metabolites ( 135 dipeptides , 183 tripeptides and 132 tetrapeptides ) ( Figure 1 and File S1 ) . The next largest class of metabolites was amino acids and compounds associated with amino acid metabolism ( amino acids , thiol compounds and polyamines ) , which represented 14 . 6% of the total putatively identified metabolites . In addition to peptides and amino acids , metabolites from a diverse range of metabolic pathways were detected ( Figure 2 ) , including lipid ( 110 ) , carbohydrate ( 44 ) , nucleotide ( 33 ) and cofactor metabolism ( 26 ) . Putatively identified metabolites that lack KEGG [28] or Lipidmaps [29] pathway annotations were classified as unmapped ( 210 ) , among these there are N-acetylated amino acids and polyamines , acyl-carnitines and acyl-glycines . A hallmark metabolite in kinetoplastid parasites , including trypanosomes , is trypanothione . This di-thiol is composed of two molecules of glutathione linked by one molecule of spermidine , and it is usually detected as a multi charged ion by ESI-MS [30] . Under the conditions used in this study , the tri-charged form of trypanothione disulphide was detected in all parasite samples with very high S/N ratios . Furthermore , a mass consistent with tri-charged homotrypanothione , a metabolite unique to T . cruzi [31] , was also detected although with eighty times lower signal intensity than trypanothione . It is important to note that the protocol used here leads to extensive oxidation of thiols , thus the relative quantification of the intracellular redox state of these molecules is not possible . Other common small thiols detected were glutathione , cysteine/cystine and homocysteine . Detected polyamines included cadaverine , spermidine , putrescine and some modified forms such as N-acetylspermidine , N-acetylputrescine and gamma-glutamylputrescine . In an attempt to analyse the metabolic changes induced by Bzn treatment , T . cruzi epimastigotes were exposed to 20 µM Bzn over six hours ( cBt samples ) after which metabolites were extracted . Approximately 80% of the parasites remained viable after this treatment ( not shown ) . Parasites that were not exposed to Bzn ( cTc ) and parasites to which Bzn was added just prior to the extraction of metabolites ( cBc ) ( to control for any mass spectrometry related effects due to drug ) were included as controls . Medium samples were analysed in parallel including fresh medium ( Med ) and spent mediums collected from cBc ( mBc ) and cBt samples ( mBt ) . Principal Component Analysis ( PCA ) of the automatically filtered data indicated that no obvious differences were found among the different groups of cells samples ( cBc , cBt and cTc ) , although medium samples could be readily separated from cell samples ( Figure S1 ) . This indicated that the global structure of the T . cruzi metabolome was little changed by this treatment with Bzn . Univariate analysis of individual metabolites using p<0 . 05 as a significance threshold in t-tests and a fold abundance change above 1 . 4 revealed relatively few differences between treated and untreated parasites ( see “Comparison” sheet , File S1 ) . Among these , trypanothione disulphide , homotrypanothione disulphide and cystine ( cysteine disulphide ) were significantly diminished in abundance after treatment . Three glutamate containing di-peptides were elevated in Bzn treated samples ( Figure 3 ) , and although they were not structurally characterised , they likely represent the gamma-glutamyl dipeptides involved in glutathione recycling . In addition to the relatively small changes in the metabolites discussed above , two metabolites which were substantially elevated in treated cells over controls were identified through the automated screening of the datasets . One metabolite ( m/zc 278 . 14 , RT 11 . 1 ) was assigned as N-Benzoyl-D-arginine and another ( m/zc 292 . 15 , RT 10 . 2 ) as 4-coumaroyl-3-hydroxyagmatine , based on mass similarity . However , as discussed below , these were mis-identifications of Bzn metabolites that have masses ( i . e . molecular formulae ) identical to these representatives from the IDEOM metabolite database . Considering that Bzn acts as a pro-drug which undergoes reductive metabolism to generate toxic intermediates [4] , [5] , [13] , the presence of unidentified peaks at significantly higher concentrations in treated than in untreated cells prompted us to re-scan the filtered data seeking putative molecules derived from in situ Bzn metabolism . We applied a correlation analysis on the intensities of all the LC-MS peaks , searching for molecules that were highly correlated with the metabolites detected in treated samples ( and thus assumed to be derived from Bzn ) and with low or no abundance in all the other samples ( control parasites , media and solvent samples ) ( “all Data” sheet , File S1 ) . Six additional ions corresponding to putative Bzn metabolites were found after carefully removing additional MS artefacts derived from these molecules ( Table 1 ) . To further investigate the identity of the Bzn metabolites and to search for additional Bzn metabolites , an in-house database of reported and putative Bzn metabolites was built: Bznmet database ( “Targeted” sheet , File S1 ) . The theoretical m/zc values contained in the database ( 109 ions belonging to 56 different molecules ) were compared with the accurate measured m/zc values from the collected filtered data and also the raw data , resulting in identification of a number of Bzn metabolites ( Table 1 and File S3 ) . Finally , to gain additional information to support the proposed metabolite structures , MSMS fragmentation spectra were collected for the ions of interest ( File S4 ) . Once the LC-MS data were examined , a number of reduced derivatives of Bzn were found to be highly abundant in treated samples . The signal at m/zc 264 . 12 ( RT 13 min ) was attributed to a dihydroxy-dihydro derivative of Bzn: 2- ( 2-amino-4 , 5-dihydroxy-4 , 5-dihydroimidazol-1-yl ) -N-benzylacetamide ( 3 ) . Additionally , the two metabolites initially detected in Bzn-treated samples and mis-identified , as N-Benzoyl-D-arginine ( m/zc 278 . 13 , RT 11 . 1 min ) and 4-coumaroyl-3-hydroxyagmatine ( m/zc 292 . 15 , RT 10 . 2 min ) , could be assigned as methoxy derivatives of the Bzn dihydroxy-dihydro derivative ( 2 , 6 ) . Also , a molecule with an m/z value expected for the hydroxylamine or some hydroxy derivatives of Bzn was found ( m/zc 246 . 11 , RT 11 min ) ( 11 ) . N-benzyl-2-guanidinoacetamide ( m/zc 206 . 11 , RT 11 . 7 min ) ( 5 ) was also detected . This molecule was reported to arise after the Bzn dihydroxy-dihydro derivative decomposes , or reacts with other molecules , to release glyoxal [17] , [32] . Nevertheless , glyoxal adducts with nucleotides , nitrogenated bases or amino acids could not be detected in our data . Also , 2- ( 2-amino-1H-imidazol-1-yl ) -N-benzylacetamide ( m/zc 230 . 11 , RT 10 . 85 min ) ( 1 ) , a six electron reduction product of Bzn , was highly abundant in treated samples . Multiple detected signals were assigned to covalent adducts of Bzn reduction products with low molecular weight thiols . Three signals were assigned to different Bzn-trypanothione adducts , all detected as tri-charged ions ( m/zc 322 . 47 , RT 29 . 1; m/zc 316 . 46 , RT 28 and m/zc 356 . 8 , RT 29 . 9 min ) ( 10 , 13 , 16 ) . Likewise , a glutathionylspermidine adduct was detected as a tri-charged ion ( m/zc 220 . 78 , RT 34 . 5 min ) ( 15 ) . The mono-charged ion with m/zc 349 . 12 ( RT 11 min ) was assigned to a cysteine-containing adduct ( 9 ) . In addition , we found a glutathione adduct with reduced Bzn , both as a mono and a di-charged ion , with m/zc 535 . 18 and 267 . 59 respectively . Each of these ions were detected at two different retention times ( RT 17 . 6 and 18 . 4 min ) , most probably representing two isomers ( 7 , 8 ) . Ovothiol A covalent adducts were assigned to signals present at two retention times on HILIC chromatography ( m/zc 429 . 16 and m/zc 214 . 58; both at RT 19 . 5 and 21 . 5 min ) ( 14 , 12 ) . High quality MSMS spectra were obtained for a number of putative Bzn-derived molecules , even though some metabolites were detected with very low intensity signals . The analysis of the fragmentation patterns is summarized in File S4 . Although additional structural data would be necessary to unequivocally identify all the metabolites , the fragmentation data , together with the high resolution accurate mass MS and RT data , are supportive for the proposed structures . In this sense , all MSMS spectra contained a fragment of m/zc 90 . 047 assigned to the benzene ring moiety of Bzn and also present in the Bzn fragmentation pattern . In addition , molecules proposed as related structures displayed fragmentation patterns with shared peaks . Metabolites with m/zc 264 . 12 ( 3 ) and 278 . 13 ( 2 ) ( hydroxy and methoxy derivatives ) included 14 shared signals , while cysteine ( 9 ) and glutathione adducts ( 7 , 8 ) ( Bzn-thiol conjugates ) displayed five shared peaks . Furthermore , shared peaks were obtained when glutathione and trypanothione MSMS fragmentation spectra were compared with the corresponding spectra of the proposed Bzn-thiol adducts . Among the common peaks we found the typical fragment encountered in glutathione containing molecules of mass 129 Da [33] and also fragment masses that correspond to the neutral loss of pyroglutamic acid . Also , 34S isotopic peaks were observed for a number of metabolites , confirming the presence of thiol moieties in these covalent adducts ( Table 1 ) . Finally , a group of cell-free control samples were analysed separately: a control group in which the drug was incubated with the growth medium was compared with the medium alone ( not-shown ) . In this analysis we observed that no Bzn metabolites arise after the drug incubation with the medium components after 6 hours at 28°C . Thus , all of the observed Bzn derived metabolites are produced by T . cruzi-mediated metabolism of the drug . Since a number of the Bzn-related metabolites were found only with very low abundance when T . cruzi epimastigotes were incubated with 20 µM Bzn over six hours , we collected additional metabolomics data using a higher concentration of Bzn , to allow the detection of additional signals arising from Bzn derived molecules and to observe effects on endogenous metabolites . For this purpose , parasites were treated with 50 µM Bzn over 6 h in the same general conditions ( cBzt samples ) . Parasites that were not exposed to Bzn ( cBec ) and parasites to which Bzn was added just prior to the extraction of metabolites ( cBzc ) were used as controls . Fifty percent ( 50% ) of the parasites remained viable after this treatment compared to untreated controls ( not shown ) . After data filtering , and analogous to the 20 µM Bzn treatment , we observed several endogenous metabolites that showed significant differences between treated and control samples ( File S2 ) . Consistent with the lower Bzn dose , trypanothione and homotrypanothione showed diminished levels after drug exposure , probably a consequence of the formation of the drug-thiol conjugates . Three lipids putatively identified as vitamin D-related sterols showed diminished levels after treatment , whereas two long-chain acyl-carnitines showed augmented levels , along with the dipeptide γ-glutamylcysteine ( T-test P values<0 . 05 and FC>2 ) . Treatment with 50 µM Bzn induced the appearance of a high number of Bzn related signals , with 124 ions included in the filtered data . After careful removal of LC-MS artefact ions , 36 of these Bzn-specific peaks were listed as putative Bzn metabolites ( Table 2 ) , although some of the analysed signals may still represent in-source MS-derived fragments or adduct ions from true Bzn metabolites . The raw data was scanned for putative Bzn metabolites contained in the Bznmet database , and 14 additional ions were retrieved ( Table 2 ) . The Bzn metabolites identified using 20 µM Bzn were also found in the 50 µM assay , with the exception of a metabolite with m/zc 356 . 80 ( 16 ) . An additional signal at m/zc 164 . 09 ( RT 13 . 6 min ) was found and attributed to 2-amino-N-benzylacetamide ( 22 ) . This compound was described as one of the products of the in vitro enzymatic conversion of Bzn by NTRI [17] . The metabolite with m/zc 246 . 11 was found in both positive and negative ESI modes and with two different retention times ( 12 and 13 . 5 min ) ( 11 , 19 ) . These could represent both the Bzn hydroxylamine and/or hydroxy metabolites . Also , a conjugate of reduced Bzn with γ-glutamylcysteine was detected in 50 µM Bzn treated samples ( m/zc 478 . 16 and m/zc 239 . 08 , RT 17 . 5 and 18 . 3 min ) ( 20 , 31 ) . The signal at m/zc 393 . 17 ( RT 32 . 4 min ) was assigned to a molecule composed of trypanothione linked to two reduced Bzn molecules ( 41 ) . A triple and a quadruple charged ion were both attributed to a conjugate of reduced Bzn with a mixed disulphide of trypanothione and glutathione ( m/zc 418 . 82 and m/zc 314 . 12 at RT 30 . 3 min ) ( 29 ) . The remaining adducts included mercaptohistidine ( 35 ) , an intermediate in ovothiol A synthesis , and non-sulphur containing metabolites including valine ( 36 ) and pyroglutamic acid ( 27 ) . Some of the proposed structures were supported by MSMS data , although fragmentation patterns could not be obtained for all metabolites due to low abundance ( File S4 ) . Figure 4 depicts proposed structures for some of the Bzn metabolites detected in 20 µM and/or 50 µM Bzn metabolomics analysis . Many of the in situ detected Bzn-thiol conjugates display two different but close retention times , which likely correspond to structures equivalent to the ones described for reduced misonidazole adducts with glutathione [25] . These structures include the thiol moiety bound to the imidazole ring through either carbons at position 4 or position 5 ( see Figure 4 for proposed structures ) . As an example , Bzn-glutathione adduct chromatograms , and MS and MSMS spectra are shown in Figure 5 . Similar intensities were observed at the two different retention times where Bzn-glutathione adducts were detected , whereas Bzn-ovothiol A and Bzn-glutathionylspermidine adducts displayed very low intensity peaks at one of the retention times , which possibly results from preferred binding to one of the two imidazole carbons ( 4-C or 5-C ) .
Metabolomics technology has allowed us to gain further insight into Bzn mechanism of action . The MS based metabolomics analysis is a powerful tool and can be used to analyse the mode of action of different types of drugs in Trypanosoma cruzi . These in turn should help us understand resistance mechanisms as well as the natural variation in T . cruzi susceptibility to nitroheterocycles and to improve available and future drugs . Through this work we have shown that Bzn is extensively metabolized to a number of molecules once it enters T . cruzi . These molecules include reduction products and covalent adducts with low molecular weight thiols and other small molecules . In addition , the metabolomics analysis of endogenous metabolites identified low molecular weight thiol depletion and turnover as the major metabolic impact of Bzn treatment . We here propose that the covalent binding of Bzn with low molecular weight thiols as well as with protein thiols is a primary cause of the drug's toxicity against T . cruzi . | The unicellular parasite Trypanosoma cruzi infects humans , leading to Chagas disease , endemic in Central and South America and responsible for 13 , 000 annual deaths . Only two drugs have proven effective against Chagas , nifurtimox and benznidazole ( Bzn ) . Bzn has the best safety and efficacy profiles and is thus used as first line treatment . Bzn is a pro-drug , and possesses a nitro group which needs to be enzymatically reduced within the parasite to become active . We have investigated for the first time , by means of mass spectrometry based metabolomics , the global changes to small metabolites that occur once Bzn enters the parasite . A decrease in the levels of several thiols , including cysteine and trypanothione , and an increase in gamma-glutamyl containing dipeptides were observed after treatment . Reduced metabolites of Bzn were also detected , together with numerous covalent conjugates of the drug combined with low molecular weight thiols and some non-thiol metabolites . Overall , Bzn treatment primarily affects thiol containing molecules in T . cruzi , and this interference with thiol metabolism contributes to the drug's mode of action . | [
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| 2014 | Benznidazole Biotransformation and Multiple Targets in Trypanosoma cruzi Revealed by Metabolomics |
Cells adjust to hypoxic stress within the tumor microenvironment by downregulating energy-consuming processes including translation . To delineate mechanisms of cellular adaptation to hypoxia , we performed RNA-Seq of normoxic and hypoxic head and neck cancer cells . These data revealed a significant down regulation of genes known to regulate RNA processing and splicing . Exon-level analyses classified > 1 , 000 mRNAs as alternatively spliced under hypoxia and uncovered a unique retained intron ( RI ) in the master regulator of translation initiation , EIF2B5 . Notably , this intron was expressed in solid tumors in a stage-dependent manner . We investigated the biological consequence of this RI and demonstrate that its inclusion creates a premature termination codon ( PTC ) , that leads to a 65kDa truncated protein isoform that opposes full-length eIF2Bε to inhibit global translation . Furthermore , expression of 65kDa eIF2Bε led to increased survival of head and neck cancer cells under hypoxia , providing evidence that this isoform enables cells to adapt to conditions of low oxygen . Additional work to uncover -cis and -trans regulators of EIF2B5 splicing identified several factors that influence intron retention in EIF2B5: a weak splicing potential at the RI , hypoxia-induced expression and binding of the splicing factor SRSF3 , and increased binding of total and phospho-Ser2 RNA polymerase II specifically at the intron retained under hypoxia . Altogether , these data reveal differential splicing as a previously uncharacterized mode of translational control under hypoxia and are supported by a model in which hypoxia-induced changes to cotranscriptional processing lead to selective retention of a PTC-containing intron in EIF2B5 .
Reduced availability of oxygen , or hypoxia , is a major feature of solid tumors that contributes to metastasis and resistance to therapy [1] . Tumor hypoxia occurs due to several physiological factors , such as limited diffusion of oxygen and irregular vascular structure [2] . While oxygen levels can be measured directly in tumors , there is an immediate need to develop noninvasive clinical markers of hypoxic burden in tumors . Molecular imaging markers such as pimonidazole and fluorescence-based compounds [3–5] have been developed and refined to specifically label hypoxic tumors , which allows for specific interrogation of hypoxic gene expression programs within the tumor microenvironment [6] . Hypoxia-mediated changes in expression can be dynamic and robust , impacting pathways critical to tumor development and survival , such as angiogenesis , metabolism , and macromolecular synthesis [7 , 8] . Consequently , there is a nearly universal negative correlation between the level of hypoxia in tumors and overall survival in patients of many solid cancers , including head and neck squamous cell carcinoma ( HNSC ) [9] . As such , hypoxia “metagene” expression signatures have been successfully implemented as a surrogate method to classify tumor hypoxia for HNSC and other solid malignancies , including breast and prostate cancer [10 , 11] . Hypoxic stress influences processing and translation of mRNAs by regulating the levels and activity of diverse factors , including Hypoxia-Inducible transcription Factors ( HIFs ) , small noncoding RNAs and miRNAs , and RNA binding proteins ( RBPs ) [12–14] . For example , RBPs such as HuR and PTB bind to and regulate the stability and localization of key regulators of hypoxic response such as HIF1α [15 , 16] and miRNA-199a [17] . Several kinases known to phosphorylate major RBPs and splicing factors are also hypoxia responsive [18] . Consequently , alternative splicing of select target genes of HIF1α has been reported in hypoxic cells [19] . Likewise , expression of noncoding mRNA isoforms are induced under hypoxia in part due to changes in splicing [20] . Several splicing factors , including SF3B1 , are up-regulated in a HIF1α-dependent manner under physiological conditions of hypoxia in cardiac myocytes [21]; however , it remains unclear precisely how mRNA splicing is regulated during periods of oxygen deprivation in cancer cells and what the resulting biological implications are . Intriguingly , regulation of splicing is frequently altered in cancer and is affected by the same signaling pathways that are differentially regulated in hypoxic tumor microenvironments [22] . Moreover , many solid cancers affected by hypoxia display widespread alterations in splicing [23] . While splicing of specific genes has been shown to be dependent on the activity of HIF1α , differences in transcription elongation are known to impact regulation of cotranscriptional splicing [24–26] . Thus , we hypothesized that hypoxia-mediated changes to the RNA processing and transcription machinery could lead to extensive differences in mRNA splicing . Hypoxia was identified to induce phosphorylation of the C-terminal domain ( CTD ) of RNA polymerase II ( RNAPII ) and was observed to enhance binding of cofactors and increase control of transcriptional activation of HIF target genes [27] . Additional findings support the theory that changes in the activity and rate of transcription elongation play a key role in the maturation and processing of mRNAs under hypoxia . Under hypoxia , there are fewer changes in RNAPII binding near gene promoters but instead an increased accumulation of RNAPII observed along gene bodies [28] . Therefore , to better understand the link between hypoxia-mediated changes to mRNA regulation and to investigate the biological role of alternative splicing in response to hypoxia in cancer , we deeply sequenced mRNA of hypoxic and normoxic HNSC SQ20B cells . The data led to the identification of more than 1 , 000 transcripts affected by alternative splicing under hypoxia and revealed 3 types of mRNA splicing as specifically enriched , including an increase in retained introns ( RIs ) in hypoxia compared to normoxia . Strikingly , for more than 90% of genes in this category , hypoxia increased the occurrence of RIs relative to normoxia , which is a phenomenon also observed in 16 cancer types , including head and neck , colon , breast , and lung cancers [23] . We found evidence of several hypoxia-induced RIs expressed in solid tumor data , indicating that tumor hypoxia contributes to this type of splicing . Most notably , a unique RI in the master regulator of translation initiation , EIF2B5 , was significantly overexpressed in both head and neck and kidney renal clear cell tumors relative to normal tissues in a stage-dependent manner . Here , we present compelling evidence that hypoxia leads to retention of an intron in EIF2B5 that creates a phylogenetically conserved premature termination codon ( PTC ) . This alternate transcript results in a truncated protein isoform of eIF2Bε predicted to lack enzymatic guanine exchange factor ( GEF ) activity . Remarkably , we demonstrate that the resulting truncated isoform of eIF2Bε is induced under hypoxia and provide data to show that this isoform acts in opposition to full-length eIF2Bε to inhibit protein synthesis . Cellular adaptation to hypoxia entails adjusting key metabolic processes , such as translation , to low energy due to reduced availability of oxygen [29] . Control of translation initiation specifically contributes to down-regulation of protein synthesis under hypoxia , which occurs through phosphorylation of eIF2α by hypoxia-mediated induction of the integrated stress response [30] . Here , we discover hypoxia-mediated induction of a dominant-negative isoform of eIF2Bε as a secondary method to inhibit translation and increase survival of head and neck cancer cells in periods of acute or prolonged hypoxia . We further investigated how splicing of EIF2B5 is controlled and uncovered several factors that contributed to the hypoxia-induced RI , including a weak splice site at the intron:exon junction , hypoxia-induced expression and binding of SRSF3 , and an accumulation of RNAPII specifically at the RI . Some of these findings extend to the broader class of genes with RIs under hypoxia , supportive of a mechanism by which changes to RNAPII elongation contributes to retention of introns with weak 3′ splice sites under hypoxia .
The transcriptomes of normoxic and hypoxic SQ20B cells ( maintained in 0 . 5% O2 for 16 h ) were compared to identify expression differences at an individual mRNA transcript level . The RefSeq hg19 reference comprised of 46 , 017 transcript models was used for annotation . Of the 24 , 812 transcripts expressed in SQ20B cells , we detected 3 , 114 that significantly changed expression in hypoxia compared to normoxia ( P < 0 . 05 , false discovery rate [FDR] < 5% , Fragments Per Kilobase of transcript per Million mapped reads [FPKM] ≥ 0 . 5 ) . In total , 1 , 519 transcripts representing 1 , 473 genes were induced and 1 , 595 transcripts expressed from 1 , 563 genes were repressed ( FDR < 5% ) . Pathway-based analysis identified “cell adhesion , ” “response to hypoxia , ” and “metabolism” among the most enriched categories for hypoxia-induced transcripts ( P < 0 . 01 , DAVID GO [31] ) . Induction of select HIF1α target genes was validated by quantitative PCR ( qPCR ) ( S1A Fig ) . Repressed genes were involved in regulating processing , stability , and translation of RNAs , with “ribosome biogenesis , ” “nucleosome organization , ” and “RNA splicing” as highly significant ontology groups ( P < 0 . 01 ) . This included core regulators of alternative splicing , such as the major splicing factor SF1 , several serine–arginine splicing factors ( SRSF1 , SRSF3 , and SRSF7 ) , SF3 genes , and many translation initiation factors , including EIF2B family members , EIF5 , and EIF6 . Notably , a closer examination of genes involved in regulation of mRNA transcription , translation , and processing revealed that the clear majority of these genes were repressed under hypoxia ( Fig 1A ) . Variation in expression of individual transcripts of the same gene by hypoxia could be masked when analyzing expression changes at the total gene level; therefore , we used levels of individual isoforms to classify genes as hypoxia-responsive ( defined as a gene with 1 or more individual transcripts detected as significantly induced or repressed , P < 0 . 05 , FDR < 5% ) . This method uncovered 50 additional genes that may not yet be reported as hypoxia-regulated , including genes known to mediate protein localization ( SEC24B , ARL17A , PLEKHA8 , MLPH , STXBP5 , MON2 , and EXOC1 ) , Mitogen-activated protein kinase kinase signaling pathway ( MAP4K3 , DUSP22 , and MAP3K13 ) , and key regulators of RNA metabolism ( ZNF519 , JDP2 , CTBP2 , ZNF248 , RBM5 , NFAT5 , CREB3L2 , SMARCA1 and ZFHX3 ) . Consistent with the finding that transcript-level changes comprise an additional layer of hypoxia-regulated expression changes , only approximately 3% of genes that expressed multiple isoforms in SQ20B cells have been reported to be transcriptionally controlled by HIF binding at hypoxia-responsive elements ( HRE ) [32] , suggesting an HRE-independent mode of regulation . This is not surprising , as regulation of transcription and mRNA splicing are observed to be distinct processes that impact different subsets of genes [33] . Next , we focused on those genes that expressed more than 1 transcript in SQ20B cells to assess changes in patterns of isoform expression . Altogether , of the 5 , 418 genes identified to express >1 transcript , 937 of these genes showed hypoxia-induced changes in expression of 1 , 015 transcripts ( P < 0 . 05 , FDR < 5% ) . Most these genes contained a single hypoxia-responsive isoform; only 8% of genes that expressed multiple transcripts showed more than 1 isoform that significantly changed expression in hypoxia . The isoform-level changes in expression were validated for select genes that displayed differential expression of isoforms predicted to have different biological functions ( S1B Fig ) . Hypoxia selectively induced the MXI1 isoform , which codes for the shortest protein isoform that would lack 46 amino acids at the N-terminus compared to full-length isoforms . This difference was determined to alter the ability of the truncated protein to antagonize N-Myc activity and impact cell proliferation [34] . Likewise , the isoform of NDRG1 that exhibited the strongest induction arises from an alternative transcription start site , which leads to 66 fewer amino acids and exclusion of a proteolytic cleavage site that remains in the other two isoforms of NDRG1 . Transcripts of 2 additional genes , NEK6 and FAM86C1 , were validated as being selectively repressed under hypoxic conditions ( S1C Fig ) . We reasoned that hypoxia-responsive isoforms predicted to carry out different functions than isoforms expressed in normoxia would be the most biologically impactful changes that warranted further study . Therefore , we used the program MISO [35] to carry out an additional exon-level approach to identify changes in gene structure based on 8 annotated categories of alternative splicing . Hypoxia led to a change in expression for 1 , 103 alternatively spliced loci representing 819 unique genes ( Fig 1B , ΔPsi > 10% , Bayes Factor > 20 ) . Notably , there was a significant comparative enrichment for hypoxia-induced changes in 3 specific event types: expression of alternate last exons ( ALEs ) , RIs , and tandem 3′ UTRs ( TUTR ) ( Fig 1B ) . For the genes in these 3 splicing categories , gene ontology revealed processes central to hypoxic adaptation as significantly enriched , including “cellular protein metabolism , ” “programmed cell death , ” and “gene expression” ( Fig 1C , DAVID GO , P < 0 . 05 , S1 Data ) . Remarkably , nearly 90% of the genes in the RI category displayed increased retention of introns in hypoxic compared to normoxic cells ( Fig 1D ) . Among these genes was ANKZF1 , a gene implicated in mitochondrial and endoplasmic reticulum-associated protein degradation [36 , 37] , the translation initiation factor EIF2B5 , TGFB1 , and the metionyl-TRNA synthetase , MARS . The RIs in these genes were validated by PCR using primers spanning the intron junction and cDNA prepared from oligo-dT–selected mRNA ( Fig 2A–2D , S2 Fig ) . To confirm expression of these hypoxia-induced introns in additional datasets and determine if they were expressed in patient samples , we interrogated The Cancer Genome Atlas ( TCGA ) and analyzed expression solid tumors known to be affected by hypoxic fractions [38–40] . This analysis validated increased expression of the hypoxia-induced RIs for ANKZF1 , EIF2B5 , MARS , and TGFB1 in HNSC tumors relative to matched normal tissues ( Fig 2E–2H ) . The extended analysis of additional cancer types confirmed significantly increased expression of these hypoxia-induced RIs for 2 types of renal carcinoma , as well as lung , liver , and prostate cancers ( S3 Fig ) . Intron 12 of EIF2B5 showed a strong stage-dependent increase in expression for both head and neck and kidney renal clear cell carcinomas ( KIRC ) ( Fig 3A and 3B ) . This trend was even more apparent with HNSC patients in late-stage disease , with some individuals exhibiting nearly 8-fold increased expression of intron 12 compared to controls ( Fig 3A ) . These data suggest hypoxia-induced retention of EIF2B5 intron 12 may result in meaningful biological effects and encouraged us to examine the functional impact of this RI . The RI in EIF2B5 stood out as the strongest candidate for functional studies for several additional reasons: ( a ) hypoxia led to a >40% increase in retention of intron 12 in a background of an overall 2-fold decrease in total expression of EIF2B5 ( ΔѰ = 0 . 44 , Bayes Factor > 20 ) ; ( b ) the retention occurred specifically at a single locus of EIF2B5; and ( c ) retention of intron 12 creates a PTC that remains in frame with the coding sequence ( Fig 3C ) . The genomic locus around the PTC is highly conserved , even among lower organisms . Intriguingly , this stop codon is part of the 5′ splice site consensus sequence “GURAGU , ” where URA can be either UAA or UGA ( both of which would create a stop codon ) . This suggests a strong evolutionary pressure to preserve an early termination precisely at this location , where inclusion of the PTC may be influenced through regulation of splice site choice . Increased expression of EIF2B5_intron12 was further confirmed by qPCR using intron-specific primers in a reaction with cDNA prepared from oligo-dT–selected mRNA ( Fig 3D ) . Additionally , a deeper analysis of changes in splicing of EIF2B5 was carried out to closely examine inclusion of intron 12 and to validate the occurrence of this RI using another method; the software package MAJIQ [41] was used to assess local splicing variation in EIF2B5 for annotated and de novo splice events . A hypoxia-induced increase in expression of intron 12 was confirmed using this approach ( S4 Fig ) , but additional sites of local splicing variation did not show significant ( >20% ) hypoxia-influenced changes . Computational analyses approximate up to 20%–35% of alternatively spliced transcripts could contain PTCs and become targets of nonsense-mediated decay ( NMD ) [42 , 43]; however , in cases where NMD is inhibited , transcripts can be stabilized and subsequently translated into truncated proteins [44] . Moreover , NMD surveillance typically recognizes stop codons as premature if the stop occurs more than 50 nucleotides upstream of a splice junction [45] . Due to the unusual nature of this intron retention event and the role of hypoxia in suppressing NMD in an eIF2α phosphorylation-dependent manner [46] , we predicted that this isoform would not be subject to NMD but would rather be translated into a truncated protein ( Fig 4A ) . Consistent with this hypothesis , the MAJIQ splicing analysis of EIF2B5 revealed a 40%–50% decrease in expression of remaining exons following intron 12 ( S4 Fig ) . These data support the notion that transcripts that retain intron 12 and the subsequent PTC would undergo read-through of intron 12 into intron 13 , resulting in a reading frame for a truncated protein variant . Indeed , we observed induction of a 65kDa protein isoform of eIF2Bε under various conditions of hypoxia consistent with the predicted PTC inserted upon retention of intron 12 ( Fig 4B ) . Induction of phospho-eIF2α was used as a marker for hypoxia in these experiments ( Fig 4B ) . To further test whether this 65kDa protein detected in the immunoblot was indeed a truncated isoform of eIF2Bε , we used small interfering RNA ( siRNA ) to specifically target the entire EIF2B5 gene or intron 12 alone . Using this approach , we observed a substantial reduction in the levels of the 65kDa isoform under both conditions ( Fig 4C , S5A Fig ) . We additionally observed hypoxia-induced expression of 65kDa eIF2Bε in the colorectal cancer cell line RKO ( Fig 4D ) , demonstrating that this is not a cell-line specific event . Moreover , ultraviolet ( UV ) radiation ( Fig 4E ) , but not thapsigargin-induced endoplasmic reticulum ( ER ) stress ( S5B Fig ) , also led to induction of the truncated eIF2Bε , suggesting that expression of this isoform is induced by specific cell stresses . Furthermore , whole-cell lysates were isolated from hypoxic SQ20B cells , and proteins migrating at 80kDa and 65kDa were subjected to liquid chromatography tandem mass spectrometry ( LC-MS/MS ) . Peptides corresponding to eIF2Bε were detected in both the 80kDa and 65kDa size analytes . The peptides corresponding to the 65kDa-sized isoform of eIF2Bε were located near the N-terminus or middle of the eIF2Bε sequence , consistent with a C-terminal truncation ( Fig 4F ) . To additionally rule out that expression of the band migrating at 65kDa is a degradation product or may occur due to proteolytic cleavage , we used a plasmid to express a version of eIF2Bε with a C-terminal FLAG-tag in SQ20B cells and exposed these cells to UV radiation . If the tagged eIF2Bε were to undergo proteolytic cleavage , we predicted to observe a 15kDa band reactive to FLAG-tag antibody in addition to the 65kDa band reactive against eIF2Bε antibody in UV-treated cells . This experiment did not show evidence of the 15kDa tagged proteolytic cleavage product ( S5C Fig ) . Finally , we carried out an experiment to test for the involvement of NMD in leading to the expression of truncated eIF2Bε . Although NMD is known to be inhibited in conditions of oxygen deprivation [46] , NMD and alternative splicing are coupled processes , which can lead to alternative PTC-containing transcripts that ultimately become targets of NMD [47] . To rule out any major contribution of NMD in leading to expression of 65kDa eIF2Bε , we used siRNA to knock down expression of UPF1 . UPF1 is a necessary component of the SURF complex , which is required for NMD [48 , 49] . This experiment failed to produce an increase in expression of the truncated protein ( S5D Fig ) , further supporting the notion that this isoform occurs due to alternative splicing . To identify potential regulators mediating retention of intron 12 in EIF2B5 , we utilized the AVISPA tool [50] to carry out a splicing-relevant sequence feature analysis of the locus encompassing EIF2B5 exons 12–14 . The nonmotif analysis revealed a relatively short distance to the nearest AG dinucleotide upstream of exon 13 , as well as relatively unstructured RNA immediately downstream of exon 13 ( Fig 5A ) . Both features indicate weakened splicing potential of exon 13 [51 , 52] . The motif analysis revealed sequence motifs , such as potential RBP binding motifs , which could be important for regulating splicing of the locus ( Fig 5B ) . Notably , these sequence features were not identified in a search of additional loci within EIF2B5 . As a control , we used AVISPA to predict the occurrence of alternative splicing in 4 additional exon triplets within EIF2B5 ( exons 6–8 , 7–9 , 8–10 , and 9–11 ) . These loci were not predicted to be alternatively spliced . Altogether , these results suggest that alternative splicing is significantly more likely to occur at the intron 12 locus compared to the control loci tested . The motif analysis also identified potential trans regulators with binding sites within this locus predicted to regulate splicing of this region , including NOVA , HNRNPC , and members of the CUGBP Elav-Like Family ( CELF ) , as well as Ser/Arg-rich splicing factor 3 ( SRP20 , AKA SRSF3 ) , HNRNPG , NPTB , HNRNPF , and Ser/Arg-rich splicing factor 2 ( SC35 , AKA SRSF2 ) ( Fig 5B ) . Interestingly , several of the splicing factors predicted to have the greatest impact on regulation of this locus also changed expression under hypoxia , including SRSF2 , SRSF3 , HNRNPC , HNRNPF , and RBMX ( AKA HNRNPG ) , which were repressed at the mRNA level , and CELF5 ( a member of the CELF family ) , which was induced ( Fig 1A , FC ≥ |1 . 2| , FDR < 5% ) . Upon closer examination of these splicing factors , we focused in on SRSF3 and CELF5 as prime candidates to test as regulators of EIF2B5 splicing , due to a large number of binding sites near the intron12:exon13 junction ( S6A Fig ) and the fact that these factors showed >2-fold changes in mRNA expression under hypoxia . We next assayed for changes in protein expression of these splicing factors in hypoxic cells and observed a reproducible hypoxia-induced increase in SRSF3 protein ( Fig 5C , S6B Fig ) and a modest decrease in expression of CELF5 . To test for their requirement in the splicing of EIF2B5 and production of the resulting 65kDa protein isoform , we used siRNA to knock-down expression of these factors and assayed for changes in expression of 65kDa eIF2Bε . Upon knockdown of SRSF3 , we saw a concurrent disappearance in the expression of the 65kDa isoform of eIF2Bε in conditions of normoxia or hypoxia ( Fig 5D ) , while siRNAs against other RBPs , such as CELF5 , did not display the same effect ( S6C and S6D Fig ) . RNA immunoprecipitation assays were next carried out to confirm a direct interaction between SRSF3 protein and EIF2B5 RNA . The data showed enrichment of SRSF3 at both exons flanking EIF2B5_intron12 ( Fig 5E ) , validating the predicted binding sites observed in other systems ( S6A Fig ) . Furthermore , binding of SRSF3 was increased in hypoxic cells relative to normoxic cells , providing additional support for the role of SRSF3 in regulating hypoxia-induced retention of intron 12 . In addition to the influence of sequence elements , mRNA splicing is a co-transcriptionally regulated process which is impacted by coordination of RNAPII . Phosphorylation of the CTD of RNAPII is known to impact the rate of transcriptional elongation , pausing , and the relative rate of splicing [53 , 54] . Therefore , we assayed for changes in phosphorylation of the CTD of the largest subunit of RNAPII in HNSC cells . Surprisingly , we saw a large ( >10-fold ) and reproducible increase in levels of phosphorylation at serine 2 residues and a concomitant decrease in phosphorylation of serine 5 and 7 residues of the CTD in hypoxic cells compared to normoxic controls ( Fig 6A , S7 Fig ) . Interestingly , accumulation of the phospho-Ser2 form of RNAPII has been associated with RIs compared to constitutively spliced introns [55] . To assay for changes in binding of total and phospho-Ser2 RNAPII , we next used chromatin immunoprecipitation followed by qPCR . We observed a significant hypoxia-induced enrichment in both forms of RNAPII at intron 12 but did not detect the same enhanced binding at another nearby intron of EIF2B5 that did not undergo intron retention under hypoxia ( Fig 6B ) . In strong support of these data , cells expressing mutant forms of RNA polymerase with slower elongation rates led to extensive changes in mRNA splicing , including specific retention of EIF2B5 intron12 [57] . Moreover , 31 of 100 genes we identified to be affected by intron retention in hypoxia were classified as “rate-sensitive” and displayed altered expression in the RNA polymerase elongation mutants [57] , suggesting that hypoxia-mediated changes in elongation of RNAPII likely influence splicing of additional loci . Those genes alternatively spliced in conditions of slow elongating RNAPII generally exhibited weaker 3′ splice sites compared to loci insensitive to changes in elongation rate [57] , so we next carried out an analysis of splice site strength for genes affected by intron retention under hypoxia . This analysis detected significantly weaker 3′ splice sites for genes with changes in RIs under hypoxia ( score = 7 . 6 ) compared to a control set of splice sites not affected by hypoxia ( score = 8 . 9 ) ( Fig 6C , P = 0 . 00289 ) . This included intron 12 of EIF2B5 , where the 3′ splice site strength score was 1 . 1 points lower compared to the 3′ splice site strength of the control set . Next , we tested whether the expression of 65kDa eIF2Bε has an impact on the biological function of the endogenous , full-length eIF2Bε protein . The truncated protein isoform created from retention of intron 12 is predicted to lack 2 critical domains that occur in the C-terminus ( Fig 4A ) : a GEF domain and a region required for interaction with eIF2α [58] . Thus , we hypothesized that the 65kDa isoform of eIF2Bε would inhibit translation and lead to reduced protein synthesis . To test this , we constructed a plasmid expressing the truncated 65kDa isoform using site-directed mutagenesis to insert a stop codon within 3 nucleotides of where retention of intron 12 results in a PTC ( Fig 7A ) . Expression of this mutated version of eIF2Bε under normoxic conditions resulted in the appearance of a 65kDa protein isoform consistent with the size of the endogenous protein that is induced under hypoxia ( Fig 7B ) . To analyze the impact of the truncated isoform on protein synthesis , we used pulse-labeling of 35S methionine/cysteine in cells expressing 65kDa eIF2Bε , full-length eIF2Bε , or empty vector . Proteins isolated from the 35S methionine/cysteine-labeled cells were resolved on SDS-PAGE , after which the gels were dried and exposed to autoradiograph film to detect signal intensities as a measure of total protein synthesis . There was a pronounced and reproducible decrease ( approximately 30% ) in total protein synthesis in cells expressing 65kDa eIF2Bε compared to empty vector , while expression of full-length eIF2Bε did not have the same effect ( Fig 7B and 7C ) . Translation levels were assessed relative to cells expressing empty vector as a control in order to best isolate the effects of the truncated isoform from those of endogenous full-length eIF2Bε . Effects of inducing full-length eIF2Bε were not used as a control because full-length eIF2Bε remains stably expressed under hypoxic conditions and is not induced . In addition , expression of eIF2B has been shown to destabilize ternary complex formation under conditions where eIF2 is phosphorylated [59] . To further verify the effects observed in the 35S assay , we carried out polysome profiling as an additional measure of protein synthesis . The data confirmed a decrease in the total polysome profiles of cells expressing 65kDa eIF2Bε compared to control cells ( Fig 7D , S8A Fig ) . Moreover , this decrease in translation was comparable to the decrease in translation observed in hypoxic conditions ( Fig 7E ) . Consistent with these data , we observed an increase in adenosine triphosphate:adenosine monophosphate ( ATP:AMP ) ratio in cells expressing 65kDa eIF2Bε relative to cells expressing empty vector or the full-length isoform ( S8B Fig ) . Finally , because down-regulation of translation is a known mechanism by which tumor cells overcome hypoxic stress [60] , we predicted that expression of 65kDa eIF2Bε and the resulting repression of translation may promote survival of hypoxic cells . To test this , clonogenic assays were performed to measure survival and proliferation . The surviving fraction of cells expressing 65kDa eIF2Bε decreased under normoxic conditions but was significantly higher when cells were grown in 0 . 5% O2 conditions for either 16 h or 24 h ( Fig 7F and 7G , S8C and S8D Fig , P < 0 . 01 , Student t test ) . Collectively , these data establish a role for the 65kDa isoform of eIF2Bε in reducing protein synthesis in head and neck cancer cells to adapt to conditions of hypoxia ( Fig 8 ) . Mechanistically , we provide strong evidence that expression of this isoform is influenced by differential binding of SRSF3 at the locus of EIF2B5 intron12 , a weak splice site coupled with an alternate splice site , and increased binding of total and P-Ser2 RNAPII ( Fig 8 ) .
This study has validated alternative splicing as a major , additional layer of complexity to the gene expression response to hypoxia . Previous work had described hypoxia-mediated changes in splicing , including in several known HIF-target genes [19 , 61] and further identified splicing as a means to induce expression of noncoding RNAs under hypoxia [20] . Here , we expand upon the current understanding of RNA processing under hypoxia and demonstrate that decreased oxygen in cancer cells leads to extensive changes in splicing , including a striking increase in retention of over 100 introns affecting a significant number of genes with key functional roles in cellular adaptation to hypoxia . As such , a major focus of our study became to investigate the biological significance of hypoxia-induced intron retention in the master regulator of translation initiation , EIF2B5 . Specific retention of intron 12 in EIF2B5 led to a previously undescribed 65kDa isoform of eIF2Bε that decreases overall protein synthesis in head and neck cancer cells . Full-length eIF2Bε is a necessary component of the eIF2B complex , containing both the active GEF domain and a region for association with eIF2α [62]; this complex binds eIF2α and exchanges guanosine diphosphate ( GDP ) for guanosine triphosphate ( GTP ) to initiate translation . During hypoxia , eIF2α is phosphorylated , and translation initiation is inhibited and was demonstrated to be critical for cell survival under extreme hypoxia [60] . However , this phosphorylation is not long-lasting and can be removed by GADD34 [29 , 63] . Moreover , in cells stably expressing an unphosphorylatable form of eIF2α with an S51A mutation , translation initiation resumed under hypoxia , but only to approximately 75% of the level observed in control cells [60] . These data suggested the existence of additional mechanisms to sustain reduced protein synthesis in conditions of low oxygen . Thus , we propose that , during periods of acute or prolonged hypoxia , intron retention in EIF2B5 leads to expression of a truncated dominant-negative isoform to further inhibit protein synthesis in cancer cells ( Fig 8 ) . Expression levels of 65kDa eIF2Bε are consistently up-regulated in hypoxia , with the greatest induction of this isoform observed at stringent ( 0 . 2% O2 ) and prolonged ( ≥ 16 h ) hypoxic conditions ( Fig 4B ) . Moreover , SQ20B cells expressing 65kDa eIF2Bε demonstrated an increase in ATP:AMP ratio and increased clonogenicity in conditions of low oxygen ( S8 Fig ) . Altogether , these data establish a biological role for the 65kDa isoform of eIF2Bε in promoting adaptation to hypoxic stress in cancer cells . The question remains as to whether induction of this isoform is a cause or effect of tumorigenesis; additional animal studies are necessary to evaluate its potential to influence tumor formation and will be a focus of future work . Intriguingly , altered regulation of eIF2B GEF activity and translational control has been observed in transformed cells relative to primary cells [64] . In conditions that activate the unfolded protein response ( UPR ) , transformed cells displayed increased levels of GDP exchange from eIF2 relative to normal human cell lines , despite comparable levels of phospho-eIF2α [64] . These data provide an additional line of evidence that cancer cells in conditions of stress may require mechanisms outside of phospho-eIF2α to control eIF2B GEF activity and overall levels of protein synthesis . Our findings led to the first reported case of splicing as a previously uncharacterized mode of translational control under conditions of hypoxia . The mechanism behind hypoxia-induced alternative splicing is influenced by several factors , including both -cis RNA sequence determinants and oxygen-sensitive -trans regulators . We used retention of intron 12 in EIF2B5 as a starting point to investigate regulation of intron retention in hypoxic cells . Several unique aspects of intron 12 may lead to its retention in EIF2B5 , such as a weak 3′ splice site and a weakened splicing potential in this region of EIF2B5 influenced by a relatively short distance to the nearest AG dinucleotide upstream of exon 13 , as well as the relatively unstructured RNA immediately downstream of exon 13 . Intron length and GC content near the splice site can also influence splice site choice [65 , 66] . Alternatively , RIs often exhibit lower GC content compared to the immediate upstream and downstream exons [66] . Interestingly , the GC content of EIF2B5 intron 12 was 6% lower than that of the adjacent downstream exon 13 . This was the intron:exon junction where our AVISPA analysis detected a relatively weaker splice site and a possible “GT” alternate splice site downstream in intron 13 . The unique properties of this locus may promote specific retention of intron 12 compared to other introns under hypoxia . The influence of hypoxia-responsive -trans regulators on splicing of EIF2B5 was also substantial . Our data support the theory that hypoxia-mediated changes in expression of splicing factors and regulation of transcription elongation contribute to splice site choice . We observed down-regulation of many genes that regulate splicing and RNA processing , including some that were predicted to bind at the EIF2B5 intron 12 locus . There is evidence that global down-regulation of splicing factors and RBPs can promote intron retention in a regulated manner during other physiological responses or processes , such as hematopoiesis [67] . This mechanism likely contributes to the RIs in some genes under hypoxia . However , we uncovered that the splicing factor predicted to contain the most binding sites within the EIF2B5 intron12 locus , SRSF3 , was induced at the protein level under hypoxia and exhibited increased binding at the alternatively spliced locus under hypoxia ( Fig 5C and 5E ) . Reducing levels of SRSF3 substantially decreased expression of the 65kDa protein isoform . The full-length eIF2Bε protein was reduced as well , suggesting a major role of SRSF3 in the processing and splicing of EIF2B5 transcripts . While there was not a significant enrichment for predicted SRSF3 binding sites for the class of 100 genes affected by RIs under hypoxia , we did identify a common feature of relatively weaker 3′ splice sites for this group of genes ( Fig 6C ) . Previous work described that alternatively spliced loci with relatively weaker 3′ splice sites were more sensitive to changes in transcription elongation [57] . Thus , we propose that regulation of EIF2B5 intron12 and additional introns retained under hypoxia are likely influenced by hypoxia-mediated changes in activity of RNAPII . Under hypoxia , changes in phosphorylation of the CTD of RNAPII influence expression of HIF-1α target genes by affecting the binding of cofactors and the kinetics of transcriptional activation [27] . Our observation that UV radiation was another stress that also led to an induction of 65kDa eIF2Bε strongly supports this hypothesis . UV exposure is known to induce pyrimidine dimers and other blocks to transcription elongation , which alter RNAPII transcription kinetics and subsequently impact regulation of splicing [68 , 69] . We posit that other stresses that have an impact on transcriptional elongation will likely impact expression of 65kDa eIF2Bε as well . Furthermore , hypoxia-mediated changes in phosphorylation of RNAPII may explain why we observed an enrichment of splicing changes at the 3′ end of genes ( i . e . , ALE , TUTR , and RI categories ) . Several RBPs , including polyadenylation factors and splicing regulators , interact specifically with phospho-Ser2 modifications of the CTD [70] . Intriguingly , increased occupancy of phospho-Ser2 RNAPII is associated with RIs compared with constitutively spliced introns [55] . Our data detected an enrichment of phosphor-Ser2 binding under hypoxia at intron 12 in EIF2B5 , and future work by our group will determine whether this translates to other hypoxia-induced RIs with weak 3′ splice sites as well . The physiological relevance of these findings is underscored by the fact that EIF2B5 intron 12 is overexpressed in tumor versus normal tissues of patients affected by 6 different cancers , including HNSC ( Fig 2 , S3 Fig ) . Interestingly , intron retention is relatively increased for nearly all solid tumors compared to normal tissue [23] . An interrogation of the TCGA database uncovered evidence of several hypoxia-induced RIs identified in this study as overexpressed in solid tumors relative to matched normal tissues ( Fig 2 , S3 Fig ) , supporting the notion that the hypoxic tumor microenvironment is a contributor to the intron retention observed in solid tumors . There is a critical need to understand this important form of RNA processing and regulation in a cancer context . Many hypoxia-responsive isoforms , including those with RIs , have the potential to influence key biological pathways in cancer cells . For example , the hypoxia-induced RI in TGFB1 is predicted to create a different 5′ UTR and alternate transcription start site , which would code for a 416 amino acid peptide instead of the full-length 689 amino acids . This change in the C-terminus would affect part of a FAS1 domain involved in binding integrin to regulate cell adhesion , as well as an EMI ( EMILIN protein family ) domain that is thought to be a protein–protein interaction domain . Additional work is needed to investigate the functional consequences of additional hypoxia-induced introns , such as those in TGFB1 and MARS , predicted to create alternative protein isoforms . The identification of stress-responsive isoforms with biological functions that may differ from isoforms expressed under normal conditions will enable a deeper understanding of the link between stresses within the tumor microenvironment and regulation of RNA processing and splicing . These data will provide a new layer of information to refine prognostic hypoxia gene expression signatures and to investigate appropriate biological pathways to target hypoxic cancer cells .
SQ20B cells , derived from human head and neck squamous cell cancer , were obtained from American Type Culture Collection ( Rockville , MD ) . The RKO cells were a generous gift from Dr . Cho ( University of Chicago ) . Both cell lines were maintained in Dulbecco’s Modified Eagle Medium ( DMEM ) media supplemented with 4 . 5 g/L D-Glucose , 1X L-glutamine , 10% Fetal Bovine Serum , and 1X Penicillin/Streptomycin and cultured in a 37°C humidified 5% CO2 atmosphere . For oxygen deprivation experiments , cells were incubated in 37°C humidified 5% CO2 conditions with varying concentrations of O2 in an INVIVO2 400 chamber ( Baker BioScience Solutions ) . RNA was isolated from cells using the Trizol reagent ( ThermoFisher ) and purified according to the manufacturer’s protocol . All purified RNA was subsequently treated with DNaseI digestion to remove possible DNA contaminants ( Qiagen ) . The quality of RNA used for cDNA library preparation was verified using the RNA nano 6000 analysis chip on a BioAnalyzer 2000 series instrument ( Agilent Technologies ) to ensure an RNA integrity value greater than or equal to 9 . The cDNA libraries for sequencing were prepared from poly ( A ) +-selected mRNA , according to Illumina’s TruSeq Stranded mRNA sequencing preparation kit . Briefly , 1 μg RNA was purified for mRNA . Then mRNA material was fragmented and denatured , in preparation for first- and second-strand cDNA synthesis steps . Finally , the 3′ ends were adenylated to ligate strand-specific adapter sequences to cDNA material and amplified using PCR . Purity and size of cDNA library products were confirmed using a BioAnalyzer instrument . Library concentrations were determined via RT-qPCR using the Library Quantification Kit ( KapaBiosystems ) . The samples were then prepared and sequenced on an Illumina HiSeq Series instrument , with 1 sample per sequencing lane to achieve > 2 x 108 reads per sample . The sequencing data were aligned using a RefSeq hg19 reference with STAR software ( version 2 . 3 . 0 . 1 ) , resulting in an average of 1 . 7 x 108 uniquely aligned reads per sample . The Cufflinks software suite ( version 2 . 1 . 1 ) was used for differential expression analysis , with standard parameters and RefSeq hg19 reference annotations . Gene ontology analyses were carried out using DAVID software [31] . The mixture of isoforms software , MISO , version 0 . 5 . 1 ( February 23 , 2014 release ) was used for the exoncentric isoform quantification analysis . Each of the 4 hypoxia and normoxia replicates were merged into 1 file for each treatment for MISO analysis . For the exoncentric analysis , the hg19 GFF3 annotation files for each of the splicing event categories ( A3SS , A5SS , AFE , ALE , MXE , RI , SE and Tandem UTR ) were downloaded from http://genes . mit . edu/burgelab/miso/ as human genome ( hg19 ) alternative events v1 . 0 . Standard analysis parameters were used , with a filter option applied to require a minimum of 20 reads to support an event identification . To identify regulatory elements that may affect retention of EIF2B5 intron 12 , we used AVISPA [50] . This method , based on computationally derived splicing codes , has been used previously to detect and experimentally verify novel regulators of exon splicing in a variety of experimental conditions [71–73] . Since AVISPA was built for analyzing differential splicing determinants around cassette exons , we extracted hg19 coordinates for Ensembl-defined exons 11–13 and 12–14 , then analyzed the genomic regions of these 2 triplet exons using AVISPA . The loci containing triplets of exons 6–8 , 7–9 , 8–10 , and 9–11 were also analyzed to serve as negative controls , as these exons are known to be constitutive and not exhibit intron retention . The splicing-related top motifs and regulatory features were defined by their normalized feature effect ( NFE ) and their relative enrichment compared to alternative and constitutive exons . Briefly , the NFE value represents the effect on splicing prediction outcome if a motif is removed in silico , normalized by the total effects observed from removing each of the top features in this way . PCR reactions were carried out with cDNA prepared from RNA treated with DNaseI ( Qiagen ) to minimize contamination of DNA and in a reverse transcription reaction using oligo-dT primers to enrich for mature mRNA . Reverse transcription was carried out according to manufacturer’s protocol ( Taqman RT reagents ) . PCR reactions were carried out in a PTC-100 Thermocycler ( MJ Research , Inc . ) for 40 cycles , using annealing temperatures optimal for each primer set . ( Primer sequences available in Supporting Material ) . qPCR reactions were prepared using Power SYBR Green PCR Master Mix ( Applied Biosystems ) and carried out on a QuantStudio 6 Flex Real-Time PCR Instrument ( Thermo Fisher Scientific ) . Primer sequences made available in S1 Table . SQ20B cells were plated 24–48 h before transfection and grown to approximately 60%–70% confluency . RNAi transfections were carried out using a mixture of lipofectamine RNAi Max ( ThermoFisher ) diluted in OptiMEM media with siRNA to 10–50 nM , which was added to cell culture plates in complete DMEM . Cells were placed in incubator for 24 h , at which point the media was replaced . After an additional 24 h , cells were either harvested or used for subsequent experiments . For expression experiments , plasmids were purchased from Origene . Site-directed mutagenesis was used to alter the original plasmid and introduce a TAG stop codon , and confirmed by Sanger sequencing ( see Fig 7A ) . Expression plasmids were transfected in cells plated to the same confluency as described above using a mixture of lipofectamine 2000 reagent ( ThermoFisher ) diluted in OptiMEM media with varying concentrations of pCMV expression plasmid added to cells in complete DMEM . The plates of cells were incubated for 4–12 h , at which point the media was washed off and replaced . Cells were harvested or used for downstream experiments 24–48 h post-transfection . Whole cell lysates were collected using a lysis buffer of 2% Triton-X , 1X Complete Mini protease inhibitor cocktail ( Roche ) , and 1X phosphatase inhibitor cocktail 2 ( Sigma ) in PBS . The nuclear/cytosol fractionation reagents ( BioVision ) supplemented with 1X phosphatase inhibitor cocktail 2 ( Sigma ) were used to extract cytoplasmic and nuclear extracts from the same sample . Lysates isolated from frozen tumor and normal mouse tissue were lysed in a buffer containing 1% Triton X-100 , 50 mM HEPES , ph 7 . 4 , 150 mM NaCl , 1 . 5 mM MgCl2 , 1 mM EGTA , 100 mM NaF , 10 mM Na pyrophosphate , 1 mM Na3VO4 , 10% glycerol , protease inhibitors ( Roche #04693116001 ) , and phosphatase inhibitors ( Roche #04906845001 ) . All protein concentrations were determined using DC protein assay ( BioRad ) . Equal amounts of protein were resolved on 10% or 12% sodium dodecyl sulfate polyacrylamide gels and transferred to polyvinylidene fluoride membranes . Membranes were blocked with 5% nonfat dried milk in TBS-T ( 20 mM Tris , 137 mM NaCl , 0 . 1% Tween-20; pH 7 . 6 ) and then incubated in a 1:1 , 000 dilution of primary antibody in 5% milk/TBS-T followed by 1:5 , 000 dilution of secondary antibody in 5% milk/TBS-T . After washing , membranes were treated with ECL chemicals and exposed to autoradiograph film . The LC-MS/MS was carried out at the Wistar core facility . Complete protocol available online: https://www . wistar . org/our-science/shared-facilities/proteomics-facility/helpful-information . Following transfection experiment , SQ20B cells were incubated with methionine-/cysteine-free media for 30 m . For labeling , 1 set of experimental cell plates were incubated for 30 m with media supplemented with 0 . 075 mCi/ml [35S]-methionine/cysteine , while a second set of plates were incubated with “cold” media supplemented with nonradioactive 1X methionine/cysteine . Protein was harvested from the “cold” samples , and a protein assay was performed to obtain concentrations . These proteins were analyzed using standard immunoblot assay conditions . Proteins collected from the [35S]-methionine/cysteine labeled cells were resolved on SDS-PAGE . The resulting gel was then fixed using a solution of 20% methanol and 10% acetic acid for 30 m , washed with deionized water , and then incubated on a rocker at room temperature with enlightening solution ( PerkinElmer ) for 15–30 m . The gel was then dried for 16 h using a Bio-Rad gel-drying apparatus and then exposed to autoradiography film at -80°C for 2 h before processing . Polysome profiling was carried out per previously described conditions [74] . The HPLC ATP:AMP protocol was performed using previously established conditions determined by our group [75] . For ChIP experiments , SQ20B cells were plated 24 h before placing into hypoxia chamber or standard incubator . Six 10-cm plates at 70% confluency were grown in normoxic or hypoxic ( 0 . 5% O2 ) conditions for 16 h , at which time cells were crosslinked for 10 m at room temperature using 1% formaldehyde in minimum essential medium . Crosslinking was stopped and cells were washed and lysed according to manufacturer’s protocol ( Active Motif ChIP kit #53035 ) . Shearing conditions were carried out for six 20-s pulses at 25% power , with 30 s rest in between pulses on ice . Immunoprecipitations were carried out according to protocol , with 40 ug chromatin and 3 ug antibody for RNAPII ( Active Motif #39097 , mouse IgG #53010 ) and 60 ug chromatin with 3 ug ( rabbit IgG Santa Cruz #sc-2027X ) or 30 ug P-Ser2 RNAPII ( Abcam #5095 ) . For the resulting qPCR reactions , primers specific to EIF2B5 intron 10 and intron 12 were used ( S1 Table ) , and control primers were purchased from Active Motif ( GAPDH-2 for RNAPII control #71006 , GAPDH-1 for P-Ser2 RNAPII control #71004 , and Negative-1 for a 78-bp intergenic region of chromosome 12 as a negative control #71001 ) . SQ20B cells were grown in 2 x150-cm tissue culture plates for each condition ( normoxia and a 0 . 5% hypoxia time point ) . Antibody against SRSF3 ( SRp20 ) was purchased from MBL , Inc . ( #RN080PW ) . Rabbit IgG was provided by the RIP kit for use as a control ( RIP Assay kit , MBL , Inc . # RN1001 ) . Protein A Sepharose CL-4B ( GE Healthcare ) were prepared fresh for the beginning of the experiment in a slurry of 75% beads in 25% 50 mM Tris pH 7 . 5 . Beads were prewashed in PBS before adding 15 μg of antibody and then incubated at 4 degrees Celsius with rotation for 4 h . At this time , beads were prewashed before lysing cells and adding the lysates to the Protein A beads to preclear for 1 h at 4 degrees with rotation . Input samples were collected at this time before adding the precleared lysate to the antibody-bound beads . Samples were incubated for 3 h at 4 degrees with rotation . After wash steps , samples were aliquoted for quality control analysis of the protein . RNA was isolated according to the manufacturer’s protocol and analyzed using the Nanodrop1000 UV spectrometer . Equal amounts of RNA were then processed into cDNA and used for subsequent qPCR analysis . Input RNA was used as a standard to calculate the quantity for each primer . Expression data are reported as the relative enrichment of SRSF3 immunoprecipitation over the control Rabbit IgG immunoprecipitation . All statistical analyses and drawings were done in R ( version 3 . 2 . 5 ) ( http://www . r-project . org/ ) , and the statistical significance was defined as a P value <0 . 05 . For gene expression levels of the regions of interest , we downloaded RPKM data from TANRIC ( http://ibl . mdanderson . org/tanric/_design/basic/index . html ) [76] . The expression data is log2 transformed . We restricted ourselves to the 8 types of cancers with available data for at least 30 normal samples . These are breast invasive carcinoma ( BRCA ) , HNSC , KIRC , kidney renal papillary cell carcinoma ( KIRP ) , liver hepatocellular carcinoma ( LIHC ) , lung adenocarcinoma ( LUAD ) , prostate adenocarcinoma ( PRAD ) , and thyroid carcinoma ( THCA ) . We downloaded patient clinical information for the patients of these cohorts from cbioPortal ( http://www . cbioportal . org/ ) . To be able to determine the expression difference for the regions of interest between normal and tumor tissue , respectively , among normal and tumor tissue separated according to the cancer stage , we first employed a Shapiro-Wilk test to verify if the data follows a normal distribution . Accordingly , t test , respectively ANOVA test ( depending on the number of groups considered ) , or the nonparametric test Mann-Whitney-Wilcoxon , respectively Kruskal-Wallis test , was applied to assess the relationship between mRNA expression and tissue type . A box-and-whisker plot ( Box plot represents first [lower bound] and third [upper bound] quartiles , whiskers represent 1 . 5 times the interquartile range ) was used to visualize the data . | Tumor hypoxia is a negative prognostic factor for many solid cancers . Cellular adaptation to hypoxia is largely mediated by widespread changes in gene expression and enables cancer cells to adjust and survive . Recently , alternative splicing has been implicated in this process . To identify biologically impactful hypoxia-responsive isoforms , we took an unbiased approach to deeply sequence RNA of normoxic and hypoxic head and neck cancer cells . This analysis identified >1 , 000 mRNAs as alternatively spliced under hypoxia , including a significant enrichment of alternatively spliced genes involved in adaptation to hypoxia . Most notably , we discovered a unique retained intron in the translation initiation factor EIF2B5 that creates a premature termination codon . We show that this retained intron leads to a 65kDa truncated isoform that opposes full-length eIF2Bε to inhibit global translation and enhances survival of head and neck cancer cells under hypoxia . Strikingly , this intron and several additional hypoxia-induced retained introns were overexpressed in solid tumors relative to normal tissues . Mechanistically , we propose that intron retention under hypoxia is influenced by changes to RNA polymerase II ( RNAPII ) activity at weak 3′ splice sites and carry out experimental validation for the retained intron in EIF2B5 to investigate intron retention under hypoxia . | [
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| 2017 | Transcriptome analysis of hypoxic cancer cells uncovers intron retention in EIF2B5 as a mechanism to inhibit translation |
As some of the most widely utilised intercellular signalling molecules , transforming growth factor β ( TGFβ ) superfamily members play critical roles in normal development and become disrupted in human disease . Establishing appropriate levels of TGFβ signalling involves positive and negative feedback , which are coupled and driven by the same signal transduction components ( R-Smad transcription factor complexes ) , but whether and how the regulation of the two can be distinguished are unknown . Genome-wide comparison of published ChIP-seq datasets suggests that LIM domain binding proteins ( Ldbs ) co-localise with R-Smads at a substantial subset of R-Smad target genes including the locus of inhibitory Smad7 ( I-Smad7 ) , which mediates negative feedback for TGFβ signalling . We present evidence suggesting that zebrafish Ldb2a binds and directly activates the I-Smad7 gene , whereas it binds and represses the ligand gene , Squint ( Sqt ) , which drives positive feedback . Thus , the fine tuning of TGFβ signalling derives from positive and negative control by Ldb2a . Expression of ldb2a is itself activated by TGFβ signals , suggesting potential feed-forward loops that might delay the negative input of Ldb2a to the positive feedback , as well as the positive input of Ldb2a to the negative feedback . In this way , precise gene expression control by Ldb2a enables an initial build-up of signalling via a fully active positive feedback in the absence of buffering by the negative feedback . In Ldb2a-deficient zebrafish embryos , homeostasis of TGFβ signalling is perturbed and signalling is stably enhanced , giving rise to excess mesoderm and endoderm , an effect that can be rescued by reducing signalling by the TGFβ family members , Nodal and BMP . Thus , Ldb2a is critical to the homeostatic control of TGFβ signalling and thereby embryonic patterning .
In vertebrates , the transforming growth factor β ( TGFβ ) superfamily comprises a large number of ligands , including TGFβs , Nodal , Activin , and bone morphogenetic proteins ( BMPs ) , each of which can direct lineage-specific transcriptional responses that regulate biological processes as diverse as cell proliferation , differentiation , apoptosis , and severe diseases caused by their mis-regulation [1] . In response to extracellular ligand binding , trans-membrane receptors phosphorylate receptor-activated Smads ( R-Smads ) in the cytoplasm . Different ligand-stimulated pathways converge and signal through two main R-Smad pathways , with Nodal/TGFβ/Activin mediated by R-Smad2/3 and BMP by R-Smad1/5/8 [2] . Activated R-Smads interact with the common partner Smad4 ( Co-Smad4 ) to carry the signals into the nucleus , where the Smad complexes associate with additional transcription factors ( TFs ) and co-factors , as well as co-activators or co-repressors , to regulate downstream target genes [3] . The level of TGFβ signalling is established by homeostatic regulation , which dynamically adds or removes signalling components to maintain a sufficient and constant level of activity . For example , TGFβ signals activate expression of their own ligands [4–9] . After secretion from the cell , these ligands bind transmembrane TGFβ receptors , implementing positive feedback to self-amplify and sustain signals at a sufficient level and to propagate the signals into neighbouring cells . The inhibitors of TGFβ signalling , such as Leftys and inhibitory Smad6 and Smad7 ( I-Smad6/7 ) , can also be induced by TGFβ family signals , thereby generating negative feedback to dampen excess signalling [8–12] . These positive and negative feedbacks are coupled , as the TGFβ-responsive induction of both is by direct binding of R-Smads and Co-Smad4 to ligand or inhibitor genes [2 , 6 , 8 , 9 , 13–17] . Activation of TGFβ family signalling pathways results in rapid recruitment of transcriptional co-activators to ligand and I-Smad genes , leading to their up-regulation in vivo [8 , 9] . In zebrafish , the expression of Nodal ligand genes and Smad7 can be induced by R-Smad3 expression [12] . It has been demonstrated that coupled positive and negative feedback confers flexibility on signal switches and enables precise modulation of signal responses [18–20] . However , whether and how the activation of negative and positive feedbacks can be uncoupled is not known . LIM domain binding proteins ( Ldbs ) are multi-functional non-DNA binding adaptor proteins that assemble TF complexes on target genes [21–25] . Components of such Ldb complexes , Lmo4 and Gata1/2 for example , have been shown to recruit R-Smad complexes onto TGFβ target genes [9 , 26 , 27] . By comparing published chromatin immunoprecipitation ( ChIP ) -seq datasets of genome-wide protein-DNA binding profiles for R-Smad1/3 and Ldb1 [8 , 9 , 21] , we have obtained evidence that Ldb1 co-localises with R-Smad1/3 at a substantial subset of R-Smad target sites across the genome , suggesting that Ldb1 might function together with R-Smads to implement transcriptional responses to TGFβ family signalling . In vertebrates , a paralogue , Ldb2 , shares a high percentage of amino acid sequence identity and structural similarity with Ldb1 [28] , but its functions are largely unknown . In this study , we present in vivo functional and phenotypic data showing that Ldb2 regulates Nodal/BMP signalling and is required for early embryogenesis . Furthermore , we identify I-Smad7 and a Nodal ligand , Sqt , as direct target genes activated or suppressed respectively by Ldb2a , and show that the fine tuning of TGFβ family signalling requires both positive and negative control by Ldb2a complexes .
We compared published ChIP-seq datasets of Ldb1 , the BMP effector , R-Smad1 , and the Nodal/Activin/TGFβ effector , R-Smad3 [8 , 9 , 21 , 29] . We found that the binding of Ldb1 overlaps R-Smad1 or R-Smad3 binding at a substantial subset of R-Smad targets across the genome ( Fig . 1A and 1B ) , including at the known TGFβ target genes , I-Smad6 and I-Smad7 ( Fig . 1C and 1D ) . Ldb1 binding at these loci was validated in murine cells by ChIP-quantitative PCR ( qPCR ) ( Fig . 1E ) . The ChIP-seq of Ldb1 had been performed in murine bone marrow cells or day 4 embryoid body ( EB ) -derived Flk1+ haemato-endothelial precursor cells [21 , 29] , whereas the ChIP-seq of R-Smad1 and R-Smad3 had been carried out in murine G1ER erythroid progenitor cells and murine pro-B cells , respectively [8 , 9] . Nevertheless , the widespread co-localisation of Ldb1 and R-Smads , albeit in different cell types , suggests the potential for functional cooperation between these factors . Ldb1 does not bind DNA directly but has been shown to assemble complexes containing Scl ( Tal1 ) and Gata1/2 on DNA via motifs including Ebox , GATA , and Ets [23 , 24 , 29] . Genome-wide comparison of ChIP-seq datasets suggests that Scl and Gata1/2 co-occupy a substantial subset of Ldb1-binding sites with R-Smad1 or R-Smad3 ( S1 Fig . ) . Indeed , the most enriched motifs identified in genomic sequences bound by R-Smads also include GATA , Ebox and Ets [8 , 9] . Taken together , these observations identify Ldb proteins as potential modulators of TGFβ superfamily signalling , possibly by associating with R-Smads to regulate TGFβ targets . To analyse the role of Ldbs in TGFβ signalling in vivo , we first monitored their expression during early embryonic development when TGFβ family members are known to be critical . Throughout early zebrafish development , ldb2a shows greater specificity than the ubiquitous ldb1a , ldb1b , or ldb2b ( S2 Fig . and data retrieved from the Zebrafish Information Network ( ZFIN ) [30] ) . At 15 hours post fertilisation ( hpf ) , ldb2a is present in the notochord and the lateral mesoderm , which gives rise to haematopoietic , endothelial , and pronephric derivatives ( S2A Fig . ) . At 26 hpf , ldb2a expression continues in and around the blood vessels ( S2B Fig . ) . Maternal/zygotic ldb2a is expressed ubiquitously throughout cleavage and blastula stage ( 0–4 . 7 hpf ) embryos ( S2C–S2F Fig . ) , but immediately before and during gastrulation ( 4 . 7–10 hpf ) , ldb2a becomes more specific in the yolk syncytial layer ( YSL ) ( Figs . 2A and S2F , white arrowheads ) , an important source of Nodal signalling crucial for the specification of gastrula germ layers . This suggests a possible role for Ldb2a in signalling by this TGFβ superfamily member , we therefore initially focussed our studies on the function of Ldb2a in Nodal signalling during gastrula embryonic development . To determine if Ldb2a functions in Nodal signal transduction ( illustrated in Fig . 2B–2E ) , we knocked it down using two antisense morpholinos ( MOs ) , a splice MO targeting the boundary of intron3 and exon4 , and a MO targeting the ATG site ( S3A–S3E Fig . ) . Both MOs cause similar defects ( S3F–S3K Fig . ) , and co-injection of ldb2a mRNA with the splice MO was able to rescue ldb2a morphant phenotypes ( S3L–S3T Fig . ) . Moreover , we injected NLS-Cas9 protein together with a small guide RNA targeting the ATG of ldb2a , and observed that a significant proportion of resultant mosaic F0 mutants phenocopy the morphants ( S3U–S3W Fig . ) . Altogether , these data confirm the specificity of the ldb2a MOs . Upon ldb2a knockdown , we saw an increase in the level of the phosphorylated Nodal effector , p-Smad2 , by the shield stage ( 6 hpf ) , while the level of total Smad2/3 was comparable to the wild-type control ( Fig . 2B ) . We also observed up-regulated activity of a TGFβ reporter ( SBE-luciferase [31] ) ( Fig . 2C ) . Thus , ldb2a knockdown up-regulates Nodal signalling , suggesting that Ldb2a normally acts to suppress Nodal signalling . Another TGFβ superfamily member , BMP , plays critical roles during gastrulation and signals through R-Smad1 , which also co-occupies the genome with Ldb1 ( Figs . 1A and S1A ) . We therefore examined the BMP signal transduction pathway in ldb2a morphants . The activity was unaffected at the shield stage ( S4A and S4B Fig . ) but significantly increased by the end of gastrulation ( the tailbud stage , 10 hpf ) , as shown by the level of p-Smad1/5/8 and the activity of a BMP-specific reporter ( Id1-BRE2-luciferase [32] ) ( Fig . 2D and 2E ) . Thus , ldb2a loss-of-function promotes BMP signal transduction , suggesting that Ldb2a normally acts to suppress BMP signalling . The consequences of the excessive Nodal signalling in ldb2a morphants included up-regulation of the Nodal-induced genes , cyclops ( cyc ) and squint ( sqt ) ( Fig . 2F–2I’ ) . Expression of bmp4 was also increased by the tailbud stage ( Fig . 2J–2K’ ) and remained up-regulated during somitogenesis ( S4C and S4D Fig . ) . These genes code for ligands that implement positive feedback to sustain and propagate signalling . Taken together , ldb2a knockdown enhances expression of Nodal and BMP ligands , suggesting a negative effect of Ldb2a on positive feedback for Nodal and BMP signalling . In addition to the expression of ligands , readout of Nodal signalling also includes expression of various germ layer genes , as Nodal induces the mesendoderm while restricting the ectoderm [33–35] . Consistent with the excessive Nodal signalling observed in ldb2a morphants , expression of ntl , a mesendoderm marker , was expanded towards the presumptive ectoderm ( Fig . 3A and 3B ) , while expression of gata2 , a non-neural ectoderm marker , and otx2 , a neural ectoderm marker , was reduced ( Figs . 3C , 3D , S5A–S5B’ ) . In addition , another Nodal target , mixer/bon , expressed in the mesendoderm at the onset of gastrulation and becoming restricted to the endoderm during late gastrulation [36 , 37] , and critical for proper endoderm specification in a Nodal-dependent manner [38] , displayed increased expression in ldb2a morphants at the shield and 80% epiboly stages , suggesting a critical role for Ldb2a in the specification of endoderm ( S5C–S5F’ Fig . ) . Taken together , these data suggest that some of the ectoderm is converted to mesoderm and endoderm in ldb2a morphants , consistent with the excessive Nodal signalling observed in these embryos . To monitor the stability of the patterning effects of Ldb2a via Nodal , we examined genes expressed in mesendoderm-derived tissues of ldb2a morphants at later stages . At the 13-somite stage ( ∼15 hpf ) , markers of the mesendoderm-derived lateral mesoderm , such as a lateral mesodermal gene , hand2 , a pronephric duct gene , pax2 . 1 , and a haemangioblast gene , scl , displayed up-regulated expression in ldb2a morphants ( Fig . 3F and 3G ) . We also observed up-regulation of other lateral mesodermal genes , including the haemangioblast genes lmo2 , gata2 , and fli1 , erythroid genes gata1 and draculin , a myeloid gene pu . 1 , and the pronephric duct genes pax8 and lim1 ( Figs . 3H , 3I , and S6A–S6N ) . To quantify expression of genes in the lateral mesoderm , we performed quantitative real-time PCR ( qPCR ) analyses and observed an increased level of fli1 RNA in ldb2a morphants at the 12-somite stage ( S6O Fig . ) . In addition , Tg ( gata1a:GFP ) la781 zebrafish embryos injected with the ldb2a MO showed a clear up-regulation of GFP expression ( S6P and S6Q Fig . ) , indicating an increase in the protein level of Gata1 , but also in the number of Gata1 positive cells . Consistent with the unchanged BMP activity at the beginning of gastrulation , dorsoventral patterning of ldb2a morphants remained balanced , shown by increased expression of both a ventral mesendoderm marker , eve1 , and a dorsal mesendoderm marker , gsc ( S5G–S5J’ Fig . ) . However , the activity of BMP signalling and expression of bmp4 became up-regulated in ldb2a morphants during late gastrulation ( Fig . 2D , 2E , 2J , and 2K’ ) , when high level BMP continues to specify ventral and posterior mesodermal tissues . After gastrulation , we indeed observed increased expression of genes marking the lateral mesoderm , derived from the ventro-posterior mesoderm ( Figs . 3F–3I and S6 ) . To further investigate the effects of Ldb2a activity via a combination of Nodal and BMP after gastrulation , we examined expression of paraxial and dorsal mesodermal genes in ldb2a morphants . They were indeed up-regulated ( by excessive Nodal ) but less severely compared to the ventrally expressed genes ( influenced by both Nodal and BMP ) , as shown by increased expression of shh ( notochord ) and myoD ( somite ) in the 10%–30% most affected ldb2a morphants ( Fig . 3J–3M ) . Furthermore , the effect of ldb2a knockdown in the ventro-lateral mesendoderm-derived tissues remained evident until 24 hpf , when we observed up-regulated expression of flk1 , tie1 , dll4 , and deltaC in endothelial cells of ldb2a morphants ( Figs . 3N , 3O , and S7A–S7F ) . Taken together , our findings indicate that ldb2a loss-of-function induces mesodermal and endodermal while restricting ectodermal fates , especially in the ventro-lateral regions , and that this fate change is stable ( Fig . 3P ) . To confirm that the ectopic mesendoderm formation in ldb2a morphants is due to the up-regulation of Nodal and BMP signalling , we tried to reverse the effects by reducing these signals . When treated with an Alk4/5/7 ( Nodal/Activin/TGFβ receptors ) inhibitor , SB431542 , ldb2a morphants were rescued with respect to ectopic expression of cyc ( Fig . 4A–4C ) and of scl and pax2 . 1 ( Fig . 4D–4F ) . Moreover , bmp4 knockdown by MO injection also rescued the increased expression of scl and pax2 . 1 in ldb2a morphants ( Fig . 4G–4I ) . These observations suggest that Ldb2a functions through Nodal signalling to restrict the specification of mesendoderm and through BMP signalling to restrict the specification of ventro-lateral mesendoderm . Under normal circumstances , once Nodal signalling is up-regulated , negative feedback dampens excess signalling . However , the fact that a stable Nodal-dependent effect of ldb2a knockdown was seen suggests that negative feedback might not be fully active . Such feedbacks for both Nodal and BMP can be mediated by their common inhibitor , I-Smad7 [10–12 , 39] . Smad7 antagonises Nodal and BMP signal transduction via multiple mechanisms , dampening the phosphorylation of R-Smads , the formation of R-Smad/Co-Smad4 complexes , or the binding of R-Smad/Co-Smad4 to DNA [40–45] . By causing disruption of these mechanisms , altered Smad7 levels can eventually lead to changes in expression of Nodal targets , including ligand and mesendodermal genes . We first confirmed the role of Smad7 as a Nodal inhibitor in zebrafish embryos , showing that cyc expression was increased by smad7 MO knockdown ( S8 Fig . ) [12] , but decreased by smad7 overexpression ( Fig . 5A–5C ) . Loss-of-smad7 also increased expression of the Nodal target , mixer , in the mesendoderm ( Fig . 5D and 5E ) . We then showed that indeed Smad7-mediated negative feedback is defective in ldb2a morphants , as shown by decreased levels of Smad7 mRNA and protein ( Fig . 5F and 5G ) . Importantly , the increased cyc expression in ldb2a morphants was further up-regulated by co-injection of a level of smad7 MO that did not give a phenotype on its own . This synergistic effect between ldb2a and smad7 MOs implies that they function in the same pathway . Leftys also mediate auto-regulatory negative feedback for Nodal signalling [4] . However , as a direct target induced by Nodal , expression of lefty1 was increased , as opposed to decreased like smad7 , in ldb2a morphants ( Fig . 5L and 5M ) , consistent with the excessive Nodal signalling in these embryos . Moreover , Ldb2a and Smad7 are synergistic on lefty1 expression ( Fig . 5N and 5O ) , as seen for mixer . Therefore , Ldb2a is required for the negative feedback driven by Smad7 but not by Lefty1 . Upon ldb2a knockdown , expression of Nodal ligands and I-smad7 was affected immediately after the mid-blastula transition ( MBT ) ( Fig . 5F , 5I , and 5M ) , suggesting that the regulation of these genes by Ldb2a may be direct . Indeed , ChIP of zebrafish shield-stage embryos followed by qPCR analysis showed an enrichment of Ldb2a at the promoter of smad7 and upstream of the Sqt ATG site ( Fig . 6A , with primers shown in 6B ) . For ChIP-qPCR analysis in zebrafish , we adapted the in vivo biotinylation method described by de Boer and colleagues [46] for the zebrafish system . We injected low-level Avi ( biotin acceptor peptide ) -tagged ldb2a mRNA that does not cause any defect on its own ( S9 Fig . ) , together with NLS-BirA ( bacterial biotin ligase ) , in order to biotinylate Ldb2a in vivo; we then precipitated Biotin-Ldb2a-chromatin using streptavidin beads for subsequent analyses . We previously showed that the loss of Ldb2a exerted opposite effects on expression of different sets of genes induced by the same R-Smad pathways ( i . e . , down-regulation of I-Smad7 and up-regulation of Nodal/BMP ligands ) . Altogether these data suggest that Ldb2a directly activates expression of Smad7 but suppresses that of TGFβ family ligand genes , uncoupling the negative and positive feedbacks that are otherwise induced by the same R-Smad signalling . To further explore how Ldb2a regulates expression of these genes , we mined published protein partner and DNA binding site datasets for Ldbs . Most of our current knowledge of the Ldb family is from studies of Ldb1 . Since Ldb1 and Ldb2 share highly conserved protein sequence and structure , they likely function through similar mechanisms . In haematopoietic lineages , Ldb1 functions as a bridging molecule , with Lmo2/4 , to assemble TF complexes that bind DNA through SBE , E-box , GATA , and Ets motifs [21 , 23 , 24] . LMO4 interacts with R-SMADs to mediate the TGFβ inputs in human epithelial cells [27] . Other components of Ldb1 complexes , such as Gata1/2 , have also been shown to modulate TGFβ family signalling by assembling and recruiting Smad complexes onto TGFβ target genes [9 , 26] . The Smad7 and Sqt genes contain conserved SBE , E-box , and GATA motifs ( Fig . 6B ) [47] , which are known to be enriched at Smad and/or Ldb binding sites [8 , 9 , 21] . As ChIP-seq data comparison suggests that Ldb1 co-localises with R-Smad3 at the I-Smad7 gene ( Fig . 1D ) , Ldb1/2 might assemble TF complexes to recruit R-Smads to the Smad7 locus . As previously shown , direct binding of Ldb1 at I-Smad7 was confirmed by ChIP-qPCR in either murine EBs or Flk1+ cells ( Fig . 1E ) , supporting our observations . Taken together , we provide evidence that Ldb2a acts together with R-Smads to bind Smad7 at the SBE/E-box and directly activates TGFβ-induced expression of Smad7 . On the other hand , Ldb2a suppresses Sqt expression , possibly via forming a repressor complex binding to the Sqt locus . Thus the homeostatic mechanism regulating Nodal/BMP levels of signalling requires both positive and negative control by Ldb2a complexes . Deficiency of ldb2a caused dysregulation of I-smad7 expression , which subsequently disrupted the negative auto-regulating circuit , contributing to excessive activation of Nodal/BMP signalling via unrestricted positive feedback . We conclude that Ldb2a plays critical roles in controlling both negative and positive feedback on TGFβ signalling in vivo , discriminating the responses of the I-Smad7-mediated negative feedback from the ligand-driven positive feedback . Disruption of this apparatus makes a substantial impact on embryonic development . To gain further insights into the role of Ldb2a in TGFβ signalling , we studied the regulation of ldb2a expression by Nodal signalling . ChIP-seq datasets show an enrichment of R-Smad3 at the Ldb2 locus in various cell types and this enrichment can be stimulated by Activin/Nodal signalling ( Fig . 6C ) [8 , 9 , 48] . To study whether Ldb2a is regulated by TGFβ signals , we treated zebrafish embryos with the Nodal inhibitor SB431542 , from the MBT stage . We examined expression of ldb2a and other Nodal targets at 0 . 5 and 3 hours after treatment , and showed that their expression was decreased by the blockade of Nodal signalling ( Fig . 6D ) . Thus , an Ldb2a-mediated coherent feed-forward loop delays the activation of Smad7 expression and the suppression of ligand gene expression . As a consequence , Ldb2a discriminates the response speed of the positive and negative feedback circuits during signal propagation , allowing the accumulation of signalling through unrestricted positive feedback before negative feedback becomes fully established . Altogether our data suggest the following model: during the initiation and propagation of TGFβ signalling , expression of ligands is immediately up-regulated , whereas I-Smad7 transcription is delayed by its requirement for Ldb2a , which gradually accumulates in response to the same signal ( Fig . 6E ) . This mechanism allows signalling to self-amplify until adequate levels of Ldb2a enable the fully active Smad7-driven negative feedback , together with the direct restriction of positive feedback , to dampen excess signalling . Thus , the coherent feed-forward loop involving Ldb2a serves to delay the activation of negative feedback and the suppression of positive feedback . Despite the maternal expression of Ldb2a , this mechanism is likely to be specifically active during zygotic transcription , as phenotypes shown here were mainly caused by a splice MO that only knocks down zygotic Ldb2a . In agreement with this hypothesis , the level of maternal ldb2a RNA drops around the MBT stage , just before its zygotic expression increases ( S2C Fig . ) . We conclude that Ldb2a plays critical roles in stabilising TF complexes that control both negative and positive feedback on TGFβ signalling in vivo . It utilises a feed-forward circuit that discriminates the responses of the Smad7-mediated negative feedback from ligand-driven positive feedback . Disruption of this apparatus makes a substantial impact on embryonic development .
We have compared published ChIP-seq datasets of R-Smads and Ldb1 complex components , and shown that they co-occupy a significant proportion of the genome in different cell types , which suggests potential roles for Ldbs in TGFβ signalling . This was validated by in vivo studies showing that Ldb2a does indeed modulate R-Smad/TGFβ family signalling during zebrafish development . Ldbs are non-DNA binding adaptor proteins , mediating the formation of TF complexes containing partners that are also crucial for TGFβ pathways . For example , LMO4 , another non-DNA binding protein in Ldb complexes , interacts with R-SMAD1 , 2 , 5 , 8 , and Co-Smad4 , in response to TGFβ signalling in human epithelial cells [27] . GATA1 , a TF in Ldb complexes , has been shown to assemble with SMAD1 on BMP response elements ( BREs ) in human HepG2 ( liver hepatocellular ) cells and is required for strong activation of a BRE in the first intron of Smad7 [26] . In addition , another TF in Ldb1 complexes , Gata2 [21] , also co-occupies genomic sites with Smad1 in murine erythroid progenitors [9] . Gata1 was also shown to direct Smad1 binding to erythroid-specific genes during erythroid differentiation . Altogether these observations suggest that Ldbs may nucleate R-Smad complexes to modulate TGFβ family signalling . Known DNA binding motifs for the Smad and Ldb complexes were found in the Smad7 locus , including GATA , Ets , SBE , and Ebox , some of which having already been identified as active regulatory elements and required for TGFβ inducibility of I-Smads in human cells [14 , 26 , 49] . We have also shown that Ldb2a co-binds the conserved R-Smad binding site in the I-Smad7 promoter and directly activates I-Smad7 expression . On the other hand , Ldb2a also binds the Nodal ligand gene , Sqt , but represses its expression . This effect is also likely to be direct , because the expression of Nodal ligands increased immediately after the MBT when the ldb2a splice MO could only just have begun to have an effect . It has been shown that the first intron of Sqt , the promoter/proximal upstream region , and a distal upstream sequence together drive expression of the reporter gene in axial mesoderm , which does not reflect endogenous sqt expression [47] , suggesting the existence of an element responsible for repressing sqt expression beyond the genomic regions used . Our ChIP-qPCR analyses showed that Ldb2a binds the Sqt locus , and expression of Nodal ligands/targets in the axial mesoderm was indeed increased by ldb2a knockdown . Thus , our findings and evidence from the literature suggest that Ldb2a represses sqt expression by binding to an unknown regulatory element . The ChIP assay of Ldb2a in zebrafish has been a great challenge because Ldb proteins do not directly bind DNA . Moreover , few antibodies work for ChIP assays in zebrafish , including the zLdb2a antibody we generated . We therefore injected ldb2a mRNA tagged by HA or biotin at low enough doses to not cause any morphological or phenotypic disruption . The biotin-ChIP succeeded in detecting the direct binding of Ldb2a at I-Smad7 and Sqt . The ChIP assays were performed during early gastrulation when , like the injected RNA , Ldb2a is active in most cells of the embryo . Thus , our observations are likely to reflect physiological interactions . The loss of ldb2a in zebrafish embryos increased the phosphorylation of R-Smads and the activity of TGFβ-responsive cis-regulatory elements , as well as the expression of TGFβ target genes . These observations suggest that Ldb2a normally restricts Nodal/BMP signal transduction and our subsequent experiments show that both an increase in ligand expression and a loss of smad7 expression contribute to the signalling perturbation seen in ldb2a morphants . Knockdown of ldb2a led to excessive specification of mesendoderm and derivatives during development . Chemically restricting Nodal activity rescued the ectopic mesendoderm induction caused by ldb2a knockdown , while bmp4 loss-of-function rescued the extra increase in lateral mesoderm specification . Therefore , Ldb2a functions in embryonic patterning through Nodal and BMP signalling . Reflecting the elevation of both signalling pathways , the effect of ldb2a depletion on the ventro-lateral and posterior mesendoderm fates ( e . g . , blood , vasculature , pronephric , and tail mesodermal tissues ) was more significant than on other mesodermal lineages ( e . g . , trunk somites , notochord , heart , and head mesodermal tissues ) , as the ventro-lateral and posterior mesendoderm is formed by exposure to a combination of Nodal and BMP morphogens during gastrulation [33–35] . We have therefore shown that disruption of the Ldb2a-controlled responses to TGFβ signals makes a substantial impact on embryonic development . Insight into the biological significance of the discrimination among R-Smad targets by Ldb2a was provided by the discovery that the Ldb2a gene might itself be bound by R-Smads and transcribed in response to TGFβ family signalling . Thus , an Ldb2a-mediated coherent feed-forward loop slows down the transcriptional response of I-Smad7 . As a consequence , Ldb2a discriminates the response speeds of the positive and negative feedback circuits during signal propagation , allowing the accumulation of signalling through positive feedback before the negative feedback is fully established . Recent publications [20 , 50] have provided mathematical simulations and experimental investigations suggesting that coupled positive and negative feedback circuits enable cellular systems to produce optimised responses to stimuli with respect to signal duration and amplitude . Here for the first time , we have shown that the two feedback pathways can be uncoupled .
All animal experiments were performed under a Home Office Licence according to the Animals Scientific Procedures Act 1986 , UK , and approved by local ethics committees . The ChIP-seq datasets of each protein ( Smad1 , Smad3 , Ldb1 , Scl/Tal1 , Gata2 , and Gata1 ) were downloaded from the NCBI gene expression omnibus ( GEO , http://www . ncbi . nlm . nih . gov/geo ) . For Smad1 ( ChIP-seq in murine G1ER cells ) , Smad3 ( murine pro-B cells ) , and Ldb1 ( murine bone marrow cells ) , their mapped reads on the MM8 genome ( bed format ) were used for peak calling analysis using MACS ( version 1 . 4 . 2 ) , while IgG was used as the negative control . Genome-wide comparison of ChIP-seq datasets was performed as previously described [8] . Briefly , the location of Smad1/3 binding ( query datasets , shown in x-axes ) ( Fig . 1A and 1B ) in relation to Smad1/3- or Ldb1-enriched sites ( base datasets , y-axes ) was visualised by Java Treeview with the average reads density calculated in 100-bp bins ±2 . 5 kb around each Smad peak position suggested by MACS . These plots show the overlaps between Ldb1 binding regions and the enriched sites of Smad1/3 genome-wide . The location of Scl , Gata2 , Smad1 , Gata1 , and Smad3 binding in relation to Ldb1-enriched sites was also visualised ( S1 Fig . ) . These plots show the overlaps between Ldb1 binding sites and the enriched regions of the other five proteins genome-wide . Wild-type and Tg ( gata1a:GFP ) la781 [51] embryos and adult fish were bred and maintained as described [52] . MO oligonucleotides ( S2 Table , GeneTools ) were dissolved in Milli-Q water to 25 ng/μl and stored at room temperature . Micro-injections were performed with 1 nl of each MO injected into the yolk cell of 1–2-cell stage embryos , at concentrations shown in S2 Table . To generate GFP-tagged ldb2a mRNA for injection , the entire ldb2a reading frame was first cloned into the Gateway vector pDONR™221 . Full-length ldb2a PCR fragments were generated via superscript III one-step RT-PCR system ( Invitrogen ) using total RNA extracted from 24 hpf embryos , with gLdb2 FWD1 and gLdb2 REV1 primers ( S1 Table ) . Gateway cloning technology ( Invitrogen ) generated an ldb2a entry vector in pDONR221 back bone , which was sequenced and recombined with pCSGFP2 [53] to create a full length ldb2a-GFP plasmid , in which the ldb2a gene was placed immediately upstream of the GFP coding sequence . Untagged or HA-tagged ldb2a fragments were amplified from 24 hpf cDNA with Ldb2-F3/Ldb2-R4 , Ldb2-F2/Ldb2-R4 , or Ldb2-F2/Ldb2-R5 primer pairs ( S1 Table ) , cloned into pGEM-T easy vectors ( Promega ) and sequenced . To generate HA-tagged ldb2a mRNA for injection , ldb2a fragments were excised from ldb2a-pGEM-T entry vectors and cloned into the pCS2+ vector . To generate Avi-tagged ldb2a mRNA for injection , the Flag-ldb2a-2A fragment was amplified from the ldb2a-pGEM-T entry vector with 5′-Flag-ldb2a/3′-2A-ldb2a primers ( S1 Table ) by 2-step PCR using Phusion DNA Polymerase ( NEB ) . The Flag-ldb2a-2A fragment was then annealed with an Avi-Tev-Flag oligo ( S1 Table ) , followed by amplification of the Avi-Tev-Flag-ldb2a-2A fragment with 5′-Avi-fusionF/3′-2A-fusionR primers ( S1 Table ) and cloning into the pMTB2-eGFP vector using In-Fusion HD Cloning kit ( Clontech ) . Capped mRNA for micro-injection was in vitro transcribed from 1 μg linearised DNA template , using the Ambion mMESSAGE mMACHINE kits , and purified by QIAGEN RNeasy Micro kit , according to manufacturers’ instructions . Murine or zebrafish Smad7 mRNAs were synthesised from published Flag-pcDNA3-mSmad7 vectors [10] or a PCS2-zSmad7 construct [12] , respectively . Synthesised mRNA was aliquoted and stored at −80°C , and injected to 1-cell stage zebrafish embryos . Wild-type and ldb2a morphant embryos were treated with 25 μM or 100 μM SB431542 [54] from the 8-cell stage until collection at the sphere , shield , tailbud , or somitogenesis stages . Control embryos were treated with an equal volume of DMSO added to fish water . Whole mount in situ hybridisation on zebrafish embryos was carried out as described [55] . Digoxigenin ( DIG ) or fluorescein labelled antisense RNA probes were transcribed from linearised templates using T3 , T7 , or Sp6 RNA polymerases ( Roche ) . DIG and fluorescein antibodies were detected using BM-purple ( Roche ) or Fast Red [56] , respectively . Protein extracts were prepared according to Link and colleagues [57] . Primary antibodies were used at 1:500–1:2 , 000 dilutions . Antibodies used included: Phospho-Smad1/5 ( Ser463/465 ) ( 41D10 ) ( Cell Signaling number 9516 ) ; Phospho-Smad2 ( Ser465/467 ) ( Cell Signaling number 3101 ) ; Smad2/3 ( N-19 ) : sc-6032 ( Santa Cruz ) ; Smad6/7 ( N-19 ) : sc-7004 ( Santa Cruz ) . 50 pg SBE-luciferase [31] or Id1-BRE2-luciferase [32] constructs were co-injected with ldb2a MO into the streaming yolk or the yolk-free cell of 1-cell stage zebrafish embryos . 50 pg pCMV-LacZ plasmids were co-injected to normalize injection efficiency . Gastrula stage embryos were collected and washed with PBS . 20–50 embryos were homogenised in 200 μl lysis buffer ( provided in the Roche Luciferase Reporter Gene Assay kit ) by aspirating through 23G syringes and incubated on ice for 10 minutes , followed by a brief centrifugation . Supernatants were separated into duplicates for each assay . 50 μl and 25 μl of the supernatant were used to measure the activity of luciferase and β-galactosidase , respectively , as described [58] . Total RNA was isolated with the RNAeasy Microkit ( QIAGEN ) . Quantitative PCR was performed with SybrGreen ( Applied Biosystem ) . Data were collected with the ABI-PRISM 7000 or 7500 Sequence Detection system . β-actin1/2 , EF1α , and GAPDH were used as internal controls . The relative abundance for each sample was computed by the comparative method ( ∆∆Ct ) . Statistical analysis was by the two-sample equal variance t-test . Error bars indicate the standard deviation . Primers are listed in S1 Table . Previously published primers as described [59] . The sgRNA sequence targets the sense strand near the ATG of ldb2a . The template DNA of sgRNA was generated by PCR with Phusion polymerase ( NEB ) in HF buffer with a unique oligonucleotide encoding a T7 polymerase-binding site and the sgRNA target sequence ( zLdb2a-ATG sgRNA F ) and a reverse oligonucleotide encoding the remainder of the sgRNA sequence ( sgRNA-R ) . In vitro transcription was performed with 100 ng purified DNA template using the Megascript T7 kit ( Ambion ) , and sgRNA purified by phenol chloroform extraction and isopropanol precipitation . sgRNA was stored in aliquots at −80 °C . To generate ldb2a mutants , 1 ng NLS-Cas9 protein and 500 pg sgRNA were injected into the cell of 1-cell stage embryos . The control group was injected with 1 ng NLS-Cas9 alone . Genomic DNA was extracted by homogenizing single zebrafish embryos in 20 μl of 50 mM NaOH , followed by incubation at 95°C for 8 minutes ( gastrula embryos , older embryos require longer incubation ) , cooling to 4°C , and addition of 2 μl ( 10% ) of 1 mM Tris-HCl ( pH = 8 ) to neutralize the solution [60] . A 178-bp fragment spanning the sgRNA target site was amplified from control or mutant gDNA using the LC-Green Plus ( BioFire Inc ) , HotShot Diamond PCR Master mix ( Clent Lifescience ) , with ldb2a HRMA F1/ldb2a HRMA R1 primers . Details of the qPCR followed by HRMA were described previously [61] . PCR products from HRMA were cloned into pGEM-T vectors ( Promega ) and 16 colonies from each embryo were sequenced with T7 and SP6 primers . ChIP-seq and ChIP-qPCR of endogenous Ldb1 ( using anti-Ldb1 antibody N-18 , Santa Cruz ) on murine Flk1+ BL-CFCs isolated from day 4 EBs was performed as described [29] . 36-bp raw reads were mapped against NCBI build 37 . 1 of the mouse genome with ELAND ( Illumina ) . Uniquely mapped reads were extended to 200 bp and then transformed into the genome-wide reads density ( coverage ) with the ShortRead Bioconductor package [62] . The coverage from ChIP and IgG control was visualized on a mirror of the UCSC genome browser . ChIP-qPCR analyses of Ldb2a in zebrafish gastrula embryos were performed as described [63] , using two different methods for the IP: ( a ) inject low-level ( 50 pg ) HA-Ldb2a mRNA that does not cause any defects on its own , and then precipitate HA-Ldb2a using HA antibody-coupled dynabeads ( Anti-HA tag antibody: ChIP Grade , abcam ab9110; Dynabeads Protein A for Immunoprecipitation , Novex ) ; ( b ) inject 50 pg Avi-Ldb2a ( Avi: biotin acceptor peptide ) together with NLS-BirA ( bacterial biotin ligase ) , and then precipitate Biotin-Ldb2a using Streptavidin-coupled Dynabeads ( Dynabeads MyOne Streptavidin T1 , Invitrogen ) . For ( b ) , we adapted the in vivo biotinylation method described previously [46] for the zebrafish system . The following previously published datasets were used: Ldb1 , Scl , and Gata2 in murine bone marrow cells [21] , Ldb1 in murine day 4 EB-derived Flk1+ cells [29] , Smad1 and Gata1 in murine G1ER erythroid progenitors cells [9] , Smad3 in murine pro-B cells [8] . | Cells depend on signals from their microenvironment to carry out their normal functions and coordinate responses . Once initiated , such signals often self-amplify via positive feedback to reach a sufficient level , when negative feedback can then be employed to dampen excess signalling . These feedback loops dynamically add or remove signalling components to maintain homeostasis . Their activation is often driven by the same signal transduction components , making it difficult to understand how signalling builds up in the first place . Here we find that the transcription co-factor Ldb2a enables differential response dynamics of negative and positive feedback upon the induction of TGFβ signalling . We show that Ldb2a directly activates expression of a TGFβ inhibitor that mediates negative feedback , while also repressing expression of TGFβ ligands that drive positive feedback . Moreover , expression of Ldb2a is itself activated by TGFβ signals . Thus , when Ldb2a levels are initially low , TGFβ signalling can self-amply and build up signal via positive feedback without being countered by negative feedback . We show that this regulatory mechanism is active in developing zebrafish embryos , where a loss of Ldb2a results in the over production of mesodermal and endodermal tissue types as a consequence of elevated TGFβ family signalling . | [
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| 2015 | A Novel TGFβ Modulator that Uncouples R-Smad/I-Smad-Mediated Negative Feedback from R-Smad/Ligand-Driven Positive Feedback |
The unicellular eukaryote Tetrahymena thermophila has seven mating types . Cells can mate only when they recognize cells of a different mating type as non-self . As a ciliate , Tetrahymena separates its germline and soma into two nuclei . During growth the somatic nucleus is responsible for all gene transcription while the germline nucleus remains silent . During mating , a new somatic nucleus is differentiated from a germline nucleus and mating type is decided by a stochastic process . We report here that the somatic mating type locus contains a pair of genes arranged head-to-head . Each gene encodes a mating type-specific segment and a transmembrane domain that is shared by all mating types . Somatic gene knockouts showed both genes are required for efficient non-self recognition and successful mating , as assessed by pair formation and progeny production . The germline mating type locus consists of a tandem array of incomplete gene pairs representing each potential mating type . During mating , a complete new gene pair is assembled at the somatic mating type locus; the incomplete genes of one gene pair are completed by joining to gene segments at each end of germline array . All other germline gene pairs are deleted in the process . These programmed DNA rearrangements make this a fascinating system of mating type determination .
Unicellular eukaryotes reproduce asexually , but most also have a sexual stage to their life cycle that increases genotypic variability . Sexual partners are usually morphologically indistinguishable and mating types , as part of a self/non-self recognition system , foster outbreeding . Mating types were first discovered by Sonneborn in the ciliate Paramecium aurelia [1] . This discovery initiated the field of microbial genetics , as mating types were subsequently found in bacteria and a diversity of microbial eukaryotes . The number of mating types and the mechanisms of mating type determination vary widely among unicellular eukaryotes [2]–[6] . T . thermophila is a ciliate that segregates germline and somatic functions into two nuclei with distinct genome structures: the diploid micronucleus ( germline ) and the polyploid macronucleus ( somatic ) . Starvation induces mating ( conjugation ) between two cells of different mating types . During conjugation ( Figure S1 ) the parental somatic nucleus is destroyed while new somatic and germline nuclei are differentiated from a zygote nucleus . This differentiation includes extensive site-specific genome rearrangements , including fragmentation of the germline chromosomes , de novo telomere addition , and deletion of thousands of internal eliminated sequences ( IESs ) [7] . Mating type is also determined at this stage [8] . The T . thermophila germline mat locus was first described by Nanney et al . in 1953 [9] and remains the only locus known to control mating type specificity in this organism . These authors reported that the mat locus determines a spectrum of seven mating types ( I–VII ) , one of which is stochastically and irreversibly expressed in each new somatic nucleus . Extensive field collections have revealed no additional mating types [10] . Two classes of germline mat alleles are known [10]–[13] . The mat-1-like alleles encode mating types I , II , III , V , and VI , while mat-2-like alleles encode mating types II , III , IV , V , VI , and VII [9] . All the strains used in this work are homozygous for the mat-2 allele of inbred strain B . Alternative DNA deletions , rather than epigenetic gene silencing , were proposed to be responsible for mating type determination [14] . The work reported here , made possible by the molecular identification of the mating type genes , has revealed a type of programmed DNA rearrangement in the somatic nucleus that assembles a gene pair of one mating type and deletes the rest .
The genetically mapped mat locus [15]–[17] was assigned to a roughly 300-kb segment of a somatic chromosome sequence assembly ( Figure S2 ) . As cells must be starved to mate , we assumed that a candidate mating type gene would be expressed in a mating type-specific manner during starvation and not expressed during growth . In a previous whole-transcriptome RNA-seq study [18] , mRNA was prepared and sequenced from starved SB4217 ( mating type V or mt V ) cells as well as from starved and growing SB4220 ( mt VI ) cells ( Table S1 ) . To identify mating type candidate genes , we mapped the RNA-seq reads to the 300-kb segment of the mt VI somatic reference genome [15] , [19] . Two adjacent genes in this region showed mating type-specific expression in starved cells and no expression during growth ( Figure 1A ) making them good mating type gene candidates . We named these genes MTA and MTB . A transcript for each gene was assembled primarily from reads that mapped to the mt VI reference genome . Reads from mt VI covered both genes except for one small gap in MTA , which was filled in by cDNA sequencing ( unpublished data ) . Northern blot analysis ( Figures 2 and S3 ) confirmed a single transcript for each mt VI gene . Only the terminal exons of MTA and MTB could be assembled from the mt V reads that mapped to the mt VI reference genome ( Figure 1B ) . In addition , a partial transcript was assembled de novo from the mt V RNA-seq reads ( Text S1 ) . Two thirds of this partial transcript has 99 . 9% identity with the terminal exon of mt VI MTA gene but the remainder is absent from the mt VI somatic reference genome and could encode a mating type-specific segment . The MTA and MTB genes identified above are arranged head to head , are divergently transcribed ( Figure 1B ) and are predicted to code for unique proteins . The MTA gene ( TTHERM_01087810 , KC405257 ) is predicted to encode a 161-kD protein while the MTB gene ( TTHERM_01087820 , KC405257 ) is predicted to encode a 194-kD protein . Each terminal exon is unique in the somatic mt VI genome sequence and both are predicted to encode transmembrane ( TM ) helices . TM domain proteins that can localize to the cell surface could play a role in self/non-self recognition , since cell-cell contact is required to stimulate cells to mate [20]–[22] . If the MTA and MTB genes determine mating type , they may also be essential for mating . This was addressed by removing the entire somatic gene pair of mt VI ( SB210 ) by homologous gene replacement ( Figure S4A and S4B ) [23] . The gene pair knockout ( MT– ) abolished the cell's ability to pair or produce progeny when mixed with starved wild-type ( wt ) cells of a different mating type or with cells of the same mating type . Identical results were obtained with three independent knockout strains . In contrast , control assays of mating between two wt strains of different mating types showed high levels of pair formation and produced abundant ( >85% ) progeny . Each gene of the mt VI gene pair was deleted independently to investigate the functional relationship between the two genes . For both single knockouts , RT-PCR showed that removal of one gene did not abolish expression of the remaining gene ( Figure S4C ) . Three independent MTB knockouts ( MTB– ) gave the same results as the gene pair knockout . No progeny were produced when MTB– cells were mixed with wt cells of a different mating type . The MTA knockout ( MTA– ) retained mating specificity but very little mating competence . It paired extremely poorly and rarely produced progeny ( 0 . 16% on average ) when mated with wt cells of a different mating type . No pairs or progeny were detected when it was mated to cells of the same ( mt VI ) mating type . Identical results were obtained with three independent knockout clones . To determine whether other mating types express genes containing the TM exons shared by mts V and VI , we isolated RNA from starved , mature strains of each mating type ( Table S1 ) . Northern blot analysis revealed that cells of every mating type have MTA– and MTB-like transcripts ( Figures 2 and S3 ) . The length of the transcripts is similar to the lengths of the RNA-seq assembled transcripts , 4 . 8 kb for MTA6 ( mt VI MTA ) and 5 . 7 kb for MTB6 ( mt VI MTB ) . These results , in combination with the RNA-seq results , support the hypothesis that all mating types have MTA and MTB genes consisting of two segments: one encoding a highly conserved TM segment found in all mating types and the other encoding a larger mating type-specific segment . To identify the genes of the germline mat locus , we used the mt VI MTA6 and MTB6 gene pair sequence as query in a BLAST search of the SB210 germline genome sequence ( Tetrahymena Comparative Sequencing Project , Broad Institute of Harvard and MIT , http://www . broadinstitute . org/ ) . Multiple matching discontiguous segments were observed over a 91-kb region of the germline . The mating type-specific segments of MTA6 and MTB6 matched once in the middle of this region . Additional matches were due to the conserved TM exons of MTA6 and MTB6 , each of which matched six times within this region . This led us to identify five additional gene pairs containing sequences homologous to those of the TM exons of MTA6 on the left and MTB6 on the right . The genes are arranged in a tandem array of six similarly oriented gene pairs , the number of mating types encoded by the mat-2 allele ( Figure 3 ) . Sequence immediately flanking the mat locus is identical in the germline and somatic genomes . Before carrying out detailed analysis of the mat locus , we filled all sequence gaps in this region and corrected sequence errors ( Tables S2 and S3 ) . In the finished sequence we found that each gene pair consists of an MTA- and an MTB-like gene . These are composed of a unique mating type-specific segment , and a terminal TM exon segment that is highly conserved among the MTA ( or MTB ) genes . The germline mat locus lacks a complete gene pair . The mat locus array begins and ends with the only complete genes within the array , later shown to be MTA2 and MTB3 , respectively ( Figure 3 ) . The TM exons of all the other mating type genes are truncated , indicated by the use of lower case “tm” ( for example , MTA-tm or tm ) . Assembly of a somatic mating type gene pair requires joining of mating type-specific segments to the full-length copies of the MTA2– and MTB3-TM exons located at the ends of the array . A mating type was assigned to each germline gene pair segment by Southern blot analysis using probes from unique regions of each germline gene pair . Each probe was found to be mating type-specific , hybridizing to a single band from the somatic nucleus of one mating type ( Figure 4 ) . This result clearly shows that only one mating type gene pair remains in the somatic nucleus . The order of the mating type gene pairs in the germline was identified as II – V – VI – IV – VII – III ( Figure 3 ) . Using the above information , the somatic mat locus of each mating type was sequenced from mature mating type strains ( Tables S1 and S3 ) derived from a mating between strains SB210 mt VI and SB1969 mt II . The entire germline mat locus from SB1969 mt II was sequenced and found to be identical to that of SB210 ( Table S3 ) . In the mature mating type strains every somatic gene pair has full-length MTA– and MTB-TM exons joined to a mating type-specific segment , an arrangement identical to that of the somatic mt VI gene pair . The TM exons of the other mating types revealed several single nucleotide polymorphisms when compared to the mt VI gene pair ( see below ) , but otherwise are identical . The mating type genes represent two gene families . Predicted proteins within the MTA family are of similar size ( 1423–1494 aa ) . Clustal Omega alignment [24] , [25] of the six predicted MTA proteins reveals their TM exons share 99 . 6% amino acid identity ( Text S2A ) . Mating type-specific regions were compared by means of all-by-all pairwise alignments of every MTA mating type-specific amino acid sequence using BLASTP . On average , the alignments covered 98% ( range 92%–100% ) of the sequences , and showed 42% ( range 38%–47% ) sequence identity and 60% ( range 58%–65% ) sequence similarity ( identical and conservative substitutions ) ; expected values ranged from 1E-162 to less than 1E-200 . Predicted proteins within the MTB family are also of similar size ( 1 , 733–1 , 749 aa ) . Clustal Omega alignment of the six MTB proteins shows their TM exons share 99 . 4% amino acid identity ( Text S2B ) . Analogous pairwise alignments of every MTB mating type-specific amino acid sequence on average covered 99% ( range 97%–100% ) of the sequences , and showed 43% ( range 41%–46% ) sequence identity and 62% ( range 60%–64% ) sequence similarity; expected values were all less than 1E-200 . The two protein families were compared by all-by-all BLASTP alignments of MTA versus MTB predicted amino acid sequences; in every case , the only significant match ( expected value around 1E-08 ) was restricted to a ∼80 amino-acid cysteine-rich segment containing furin-like repeats , starting about 50 amino acids into the TM exon-encoded sequence . Clustal Omega alignment of the furin-like repeats within the 12 TM exons is shown in Text S3 . Cysteines at 12 positions and other amino acids at 14 positions are absolutely conserved among the furin-like repeats of the 12 TM exons . The function of cysteine rich , furin-like repeat domains is not known , but they are found in some endoproteases and cell surface receptors [26] . The mating type-specific segments of the germline gene pairs differ in size by up to 8 . 5 kb . This variation is due to the presence of IESs , germline-specific sequences that interrupt a contiguous region of somatic-destined sequence , within the array . By comparing somatic sequences to the germline genome sequence , we identified six IESs , ( Figure 3; Table S4 ) . Each was confirmed by cloning and sequencing PCR products from the germline and somatic nuclei ( unpublished data ) . The IESs lie within introns in mating type-specific segments or in an intergenic region; they range in size from 299 to 5 , 989 bp . No other differences were found between the germline and somatic sequences in the mating type-specific segments . Additional germline-limited sequence separates adjacent mating type gene pairs in the germline array ( Table S4 ) . We identified homologs of the MTA and MTB genes in the somatic genome sequence of several additional species ( Figure 5 ) . Somatic genome sequence is available for two Tetrahymena species that are within the same subgroup [27] as T . thermophila ( T . malaccensis and T . elliotti ) and two more distantly related species ( T . borealis and T . pyriformis ) ( T . malaccensis , T . elliotti , T . borealis at the Broad Institute website , T . pyriformis strain GL by W . Miao , unpublished data ) . T . malaccensis and T . borealis have systems with six and seven mating types , respectively , and like T . thermophila , mating type determination is stochastic , without influence of the parental mating types [28] . The mating type system of T . elliotti is unknown . The same is true of T . pyriformis , where the GL strain is sole representative of this species . This strain also lacks a germline nucleus and thus would be sterile if it could mate . Nucleotide and protein BLASTN and TBLASTN searches using the sequence of the conserved TM exons led us to identify single-copy , head-to-head MTA and MTB homologs of approximately the same length for all four related species ( Text S4 ) . The results of a phylogenetic analysis ( Figure 5 ) and Clustal Omega alignment ( Text S5 ) showed the mating type of the sequenced strain of T . elliotti to be most closely related to T . thermophila mating type III . Similarly , the mating type of the sequenced strain of T . malaccensis is most closely related to mt IV . Alignments of the predicted amino acid sequences are shown in Text S5 . For the remaining species , specific mating type relationships could not be recognized either because they carry a homolog of the mt I gene of the T . thermophila mat1 allele , which has not yet been sequenced , or the sequence divergence is too great . Neither T . thermophila MTA nor MTB protein show similarity to any of the other ciliate mating type protein deposited in GenBank , a total of 19 distinct proteins from four Euplotes species and one Blepharisma japonicum protein , as determined by BLASTP with expected value threshold = 10 . The six MTA TM exon segments of the germline SB210 mat locus were aligned , delineating the position at which each germline tm segment is truncated ( Figure 6 ) and revealing 59 polymorphic sites ( Table S5 ) . The MTB TM exon segments were similarly examined and 52 polymorphic sites were found ( Table S6 ) . With only one exception , none of the polymorphic nucleotides generate stop codons or reading frame shifts and most are unique to a particular gene pair . Unique polymorphic nucleotides within the germline TM exon segments allow us to deduce the germline origin of somatic MTA-TM and MTB-TM exon DNA . During differentiation of a new somatic nucleus a pair of intact MTA and MTB genes must be assembled from the germline genes . One possibility is that joining occurs between the ends of the mating type-specific segment and the start of the MTA2 and MTB3 full-length TM exons . If this were the case , all the progeny would have full-length TM exons identical to those of the MTA2 and MTB3 germline genes . Alternatively , joining could occur at internal locations within the germline TM exons . In this case , the somatic TM exons would contain novel combinations of the unique polymorphic nucleotides found in the germline tm segments . Somatic mating type gene pair sequences from the mature strains mentioned above , and the SB210 shotgun macronuclear genome sequence , were found to contain novel combinations of these polymorphic nucleotides ( see below ) , suggesting that joining can occur within the germline TM exons . To determine more precisely where the incomplete gene pairs are joined to full-length germline TM exons , we compared the sequences of TM exons from newly differentiated somatic nuclei to those of germline TM exon segments . We sequenced individual somatic TM exons from progeny that had not yet undergone the first cell division ( exconjugants , Figure S1 stage 3 ) . We constructed “collapsed alignments” to concisely represent all the polymorphisms in the somatic and germline nuclei ( Texts S6–S8 ) . Schematic representations of the complete set of sequenced exons are shown in Figure 6 . Somatic MTA2 and MTB3 genes , which are already complete in the germline , showed no evidence of any joining event . The TM exons of every other somatic mating type gene showed polymorphic nucleotide combinations not present in the germline genome ( Figure 6; Texts S7 and S8 ) . A single , simple joining event connecting a truncated germline tm segment to the full-length germline MTA2-TM exon explains 98% of the somatic MTA-TM exons present in early progeny ( Table S7A ) . The MTA join sites were mapped to a 269-bp segment near the start of the MTA2-TM exon . A single , simple event explains joining to the full-length germline MTB3-TM exon in 74% of the sequenced exons ( Table S7A ) . This percentage varies from 42% for somatic MTB2 to 89% for somatic MTB6 . The MTB join sites mapped to intervals distributed throughout the germline TM exon sequence . The number of distinct join sites may have been exaggerated if PCR template switching [29] reshuffled nucleotide diversity in these sequenced TM exons ( Text S9 ) . These data confirm that many if not all of the joining events occur within the TM exon rather than exclusively between the TM exon and the mating type-specific segment . The frequency of novel nucleotides ( not present in the germline ) is less than one in 50 , 000 sequenced base pairs ( Figure 6 legend ) , showing that the joining events are highly precise . Analysis of the TM exons of twenty 120-fission strains ( Table S7; Texts S10 and S11 ) shows that MTB-TM exons undergo additional recombination after the resumption of vegetative multiplication . Highly significant differences between 0- and 120-fission cells are observed for the MTB-TM exons , whether one compares the number of haplotypes explained by a single joining event , or by recombination events involving more than two germline genes , or by gene conversions ( Table S7 ) . PCR template switching is excluded as a spurious source of recombination in these results ( Text S9 ) . We believe these events largely represent intragenic secondary recombination , distinct from the single , simple recombination events responsible for mating type determination .
Our findings suggest that mating type determination in T . thermophila involves a remarkable type of programmed genome rearrangement . We have identified a pair of mating type genes that are arranged head-to-head . Each mating type is characterized by a similarly organized pair of somatic genes and each gene of the pair encodes a TM domain shared by all mating types . Starvation is required for mating and induces transcription of both genes . Both genes are required for wt levels of pair formation and progeny production . The germline genome contains an array of incomplete gene pairs , one for each mating type . During development of the somatic nucleus in progeny cells , the germline array undergoes rearrangement to assemble one complete gene pair and delete all others in the somatic chromosome . Thus , mating type determination occurs by deletion rather than by an epigenetic gene silencing mechanism . These findings account for the irreversibility of mating type determination . The mating type locus can be thought of as a multi-state developmental switch where the switch is stochastically and permanently set to one state in the somatic genome . The removal of either or both genes caused a significant inhibition of pairing between cells of different mating types , suggesting the MTA and MTB genes are both fundamental for recognition of cells of a different mating type ( allorecognition ) . This inhibition of pairing suggests that the gene products may be functioning cooperatively for allorecognition . In addition to allorecognition , the gene products could be distinguishing self to prevent homotypic pairing . If this were the case , homotypic pairing would be observed in the absence of one or both genes . This does not appear to be a function of the MTA and MTB genes because pairing between starved cells of the same mating type was not observed in our knockouts . At least two events are required to assemble a complete somatic mating type gene pair from the mat germline array ( see model shown in Figure 7 ) . At the left end of the gene pair , the MTA-tm segment must be joined to the single copy , full-length MTA2-TM exon located at the far left end of the array . At the right end of the same gene pair , the MTB-tm segment must join to the single copy , full-length MTB3-TM exon located at the far right end of the array . The breakage and rejoining mechanism is highly precise . Since both joining events occur within translated exons segments , without this precision mating competence could be lost . Possible mechanisms include homologous recombination and precise nonhomologous end joining . The mechanism will become clearer once we experimentally determine which of the observed recombination events are essential to mating type determination and which are unrelated to this process . Regardless of the mechanism , an interesting question is how joining at opposite ends is coordinated to result in the assembly of a somatic gene pair . A stochastically selected germline gene pair may be epigenetically marked , its two ends cut , and full length TM exons joined coordinately . Alternatively , each end could be processed independently resulting in the deletion of one or more gene pairs from either end , until only one complete gene pair remains . Additional knowledge of the mechanism will be needed to understand how mating type frequencies are influenced by environmental conditions , such as temperature and nutritional state [30] , [31] . In addition to the single , simple recombination events associated with mating type determination , we have observed secondary recombination events in somatic TM exons , especially MTB TM exons . These events are particularly frequent in the MTB TM exons of mature cell lines ( Table S7 ) . As explained in Text S9 , artifacts of PCR template-switching are excluded in these results . Since the majority of joined TM exons from 24-h exconjugants show no evidence of secondary recombination , these events are probably unrelated to mating type determination . Presumably they chiefly reflect recombination between multiple somatic chromosome copies carrying independently differentiated TM exons prior to the purification brought about by assortment during vegetative multiplication ( Figure S1 ) . A number of recombination events , most simply interpreted as gene conversions , have also been detected among MTB exon haplotypes . We believe that these MTB gene conversions are also due to the secondary recombination described above and are unrelated to Tetrahymena mating type determination , in part because gene conversions are found in only a small minority of the sequenced TM exons in 24-h exconjugants . In addition , gene conversion per se cannot result in the loss of intervening mating type gene pairs . Gene conversion is responsible for mating type switching in yeast , but no intervening DNA is lost in yeast mating type switching [32] . Programmed somatic DNA rearrangements are well known among the ciliates [33] , [34] . In T . thermophila , approximately 6 , 000 IESs in the germline genome are excised during differentiation of a new somatic nucleus [35] . The deletions that join TM exons to mating type-specific segments differ in several important ways . IES excision is imprecise; precision is not required , as nearly all IES are found in intergenic regions or within introns [36] . In contrast , the deletions involved in mating type determination are highly precise and occur within the coding segment of the TM exon . Furthermore , IES excision is maternally controlled; only sequences absent from the parental somatic genome are targeted for elimination [37] , [38] . Mating type , on the other hand , is stochastically inherited; determination of mating type in each progeny cell occurs autonomously during the differentiation of the new somatic nucleus . Mating type-specific sequences absent from the parental somatic nucleus escape deletion by the IES excision mechanism and are retained in progeny somatic nuclei . Finally , preliminary experiments ( unpublished data ) indicate that mating type determination occurs several hours after excision of IES within the mat locus . All these considerations lead us to conclude that these two processes , which occur in the differentiating somatic nucleus , proceed by different mechanisms . In mating type determination , DNA breakage and rejoining occurs physically independently and precisely at both ends of one gene pair . This leads to the assembly of one complete gene pair and the excision of the other germline gene pairs from the somatic chromosome . To our knowledge , this type of programmed genome rearrangement is novel , at least in ciliate molecular biology . The modular organization of the T . thermophila germline mat locus ( Figure 3 ) in combination with rare unequal meiotic crossing-over between homologous germline TM/tm domains could facilitate rapid evolutionary change in the number of available mating types . This hypothesis is consistent with the existence of two T . thermophila germline mat allele classes specifying different numbers of mating types ( five for mat-1 and six for mat-2 ) . mat-1-like alleles carry mt I but are missing mts IV and VII . mat-2-like alleles are the opposite , carrying mts IV and VII in adjoining gene pairs while missing mt I . Using somatic genome sequence data we assigned a mating type to the sequenced strains of two other Tetrahymena species by virtue of their similarities to T . thermophila mating types . This suggests that a similar mating type system is conserved in multiple Tetrahymena species . If so , the mechanism proposed above could also explain the finding that the number of mating types described in species of the genus Tetrahymena is dynamic , ranging from 3 to 9 ( reviewed in [39] ) . Using the strong sequence conservation observed at the TM exons , it may be possible to isolate and sequence mating type genes from many species of the genus Tetrahymena to investigate the evolution of their mating type system . T . thermophila is a model organism for eukaryotic biology [15] . Future research of this mating type system should advance our knowledge in several areas of biology . The biochemical functions of the MTA and MTB gene products are of interest for understanding the principle of self/non-self discrimination . The study of genomic rearrangements employed for mating type determination can inform mechanisms of genome dynamics in other systems .
All of the T . thermophila strains used here have the inbred strain B genetic background [40] . As such , they are mat2/mat2 homozygotes and can be of any one of mating types II–VII . The somatic and germline genomes have been sequenced from strain SB210 [41] . A panel of mature strains of different mating types , F1s of a cross of SB210×SB1969 , was obtained by propagating F1 cells for ∼120 fissions , subcloning and determining their mating type , all using established methods [42] . The germline mat locus alleles of SB210 and SB1969 are identical to the nucleotide , as determined by sequencing SB1969 ( Table S3 ) . This identity should extend to the germline of all members of the F1 panel . Strains are listed in Table S1 and are available through the National Tetrahymena Stock Center ( http://tetrahymena . vet . cornell . edu ) . An RNA-seq-based whole transcriptome analysis was done using strains SB4217 ( mt V ) and SB4220 ( mt VI ) ; detailed information of RNA extraction , library construction , and deep RNA sequencing can be found in our previous work [18] . For the work here , we compared three conditions: starved mating type VI cells , growing mating type VI cells , and starved mating type V cells . To look for transcription differences between mating type V ( SB4217 ) and mating type VI ( SB4220 ) , RNA-seq reads were first mapped to the ∼300-kb region of SB210 ( mt VI ) that includes the mat locus ( Figure S2 ) using TopHat [43] . Mapped reads were assembled as transcript fragments using Cufflinks [44]; the command lines of this mapping-then-assembly pipeline were described previously [18] . Gbrowse genome viewer ( http://gmod . org/wiki/GBrowse ) was setup to visually check the transcription differences using as input the TopHat mapping results and the Cufflinks assemblies . Necessary data format transformations were performed using SAMTools [45] and ad hoc Perl scripts . The ∼300-kb region was then manually examined in Gbrowse for significant mating type-specific transcription differences . We de novo assembled a mating type V partial transcript using the Trinity transcriptome assembler ( 2011-08-20 release version ) [46] . The command line used was: Trinity . pl –seqType fq –output output –left left . fq –right right . fq –run_butterfly –bflyHeapSpace 20000M . To identify de novo assembled mating type V partial transcripts related to the mating type locus , similarity searches ( BLASTN ) were performed against the de novo transcript assemblies using as query the sequences of genes TTHERM_ 01087810 and TTHERM_ 01087820 . All identified mating type V transcripts were aligned with mating type VI transcripts of the two genes to discern conserved and mating type-specific sequences . Table S2 lists primers used to PCR amplify gaps in the SB210 germline sequence in the region of the mat locus , the mat locus from the SB1969 germline sequence , regions flanking IESs in the somatic genome , segments for knockout constructs , and the TM exons from the somatic nuclei of mature strains . DNA was prepared as described [47] . PCR products were amplified with Finnzymes Phusion High-Fidelity DNA Polymerase and cloned using the Zero Blunt TOPO PCR Cloning Kit for Sequencing ( Invitrogen ) . Primers for sequencing were chosen using the SB210 genome sequence . Sequencing by Sanger dideoxy sequencing was carried out at Eton Bioscience Inc . RNA was extracted using the Qiagen RNeasy kit from 10-ml cultures at 2×105 cells/ml that had been starved for 3 h at 30°C in 10 mM Tris ( pH 7 . 4 ) . 15 µg of RNA was loaded per lane on a 1% gel , subject to electrophoresis for ∼2 h at 120 V in formaldehyde buffer , and set up for downward transfer in denaturation buffer to a charged nylon membrane . To prepare hybridization probe , 150 ng of PCR product was labeled with dATP32P by random primer labeling for 72 h at room temperature , followed by removal of unincorporated dATP32P nucleotides using the QIAquick nucleotide removal Kit ( Qiagen ) . Pre-hybridization and hybridization with ULTRAhyb solution ( Ambion ) was at 45°C for 2 and ∼16 h , respectively . Blots were washed twice for 10 min in 0 . 1× SSC/0 . 1% SDS at 45°C and hybridization was visualized on film . The MTA and MTB genes in the somatic nucleus of mating type VI were replaced , separately or together , by a neo-cassette that confers Cd-inducible resistance to paromomycin [23] . Each construct contains the neo cassette , flanked by a minimum of 500 bp of sequence from each side of the coding sequence to be replaced . Replacement occurs by precise homologous recombination . In constructing the single knockouts , 8 bp of the spacer region before the start codon of MTA6 and 67 bp before the start codon of MTB were removed in addition to the coding sequence . For the double knockout both coding sequences and the spacer region were replaced . Constructs were created by overlapping PCR [48] . Biolistic transformation was carried out as described [49] . To select for complete phenotypic assortment to the KO allele , transformants were initially selected in 0 . 1 µg/ml CdCl2 and 100 µg/ml paromomycin and were propagated for more than ∼100 successive cell divisions in the presence of increasing amounts of CdCl2 and paromomycin ( final concentrations 0 . 2 µg/ml CdCl2 and 1 mg/ml paromomycin ) . These transformants were then screened by PCR using Choice-Taq DNA Polymerase ( Denville Scientific Inc . ) and IES-bracketing primers to verify that they had no remaining copies of the wt gene pair in their somatic nucleus ( see Table S2 for primers ) . RNA from the MTA knockout strains , MTB knockout strains , SB210 , SB4208 , SB4211 , SB4214 , SB4217 , and SB4223 was prepared as for the Northern blots , and used in cDNA synthesis with the ThermoScript RT-PCR System ( Invitrogen ) . Subsequent PCR was done with primers in Table S2 . Somatic mat knockout strains are derived from SB210 ( mt VI ) and thus are homozygous for 2-deoxygalactose-resistance in their germline , but have only the wt , 2-deoxygalactose-sensitive , allele in their somatic genome ( Table S1 ) . The fully assorted mat knockout strains were crossed to SB1969 , a mating type II strain that is homozygous for cycloheximide-resistance in its germline , but has only the wt , cycloheximide-sensitive , allele in its expressed somatic genome . As a control , SB1969 was mated to SB210 . Established methods for mating Tetrahymena strains and progeny selection were used [42] . The mixtures were allowed to mate for 24 h in starvation medium and upon re-feeding they were diluted and distributed to 96-well plates at 5 , 50 , 500 , or 5 , 000 cells per well ( 50 µl per well ) . Progeny were serially selected for resistance to cycloheximide and 2-deoxygalactose to verify the expected double-resistant phenotype and to quantitate the fraction of cells producing progeny . Cells ( ∼8×105 cells per plug ) of mature strains of mating types II through VII ( SB4208 , SB4211 , SB4214 , SB4217 , SB4220 , SB4223 ) were washed in 10 mM Tris ( pH 8 . 0 ) , resuspended to 50 µl per plug , mixed with 1 . 5% low melting point agarose ( SeaPlaque GTG ) , and distributed into plug molds ( Bio-Rad ) . Plugs were incubated overnight at 55°C in NDS/proteinase K ( 1% NDS , 10 mM Tris [pH 7 . 6] , 0 . 5 M EDTA , proteinase K 100 µg/ml ) , washed 2× for 2 h in 0 . 5 M EDTA at room temperature and stored at 4°C in 0 . 5 M EDTA . Plugs were then washed 2×10 min in TE at 4°C and digested with PvuII restriction endonuclease . A 1% gel ( Pulsed Field Certified Agarose , Bio-Rad ) was poured around plugs adhered to the teeth of a gel comb , and subjected to pulsed-field gel electrophoresis in CHEF-DR III Pulsed Field Electrophoresis System ( 0 . 5× TBE , ramping switch time of 0 . 5 to 3 s , 6 V/cm , angle 120° for 15 h ) . The gel was transferred under alkali conditions to positively charged nylon membrane following the protocol in CHEF-DR III Pulsed Field Electrophoresis Systems Instruction Manual , BioRad . Primers for the mating type-specific PCR products used as probes are listed in Table S2 . Probes were prepared as for Northern blot analysis above . Pre-hybridization at 45°C for 2 h and hybridization at 60°C for 16 h were done in hybridization solution ( 6× SSC , 5× Denhardt's solution , 20 mM Tris-HCl [pH 8 . 0] , 0 . 1% sodium dodecyl sulfate , 2 mM EDTA , and 27 µg/ml denatured salmon sperm DNA ) . Blots were washed once in 1× SSC/0 . 1% SDS at room temperature for 10 min , and twice in 0 . 1× SSC/0 . 1% SDS at 55°C for 10 min . Hybridization was visualized on film . Structural annotation of mat genes was performed on genomic DNA sequences derived from the somatic genomes of each of six mature strains [SB1969 , SB4213 , SB4214 , SB4218 , SB210 , and SB4223] expressing different mating types . The strategy was developed as part of an ongoing reannotation of the T . thermophila macronuclear and micronuclear genomes . The gene-finding algorithms AUGUSTUS [50] and GeneZilla [51] were re-trained for T . thermophila using a set of full-length RNA-seq transcripts and used to perform gene predictions . Additionally , we ran the AAT tool [52] against a JCVI in-house non-redundant protein database ( AllGroup ) to map known proteins to the loci and PASA [53] to map transcripts assembled from RNA-seq data [18] using Trinity [46] . Using this evidence , each gene , including 500 bp of predicted downstream sequence , was manually curated using Annotation Station ( Neomorphic , Inc . ) . The genes for the mat loci of the other Tetrahymena species ( see below ) were predicted using a similar approach . In addition to evidence generated as above , we included predictions made by GeneZilla trained on the T . borealis genome and we used AAT to compare the six T . thermophila mat genes to each other . The SB210-derived sequence of the entire mt VI gene pair was aligned by BLASTN and TBLASTN to the Broad Institute assemblies of the somatic genomes of T . malaccensis , T . elliotti , and T . borealis ( http://www . broadinstitute . org/annotation/genome/Tetrahymena/MultiHome . html ) . The same sequence was aligned to the T . pyriformis strain GL somatic genome sequence ( W . Miao , unpublished data ) . These matches allowed us to delineate mating type gene pair homologs in the sequenced strains of all four species . Since the strongest matches were to the TM exons at the 3′ end of each mating type gene , the first in-frame stop codon after the end of the matching segment was tentatively defined as the 3′ end of the gene . The entire four gene pair sequences were then extracted and subjected to phylogenetic analysis in conjunction with the somatic gene pair sequences of T . thermophila strains of every mating type ( SB1969 , mt II; SB4218 , mt V; SB210 , mt VI; SB4214 , mt IV; SB4223 , mt VII; and SB4218 , mt III ) . The entire gene pair sequences were aligned with ClustalW . Phylogenetic analysis was done with Maximum Likelihood , implemented in RAxML 7 . 2 . 8 , Model = GTR+Gamma , 100 bootstrap replicates . The TM exons of somatic mating type genes were PCR amplified from progeny cells before the newly differentiated somatic nucleus has undergone its first division ( Figure S1 , stage 3 ) . Two independent matings were necessary to avoid amplifying parental somatic TM exons from cells in the culture that failed to mate . Exons of mt IV , mt V , and VII were obtained from the DNA of progeny from a SB210 VI mating SB1969 II . Exons of mt II , mt III , and mt VI were obtained from DNA of progeny from a SB4216 IV mating SB4224 VII . PCR primers were designed to amplify mating type-specific segments whose truncated tm had been joined to a full-length TM exon generating the complete somatic gene . Unlike the other mating type genes , the MTA2 and MTB3 genes have a full-length TM exon in the germline as well as in the somatic nucleus . The somatic and germline products for these two genes can be distinguished from each other by IESs , which are only present in the germline ( see Table S4 ) . In Table S2 primers for MTA-TM amplification begin with an A while those for MTB-TM amplification start with a B . For PCR amplification of the MTA-TMs from the somatic nucleus , the primers and size of resulting PCR products are as follows: MTA2-TM: Two rounds of PCR , first round to amplify a product specific to the somatic nucleus - Primers A2 , A1 , Germline 10 . 9 kb Somatic 5 . 7 kb . The 5 . 7-kb product was gel purified with the QIAquick Gel Extraction Kit ( Qiagen ) , and used for a second round of PCR to amplify a smaller TM exon product using primers A3 , A4 , Germline , and Somatic 1 kb; MTA3-TM: Primers A5 , A6 Germline 81 . 4 kb Somatic 2 . 6 kb; MTA4-TM: Primers A7 , A8 Germline 52 . 5 kb Somatic 1 . 99 kb; MTA5-TM: Primers A9 , A10 Germline 21 . 5 kb Somatic 1 . 38 kb; MTA6-TM: Primers A9 , A11 Germline 36 . 5 kb Somatic 1 . 38 kb; MTA7-TM: Primers A9 , A12 Germline 63 . 4 kb Somatic 1 . 22 kb . For PCR amplification of the MTB-TMs from the somatic nucleus , the primers and size of resulting PCR products are as follows: MTB2-TM: Primers B1 , B2 Germline 74 . 7 kb Somatic 1 . 73 kb; MTB3-TM: Two rounds of PCR , first round to amplify a product specific to the somatic nucleus – Primers B3 , B4 Germline 16 . 9 kb Somatic 11 kb . The 11-kb product was gel purified with the Qiagen QIAquick Gel Extraction Kit , and used for a second round of PCR to amplify a smaller TM exon product using primers B5 , B4 Germline , and Somatic 3 . 0 kb; MTB4-TM: Primers B6 , B2 Germline 30 kb Somatic 1 . 8 kb; MTB5-TM: Primers B7 , B2 Germline 58 . 1 kb Somatic 1 . 7 kb; MTB6-TM: Primers B8 , B2 Germline 42 . 9 kb Somatic 1 . 8 kb; MTB7-TM: Primers B9 , B2 Germline 16 . 8 kb Somatic 1 . 7 kb . Matings were carried out as for the knockout strains [42] . Starved cells of different mating types were mixed to start mating; after 24 h in starvation medium they were lysed and whole cell DNA prepared as described previously [47] . PCR products were amplified with Finnzymes Phusion High-Fidelity DNA Polymerase and cloned using the Zero Blunt TOPO PCR Cloning Kit for Sequencing ( Invitrogen ) . Plasmid DNA was isolated using QIAprep Spin Miniprep kit ( Qiagen ) . At least two clones of each PCR product were sequenced by Sanger dideoxy sequencing at Eton Bioscience Inc . CAP3 [54] was used to assemble exon sequences from multiple reads . ClustalW with default settings at EBI ( http://www . ebi . ac . uk/Tools/msa/clustalw2/ ) was used to determine the germline consensus sequence ( Text S6 ) by alignment of the sequences for the TM exon segments of the six MTA and six MTB germline genes . BLASTN ( http://blast . ncbi . nlm . nih . gov ) was used to align assembled sequences to the consensus sequence and thus identify polymorphic sites . NCBI Accession numbers for the complete sequence of the mat locus from the germline of SB210 and SB1969 and of the somatic mat gene pair for one strain of each mating type are listed in Table S3 . | Tetrahymena thermophila is a single-celled organism with seven sexes . After two cells of different sexes mate , the progeny cells can be of any one of the seven sexes . In this article we show how this sex decision is made . Every cell has two genomes , each contained within a separate nucleus . The germline genome is analogous to that in our ovaries or testes , containing all the genetic information for the sexual progeny; the somatic or working genome controls the operation of the cell ( including its sex ) . We show that the germline genome contains a tandem array of similarly organized but incomplete gene pairs , one for each sex . Sex is chosen after fertilization when a new somatic genome is generated by rearrangement of a copy of the germline genome . One complete sex gene pair is assembled when the cell joins DNA segments at opposite ends of the array to each end of one incomplete gene pair; this gene pair is thus completed and becomes fully functional , while the remaining sex gene pairs are excised and lost . The process involves programmed , site-specific genome rearrangements , and the physically independent rearrangements that occur at opposite ends of the selected gene pair happen with high reliability and precision . | [
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| 2013 | Selecting One of Several Mating Types through Gene Segment Joining and Deletion in Tetrahymena thermophila |
To mate , the fungal pathogen Candida albicans must undergo homozygosis at the mating-type locus and then switch from the white to opaque phenotype . Paradoxically , opaque cells were found to be unstable at physiological temperature , suggesting that mating had little chance of occurring in the host , the main niche of C . albicans . Recently , however , it was demonstrated that high levels of CO2 , equivalent to those found in the host gastrointestinal tract and select tissues , induced the white to opaque switch at physiological temperature , providing a possible resolution to the paradox . Here , we demonstrate that a second signal , N-acetylglucosamine ( GlcNAc ) , a monosaccharide produced primarily by gastrointestinal tract bacteria , also serves as a potent inducer of white to opaque switching and functions primarily through the Ras1/cAMP pathway and phosphorylated Wor1 , the gene product of the master switch locus . Our results therefore suggest that signals produced by bacterial co-members of the gastrointestinal tract microbiota regulate switching and therefore mating of C . albicans .
The white-opaque transition in MTL-homozygous strains of Candida albicans affects cellular physiology , cell morphology , gene expression , virulence and biofilm formation [1]–[3] . It is repressed by the a1-α2 co-repressor in a/α cells and derepressed in cells that have undergone MTL-homozygosis to become either a/a or α/α [4] . White-opaque switching , which occurs spontaneously and reversibly , is controlled through expression of a master switch locus , WOR1 , which also has been referred to as TOS9 [5]–[7] . The frequency of switching is regulated in part at the level of WOR1 transcription by a number of genes through a network of positive and negative regulatory loops [8] , [9] and through changes in chromatin state [10]–[12] . After the discovery of a mating system in C . albicans [13] , it was demonstrated that MTL-homozygous cells had to switch from white to opaque in order to mate [4] , [14] . Paradoxically , it was demonstrated that in vitro this switch was sensitive to physiological temperature [15] , [16] . When the temperature of opaque cell cultures grown at 25°C was raised to 37°C , cells switched en masse and semi-synchronously to white [17] , suggesting that the opaque phenotype was unstable at physiological temperatures and that mating would , therefore , be compromised in a host , the major niche of C . albicans . Recently , we demonstrated that high levels of CO2 comparable to those found in the host gastrointestinal tract and some host tissues induced switching from white to opaque , maintained cells in the opaque phenotype , and blocked switching from opaque to white [18] . CO2 had been demonstrated previously to be a potent inducer of filamentation as well [19] , [20] . Because N-acetylglucosamine ( GlcNAc ) , which is produced by bacteria in the gastrointestinal tract [21] , is also a potent inducer of filamentation [22] , we therefore considered the possibility that it , like CO2 , was an inducer of the white to opaque transition . We found that G1cNAc represents a second strong inducer of the white to opaque transition and stabilizes the opaque phenotype . GlcNAc induction occurs at 25°C and is enhanced at 37°C . In addition , because there were indications that the induction of filamentation by GlcNAc was mediated by the Ras1/cAMP pathway [23]–[30] , we tested whether G1cNAc induction of switching was regulated by this pathway . Our results demonstrate that GlcNAc induction is transduced primarily by the same Ras1/cAMP pathway that has been implicated in the regulation of filamentation and requires phosphorylated Wor1 , the product of the master switch locus . We therefore suggest that two different signals in the host gastrointestinal tract , both produced by bacterial co-members of the gastrointestinal tract microbiota , can regulate the white to opaque transition , an essential step in C . albicans mating .
To test whether GlcNAc induces the white to opaque transition and does so as a function of culture age , as is the case for the induction of filamentation [22] , [24] , white cells of a/a and α/α derivatives of strain SC5314 , 5314a and 5314α , respectively , were first grown at 25°C in suspension in liquid modified Lee's medium in which glucose was the sole carbon source ( “liquid glucose medium” ) [31] ( Figure 1A ) . To assess GlcNAc induction as a function of culture growth [23] , cells were removed at time intervals from the liquid culture , plated on nutrient agar containing either 1 . 25% ( w/v ) glucose ( “glucose agar” ) or 1 . 25% ( w/v ) GlcNAc ( “GlcNAc agar” ) as the sole carbon source ( Figure 1A ) , and incubated at 25°C . This temperature was selected to assess induction initially , because physiological temperature ( 37°C ) induces the reverse switch from opaque to white [15] , [17] , and we wanted the initial assessment to be performed in the absence of reverse induction . After five days on agar , the proportion of opaque colonies plus white colonies with opaque sectors was measured in glucose or GlcNAc agar . This proportion will be referred to as the “switching frequency” for convenience , but should not be confused with the rate of switching [1] , [16] , [32] . Although a/a and α/α cultures reached different final cell densities , they entered the saturation phase in liquid glucose medium at approximately the same time ( Figure 1B ) . For a/a cells plated on glucose agar at 25°C , the switching frequency increased from 0 . 4±0 . 4% for cells taken from exponential phase cultures after one day , to 3 . 5±1 . 2% for cells taken from late saturation phase cultures after 10 days ( Figure 1C , D ) . For α/α cells , the proportion increased similarly from 0 . 5±0 . 5% to 2 . 9±0 . 2% ( Figure 1C , D ) . Hence , the switching frequency of a/a and α/α cells grown in liquid glucose medium increased 9 . 5- and 6 . 0-fold , respectively , over the course of exponential growth and entrance into the saturation phase . For a/a cells plated on GlcNAc agar , the frequency of switching increased from 5 . 1±1 . 3% after one day to 88 . 7±4 . 9% after 10 days , and for α/α cells , the frequency increased from 7 . 3±3 . 4% to 92 . 5±3 . 3% ( Figure 1C , D ) . Plating on GlcNAc agar , therefore , caused an increase in the frequency of switching of a/a and α/α cells after one day that was approximately 13- and 15-fold higher , respectively , than the frequencies on glucose agar after one day ( Figure 1C , D ) . After 10 days , the frequency was 25- and 32-fold higher , respectively , than the frequency on glucose agar ( Figure 1C , D ) . In Figure 1E , examples are presented of cultures from three day liquid glucose cultures of a/a cells plated on glucose agar or GlcNAc agar . Note that on GlcNAc agar , the majority of colonies were completely opaque rather than sectored , indicating that in these cases GlcNAc induction occurred very early in the life history of the colonies . Similar results were obtained for cells grown in liquid glucose medium for five days and plated on agar containing either glucose or GlcNAc ranging in concentration from 0 . 2% to 5% ( w/v ) , indicating that the concentration employed ( 1 . 25% , w/v ) resulted in maximum G1cNAc induction ( data not shown ) . The preceding experiments were performed at 25°C . To test whether GlcNAc also induced white to opaque switching at physiological temperature ( 37°C ) , we then performed experiments in which white cells of a/a and α/α derivatives of strain SC5314 were grown to mid-log phase on liquid glucose medium for 48 hr at 25°C , then plated on glucose or GlcNAc agar at either 25 or 37°C . Increasing the temperature from 25 to 37°C on glucose agar resulted in a lower frequency of switching for white cells of both the a/a and α/α strains ( Table 1 , data in air ) . In direct contrast , when white cells of both strains were grown in liquid glucose medium at 25°C and then plated on GlcNAc agar at 37°C , there was a dramatic increase in the switching frequency ( Table 1 , data in air ) . These data demonstrate that physiological temperature enhances GlcNAc induction . The cAMP pathway , which plays a role in filamentation , includes Ras1 , Cdc35 , Pde2 and the protein kinase isoforms Tpk1 and Tpk2 [25]–[30] . To test whether GlcNAc induction of white to opaque switching was mediated by the cAMP pathway , we first analyzed the RAS1 deletion mutant , ras1/ras1 . These experiments were performed at 25°C because a temperature of 37°C induced a portion of the cells of mutants of the Ras1/cAMP pathway to undergo filamentation ( data not shown ) , making it difficult to assess switching from white to opaque at the level of cell phenotype . White cells of ras1/ras1 and the control strain ( WT ) were grown at 25°C in liquid glucose medium to saturation phase ( seven days ) , plated on either glucose or GlcNAc agar , and analyzed for switching frequencies after five days at 25°C . The switching frequency on GlcNAc agar was 90 . 5±3 . 8% for WT cells , and 11 . 2±1 . 5% for ras1/ras1 cells ( Figure 2A ) , indicating that Ras1 played a major , but not exclusive , role in GlcNAc induction . The frequency of switching of ras1/ras1 cells on GlcNAc agar was 9-fold lower than that of WT cells , and 16-fold higher than that on glucose agar ( Figure 2A ) . Complementation of ras1/ras1 with RAS1 under the control of the MET3 promoter partially rescued the mutant phenotype in the activated state ( Figure 2A ) . Rescue was incomplete due to the fact that RAS1 was controlled in the complemented strain by the MET3 rather than the natural promoter [7] , [33] . It should also be noted that on glucose agar , the frequency of switching of WT cells was two-fold higher than that of ras1/ras1 cells ( Figure 2A ) , indicating that a RAS1-dependent pathway also played a role in spontaneous switching on glucose agar . To explore further the role of RAS1 in G1cNAc induction , we transformed strain WUM5A , a derivative of α/α strain WO-1 , with RAS1V13 , which encodes a constitutively activated form of Ras1 ( Ras1V13 [25] ) under the control of the MET3 promoter MET3p [33] , to generate strain WT+MET3p-RAS1V13 . The control strain was also transformed with the vector lacking the RAS1V13 to generate the control strain WT+vector . The addition of 2 . 5 mM methionine plus 2 . 5 mM cysteine ( +Met , +Cys ) represses MET3 promoter activity ( the repressed state ) and the absence ( -Met , -Cys ) activates it ( the activated state ) [33] . White cells of WT+METp-RAS1V13 and WT+vector were plated directly onto glucose agar in the presence or absence of methionine and cysteine at 25°C . In the repressed state ( +Met , +Cys ) , the majority of colonies were white , with few sectors , but in the activated state ( -Met , -Cys ) , nearly every colony was highly sectored ( Figure 2B ) , indicating that overexpression of RAS1V13 in the absence of GlcNAc induced switching . Next , white cells of strains WT+vector and WT+METp-RAS1V13 were grown in liquid glucose medium in the repressed state for one day to the mid-exponential phase , then plated on either glucose or GlcNAc agar in the induced state . On glucose agar , the switching frequency of white WT+vector cells was 2 . 1±1 . 0% , and for the overexpression strain , 100% ( Figure 2C ) . The majority of colonies of the overexpression mutant on glucose agar were highly sectored white colonies ( Figure 2D ) . Only 2 . 3±1 . 2% were homogeneous opaque colonies . On GlcNAc agar , the switching frequency of control cells was 3 . 2±0 . 2% , while that of the overexpression mutant was 100% ( Figure 2C ) . All of the latter colonies were homogeneously opaque ( Figure 2D ) . The near uniformity of the opaque phenotype in the latter colonies was evident at the cellular level ( Figure 2E ) . These results reinforce the conclusion that induction of white to opaque switching by GlcNAc is mediated primarily by Ras1 . In the cAMP pathway that is involved in filamentation , Ras1 activates adenylate cyclase , which is encoded by CDC35 [26] . The resulting increase in cAMP is kept in check by a cAMP-phosphodiesterase , which is encoded by PDE2 [34] , [35] . If GlcNAc induction of white-opaque switching is mediated by the same cAMP pathway , then deletion of CDC35 should reduce the effect , while deletion of PDE2 should enhance it . When white cdc35/cdc35 cells were grown at 25°C to saturation phase in liquid glucose medium ( seven days ) and then plated on GlcNAc agar , the frequency of switching was 8 . 0±3 . 5% , whereas that of the WT parental control was 86 . 9±4 . 3% ( Figure 3A ) . Complementation of the mutant cdc35/cdc35 with CDC35 under control of the MET3 promoter partially rescued the mutant phenotype ( Figure 3A ) . These results indicate that CDC35 is necessary for the major response to GlcNAc , as was the case for RAS1 . GlcNAc did , however , induce low level switching in white cdc35/cdc35 cells , indicating that although CDC35 is necessary for the major response to GlcNAc , there is a minor response that is CDC35-independent , just as we observed there is a minor response that is RAS1-independent . When white pde2/pde2 cells were grown at 25°C to saturation phase in liquid glucose medium ( five days ) and then plated on GlcNAc agar , the switching frequency was 100% , compared to 78 . 6±6 . 5% for control cells ( Figure 3A ) . When plated on glucose agar , the frequency of switching was 96 . 0±1 . 5% , compared to 0 . 6±0 . 6 for WT cells ( Figure 3A ) . Complementation of the pde2/pde2 mutant with PDE2 under the control of the MET3 promoter partially rescued the mutant phenotype ( Figure 3A ) . To explore further the role of Pde2 in switching , the deletion mutant pde2/pde2 was also transformed with a vector containing PDE2 under the control of the MET3 promoter to generate the strain pde2/pde2+MET3p-PDE2 . The deletion mutant pde2/pde2 was transformed with the vector lacking PDE2 to generate the control strain pde2/pde2+vector . When white cells of the parental control ( WT ) were grown at 25°C as a streak on glucose agar lacking methionine and cysteine ( activating conditions ) , only rare opaque sectors formed at the periphery ( Figure 3B ) . When the mutant pde2/pde2+vector was streaked at 25°C , opaque sectors rimmed the entire streak ( Figure 3B ) . In contrast , opaque sectors were absent at the periphery of the streak of the overexpression mutant pde2/pde2+MET3p-PDE under activating conditions ( Figure 3B ) . When the overexpression mutant pde2/pde2+MET3p-PDE was grown at 25°C in liquid glucose medium under activating conditions to early exponential phase ( two days ) , then plated on glucose agar at 25°C under activating conditions , the frequency of sectoring was 2 . 8±1 . 2% , compared to 98 . 9±1 . 9% for the deletion mutant ( Figure 3C ) . When plated at 25°C on GlcNAc agar under activating conditions , the frequency of switching of the rescued strain was 26 . 8±5 . 8% compared to 100% for the mutant ( Figure 3C ) . Examples of colonies of strain pde2/pde2+vector and pde2/pde2+MET3p-PDE grown at 25°C on glucose or GlcNAc agar under activating conditions are presented in Figure 3D , and examples of cells from these colonies are presented in Figure 3E . These results support the conclusion that cAMP is involved in the regulation of the GlcNAc response and that Pde2 plays the role of a negative regular . In the Ras1/cAMP pathway , cAMP activates protein kinase A ( PKA ) [27] , [36] . In S . cerevisiae there are three PKA catalytic subunits , Tpk1 , Tpk2 and Tpk3 , which play roles in the cAMP pathway regulating pseudohypha formation [36] , [37] . C . albicans possesses two isoforms , Tpk1 and Tpk2 , which have been demonstrated to play functionally different roles in filamentation , depending upon environmental conditions [27] , [38] . Consistent with previous reports [27] , our lack of success in generating a double mutant of TPK1 and TPK2 suggested that this mutant may not be viable . We therefore analyzed the individual deletion mutants tpk1/tpk1 and tpk2/tpk2 . White cells of the individual deletion mutants were grown at 25°C in liquid glucose medium to saturation phase ( seven days ) and then plated on glucose or GlcNAc agar and examined for switching after five days . Deletion of either TPK1 or TPK2 had no detectable effect on the frequency of switching on glucose or on GlcNAc agar ( Figure 3F ) . There was , however , one noticeable difference in the GlcNAc-induced opaque colonies of tpk1/tpk1 . They possessed a mixture of opaque cells and cells that had formed hyphae , suggesting that Tpk1 plays a role in the regulation of the bud-hyphae transition ( data not shown ) . Given that each of the alternative PKA isoforms may perform redundant function in the two mutants tpk1/tpk1 or tpk2/tpk2 , we generated overexpression mutants in the wild type background WUM5A ( WT ) in which TPK1 or TPK2 was placed under the regulation of the strong constitutive ACT1 promoter [5] . White cells of the overexpression strains WT+ACTp-TPK1 and WT+ACTp-TPK2 , as well as white cells of the control strain , were grown for five days at 25°C to saturation phase in liquid glucose medium and then plated on glucose or GlcNAc agar and examined after five days at 25°C for switching ( Figure 1A ) . Overexpression of TPK1 had no effect on the switching frequency on glucose agar and actually suppressed switching on GlcNAc agar ( Figure 3F ) . Overexpression of TPK2 , however , caused a tenfold increase in the switching frequency on glucose agar over that of wild type cells and enhanced the frequency of switching by approximately 20% on GlcNAc agar ( Figure 3F ) . These results suggested that in wild type cells , Tpk2 may function as the major downstream kinase in the GlcNAc induction pathway . At 37°C , GlcNAc induction was impaired in the mutants ras1/ras1 and cdc35/cdc35 ( supplemental Table S1 ) but to a lesser extent than at 25°C . High temperature induction , however , reinforced our conclusion that it is Tpk2 that plays the crucial role in wild type cells in transducing GlcNAc induction . Whereas at 25°C GlcNAc induction was unaffected in tpk1/tpk1 and tpk2/tpk2 , at 37°C it was reduced in the tpk2/tpk2 , but not tpk1/tpk1 ( supplemental Table S1 ) . These results supported our conclusion based on overexpression data ( Figure 3B-E ) that Tpk2 plays a role in transducing GlcNAc induction in wild type cells . The WOR1 ( TOS9 ) locus has been demonstrated to regulate spontaneous white-opaque switching [5]–[7] . It has been proposed that a stochastic increase in WOR1 expression above a threshold causes cells to switch from white to opaque , and that continued expression above that threshold maintains the opaque phenotype [5]–[7] . Wor1 has been shown to auto-induce at the level of transcription [5]–[7] . When activated , the cAMP pathway , which traditionally functions by cAMP-activation of protein kinase A , might increase the frequency of switching by phosphorylating either Wor1 or one of the several proteins that modulate WOR1 function through transcriptional regulatory loops [8] , [9] or chromatin modification [10]–[12] . Interestingly , Wor1 possesses a single consensus PKA phosphorylation motif , between amino acids 64 and 69 with a phosphorylatable threonine at amino acid 67 [5] . To test whether Wor1 was essential for GlcNAc-activated switching , white cells of the parental strain ( WT ) and the WOR1 deletion mutant wor1/wor1 were grown at 25°C to saturation phase ( seven days ) in liquid glucose medium and then plated on nutrient agar containing glucose or GlcNAc at 25°C . The wor1/wor1 mutant did not switch on either glucose or GlcNAc agar ( Figure 4A , B ) . Neither a single opaque colony or opaque sector was observed among more than 1 , 000 colonies . This was also true at 37°C ( data not shown ) . We then tested whether overexpression of WOR1 drove the phenotype to opaque in the ras1/ras1 , pde2/pde2 , cdc35/cdc35 , tpk1/tpk1 and tpk2/tpk2 mutants by transforming these mutants with a construct in which WOR1 was under the regulation of the inducible MET3 promoter [33] . In the activated state , 100% of white cells of all five strains , when plated on either glucose or GlcNAc agar at 25°C , switched to opaque ( Figure 4C ) . These results demonstrated that WOR1 is essential for the induction of switching by GlcNAc and that it plays a role downstream of the Ras1/cAMP pathway . To test whether threonine phosphorylation is necessary for Wor1 function , the homozygous deletion mutant wor1/wor1 was transformed with the WOR1TA construct , in which the phosphorylatable threonine 67 residue was replaced with the nonphosphorylatable amino acid alanine and the construct placed under the control of the inducible tetracycline promoter ( TETp ) to generate strain wor1/wor1+TETp-WOR1TA . A control strain wor1/wor1+TETp-WOR1 , was generated in which the mutant wor1/wor1 was transformed with a construct containing the native WOR1 ORF under the regulation of the tetracycline promoter . A second control strain , wor1/wor1+vector , was also generated in which wor1/wor1 was transformed with the vector lacking a WOR1 derivative . White cells of the three test strains were grown in liquid glucose medium at 25°C to saturation phase ( five days ) and then plated on glucose or GlcNAc agar at 25°C . Both the liquid and agar media contained either 50 or 200 µg per ml of the tetracycline analog doxycycline , which had been shown to induce submaximal and maximal levels of expression , respectively [39] . When native WOR1 was overexpressed both in glucose liquid medium and on glucose agar , the switching frequency was 100% at both 50 and 200 µg per ml of doxycycline ( Figure 4D ) . When WOR1TA was overexpressed in both liquid glucose medium and then after plating on glucose agar at either 50 or 200 µg per ml of doxycycline , the frequency of switching was zero percent ( Figure 4D ) . When native WOR1 was overexpressed in both GlcNAc liquid medium and then after plating on GlcNAc agar at 50 and 200 µg per ml of doxycycline , 100% of the colonies underwent switching ( Figure 4D ) . At 200 µg per ml of doxycycline , over 70% of the cells in the opaque colonies exhibited the elongate opaque cell phenotype ( Figure 4E ) . When WOR1TA was overexpressed in both GlcNAc liquid medium and then after plating on GlcNAc agar at 50 µg per ml of doxycycline , 0% of the colonies exhibited switching ( Figure 4D ) . However , when WOR1TA was overexpressed in GlcNAc media at 200 µg per ml of doxycycline , 100% of the colonies were light pink ( Figure 4D ) . Microscopic analysis revealed that 10% of the cells in these pink colonies exhibited the elongate opaque phenotype ( Figure 4E ) . These results suggested that expression of the unphosphorylatable derivative of Wor1 , Wor1TA , was capable of inducing switching , but with a 10-fold reduction in efficiency . Because WOR1 and WOR1TA were fused in frame with GFP in the overexpression mutants , we used confocal microscopy to test whether Wor1TA localized normally to the nucleus and was expressed at the same level as Wor1 . Both Wor1 and Wor1TA localized to the nucleus of a majority of cells of the overexpression mutants treated with doxycycline , as demonstrated by overlapping GFP fluorescence and staining with DAPI , a DNA indicator ( Figure 5A ) . Moreover , GFP fluorescence of nuclei was qualitatively comparable for Wor1-GFP and Wor1TA-GFP ( Figure 5A ) . These results demonstrated that although the replacement of threonine with alanine caused a dramatic decrease in its capacity to support switching , it did not affect nuclear localization or cause a decrease in the transcript level . The levels of the Wor1 and Wor1TA protein were then compared by western blot analysis using anti-GFP antibody . The levels of Wor1 and Wor1TA expressed in white cells of strains wor1/wor1+TETp-WOR1 and wor1/wor1+TETp-WOR1TA , respectively , treated with 200 µg per ml of doxycycline were similar ( Figure 5B ) . These results indicate that the decrease in Wor1 function resulting from the replacement of threonine with alanine in the PKA consensus motif of Wor1 was due to a decrease in function , rather than to a decrease in the level of the Wor1 protein or mis-localization . We had demonstrated that 1% CO2 induced switching submaximally and that at this concentration induction was dependent primarily upon the Ras1/cAMP signal transduction pathway [18] . We have shown here that GlcNAc induction was also submaximal when cells were grown for only two days in glucose liquid medium to mid-log phase ( Figure 1C , D ) . We therefore tested whether cells growing at 25 or 37°C in a suboptimal concentration of CO2 ( 1% ) and for a suboptimal period of time in glucose liquid medium enhanced GlcNAc induction . White cells of an a/a and an α/α strain were first grown at 25°C in glucose liquid medium in air for two days and then plated on either glucose or GlcNAc agar either in air or in air containing 1% CO2 at 25 or 37°C . On glucose agar in 1% CO2 at both temperatures , cells of the a/a and α/α strains exhibited switching frequencies that were significantly higher than in air alone ( Table 1 ) . When plated on GlcNAc agar in air at 25°C , the respective frequencies of the two strains were 26 . 7±1 . 2% and 22 . 4±3 . 3% , but at 37°C , they were 98 . 9±2% and 99 . 7±0 . 6% . However , when plated on GlcNAc agar in 1% CO2 , the switching frequency at the two temperatures were 99 to 100% in both strains ( Table 1 ) . These results indicate synergy for CO2 and GlcNAc induction , and enhancement by physiological temperature .
Recently , high CO2 was demonstrated to be a potent inducer of the white-opaque transition at 37°C [18] . The high concentrations of CO2 that induce and maintain the opaque phenotype at 37°C are found in select host tissues and in the gastrointestinal tract [40] , [41] . The main source of CO2 in the gastrointestinal tract is the result of metabolism by colonic bacteria [40] . Here , we demonstrate GlcNAc , also a product primarily of bacteria that cohabit the host gastrointestinal tract with C . albicans [21]–[23] , [42] represents a second potent inducer of the white to opaque transition . By mutational analyses , we have found that the components of the Ras1/cAMP pathway , Ras1 , Cdc35 , Pde2 and PKA ( Tpk1 , Tpk2 ) , mediate the major portion of GlcNAc induction . Our results also indicate that switching in glucose medium , which is at a far lower frequency than that in GlcNAc medium , is mediated in part by the Ras1/cAMP pathway . The low level of induction in deletion mutants of the Ras1/cAMP pathway is still approximately ten-fold higher than the level caused by glucose in wild type cells . Because the pathway that transduces high CO2 induction is also Ras1/cAMP-independent and unidentified , the possibility must be entertained that it may be the same Ras1/cAMP-independent pathway that mediates the minor portion of GlcNAc induction . Our results , especially those at 37°C , suggest that the PKA isoform Tpk2 functions as the major downstream target of cAMP in the GlcNAc response pathway . Tpk1 appears to be capable of substituting for Tpk2 in the mutant tpk2/tpk2 , but when overexpressed , inhibits GlcNAc-induced switching . One possible explanation is that the two Tpk isoforms play inhibiting and stimulating roles , respectively , in the white to opaque transition , especially in light of the fact that Efg1 , a negative regulator of Wor1 [8] , contains a PKA phosphorylation site . Tpk isoforms have been found to play different roles in the same regulatory networks in other systems . In S . cerevisiae , while Tpk2 activates filamentation downstream of cAMP in the glucose induced pathway , Tpk1 and Tpk3 inhibit filamentation by a feedback loop [36] . In addition , in the pheromone response pathway of C . albicans , the downstream MAP kinases Cek1 and Cek2 also play both distinct and overlapping roles [43] , suggesting a general pattern of functional complexity of downstream protein kinase isoforms in signal transduction pathways . Mutant analysis revealed that both the major and minor GlcNAc induction pathways required the master switch locus WOR1 . Because cAMP activates PKA , we considered the possibility that Wor1 , which contains one conserved PKA phosphorylation motif between amino acids 64 and 69 , might have to be phosphorylated to function in the switch event . By converting the single threonine residue at that site to alanine , GlcNAc induction was impaired dramatically , indicating that phosphorylation of threonine 67 of Wor1 is necessary for maximum induction by GlcNAc . The observation that GlcNAc induction was completely blocked in the wor1/wor1 mutant , but only impaired in the wor1/wor1+TETp-WOR1TA mutant suggested that the constitutively unphosphorylated form of Wor1 was still functional , but at far lower efficiency than the phosphorylated form . In Schizosaccharomyces pombe , the gluconate transport inducer 1 ( Gti1 ) , an ortholog of Wor1 , also harbors one conserved PKA phosphorylation motif between amino acids 65 and 70 , and conversion of the single threonine residue at that site to alanine causes severe impairment of function [44] . As is the case in C . albicans , Pka1 , which is the only PKA in S . pombe , is involved in the regulation of Gtil . Given that in C . albicans , Wor1 has one conserved PKA phosphorylation site that must be phosphorylated to attain the major portion of GlcNAc induction , and that Tpk2 appears to be the downstream PKA involved in GlcNAc induction , it seems reasonable to suggest that GlcNAc induction may involve the direct phosphorylation of Wor1 by Tpk2 , but that remains to be demonstrated . The Ras1/cAMP-dependent pathway has been found to be the predominant one for GlcNAc induction and the minor one for low level CO2 induction [18] ( Figure 6 ) . An unidentified pathway has been found to be the predominant one for CO2 induction [18] and an unidentified pathway has the minor one for GlcNAc induction ( Figure 6 ) . Our data further suggest that glucose represents a weak but significant inducer of switching that also functions through both a Ras1/cAMP-dependent pathway and a Ras1/cAMP-independent pathway , the latter again unidentified ( Figure 6 ) . The fact that each inducer functions not only through the Ras1/cAMP pathway , but also through an unidentified pathway , leaves open the possibility that the Ras1/cAMP-independent pathway may also be common to all three inducers . We demonstrated previously that both the Ras1/cAMP-dependent and -independent pathways for CO2 induction are dependent on Wor1 [18] , and we have demonstrated here that the dependent and independent pathways for G1cNAc and glucose induction are also dependent on Wor1 . We have demonstrated that only the major portion of G1cNAc induction , which is transduced by the Ras1/cAMP pathway , requires the phosphorylated form of Wor1 . The induction of switching by environmental cues shares several characteristics with that of filamentation . First , both CO2 and GlcNAc induce filamentation [19] , [20] , [24] as they do switching . Second , the Ras1/cAMP pathway has been demonstrated to play a role in the induction of filamentation by CO2 [20] , as it does in switching . The Ras1/cAMP pathway has also been demonstrated to play a role in the induction of filamentation in S . cerevisiae [36] , [45] , suggesting that the pathways regulating of filamentation represent an ancestral process conserved in the evolution of both the Candida and Saccharomyces groups of the hemiascomycetes . Several characteristics of the opaque phenotype are shared with hyphae , including an elongate shape , a prominent vacuole and cell surface antigens [46] , [47] . Because white-opaque switching is a specific and unique characteristic of C . albicans and the closely related species Candida dubliniensis [48] , it represents a newly evolved developmental process , in contrast to filamentation . Switching of C . albicans from white to opaque at physiological temperature can therefore be influenced by two factors in the gastrointestinal tract that result primarily from gastrointestinal bacteria: high CO2 [18] and free GlcNAc . In host tissue , high CO2 is the result of metabolism by the host , but in the gastrointestinal tract , it is the product of bacterial metabolism [40] , [41] . GlcNAc in the gastrointestinal tract is also a product primarily of gastrointestinal tract bacteria , but also to a lesser extent of host goblet cells [41] . Hence , bacteria of the gastrointestinal tract produce two potent inducers of the white to opaque transition , a prerequisite for mating between a/a and α/α cells [4] . These results suggest that two developmental programs of C . albicans , filamentation and switching , have evolved to respond to signals originating from bacterial co-members of the gastrointestinal microbiota .
The strains of C . albicans used in this study are listed in supplemental Table S2 . For routine growth , modified Lee's medium without methionine was used [31] , unless stated otherwise . For repression of MET3 promoter-controlled gene expression , 2 . 5 mM methionine and 2 . 5 mM cysteine were added to the medium . For GlcNAc induction , the carbon source glucose was replaced with GlcNAc ( 1 . 25% w/v ) in nutrient medium . Here , agar containing Lee's medium , in which glucose was the carbon source , was referred to as glucose agar and Lee's medium containing GlcNAc as a carbon source was referred to as GlcNAc agar . Agar cultures were grown at a density of 80–120 colonies per 85 mm plate . Phloxine B was added to nutrient agar for opaque colony staining [46] . The PDE2 gene was disrupted using a modified Ura-blaster method [49] . Two long primers ( PDE2-5DR , PDE2-3DR ) , each containing a different 60 nucleotide sequence homologous to the gene PDE2 , were used for PCR amplification ( supplemental Table S3 ) . pDDB57 , which contains the recyclable URA3-dpl200 marker , was used as template . The PCR product was transformed into WUM5A , a WO-1 derivative [50] . Transformants were grown on selective synthetic defined ( SD ) medium SD-Ura agar plates . To delete the second allele of PDE2 , the PCR product was transformed into a spontaneous Ura- derivative of PDE2/pde2 obtained from SD agar containing 5-fluoro-orotic acid . pde2/pde2 null mutants were selected from SD-Ura agar plates and confirmed by PCR . TPK1 and TPK2 were deleted by a PCR product-directed disruption protocol , as described in [18] . Briefly , the HIS1 and ARG4 markers were amplified by PCR from pGEM-HIS1 and pRS-ARG4-SpeI , respectively . The oligonucleotide pairs TPK1-5DR , TPK1-3DR; TPK2-5DR , TPK2-3DR ( supplemental Table S3 ) were used for PCR amplification . The HIS1 and ARG4 markers were sequentially transformed into the host strains GH1013 [51] , and heterozygous mutants . The null mutants were selected on SD-His-Arg plates and confirmed by PCR . The primers used for plasmid constructions are listed in supplemental Table S3 . To generate pMET-RAS1V13 , the RAS1 ORF containing a mutation at the thirteenth amino acid ( glycine to valine mutation ) was amplified from pQF145 . 2 [25] by using primers including PstI and SphI sites , and then cloned into pCaEXP [33] . pMET-CDC35 was constructed by inserting the BamHI-SphI digested CDC35 ORF fragment into pCaEXP . To generate pMET-PDE2 , the PDE2 ORF was amplified from CAI4 genomic DNA by using the primers PDE2F and PDE2R that contained BamHI and SphI sites ( supplemental Table S3 ) and the PDE2 ORF was cloned into pCaEXP . To generate pACT-TPK1 , the TPK1 ORF was amplified from CAI4 genomic DNA by using the primers TPK1F and TPK1R that included EcoRV and HindIII sites ( supplemental Table S3 ) , and the TPK1 ORF was cloned into pACT1 [18] . The TPK2 ORF was amplified from CAI4 genomic DNA by using primers TPK2F and TPK2R ( supplemental Table S3 ) . To generate pACT-TPK2 , the PCR product was digested by HindIII and cloned into EcoRV/HindIII-digested pACT1 . pNIM-WOR1 was constructed by inserting a SalI digested PCR fragment of WOR1 into the SalI site of pNIM1 [52] . The WOR1 ORF was amplified from CAI4 genomic DNA . The primers WOR1salF and WOR1salR were used for PCR amplification ( supplemental Table S3 ) . To generate a mutation in which threonine 67 is replaced with alanine in the WOR1 gene , a two-step PCR method [53] was used with slight modification . The primers WOR1TAF and WOR1TAR ( supplemental Table S3 ) were used to generate site-directed mutation . The second-round PCR product was digested with SalI and subcloned into the SalI site of pNIM1 . The resulting plasmid was referred to as pNIM-WOR1TA . The correct direction of WOR1 ORF and WOR1TA fragment in pNIM1 was confirmed by sequencing . White-opaque switching on agar was analyzed as described previously [15] . Briefly , strains were first grown on agar containing supplemented Lee's medium for 6 days at 25°C . Colonies were then replated onto plates containing supplemented Lee's medium [31] . These plates were then incubated at 25°C for five days , and the proportion of colonies exhibiting different phenotypes counted . White colonies were inoculated into a test tube containing 1 ml of supplemented Lee's medium [31] with glucose as carbon source and incubated at 25°C . The overnight culture was diluted ( to 2×105 cells/ml ) in 20 ml of fresh medium with glucose as the carbon source and incubated at 22°C in a shaker . Aliquots were taken out at different time points , diluted and plated onto both glucose agar and GlcNAc agar plates ( Figure 1A ) . The plates were then incubated at 25°C for five days , and the colonies exhibiting different colony phenotypes counted . For the experiments performed at 37°C , plates were cultured at both 25 and 37°C . Cells from liquid cultures were spun down following doxycycline treatment for 12 hours . Total protein extract was obtained using a bead beater in lysis buffer that contained 50 mM Tris-HCl , 100 mM NaCl , 5 mM MgCl2 , 1 mM DTT , 1 mM EDTA , 1 mM EGTA , 0 . 1% Tween-20 , and 5% glycerol , supplemented with a protease inhibitor cocktail ( Sigma-Aldrich , St Louis , MO ) and 1 mM phenyl-methylsulphonyl fluoride . An equal amount of total protein from each sample was then subjected to protein G beads ( Active Motif , Carlsbad , California ) for pre-clearing , followed by immuno-precipitation ( IP ) using rabbit GFP antibody-conjugated agarose beads ( Santa Cruz Biotechnology , Santa Cruz , California ) . IP protein samples were subjected to SDS-PAGE ( 8% polyacrylamide ) electrophoresis . After electrophoresis , the SDS-PAGE protein gel was transferred to a PVDF membrane ( Immobilon-P , Millipore Corporation , Bedford , MA ) , blocked for 1 h in 3% non-fat dry milk in TBS-T ( 20 mM Tris-HCl , pH 7 . 5 , 150 mM NaCl , 0 . 05% Tween-20 ) , and then incubated with rabbit polyclonal GFP antibody ( Santa Cruz Biotechnology , Santa Cruz , CA ) overnight at 4°C [54] . After washing six times in TBS-T , the proteins on the membrane were detected with horseradish peroxidase-labelled goat anti-rabbit IgG ( Promega , Madison , WI ) and SuperSignal West Pico Chemiluminescent Substrate ( Pierce , Rockford , IL ) . Cells expressing tetracycline ( doxycycline ) -inducible GFP-labeled Wor1p were grown to midlog phase in the presence of 50 µg/ml doxycycline ( Sigma-Aldrich , St Louis , MO , USA ) , harvested and simultaneously permeabilized and the nuclei labeled with 4′ , 6′-Diamidino-2-phenylindole ( DAPI , Invitrogen , Inc . ) by incubating them for 10 min at room temperature in the dark in a solution containing 5 µg/ml DAPI in 1 M Sorbitol , 0 . 1% Saponin , 150 mM NaCl and 20 mM Tris buffer , pH 7 . 4 , followed by a 15–20 min incubation period on ice . Without washing , the cells were imaged using a Bio-Rad Radiance 2100 MP multi-photon microscope ( Bio-Rad , Hermel , Hamstead , UK ) . Cells were excited at 780 nm by a Mai-Tai laser ( Spectra- Physics , Newport Corp . , Mountain View , CA ) and three channel emission images ( GFP , DAPI and transmitted ) were gathered using a sequential 2 . 0 µm Z-series , gathered at 0 . 2 µm intervals to include the entire cell nucleus . GFP and DAPI images were visualized as Z-series projections . Transmitted images were a single scan at the focal plane selected from the Z-series . | To mate , the human fungal pathogen Candida albicans must undergo a complex phenotypic change from a round “white” to large , elongated “opaque” cell . This involves the regulation of approximately 5% of the organism's genes . Surprisingly , this complex transition is not required for mating in other related yeast . Even more surprisingly , it was found that in vitro the mating-competent opaque phenotype was unstable at 37°C , the temperature of the host body . This observation led to a paradox . If C . albicans lives primarily in an animal host , physiological temperature would thwart mating , so where does C . albicans mate ? This led to the suggestion that some physiological condition in the host niche stabilizes the opaque phenotype or even induces switching from white to opaque , so cells can mate . Recently , we demonstrated that the high concentrations of CO2 found in tissue and the gastrointestinal tract induced switching from white to opaque and then stabilized the opaque phenotype . Here , we demonstrate that a second factor , N-acetylglucosamine ( GlcNAc ) , a sugar released primarily by bacteria in the gastrointestinal tract , also induces the switch from white to opaque and stabilizes the opaque phenotype . We demonstrate by mutational analysis that GlcNAc induction is regulated primarily by the Ras1/cAMP pathway , which also regulates filamentation of C . albicans . This is perhaps not surprising given that white-opaque switching shares with filamentation several phenotypic characteristics . Finally , we show that induction by GlcNAc requires the phosphorylated master switch gene that regulates spontaneous switching , suggesting that it induces the switch from white to opaque by activating the gene product of the master switch gene . Together , our results suggest that multiple signals from bacterial co-members of the gastrointestinal tract microbiota regulate switching and , therefore , mating of C . albicans in the colonized host . | [
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| 2010 | N-Acetylglucosamine Induces White to Opaque Switching, a Mating Prerequisite in Candida albicans |
Transposable elements ( TEs ) are repetitive nucleotide sequences that make up a large portion of eukaryotic genomes . They can move and duplicate within a genome , increasing genome size and contributing to genetic diversity within and across species . Accurate identification and classification of TEs present in a genome is an important step towards understanding their effects on genes and their role in genome evolution . We introduce TE-Learner , a framework based on machine learning that automatically identifies TEs in a given genome and assigns a classification to them . We present an implementation of our framework towards LTR retrotransposons , a particular type of TEs characterized by having long terminal repeats ( LTRs ) at their boundaries . We evaluate the predictive performance of our framework on the well-annotated genomes of Drosophila melanogaster and Arabidopsis thaliana and we compare our results for three LTR retrotransposon superfamilies with the results of three widely used methods for TE identification or classification: RepeatMasker , Censor and LtrDigest . In contrast to these methods , TE-Learner is the first to incorporate machine learning techniques , outperforming these methods in terms of predictive performance , while able to learn models and make predictions efficiently . Moreover , we show that our method was able to identify TEs that none of the above method could find , and we investigated TE-Learner’s predictions which did not correspond to an official annotation . It turns out that many of these predictions are in fact strongly homologous to a known TE .
Transposable elements ( TEs ) are DNA sequences that can move and duplicate within a genome , autonomously or with the assistance of other elements . The field of TE annotation includes various steps such as the identification and classification of TE families . In this article , we focus on these activities since accurate identification and classification of TEs enable researches into their biology and can shed light on the evolutionary processes that shape genomes [1] . TEs in eukaryotes can be classified according to whether reverse transcription is needed for their transposition ( Class I or retrotransposons ) or not ( Class II or DNA transposons ) . A consensus for a universal TE classification has not been reached yet [3] , but this lack of consensus does not affect the focus of our study . Here , we will follow the hierarchical system proposed by Wicker et al . [2] , which includes the levels of class , subclass , order , superfamily , family and subfamily . Fig 1 presents an illustration of Wicker’s hierarchy considered in our study . Class I is composed of five orders: LTR retrotransposons , DIRS-like elements , Penelope-like elements ( PLEs ) , long interspersed nuclear elements ( LINEs ) and short interspersed nuclear elements ( SINEs ) . Similar in structure to retroviruses , LTR retrotransposons have long terminal repeats ( LTRs ) , two normally homologous non-coding DNA sequences that flank the internal coding region and that range in size from a few hundred base pairs to more than 5 kb . Superfamilies within an order are distinguished by uniform and widespread large-scale features , such as the structure of protein or non-coding domains and the presence and size of the target site duplication ( TSD ) . Families are defined by DNA sequence conservation and subfamilies on the basis of phylogenetic data . Class II is divided into two subclasses , which are distinguished by the number of DNA strands that are cut during transposition . Subclass 1 consists of TEs of the order TIR , which are characterized by terminal inverted repeats ( TIRs ) . Subclass 2 groups the Helitron and Maverick orders . Methods identifying TEs in a genome are homology-based , employ structural information or do not use prior information at all about the TEs to be identified [4–6] . The latter methods , known as de novo repeat discovery methods , search for example for repeats in the genome . A widely used method for TE identification is RepeatMasker [7] . This tool screens a query sequence searching for repeats , taking into account their similarity with sequences from a reference library , using an optimal pairwise alignment algorithm . Censor [8] works similarly as RepeatMasker but uses BLAST for the comparison . Afterwards , both RepeatMasker and Censor remove overlaps and defragment detected repeats . Loureiro et al . [9] show that machine learning can be used to improve the identification of TEs . They assessed a set of ( non-machine learning based ) identification methods and learn a classifier that combines their predictions to determine whether a sequence is a TE or not . Another classifier predicts the best method to determine the exact boundaries of a TE . In their analysis , both RepeatMasker and Censor were the most accurate tools . While Loureiro et al . demonstrate the benefit of using machine learning models to improve predictions , they do not use machine learning to obtain the predictions , which we address in this article . A few methods have been proposed to classify TEs . LtrDigest [10] evaluates a list of LTR retrotransposons generated by another tool called LTRharvest [11] , annotating these sequences w . r . t . the protein domains and other structural characteristics that were found in them . LtrDigest can then be used for de novo ( unsupervised ) classification , i . e . , finding groups within the LTR retrotransposons without any predefined classification scheme . To evaluate whether the resulting groups represent known LTR retrotransposon superfamilies , Steinbiss et al . [10] have matched representative sequences of the groups to a reference set of known transposon sequences using a fixed set of rules . LtrSift [12] takes the LtrDigest output and clusters the candidate sequences . It then tries to find patterns of shared cluster membership that might indicate multiple TE families , e . g . different Copia-like , Gypsy-like or Bel-Pao families . It is a generic tool that uses sequence clusters to find family-specific patterns , based on the LtrDigest detected features . These patterns are then used as evidence for family discrimination . TEClass [13] classifies TE sequences into Class I and Class II TEs . The Class I elements can further be classified into LTRs and non-LTRs , and the non-LTRs are classified into the SINE or LINE orders . This classification is obtained by a hierarchy of binary classifiers based on machine learning support vector machines , using oligomer frequencies as features . RepClass [14] consists of three independent classification modules: a module based on homology information , a module that searches for structural characteristics such as LTRs or TIRs , and a module that searches for target site duplication . The three modules provide classifications at different levels of granularity , typically at the subclass or order level , sometimes at the superfamily level . Finally , an integration module aims to compare , rank , and combine the results of the three modules providing a single tentative classification . Pastec [15] also uses multiple features of TEs to classify TE sequences: structural features ( TE length , presence of LTRs or TIRs , presence of simple sequence repeats , etc . ) , sequence similarities to known TEs , and conserved functional domains found in HMM profile databases . It provides classifications on the order level , including all orders from the classification hierarchy defined by Wicker et al . [2] , whereas TEClass and RepClass only consider a subset of the orders . Importantly , none of the above classification systems is able to provide classifications for LTR retrotransposons at the superfamily level . One exception is a recently introduced method called LtrClassifier [16] , which performs both annotation ( i . e . , identifying structural elements ) and classification ( but not identification ) for plant genomes , and returns predictions for the Copia and Gypsy superfamilies . In this article we introduce TE-Learner , a framework for the identification of TEs of a particular order , and for the classification of these TEs on the superfamily level . TE-Learner consists of three steps . First , based on the characteristics of the order under consideration , it extracts from the genome a set of candidate sequences , which may include false positives . Second , it automatically annotates these candidates with features . Finally , the features are given as input to a machine learning model , which predicts whether a given candidate sequence is indeed a TE of the considered order , and if so , predicts its superfamily . In particular , we present TE-LearnerLTR , an implementation of this framework for LTR retrotransposons , which include the superfamilies Copia , Gypsy and Bel-Pao [2] . As features we consider the occurrence of conserved protein domains , which help TEs perform the transposition process . The machine learning method we apply is random forests . This last step is essential , since the model of the three superfamilies contains the same protein domains [2]; for Gypsy and Bel-Pao some domains even occur in the same order . As LTR retrotransposons have a high abundance in the genomes of Drosophila melanogaster [17] and Arabidopsis thaliana [18 , 19] , and as these genomes are well annotated , they present the ideal candidates for evaluating how well our proposed method can identify and classify the LTR retrotransposons without using any prior information about these genomes . We present an extensive quantitative analysis on D . melanogaster and A . thaliana comparing the obtained results to three widely used methods ( each dealing with one of the two tasks considered ) and we show that TE-LearnerLTR outperforms the state-of-the-art methods w . r . t . predictive performance and runtime . The novelty of our proposed method w . r . t . the available methods lies mainly in three aspects . First , in contrast to the other methods , which focus on one task , here we consider the tasks of identifying and classifying TEs together . Second , we propose a general framework for these tasks . Even though the implementation we provide in this article focuses on LTR retrotransposons , our framework can be extended to other TE orders . Third , in contrast to classification methods such as LtrDigest , LtrSift , RepClass , Pastec and LtrClassifier , our method is not based on a predefined set of rules . Instead , we exploit the strength of machine learning to automatically derive rules from the available data , with no need of prior knowledge . Our framework is the first step towards completely automatic identification and classification of TEs in superfamilies .
We address the following problem: given an unannotated genome , find subsequences in it corresponding to a particular order from the classification scheme [2] , and predict their superfamily . We propose the following three-step framework , called TE-Learner: We now discuss TE-LearnerLTR , one particular implementation , for every step in detail , focusing on the LTR retrotransposon order . In Step 1 we use a simple splitting strategy to obtain subsequences of the genome . The features used in Step 2 are conserved protein domains known to occur in LTR retrotransposons , and the machine learning model used in Step 3 is a random forest . Fig 2 shows a schematic representation of our framework based on this implementation . Note the modularity of the framework: every step can be implemented independently of the other steps . For instance , an alternative implementation could use an LTR pair detection tool in Step 1 , annotate the candidates with oligomer frequencies in Step 2 , and apply an artificial neural network in Step 3 . Any machine learning classifier can be used , as long as it outputs a probability . We evaluate the predictive performance of our framework on the genomes of D . melanogaster and A . thaliana . We use version 6-15 of the annotated genome from Flybase ( http://flybase . org/ ) , as the official annotation for D . melanogaster , which was made publicly available in April 2017 . We use the Flybase annotations “Transposable Elements” and “Repeat Regions” to constitute the golden standard in our experiments . For A . thaliana , we used the Araport11 annotation , released in June 2016 , for genome TAIR10 , from The Arabidopsis Information Resource ( TAIR ) ( http://www . arabidopsis . org ) . We will compare our results for the Copia , Gypsy and Bel-Pao superfamilies ( Bel-Pao only for D . melanogaster because there is no annotation for it in A . thaliana ) with those of three methods for TE identification or classification that can make predictions at the superfamily level: RepeatMasker , Censor and LtrDigest . For each superfamily , we also compare the results to those of a baseline model . We now discuss the specific parameter settings for each of the tools used in our framework , as well as for the methods we compare to . Baseline: The baseline model starts from the TE candidates obtained in Step 2 of our framework and makes predictions solely based on the presence of one key protein domain ( as predicted by the RPS-Blast program ) : RNase_HI_RT_Ty1 , RNase_HI_RT_Ty3 , and RT_pep_A17 for Copia , Gypsy , and Bel-Pao , respectively . As such , it evaluates the impact of the machine learning aspect ( step 3 ) in our framework . RPS-Blast: We constructed the database used by RPS-Blast by taking for each domain of interest the set of sequences from the Conserved Domain Database ( CDD ) ( http://www . ncbi . nlm . nih . gov/Structure/cdd/cdd . shtml ) [28] , used to generate the original multiple sequence alignment , except that we excluded the sequences of the organisms used in our tests ( D . melanogaster and A . thaliana , respectively ) . The reason to exclude the D . melanogaster or A . thaliana sequences is that we want to provide an evaluation as blindly as possible , without using any known information from the target organism . The PSI-Blast ( Position-Specific Iterative Blast ) program was used to obtain the new PSSMs and Makeprofiledb application for creating the database for RPS-Blast . FORF: The relational trees were built with default parameters , except for the minimum number of examples in a leaf , which was set to 5 . No pruning was used . The forests consist of 100 trees , with a feature sample size at each node equal to the square root of the number of possible features . For the training of FORF we used sequences from Repbase ( http://www . girinst . org/server/RepBase/ ) , volume 17—issue 3 , one set for each superfamily of interest here . In order to provide a fair evaluation , as before , we excluded from these sets the sequences of the target organism . Note that each analysis was performed twice: each time leaving out one target organism . The resulting sets were also used as the databases for RepeatMasker and Censor applications—as described further . We ran the RPS-Blast program , with the PSSM database created in Step 2 of our framework , to search these sequences for regions related to the conserved domains of interest ( Table 1 ) , retrieving the same types of information obtained from the screening of the candidates ( Step 2 ) . We observed that the longest predicted domain region in these sequences has a length smaller than 800 nucleotides , which indicates that the overlap size of 1 , 000 nucleotides we used in Step 1 of our framework , is sufficient . We removed training sequences without domain hits and those that contained domain hits in both strands of their genomic sequence . The resulting number of sequences is 3188 for Copia , 4718 for Gypsy , and 891 for Bel-Pao when leaving out D . melanogaster sequences . Leaving out A . thaliana , the numbers become 3077 for Copia and 4728 for Gypsy . These sequences constitute the positive training set . For each superfamily , we also constructed a negative set , by sampling without replacement from the other superfamilies . For Copia and Bel-Pao the negative set has an equal size as the positive set; however for Gypsy , given the size of its positive set , the negative set contains less sequences ( all Copia and Bel-Pao sequences ) , which still yields a balanced classification task . RepeatMasker and Censor: These systems were run using their standard parameter settings . For a fair evaluation , we used as reference library the same training sets as for FORF as described above . As each of the training sets belongs to a particular superfamily , we can label hits with the corresponding superfamily . Both applications were run on the complete genomes . LtrDigest: This method was also run with its standard parameter settings on the complete genomes . We only retained predictions with an assigned DNA strand and used the authors’ guidelines to assign a particular superfamily to each prediction as follows . Every predicted sequence is annotated with protein domain hits . If the sequence has a “Peptidase_A17” hit it is classified as BelPao; otherwise , if the sequence has a “Gypsy” hit , it is classified as Gypsy; otherwise , following [10] , if the sequence has an “INT” followed by an “RT” ( there may be other hits in between ) , it is classified as Copia and if an “RT” is followed by an “INT” , it is classified as Gypsy . The remaining sequences are not classified . We report the predictive performance of the different methods with precision-recall ( PR ) curves [29] . The motivation for preferring PR curves over the more popular ROC curves is as follows . Only a small fraction of the genome contains TE sequences of a specific superfamily , thus we are more interested in recognizing the positive cases , i . e . the candidate sequences that actually belong to the superfamily , than in correctly predicting the negatives . Precision is the percentage of predictions that are correct and recall is the percentage of annotations that were predicted . A PR curve plots the precision of a model as a function of its recall . Assume the model predicts the probability that a new example is positive , and that we threshold this probability with a threshold t to obtain the predicted class ( positive or negative ) . A given threshold corresponds to a single point in PR space , and by varying the threshold we obtain a PR curve: while decreasing t from 1 . 0 to 0 . 0 , an increasing number of examples is predicted positive , causing the recall to increase whereas precision may increase or decrease ( with normally a tendency to decrease ) . A domain expert can choose the threshold corresponding to the point on the curve that looks most interesting . To consider a prediction as a true positive , we do not require it to match the exact same boundaries of the corresponding annotation of the genome , as this would be an overly strict criterion . Instead , we allow some tolerance by defining a true positive as a prediction which has a minimum overlap of 100 nucleotides with an annotation , or a prediction which overlaps a complete annotation and vice versa . Our motivation for this evaluation is that a domain expert can inspect each prediction and determine the exact boundaries of the complete TE . The random forests in TE-LearnerLTR only make predictions w . r . t . the superfamily for which they were built . For example , one forest outputs the probability whether a sequence belongs to Copia or not . However , one might be interested in having a model that can make predictions w . r . t . many superfamilies at the same time . An advantage of such a model is that the user does not need to combine the results of individual models , avoiding conflicting predictions . As our models output probabilities , one straightforward idea to obtain this more general model consists of selecting the superfamily with the highest probability . To avoid that a superfamily with a very low probability is predicted , we include the category None ( i . e . , the sequence does not belong to any of the considered superfamilies ) , which is predicted when none of the probabilities exceeds a certain threshold . In this setting , we construct a single average PR curve for all superfamilies together as follows . When a sequence is predicted to have a certain superfamily , we consider it correct if the sequence indeed belongs to that superfamily . The definition of precision and recall is then as before . Thus , for precision , the denominator contains all candidate predictions , minus those predicted as None; for recall , the denominator contains all annotations ( for all considered superfamilies ) . We compare our results to those of LtrDigest , which is also able to make predictions w . r . t . different superfamilies at the same time .
Before discussing each superfamily in detail , we first show for both genomes the number of predictions that were made and the average prediction length of each method and for each superfamily ( Table 3 ) . Note that TE-LearnerLTR presents the same numbers of candidates and average length of candidates for the three superfamilies . This happens because in our implementation Steps 1 and 2 output one common candidate set for the three superfamilies . From the table it is clear that RepeatMaskerand Censor make a lot of predictions , which are on average much smaller than the predictions of TE-LearnerLTR . LtrDigest on the other hand , makes much less predictions , which are considerably longer . Figs 10 and 11 report the combined PR curves of TE-LearnerLTR and the point of LtrDigest . For D . melanogaster , the point of LtrDigest obtains a slightly higher precision ( 0 . 80 ) than TE-LearnerLTR ( 0 . 77 ) at a recall of 0 . 15 . For A . thaliana , LtrDigest and TE-LearnerLTR both obtain a precision of 1 , however , the latter obtains a higher recall . Moreover , our combined model has the advantage of allowing the user to choose an appropriate threshold .
In this paper we have proposed a framework based on machine learning to identify and classify TEs in a genome . We evaluated our approach on three Class I TE superfamilies in D . melanogaster , and two Class I TE superfamilies in A . thaliana , using a relational random forest model . We found a better predictive performance ( w . r . t . F1 measure ) and runtime compared to three widely used methods for TE identification and classification . In terms of F1 , the performance of RepeatMasker comes close to TE-LearnerLTR , however , it obtains a higher recall , because it is able to recover TEs that have no conserved protein domains . The fact that we rely on these protein domains is a clear limitation of our method , yet , we are able to find TEs that other methods did not find . This suggests that TE-LearnerLTR presents a viable alternative to the state-of-the-art methods , in case one prefers predictions with a very high precision , or as a complement to the other methods when one is interested in finding more TEs . Furthermore , for our top predictions not confirmed by the official annotations , we validated their homology to known TEs of the corresponding superfamilies , showing that our method could be useful to detect missing annotations . While our implementation has been focusing on LTR retrotransposons , it is possible to train it on other TE orders with superfamilies that have recognizable protein domains . Alternatively , one could change the implementation of any of the steps of the framework: the machine learning model , the features used , and the candidate generation procedure . For instance , to identify TEs from the TIR order ( a Class II order with Terminal Inverted Repeats ) , the first step could use software tools to identify a candidate set of sequences surrounded by TIRs ( such as [30 , 31] ) . A possible direction for further work is to explore hierarchical classification methods in the machine learning step of the framework . This would allow to exploit the underlying structure of the TE classification scheme . Additionally , one could try to still boost the performance of the different steps of the framework , e . g . , by improving protein domain detection , or by including additional features ( including features not related to protein domains ) in the decision trees . | Over the years , with the increase of the acquisition of biological data , the extraction of knowledge from this data is getting more important . To understand how biology works is very important to increase the quality of the products and services which use biological data . This directly influences companies and governments , which need to remain in the knowledge frontier of an increasing competitive economy . Transposable Elements ( TEs ) are an example of very important biological data , and to understand their role in the genomes of organisms is very important for the development of products based on biological data . As an example , we can cite the production biofuels such as the sugar-cane-based ones . Many studies have revealed the presence of active TEs in this plant , which has gained economic importance in many countries . To understand how TEs influence the plant should help researchers to develop more resistant varieties of sugar-cane , increasing the production . Thus , the development of computational methods able to help biologists in the correct identification and classification of TEs is very important from both theoretical and practical perspectives . | [
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| 2018 | A machine learning based framework to identify and classify long terminal repeat retrotransposons |
Melioidosis is a severe disease caused by Burkholderia pseudomallei . Clinical manifestations are diverse and acute infections require immediate treatment with effective antibiotics . While culture is the current diagnostic standard , it is time-consuming and has low sensitivity . In endemic areas , inaccessibility to biosafety level 3 facilities and a lack of good serodiagnostic tools can impede diagnosis and disease surveillance . Recent studies have suggested that O-polysaccharide ( OPS ) and hemolysin co-regulated protein 1 ( Hcp1 ) are promising target antigens for serodiagnosis of melioidosis . We evaluated rapid ELISAs using crude antigens , purified OPS and Hcp1 to measure antibody levels in three sets of sera: ( i ) 419 serum samples from melioidosis patients , Thai and U . S . healthy donors , ( ii ) 120 serum samples from patients with other bacterial infections , and ( iii ) 423 serum samples from 200 melioidosis patients obtained upon admission and at 12 and 52 weeks post-recovery . We observed significantly higher antibody levels using the crude antigen prepared from wild type B . pseudomallei K96243 compared to that of an OPS-mutant . The areas under receiver operator characteristics ( AUROCCs ) for diagnosis were compared for individual Hcp1-ELISA or OPS-ELISA or combined Hcp1/OPS-ELISA . For Thai donors , AUROCCs were highest and comparable between the Hcp1-ELISA and the combined Hcp1/OPS-ELISA ( 0 . 95 versus 0 . 94 ) . For U . S . donors , the AUROCC was highest for the combined Hcp1/OPS-ELISA ( 0 . 96 ) . Significantly higher seropositivity was observed in diabetic patients compared to those without diabetes for both the Hcp1-ELISA ( 87 . 3% versus 69 . 7% ) and OPS-ELISA ( 88 . 1% versus 60 . 6% ) . Although antibody levels for Hcp1 were highest upon admission , the titers declined by week 52 post-recovery . Hcp1 and OPS are promising candidates for serodiagnosis of melioidosis in different groups of patients . The Hcp1-ELISA performed better than the OPS-ELISA in endemic areas , thus , Hcp1 represents a promising target antigen for the development of POC tests for acute melioidosis .
Melioidosis is a severe infectious disease caused by the Gram-negative environmental bacterium , Burkholderia pseudomallei . It is an under-recognized tropical disease that is a common cause of community-acquired infections in Southeast Asia and northern Australia . It is recognized that melioidosis is a more significant global public health concern than previously thought , with increasing numbers of cases reported in many countries [1] . A recent report estimated the incidence of melioidosis to be 165 , 000 cases per year worldwide , with a predicted annual mortality of 89 , 000 [2] . In Thailand , the estimated incidence rate is 12 . 7 cases of melioidosis per 100 , 000 people per year and the mortality rate is 43% [3] . Melioidosis is the third most common cause of death from infectious diseases in northeast region after HIV infection and tuberculosis [3] . Up to 80% of patients with melioidosis have one or more risk factors which include diabetes , alcohol use , renal disease , thalassemia , cancer and glucocorticoid therapy [1 , 4] . Among these , diabetes is the most common underlying disease with 60% of melioidosis patients being diabetic [1] . B . pseudomallei is a facultative intracellular pathogen [5] that can invade host cells , escape from phagosomes , survive within the cytosol and spread from cell-to-cell in many organs [6 , 7] . These processes are dependent upon virulence-associated type III and type VI secretion systems ( T3SS and T6SS ) expressed by this pathogen [8 , 9] . Lipopolysaccharide ( LPS ) and capsular polysaccharide ( CPS ) are additional virulence factors that contribute to the pathogenesis of B . pseudomallei [10] . The clinical manifestations of melioidosis are diverse and can mimic other infections , ranging from skin and soft tissue infections to acute pneumonia and septicemia frequently resulting in misdiagnosis . Treatment of melioidosis requires immediate administration of ceftazidime or carbapenems , which are generally not used as empirical treatment for other bacterial sepsis [1] . Making an early and accurate diagnosis of melioidosis to guide treatment is critical for reducing patient mortality . The diagnosis of melioidosis and subsequent appropriate treatment depends on culture of B . pseudomallei from clinical specimens , or evidence of sepsis in people with a high risk of exposure and predisposing factors ( e . g . diabetes ) for melioidosis . However , identification of B . pseudomallei by culture is time-consuming ( typically 72 hours ) , has low sensitivity ( 60% ) [11 , 12] and requires both experience and stringent laboratory health and safety for this Hazard Group 3 pathogen . Using culture methods , laboratories unfamiliar with B . pseudomallei frequently misidentify the bacterium as an inconsequential environmental Pseudomonas species [13] . An alternative approach to the gold standard of bacterial culture for diagnosis of melioidosis is antigen detection using a monoclonal antibody to B . pseudomallei capsule as a point-of-care ( POC ) diagnostic lateral flow assay ( LFA ) . Although rapid and low cost , the LFA only achieves 40% sensitivity in blood of culture-positive patients , limiting its diagnostic utility in acute melioidosis [14] . Quantitative real-time polymerase-chain reaction ( qPCR ) assay of clinical samples may provide a more rapid result than culture , but has a disappointing sensitivity at 61% in northeast Thailand , especially when performed on blood ( sensitivity at 25% ) [15] . Additional tests such as latex agglutination assays , immunofluorescence assays or matrix-assisted laser desorption ionization-time of flight mass spectrometry ( MALDI-TOF MS ) are required to accelerate the identification of positive cultures [16–19] . To improve the time for diagnosis of melioidosis , an indirect hemagglutination assay ( IHA ) is used to determine antibody titers that are indicative of exposure to B . pseudomallei . While rapid compared with bacterial culture , the sensitivity and specificity of the IHA in Thailand are low ( 69 . 5% and 67 . 6% respectively ) [20] . We recently developed a simpler O-polysaccharide ( OPS ) -based latex agglutination assay which shows potential for detecting exposure to B . pseudomallei in individuals from non-endemic areas but lacks specificity in long term residents from endemic regions [20] . A rapid POC serological test with high sensitivity and specificity would be ideal for use in resource-poor areas where melioidosis is endemic . To develop such assays , identification of good serologic markers is critical . It is also important to evaluate whether an assay can differentiate between acute melioidosis and previous infection or exposure . Our recently developed rapid indirect enzyme-linked immunosorbent assay ( ELISA ) provides a platform for evaluation of different antigen candidates [21] . Among several antigens tested , our studies and others have highlighted the potential of B . pseudomallei OPS and hemolysin co-regulated protein 1 ( Hcp1 ) as targets for further development of serodiagnostic tests for melioidosis [20–24] . Hcp proteins are both structural components and substrates of T6SSs [24] , and in B . pseudomallei are known to be expressed in vivo [9 , 22 , 23] . To identify the best candidate for further development of POC , we used rapid ELISAs to measure antibodies to OPS and Hcp1 using our large collections of serum samples from both endemic and non-endemic areas . The aims of this study were 1 ) to compare the antibody responses measured by ELISA to OPS and non-OPS antigens in sera from melioidosis patients and healthy donors , 2 ) to develop a rapid ELISA using Hcp1 as the target antigen for antibody detection and then compare the results of Hcp1-ELISA with the OPS-ELISA , 3 ) to evaluate the diagnostic potential of Hcp1 and OPS for determination of antibody titers in different groups of melioidosis patients , and 4 ) to evaluate the dynamics of the antibody responses to OPS and Hcp1 over 12 months in individual melioidosis patients by comparing titers during acute infection with the titers observed at 3 and 12 months post-recovery .
Initially , two sets of anonymous human serum samples were used to evaluate the ELISAs as described previously [21] . The first set included 141 on-admission sera from culture-confirmed B . pseudomallei infected patients who were admitted to Sappasithiprasong hospital , Ubon Ratchathani , northeast Thailand , 188 serum samples obtained from healthy donors from the same area in northeast Thailand and 90 serum samples obtained from healthy U . S . donors ( Innovative Research , Novi , MI , USA ) . The second set was three groups of on-admission anonymous human sera that were used to further evaluate the specificity of the ELISAs . These included the following groups: 1 ) 20 acid-fast stain positive tuberculosis patients from Chiangrai , north Thailand , 2 ) 50 culture-proven scrub typhus patients from Udon Thani , northeast Thailand , and 3 ) 50 culture-proven leptospirosis patients from Udon Thani , northeast Thailand . To evaluate the diagnostic potential of Hcp1 and OPS antigens for determination of antibody titers in different groups of melioidosis patients , a third set of independent serum samples was used . This set included serum samples obtained from patients with culture-confirmed melioidosis collected a median of 5 days ( Interquartile range , IQR 3–6 days , range 2–13 days after admission ( N = 200 ) , and at 12 weeks ( N = 113 ) and 52 weeks ( N = 110 ) post-recovery . The patients were recruited in a longitudinal clinical and immunological study at Sappasithiprasong hospital during September 2012-October 2015 [25] . All participants were ≥ 18 years old . All serum samples were stored at -80°C . The study was approved by Ethics Committee of Faculty of Tropical Medicine , Mahidol University ( approval number MUTM 2014–079 and MUTM 2012–018 ) , Sappasitthiprasong hospital ( approval number 018/2555 ) , and the Oxford Tropical Research Ethics Committee ( reference 64–11 ) . Written informed consent was obtained from the participants enrolled in the study . Whole-cell ( WC ) antigen was extracted from the wild type strain B . pseudomallei K96243 ( from a Thai patient in northeast Thailand; expresses type A OPS ) and an OPS mutant ( ΔwbiD K96243 ) by heating at 80°C for 1 h . The supernatant was used as the antigen described previously [21 , 26] . The OPS mutant defective in wbiD ( BPSL2677 ) was constructed as described in our previous study [27] . B . pseudomallei LPS type A was extracted from the select agent excluded strain RR2808 ( capsule mutant ) using a modified hot phenol method [28 , 29] . Purified OPS antigen was then obtained using acid hydrolysis and gel permeation chromatography as previously described [30] . For expression of recombinant Hcp1 ( rHcp1 ) with a N-terminal 6xHis-Tag , the hcp1 ORF ( BMAA0742 ) was PCR amplified from B . mallei ATCC 23344 genomic DNA using the Bmhcp1-6HisF ( 5’-CCCAACGGTCTCACATGGCGGCGCATCATCATCATCATCATCTGGCCGGAATATATCTCAAGG-3’ ) and Bmhcp1-R1 ( 5’-CCCAACGGTCTCAAGCTTCAGCCATTCGTCCAGTTTGCGGC-3’ ) primer pair; BsaI linkers are underlined . The resulting DNA fragment was digested with BsaI and cloned into pBAD/HisA digested with NcoI/HindIII producing plasmid pBADBmhcp1-6HisF . Notably , B . pseudomallei and B . mallei Hcp1 proteins are 99 . 4% identical . Recombinant DNA techniques were conducted as previously described [31] . Oligonucleotide primers were obtained from Integrated DNA Technologies . DNA sequencing was performed by ACGT Inc . For purification of rHcp1 , E . coli TOP10 ( pBADBmhcp1-6HisF ) was grown to mid log phase in LB broth and protein expression was induced using 0 . 02% L-arabinose ( Sigma ) . Bacterial pellets were resuspended in B-PER ( Pierce ) plus Benzonase ( Novagen ) and Lysozyme ( 100 μg/ml ) and incubated for 20 min at room temperature with gentle agitation . Insoluble material was removed by centrifugation and the resulting supernatant was loaded onto a gravity-fed Ni-NTA agarose ( Invitrogen ) column . The column was washed with Wash Buffer ( 50 mM Tris pH 8 . 0 , 300 mM NaCl and 40 mM Imidazole ) , protein was eluted with Elution Buffer ( 50 mM Tris pH 8 . 0 , 50 mM NaCl and 300 mM Imidazole ) then dialyzed against PBS and loaded onto a gravity-fed His-Pur Cobalt Resin ( Thermo Scientific ) column . The column was washed with PBS and rHcp1 was eluted with Wash Buffer , dialyzed against PBS , concentrated and stored at 4°C . Protein concentrations were determined using a BCA protein assay kit ( Pierce ) . Endotoxin removal was performed using High Capacity Endotoxin Removal Resin ( Pierce ) . The amount of endotoxin in the rHcp1 preparations was quantitated using a LAL Chromogenic Endotoxin Quantitation Kit ( Pierce ) . The ELISAs were performed using these following antigens: 1 ) WC antigen prepared from wild type B . pseudomallei K96243 , 2 ) WC antigen prepared from an OPS mutant defective in OPS ( ΔwbiD K96243 ) antigen , 3 ) rHcp1 protein , 4 ) the purified OPS antigen , and 5 ) OPS antigen in combination with rHcp1 ( Hcp1/OPS ) . The optimal concentration of coating antigen was determined using pooled melioidosis and pooled healthy sera as previously described [21] . Following evaluation for antigen concentration and serum dilution , the plates were prepared for ELISA using the optimized antigen concentration as follows: WC 0 . 25 μg/ml , OPS 1 μg/ml , Hcp 2 . 5 μg/ml and OPS/Hcp1 ( 0 . 5 μg/ml OPS/1 . 25 μg/ml Hcp1 ) . The serum samples used for evaluation of the various ELISAs included culture-confirmed melioidosis patients ( N = 141 ) , U . S . healthy donors ( N = 90 ) , Thai healthy donors ( N = 188 ) , tuberculosis patients ( N = 20 ) , scrub typhus patients ( N = 50 ) and leptospirosis patients ( N = 50 ) at a dilution of 1:2 , 000 . All ELISAs were performed using a 1:2 , 000 dilution of horseradish peroxidase-conjugated rabbit antihuman IgG as previously described [21] . To examine the antibody titers specific to Hcp1 and OPS , ELISAs were performed with a third set of serum samples obtained from acute phase and recovery phase melioidosis patients using undiluted sera and two-fold serial dilution sera at range of 1:125 to 1:2 , 048 , 000 . The endpoint antibody titer was read at the serum dilution which showed positive OD values of each ELISA . Positive results for individual serum samples were determined using OD cut-off values at specificity of 95% . The antibody titers of individual patients were compared between week 0 , week 12 and week 52 . Only samples from different time points with two-fold changes in titer were considered as increased or decreased antibody titers . Statistical analyses were performed using Stata version 12 ( StataCorp LP , College Station , TX ) and Prism 5 Statistics ( GraphPad Software Inc , La Jolla , CA ) . All data in box plots are presented as 25th and 75th percentile boundaries in the box with the median line within the box; the whiskers indicate the 10th and 90th percentiles . The Mann-Whitney test was used to test the difference of median between different serum groups . Spearman’s rank correlation was used to determine the pairwise correlation coefficient for the pairs of tests [32] . The McNemar test was used to compare the sensitivity between tests . Fisher’s exact test was performed to compare the ELISA results and clinical presentation and outcomes . Differences were considered statistically significant at a p-value < 0 . 05 . A receiver operator characteristic ( ROC ) curve was created to monitor the shifting the positive cut off value on true-positive ( sensitivity ) and false positive ( 1-specificity ) rates . Areas under the ROC curves ( AUROCC ) were compared using a nonparametric method as previously described by DeLong et al . [33] . The ELISA data of the melioidosis group and Thai donors were evaluated separately from the data of the melioidosis group and U . S . donors using OD cut-off values at specificities of 95% .
Results from our previous study using a crude antigen ELISA ( WC-ELISA ) to determine the levels of B . pseudomallei-specific antibodies in five individual melioidosis patients , to either wild-type ( K96243 ) or an OPS mutant ( K96243ΔwbiD ) , indicate that OPS appears to be the predominant antigen recognized by human antibodies [21] . In the present study , we expand these experiments to determine the antibody levels in 419 individual sera obtained from melioidosis patients ( N = 141 ) , Thai healthy donors ( N = 188 ) , U . S . healthy donors ( N = 90 ) using the same WC-ELISA with coating antigens prepared from either the wild type or the OPS mutant ( Fig 1 ) . Our results revealed that the median OD value for the melioidosis group was statistically higher compared to Thai donors ( P < 0 . 001 for both ELISAs ) and U . S . donors ( P < 0 . 001 for both ELISAs ) . The median OD value for the melioidosis group was 5 . 9 times lower in the OPS mutant-WC-ELISA compared to the wild type-WC-ELISA . Similarly , the median OD value for Thai healthy donors was 3 times lower in the OPS mutant-WC-ELISA in comparison to the wild type-WC-ELISA [median OD 0 . 04 ( IQR 0 . 02–0 . 08 ) versus 0 . 12 ( IQR 0 . 06–0 . 22 ) ; P < 0 . 001] . In contrast , the median OD value for U . S . healthy donor serum was not significantly different between the two ELISAs [median OD 0 . 12 ( IQR 0 . 05–0 . 27 ) for OPS mutant versus 0 . 11 ( IQR 0 . 05–0 . 38 ) for wild type; P < 0 . 954] . These findings suggest that OPS is a predominant antigen recognized by antibodies in Thai melioidosis patients and Thai healthy donors sera . This was not the case , however , for sera from U . S . donors . Interestingly , results from the OPS mutant-WC-ELISA also revealed that several melioidosis patients appeared to have high antibody levels to antigens other than OPS . Recently , our group and others have shown that Hcp1 is a promising candidate serodiagnostic marker for melioidosis [22–24] . B . pseudomallei Hcp1 is a T6SS component that is expressed in vivo or under iron-limiting conditions when the organism is grown in vitro [22 , 24] . To assess the serodiagnostic potential of Hcp1 , we developed a rapid ELISA using rHcp1 as the target antigen and compared it with our established OPS-ELISA [21] . The optimal conditions for our Hcp1-ELISA were initially determined using pooled serum from either melioidosis patients or healthy donors . The optimized concentration of rHcp1 for coating wells was 2 . 5 μg/ml . For the primary antibody incubation step , we used a serum dilution of 1:2000 at room temperature ( 25°C ) for 30 minutes . The assay was standardized throughout the study using these conditions for all serum samples as previously described [21] . For comparison with our previous study using an OPS-ELISA , a total of 539 serum samples were tested in our Hcp1-ELISA [21] . These included on-admission sera from culture-proven melioidosis patients ( N = 141 ) , Thai healthy donors ( N = 188 ) , U . S . healthy donors ( N = 90 ) , tuberculosis patients ( N = 20 ) , scrub typhus patients ( N = 50 ) and leptospirosis patients ( N = 50 ) [21] . Quantitative results of OD values in both ELISAs are summarized in Table 1 . The median OD of melioidosis patients for the Hcp1-ELISA was higher than that of OPS-ELISA [median OD 3 . 16 ( IQR 2 . 22–3 . 40 ) versus 1 . 78 ( IQR 0 . 67–3 . 11 ) ; P < 0 . 001] . The median OD value of the melioidosis group was statistically different from Thai healthy donors , U . S . healthy donors , tuberculosis patients , scrub typhus patients and leptospirosis patients for both ELISAs ( P < 0 . 001 for both ELISAs for all comparisons between melioidosis patients versus each of other groups ) . We determined the correlation between individual results of the Hcp1-ELISA and OPS-ELISA using serum samples from melioidosis patients , Thai healthy donors , U . S . healthy donors , tuberculosis patients , scrub typhus patients and leptospirosis patients . The pairwise correlation coefficient ( rho ) of all serum samples was 0 . 80 , however , the relatedness between antibody response to the Hcp1 and OPS antigens was different between groups of serum samples . The results indicate a strong relatedness only in U . S . healthy donor group ( rho = 0 . 93 ) but the results of the Hcp1-ELISA and OPS-ELISA were less correlated with the groups of Thai melioidosis patients ( rho = 0 . 38 ) and Thai healthy donors ( rho = 0 . 60 ) . The correlation coefficients were 0 . 72 , 0 . 50 and 0 . 74 for tuberculosis , scrub typhus and leptospirosis patients , respectively . We next compared the diagnostic potential of each antigen individually ( Hcp1-ELISA or OPS-ELISA ) or combined ( Hcp1/OPS-ELISA ) . ROCs were plotted by calculating the sensitivity and specificity of increasing numbers of the true-positive rate and false-positive rate . The results for comparisons of these ELISAs using the melioidosis group and Thai donors are shown in Fig 2A , and those using the melioidosis group and U . S . donors are shown in Fig 2B . When the results of Thai donors were analyzed , the areas under the receiver operator characteristic curves ( AUROCCs ) for diagnosis of melioidosis were highest and comparable between the Hcp1-ELISA and the combined Hcp1/OPS-ELISA ( 0 . 95 versus 0 . 94 , P = 0 . 153 ) ( Fig 2A ) . The AUROCC of the OPS-ELISA was significantly lower than that of the Hcp1-ELISA ( 0 . 91 versus 0 . 95 , P = 0 . 001 ) ; and lower than that of Hcp1/OPS-ELISA ( 0 . 91 versus 0 . 94 , P = 0 . 003 ) . When the results from the U . S . donors were analyzed ( Fig 2B ) , the AUROCC for diagnosis of melioidosis was highest for the combined Hcp1/OPS ELISA ( 0 . 96 ) and significantly higher when compared to the AUROCC of Hcp1-ELISA ( 0 . 93 , P = 0 . 009 ) and the OPS-ELISA ( 0 . 92 , P < 0 . 001 ) . The AUROCC of the Hcp1-ELISA was not significantly different from that of the OPS-ELISA ( 0 . 93 versus 0 . 92 , P = 0 . 353 ) We further analyzed the sensitivity and specificity of Hcp1-ELISA in comparison to OPS-ELISA and Hcp1/OPS-ELISA using the 539 serum samples described above . To compare the performance of assays , we used an OD cut-off corresponding to a specificity of 95% using Thai healthy donors as controls ( OD 1 . 165 ) ( Table 2 ) . The results demonstrated that the diagnostic sensitivity of the Hcp1-ELISA was significantly higher than that of the OPS-ELISA ( 83 . 0% versus 71 . 6% , P = 0 . 003 ) . The sensitivity of the Hcp1-ELISA was not significantly different from the sensitivity of combined Hcp1/OPS-ELISA ( 83 . 0% versus 81 . 6% , P = 0 . 527 ) . The specificity of the Hcp1-ELISA using U . S . healthy donors , tuberculosis patients , scrub typhus patients and leptospirosis patients as non-melioidosis controls were 95 . 6% , 100% , 98 . 0% and 100% , respectively . The specificity of the OPS-ELISA using U . S . healthy donors , tuberculosis patients , scrub typhus patients and leptospirosis patients as non-melioidosis controls were 96 . 7% , 100% , 94 . 0% and 98 . 0% , respectively . We next investigated whether the use of antibody levels to Hcp1 and OPS as serodiagnostic markers of acute infection in melioidosis patients was influenced by the presence or absence of diabetes ( Table 3 ) . The sensitivity and antibody titers to Hcp1 and OPS were determined at week 0 for 200 follow-up patients . At serum dilution 1:2000 , we found that the sensitivity of the Hcp1-ELISA for diabetic patients ( N = 134 ) was significantly higher than for non-diabetic patients ( N = 66 ) ( 87 . 3% versus 69 . 7% , P = 0 . 004 ) . Similarly , the sensitivity of the OPS-ELISA for diabetic patients was significantly higher than for non-diabetic patients ( 88 . 1% versus 60 . 6% , P < 0 . 001 ) . The median antibody titers for Hcp1 and OPS were significantly higher in diabetic patients compared to non-diabetic patients ( P < 0 . 001 for both Hcp1 and OPS ) ( Fig 3 ) . The median antibody titers for Hcp1 in diabetic patients was 26 , 322 , ( IQR 8 , 898–59 , 420 ) and non-diabetic patients was 10 , 327 ( IQR 1 , 250–35 , 842 ) . The median antibody titers to OPS in diabetic patients was 10 , 657 ( IQR 4 , 487–29 , 157 ) and in non-diabetic patients was 3 , 499 ( 483 . 8–10 , 863 ) . We next compared the antibody titers for Hcp1 and OPS in melioidosis patients with or without bacteremia . The levels of Hcp1- or OPS-specific antibodies were determined at week 0 in 200 follow-up patients ( Table 3 ) . Using a serum dilution of 1:2000 , the sensitivity of the Hcp1-ELISA for patients with bacteremia ( N = 105 ) was not significantly different from that of the patients without bacteremia ( N = 95 ) ( 82 . 9% versus 80 . 0% , P = 0 . 654 ) . The sensitivity of the OPS-ELISA for patients with bacteremia was not statistically different from patients without bacteremia ( 83 . 8% versus 73 . 7% , P = 0 . 085 ) . Although the median antibody titer for Hcp1 was not significantly different between bacteremic and non-bacteremic patients ( median 22 , 108 , IQR 9 , 045–66 , 612 versus median 14 , 769 , IQR 4 , 154–39 , 255 , P < 0 . 057 ) , the median antibody titer for OPS was higher in bacteremia patients compared to non-bacteremia patients ( median 9 , 636 , IQR 4 , 150–28 , 886 versus median 6389 , IQR 1817–16106 , P = 0 . 019 ) ( Fig 4 ) . The sensitivities of the Hcp1-ELISA and OPS-ELISA were determined using on-admission ( week 0 ) serum collected from 198 melioidosis patients whose survival status was available ( Table 3 ) . The sensitivity of the Hcp1-ELISA for non-survivors ( N = 64 ) was not significantly different from survivors ( N = 134 ) ( 78 . 1% versus 82 . 8% , P = 0 . 629 ) . The sensitivity of the OPS-ELISA for non-survivors was not different from the patients who survived ( 78 . 1% versus 79 . 1% , P = 0 . 91 ) . The median antibody titers for Hcp1 in 134 patients who were survived was 18 , 035 ( IQR 6 , 022–48 , 678 ) which was not significantly different from the median antibody titer for 64 non-survivors 25 , 873 ( IQR 4 , 086–45 , 678 ) ; P = 0 . 822 ) ( Fig 5 ) . The median antibody titer for OPS in survivors was 9 , 588 ( IQR 2 , 793–24 , 096 ) which was higher than the median of non-survivors 5 , 330 ( IQR 2 , 114–17 , 670 ) but was not significantly different ( P = 0 . 074 ) . We next investigated whether the antibodies to Hcp1 and OPS might be useful serodiagnostic markers during acute infection and/or following recovery from melioidosis . The specific antibodies were determined in 423 archived serum samples obtained from 200 melioidosis patients recruited in our recent longitudinal study [25] . Using a serum dilution of 1:2000 , the percentage of positive serum samples in the Hcp1-ELISA was 81 . 5% ( 163/200 ) , 82 . 3% ( 93/113 ) and 81 . 8% ( 90/110 ) at week 0 , week 12 and week 52 , respectively . The percentage of positive serum samples in the OPS-ELISA was 79 . 0% ( 158/200 ) , 85 . 8% ( 97/113 ) and 88 . 0% ( 88/110 ) at week 0 , week 12 and week 52 , respectively . To compare the antibody levels , we determined the endpoint antibody titers to Hcp1 and OPS in 103 individual patients who survived one year after acute infection ( Fig 6 ) . The median titer of melioidosis patients for the Hcp1-ELISA at week 0 was not different from that at week 12 [median 19 , 792 ( IQR 6 , 654–57 , 596 ) versus 17 , 677 ( IQR 4 , 327–44 , 079 ) ; P = 0 . 255] , but the titer was significantly lower at week 52 [median 10 , 427 ( IQR 3 , 731–19 , 046 ) ; titers of week 12 versus week 52 , P < 0 . 019; week 0 versus week 52 , P < 0 . 001] ( Fig 6A ) . The results of individual patients for Hcp1 are shown in S1 Fig . Of the 103 patient samples tested , 49 ( 47 . 6% ) showed decreased antibody titers for Hcp1 at week 52 compared to week 0 and 12 while only 31 ( 30 . 1% ) showed decreased antibody titers for Hcp1 at week 12 compared to week 0 ( Fig 6B ) . However , 14 ( 13 . 6% ) , 20 ( 19 . 4% ) and 18 ( 17 . 5% ) of patients had increased titers at week 52 compared to week 0 , week 12 compared to week 0 , and week 52 compared to week 12 , respectively . We found 40 ( 38 . 8% ) , 52 ( 50 . 5% ) and 36 ( 35 . 0% ) of patients had no change in antibody titers at week 52 compared to week 0 , week 12 compared to week 0 , and week 52 compared to week 12 , respectively . In contrast , the median titer of melioidosis patients for OPS-ELISA increased at week 12 compared to week 0 [median 10 , 192 ( IQR 2 , 783–26 , 502 ) versus 17 , 464 ( IQR 7 , 346–38 , 834 ) ; P = 0 . 006] but decreased at week 52 [median 8 , 848 ( IQR 3 , 293–15 , 359 ) ; P <0 . 001 for comparison between week 12 and week 52] ( Fig 6A ) . The results of individual patients are shown in S2 Fig . The median titer was not different between week 0 and week 52 ( P = 0 . 329 ) . Of a total 103 patients , the number of patients that had decreased antibody titer for OPS at week 52 compared to week 0 was 30 ( 29 . 1% ) , at 12 compared to week 0 was 7 ( 6 . 8% ) , decreased titer at week 52 compared to week 12 was 53 ( 51 . 5% ) ( Fig 6B ) . However , 22 ( 21 . 4% ) , 41 ( 39 . 8% ) and 3 ( 2 . 9% ) of serum samples showed increase titer at week 52 compared to week 0 , week 12 compared to week 0 and week 52 compared to week 12 respectively . We found 51 ( 49 . 5% ) , 55 ( 53 . 4% ) and 47 ( 45 . 6% ) of the patients had no change in antibody titer at week 52 compared to week 0 , week 12 compared to week 0 and week 52 compared to week 12 , respectively . We next determined the correlation between the individual results from the Hcp1-ELISA and OPS-ELISA conducted using serum samples from 103 follow-up melioidosis patients who had survived at one year after admission ( Fig 7 ) . The pairwise correlation coefficient ( rho ) of all serum samples was 0 . 58 ( P < 0 . 001 ) . The relatedness between the antibody responses against Hcp1 and OPS was different for the 0 , 12 and 52 week sample sets . The rho values at week 0 and week 12 were only 0 . 46 ( P < 0 . 001 ) and 0 . 55 ( P < 0 . 001 ) , respectively . A stronger correlation ( rho = 0 . 80 , P < 0 . 001 ) was observed with the serum samples collected at week 52 .
Melioidosis is a potentially fatal disease that is more widely distributed globally than previously recognized [2 , 34] . A rapid and reliable POC serological test would be particularly useful as a tool for serodiagnosis and for seroprevalence studies in highly endemic regions as well as in countries where melioidosis is underreported . In the present study , we used a rapid ELISA platform to assess the serodiagnostic potential of various candidate target antigens . Our results showed that consistent with previous studies , antibodies specific for B . pseudomallei OPS were predominant in melioidosis patient sera . Interestingly , our WC-ELISA using both a wild type B . pseudomallei strain and an OPS mutant demonstrated that the median OD value for Thai healthy donors group was significantly lower in the OPS mutant-WC-ELISA compared to the wild type-WC-ELISA . The data suggested that the OPS-specific antibodies might also contribute to the high rate of seropositivity in healthy individuals from endemic areas and may influence the specificity of the assay . In contrast , the median OD value for U . S . healthy donor serum was not significantly different between the two ELISAs . A possible explanation for this finding is that individuals in Thailand and other endemic regions may be previously exposed to environmental Burkholderia species that express Type A OPS ( e . g . B . thailandensis ) whereas U . S . healthy donors would not . [35 , 36] . Hcp1 is a major virulence factor that plays a critical role in the intracellular lifestyle of B . pseudomallei . Results of this study are consistent with our previous study [37] demonstrating that Hcp1 is immunogenic and is recognized by serum from melioidosis patients . We and others have demonstrated that Hcp1 is expressed at a low level when B . pseudomallei is cultured in vitro , but is produced at a high level in vivo within an intracellular environment [24 , 37 , 38] . Our findings suggest that the detection of antibodies to Hcp1 in a high percentage of melioidosis patients upon admission is likely to reflect infection with B . pseudomallei rather than non-infective exposure . In addition , since Hcp1 expressed by B . pseudomallei ( and B . mallei ) is structurally different than B . thailandensis Hcp1 , it is likely that seropositivity to this protein antigen will be less prevalent than seropositivity to OPS in healthy donors in endemic areas . Results of ROC analyses revealed that the Hcp1-ELISA had a significantly higher AUROCC than the OPS-ELISA when serum from Thai healthy donors was used as a control . While the OPS-ELISA alone may not be ideal for detection of acute infections in endemic areas , our data suggest that it may be more useful when combined with Hcp1 for use in non-endemic regions such as the USA . In support of these results , we demonstrated that the median OD value of Hcp1-ELISA in the melioidosis group was significantly higher than the median OD value of the OPS-ELISA . Our results also indicated that antibodies to Hcp1 are significantly elevated in patients with B . pseudomallei infections . The pairwise correlation coefficient for the results of the two ELISAs for all of the serum samples was high ( 0 . 80 ) , however , the relatedness between antibody response to the Hcp1 and OPS antigens was lower in the patient group compared with the two healthy control groups . It is possible that the immune pathways activated by polysaccharide and protein antigens are different among individuals . Hcp1 is a T-cell dependent protein antigen while OPS is a carbohydrate that can induce humoral immune responses via a T-cell independent pathway . A recent report demonstrated that Hcp1 can bind to the surface of host antigen-presenting cells , which may contribute to its immunogenicity by inducing high antibody titers in melioidosis patients [24] . The diagnostic performance of Hcp1-ELISA for antibody detection using the first set of serum samples including 141 Thai melioidosis , 188 Thai healthy donors and 90 U . S . healthy controls showed a significant improvement over the conventional IHA . Using the same serum set , the Hcp1-ELISA had 83% sensitivity and 96% specificity compared with the IHA ( sensitivity 69 . 5% and specificity 67 . 6% ) in our previous study [20] . The results obtained from a second set of 200 melioidosis patient serum samples confirmed the high sensitivity ( 82% ) at the time of admission . Our findings are consistent with a previous report by Lim and colleges [24] which demonstrated that anti-Hcp1 IgG titers in 20 melioidosis patient serum samples were significantly higher compared to serum from 20 healthy controls . In addition , an Hcp1-ELISA developed by Cheng et al using serum from 32 melioidosis patients and 20 healthy donors from Malaysia showed a sensitivity of 93 . 7% with a specificity of 100% [23] . Antibodies to OPS are highly elevated in melioidosis patients . Analysis of a follow-up set of 200 serum samples from diabetic or non-diabetic melioidosis patients in this study showed sensitivity to be 79% , which was consistent with the high sensitivity result of our previous study ( 72% ) [21] . We observed higher seropositivity for both Hcp1 and OPS in diabetic melioidosis patients compared to those without diabetes . This has been previously seen for the IHA in Northern Australia [39] and for IHA in our laboratory in Thailand ( manuscript in preparation ) . One possible explanation for the high seropositivity observed in diabetic patient is the alteration in the balance between cell-mediated immunity and humoral immunity in response to B . pseudomallei infection , for example due to enhanced polyclonal B-cell stimulation in Type 2 diabetes secondary to chronic hyperactivation of the innate immune response [40] . Ongoing studies are exploring the causality and exact mechanisms of higher antibody titers to B . pseudomallei in patients with diabetes . We reported no significant difference in diagnostic sensitivity between bacteremic versus non-bacteremic patients and survivors versus non-survivors . Thus , Hcp1 and OPS appear to be two promising antigens for further development of POC serological tests for all melioidosis patients including diabetics . The induction of antibody responses to Hcp1 in melioidosis patients is relatively rapid with our results showing high median anti-Hcp1 titers at week 0 . Interestingly , these titers were decreased at week 12 and week 52 for patients who recovered from the disease . Based on this , it appears that antibody titers to Hcp1 may be a useful serodiagnostic marker for acute infections as well as for monitoring the disease progression or recovery . The induction of antibody to OPS appeared to be slower with OPS-specific antibodies detectable at week 12 but declining by week 52 . Further studies will be necessary to determine when OPS-specific antibody titers reach peak levels . Our experiments focused on follow-up patients highlights the variation of individuals in the direction and rate of antibody titer changes over time . These results provide evidence of inter-individual variation in responses to the same B . pseudomallei antigens which may involve specific immune statuses , variable past exposures , infecting bacterial strains , and clinical disease factors . We recognize that our serological tests using a single serum dilution ( 1:2 , 000 ) may be of relatively limited value for following disease progression and that determining endpoint antibody titers will be more useful for assessing the variability of antibody levels in melioidosis patients . In conclusion , this study establishes that Hcp1 and OPS are useful targets for serodiagnosis of melioidosis in various groups of patients . Overall , the Hcp1-ELISA provided better diagnostic assay values than the OPS-ELISA . When used in non-endemic areas , a combined Hcp1/OPS-ELISA showed increased sensitivity compared to the ELISAs using Hcp1 or OPS alone . Our results support accelerated development of Hcp1-based assays for a much needed POC test for the diagnosis of acute melioidosis . | Melioidosis , caused by Burkholderia pseudomallei , is a life-threatening infection endemic in tropical countries . Definitive diagnosis of melioidosis relies upon bacterial culture which requires suitable laboratory facilities and reliable antibody testing . To obtain an effective target antigen for use in a simple point-of-care ( POC ) test , rapid ELISAs using crude B . pseudomallei antigen preparations or purified O-polysaccharide ( OPS ) and hemolysin co-regulated protein ( Hcp1 ) were compared using serum samples from three large collections obtained from melioidosis patients and patients with other bacterial infections . We detected high levels of antibodies to Hcp1 and OPS in serum from melioidosis patients upon admission and showed that anti-Hcp1 levels declined post-recovery . When serum samples from endemic areas were tested , the performance of the Hcp1-ELISA and combined Hcp1/OPS-ELISA were higher than the OPS-ELISA . When serum from non-endemic areas was tested , the combined Hcp1/OPS-ELISA gave the highest performance . Both the OPS- and Hcp1-based ELISAs were useful for detection of antibodies in various groups of patients including diabetics . Since anti-Hcp1 titers in melioidosis patient serum were higher than anti-OPS titers , Hcp1 is an attractive candidate for further development of a rapid POC test for use in endemic areas . | [
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| 2017 | Comparison of O-polysaccharide and hemolysin co-regulated protein as target antigens for serodiagnosis of melioidosis |
Trophic relationships , such as those between predator and prey or between pathogen and host , are key interactions linking species in ecological food webs . The structure of these links and their strengths have major consequences for the dynamics and stability of food webs . The existence and strength of particular trophic links has often been assessed using observational data on changes in species abundance through time . Here we show that very strong links can be completely missed by these kinds of analyses when changes in population abundance are accompanied by contemporaneous rapid evolution in the prey or host species . Experimental observations , in rotifer-alga and phage-bacteria chemostats , show that the predator or pathogen can exhibit large-amplitude cycles while the abundance of the prey or host remains essentially constant . We know that the species are tightly linked in these experimental microcosms , but without this knowledge , we would infer from observed patterns in abundance that the species are weakly or not at all linked . Mathematical modeling shows that this kind of cryptic dynamics occurs when there is rapid prey or host evolution for traits conferring defense against attack , and the cost of defense ( in terms of tradeoffs with other fitness components ) is low . Several predictions of the theory that we developed to explain the rotifer-alga experiments are confirmed in the phage-bacteria experiments , where bacterial evolution could be tracked . Modeling suggests that rapid evolution may also confound experimental approaches to measuring interaction strength , but it identifies certain experimental designs as being more robust against potential confounding by rapid evolution .
Empirical and theoretical studies suggest that the structure and strength of trophic links have large influences on ecosystem attributes such as species diversity [1 , 2] , the abundance and productivity of different trophic levels [3 , 4] , and the stability and dynamical behavior of component populations ( e . g . , [5–8] ) . One of the principal methods for assessing the existence and strengths of trophic interactions is an analysis of temporal patterns of change in species abundances [9]; either natural variations in abundance or transient dynamics that occur following natural or experimental disturbances of a steady state . Time-series data on changes in species abundance ( and possibly on environmental covariates ) are used to parameterize a multispecies dynamic model , whose parameters can then be used to calculate the various summary measures of interaction strength [10] . This approach has some advantages over strictly experimental approaches , such as species removals: in principle , the interaction strengths between all species pairs in a community can be estimated with one experiment ( rather than requiring a comprehensive set of single-species removals ) , and estimates of direct pairwise interaction strengths are not confounded by indirect effects [9] . Recent applications include tide pool fish communities [11] , freshwater plankton communities [12–14] , the wolf-moose interaction in Isle Royale National Park [15 , 16] , forest insects [17–19] , and laboratory systems using microbes [20–24] and insects [25] . The fundamental premise of the system-dynamics approach to measuring interaction strengths is that the impact of one species on another is revealed by patterns of covariation in their changes of abundance through time . Although a variety of different methodologies have been used , they are all based on estimating the relationship between the abundance of one species and the rate of change in the abundance of a second , and then using some summary measure of the strength of this relationship as the estimated interaction strength . Conclusions reached in this way may often be valid , but we have discovered that ecologically relevant conditions exist under which there is little or no relationship between the abundance of a predator and the population growth rate of its prey , and vice versa , despite the existence of what is known to be a tightly coupled interaction . Under these conditions , a strong interaction would be completely missed in an analysis based on observed changes in species abundance . Both theoretical and experimental studies of predator–prey dynamics have assumed , explicitly or tacitly , that populations are genetically homogeneous , so that , e . g . , predation does not drive nontrivial changes in prey genotype distributions even while it causes substantial changes in prey abundance . There is now considerable evidence , however , that populations may experience rapid evolutionary change contemporaneous with the ecological processes that drive changes in abundance ( e . g . , [26–37] ) . An important consequence is that if the evolving morphological , physiological , behavioral , or life-history traits influence birth or death rates , evolution may thereby radically alter population and community dynamics [38–41] . We report here a phenomenon that we call “cryptic dynamics , ” in which the strength or even the existence of a predator–prey trophic link is masked by evolutionary dynamics . This phenomenon is a subset of the category of evolutionary cycles that we previously documented in a predator–prey interaction between a rotifer , Brachionus calyciflorus , and a unicellular alga , Chlorella vulgaris , in laboratory chemostat cultures [42] . When cultures were initiated with only a single algal genotype present , and therefore no possibility for algal evolution , we observed short-period standard predator–prey oscillations with a quarter-period phase lag between rotifer and algal peak densities ( Figure 1A ) . This pattern was completely different , however , when multiple algal genotypes were present . The prey population cycled between dominance by genotypes defended against predation and genotypes that grew better in severely nutrient-limited conditions . Rather than short-period standard predator-prey oscillations , we observed longer-period cycles in which the rotifer and algal densities were exactly out of phase ( Figure 1B ) [40 , 42] . We have also seen qualitatively similar results in the interaction between the bacterium Escherichia coli and the lytic phage T4 that infects it [32 , 43] . When only sensitive hosts are present , the densities of bacteria and phage exhibit rapid high-amplitude cycles . These cycles become longer and less pronounced after phage-resistant bacteria evolve or are deliberately introduced . The striking similarity in dynamics caused by host variability in two such different systems suggests a common and fundamentally important mechanism . Here we present experimental evidence and theoretical analyses showing that ecologically realistic conditions exist in which the prey genotype oscillations almost exactly counterbalance each other , so that total prey density remains essentially constant while predator density oscillates . When this occurs , observations of predator and prey population dynamics cannot be trusted to be informative about the strength or even the existence of interspecific trophic links .
Our studies of rotifer–algal predator–prey cycles are carried out in single-stage chemostats , which are culture vessels with continuously pumped inflow of sterile nitrogen-limited medium and continuous outflow of medium , rotifers , and algae . Under well-understood conditions of nutrient concentration and flow rate , the tight predator–prey link between these species produces coupled population cycles [40 , 42 , 44] . Microevolution in the prey population produces the longer out-of-phase cycles described above , and alternative mechanisms ( resource-dependent egg viability , stoichiometric changes in algal quality , and metabolite-dependent algal physiology ) do not [45] . Theory and experimental results using identifiable algal clones show that these dynamics result from changes in algal genotype frequencies occurring in parallel with changes in algal abundance [40 , 42 , 46] . We observed unexpected population dynamics , however , in several extended chemostat runs where evolutionary cycles were expected . In the first case ( Figure 2A ) , multiple algal clones were inoculated at high density and rotifers at low density at the start of the experiment . These initial conditions should have led to long out-of-phase evolution-driven cycles , similar to those shown in Figure 1B . Instead , algal density declined initially and then stayed consistently low . At the same time , rotifer density oscillated as in a typical evolutionary cycle ( especially after the drop in chemostat dilution rate on day 35 of the experiment ) . Based on independently measured rotifer and algal functional responses and observations of other chemostat runs , the magnitude of the changes in rotifer population size should have been more than sufficient to induce a response in algal density and , hence , in predator–prey cycling [42 , 44 , 47] . Even though we know that the rotifers and algae are bound in a tight predator–prey relationship , a plot of their densities in a predator–prey phase plane suggests instead that rotifers and algal populations are completely decoupled ( Figure 2B ) . In a second rotifer–algal chemostat experiment , the algal population was again initiated with multiple genotypes . In this case , however , the rotifers were not introduced until day 12 , after which we observed three clear short-period cycles ( Figure 2C ) with the quarter-period phase delay that occurs in the absence of algal evolution ( Figure 2D ) . In mathematical models for this system , one competitively superior clone comes to dominance during a period when , in the absence of rotifer grazing , algae at high density experience strong competition because of severe nutrient limitation . This low genetic variation would cause the system to behave initially as if no evolution were taking place , even if the frequency of a defended clone was slowly increasing during each period of high rotifer density . But once the frequency of the defended clone became high enough , the system would be expected to shift to long-period , out-of-phase evolutionary cycles . Between days 45 and 55 , the dynamics did change markedly , despite there being no change in experimental conditions ( Figure 2C ) . Consistent with expectations , the rotifers continued to oscillate with cycle period increasing from about 12 d to ∼16 . 5 d and with reduced amplitude . But instead of cycling out of phase with the rotifers , the algal population dropped to constant low abundance ( Figure 2C ) . Again , the changes in rotifer density should have been sufficient to elicit fluctuations in the algal prey , but they did not , and thus dynamical evidence for trophic coupling was lacking in the predator–prey phase plane ( Figure 2D; blue dots ) . From the plots in Figures 2B and 2D ( blue dots ) alone , one would infer that B . calyciflorus and C . vulgaris did not interact in our chemostats , when in fact they are strongly and directly linked . This inconsistency led us to ask if the evolutionary cycling of genotypes within the prey population allows total prey abundance to remain essentially constant even while the predator population is cycling . We used simple models to explore whether rapid prey evolution in response to fluctuating predation risk can produce cryptic dynamics in which prey abundance cycles with an amplitude so small that it could easily be obscured by sampling variation , environmental stochasticity , or both . Consider first the simplest case of a single predator ( y ) feeding on two clones ( x1 , x2 ) of an asexual prey species . The prey types are assumed to differ in their palatability to predators ( p ) and in a parameter θ affecting their birth rate: where X = x1 + x2 is total prey abundance and Q = p1x1 + p2x2 is total prey quality as perceived by the predator . The function f represents prey births and deaths unrelated to predation , and allows for nonlethal effects of the predator ( e . g . , [48–51] ) . We assume that f is decreasing in X , nonincreasing in y , and increasing in a parameter θ affecting prey birth rate . The function g is the predator per capita “grazing rate , ” and p represents prey palatability with a low-p clone having a lower probability of death due to predation . [Note that we use “palatability” here to signify a variety of ways that prey might be vulnerable to predator-caused mortality , not all of which have to do with true palatability ( e . g . , our defended algal cells survive gut passage , see below ) . ] We number the prey types so that p1 < p2 , and therefore θ1 < θ2 to represent a reproductive cost for reduced palatability . Predator population dynamics result from conversion of prey into offspring , and from a density-independent intrinsic mortality rate d . We also scale the model so that a unit of prey consumption yields one net predator birth . In the Materials and Methods section , we discuss in more detail the biological assumptions underlying model ( 1 ) and their rationale . Examples of the general model ( 1 ) include the model of Abrams and Matsuda [38] , in which competition between prey types is represented by Lotka-Volterra interaction terms , and a chemostat system model with two prey types competing for a limiting resource . These models are described in Protocol S1 . “Cryptic” cycles in model ( 1 ) are a limiting case of the evolutionary cycles described above . A general analysis of model ( 1 ) [52] shows that evolutionary cycles occur when: ( a ) defense is effective but not too costly ( p1 ≪ p2 but θ1 ≈ θ2 ) , and ( b ) the coexistence steady state for model ( 1 ) , at which both prey types coexist with the predator , is locally unstable ( see Figure 3 ) . A coexistence steady state always exists for p1 ≪ p2 and θ1 ≈ θ2 , and it is always a spiral point but may be stable or unstable . In both the Abrams-Matsuda and our chemostat models , these conditions are all satisfied for p1 ≈ 0 when the reduced system consisting of the predator and the more vulnerable prey exhibits consumer-resource cycles [52] . However , as p1 increases with θ1 close to θ2 , the coexistence steady state in model ( 1 ) always becomes locally stable ( before eventually disappearing with the vulnerable prey type dropping out due to indirect “apparent” competition with the defended prey ) . These qualitative behaviors are not dependent on either parameter values or the functional forms of f and g in model ( 1 ) . The mechanism for cryptic cycles is density compensation , by which we mean that the two prey types cycle out of phase with each other in such a way that their total abundance remains nearly constant . Evolutionary cycles in model ( 1 ) always develop this character as the cost of defense becomes small ( θ1→ θ2 ) . An asymptotic analysis treating ε = θ2 − θ1 as a small parameter [52] shows that the dominant eigenvector of the Jacobian at the coexistence equilibrium ( which gives the linear approximation to small-amplitude cycles ) is where and B and C are positive constants . The angle in the complex plane between the eigenvector components for different state variables corresponds to the phase lag between their cycles , e . g . , the approximately 180° angle between Prey 1 and Prey 2 corresponds to half of a cycle period . The entries in ( 2 ) therefore imply that the following: ( a ) the two prey types are almost exactly out of phase with each other; ( b ) the predator and total prey are exactly out of phase with each other; ( c ) the cycles of the predator and the vulnerable prey type exhibit the quarter-period lag typical of classical consumer-resource cycles . A dimensionless measure of each population's variability is given by the ratio between its cycle amplitude and its abundance at the coexistence equilibrium ( which lies approximately at the center of near-equilibrium cycles ) . Cycle amplitudes are proportional to the magnitudes of the eigenvector components in ( 2 ) . As the cost of defense becomes very small ( ε → 0 ) , the equilibrium total prey abundance approaches a finite limit while the predator equilibrium is order ε ( e . g . , a 50% reduction in ε leads to a roughly 50% reduction in the predator equilibrium density ) . Therefore , relative to the evolutionary variability in the prey ( i . e . , the relative proportion of the two types ) , the variability of total prey abundance is order smaller , whereas the predator variability is order larger . In addition , the cycle period converges to infinity as ε → 0 [52] . Prey evolution is driven by occasional predator outbreaks , but prey density remains nearly constant because the consumption of the vulnerable prey is almost exactly balanced by growth of the better-defended prey when they are released from competition with the vulnerable type . A caveat to these conclusions is that they are based on the Jacobian at the unstable coexistence equilibrium , so they are only guaranteed to be a good approximation if cycles stay fairly close to the equilibrium . That is , our analysis guarantees the occurrence of cryptic cycles only for parameter values near the intersection of the green and yellow bifurcation curves in Figure 3 . However , model simulations show that the cycles can remain cryptic—in the sense that the cycle amplitude of total prey density is very low—even far from the transition between cycling and stability . Figure 4 shows numerical solutions of the chemostat model , with parameters estimated for our algae-rotifer experimental system [42] . In these simulations , we set p1 = 0 . 25 , p2 = 1 , and δ = 1 , and the defended prey had a half-saturation constant for nutrient uptake 5% higher than that of vulnerable prey; with these parameters , the transition between stability and cycling occurs at p1 ≈ 0 . 4 . Plotting the predator and total prey densities and the mean prey palatability relative to their temporal averages , the scales of variability match the near-equilibrium analysis even though the predator density varies by nearly a factor of 2: predator density variability > prey evolution > total prey variability . Predator and total prey densities are exactly out of phase , whereas there is a quarter-period lag between predator density and the mean prey palatability . So instead of a predator–prey cycle , we observe a “predator–trait cycle , ” in which the “resource” being “consumed” is the mean palatability of the prey population . The algae and rotifers in our chemostat system are both obligately asexual , and our model up to this point is structured in accordance with this fact . To show that the phenomenon of cryptic cycles is not limited to asexual species , we consider a simple model with a sexually reproducing diploid prey species . For simplicity , we assume that defense is determined by a single locus with alleles A1 , A2 such that A1 A1 genotypes have palatability p11 ≪ 1 , A2A2 genotypes have palatability p22 = 1 , and A1A2 genotypes are intermediate . Model equations are given in Protocol S1 . Figure 5 shows that a chemostat model with sexual reproduction can still exhibit cycles in which total prey abundance remains nearly constant . However , the model's behavior is sensitive to the assumed tradeoff curve between predator defense and nutrient uptake ability ( the asexual model is much less sensitive to the shape of the tradeoff curve because only extreme types coexist [p1 ≈ 0 , p2 ≈ 1] ) . In Figure 5 , the heterozygote has half the cost of the defended genotype , but gains only 30% of the benefit , p12 = 0 . 3p11 + 0 . 7p22 . If p12 is instead closer to p11 , which gives more of an advantage to heterozygotes , the cycles of predator abundance and prey genotype frequencies are much smaller . The data from rotifer–algal chemostats led us to explore the possibility that rapid prey evolution could underlie our experimental observations of predator cycling without an accompanying response in total prey density , and mathematical modeling demonstrates that this is a plausible explanation . However , for the rotifer–algal experiments , we do not have direct evidence of changes in algal genotype frequencies that would confirm this interpretation . There is , however , another predator–prey chemostat system that provides direct evidence of cryptic dynamics: a bacterial prey , E . coli , attacked by lytic bacteriophage T4 [32 , 43] . Figure 6 shows results from two experimental runs that were initiated with phage and a bacterial strain that was susceptible to phage attack . After ≈75 h , a second bacterial strain , resistant to phage attack , was introduced . Critical for our purposes here , these resistant bacteria carried a neutral marker that made it possible to reliably estimate the separate densities of the resistant and sensitive strains ( see Materials and Methods ) . In both cases ( Figure 6A and 6C ) , there were 1–2 population cycles of the phage and sensitive bacteria; addition of the resistant strain was followed by stabilization of total bacterial density ( within experimental error ) but continued cycling of phage density . The fraction of the susceptible genotype in the total bacteria population clearly showed evolutionary cycles in concert with cycles in bacteriophage density , as our model predicts . Changes in the fraction of sensitive bacteria produced oscillations in phage density over five orders of magnitude , so the tight coupling between the populations is revealed by a phase-plane plot of bacteriophage density against the fraction of susceptible bacteria ( Figure 6B ) . These experiments thus confirm that cryptic cycles were produced by genetic diversity and rapid evolutionary dynamics in the host population . In Figure 6A , the density of the sensitive strain becomes so low relative to the total bacterial density ( note that density is plotted on a log scale ) that we have no evidence either for or against the theoretical prediction of density compensation between the sensitive and resistant strains . The changes in sensitive-strain density were swamped by the uncertainty in resistant-strain density ( as estimated from the unexplained between-sample variability ) . Several other runs with the same experimental design ( [32 , 43] and unpublished data ) exhibited the same features: total bacterial density ceased to cycle ( within experimental error ) because only a small fraction of the total population was interacting with the phage . However , when the sensitive-strain density did increase appreciably , during a period of low phage density ( Figure 6C and 6D ) , we observe nearly exact density compensation between the two host strains , as predicted . Figure 6E and 6F shows results from two other experiments in which a resistant bacterial strain arose spontaneously by mutation within the experimental cultures . Because this strain lacked the neutral marker carried by deliberately introduced resistant strains , we know when the resistant strain arose but do not have reliable separate estimates of sensitive- and resistant-strain densities ( see Materials and Methods ) . However , because the resistant strain arose later in these experiments than in those where the resistant type was deliberately added , they provide the clearest evidence for the predicted changes in population-level dynamics . Once the resistant strain arose and achieved high density , the total host density stabilized while the density of the phage continued to cycle , but with the markedly longer cycle period characteristic of evolution-driven cycles .
Cryptic population dynamics take place when the nature of an ecological interaction is obscured by rapid evolution in one or more of the interacting species . Here we have provided experimental examples in which cycling by a resource species was effectively eliminated through compensatory changes in the frequencies of prey genotypes that differed in their vulnerability to a consumer . Using a mathematical model , we have established the conditions necessary for these cryptic cycles: ( 1 ) predator–prey cycles would occur between the consumer and the more vulnerable genotype of the resource species if that genotype were the only one present , ( 2 ) the less vulnerable of the resource genotypes has an effective defense against the consumer , and ( 3 ) the cost of defense is fairly low in terms of ability to compete for limiting resources . Empirical studies suggest that these requirements often hold in natural systems . Cyclic population dynamics are widespread across all major animal taxa and biomes , occurring in roughly 30% of the available long-term data sets on population variability [69] . Fitness costs for defensive compounds and structures have often proved difficult to demonstrate , and in many cases , either no tradeoff or only a weak tradeoff was found ( e . g . , [47 , 70–76] ) . Therefore , efforts to establish the nature and strength of interactions in ecological communities that fail to consider the potential for evolution ( which is to say virtually all efforts to date ) run a risk of being incorrect . Because essentially all natural populations have heritable variation for ecologically important traits , and the number of examples of rapid contemporary evolution is large and growing , ignoring the potential for evolution to affect measurements of species interaction strengths becomes untenable . Although our microcosms are extremely simple systems , they mimic the consumer–resource interactions occurring in natural systems . Our rotifer–algal interaction is an herbivore consuming a primary producer ( though we call it “predator–prey” because each algal cell is consumed whole ) , and our phage–bacterial interaction can be considered as either predator–prey or host–parasitoid ( in which successful infections are lethal ) . Parasite–host dynamics can be significantly altered by contemporary evolution [30 , 77] , so epidemiological predictions of disease outbreaks may well need to take account of evolution . The focus here has been on the ecological consequences of evolution in prey populations , in particular showing how such evolution can obscure the coupling between predator and prey dynamics . Of course the processes and patterns we have described in prey populations can also have further evolutionary consequences . We suggest two . Cryptic evolutionary cycles result in the maintenance of non-neutral genetic variation in isolated populations at equilibrium abundance . In nature , variation harbored by this mechanism would be grist for the mill of rapid adaptive evolutionary response to environmental change of the kind reported with increasing frequency [26–37 , 41] . Second , predators might evolve that partially or completely overcome the defenses of the more resistant prey type . This outcome could , in turn , lead to cycles of predator–prey coevolution , perhaps leading to adaptive radiation in the prey , the predator , or both populations , depending on the existence and pattern of tradeoffs between competitive ability and resistance in the prey population , and between growth rates on different prey types in the predator population . However , the particular evolutionary path will depend on details of any given predator–prey interaction [28 , 39] . We have , in fact , sometimes observed evolutionary changes in rotifers ( but for a character unrelated to diet ) , and certain phage species can sometimes evolve to overcome bacterial resistance ( but not the phage T4 we used ) [28 , 40] . If a predator eventually evolves the renewed ability to consume the defended prey , then traditional predator–prey cycles might reappear . However , it has not been necessary to incorporate evolution of the predator in order to explain the cryptic dynamics that we observe in both of these systems . More generally , it is little consolation that the traditional predator–prey coupling might ( or might not ) be evident depending on the patterns of genetic variation in one or both populations , because so little is usually known about that variation . We have shown that the coupling of ecological and evolutionary dynamics can have unexpected consequences even in the simplest possible ecological community . We expect that further surprises will be found as the effects of evolution are traced in more complex communities and ecosystems . If rapid evolution is pervasive , then all of ecological theory needs to be re-examined to take into account the fact that changes in distribution and abundance are likely to be accompanied by evolutionary dynamics that , in turn , alter the very changes in distribution and abundance that we are striving to understand . It is a daunting and exciting prospect .
Our rotifer–algal chemostat system has been described in detail elsewhere [40 , 44 , 57] so we give here only an outline . We established stock cultures of C . vulgaris ( UTEX C . vulgaris culture no . 26; http://www . utex . org ) and Brachionus calyciflorus ( taken originally from the harbor at Milwaukee , Wisconsin , United States , and provided by M . Boraas ) . We established that our algal stock culture is genetically variable for ecologically relevant traits , because clonal populations that were derived from it exhibit heritable phenotypic changes in response to selection [47] . Defended cells survive gut passage when consumed by rotifers , but have a reduced growth rate at low nutrient concentrations [46 , 47] . The experiments reported here used 380-ml , single-stage chemostats to culture these organisms in sterilized medium with nitrogen ( in the form of nitrate ) as the limiting nutrient , under constant light ( 120 μE m−2 s−1 ) and temperature ( 25 °C ) . We set the dilution rate ( 0 . 80–0 . 98 d−1 ) and nutrient concentration ( 80 μmol/l nitrate ) to give population cycles based on results in [42 , 44] . Organisms were sampled daily through ports near the bottom and top of each chemostat . Rotifers were counted under a dissecting microscope and algae were counted with a particle counter ( CASY 1 , Schärfe; http://www . casy-technology . com ) . Organism abundance data are presented as means of duplicate samples . For our work , it is important to know that the chemostats were not accidentally contaminated with other species , so that we can be sure of a strong direct trophic link between algae and rotifers . No other species were observed during visual counting of rotifers . The size distribution of suspended particles ( obtained from the particle counter ) showed a clear single peak corresponding to algal cell size . This suggests the absence of other organisms or bacteria , which produce a peak at smaller size than our algae if they are present ( T . Yoshida , unpublished data ) . Also , nitrate concentrations were consistently very low ( 0 . 24–0 . 56 μmole l−1 ) compared with fresh medium ( 80 μmole l−1 ) , while algal density stayed unchanged and rotifer predator density fluctuated ( T . Yoshida , unpublished data ) , suggesting that the limiting resource was not being captured by some other species that the rotifers could then consume . Thus , it is unlikely that any other species were present in sufficient numbers to affect the population dynamics of the rotifer-algal system . The bacteria–phage chemostat system has been described in detail previously [32 , 43] so we give here only an outline . The experiments used E . coli and bacteriophage T4 cultured in single-stage chemostats , with limiting glucose supplied at 0 . 5 or 0 . 1 mg/l of fresh culture medium . Chemostats were maintained at a volume of 30 ml , temperature 37 °C , and dilution rate 0 . 2/h . Experimental runs were inoculated with phage T4 and E . coli B strain REL607 , which is susceptible to attack by T4 . T4-resistant mutants were either inoculated deliberately at ≈75 h into the run ( E . coli B strain REL6584 ) , or arose spontaneously in control chemostats . All T4-resistant mutants in this E . coli strain achieve resistance through the loss of particular moieties on the lipopolysaccharide core surface receptor to which T4 binds to initiate infection [62] . This loss confers complete invulnerability to attack by T4 , at the cost of a competitive disadvantage under glucose-limited conditions when phage are absent [28 , 32 , 43 , 62] . Total bacteria and phage densities were estimated twice daily by dilution and plating . REL607 density was estimated on agar plates containing arabinose as a sole carbon source; REL6584 is unable to use arabinose , so this medium allows growth of REL607 but not REL6584 ( the inability to use arabinose is selectively neutral in the culture medium used for all chemostat runs ) . Phage density was estimated by plating on a lawn of REL607 . REL6584 density was estimated by mixing a second bacterial sample with a concentrated T4 lysate ( which kills the sensitive strain , REL607 ) and then plating on a lawn of glucose medium . See [32 , 43] for details of these procedures . Spontaneously arising T4-resistant mutants did not carry the marker ( inability to use arabinose ) and so could not be counted separately by these methods . When resistant mutants are rare , plating after mixture with T4 lysate gives an estimate of the resistant strain , while plating on arabinose gives an estimate of total bacterial density . But once the resistant strain becomes numerically dominant , sampling variability is too high for the sensitive-strain density to be estimated by the difference between total and resistant strain density , as both plating methods are really estimating total bacterial density . A key biological assumption in model ( 1 ) is total niche overlap between the prey types , which is reflected in f being a function of total prey density X . This assumption seems reasonable for within-species heritable variation , especially when the prey's resource base is homogeneous . Another important biological assumption is that the function g , which can be thought of as the predator attack rate , depends on Q rather than on X . This assumption can be justified mechanistically in at least two different situations: First , suppose that the mechanism of prey defense is crypsis , with p representing the probability that a prey individual is detected by a predator searching the area containing that individual . Then the instantaneous capture rate is the same as if the prey abundances were p1x1 and p2x2 , but each predator can detect all prey within its search area . This leads to the predation rates in model ( 1 ) , with xg ( x ) being the capture rate by one predator at density x of visible prey . Second , and more relevant to our rotifer-algal chemostat system , suppose that the predator is an aquatic filter-feeder and defended prey have a higher probability of passing through the predator gut undigested and unscathed , as in our rotifer-alga experimental microcosms [46] . If the predator adjusts the volume of water it filters per unit time ( clearance rate ) in response to the total number of digestible prey Q , then the per-predator rate of prey consumption will be of the form Qg ( Q ) , as assumed in model ( 1 ) . Although we have no direct evidence on clearance rates in our microcosms to support this assumption , models with Q-dependent clearance rate were more successful at quantitatively matching experimental data on cycles in predator and total prey abundance [42] than were models with X-dependent clearance rate , and the grazer population growth rate is Q-dependent [47] . | The presence and strength of interactions between species has frequently been inferred from observational data on changes in species abundance . For example , correlated cycles in potential predator and prey species may be interpreted as evidence that the species interact , while the absence of such coupled oscillations might be interpreted as evidence for lack of interaction . Here we show that prey abundance can be decoupled from changes in predator abundance when there is genetic variability in the prey for antipredator defense traits , allowing rapid evolutionary changes in prey defense levels . It then appears that the two species are not interacting , when in fact they are . We deduce this from studies of two laboratory microcosm systems , one with algae consumed by rotifers and the other with bacteria attacked by phage . In each , when the prey vary genetically for defense traits and undefended genotypes are superior competitors , defended and undefended prey frequencies evolve in a cyclical way that is almost exactly counterbalancing , so that total prey density remains nearly constant . We show mathematically that these “cryptic cycles” occur whenever conditions are right for predator-prey cycles , when prey vary genetically for defense traits , and when prey defense against predation is effective but inexpensive to produce . Under these conditions , observations of predator and prey population dynamics cannot be trusted to be informative about the strength or even the existence of interspecific trophic links . | [
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| 2007 | Cryptic Population Dynamics: Rapid Evolution Masks Trophic Interactions |
In mid-2015 , Salvador , Brazil , reported an outbreak of Guillain-Barré syndrome ( GBS ) , coinciding with the introduction and spread of Zika virus ( ZIKV ) . We found that GBS incidence during April–July 2015 among those ≥12 years of age was 5 . 6 cases/100 , 000 population/year and increased markedly with increasing age to 14 . 7 among those ≥60 years of age . We conducted interviews with 41 case-patients and 85 neighborhood controls and found no differences in demographics or exposures prior to GBS-symptom onset . A higher proportion of case-patients ( 83% ) compared to controls ( 21% ) reported an antecedent illness ( OR 18 . 1 , CI 6 . 9–47 . 5 ) , most commonly characterized by rash , headache , fever , and myalgias , within a median of 8 days prior to GBS onset . Our investigation confirmed an outbreak of GBS , particularly in older adults , that was strongly associated with Zika-like illness and geo-temporally associated with ZIKV transmission , suggesting that ZIKV may result in severe neurologic complications .
Guillain-Barré syndrome ( GBS ) is a peripheral polyneuropathy characterized by acute onset of bilateral , symmetric limb weakness with decreased or absent deep-tendon reflexes . GBS is a progressive illness with clinical nadir occurring generally within 2–4 weeks . The underlying disease mechanism by which GBS develops is thought to be related to an aberrant immune response following an infection or other immune stimulation [1] . The most common known inciting infection is Campylobacter jejuni , though sporadic cases of GBS have been described temporally following a myriad of other viral , bacterial , and parasitic infections [2] . Several emerging and re-emerging arboviruses , including dengue , chikungunya , West Nile , and Zika viruses , have been associated with isolated cases of GBS [3–6] . The onset of GBS symptoms typically manifests within 6–8 weeks , and particularly the following 10–14 days , after exposure [7] . Although clinical outcomes are generally favorable , approximately 20–30% of cases may develop autonomic disturbances and/or neuromuscular respiratory failure , which are the most common causes of death in GBS [8] . Reported mortality rates range from 3–7% in North America and Europe to 13% in parts of Asia [8 , 9] . Zika virus ( ZIKV ) , a flavivirus primarily transmitted by Aedes spp . mosquitoes [10–12] , was originally identified in Uganda in 1947 [13] . Historically , it has been associated with sporadic cases of rash illness in Africa and Southeast Asia [14–17] , though outbreaks of ZIKV began to emerge in the Western Pacific region during the late 2000s . In 2014 , an outbreak of ZIKV in French Polynesia was followed by increased reports of GBS . An investigation into the GBS outbreak provided evidence for a possible etiologic association between ZIKV and the cluster of GBS cases [18] . Subsequent reports have further supported this association of ZIKV with severe neurologic sequelae such as GBS and congenital malformations [6 , 18–21] . In April 2015 , ZIKV was first identified in Brazil , causing an outbreak of exanthematous illness centered in the northeast region [22] . Subsequent to the ZIKV outbreak , clustering of GBS diagnoses was noted in mid-2015 in northeastern Brazil [23–25] . However , to identify risk factors and potential infectious pathogens associated with the reported increase in GBS cases , we performed a case-control investigation to evaluate the relationship between ZIKV and increased reports of suspected GBS . In particular , we sought to establish an etiology for the outbreak of GBS through a case-control investigation using information collected through interviews , and evaluate the relationship of GBS to arboviral infections in the population .
We conducted our investigation in the Salvador metropolitan area during January 16 –February 5 , 2016 . We identified suspected GBS case-patients reported by physicians and hospitals to the Center for Information and Epidemiologic Surveillance of Bahia ( Centro de Informações Estratégicas em Vigilância em Saúde [CIEVS] ) with onset of neurologic symptoms during January 1– August 31 , 2015 . To determine compatibility with a GBS diagnosis , we performed medical record reviews to ascertain characteristics of the clinical illness and diagnostic testing , including cerebrospinal fluid , neuroimaging , and electrodiagnostic test results . Suspected GBS case-patients were classified according to diagnostic certainty of the Brighton Collaboration Criteria case definitions for GBS [26] . Case-patients meeting levels 1–3 of diagnostic certainty , and who were at least 12 years of age at time of interview , were classified as confirmed GBS and eligible for enrollment in the case-control investigation . We obtained numbers of suspected ZIKV infections in Salvador during January 1 –August 31 , 2015 , from CIEVS . We also obtained incidence for suspected and confirmed dengue and chikungunya infections from routine surveillance through the National Notifiable Disease Information System ( Sistema de Informação de Agravo de Notificação ) . We juxtaposed these data to evaluate temporal relationships between dengue , chikungunya , and ZIKV infections compared with confirmed GBS cases . For each GBS case-patient , we selected two neighborhood controls from the same general age grouping ( 12–19 , 20–39 , 40–59 , 60+ ) as the case-patients . We did this to ensure a relatively equal age distribution between case-patients and controls given that age is a known risk factor for GBS [27] . To identify controls , we flipped a coin to determine the direction of travel from the case-patient’s house , and we used a random number generator to determine how many properties ( 1–20 ) to skip to choose the first house . We continued to move in the same direction until finding the first control , and we repeated the random number selection to find the second control , maintaining the direction of travel . We interviewed all available case-patients and controls to obtain information about demographics , risk factors , and exposures ( S1 Table ) in the 2 months prior to onset of neurological symptoms of the GBS case-patients . We considered case-patients and controls as having suspected ZIKV disease if they had self-reported symptoms of rash with at least two other ZIKV-like symptoms: fever , conjunctivitis , arthralgia , myalgia , and peri-articular edema [28] . At the time of interview , to determine intermediate-term functional outcomes , we assessed for residual motor deficits using the Hughes GBS Disability Scale [29] . Following the interviews , serum samples were collected from case-patients and controls . We tested serum samples by capture enzyme-linked immunosorbent assay for IgM antibodies against ZIKV ( MAC-ELISA ) and dengue viruses serotypes 1–4 ( DENV Detect IgM Capture ELISA , InBios , Inc . , Seattle , WA ) [30] . We determined neutralizing antibody titers against ZIKV and dengue serotypes 1 and 2 using a 90% cutoff value for plaque-reduction neutralization tests ( PRNT90 ) [31] . We defined a recent flavivirus infection as a positive or equivocal IgM test result for ZIKV or dengue . We discriminated between ZIKV and dengue infections if only one PRNT was positive ( Table 1 ) [32] . We estimated the incidence of GBS using 2015 population estimates by the Brazilian Institute of Geography and Statistics ( Instituto Brasileiro de Geografia e Estatística ) [33] . To determine a possible association between GBS and a preceding ZIKV infection , we estimated that 37 case-patients and 74 controls would provide a power of 80% to detect a difference of 30% in ZIKV prevalence , with an alpha level of 5% . We performed logistic regression to calculate odds ratios and 95% confidence intervals for the association of GBS and demographics , known GBS risk factors , antecedent illness , and dengue and ZIKV infections . We also performed sensitivity analyses on combinations of symptoms and laboratory findings to evaluate consistency of results . The human subjects review board at CDC and the Brazil Ministry of Health approved the investigation and determined it to be part of a public health response and not research [National Council of Ethics in Research ( Conselho Nacional de Ética em Pesquisa ) approval number 1 . 391 . 200] . All adult subjects provided informed written consent prior to interview participation and collection of specimens , and a parent or guardian of any child participant ( under 18 years old ) provided informed consent on their behalf .
During January 1 –August 31 , 2015 , 77 suspected GBS case-patients within the Salvador metropolitan area were reported to CIEVS . We reviewed all available medical records , and 50 ( 65% ) patients had sufficient information to be classified as levels 1–3 of the Brighton Collaboration criteria for GBS: 7 ( 9% ) met level 1 of diagnostic certainty , 43 ( 56% ) met level 2 , and none met level 3 . Of the remaining 27 individuals , 24 ( 31% ) did not have enough information for classification as GBS or had alternative diagnoses . The remaining 3 ( 4% ) were excluded because of factors such as age below enrollment criterion or inaccurate address . The majority ( 94% ) of Brighton-confirmed case-patients based on medical chart review had neurologic illness onset during epidemiologic weeks 17–29 ( April 26 –July 25 ) ( Fig 1 ) . During this period of peak GBS occurrence , the annualized incidence of GBS was 5 . 6 cases/100 , 000 population for the Salvador metropolitan area . Annualized age-group-specific incidence increased with age . The incidence was 1 . 5 cases/100 , 000 population for the 12–19 years age group , 3 . 9 for the 20–39 years age group , 7 . 3 for the 40–59 years age group , and 14 . 7 among persons ≥60 years of age . The median age of these cases was 47 years ( range , 14–79 years ) . There was no significant difference in incidence between men and women ( 5 . 8 versus 5 . 5 , p = 0 . 37 ) . Based on medical chart review of the 50 confirmed GBS case-patients , 44 were reported to have had a preceding illness ( S2 Table ) ; the median time between antecedent illness and neurologic symptom onset was 8 days ( IQR 5–15 ) . Prominent neurologic signs/symptoms of the GBS case-patients included leg and arm weakness , dysphagia , and facial weakness . Median time from onset of neurologic symptoms to nadir was 6 days ( IQR 4–9 ) . Nine case-patients had electrodiagnostic studies available for review . Of the available reports , 5 were interpreted as being consistent with the acute motor axonal neuropathy ( AMAN ) subtype of GBS , and the other 4 demonstrated patterns interpreted as the acute inflammatory demyelinating polyradiculoneuropathy ( AIDP ) subtype of GBS . All case-patients were hospitalized; 46 ( 92% ) received intravenous immunoglobulin ( IVIG ) , 17 ( 34% ) required ICU-level care , 11 ( 22% ) required mechanical ventilation , and 3 ( 6% ) died ( Table 2 ) . The reports of suspected symptomatic ZIKV infections occurred as a prominent clustering during April–June of 2015 while no apparent fluctuations were reported in either dengue or chikungunya infections throughout 2015 . The outbreak of GBS peaked approximately 7 weeks after the peak of suspected ZIKV infections ( Fig 1 ) . Of the 47 GBS case-patients who were alive at the time of the investigation , 2 declined participation and we could not locate 4 , leaving 41 individuals that we could further evaluate through interview . The median age for these case-patients was 44 years ( range 14–78 ) , which was not significantly different from the controls with a median age of 50 ( range 13–87 ) . Additional demographics and exposure histories did not differ between case-patients and controls ( S1 Table ) with the exception that a higher proportion of GBS case-patients compared to controls reported an antecedent illness in the 2-month period prior to neurologic symptom onset of GBS case-patients ( Table 3 ) . Symptoms most frequently reported by GBS case-patients included rash , headache , fever , myalgias , and arthralgias . At the time of our assessment , all living GBS case-patients were at least 5 months out from neurologic symptom onset ( median 220 days , range 160–321 days ) . Thirty-five case-patients ( 85% ) reported at least minor residual motor deficits; 17 ( 41% ) had substantial residual motor deficits and could not walk without assistance . Case-patients were found to meet criteria for suspected ZIKV disease significantly more often than controls ( Table 3 ) . There were no case-patients or controls who met laboratory criteria for recent or prior ZIKV infection or recent dengue infection . Recent flavivirus infections were equally prevalent between case-patients and controls . However , being a case-patient was significantly associated with evidence of recent flavivirus infection when combined with clinical criteria for suspected ZIKV disease . Only a small number of case-patients and controls had no evidence of prior ZIKV exposure based on negative PRNTs . Nearly all of the samples tested positive for dengue ( serotypes 1 and 2 ) virus-neutralizing antibodies ( 100% of case-patients versus 96% of controls ) , leaving only 3 controls with no evidence of prior exposure to ZIKV , dengue , or other flaviviruses .
Our investigation demonstrated a high incidence of GBS geographically and temporally clustered in the setting of an ongoing large outbreak of ZIKV . GBS case-patients in this investigation were more likely than non-GBS controls to report symptoms suggestive of ZIKV illness in the 6–8 weeks prior to neurologic illness onset; in addition , based on both medical record review and self-report , the distribution of onsets of the antecedent illnesses clustered during the 2 weeks before the onset of neurologic symptoms . This temporal relationship supports an etiological association between these two illnesses . Despite this epidemiological evidence , the serologic data could not confirm this relationship . However , the serologic findings from specimens collected an average of 7 months after GBS onset showed a high prevalence of ZIKV-neutralizing antibodies in cases and controls , consistent with the ZIKV epidemic that was recognized in the community during the preceding months [22] . Baseline population estimates of GBS incidence in Brazil are limited but are reported at 0 . 05–0 . 6 cases/100 , 000 people per year , which are substantially lower than expected [34–36] . The incidence of GBS in North America and Europe is 0 . 81–1 . 89 with an expected worldwide incidence of 1 . 1–1 . 8 cases per 100 , 000 people per year [27 , 37] . Applying a baseline estimate of 1 . 5 cases per 100 , 000 people per year results in a 3 . 7-times increased incidence of GBS for the outbreak period for persons at least 12 years of age . The characteristics of GBS illness during this outbreak were largely similar to what would be expected for typical GBS disease patterns with some notable exceptions . The high attack rates in the older individuals reflects an unusually steep increase in GBS incidence during the outbreak . The incidence of GBS in this investigation is 10-times higher among the oldest age group compared with the youngest , in contrast to a 2–3-times increase in incidence reported elsewhere for the same population groups [27] . Indicators of GBS severity , such as need for intensive care monitoring and mechanical ventilation , were comparable to other reports of GBS [1] . Overall , the 6% mortality rate was similar to rates in North America and Europe and possibly reflected the high utilization of IVIG and supportive care of the GBS case-patients in this outbreak . Most of the case-patients regained lost function after their GBS illness; however , 41% of case-patients still required assistance with walking 6 months later , which is higher than the 20% that is more commonly expected [38] . Additionally , there was a more rapid progression to nadir than the 2–4 weeks usually observed for GBS , though this was similar to findings in French Polynesia [18 , 39 , 40] . Few studies exist that characterize the subtypes and electrophysiologic findings of GBS in Brazil [41] . Though there was a limited number of electrophysiologic studies performed for case-patients in this investigation , there appeared to be a relatively equal distribution of AMAN and AIDP subtypes . This contrasts with French Polynesia where AMAN was the predominant subtype and with Puerto Rico and Colombia where AIDP was most frequently reported [18 , 20 , 21] . Additional investigations are required to define the electrophysiologic features of ZIKV-associated GBS , which could contribute to understanding the underlying pathophysiologic mechanisms . We noted a 7-week interval between the peaks of reported ZIKV infections and GBS , which is longer than would be expected if ZIKV was biologically associated with GBS . This finding was consistent with data reported elsewhere [25] . However , this differed from individual-level data , in which the median interval between onset of ZIKV-like illness and GBS was 8 days . Several factors may have contributed to this effect . One possibility suggested by Paploski , et al . [25] is that once the community perceived ZIKV infections as benign , persons may have stopped seeking care , artificially foreshortening the epidemiologic peak of ZIKV infections . Alternatively , there may be limitations in the surveillance data for ZIKV infections given that very few case-patients had laboratory confirmation . This is especially notable considering recent reports that more closely align occurrences of ZIKV infections and GBS in Bahia [6] . The preceding illnesses of the case-patients were most prominently characterized by rash , headache , fever , myalgias , and arthralgias—symptoms commonly reported with ZIKV infections [42] . The occurrence of acute illness among the case-patients 8 days prior to onset of GBS provides a biologically plausible argument for a causal association between the acute illness and GBS and has been similarly reported in other studies of Zika-associated GBS [20 , 21] . We found that there was also a significant , but much less robust , association for gastrointestinal symptoms , such as nausea/vomiting and diarrhea . Gastrointestinal symptoms have been reported in the setting of other ZIKV outbreaks [20 , 43] , and it is possible that these symptoms are an under-recognized clinical feature of ZIKV illness , rather than manifestations of Campylobacter infection leading to the GBS cases observed in this investigation . However , it is probable that not all of the case-patients had the same antecedent etiology , which is supported by the fact that several of the case-patients did not report symptoms of Zika-like illness or have laboratory evidence of previous ZIKV infection . The attack rate of ZIKV in northeastern Brazil is unknown . However , if it was as high as reported in French Polynesia , it could limit our ability to detect differences in seropositivity between case-patients and controls . Tests of association cannot discriminate between such high rates of infection without a very large sample size . Notwithstanding , the laboratory data can be used to demonstrate likely ZIKV exposure , which hence could be presumed to represent a relatively recent infection since the virus has only been identified in Brazil since early 2015 . Additionally , given this finding of greater ZIKV-like symptoms in the case-patients , even if rates of ZIKV infection are not different between the case-patients and controls , it may indicate that individuals with symptomatic ZIKV infections are more predisposed to the development of GBS . It is not yet understood what factors lead to symptomatic versus asymptomatic infections , though possibilities may include prior infectious exposures causing potentiation , initial infectious viral load , or host immunologic response . The latter theory is supported by this observed correlation with GBS , which is itself a manifestation of an aberrant immune response . Further exploration of the immunopathogenesis of ZIKV may provide insight into the mechanism of Zika-associated GBS . Because patients for whom GBS case status was not ascertained were not interviewed , we did not systematically collect demographic information on them , so no comparisons could be made with case-patients enrolled in the investigation . The major limitations in antecedent illness analysis are the non-specific nature of the symptoms , possible underreporting of acute illnesses by controls , and recall bias in reporting of symptoms by case-patients . Because the investigation was performed approximately 7 months after the acute illness onset , laboratory findings were limited to serology . Because there is no information on how long ZIKV-specific IgM persists , the finding of a negative IgM result so far out from the initial suspected ZIKV infection is difficult to interpret . Furthermore , the cross-reactivity of dengue and ZIKV antibodies makes accurate discrimination between these pathogens particularly challenging since all samples with ZIKV-specific neutralizing antibodies also had dengue virus-specific neutralizing antibodies [32 , 44] , though measuring ZIKV-specific neutralizing antibodies several months from the acute infection may be a more specific indicator [45] . Regardless , the laboratory findings more effectively document flavivirus exposure rather than being able to confirm Zika infection or exposure , and other possible infectious triggers cannot be ruled out; in particular , Campylobacter-specific surveillance data was not available and could not be retrospectively ascertained . Additionally , ascertainment of neurological impairment was based on retrospective abstraction of medical records , which may be limited by lapses in documentation . The findings of this investigation , along with similar epidemiologic findings throughout Latin America and previously published data from French Polynesia [6 , 18 , 20 , 21 , 25] , strongly suggest a ZIKV-associated GBS . The apparent etiological association between ZIKV and GBS and the observed higher ZIKV-related GBS attributable risk in older persons should be substantiated through additional prospective studies of GBS during ZIKV outbreaks . These studies should include molecular confirmation of infection to further define distinctive clinical and laboratory features of such cases . If this etiologic relationship is true , prevention of GBS may be enhanced by minimization of exposure to mosquitoes through personal protection and environmental control methods . It may also be prudent to target public health messaging about GBS to older adult populations during ZIKV outbreaks . Additionally , these findings may inform future preparedness efforts to build GBS-related diagnostic , treatment , and hospital capacity in areas at risk for ZIKV infection . | Shortly following the introduction of Zika virus ( ZIKV ) , a type of flavivirus transmitted by mosquitoes , into Brazil in early 2015 , the Brazil Ministry of Health began receiving increased reports of a paralyzing condition known as Guillain-Barré syndrome ( GBS ) . The areas with the greatest number of GBS cases appeared to correlate geographically and temporally with the areas reporting the highest rate of ZIKV infections . This association had been previously observed during a ZIKV outbreak in French Polynesia , however , this had not been systematically examined in a case-control investigation for the ZIKV outbreak in South America . In this investigation , the authors found that the occurrence of GBS in the affected population was nearly four times higher than would be expected , and the risk for GBS was particularly elevated among older adults . GBS was associated with ZIKV-like symptoms and with a combination of ZIKV-like symptoms plus laboratory evidence of a recent flavivirus infection . Taken together , these findings provide strong support for and greater understanding of the link between ZIKV and GBS . | [
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| 2017 | Increased rates of Guillain-Barré syndrome associated with Zika virus outbreak in the Salvador metropolitan area, Brazil |
Ants are a highly successful family of insects that thrive in a variety of habitats across the world . Perhaps their best-known features are complex social organization and strict division of labor , separating reproduction from the day-to-day maintenance and care of the colony , as well as strict discrimination against foreign individuals . Since these social characteristics in ants are thought to be mediated by semiochemicals , a thorough analysis of these signals , and the receptors that detect them , is critical in revealing mechanisms that lead to stereotypic behaviors . To address these questions , we have defined and characterized the major chemoreceptor families in a pair of behaviorally and evolutionarily distinct ant species , Camponotus floridanus and Harpegnathos saltator . Through comprehensive re-annotation , we show that these ant species harbor some of the largest yet known repertoires of odorant receptors ( Ors ) among insects , as well as a more modest number of gustatory receptors ( Grs ) and variant ionotropic glutamate receptors ( Irs ) . Our phylogenetic analyses further demonstrate remarkably rapid gains and losses of ant Ors , while Grs and Irs have also experienced birth-and-death evolution to different degrees . In addition , comparisons of antennal transcriptomes between sexes identify many chemoreceptors that are differentially expressed between males and females and between species . We have also revealed an agonist for a worker-enriched OR from C . floridanus , representing the first case of a heterologously characterized ant tuning Or . Collectively , our analysis reveals a large number of ant chemoreceptors exhibiting patterns of differential expression and evolution consistent with sex/species-specific functions . These differentially expressed genes are likely associated with sex-based differences , as well as the radically different social lifestyles observed between C . floridanus and H . saltator , and thus are targets for further functional characterization . Our findings represent an important advance toward understanding the molecular basis of social interactions and the differential chemical ecologies among ant species .
The family of insects commonly known as ants ( family Formicidae ) originated during the Cretaceous period , approximately 140 million years ago [1] . Since that time , they have established a global presence , with only the most remote locations lacking ant species [2] . Indeed , in some cases , such as lowland tropical rainforest canopies , ants have come to dominate the biomass [3] , [4] . Their ecological success is reflected in the number and diversity of ants , of which there were 283 known genera [5] . There is a wide diversity in the behavior and morphology of different ant subfamilies that includes both the level and complexity of social organizations . For instance , Camponotus floridanus ( the Florida Carpenter Ant ) , is a Formicine ant from the South-Eastern United States which belongs to one of the most globally prevalent ant genera [6] . These ants feature a rigid caste structure , with strict division of labor between the reproductive queens and the non-reproductive workers that is primarily regulated through pheromones [7] , [8] , [9] . Workers have a high threshold to lay eggs , and regulation of their reproduction through aggressive interactions does not occur [10] . Furthermore , the worker caste is divided into two classes: minor workers and major workers , which differ in size and morphology [2] , [6] . On the other hand , Harpegnathos saltator , a predatory species of Ponerine ant endemic to India and Sri Lanka is characterized by a more flexible reproductive system . H . saltator colonies are relatively small ( averaging 65 to 225 individuals , depending on season and region ) [11] , and queen to worker dimorphism is weak [11] , [12] . When a H . saltator colony loses its queen , one or more of the workers will begin laying eggs and become functional reproductives ( referred to as gamergates ) [12] and this behavioral transition is initiated with strong aggressive interactions [13] . Sociality in ants is considered to be a simple model for complex behaviors in humans and other mammals [14] . The success of ants is thought to have arisen in large part from their well-developed eusociality , wherein individuals live together in colonies with one or several highly fertile female “queens” surrounded by a host of non-reproductive female “workers . ” These workers then support and defend the queen and her progeny . The fact that the workers are the queen's own daughters is thought to provide the evolutionary advantage for the workers to protect and support the queen [6] . While it is generally accepted that a variety of chemical signals mediate many of the interactions between these castes , as well as interactions between individuals from competing colonies , there is great interest in determining the particular pheromones and their cognate molecular receptors that mediate these interactions [2] . It is likely that these semiochemicals are initially detected in peripheral sensory neurons by members of three major insect chemosensory receptor gene families: odorant receptors ( Ors ) [15] , [16] , [17] , [18] , [19] , gustatory receptors ( Grs ) [15] , [20] , [21] , [22] , [23] , and the more recently discovered variant ionotropic glutamate receptors ( Irs ) [24] , [25] , [26] . Ors and Grs belong to the same superfamily and both encode seven-transmembrane-domain proteins [17] , [22] . Ors are mainly expressed in olfactory receptor neurons ( ORNs ) within sensory appendages such as antennae and maxillary palps , where they are responsible for the perception of volatile chemical signals [17] , [19] . Conventional insect Ors ( so-called “tuning” Ors ) are associated with odorant specificity . They are typically highly divergent and their orthologous relationships are usually difficult to determine even within order ( e . g . Drosophila vs . Anopheles [27] , and Nasonia vs . Apis [28] ) . In contrast , one member of this gene family , which is now uniformly known as Orco , is both highly conserved across insect orders and widely expressed in a majority of ORNs [29] , [30] . Orco is necessary and sufficient for the proper localization and retention of other tuning Ors at the dendritic membrane , and is required for proper function of tuning Ors [29] , [31] . Rather than playing a role in odorant specificity , Orco forms an essential part of a heteromeric ion channel in cooperation with a tuning Or that is gated by its cognate odor ligand [32] , [33] , [34] , [35] , [36] . In contrast with the Ors , Grs are highly expressed in gustatory organs [20] , [21] , [22] , and a large portion of these receptors respond to soluble tastants [37] , [38] , [39] and pheromones [40] , [41] , [42] , leading to the “gustatory” designation for this group of chemoreceptors . However , there are some exceptions; for example , one unusual group of Grs respond to the volatile chemical carbon dioxide [43] , [44] , demonstrating that members of this receptor family are not necessarily limited to gustatory or pheromonal responses . This is further supported by the expression of some Grs in non-gustatory organs such as the arista and Johnston's organ [45] . Irs are homologous to ionotropic glutamate receptors ( iGluRs ) and thus are evolutionarily unrelated to Ors and Grs [24] , [26] . The role of IRs as chemosensory receptors has recently been uncovered based on multiple lines of evidence , including their divergence from conventional iGluRs at sequence level and the expression of several Irs in chemosensory neurons [24] . While Irs are generally thought to mediate responses to acids and amines [25] , members of this family of chemosensory receptors may also sense other classes of chemicals . We hypothesize that the striking contrast between C . floridanus , with its strict queen-worker dimorphism and largely pheromone-regulated reproduction , and H . saltator , with its flexible reproductive system that is associated with behavioral and pheromonal regulation of reproduction , is correlated with distinctive semiochemical and chemoreceptor profiles , which in turn generate differences in their chemical ecologies . The same is likely to be true of caste- or sex-based differences in behavior within each species . To test these hypotheses , we first developed a custom gene annotation pipeline to comprehensively describe the chemosensory receptor repertoires of C . floridanus and H . saltator . We then investigated the evolutionary patterns ( e . g . gene gain-and-loss ) of these chemosensory receptor genes , in order to gain insight on their functional diversification . Furthermore , we performed RNAseq analyses of caste- and sex-specific antennal transcriptomes to identity chemoreceptors that are differentially expressed between males/females and between species . We found multiple clades of chemosensory receptor genes that show differential expansion/contraction among ant species . In addition , a large number of chemosensory receptor genes exhibited sex-specific expression or male/female-enrichment . These chemosensory receptor genes exhibiting interesting evolutionary and expression patterns may have potentially contributed to the different chemical ecology between sexes/species . We also successfully identified agonists for two Or genes to further validate these annotations . The findings of this study inform us as to the genetic basis for the differences in chemical ecology between C . floridanus and H . saltator , as well as the potential role of chemosensory receptors in the biology and evolution of eusociality in ants .
The automated genome annotations of C . floridanus and H . saltator revealed about 100 Or and about 10 Gr genes [46] , which is substantially fewer than the number of Or and Gr genes in two other sequenced ant genomes ( e . g . argentine ant: Linepithema humile [47] , and harvester ant: Pogonomyrmex barbatus [48]; Figure 1 ) . These low numbers were not surprising because the annotation of Or/Gr genes in other insect genomes has been difficult and usually requires extensive manual efforts [47] , [48] . In order to address this potential discrepancy and comprehensively elucidate the genomic repertoire of chemosensory receptor genes in C . floridanus and H . saltator , we rigorously re-annotated Or , Gr , and Ir genes in these two ant species using a custom automated pipeline followed by careful manual inspection . To maximize the sensitivity of our re-annotation , we collected reported Or , Gr , and Ir gene sequences from other sequenced Hymenoptera and insect relatives of C . floridanus and H . saltator , including Apis mellifera , Acyrthosiphon pisum , Drosophila melanogaster , Nasonia vitripennis , L . humile , and P . barbatus . These insect chemosensory receptor genes were used to identify putative Or/Gr/Ir coding regions within the C . floridanus and H . saltator genomes and to guide homology-based gene prediction . As a result , we discovered a large number of previously unannotated chemosensory receptor genes and corrected several previously reported gene models [46] . All these annotations were manually inspected in multiple sequences alignments to identify and correct for potential errors ( e . g . missing exons , unrelated sequences ) . This analysis indicates that C . floridanus contains 407 putative Or coding loci , of which 352 loci encode intact Or genes , which is similar to those newly annotated in H . saltator , with 377 loci in total and 347 intact loci ( all chemosensory receptor genes annotated in this study are available in Dataset S1 ) . The number of Ir predictions is also similar between the two ants , with 31 Ir genes in C . floridanus and 23 in H . saltator . On the other hand , C . floridanus contains 46 intact Gr genes , which is significantly higher than the 17 intact Gr genes found in H . saltator ( Figure 1 ) . Moreover , all three families of chemosensory receptor genes exhibited high degrees of sequence divergence among family members ( Table S1 ) . In addition to the chemosensory receptor genes listed above , we also found a large number of incomplete gene models in these two ant genomes . For example , in C . floridanus and H . saltator , there are respectively ∼100 and ∼80 Or gene models encoding proteins shorter than 300 amino acids . In parallel to the difference in intact Gr genes , only three fragmented Gr gene models were found in H . saltator , while C . floridanus has ∼30 short Gr genes . Close examination of their genomic sequences revealed two principal mechanisms apparently leading to these fragmented Or/Gr gene models: 1 ) the presence of multiple frame-shift mutations and premature stop-codons , suggesting that they represent pseudogenes; and 2 ) their locations around undetermined genomic regions ( e . g . edges of contigs/scaffolds ) , indicative of incomplete assembly as expected from a draft genome . The latter mechanism explains about 80% of the incomplete gene models . Furthermore , similar to other insects [28] , [47] , [48] , [49] , [50] , [51] , most chemosensory receptor genes are tandemly arrayed in the C . floridanus and H . saltator genomes . In both cases , about 75% of Or genes are located in gene clusters of 4 to about 40 genes , and these occur in 24 and 20 Or gene clusters ( n≥4 ) in C . floridanus and H . saltator , respectively ( Figure S1 ) . Although to a lesser degree than the Ors , half of the Gr and Ir genes in both ants have at least one neighboring homolog . To better understand the evolutionary history of chemosensory receptor genes in the two ant species , we performed Hymenoptera-wide phylogenetic analysis on each of the OR , GR , and IR gene families . Additional analyses including D . melanogaster and Tribolium castaneum showed that most relationships among hymenopteran and non-hymenopteran sequences were not resolved within the OR and GR families ( see below ) . In this study , while they are generally categorized as belonging to the same receptor superfamily [22] , we elected to analyze the OR and GR families separately due to their high level of divergence . To further understand the evolutionary dynamics of chemosensory receptor genes , we quantified the gene birth and death events and estimated the number of ancestral gene copies in each family using both the maximum-likelihood ( ML ) and the parsimony based methods implemented in CAFÉ [62] and Notung [63] , respectively . For all three families , the ML method suggested relatively high copy numbers in the ancestor of Hymenoptera ( Figure 5 ) . For instance , it estimated a repertoire of 266 Or genes in the hymenopteran ancestor , which was expanded in all ant lineages , but significantly contracted in both N . vitripennis and A . mellifera . A similar pattern was also observed in both the GR and IR families . Moreover , the ML analysis suggested that the low number of Gr genes in H . saltator is due to a significant gene loss in this lineage . On the other hand , the parsimony approach gave conservative estimates of ancestral copy numbers and showed that many more gene-gain events occurred during later stages of hymenopteran evolution . According to the parsimony analysis , the number of Or genes increased from 25 in the last common ancestor of Hymenoptera to about 200 in N . vitripennis and A . mellifera , and more than 300 in all four ants ( Figure 5A ) . Most notably , the repertoire of Or genes increased by three-fold in the ancestor of ants ( from 51 to 204 copies ) , after the separation of A . mellifera , and continued to expand greatly along each ant lineage . Interestingly , although to a lesser degree , the ML method also identified significant expansion on the branch leading to the ant ancestor . In addition to the large number of gene gains , substantial gene losses also occurred in all ants . On the other hand , most duplications of ant Grs occurred in C . floridanus , L . humile , and P . barbatus , while there were only one gene gain and four gene loss events on the lineage to H . saltator ( Figure 5B ) . Similar to the OR and GR families , the number of Ir genes also doubled in the ancestor of ants after its separation from other Hymenoptera ( Figure 5C ) . Subsequent increase of Ir gene number was only observed in C . floridanus and L . humile . Overall , the ML and parsimony analyses gave different estimates of the ancestral copy numbers and gene gain and loss events . The ML method assumes a random gene birth and death process [64] , which is significantly violated by both the OR and GR families ( p-values<0 . 01 ) . On the other hand , the parsimony approach aims to minimize the number of gene gain and loss events , and thus might underestimate the number of ancestral copies . Nonetheless , both analyses support the hypothesis that chemosensory genes have distinct evolutionary dynamics in ant lineages in comparison to the other two hymenopterans . In insects , most Ors and some Grs/Irs are expressed in antennal ORNs [18] , [24] , [49] , [65] . As best illustrated in studies of the Drosophila olfactory system , each ORN expresses a single tuning Or which is responsible for the odorant response profile and all the ORNs expressing that singular tuning Or send axonal connections to a single antennal lobe glomerulus thereby providing a mechanistic basis for the initial stages of odor coding [18] . Therefore , we analyzed antennal transcriptomes of workers and males for both C . floridanus and H . saltator , to identify chemosensory receptor genes that are differentially expressed between castes ( minors and majors in C . floridanus ) and between different sexes , and which might play salient roles in social communication ( see Table S2 for information on transcriptome datasets ) . We performed pairwise comparisons between males and females within C . floridanus and H . saltator ( Dataset S5 ) . At the whole transcriptome level , there was a very high similarity between major and minor worker of C . floridanus ( r2 = 0 . 99; Figure S10A ) , while greater diversity was found between workers and males ( r2 values around 0 . 85 for all comparisons ) , largely due to mild up-regulation of many genes in males ( Figure S10B , S10C ) . Similar trends were also observed for chemosensory receptor genes ( Figure S10D ) . In order to validate our bioinformatic annotations and in an attempt to link functional data to the antennal expression data , we have cloned a small subset of 14 C . floridanus and H . saltator Or genes , drawn from 6 subfamilies in the Or phylogeny ( D , E , H , L , V , and 9-exon ) . These include four genes ( CfOr263 , HsOr212 , HsOr213 , and HsOr279 ) that display significant differential expression in our transcriptome analysis ( see Methods and Materials for full list ) . This allowed us to carry out deorphanization studies to decipher the odorant response profiles of these receptors through the use of two-electrode voltage clamp recordings in Xenopus oocytes heterologously expressing ant Ors [44] , [66] . After first confirming that the C . floridanus and H . saltator Orco proteins showed coreceptor function in combination with a previously deorphanized mosquito tuning Or ( Figure 8A , 8B ) , candidate ant tuning Ors were screened against a panel of 73 unitary and complex stimuli ( Table S4 ) . These stimuli consisted of a variety of general odorants , as well as hydrocarbons known to be produced by H . saltator or C . floridanus . Out of the 14 tuning Ors initially screened , CfOr263 ( from OR subfamily D; Figure 2 ) , which is highly expressed in workers as compared to males ( Figure 6A ) , produced specific and dose-dependent responses to 2 , 4 , 5-trimethylthiazole ( Figure 8D , 8F ) , a naturally occurring odorant found in cooked beef and pork [67] found in the library of general odorants that we screened . An additional Or from H . saltator , HsOr55 ( from OR subfamily L; Figure 2 ) , showed a dose-dependent response to another odorant from our general odorant library , 4-methoxyphenylacetone ( Figure 8E , 8G ) , which is a naturally occurring odorant found in anise essential oil [68] . However , this particular Or has not been shown to be differentially expressed between males and females . It should also be noted that , as is the case for most ant Ors , both receptors have multiple closely related homologs that may possess similar chemosensory functions ( Figure 6A ) .
We have developed and used a dedicated annotation scheme to comprehensively elucidate the repertoire of chemosensory receptor genes in both C . floridanus and H . saltator . Through exhaustive homology search and careful manual curation , we significantly improved upon previous studies to identify roughly equivalent numbers of Or/Gr/Ir genes in the genomes of C . floridanus and H . saltator as compared to two other sequenced ant genomes [47] , [48] , providing a solid foundation for subsequent study . It is striking that , in general , ants have the most expanded repertoire of chemosensory receptor genes in Hymenoptera ( Figure 1 ) . The numbers of ant OR and IR family members are much greater than those of the other two hymenopteran genomes currently available . Indeed , thus far , ant genomes have the largest number of Or genes among all insects [69] . Furthermore , although the number of the Gr genes varies greatly among hymenopterans and also within ants , L . humile carries the largest Gr family; it has about 2- and 10-fold more Grs than N . vitripennis and A . mellifera , respectively . Interestingly , although ants and honey bees are both social insects , ants have much larger repertoires of all three chemosensory receptor gene families than honey bees , possibly indicative of a more sophisticated communication system relying on chemicals [70] . Our phylogenetic analyses of hymenopteran chemosensory receptor genes reveal distinct evolutionary patterns among gene families . Among chemosensory receptors , the OR family shows the most dramatic birth-and-death evolution , with many OR subfamilies displaying diversified patterns of gene gain-and-loss . For example , the 9-exon subfamily and others have experienced rapid gene duplications at almost all stages of Hymenoptera evolution , followed by numerous losses of duplicates . In contrast , there are 35 subclades that have only one ortholog in all four ants . Further , the IR family has maintained relatively stable copy numbers in ants; lineage-specific expansion only occurred in C . floridanus and L . humile for two of the 13 “divergent IRs” . In between these extremes is the GR family that has expanded moderately in N . vitripennis and three of the four ants . Recent studies of chemosensory receptors in mammals and Drosophila , as well as other genes with important regulatory and physiological functions , have suggested a possible correlation between functional requirements and the variations of gene numbers [52] , [71] , [72] . Genes with conserved roles tend to have relatively stable copy numbers while those with diversified functions have higher rates of birth-and-death , although the degrees of copy number changes are somewhat random . Our results suggest that this pattern could also hold true for the evolution of the hymenopteran chemosensory receptor genes . For example , as an obligatory co-receptor for all other Ors [29] , Orco is the most conserved insect Or gene and also the only one that has maintained unambiguous orthology in all insects studied to date , including ants [69] . Similarly , orthologs of most “antennal IRs” [26] have also maintained strict single-copy in Hymenoptera . It has been proposed that these conserved “antennal IRs” represent the earliest insect chemosensory receptors and perform functions important for all insects [26] . Therefore , we suggest that the chemosensory receptor genes that have constant copy numbers in ants ( e . g . the 35 single-copy tuning Ors ) are likely to carry out important functions common for all ants . On the other hand , prevalent rapid expansions in chemosensory receptor gene families could allow for diversification in ligand specificity/sensitivity among duplicated receptor genes . Such functional divergences would offer tremendous opportunities for organisms to explore different chemical niches , thus facilitating the adaption to new environments and/or the evolution of novel life styles such as sociality . In all three gene families , we found either retention of the complete ancestral repertoire ( according to the ML method ) or dramatic increases in gene numbers ( according to the parsimony method ) in the ancestor of ants ( Figure 5 ) , which might have contributed to the success and subsequent diversification of this group . In addition , there are many cases of unbalanced expansions/contractions among lineages in specific ( sub- ) families , suggesting that the chemosensory receptor repertoire has been differentially exploited among ants , which might shed light on the evolution of different lifestyles of ants . For example , our results indicate expansions of Grs in C . floridanus , L . humile , and P . barbatus , but not H . saltator , which are likely to reflect differences in their feeding behaviors . In this view , scavengers like C . floridanus might require a highly expanded repertoire of taste receptors to discriminate nutritious food sources from spoiled , contaminated , or poisoned substrates . In contrast , H . saltator workers likely rely more on visual cues to track down prey , as suggested by their large eyes and expanded number of ommatidia [73] . Furthermore , Grs which act as contact chemoreceptors would be far less useful for identifying and capturing prey . In fact , ponerine ants in general rarely use liquid food sources , since they normally lack the ability to exchange liquids stored in their crop [74] which further reduces the potential benefit of a large Gr repertoire . Another intriguing possibility is that Grs are involved in the contact chemosensation of species-specific , nonvolatile CHCs ( e . g . queen pheromone , nestmate recognition signals , etc . ) , and that C . floridanus has more Grs precisely because they utilize a greater number and variety of pheromones to support their more rigid and complex social lifestyle . Presumably , these Grs would be in addition to the large number of worker enhanced Ors that are likely to be involved in the same process . Furthermore , C . floridanus has expansions in multiple GR subfamilies , including 5 homologs of the DmGr43a/AmGr3 gene , which has been recently shown to be a fructose receptor [75] . Taken together , our results indicate a correlation between the expanded GR family and the more complex chemical ecology of C . floridanus . The antenna is perhaps the most important chemosensory organ for ants , where a variety of ant species have been observed to closely inspect their environment and each other by touching their antennae in a process known as antennation [2] . This makes it likely that most of the behaviorally important chemosensory neurons ( and their corresponding chemosensory receptors ) are located in this organ . Our comparative analysis of antennal transcriptomes of workers and males in both C . floridanus and H . saltator reveal differential expressions of chemosensory receptor genes both within and between species , providing important clues on their functional divergence . One major pattern revealed by our results is the substantial sexual dimorphism in chemosensory receptor gene expression in ants . For both C . floridanus and H . saltator , almost all Ors were expressed in workers , but only one third were expressed in male . Similarly , workers consistently had more expressed Grs and Irs than males . In contrast , expression of chemosensory receptor genes was highly similar between major and minor workers in C . floridanus . Previous studies have shown that the antennal lobes of males from both C . floridanus and H . saltator lack a large subset of glomeruli relative to workers [76] , [77] , [78] , which may explain the low number of chemosensory receptor genes expressed in males . Given that the number of glomeruli in insects generally correlates with the number of functional odorant receptors [18] , [65] , it is likely that most of the Ors that are only expressed in C . floridanus and H . saltator workers project to these female-specific glomeruli . Furthermore , it has been shown in another Camponotus species ( Camponotus japanicus ) that females exclusively possess the olfactory sensilla necessary to detect non-nestmate CHCs , [79] , [80] . It is therefore likely that the CHCs receptors are encoded by some of the worker-specific Ors in C . floridanus . In particular , the 9-exon subfamily represents the largest expansion of Ors in all ants and it harbors close to 100 worker-specific Ors in both C . floridanus and H . saltator . These results strongly support previous hypothesis that members of the 9-exon subfamily are likely candidates for ant CHCs receptors [47] , [48] . These Ors are potentially involved in detecting CHCs involved in worker-to-worker or worker-to-queen intracolonial social communication . Interestingly , we also noticed discrepancies between the overall number of Ors and the number of glomeruli in the adults of these two ant species . H . saltator workers and males both have far more expressed Ors than the number of glomeruli in the adult antennal lobe ( approximately 78 in the adult male and 178 in the adult worker [77] ) . The discrepancy in H . saltator could possibly be the result of co-expression of multiple tuning Ors in the same ORN and/or the projection of ORNs expressing different , but related tuning Ors to the same glomerulus , which have both been observed for a small number of Ors/ORNs in D . melanogaster [81] , [82] , [83] , [84] . However , given that the number of expressed Ors is about twice the number of observed glomeruli , this would mean that each glomerulus received input from , on average , two odorant receptors . Although co-expression of tuning Ors has not been observed to such a broad extent in any insect olfactory system studied to date , it should be noted that many of the receptor pairs that are co-expressed in Drosophila appear to be the result of tandem duplication events [84] . Therefore , it is possible that the extensive tandem duplication of H . saltator Or genes may also result in the co-expression of closely related odorant receptors from the same clusters . All of these are highly interesting hypotheses that may be examined in future studies . In contrast to H . saltator , C . floridanus has approximately 80 fewer Ors than the number of adult worker glomeruli ( about 454 [76] ) . In this instance it is possible that many of those glomeruli receive projections from Gr and Ir expressing ORNs , as there is precedence for this in Drosophila [24] , [43] and the number of predicted Grs and Irs would be enough to fill the gap . Moreover , it could be that several Ors have been missed by the current analysis due to incomplete genome assembly; some of the fragmented Or gene models might represent genuine genes , and further genomic/transcriptomic data would help address this possibility . Although chemosensory receptor genes in general had higher expression in workers , our studies have nevertheless identified a single Or ( CfOr267 , in subfamily 9-exon ) and a single Ir ( CfIR8a ) in C . floridanus , as well as 4 Ors ( HsOr32 , HsOr35 , and HsOr37 , in subfamily L; and HsOr224 , in subfamily E ) and 2 Irs ( HsIR8a and HsIR75u . 2 ) in H . saltator that were significantly male-enriched . The male-enrichment of a receptor gene could be due to elevated expression of the gene in ORNs of males relative to workers , and/or increased number of ORNs expressing the gene in males . No matter which of the possibilities is indeed the case , our results indicate higher overall abundances of these chemosensory receptor genes in male antennae . These genes are viable candidates for receptors that are specifically tuned for male-specific social cues , including queen pheromones . In fact , at least one male-specific honeybee odorant receptor that responds to a queen-specific pheromone has already been revealed through microarray analysis and subsequent functional characterization in Xenopus oocytes [85] . It would not be surprising to see that similar results will be found with the male-enriched ant Ors . In insects , the co-receptors IR8a and IR25a are the two most conserved Irs [26] . Although a systematic profiling of sexual dimorphic Ir expression is still lacking , a previous study has shown that the Anopheles gambiae orthologs of both IR8a and IR25a have higher expression in female than male [49] . Interestingly , IR8a was the most male-enriched Ir in both C . floridanus and H . saltator . While IR25a also displayed higher expression in C . floridanus male , it was not expressed in the male of H . saltator . These results could possibly indicate a functional divergence of IR8a and IR25a between Diptera and Hymenoptera . In addition , the high expression of IR25a in males of C . floridanus , but not H . saltator , suggests that IR25a-mediated signaling might have contributed to the more expanded roles for males within the colony of the former species . It may be that C . floridanus males are more involved in intracolonial interactions than H . saltator males , since males from other Camponotus species are known to participate in food exchange in the colony [86] , which has not observed in H . saltator males . We have also found diversified expression of closely related Ors within and between species . For example , in the basal clades of the 9-exon OR subfamily , closely related C . floridanus and H . saltator Ors showed opposite sexual dimorphism in their expression ( Figure 6B ) . Although the well-supported monophyletic clade within the 9-exon OR subfamily mostly consists of worker-enriched genes , it also harbors a few genes that are highly enriched in male ( Figure 6C ) . Thus , while our expression results are generally ( and strongly ) consistent with the idea that members of the 9-exon OR subfamily are involved in the detection of CHCs by workers [47] , a subset of these receptors have apparently been adapted for use in males , possibly for detecting queen mating pheromones . Taken together , these results indicate that ant Or genes have experienced not only extensive gain-and-loss , but also rapid changes in their expression , once again highlighting the highly dynamic nature of chemosensory receptor gene evolution . Our phylogenetic and transcriptomic analyses , in combination , have identified ant chemosensory receptor genes that exhibit evolutionary and expression patterns indicative of species/sex-specific functions . Ultimately , deorphanization of these receptors will greatly facilitate our understanding of the chemical ecology of social lifestyle in ants . In our heterologous studies of ant tuning Ors , we have identified chemical agonists for a single receptor from each of the two species analyzed . These data provide conclusive validations for our bioinformatic-based annotations . Although a honeybee odorant receptor has been previously shown to respond to the queen substance 9-oxo-2-decenoic acid [85] , we believe that this represents the first published report of ligand activators for odorant receptors from ants . In these studies , HsOr55 from H . saltator , display significant responses to 4-methoxyphenylacetone , a naturally occurring odorant found in anise essential oil [68] . Since anise essential oil has been shown to have a repellent and/or insecticidal effect on at least some species of insects [87] , [88] , 4-methoxyphenylacetone might represent a general insect repellent , with HsOr55 acting as the detector for this repellent in H . saltator . Whatever HsOr55's role may be , it is likely to be a very general one , since HsOr55 transcripts do not appear to be differentially expressed between workers and males . The other odorant receptor characterized in this study , CfOr263 from C . floridanus , displayed sensitivity to 2 , 4 , 5-trimethylethiazole , a naturally occurring odorant found in cooked beef and pork [67] that has been previously shown to induce strong responses in the CpC neuron of the maxillary palp in the mosquito Anopheles gambiae [44] . While the relevance of this chemical to C . floridanus remains unclear , the fact that CfOr263 transcripts are enriched in workers relative to males suggests that this odorant may be an important volatile semiochemical for C . floridanus workers . Regardless , the successful identification of odors that activate CfOr263 and HsOr55 strongly validates the role of ant Ors as chemosensory receptors . Furthermore , the large differential expression of CfOr263 between workers and males indicates that it is detecting a sex- specific signal that is relevant to workers but not to males , and testing a broader panel of odorants in the future will provide a better understanding of what that signal might be . We have revealed a greatly expanded repertoire of chemosensory receptor genes for a pair of divergent ant species , including about 400 Ors and an order of magnitude smaller number of Grs and Irs . Phylogenetic analysis of these newly annotated genes indicates that there are likely to be vast differences in the importance of particular chemoreceptor families and subfamilies between the four ant species examined , which is likely to reflect the variety of ecological and social demands experienced the members of each species . These analyses also reveal high rates of gene birth-and-death evolution among the olfactory and gustatory receptor genes , suggesting that some factor ( such as changes in the complex CHC profiles that control ant social behavior ) is driving rapid evolution in their chemical response profiles . The large repertoire of ant chemosensory genes might be either due to preferential retention of ancestral genes or rapid expansions in the ant ancestor and during later stages of ant evolution . To further complement these phylogenetic results , we have generated and analyzed antennal-specific RNAseq expression data to identify ∼40 C . floridanus and ∼120 H . saltator chemosensory receptors that exhibit significant sexual dimorphism in expression . This expression data has , in turn , informed studies towards the identification of odorant ligands for socially relevant receptors , a process that we have already successfully accomplished in a heterologous system for one of the differentially expressed C . floridanus Ors . Taken together , our evolutionary analysis , transcriptome profiling , and heterologous characterization provide new insights into the roles of the chemosensory receptors in inter-sex behavioral and social differences of ants .
The assemblies of C . floridanus ( version 3 . 5 ) and H . saltator ( version 3 . 5 ) were downloaded from the Hymenoptera Genome Database [89] . Protein sequences of reported chemosensory gene were also collected from Apis mellifera , Acyrthosiphon pisum , Drosophila melanogaster , Nasonia vitripennis , L . humile , and P . barbatus [15] , [26] , [28] , [47] , [48] , [50] , [54] . An in-house bioinformatics pipeline was developed to identify candidate chemosensory genes in C . floridanus and H . saltator . First , all collected chemosensory gene sequences were searched against the two ant genomes using TBLASTN [90] with an e-value cutoff of 1e-5 . Resulting High-scoring Segment Pairs ( HSPs ) were sorted by their blast bit-scores , and an average bit-score of the top 75% HSPs were calculated . Any HSPs with a bit-score less than 25% of the average was discarded . Chains of HSPs were than created from retained HSPs . Two HSPs were chained together if the following criteria were met: 1 ) they are derived from the same query; 2 ) they are located within 3 kb on the same strand of a scaffold/contig; and 3 ) the corresponding query region of the upstream HSPs must also be N-terminal to that of the downstream HSPs . The third criterion was applied to avoid artificial concatenation of neighboring chemosensory genes . Genomic regions covered by HSPs chains were considered putative chemosensory gene coding regions . For each putative gene , we then selected the query corresponding to the highest scoring HSPs at that region as reference sequence for homology-based gene prediction using GeneWise ( version 2 . 2 . 0 ) [91] . All predictions were sorted by ORF length and the lowest 25% was filtered . This pipeline was iterated by adding results of previous run to input until no additional genes were found . Multiple sequence alignments ( MSAs ) of predicted OR/GR/IRs were constructed using MUSCLE ( version 3 . 8 ) [92] and manually inspected . Attempts to improve annotations were made whenever an obvious problem was identified ( e . g . missing exon , incorrect exon-exon junction ) . In addition , in the OR and GR families , we observed many fragmented gene models , likely due to pseudogenization and incomplete genome assembly . For the convenience of subsequent analyses , a minimum size cutoff of 300 amino acids was used for the ORs and GRs . For IRs , we screened all predicted protein sequences with InterProScan ( V4 . 8 ) [93] and filtered the ones without characteristic domains of IR ( PF10613 and PF00060 ) [26] . We included in our phylogenetic analysis chemosensory receptor genes in six hymenopteran species , including A . mellifera , C . floridanus , H . saltator , N . vitripennis , L . humile , and P . barbatus . For each of the OR/GR/IR families , all family members were firstly aligned at once using MUSCLE ( version 3 . 8 ) and a preliminary phylogenetic tree was built using RAxML ( version 7 . 2 . 8 ) [94] . Sequences were then divided into groups corresponding to highly supported clades in the preliminary phylogeny . Groups were aligned individually using PROBALIGN ( version 1 . 4 ) [95] and then combined together using the profile alignment function of MUSCLE . The complete alignment were further manually inspected and adjusted using GeneDoc ( version 2 . 6 ) [96] . In addition , poorly aligned regions in the alignment were removed using trimAl ( version 1 . 4 ) [97] . The final maximum-likelihood tree was constructed using RAxML with Le-Gascuel ( LG ) substitution model [98] and GAMMA correction for rate variation among sites . Reliability of tree topology was evaluated by 100 bootstrap replicates . To estimate the number of gene gain and loss events , we used a maximum-likelihood based approach implemented in CAFÉ ( version 2 . 2 ) [62] with default settings . As an alternative approach , we also used the parsimony based “modified reconciliation method” [99]; we first collapsed branches with bootstrap support lower than 70 in phylogenies of OR/GR/IR families and then reconciled condensed trees with known organismal relationships using Notung ( version 2 . 6 ) [63] . Samples originated from C . floridanus colonies that had been founded in the Liebig lab from queens captured in southern Florida between 2002 and 2009 and from H . saltator colonies collected in Karnataka , India between 1995 and 1999 . Antennae were collected from each of five groups of adult ants: H . saltator workers and males and C . floridanus major workers , minor workers , and males . Whole ants were flash-frozen in liquid nitrogen and kept on dry ice as 100 antennae from each group were removed with forceps . Antennae were placed directly into RNAlater ICE ( Ambion ) that had been pre-chilled on dry ice in a conical , ground-glass , tissue homogenizer . RNAlater ICE was replaced with 1 ml Trizol ( Invitrogen ) , in which antennae were homogenized . Total RNA was isolated following Trizol manufacturer instructions; briefly , after addition of 200 µl of a chloroform∶isoamylalcohol mixture ( 24∶1 ) , each sample was mixed vigorously and the RNA-containing aqueous layer was isolated with centrifugation . RNA was further purified and DNAse-treated with the RNeasy Miniprep kit ( Qiagen ) . After ethanol-precipitation , the RNA pellet was resuspended in 30 µl nuclease-free water . Male samples were sequenced using Illumina HiSeq2000 at the NYULMC Genome Technology Center , generating ∼33 million 50 bp single-end reads for C . floridanus male and ∼164 million 51 bp single-end reads for H . saltator male . All worker samples were sequenced at Hudson Alpha , generating more than 20 million 50 bp paired-end reads for each sample ( sum of two technical replicates ) . Reads of C . floridanus male sample were trimmed to 34 bp ( 8 bp trimmed from both ends ) to remove low-quality positions . In addition , for all worker datasets , we treated each paired-end read as two single-end reads . Therefore , all datasets in our subsequent analyses consist of only single-end reads . Alternative strategies for data processing led to highly similar estimations of gene expression values ( Table S5 ) . For each dataset , reads were mapped to the corresponding ant genome using TopHat ( version 1 . 3 . 3 ) [100] with default setting . Gene annotations for C . floridanus ( version 3 . 5 ) and H . saltator ( version 3 . 5 ) were downloaded from the Hymenoptera Genome Database and used in combination with our annotation of chemosensory genes to guide the reads mapping . Gene expression levels ( in FPKM values ) and differentially expressed genes were determined using Cuffdiff v1 . 3 . 0 [101] with frag-bias-correct , multi-read-correct , and upper-quartile-norm options turned on . Predicted Or coding sequences were amplified , by PCR , from H . saltator and C . floridanus worker antennal cDNA samples obtained from colonies established at Arizona State University ( Tempe , AZ ) . The PCR-amplified sequences were then TOPO cloned into the Gateway Entry vector pENTR/D-TOPO ( Life Technologies ) , followed by an additional cloning step into a destination vector derived from pSP64T . To obtain cRNA for each Or , the pSP64T vector containing the appropriate coding sequence was linearized by restriction digest and used as a template for cRNA synthesis using the mMessage mMachine Sp6 Kit ( Ambion ) . Heterologous expression of ORs was accomplished as described previously [66] . Briefly , mature oocytes were surgically extracted from Xenopus leavis adult females , treated with 2 mg/mL collagenase II in 1× Ringer's solution ( 96 mM NaCl , 2 mM KCl , 5 mM MgCl2 , and 5 mM Hepes , pH 7 . 6 ) for 30–45 minutes at room temperature , and then injected with 27 . 6 nL of a 1∶1 mixture ( by mass ) of a given tuning Or in combination with the appropriate Orco ortholog ( either HsOrco or CfOrco ) . After injection , oocytes were stored in Incubation Medium ( 10% dialyzed horse serum in 1× Ringer's solution ) at 18C for 3–7 days before testing . Responses to odorants were measured by recording whole-cell currents in Clampex 10 . 2 ( Molecular Devices ) using a two-electrode voltage-clamp setup ( OC-725C , Warner Instruments ) maintained at a −80 mV holding potential . Odorants were first dissolved in DMSO , and then further diluted into Ringer's solution before being introduced to the oocyte recording chamber using a perfusion system . For the hydrocarbons that were tested , 0 . 01% Triton X-100 ( Sigma ) was also added to the Ringer's solution to aid in dissolving the odorant . The following odorant receptors were tested with the odorants listed in Table S4: CfOr183 , CfOr215 , CfOr263 , HsOr19 , HsOr55 , HsOr132 , HsOr170 , HsOr175 , HsOr212 , HsOr213 , HsOr234 , HsOr239 , HsOr279 , HsOr287 . Odorant chemicals were purchased from commercial sources at the highest purity available . Henkel 100 , a mixture of 100 different volatile chemicals , was obtained from Henkel ( Düsseldorf , Germany ) , and the C7–C40 saturated alkane mixture was purchased from Supelco ( Bellefonte , PA , USA ) . | Chemical communication is an important factor in the regulation of social interaction in animals . The family of eusocial insects commonly known as ants offers an almost unique opportunity for examining the genetic basis for the chemosensory pathways that underlie ant sociality . In order to address this issue , we have manually and comprehensively reannotated the chemoreceptor repertoire in a pair of evolutionarily and behaviorally divergent ant species , Camponotus floridanus and Harpegnathos saltator . In addition , we have used next-generation RNA sequencing to examine the chemosensory receptor transcriptome between males and females within these species . Our analysis demonstrates rapid gene birth-and-death for the ant odorant and gustatory receptor gene families , as well as clear differences in the expression of particular subsets of chemoreceptor genes between males and females . Finally , we have begun to examine the odor space within these discrete social units by heterologous characterization of the first C . floridanus odorant receptor that also exhibits sex-specific differential expression . Taken together , our results provide a foundation for future studies of the genetic basis for the chemical signaling and chemical ecology underlying the dramatically different social lifestyles exhibited by these and other species of ants . | [
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| 2012 | Phylogenetic and Transcriptomic Analysis of Chemosensory Receptors in a Pair of Divergent Ant Species Reveals Sex-Specific Signatures of Odor Coding |
Between December 2015 and July 2016 , a yellow fever ( YF ) outbreak affected urban areas of Angola and the Democratic Republic of the Congo ( DRC ) . We described the outbreak in DRC and assessed the accuracy of the YF case definition , to facilitate early diagnosis of cases in future urban outbreaks . In DRC , suspected YF infection was defined as jaundice within 2 weeks after acute fever onset and was confirmed by either IgM serology or PCR for YF viral RNA . We used case investigation and hospital admission forms . Comparing clinical signs between confirmed and discarded suspected YF cases , we calculated the predictive values of each sign for confirmed YF and the diagnostic accuracy of several suspected YF case definitions . Fifty seven of 78 ( 73% ) confirmed cases had travelled from Angola: 88% ( 50/57 ) men; median age 31 years ( IQR 25–37 ) . 15 ( 19% ) confirmed cases were infected locally in urban settings in DRC . Median time from symptom onset to healthcare consultation was 7 days ( IQR 6–9 ) , to appearance of jaundice 8 days ( IQR 7–11 ) , to sample collection 9 days ( IQR 7–14 ) , and to hospitalization 17 days ( IQR 11–26 ) . A case definition including fever or jaundice , combined with myalgia or a negative malaria test , yielded an improved sensitivity ( 100% ) and specificity ( 57% ) . As jaundice appeared late , the majority of cases were diagnosed too late for supportive care and prompt vector control . In areas with known local YF transmission , a suspected case definition without jaundice as essential criterion could facilitate earlier YF diagnosis , care and control .
Yellow fever ( YF ) is a mosquito-borne viral infection characterized by an initial non-specific flu-like phase that lasts for 3 to 6 days and includes fever , headaches and myalgia . In 15%–25% of cases , a toxic phase follows with mild or severe jaundice , liver and kidney failure , which might lead to shock or bleeding [1 , 2] . No specific treatment exists . Approximately half of the severe cases lead to death within 7 to 10 days [2 , 3] . YF virus circulates primarily among forest-bound primates in a sylvatic cycle . Like other flaviviruses , YF can spread widely in urban areas , when transmitted from human to human by mosquito vector Aedes aegypti or potentially Aedes albopictus [4] . Female mosquitos become infected from a blood meal of an infected human . The incubation period in humans is 3–6 days [5] . Outbreak control relies on mosquito bite prevention , vector control , and mass vaccination campaigns . Early case detection by using an adapted case definition could allow earlier implementation of control measures for outbreak containment . In the Democratic Republic of the Congo ( DRC ) each year a large number of sporadic YF infections occur following sylvatic transmission , when YF virus is transmitted by mosquitoes from non-human primates to persons living or working in forest areas [6] . Because of limited mobility of patients infected in forest areas , YF transmission rarely reaches urban environments . In December 2015 YF cases were detected in the Angolan capital Luanda . In March 2016 , the outbreak in Angola intensified , resulting in cases spreading to bordering provinces of DRC and its capital Kinshasa [7] . We carried out an investigation of the urban DRC outbreak to identify cases and describe the outbreak . Furthermore , we compared the performance of the case definition applied during the outbreak to alternative case definitions , aiming at an improved , timelier detection of cases in future urban outbreaks .
We present a detailed description of the 2016 YF outbreak in DRC and an analysis of the diagnostic accuracy of the case definition used , compared to alternatives . The YF cases related to this outbreak were reported to the national surveillance system between January and August 2016 . In the analysis of the case definitions , we excluded vaccinated patients ( at least ten days before symptom onset ) and patients infected through sylvatic YF transmission ( staying in a forest area in the two weeks to three days before symptom onset ) . During the outbreak in DRC , the suspected case definition for routine surveillance in DRC was used , as also proposed in WHO guidelines [8]: an acute onset of fever followed by jaundice within 14 days after first symptoms onset . Any patient presenting at a healthcare facility meeting with this suspected case definition was notified to the Ministry of Health . Blood samples collected for every suspected case were tested for laboratory confirmation of yellow fever at the Institut National de Recherche Biomédicale ( INRB ) , Kinshasa . A suspected case became confirmed when anti-YF IgM antibodies or YF viral RNA was detected in serum , if the patient was not immunized against YF . YF IgM detection consisted of an initial enzyme-linked immunosorbent assay ( ELISA ) to detect anti-flavivirus IgM antibodies , followed by a series of consecutive virus-specific ELISA tests to exclude other flavivirus infections such as Zika , dengue , and West Nile viruses . The ELISA results needed to be further confirmed by demonstrating a four-fold increase in YF neutralizing antibodies or by a Plaque Reduction Neutralization Test . Simultaneously a RT-PCR assay tested the presence of YF viral RNA in the blood sample . A suspected case was discarded when neither YF specific IgM antibodies nor YF viral RNA were detected . Confirmed cases were further classified as imported or autochthonous relying on travel history to Angola within two weeks to three days before symptom onset . Current or recent malaria ( co- ) infection was tested during July-August 2016 among patients admitted to a YF management facility , through detection of P . falciparum HRP-2 antigen ( SD BIOLINE Malaria Ag P . f , Standard Diagnostics Inc . ) . Patient demographics , symptoms , malaria co-infections , laboratory YF confirmation results , travel and vaccination history were extracted from case investigation forms ( with suggested symptoms ) , patient medical files , and daily reports of notified suspected cases . Symptoms and malaria co-infections were systematically recorded in Kinshasa between 28 May and 02 August 2016 , and thus only available for 14 confirmed cases and 97 discarded cases . GPS coordinates of places visited by patients while infectious , during the first 6 days after symptom onset , were used to map areas with possible ongoing transmission of YF . We described recorded characteristics and deaths as frequencies , percentages or medians with interquartile range . We compared differences in frequencies between cases by using Pearson’s Chi-squared test ( or Fisher’s exact test , as appropriate ) and differences in median age using the Wilcoxon rank-sum test ( when not normally distributed ) . We used QGIS 2 . 18 with OpenStreetMap shapefiles to generate a geographical dot distribution map of cases in Kinshasa . To avoid revealing the exact locations of the cases , we rounded longitude and latitude coordinates to 10−3 degrees , to assign a random point location within a 110m radius of the patients’ recorded residences . We identified potential predictors of YF by calculating positive and negative likelihood ratios ( LR+ and LR- ) for the presence or absence of every recorded symptom , severe anemia and a positive malaria test among confirmed and discarded cases for which those symptoms were recorded . LR+ is the increase in the probability of YF infection when the symptom is present , in other words sensitivity/ ( 1-specificity ) of that symptom to detect infection . LR- is the decrease in the probability of YF infection when the symptom is absent . We used combinations of predictive signs ( LR+ or LR- larger than 2 . 5 ) to create new case definitions ( four options ) . We drew receiver operating characteristic ( ROC ) curves to compare the diagnostic performance ( sensitivity , specificity and Area Under the Curve ( AUC ) ) of the DRC outbreak case definitions with our optional case definitions and those used in previous urban YF outbreaks in Uganda ( 2010/11 ) , Brasil ( 2009 ) and Bolivia ( 1997/98 ) [9–11] . We performed analyses in R 3 . 4 . 1 and STATA 12 . The Ethical Review Committee at the University of Kinshasa approved the study ( reference ESP/CE/049/2017 ) . Only anonymized routine surveillance data , collected for the outbreak investigation was retrospectively analyzed . Therefore , no individual patient informed consent was asked .
Between 1 January and 11 August 2016 , 2 , 269 suspected cases were reported in DRC . Of the 2 , 025 cases that underwent confirmatory testing , 78 ( 4% ) were confirmed . Cases were confirmed in Kinshasa and two provinces neighbouring the Angolan border , Kongo-Central and Kwango . The first confirmed case had onset of symptoms on 22 February , the final case on 12 July ( Fig 1 ) . Of the 78 confirmed cases , 57 ( 73% ) were imported from Angola . Imported cases occurred mostly among adult male patients ( Table 1 ) : 88% ( 50/57 ) men; median age 31 years ( interquartile rate ( IQR ) 25–37 ) . Fifteen ( 19% ) YF confirmed case patients had not travelled to Angola , and acquired YF in urban settings in Kinshasa ( n = 8 ) , in the Angola-bordering Kwango ( n = 4 ) , and Kongo-Central ( n = 3 ) provinces . Of these autochthonous cases 67% ( 10/15 ) were male; median age was 20 years ( IQR 12–29; p<0 . 01 ) . For six cases , no travel history could be retrieved ( not classified ) . Six sylvatic cases , not related to this outbreak , were confirmed during the same period . Mapping the imported and autochthonous confirmed cases led to the identification of one geographical cluster of three autochthonous confirmed cases occurring between 30 May and 10 June 2016 in the same neighborhood of Kinshasa , where case investigations revealed another 3 deaths of suspected cases with symptom onset in the same period ( https://osf . io/tk3gn/ ) . This cluster was linked to a previously unidentified case who had returned from Angola with a fever 22 days before ( 8 May ) . All other cases were widespread , and could not be linked to one another . The median time from symptom onset to a first healthcare consultation in any healthcare facility was 7 days ( IQR 6–9 ) , to appearance of jaundice 8 days ( IQR 7–11 ) , to sample collection 9 days ( IQR 7–14 ) , and to hospitalization 17 days ( IQR 11–26 ) ( Table 2 ) . The delay to sample collection was not significantly different ( p = 0 . 88 ) among imported and autochthonous confirmed cases . Among the 74 confirmed cases tested by RT-PCR , 9 ( 12% ) had detectable YF viral RNA . The blood samples of the 9 PCR-positive cases were collected at a median of 7 ( range 1–14 ) days after onset of symptoms . We recorded 18 deaths among confirmed cases , resulting in a case fatality of 23% . Confirmed cases died after a median of 15 days following the onset of symptoms . Symptoms and malaria ( co- ) infection were recorded for 14 confirmed and 97 discarded cases from Kinshasa . The median age of those confirmed cases was 24 years compared with 31 years among confirmed cases without recorded symptoms ( p = 0 . 01 ) ; 64% and 86% ( p = 0 . 06 ) were male , respectively . The 97 discarded cases had a median age of 15 years , compared with 16 years among discarded cases without recorded symptoms ( p = 0 . 09 ) ; 53% and 56% ( p = 0 . 36 ) were male , respectively . Thirteen ( 92 . 9% ) confirmed cases had fever and 10 ( 71 . 4% ) had jaundice ( Table 3 ) . Of symptoms not included in the routine suspected case definition , myalgia , vomiting and headaches were most frequently reported , respectively among 88 . 9% , 77 . 8% and 66 . 7% of confirmed cases . One had hemorrhagic signs . We identified no confirmed cases with severe anemia at admission . Of 9 tested confirmed cases , 3 ( 33 . 3% ) were malaria co-infected . None ( 0/3 ) showed rapid clinical improvement after starting antimalarial treatment . Also 88 ( 91% ) discarded cases had both fever and jaundice , i . e . the DRC suspected case definition , resulting in a 9% specificity of the case definition . Considering that 1 , 947 of 2 , 025 tested suspects were discarded , it had a positive predictive value of 3 . 2% . Of 90 tested discarded cases , 73 ( 81 . 1% ) were malaria positive . Malaria positive discarded cases had a median age of 12 ( IQR 5–20 ) years , with 67% being under 18 years old . Malaria negative discarded cases were older ( p = 0 . 03 ) , median age 22 years ( IQR 12–36 ) , with 35% being under 18 years old . Of 13 malaria infected discarded cases , two ( 15 . 4% ) did not improve after starting malaria treatment . Decreased diuresis , myalgia and bleeding signs had the highest positive likelihood ratios , respectively 4 . 6 , 2 . 9 and 2 . 8 . Absence of malaria had the highest negative likelihood ratio , of 3 . 5 . When comparing the DRC suspected case definition with four case definitions based on the most predictive and frequent signs ( Options A , B , C and D ) , and case definitions used during urban outbreaks , we found that a combination of fever or jaundice and myalgia or a negative malaria test ( Option C ) , yields the best combination of sensitivity ( 100% ) and specificity ( 57% ) resulting in an AUC of 0 . 78 ( Fig 2 and Table 4 ) . Other combinations with early symptoms result in lower sensitivity , but improved specificity . The two 2010/11 Ugandan outbreak case definitions had better specificity than the DRC case definition , but at the cost of lower sensitivity ( AUC 0 . 58 in 2010 and 0 . 69 in 2011 ) . The 1997/98 Bolivia case definition improved sensitivity ( 79% ) , but did not improve the specificity ( 7%; AUC 0 . 43 ) . The 2009 Brazil case definition was not substantially different from that in DRC to allow comparison .
Although >2000 suspected YF cases were reported and tested in DRC , only 78 cases were confirmed , with symptom onset between February and July 2016 . The peak of the YF outbreak in DRC followed and mirrored the ongoing outbreak in Angola , and started to wane as vaccination went on in Angola . The majority of confirmed YF affected young men from DRC working in Angola , who returned to DRC to seek healthcare following a YF infection contracted in Angola . Despite evidence of 15 locally transmitted cases in three different provinces , we observed no widespread urban YF transmission , as in Angola . Possible reasons for this might be ( i ) the timing of the first local transmission when the dry season took off , not allowing the vector carrying YF virus to replicate , ( ii ) the implementation of vector control measures around confirmed cases’ homes , or ( iii ) the mass YF vaccination campaigns before the end of the dry season in the affected health zones . Patients were diagnosed too late for effective supportive care and to guide potential vector control measures . By the time infected patients with severe symptoms received appropriate healthcare ( median time to hospitalization 17 days after symptom onset ) , the 12 to 15 critical days to prevent death through supportive care had already passed [2 , 3] . Several elements contributed to this delay: First , the majority of patients did not seek healthcare when going through the febrile phase of the disease within 5 days after symptom onset . YF testing is free in DRC but patients waited until symptoms worsened , afraid they might bear the cost of tests and treatments of other diagnosed conditions . Second , the suspected case definition applied during this outbreak encouraged notifying and testing only once jaundice appeared , 9 days after symptom onset . Finally , test results could take days to weeks because YF confirmatory testing was carried out in only one laboratory in the capital . Low specificity of the DRC suspected case definition could only partially be explained by viral hepatitis . A 44% seroprevalence of viral hepatitis was found among suspected YF cases discarded during 2003–2012 in DRC [12] . Dengue virus RNA and chikungunya virus RNA were found in respectively 3 . 5% and in 0 . 4% of those discarded YF cases during the same period [13] . In our study , 81% of discarded cases were found to be malaria infected . Of those two thirds were children . This suggests that malaria may have been a leading cause of fever and jaundice among discarded cases in children . A case definition in which jaundice would no longer be the main clinical criterion would allow more rapid detection of cases in districts where local transmission of YF is established . Nevertheless , considering that for each confirmed case another twenty suspected cases were notified and that no options for decentralized YF testing exist , other signs than fever are needed in the case definition to improve its specificity . Suspected case definitions used in previous urban YF outbreaks have relied on at least one severe sign occurring during the toxic phase of infection , and are therefore not more appropriate for timely diagnosis [9–11] . When comparing the performance of different case definitions applied on the confirmed and discarded cases in our study , “fever or jaundice , and myalgia or a negative malaria rapid diagnostic test ( or blood slide ) ” ( Option C ) provided the most robust combination of sensitivity and specificity . Once index cases and clusters of local transmission are identified in an area using the DRC/WHO case definition , a switch to case definition option C in the area with established YF transmission could speed up the identification of YF cases . Ideally , the case definition we propose should be externally validated against clinical data from ongoing or future outbreaks in a similar urban context . A limitation to the comparison of case definitions is that our reference group is composed of discarded cases , which met the suspected case definition . Those were likely not representative of the source population ( any patient presenting at a healthcare facility ) , and therefore , the calculated specificities are probably underestimated , limiting the external validity of our estimates . Second , our study evaluated the diagnostic performance of the suspected case definition using symptoms of only 14 ( out of 78 ) confirmed and 97 ( of 1947 ) discarded cases with symptoms systematically recorded . Although age and sex distributions were slightly different of those of confirmed cases without recorded symptoms , we think this may be due to chance . We did not expect any differences in clinical presentation to occur among slightly older adult cases , or among cases reported earlier during the outbreak . Therefore , we assumed the frequencies of symptoms we reported , were representative of all cases . Our sample of cases was however too small for a precise estimate of the proportion of cases failing to respond to malaria treatment when malaria and YF co-infected . Finally , we were not able to quantitatively establish the improved timing of early diagnosis in our comparison of case definitions’ diagnostic performance , because only for jaundice the timing of onset was recorded . Recording the time of onset of each symptom could have allowed to compare the case definitions’ performance earlier through the course of the disease . Nevertheless , the case definition we proposed would probably have performed just as well when applied during the first days of illness , since fever and malaria infection would have been present already . Due to the low accuracy of the case definition used during the 2016 YF outbreak in DRC and delays in accessing healthcare , most patients were diagnosed too late to receive beneficial supportive treatment and mitigate the complications of severe YF . Timely diagnosis of YF would also allow implementing vector control measures around confirmed cases’ homes to prevent further transmission . Improving early access to healthcare and developing case definitions that do not include jaundice as essential criterion , in areas where urban YF transmission is established , will facilitate early case detection and management . | Yellow fever is a mosquito-borne viral infection characterized by fever , followed after several days by jaundice , liver or kidney failure , shock or bleeding in up to 25% of cases . Although the virus primarily circulates in forests among primates , it can also be transmitted from human to human by mosquitoes in urban areas . If infected patients are detected early , they could benefit from timely supportive treatment , and control measures such as mosquito bite prevention , mosquito control , and mass vaccination campaigns , could prevent further spread of the disease . During 2015–16 a yellow fever outbreak spread in urban areas of Angola and DRC . The present study showed that most yellow fever patients that were diagnosed in DRC had travelled from Angola where they have been infected , and that most were adult men . Nevertheless , several patients have been infected locally , in urban settings in three provinces of DRC . Patients were diagnosed only when jaundice appeared , more than a week after their illness started , too late to fully benefit from supportive treatment . During urban outbreaks , improving early access to healthcare and earlier detection of patients by recognizing acute fever when malaria infection is excluded , could improve yellow fever care and control . | [
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| 2018 | Urban yellow fever outbreak—Democratic Republic of the Congo, 2016: Towards more rapid case detection |
Sirtuin genes have been associated with aging and are known to affect multiple cellular pathways . Sirtuin 2 was previously shown to modulate proteotoxicity associated with age-associated neurodegenerative disorders such as Alzheimer and Parkinson disease ( PD ) . However , the precise molecular mechanisms involved remain unclear . Here , we provide mechanistic insight into the interplay between sirtuin 2 and α-synuclein , the major component of the pathognomonic protein inclusions in PD and other synucleinopathies . We found that α-synuclein is acetylated on lysines 6 and 10 and that these residues are deacetylated by sirtuin 2 . Genetic manipulation of sirtuin 2 levels in vitro and in vivo modulates the levels of α-synuclein acetylation , its aggregation , and autophagy . Strikingly , mutants blocking acetylation exacerbate α-synuclein toxicity in vivo , in the substantia nigra of rats . Our study identifies α-synuclein acetylation as a key regulatory mechanism governing α-synuclein aggregation and toxicity , demonstrating the potential therapeutic value of sirtuin 2 inhibition in synucleinopathies .
Sirtuins are NAD+-dependent deacylases and lifespan determinants in several model organisms . Sirtuin proteins have been implicated in neurodegenerative disorders , conditions that are strongly associated with aging [1 , 2] . In mammals , there are seven members of the sirtuin ( SIRT ) family: SIRT1–SIRT7 . SIRT2 is the most abundant sirtuin in the brain and its levels increase with aging [3] . Interestingly , SIRT2 emerged as a potential culprit in Parkinson disease ( PD ) pathology , as we showed that SIRT2 modulates α-synuclein ( aSyn ) aggregation and toxicity [4] . Pharmacological inhibition of SIRT2 ameliorates aSyn-mediated toxicity in cell models and in vivo ( in a Drosophila model of PD ) , but the molecular mechanisms underlying this effect remain unclear [4] . aSyn is the main constituent of Lewy bodies , protein inclusions typically found in the brains of PD patients [5] , and is therefore a central protein in PD . Interestingly , the balance between acetylation and deacetylation is altered in both aging and neurodegeneration , and a link between acetylation of nonhistone proteins and neuroprotection has recently emerged [6] . Given that SIRT2 is a deacetylase and that modulating its activity affects aSyn aggregation and toxicity , we hypothesized that SIRT2 may induce neurodegeneration by modulating aSyn acetylation , rendering it more prone to aggregate and to be cytotoxic . Here , we provide detailed insight into the mechanism through which SIRT2 modulates aSyn toxicity and demonstrate that acetylation on lysine ( K ) 6 and K10 might be used as targets for therapeutic intervention in PD and in other synucleinopathies .
To determine the mechanism of SIRT2-mediated protection against aSyn toxicity and aggregation , we hypothesized that aSyn might be acetylated and that this could be a substrate for SIRT2 . Thus , we first evaluated if aSyn was acetylated in mouse brain . To this purpose , endogenous aSyn was thermoenriched from the brain of wild-type ( WT ) mice and evaluated by mass spectrometry ( MS ) as we previously described [7] . Notably , we confirmed that aSyn is ubiquitously acetylated at the N-terminus ( Fig 1A and S1 Table ) [8] . In addition , we identified K6 and K10 , in the conserved N-terminal region , as aSyn acetylation sites ( Fig 1A and S1 Table ) . Next , we investigated whether SIRT2 and aSyn interact , as this would be an indication that perhaps aSyn is a substrate for SIRT2 . We coexpressed SIRT2 ( tagged with green fluorescent protein [GFP] ) with aSyn in human embryonic kidney ( HEK ) 293T cells and immunoprecipitated aSyn or SIRT2 . Using immunoblot analyses , we confirmed the coimmunoprecipitation of aSyn with SIRT2 , demonstrating that both proteins interacted ( Fig 1B ) . To confirm the interaction in a physiologically relevant context ( i . e . , without overexpressing the proteins ) , we immunoprecipitated aSyn from mouse brain extracts and , in agreement with the results obtained in transfected HEK cells , we found that SIRT2 coimmunoprecipitated with aSyn ( Fig 1C ) . Next , we investigated whether acetylated aSyn is a substrate of SIRT2 deacetylase activity . We transiently expressed aSyn together with SIRT2 or with the enzymatically inactive SIRT2-H187Y mutant in HEK cells and measured the levels of aSyn acetylation after immunoprecipitation and immunoblotting with an anti-acetyl–lysine antibody . We found that SIRT2 , but not the inactive SIRT2-H187Y mutant , reduced the levels of acetylated aSyn ( Fig 1D ) . We then assessed the levels of aSyn acetylation in the brains of SIRT2 knockout ( T2 . KO ) mice . Consistently , we found that deletion of SIRT2 leads to aSyn hyperacetylation ( Fig 1E and 1F ) . We also observed an age-associated reduction in the levels of aSyn acetylation in T2 . KO mice but not in WT animals ( S1 Fig ) . To further investigate the ability of SIRT2 to deacetylate aSyn , we performed in vitro deacetylation assays . Chemical acetylation of aSyn yielded two to eight acetylations per protein molecule , with a distribution maximum of five acetyl modifications ( Fig 1G ) . Peptide MS revealed that three of the five KxK motifs in aSyn , namely K21/23 , K32/34 , and K58/60 , showed little or no acetylation , while all other lysines—including K6 and K10—were acetylated efficiently ( Fig 1H , S2 Fig ) . In vitro treatment of acetylated aSyn with SIRT2 and NAD+ , its cofactor , caused efficient deacetylation , as demonstrated by the shift of the modification peak maximum from five to two acetyl groups ( Fig 1G ) . We also investigated the specific SIRT2-dependent deacetylation of aSyn on lysines K6 and K10 . For this , we generated acetylated forms of aSyn specifically on residues K6 or K10 ( acK6 and acK10 ) using our previously established semisynthetic approach [9 , 10] . We found that SIRT2 indeed deacetylates both acK6 and acK10 in a dose-dependent and in an NAD+-dependent manner , with similar efficiency ( Fig 1I ) . In conclusion , we identified K6 and K10 as aSyn acetylation sites that are targeted by SIRT2 . Given that we demonstrated that aSyn acetylation can be modulated by SIRT2 , we investigated whether SIRT2 modulates aSyn aggregation . To that purpose , we decreased SIRT2 levels using short hairpin RNA ( shRNA ) -mediated knockdown in an established cellular model of PD in which aSyn aggregation is recapitulated . This model consists of the coexpression of a C-terminally modified form of aSyn ( SynT ) that increases its aggregation propensity , and synphilin-1 ( an aSyn interactor that potentiates its aggregation ) in H4 cells [11 , 12] . Upon SIRT2 knockdown ( T2 . KD ) , we detected comparable levels of SynT and synphilin-1 as in cells transduced with scramble shRNA ( Scr ) ( Fig 2A ) . To quantify the effect on aSyn aggregation , T2 . KD and Scr cells were analyzed by immunocytochemistry ( ICC ) 48 h posttransfection . Remarkably , T2 . KD decreased the percentage of cells displaying SynT-positive inclusions to half of that in control cells ( Fig 2B ) . Biochemically , we confirmed that T2 . KD increased SynT triton X-100 solubility ( Fig 2C ) and decreased the levels of higher-molecular-weight ( MW ) oligomeric species , as assessed by sucrose gradient separation ( Fig 2D ) . Next , we evaluated the levels of acetylated aSyn in the same cell-based aggregation model . By immunoprecipitating SynT , we observed that T2 . KD increased the levels of acetylated aSyn ( Fig 2E ) . Importantly , concomitantly with the decrease in SynT aggregation and hyperacetylation , T2 . KD also decreased the cytotoxicity of SynT , as measured by the levels of lactate dehydrogenase ( LDH ) release from cells ( Fig 2F ) . To determine whether K6 and K10 acetylation modulated aSyn aggregation , we generated aSyn mutants in which both lysine residues were replaced by either arginine residues ( KR ) to mimic acetylation-resistant aSyn or by glutamine residues ( KQ ) to mimic constitutively acetylated aSyn [13] . Using the cell model of aSyn aggregation described above , we observed that while the acetylation-resistant mutant KR promoted SynT aggregation , the acetylation mimic mutant KQ prevented it ( Fig 3A ) . To assess if the observed phenotype was due to an intrinsic modulation of aSyn aggregation , we investigated the oligomerization kinetics of recombinant mutant aSyn , as previously described [14] . The kinetics of aSyn fibril formation was monitored using thioflavin T ( ThT ) . For the WT aSyn , we observed the expected increase in ThT fluorescence signal , confirming the formation of amyloid-like fibrils ( Fig 3B ) . Interestingly , we observed that while acetylation-resistant mutants increased , acetylation-mimic mutants prevented aSyn oligomerization ( Fig 3B ) . Moreover , when coincubated with WT aSyn , acetylation-resistant mutants also increased , while acetylation-mimic mutants prevented WT aSyn oligomerization ( Fig 3B ) . We then investigated the effects of aSyn acetylation on its structure , using nuclear magnetic resonance ( NMR ) spectroscopy , using again recombinant KR and KQ aSyn mutants produced in Escherichia coli , as above . Two-dimensional 1H-15N correlation spectra of soluble aSyn and its mutants showed a narrow spread of NMR resonances , demonstrating that both WT and acetylation-resistant and/or acetylation-mimic mutants are intrinsically disordered ( Fig 3C and S3A Fig ) . In addition , a high degree of NMR signal overlap between the WT and mutant proteins suggested that the mutations do not strongly perturb the conformational ensemble of aSyn in solution ( Fig 3C and S3A Fig ) . Next , we probed the impact of acetylation-resistant and acetylation-mimic mutants on the ability of aSyn to bind to lipid membranes , a property thought to be associated with the physiological function of the protein . In the presence of small unilamellar vesicles ( SUVs ) , aSyn constantly exchanges between the soluble disordered state and the helical , membrane-bound structure [15] . Because of this exchange process , aSyn residues involved in membrane-binding experience NMR line broadening [15] , in which residues with stronger binding generally experience more broadening . We detected the residue-specific NMR signal attenuation , which is typically observed for aSyn in the presence of SUVs: the N-terminal 100 residues are strongly attenuated—in particular the N-terminal 20–30 residues , which anchor aSyn to the membrane surface [16]—while the C-terminal domain of aSyn binds only very weakly to POPC:POPA membranes ( Fig 3D and S3B Fig ) . In the case of the acetylation-resistant mutant ( aSyn KR ) , a very similar profile was observed , demonstrating that replacement of lysine residues at positions 6 and 10 with arginine does not perturb the exchange of aSyn between the solution and membrane-bound state ( S3B Fig ) . In contrast , addition of POPC:POPA SUVs to the acetylation-mimic mutant ( aSyn KQ ) resulted in less signal attenuation , and thus higher NMR signal intensities , in the N-terminal 100 residues when compared to the WT protein ( Fig 3D ) . The strongest difference was observed for the N-terminal 20–30 residues . Because of the dynamic nature of the aSyn–membrane interaction , the overall affinity of aSyn for POPC:POPA membranes was only slightly attenuated , as estimated by circular dichroism ( Fig 3E ) . Since T2 . KD decreased SynT aggregation , we evaluated how it affected SynT clearance in the cell . For this purpose , we performed a time-chase experiment blocking de novo protein synthesis with cycloheximide ( CHX ) . We did not observe significant differences in the levels of SynT between T2 . KD or Scr cells ( Fig 4A ) . Since the autophagy lysosome pathway ( ALP ) is described as the main mechanism to clear high-molecular-weight species of aSyn that cannot be degraded by the proteasome [17] , we evaluated the effects of T2 . KD on autophagy . Remarkably , although we observed no differences in the ALP in naïve cells , T2 . KD significantly enhanced the activity of the ALP in the SynT aggregation model , as seen by the increased accumulation of LC3-II levels after 2 h of ALP inhibition with bafilomycin A ( Fig 4B ) . To further evaluate these findings , we scored the number of LC3 punctae in T2 . KD and Scr cells in both naïve cells or in the SynT aggregation model using immunocytochemistry for LC3 ( Fig 4C ) . In agreement with the previous data , LC3 punctae were not modulated by T2 . KD in naïve cells . However , in the aSyn aggregation model , T2 . KD increased the number of LC3 punctae per cell ( Fig 4C ) . Consistently , upon T2 . KD , we observe a basal decrease in the levels of SQSTM1 ( P62 ) in the cell model of aSyn aggregation ( S4 Fig ) . In total , these results suggest that while basal autophagy is not affected by SIRT2 , upon aSyn aggregation , SIRT2 inhibition potentiates the activity of ALP , thereby efficiently clearing aSyn aggregates . As we showed that SIRT2 regulates aSyn acetylation and that reduction of SIRT2 levels is protective in a cell model of PD , we next aimed at validating our findings in rat primary cortical neuronal cells . It was previously described that 10 d posttransduction of primary cortical neurons with adeno-associated viruses ( AAV ) encoding for WT aSyn results in severe neuronal loss [18] . Based on this model , rat cortical neurons were transduced with AAV6 vectors encoding WT , KR , or KQ aSyn under the synapsin promoter for neuronal expression ( S5 Fig ) . We detected similar expression levels of WT and of the two aSyn acetylation mutants 7 d post-viral transduction ( Fig 5A ) . Remarkably , we observed that cells expressing the acetylation-mimic mutant ( aSyn KQ ) displayed a significant reduction in the toxicity observed 10 d posttransduction ( 31% ) ( S6 Fig ) . However , two to three weeks after transduction , no significant differences were detected ( S6 Fig ) . Next , we performed live cell imaging to determine the effect of aSyn intrinsic acetylation in the number of living neurons over time . Neurons were cotransduced with aSyn ( WT or mutant ) and enhanced GFP ( EGFP ) , and EGFP-positive neurons were counted at different time points ( Fig 5B and 5C ) . At 7 d posttransduction , the number of transduced neurons was identical for all conditions . Between 15 and 21 d posttransduction , both WT and KR aSyn induced pronounced neuronal loss , while the number of living neurons in cultures expressing the acetylation mimic mutant KQ was significantly higher ( 2-fold ) ( Fig 5B and 5C ) . We next examined the distribution and intracellular localization of aSyn in neurons 15 d posttransduction . Using an antibody that specifically recognizes human aSyn , we did not observe major differences on the subcellular distribution of the different aSyn variants—all were distributed throughout the neurons , both in cell bodies and neurites ( Fig 5D ) . Strikingly , both the WT and the KR aSyn severely affected the dendritic phenotype , promoting an evident shortening and loss of dendritic arborisation ( Fig 5D ) . In contrast , the dendritic structure was preserved in neurons expressing the KQ mutant , as shown by microtubule-associated protein 2 ( MAP2 ) staining ( Fig 5D ) . Overall , both WT and KR—but not KQ aSyn—promoted prominent neurodegeneration and loss of dendritic arborisation , suggesting aSyn acetylation is protective . In order to investigate the effect of aSyn acetylation in vivo , we stereotaxically injected AAV6 viruses driving the neuronal expression of WT , KR , or KQ mutant aSyn into the rat substantia nigra ( SN ) . First , we established the neurotoxicity of AAV6-mediated expression of WT aSyn at three different time points ( S5A and S5B Fig ) . We observed that 6 d after injection of WT aSyn , no significant tyrosine hydroxylase ( TH ) loss was detected . However , 12 d postinjection , the number of TH-positive cells was significantly reduced in comparison to that observed in the GFP-injected group . No additional TH-cell loss was observed 18 d postinjection , indicating the lesion did not increase between 12 and 18 d postinjection . As expected , the expression of GFP did not induce TH-positive neuronal loss ( S7A and S7B Fig ) . Next , we compared the neurotoxicity of the KQ and KR aSyn mutants using the same paradigm . Based on the data obtained for WT aSyn , we assessed the effect 3 wk postinjection . Stereological quantification of TH-positive cells revealed a significant loss of TH-positive neurons 3 wk post-KR aSyn expression ( Fig 6A and 6B ) . In agreement with the in vitro results , the TH-positive cell loss in cells expressing the KQ aSyn mutant was identical to that observed in control conditions ( GFP expression ) ( Fig 6B ) . On the other hand , the KR mutant induced strong loss of TH-positive cells in the SN . We next investigated whether interfering with aSyn acetylation on K6 and K10 affected aSyn pathology in vivo . The expression of both KQ and KR aSyn variants in the SN resulted in the detection of phosphorylated aSyn on Ser129 ( pS129 ) ( Fig 6C ) , suggesting the accumulation of pathological forms of aSyn . However , the subcellular localization of the pS129 signal was different depending on the aSyn variant . While the KQ mutant displayed a predominantly cytoplasmic staining , the KR mutant displayed a homogeneous pS129 staining throughout the cells ( Fig 6C ) . We also assessed aSyn pathology by immunostaining with the 5G4 antibody , which reacts with aggregated forms of aSyn [19] ( S8 Fig ) . As expected , all variants tested resulted in immunostaining with this antibody , suggesting they all formed aSyn aggregates . Unexpectedly , we observed a stronger staining in rats expressing WT aSyn when compared to that observed in animals expressing the acetylation mutants ( S8A Fig ) . However , as observed for the pS129 staining , immunostaining with the 5G4 antibody also revealed different aSyn subcellular localization depending on the mutant expressed . The KQ mutant appeared predominantly in the cytoplasm , being excluded from the nucleus in most cells . In contrast , the signal for both KR mutant or WT aSyn was homogeneously distributed throughout the cells ( S8B Fig ) . We showed that the levels of acetylated aSyn were increased in T2 . KO mice ( Fig 1E and 1F ) . Next , to assess the effect of SIRT2 on aSyn-mediated neurodegeneration in vivo , we evaluated whether deletion of SIRT2 protected against TH-cell loss . We stereotaxically injected AAV6 virus driving the neuronal expression of WT aSyn into the SN of WT or T2 . KO mice . 2 wk after viral injection , TH neuronal loss was observed in WT mice ( 28 . 5 ± 2 . 4% surviving neurons ) . Remarkably , in T2 . KO mice , we observed a clear protection ( 62 . 8 ± 5 . 5% surviving neurons ) ( Fig 7A and 7B ) . We further investigated the protective effects of SIRT2 deletion in the chronic MPTP mouse model of parkinsonism [20] . As expected , 2 wk post-MPTP injection , we observed a significant decrease in the percentage of TH neurons in WT mice ( Fig 7C–7E ) . Overall neuronal loss was also observed ( Fig 7F ) . In contrast , we observed no neuronal loss in T2 . KO animals ( neither TH or overall neurons ) ( Fig 7C–7F ) .
Aging is , by far , the major risk factor for PD and other neurodegenerative disorders , but the precise molecular mechanisms involved are still elusive . The sirtuin family of deacetylases regulates many aspects of the aging process . Intriguingly , several sirtuins have emerged as putative therapeutic targets in several neurodegenerative diseases . For example , SIRT1 overexpression has proven beneficial in models of Alzheimer disease , Huntington disease , and PD [1 , 21] . However , while SIRT1 is ubiquitously expressed in the body , SIRT2 is the most abundant sirtuin in the brain and is primarily present in the cytoplasm of brain cells ( neurons and oligodendrocytes ) . The levels of SIRT2 increase with aging [3] , and we previously showed that pharmacological inhibition of this sirtuin is protective in cellular , Drosophila , and mouse models of PD but not in models of amyotrophic lateral sclerosis [4 , 22] . Nevertheless , the precise mechanisms of SIRT2-mediated protection against aSyn aggregation and toxicity are still unclear . In the present study , we provide strong evidence demonstrating that SIRT2 inhibition robustly alleviates neuropathology in several models of synucleinopathy by regulating the levels of aSyn acetylation . We show , for the first time , that SIRT2 interacts with and deacetylates aSyn in the highly conserved N-terminal domain . We started with the premise that acetylation balance is impaired in neurodegeneration . Thus , we first hypothesized that SIRT2-dependent modulation of aSyn acetylation could be the basis for aSyn pathogenicity . It was previously shown that aSyn is acetylated in the N-terminus [8 , 23] and that this modification is important for the interaction of aSyn with lipids , as acetylation increases membrane-binding properties of aSyn by increasing its alpha helical content and reducing its ability to aggregate [24] . Other posttranslational modifications are also known to affect the structure , membrane-binding ability , and oligomerization properties of aSyn [25] . Since aSyn is a very lysine-rich protein , with 15 putative acetylation sites located mainly in the N-terminal region of the protein [26] , it was reasonable to hypothesize that aSyn could be naturally acetylated in lysine residues in addition to the N-terminal acetylation and that these residues could , perhaps , be substrates for SIRT2 deacetylation . In our study , and in agreement with previous findings , we confirmed that aSyn is acetylated in the N-terminus . However , we also detected acetylation on residues K6 and K10 ( Fig 1A and S1 Table ) . Importantly , a previous proteomic analysis of lysine acetylation in rat tissue also detected aSyn acetylation on K6 [27] . Here , we also demonstrated that SIRT2 interacts with and deacetylates aSyn . We previously reported that pharmacological inhibition of SIRT2 reduces aSyn toxicity and decreases aggregation [4] . Therefore , to exclude potential off-target effects of SIRT2 inhibitors , we evaluated the effects of genetic inhibition of SIRT2 on aSyn aggregation and toxicity in a well-established cellular model of aSyn aggregation . Strikingly , we found that by elevating the levels of aSyn acetylation , knockdown of SIRT2 was able to reduce aSyn aggregation and toxicity . Therefore , we reasoned that aSyn acetylation might be protective . To evaluate the intrinsic implications of aSyn acetylation on K6 and K10 , we generated acetylation-mimic ( KQ ) and acetylation-resistant ( KR ) mutants . Using the cell-based model of aSyn aggregation , we confirmed that while the majority of cells expressing the KR mutant displayed aSyn inclusions , those expressing the KQ displayed almost no inclusions . Moreover , we showed that recombinant aSyn acetylation mutants display the same trend . Notably , combining WT aSyn with the KQ acetylation-mimic aSyn suppressed fibrillisation , while combining WT with the KR acetylation-resistant mutant potentiated aSyn fibrillisation . The fact that KQ aSyn is able to suppress WT aSyn fibrillisation suggests that , in normal conditions , the acetylated form of aSyn might be dominant . This observation raises the important and provocative question of how much acetylated aSyn is necessary in the brain in order to prevent fibrillisation as we age . Going one step further , this might enable us to predict what are the chances of developing PD later in life by measuring the levels of SIRT2 and/or acetylated aSyn and could shed light into the question of why some people end up developing PD later in life . Both NMR and far UV-CD data showed that the presence of acetylation-resistant modifications do not affect the binding of aSyn to lipid vesicles ( S3A Fig ) . In contrast , mimicking aSyn acetylation reduces the ability of the N-terminal region to bind membranes , preventing this region from acting as a membrane-attachment anchor ( Fig 3D and 3E ) . Importantly , this finding suggests that acetylation may regulate the physiological role of aSyn on vesicular trafficking [28 , 29] and should be further investigated in future studies . Protein acetylation is known to modulate the clearance of different proteins . For example , acetylation of huntingtin—another intrinsically disordered and misfolding-prone protein—on K444 facilitates its clearance via the ALP [30] . In addition , SIRT2 has been previously associated with autophagy . First , when SIRT2 releases FOXO1 , the latter gets acetylated and binds to ATG7 , inducing autophagy in the context of cancer [31 , 32] . Second , overexpression of SIRT2 inhibits autophagic turnover by interfering with aggresome formation [33] . Therefore , we asked whether SIRT2 could also modulate the clearance of aSyn , mainly by interfering with the ALP . Although we did not observe significant differences in the rate of aSyn clearance , we found that T2 . KD induced macroautophagy only in the presence of aSyn inclusions ( Fig 4B and 4C and S4 Fig ) . Since we observed that acetylated aSyn is less prone to aggregate and could even prevent the aggregation of the WT form and that T2 . KD promoted a more efficient clearance of the aggregates , we propose that SIRT2 acts on several fronts , potentiating neurodegeneration . On one side , SIRT2 affects the conformation of aSyn through deacetylation , thus rendering it more prone to aggregate . Simultaneously , it regulates the main clearance pathway for aggregated aSyn . Therefore , we propose a model in which the age-dependent increase of SIRT2 in the brain , with the concomitant decrease of acetylated aSyn , comes with two major deleterious consequences: an increase of aggregation and the exacerbation of the expected defects in the ALP associated with aging [34] . We then assessed the effects of aSyn acetylation in rat primary cortical cultures . By analysing the expression of aSyn acetylation mutants over time , we observed that while expression of the acetylation-resistant mutant aSyn induced a similar reduction in the number of neurons to that observed for WT aSyn , the acetylation-mimic mutant was less toxic . Expression of the same aSyn variants in vivo in the rat SN confirmed that the acetylation-mimic KQ mutant was less toxic to TH-positive neurons that the KR mutant or than WT aSyn . Interestingly , although the immunoreactivity for pS129 or aggregated forms of aSyn was identical for both the KQ and KR mutants , the intracellular distribution of the protein was altered . Altogether , this suggests that acetylation may not completely abolish the ability of aSyn to aggregate in vivo but that it may also modulate the subcellular distribution of the protein , thereby modulating its toxicity . Given the protective effects of mimicking aSyn acetylation in vivo , we also evaluated the putative protective effects of SIRT2 deletion . For this we used two in vivo models reporting on different aspects of PD: aSyn toxicity and TH-cell loss . Importantly , we found that SIRT2 deletion was protective in both the AAV-mediated model of aSyn expression in the SN and in the chronic MPTP model of parkinsonism . In summary , our data suggest that acetylation of aSyn on K6 and K10 may regulate the distribution ( and perhaps the function ) of aSyn , also modulating its aggregation potential and toxicity . We identify acetylation as a mechanism for removing aSyn by targeting it to autophagy . Thus , SIRT2 activity is an important mediator of aSyn acetylation , underlying its potential as a target for therapeutic intervention in synucleinopathies and , possibly , in other neurodegenerative disorders in which protein acetylation may play underappreciated roles in modulating protein homeostasis .
This study does not involve human participants or tissue . All experimental animal procedures were conducted according to approved experimental animal licenses , issued by the responsible animal welfare authority ( Niedersächsisches Landesamt für Verbraucherschutz und Lebensmittelsicherheit ) and controlled by the local animal welfare committee and veterinarians at the University Medical Center Göttingen after approval from the ethics committee . Total protein extracts from C57Bl/6 mice brain samples were processed as in [7] . Mass spectrometry was performed on an Applied Biosystems 4700 Proteomics Analyzer with TOF/TOF ion optics as previously described [7] . All peptide mass values were considered monoisotopic , and a MS mass tolerance was set at 100 ppm . Trypsin was assigned as digestion enzyme of aSyn . A triple miss cleavage was allowed and oxidation of methionyl residues , acetylation of the N-terminus , and acetylation of lysine residues were assumed as variable modifications . All peaks with S/N greater than 5 were included for matching against in silico digestion of corresponding aSyn sequence ( Mus muscullus ) in mMass software [35] . Cells were cultured at 37°C in a humidified incubator containing 5% CO2 ( g ) . H4 cells were grown in Opti-MEM medium ( Gibco-Invitrogen ) , and HEK 293T cells were grown in DMEM with GlutaMax medium ( Gibco-Invitrogen ) ; both were supplemented with fetal bovine serum ( FBS ) ( 10% v/v ) . Cells were transfected with FuGENE 6 transfection Reagent ( Roche ) as previously described [7] . Briefly , 293T cells were transfected with GFP or SIRT2 fused with GFP ( SIRT2::GFP ) or cotransfected with GFP with aSyn or SIRT2::GFP with aSyn . Protein immunoprecipitation was performed as we previously described [36] . Cells lysis and immunoblotting was performed as previously described [7] . For immunoblotting , we used the following antibodies: anti-aSyn ( BD Transduction laboratories , S63320 , 1:3000 ) , anti-V5 ( Santa Cruz , SC-83849-R , 1:1000 ) , anti-LC3 ( Nano Tools , 0260-100/LC3-2G6 , 1:2000 ) , anti–β-actin ( Ambion , AM4302 , 1:5000 ) , anti-GAPDH ( Ambion , AM4300 , 1:5000 ) , anti-GFP ( NeuroMab , P42212 , 1:3000 ) , and anti–acetyl-lysine ( Cell Signaling , 9441S , 1:1000 ) . aSyn double mutants were generated by site-directed mutagenesis to mimic the acetylated or the nonacetylated forms of aSyn , replacing the lysine on positions 6 and 10 by glutamine ( K6 + 10Q = KQ ) or arginine ( K6 + 10R = KR ) , respectively . Animal experiments were performed according to institutional and national regulations . All mice used were in congenic C57Bl/6 . SIRT2 knockout mice have been described previously [37] . All mice were housed at controlled temperature ( 25°C ) and 12:12 h light/dark cycle . For protein analysis , brains from mice at the different ages ( 2 and 8 mo old ) were quickly removed , striata were dissected , and samples were homogenized in RIPA buffer in the presence of protease and phosphatase inhibitors ( Roche Complete and PhosStop ) . Samples were then rotated for 1 h at 4°C and centrifuged at 18 , 000 g for 30 min . For further analyses , we used the soluble fractions . aSyn was partially purified and enriched as described in [7] . For immunoblotting , we used anti aSyn and antiacetylated lysine antibodies . Recombinant SIRT2 ( 43–356 ) was expressed in E . coli and purified through affinity and size-exclusion chromatography as previously described in [38] . The buffer of aSyn was exchanged to 100 mM sodium phosphate pH 7 . 4 using a NAP-5 column ( GE Healthcare ) . aSyn was subsequently treated with acetic anhydride , 13 mM final concentration ( 10-fold molar excess compared to the lysine concentration ) , by four consecutive , stepwise additions over an hour followed by an extra hour of incubation on ice . Excess acetic anhydride was then quenched by addition of 20 mM Tris pH 8 . 0 . For the enzymatic deacetylation , 1 . 1 μM chemically acetylated aSyn was incubated for 1 h on ice in the presence or absence of 0 . 11 μM SIRT2 and 1 mM NAD+ , and intact protein masses were then determined through HPLC-coupled ESI-MS . Proteins were concentrated and washed on a Piccolo Proto 200 C4 5μm 2 . 5 x 0 . 5mm trap column ( Higgins Analytical , Mountain View , California ) and subsequently switched in line with and separated on a Jupiter C4 5μm 300Å 150 x 1 mm analytical column ( Phenomenex , Torrance , California ) mounted onto a Shimadzu Prominence UFLC ( Shimadzu , Duisburg , Germany ) at a 70μl min-1 flow rate with the following buffers: A—5% ACN , 5% DMSO , and 0 . 1% FA; B—90% ACN , 5% DMSO , and 0 . 1% FA . Proteins were then eluted over with a gradient of 3 min of 1% B to 55% B followed by 1 min of 55% B to 90% B . Mass analysis was performed by ESI-TOF-MS on an AB Sciex TripleTOF 5600+ mass spectrometer ( Sciex , Darmstadt , Germany ) with a DuoSpray Ion Source with the following settings: floating voltage of 5 , 500 V , temperature of 350°C , and declustering potential of 120 with 4 separate TOF experiments each , respectively , with 4 , 12 , 20 , and 40 time bins summed . Spectra were integrated over a retention time period , and the summed TOF experiment with the greatest resolution was selected . The raw data was then converted and deconvoluted using the MaxEnt I algorithm ( Waters , Milford , Massachusetts ) at a resolution of 0 . 1 Da . Chemical acetylated aSyn at lysine 6 ( acK6 ) or 10 ( acK10 ) was obtained by using the semisynthetic approach [9 , 10] . acK6 or acK10 was incubated in the presence or absence of both recombinant SIRT2 in increasing amounts and NAD+ ( 1 mM ) at 37°C in SDAC buffer ( 50 mM Tris·HCl [pH 9 . 0] , 4 mM MgCl2 , 50 mM NaCl , 0 . 5 mM DTT ) . 3 h postincubation , the reaction was stopped by the addition of protein sample buffer . HEK 293T cells ( 1 . 5 x 10⁶ ) were cotransfected with Δ8 . 9 , VSVG , and SIRT2 shRNA plasmids ( 1 . 8μg , 0 . 2μg , and 2μg , respectively—Sigma RNAi facility ) using FuGENE6 ( Promega , Wisconsin , United States ) . 48 h posttransfection , the lentiviruses were collected and filtered ( 0 . 45μm ) . H4 cells ( 1 x 105 ) were infected with the lentivirus supplemented with PolyBrene ( 10μg/mL ) ( Sigma ) . After 4 h , medium was replaced by Opti-MEM supplemented with FBS . 48 h postinfection , cells were selected using Puromycin-Dihydrochloride ( 1μg/mL; Sigma ) . The aggregation model of H4 neuroglioma cells was used as previously [36] . Immunocytochemistry was performed as previously [36] . Total protein from Scr or T2 . KD H4 cells transiently expressing SynT with synphilin-1 was processed on a 5% to 30% sucrose gradient as previously described [39] . Total protein ( 500 μg ) from Scr or T2 . KD cells transiently expressing SynT and synphilin-1 for 48 h was incubated with 1% Triton X-100 on ice for 30 min . Protein fractions were separated by centrifugation at 15 , 000 g for 60 min at 4°C . Soluble protein fraction was collected and the insoluble protein fraction pellet was resuspended in 40 μL of 2% SDS Tris-HCl buffer pH 7 . 4 and sonicated for 10 s . Total protein and T-Insoluble fractions ( 5 μl of each ) were loaded and resolved by SDS-PAGE and immunoblotted as previously described . Cytotoxicity was measured using LDH kit ( Clontech ) as previously described [14] . The expression and purification of human aSyn was performed as previously described [7] . aSyn aggregation assay was performed as previously described [14] . WT and acetylation mutants K6+10Q or K6+10R were combined in a 1:1 ratio . 1H-15N HSQC spectra of 15N-labelled wild-type and acetylation mutants K6+10Q or K6+10R aSyn were acquired at 15°C on a 600 MHz Bruker spectrometer in HEPES 25 mM , pH 7 . 0 , NaCl 50 mM , 10% D2O . aSyn backbone resonance assignment was transferred from previous studies [40] . In case of K6+10Q or K6+10R aSyn , peak centers were adjusted only in regions of small signal overlap . Peaks affected by severe overlap were excluded from the analysis . NMR data were processed and analyzed by using NMRPipe [41] and Sparky ( T . D . Goddard and D . G . Kneller , University of California , San Francisco ) . For NMR and CD measurements ( see below ) of aSyn in the presence of SUVs , SUVs were prepared with 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine ( POPC ) and 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphate ( POPA ) ( Avanti Polar Lipids ) at a 1:1 molar ratio using previously published protocols [42] . Far UV-CD measurements were performed on a Chirascan ( Applied Photophysics , United Kingdom ) circular dichroism spectrometer using protein concentrations of 10 μM in HEPES buffer , 10 mM NaCl , pH 7 . 4 , in a quartz cuvette with a 0 . 1 cm light-path . To probe the binding of aSyn to SUVs , increasing concentrations of SUVs were added ( from 1:20 to 1:270 protein-to-lipid molar ratio ) . Each experiment was repeated at least twice . Baseline correction was performed with the same buffer . Data were expressed as mean residue ellipticity ( degree cm2 dmol –1 ) . Affinity curves were fitted with a single exponential equation considering a one site–specific binding model . For CHX chase experiments , Scr or T2 . KD H4 cells were transfected with SynT as previously [7] . 40 h posttransfection , cells were treated with cycloheximide ( 100 μM , added at given time points ) . Protein extracts were immunoblotted . Autophagy activity studies were performed as previously described [43] . Activity was given by the amount of accumulated LC3 after 2 h of treatment with the autophagy blocker bafilomycin A . Basal levels of SQSTM1 ( p62 ) were also measured ( sc-28359 , Santa Cruz Biotechnology ) . For LC3 punctae analysis , 1 . 2 x 105 cells were seeded on MatTek dishes . 48 h after plating , cells were carefully washed three times with PBS and processed for immunocytochemistry , as previously described [36] , using an anti-LC3 antibody . Fluorescence was detected by the use of a widefield fluorescence microscope or a confocal microscope . The number of LC3 punctae per cell was determined . For the generation of the AAV vectors of serotype 6 ( AAV-6 ) constructs encoding for human WT aSyn and lysine mutants ( K6+10Q and K6+10R ) , cDNA was amplified by PCR using the following primers: Forward Primer sequence for WT aSyn: 5′- GGCAGATCTACCGGTCGCCACCATGGATGTATTCATGAAAGGACTTTCAA AGGCCAAGGAGGG -3′; Forward Primer sequence for K6+10Q aSyn mutant: 5′- GGCAGATCTACC GGTCGCCACCATGGATGTATTCATGCAGGGACTTTCACAGGCCAAGGAGGGA -3′; Forward Primer sequence for K6+10R aSyn mutant: 5′- GGCAGATCTACCGGTCGCCACCATGGATGTATTCATGAGAG GACTTTCAAGGGCCAAGGAGGGA -3′; Reverse Primer sequence for aSyn WT sequence: 5′-CCCGC GGCCGCTTAGGCTTCAGGTTCGTAGTCTT -3′ . PCR products were digested with restriction enzymes and cloned into pAAV-6P-SEWB . WT and mutated aSyn was expressed from human synapsin-1 ( hSyn1 ) gene promoters , followed by WPRE and bovine growth hormone polyadenylation site according to standard procedures ( S2 Fig ) [44] . Briefly , recombinant AAV-6 for expression of WT , KQ ( K6+10Q ) , or KR ( K6+10R ) were produced by transient transfection in HEK293 cells , purified by iodixanol gradient ultracentrifugation and heparin affinity chromatography , and dialysed against PBS . Vector titres were determined by qPCR , and purity of viral particles was confirmed to be >98% by SDS-PAGE . Vector titres were 5 x 10e12 vg/ml for WT and K6/10R synuclein and 8 x 10e12 vg/ml for K6/10Q synuclein . Primary cortical cultures were prepared from Wistar rats at embryonic day 18 , as previously described [45] . Cortical cells were seeded in poly-ornithine ( PLO ) precoated glass coverslips ( 13 mm ) at a density of 250 , 000 cells/well , and were maintained in Neurobasal medium ( Gibco ) supplemented with 1% Penicillin-Streptomycin ( Pan-Biotech ) , 0 . 5 mg/mL Transferrin ( Sigma ) , 125mM L-glutamine ( Pan-Biotech ) , and 1 x B27 ( Gibco ) . Cultures were grown at 37°C in humidified 5% CO2 atmosphere . At DIV3 , neuronal cells were infected with equimolar amounts of recombinant adeno-associated virus ( AAV-6 ) , under the synapsin promoter , encoding for EGFP , WT , or mutant aSyn ( 1 x 108 vg/250 , 000 neurons/well ) . For live cell imaging experiments , cells were cotransduced with 1 x 104 vg EGFP/250 , 000 neurons/well . Transduced cells were imaged on an Olympus IX81-ZDC microscope system ( Olympus ) with a 10x objective . An EGFP fluorescence signal was recorded with constant exposure time ( 300 ms ) from living neurons at 7 , 10 , 15 , 18 , and 21 d posttransduction . A total of 16 images per well were randomly and automatically collected for quantification analysis . The total number of GFP-positive cells was counted for each condition using the Olympus Scan R Image Analysis Software and normalized to EGFP + WT aSyn . Supernatants were collected from primary cells on 10 , 15 , and 18 d posttransduction . Cell viability was assessed by quantitatively measuring LDH as previously mentioned . For background control , noninfected cultures were used , and maximal LDH release was measured in the supernatants of primary cells lysed with 2% Triton X-100 . Percentage of toxicity was calculated as indicated by the manufacturer . Cortical primary cells growing on glass coverslips were fixed for 10 min with 4% paraformaldehyde 15 d after transduction . Following three washes with PBS , cells were permeabilized for 15 min with 0 . 5% Triton/PBS and blocked with 3% BSA/PBS for 1 h at room temperature ( RT ) . Human anti-aSyn antibody ( MJFR1 , abcam #ab138501 ) and anti-MAP2 ( Sigma #M9942 ) were prepared 1:1 , 000 in blocking solution and incubated overnight , at 4°C . Coverslips were carefully washed three times with PBS and incubated with secondary Alexa Fluor antibodies ( Life Technologies ) diluted 1:1 , 000 in blocking buffer at RT . Nuclei were stained with Hoechst , and after three washes with PBS , coverslips were embedded in moviol . Imaging was performed using an epifluorescence microscope ( Leica DMI 6000B microscope , Leica ) . Young adult female Wistar rats ( 230–260g each; Janvier , Saint Berthevin , France ) were used for AAV injections . Rats were housed in a temperature-controlled room that was maintained on a 12-h light and/or dark cycle . Food and water were provided ad libitum . All surgical procedures , intracerebral stereotaxic vector injections into the right hemisphere SNpc ( coordinates were as follows: AP: –4 . 7; ML: –2 . 2; DV: –7 . 6 mm relative to Bregma , tooth bar: –3 . 3 mm ) ( S3C Fig ) , and tissue preparations were performed essentially as described [18] . In a first attempt , young adult female Wistar rats ( 230–260g ) were injected with 2 μL of control GFP or human WT aSyn AAV6 virus ( ~1x108 vector genomes [vg]/ml ) into the right SN at a flow rate of 0 . 2 μl/min . Animals were PFA-perfused 1 , 2 , and 3 wk after virus injection . In a second attempt , young adult female Wistar rats ( 230–260g ) were injected with 2 μL of control GFP , human WT , or mutants KR and KR aSyn AAV6 virus ( ~1x108 vg/ml ) into the right SN at a flow rate of 0 . 2 μl/min . Animals were PFA-perfused 3 wk after virus injection . 40-μm thick serial coronal sections from the SN were collected on a cryostat ( Leica ) and processed for immunohistochemistry stainings . Mice were housed in a temperature-controlled room that was maintained on a 12 h light and/or dark cycle . Food and water were available ad libitum . Both female and male WT and SIRT2-KO mice were used for AAV injections . All surgical procedures , intracerebral stereotaxic vector injections into the right hemisphere SNpc ( coordinates were as follows: AP: –3 . 2; ML: –1 . 2; DV: –4 . 4 mm relative to Bregma , tooth bar: 0 . 0 mm ) , and tissue preparations were performed essentially as described [46] . Mice were injected with 2 μL of control GFP or human WT aSyn AAV6 virus ( ~1x108 vg/ml ) into the right SN at a flow rate of 0 . 2 μl/min . Animals were PFA-perfused 2 wk after virus injection . 40-μm thick serial coronal sections from the SN were collected on a cryostat ( Leica ) and processed for immunohistochemistry . MPTP injections [20] and quantification of dopaminergic neurodegeneration [47] was performed as previously described . Counting of TH- and Nissl-positive neurons in the substantia nigra was performed blind to avoid biases . For statistical analyses , a two-way ANOVA followed by a Newman–Keuls post hoc test was performed ( R software packages , version 2 . 8 . 0 , R Development Core Team 2008 , Vienna , Austria ) . Mice were transcardially perfused with 50 ml in a phosphate buffer followed by 100 ml 4% PFA in PB at a flow rate of 15 ml/min . Brains were removed , postfixed overnight in the same solution , then cryoprotected by immersion in 30% sucrose for 36 h . Free-floating 40 μm serial coronal sections from throughout the SN were collected with a Cryostat ( Leica Microsystems , Germany ) . Brain slices were placed in a phosphate-buffered saline solution ( 1x PBS , 0 . 1% sodium azide ) and stored at 4°C before use . For the detection of proteins after infection , free-floating sections were washed three times in PBS , blocked in PBS containing 4 . 5% bovine saline albumin ( BSA ) for 1 h , then incubated for 48 h at 4°C in PBS containing 3% BSA , 0 . 2% Triton X-100 , and one of the following antibodies of interest . Sections were rinsed three times in PBS and incubated with Alexa Fluor 488-labeled antimouse or rabbit IgG or Alexa Fluor 555-labeled antimouse or rabbit IgG ( Invitrogen ) at room temperature for 1 h . The sections were rinsed in PBS and the nuclei were counterstained with DAPI ( dilution 1:10 , 000 , Wako ) for 3 min . The sections were mounted in Mowiol . Series of sections were immunostained for aSyn to localize the injection site in aSyn-injected mice with anti–N-terminal aSyn antibody ( Mouse , BD Transduction Laboratories , 610787 ) . Anti-TH antibody ( Rabbit , Millipore , ab152 ) was used to quantify the loss of dopaminergic ( DA ) neurons in the injected mice . The total number of DA neurons was determined by counting the number of TH-immunoreactive cells by stereology in every sixth ( rat ) or fourth ( mouse ) brain section in the region of the SNpc ( 6–7 sections throughout the SN ) . Ventral tegmental TH-positive cells were discarded . StereoInvestigator software ( MicroBrightField , Bioscience ) was used to count cells in an unbiased manner . The estimation of the total number of TH-positive neurons per SNpc was achieved using the optical fractionator method [48] . In rat brain samples , survival of DA neurons was determined at 1 , 2 , and 3 wk after vector administration . In mouse brain samples , survival was analyzed 2 wk after vector administration . | Parkinson disease is an age-associated neurodegenerative disorder characterized by the loss of dopamine-producing neurons from a region in the brain known as the substantia nigra and by the accumulation of the protein alpha-synuclein in intracellular clumps called inclusions . Whether these inclusions are the cause or a consequence of the pathological processes is still unclear . Sirtuin proteins are considered master regulators of the ageing process and have previously been associated with neurodegeneration . In this study , we investigated the interplay between sirtuin 2 and alpha-synuclein in order to dissect the molecular mechanisms associated with protection against alpha-synuclein toxicity . We found that sirtuin 2 interacted with and removed acetyl groups from alpha-synuclein . By decreasing the levels of sirtuin 2 , or by expressing mutant versions of alpha-synuclein that modulate its acetylation status , we found that acetylation reduces the aggregation of alpha-synuclein and its cytotoxicity in vitro . Next , we evaluated whether genetic inhibition of sirtuin 2 could prevent the deleterious effects of alpha-synuclein in vivo and found that , in two different models of Parkinson disease , deletion of sirtuin 2 was neuroprotective . Our data therefore suggest that strategies aimed at decreasing sirtuin 2 activity might prove valuable therapeutic avenues for intervention in Parkinson disease and other synucleinopathies . | [
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| 2017 | The mechanism of sirtuin 2–mediated exacerbation of alpha-synuclein toxicity in models of Parkinson disease |
Cholinergic agonists such as levamisole and pyrantel are widely used as anthelmintics to treat parasitic nematode infestations . These drugs elicit spastic paralysis by activating acetylcholine receptors ( AChRs ) expressed in nematode body wall muscles . In the model nematode Caenorhabditis elegans , genetic screens led to the identification of five genes encoding levamisole-sensitive-AChR ( L-AChR ) subunits: unc-38 , unc-63 , unc-29 , lev-1 and lev-8 . These subunits form a functional L-AChR when heterologously expressed in Xenopus laevis oocytes . Here we show that the majority of parasitic species that are sensitive to levamisole lack a gene orthologous to C . elegans lev-8 . This raises important questions concerning the properties of the native receptor that constitutes the target for cholinergic anthelmintics . We demonstrate that the closely related ACR-8 subunit from phylogenetically distant animal and plant parasitic nematode species functionally substitutes for LEV-8 in the C . elegans L-AChR when expressed in Xenopus oocytes . The importance of ACR-8 in parasitic nematode sensitivity to cholinergic anthelmintics is reinforced by a ‘model hopping’ approach in which we demonstrate the ability of ACR-8 from the hematophagous parasitic nematode Haemonchus contortus to fully restore levamisole sensitivity , and to confer high sensitivity to pyrantel , when expressed in the body wall muscle of C . elegans lev-8 null mutants . The critical role of acr-8 to in vivo drug sensitivity is substantiated by the successful demonstration of RNAi gene silencing for Hco-acr-8 which reduced the sensitivity of H . contortus larvae to levamisole . Intriguingly , the pyrantel sensitivity remained unchanged thus providing new evidence for distinct modes of action of these important anthelmintics in parasitic species versus C . elegans . More broadly , this highlights the limits of C . elegans as a predictive model to decipher cholinergic agonist targets from parasitic nematode species and provides key molecular insight to inform the discovery of next generation anthelmintic compounds .
In the absence of efficient alternative strategies , treatment and prophylaxis of parasitic nematode infections rely on anthelmintic compounds . However , resistance against these drugs compromises the sustainable control of highly pathogenic nematode species infecting humans , livestock and companion animals [1–7] . In order to optimize the use of anthelmintic drugs and identify potential molecular mechanisms associated with resistance , there is an urgent need to decipher their pharmacological targets in parasitic nematode species . Among the most widely used anthelmintics , cholinergic agonists such as levamisole and pyrantel are of prime interest as resistance against these drugs is less frequent than for the two other major anthelmintic classes: benzimidazoles and macrocyclic lactones [8–9] . Therefore , the identification and characterization of cholinergic agonist receptors from nematodes have driven important research efforts during the last decades [10–13] . Although nicotinic agonists are not currently used in the treatment or prevention of plant parasitic or filarial worms , recent reports of their in vitro efficacy further highlight the importance of elucidating their pharmacological targets [14–18] . Cholinergic agonists bind to acetylcholine receptors ( AChRs ) which are members of the pentameric Cys-loop ligand-gated ion channels super-family . These receptors play a pivotal role in fast neurotransmission and are widely used at important neuromuscular synapses that control motility and feeding behavior in nematodes . Anthelmintics such as levamisole and pyrantel induce a spastic paralysis of the worm by activating the levamisole-sensitive AChR ( L-AChR ) expressed in the body-wall muscle cell [19–21] . Taking advantage of the powerful genetic tools available in the model species C . elegans , screenings for levamisole-resistant mutants led to the identification of the five genes encoding the L-AChR subunits ( unc-38 , unc-63 , unc-29 , lev-1 and lev-8 ) [22–26] . In 2008 , Boulin et al . demonstrated that combination of these five AChR subunits with a set of three ancillary proteins ( UNC-50 , UNC-74 and RIC-3 ) gives rise to a functional L-AChR when co-expressed in the Xenopus laevis oocyte heterologous expression system [27] . Levamisole acts as a partial agonist on the recombinant C . elegans L-AChR , whereas this receptor is insensitive to nicotine [27] . The latter is consistent with evidence showing that nicotinic sensitivity of C . elegans body wall muscle is conferred by a homomeric receptor comprised of ACR-16 [28] . This first functional expression and pharmacological characterization of the C . elegans L-AChR provided a strong basis to investigate its parasitic nematode counterparts . Studies of parasitic nematode L-AChRs highlighted some striking differences with the C . elegans L-AChR [11 , 12] . Noticeably , whereas the LEV-8 subunit is a component of the C . elegans L-AChR , searches in the genomic databanks from Brugia malayi ( Clade III ) and Trichinella spiralis ( Clade I ) failed to identify a lev-8 ortholog in these parasites [29] . The absence of lev-8 orthologs was also reported in parasitic nematode species phylogenetically closely related to C . elegans ( Clade V ) for which levamisole is successfully used as an anthelmintic ( i . e . Haemonchus contortus , Teladorsagia circumcincta , Trichostrongylus colubriformis ) and has known spastic potency [30 , 31] . Taken together , these observations have prompted the hypothesis that the subunit composition of parasitic nematode L-AChRs would have to differ from the subunit composition from the C . elegans L-AChR . This has important ramifications for understanding the mode of action of the widely used anthelmintic levamisole and the emergence , monitoring and management of resistance . In H . contortus , comparative transcriptomic analysis performed on levamisole-susceptible and levamisole-resistant isolates led to the identification of an AChR subunit ( Hco-ACR-8 ) closely related to the ACR-8 and LEV-8 subunits from C . elegans [32] . In C . elegans acr-8 , like lev-8 , is expressed in body wall muscle , although it is not required for a functional levamisole receptor [33] . Using the Xenopus oocyte expression system , we previously demonstrated that Hco-ACR-8 is able to associate with the Hco-UNC-63 , Hco-UNC-29 . 1 and Hco-UNC-38 subunits to form a functional AChR ( referred to as Hco-L-AChR-1 ) highly sensitive to levamisole , but relatively insensitive to nicotine and pyrantel [34] . Strikingly , when Hco-ACR-8 was omitted from the cRNA mix , a functional receptor made from Hco-UNC-63 , Hco-UNC-29 and Hco-UNC-38 subunits was readily expressed . This second receptor ( referred to as Hco-L-AChR-2 ) was relatively unresponsive to levamisole but highly sensitive to nicotine and pyrantel . Recently , Duguet et al . reconstituted two novel L-AChRs of H . contortus: Hco-L-AChR-1 . 3 and Hco-L-AChR-1 . 4 . [35] . Both receptors that differed in their subunit composition by the presence of distinct H . contortus UNC-29 paralogs , contained Hco-ACR-8 and displayed high sensitivity to acetylcholine and levamisole . Using a similar approach , the involvement of the ACR-8 subunit in levamisole sensitivity was further confirmed in the pig parasitic nematode Oesophagostomum dentatum recombinant L-AChR [36] . This supports a critical role of the ACR-8 subunit in both levamisole sensitivity and AChR subtype diversity in parasitic nematodes . However , due to the lack of efficient stable transformation tools in parasitic nematodes , the role of their respective ACR-8 subunits in L-AChR composition , pharmacological properties and potential diversity remained to be further explored both in vitro and in vivo . In the present study , we report that acr-8 orthologs are widely distributed in parasitic nematode genomes , which is in sharp contrast to lev-8 orthologs that could be only identified in a subset of parasitic species . We expressed the ACR-8 subunits from a range of animal and plant parasitic nematode species in Xenopus oocytes and showed that they functionally complement the C . elegans L-AChR missing out the LEV-8 subunit . We expressed acr-8 from the parasitic nematode H . contortus in a C . elegans levamisole resistant lev-8 null mutant and report that H . contortus ACR-8 fully rescued the levamisole sensitivity of the transgenic worms . Finally , we optimized the use of RNAi in H . contortus and demonstrate that AChR subunits from the parasite are amenable targets for gene silencing . Taking advantage of this approach , we provide the first functional demonstration of the ACR-8 subunit involvement in the levamisole sensitivity in vivo . Intriguingly , acr-8 gene silencing in H . contortus reduced the levamisole but not the pyrantel sensitivity providing new evidence for a discrete difference in their mode of action . Overall , our findings strongly support the hypothesis that in contrast with the model species C . elegans , the ACR-8 subunit from parasitic nematodes plays a critical role in anthelmintic sensitivity . This has major importance for understanding the molecular targets of the widely used cholinergic anthelmintics and will facilitate drug discovery in this arena .
Using the C . elegans ACR-8 amino-acid sequence as a query , tBLASTn searches were performed against 68 nematode genomic databases available in WormBase-ParaSite ( version 8; http://parasite . wormbase . org/ ) ( S1 Table ) . This revealed that acr-8 homologs are present in all the available genomes from the nematode species representing the Clades III , IV and V ( 20 , 17 and 24 species , respectively ) . Strikingly , when a similar search was performed using the closely related C . elegans LEV-8 amino-acid sequence as a query , lev-8 homologs could only be identified in a subset of 21 species out of 68 , including 17 Clade V species and the five Ascaridae species from Clade III . Noticeably , no lev-8 homolog could be found in the 17 genomes of Clade IV species studied . Moreover , no acr-8/lev-8 homolog could be clearly assigned in any Clade I nematode species , although some “acr-8 related” sequences were identified . Fig 1 shows the orthologous relationships of C . elegans acr-8/lev-8 with their counterparts from other nematode species representing Clade I , III , IV and V . This analysis also highlighted that in addition to acr-8 orthologs , some parasitic nematode species from Clades III ( i . e . Toxocara canis ) and V ( i . e . O . dentatum ) possess a lev-8 ortholog . Importantly , the Clade I nematode acr-8 related sequences clustered apart from the acr-8 and lev-8 sequences from species belonging to Clades III , IV and V . This suggests that a gene duplication leading to the acr-8 and lev-8 genes occurred early in nematode evolution , soon after divergence from the Clade I nematodes . In addition to the available complete coding acr-8 sequences from the Clade V species C . elegans , H . contortus and O . dentatum ( JF416644 , EU006785 and JX429921 , respectively ) , in the present study full-length acr-8 cDNAs were obtained from other distantly related parasitic nematode species such as the vertebrate parasites Ascaris suum ( Clade III , pig worm ) and Dirofilaria immitis ( Clade III , dog heart worm ) and the plant parasite Meloidogyne incognita ( Clade IV , root-knot nematode ) . Based on their respective orthologous relationship with Cel-ACR-8 ( Fig 1 and S1 Fig ) , these novel sequences were named Asu-ACR-8 , Dim-ACR-8 and Min-ACR-8 following the recommended nomenclature proposed by Beech et al . 2010 [37] and submitted to GenBank under accession numbers: KY654347 , KY654349 , and KY654350 , respectively . All sequences shared typical features of an AChR subunit including a predicted signal peptide , a “Cys-loop” , four transmembrane domains and a YxxCC motif defining them as alpha subunits ( S2 Fig ) . Using the Cel-ACR-8 sequence as reference , mature protein sequences from parasitic species ( excluding the signal peptide ) shared identities ranging from 58% with Min-ACR-8 to 69% with Hco-ACR-8 or Ode-ACR-8 ) . As expected , sequence identities were lower when compared to Cel-LEV-8 ( 48–51% ) in agreement with Cel-ACR-8 sharing only 45% identity with Cel-LEV-8 . Interestingly , as previously reported for the Hco-ACR-8 sequence [34] , Asu-ACR-8 , Dim-ACR-8 and Min-ACR-8 share common amino acids with Cel-LEV-8 that are not conserved in Cel-ACR-8 ( S2 Fig ) . Such features could therefore suggest a role of these subunits as potential substitute of LEV-8 in the L-AChR . The C . elegans L-AChR can be robustly expressed in Xenopus oocytes by co-expressing cRNAs from the five L-AChR subunits ( UNC-38 , UNC-63 , UNC-29 , LEV-1 and LEV-8 ) in combination with cRNA encoding three ancillary proteins ( UNC-74 , UNC-50 and RIC-3 ) [27] . Noticeably , the absence of any one of these AChR subunits or ancillary proteins from the injected cRNA mix leads to the loss of expression of functional receptor highlighting their major individual requirement . Taking advantage of this , we complemented the Cel-L-AChR by replacing the Cel-LEV-8 by ACR-8 subunits from distinct parasitic nematode species . As a preliminary set of experiments , cRNAs encoding ACR-8 from parasitic nematodes were micro-injected with the three ancillary proteins in order to test their ability to form a homomeric channel . In accordance with previous studies , these experiments failed to produce responses to acetylcholine ( ACh ) application [34 , 36] . Therefore , cRNAs encoding Cel-UNC-38 , Cel-UNC-63 , Cel-UNC-29 , Cel-LEV-1 , Cel-UNC-74 , Cel-UNC-50 and Cel-RIC-3 in combination with either Hco-ACR-8 / Ode-ACR-8 / Asu-ACR-8 / Dim-ACR-8 / Min-ACR-8 were co-injected into Xenopus oocytes . Three days after injection , each of these combinations produced robust responses to 100μM ACh in oocytes ( S3 Fig ) . Similarly , application of 100μM levamisole ( Lev ) elicited a response for all complemented Cel-L-AChRs . Dose response curves were generated for both ACh and Lev in complemented L-AChRs containing ACR-8 subunits from parasitic species ( Fig 2 and Table 1 ) . In comparison to the C . elegans L-AChR , we observed a drastic reduction of the ACh EC50 values in the composite L-AChRs complemented with Hco-ACR-8 , Ode-ACR-8 , Asu-ACR-8 and Dim-ACR-8 . In contrast , when other Cel-L-AChR subunits such as UNC-63 or UNC-38 were replaced by their counterpart from H . contortus ( i . e . Hco-UNC-63 and Hco-UNC-38 ) , we did not observe any modulation of the ACh EC50 values ( Fig 2 and Table 1 ) . Therefore , these results could support a role of ACR-8 from parasitic species in the ACh sensitivity of composite receptors . Strikingly , for the complemented L-AChR expressing either Hco- or Ode-ACR-8 subunit , Lev acts as a full agonist achieving the same maximum response as ACh induced currents whereas Lev is only a partial agonist of the Cel-L-AChR . These data suggest that ACR-8 subunits from parasitic nematodes harbor important structural determinants of the Lev response . Whereas the LEV-8 subunit has been reported as a component of the C . elegans L-AChR [26 , 27 , 33] , the role of the C . elegans ACR-8 subunit remained unclear . Here , we investigated the putative functional redundancy of the C . elegans LEV-8 and ACR-8 subunits using the Xenopus oocyte as an expression system . The substitution of LEV-8 by ACR-8 in the recombinant C . elegans L-AChR led to the robust expression of levamisole-sensitive AChRs ( Fig 3 and S3 Fig ) . This first putative novel C . elegans L-AChR subtype was named Cel-L-AChR-2 . 1 ( Cel-ACR-8 , Cel-UNC-63 , Cel-UNC-38 , Cel-UNC-29 , Cel-LEV-1 ) . Strikingly , the omission of lev-1 cRNA in the injected mix also led to the robust expression of functional L-AChRs three days after micro-injection ( Fig 3 and S3 Fig ) , mirroring the subunit requirement previously reported for the H . contortus L-AChR-1 [34] and Ode ( 38-29-63-8 ) [36] . This second putative C . elegans L-AChR subtype was named Cel-L-AChR-2 . 2 ( Cel-ACR-8 , Cel-UNC-63 , Cel-UNC-38 , Cel-UNC-29 ) . In contrast , the omission of UNC-63 , UNC-38 or UNC-29 resulted in the absence of functional receptor ( Fig 3 ) . Interestingly , whereas Cel-L-AChR , Cel-L-AChR-2 . 1 and Cel-L-AChR-2 . 2 share similar ACh EC50 values , the receptors including the ACR-8 subunit had higher Lev EC50 values in comparison with the receptor including the LEV-8 subunit ( Table 1 ) . Importantly , we also observed that composite receptors containing ACR-8 subunits from a parasitic species harbored decreased Lev EC50 values in comparison with Cel-L-AChR-2 . 1 ( Table 1 ) . In addition to a decreased sensitivity to Lev , the Cel-L-AChR-2 . 1 and Cel-L-AChR-2 . 2 receptors also harbored lower Lev Imax values in comparison with the prototypical Cel-L-AChR highlighting a reduced potency of Lev on the C . elegans L-AChRs including the ACR-8 subunit ( Table 1 ) . The respective role of Cel-ACR-8 and Cel-LEV-8 in Lev sensitivity was further explored in the recombinant H . contortus L-AChR-1 receptor by substituting Hco-ACR-8 by the C . elegans subunits ( S4 Fig ) . In both cases , the robust expression of functional composite receptors was obtained . Interestingly , the composite receptor containing Cel-LEV-8 was more responsive to Lev than its counterpart including the Cel-ACR-8 subunits . Taken together , these results provide strong evidence that both ACR-8 and LEV-8 can contribute to different subtypes of L-AChR in C . elegans and further support the role of ACR-8 as a key component of Lev sensitivity modulation in nematodes . Interestingly , we also found that Pyr was more potent on Cel-L-AChR-2 . 1 and Cel-L-AChR-2 . 2 than in the prototypical Cel-L-ACh , suggesting that ACR-8 could contribute to Pyr sensitivity in C . elegans ( S5 Fig ) . In agreement with this assumption , the composite Hco-L-AChR-1 with Cel-ACR-8 replacing Hco-ACR-8 was more responsive to Pyr than the parental Hco-L-AChR-1 receptor ( S5 Fig ) . Recently , we showed that C . elegans was a tractable model to express AChR receptors from parasitic nematode species [35 , 38 , 39] . Therefore , to address the functional relationship between ACR-8 and LEV-8 , we conducted an in vivo characterization of the parasitic nematode ACR-8 using C . elegans as an expression platform . Because we demonstrated that Hco-ACR-8 was able to functionally replace the C . elegans LEV-8 subunit in the L-AChR expressed in the Xenopus oocyte , we took advantage of the C . elegans model to investigate the ability of Hco-ACR-8 to restore Lev sensitivity in a lev-8 mutant background . In the present study , we used the lev-8 null mutant strain VC1041 ( ok1519 ) that is resistant to Lev [26] . As a preliminary experiment , expression of GFP driven by the putative Hco-acr-8 promoter and mCherry driven by the muscle cell specific C . elegans myo-3 promoter was monitored in C . elegans N2 ( wild type ) . Both PHco-acr-8 and Pmyo-3 showed a similar expression pattern in body wall muscle cells ( S6 Fig ) . Thus , the myo-3 promoter was used to express the Hco-ACR-8 subunit in the C . elegans lev-8 . The motility of the N2 strain and the lev-8 null mutants ( with or without the expression of Hco-ACR-8 in body wall muscle ) was very similar , as scored from the thrashing rate in liquid ( S6 Fig ) . Thus , neither the ectopic over-expression of Hco-ACR-8 in body wall muscle , nor the deficiency of lev-8 impacted the in vivo bioassay used in our experiments to report on drug modulation of motility . From this stable background , we were able to investigate the relative Lev sensitivity of N2 , mutant and C . elegans transgenic lines by performing time dependent thrashing inhibition assays with Lev concentrations ranging from 10 to 200μM ( Fig 4 and S7 Fig ) . As expected , after 40 min exposure to Lev 25μM , N2 worms showed a strong inhibition of thrashing whereas the lev-8 null mutants were not inhibited . Strikingly , at the same dose and same time point , the lev-8 null mutant expressing Hco-ACR-8 in the body wall muscle ( lev-8 ( ok1519 ) ; cePmyo-3::hco-acr-8 ) showed a drastic reduction in thrashing rate ( Fig 4 ) . An analysis of the Lev concentration-dependent inhibition of thrashing confirms that the expression of the Hco-ACR-8 subunit functionally complements the C . elegans L-AChR lacking the LEV-8 subunit and fully restores the Lev sensitivity in the transgenic worms . In view of the results showing that Hco-ACR-8 can assemble with L-AChR subunits in Xenopus oocytes , these data suggest that Hco-ACR-8 can act in a similar fashion in C . elegans in vivo to confer sensitivity of the body wall muscle to Lev on an otherwise Lev resistant strain . In C . elegans , functional null mutants for unc-38 , unc-63 or unc-29 genes are resistant to high concentrations of pyrantel ( Pyr ) suggesting that this anthelmintic could target the L-AChR [40 , 41] . However , there was to our knowledge no available data concerning the Pyr resistance status of lev-8 null mutants . Therefore , we compared the thrashing rate of N2 and lev-8 null mutants after 10 , 20 , 30 or 40 min exposure time to Pyr concentrations ranging from 10 to 250μM ( Fig 4 and S7 Fig ) . Whereas N2 worms were completely paralyzed after 10 min incubation with 250μM Pyr , the lev-8 null mutants only showed a partial reduction of their thrashing rate ( Fig 4 ) . This result indicated that the loss of a functional LEV-8 subunit leads to a reduction of Pyr sensitivity in C . elegans . The ability of Hco-acr-8 to restore Pyr sensitivity in the lev-8 null mutant was also investigated . Incubation with increasing concentrations of Pyr led to the paralysis of both N2 and transgenic worms demonstrating that parasitic nematode ACR-8 is able to restore Pyr sensitivity to the lev-8 null mutant ( Fig 4 and S7 Fig ) . Interestingly , Pyr dose-response assays revealed an increased sensitivity to Pyr in transgenic worms expressing Hco-ACR-8 in comparison to wild-type C . elegans . The above experiments using recombinant approaches in Xenopus oocyte or utilizing heterologous subunit expression in C . elegans highlight a potential role for parasitic nematode ACR-8 in the L-AChRs and Lev sensitivity . In order to further investigate in vivo the function of ACR-8 , our objective was to optimize the use of RNAi for the silencing of L-AChR subunit genes in H . contortus and characterize the resulting phenotypes . Gene silencing in H . contortus L3 larvae has been reported to be challenging because of the unreliable siRNA uptake of the worms [42] . To overcome this limitation , we reasoned that H . contortus L2 larvae that actively feed from their environment could represent an appropriate alternative to optimize the delivery of double stranded siRNA into the parasite by soaking ( S1 Video ) . The relevance of using H . contortus L2 stage in our study is supported by two significant observations: firstly , H . contortus L2 are highly sensitive to Lev and secondly , RT-PCR experiments confirmed the expression of the AChR subunits that constitute the Hco-L-AChR-1 in this developmental stage ( S8 Fig ) . In order to investigate the potential ingestion of RNA constructs for gene silencing experiments , H . contortus L2 larvae were incubated in a culture medium containing 1μM of a non-specific siRNA targeting gfp ( non-specific target ) labeled with the fluorochrome Alexa-594 ( S8 Fig ) . After an incubation time of 2h , strong fluorescent signals were observed in the esophagus and the intestinal lumen of all the H . contortus L2 larvae providing an indication of the robust siRNA uptake by the nematode . Notably , visual scoring showed that H . contortus larvae retained normal viability even after 96 hours of siRNA treatment indicating an absence of toxicity from the ingested siRNA . Subsequently , H . contortus L2 larvae were incubated with siRNA targeting Hco-unc-38 , Hco-unc-63 or Hco-acr-8 , respectively . After 72h of siRNA incubation , the silencing of the Hco-unc-63 and Hco-unc-38 genes resulted in uncoordinated locomotion in at least 50% of the larvae illustrating a key role for L-AChR subunits UNC-63 and UNC-38 in cholinergic neuromuscular transmission ( S2–S4 Videos ) . These observations are consistent with the uncoordinated phenotype in C . elegans carrying mutation in these subunits [22] . These data indicated that L-AChR subunits are amenable to gene silencing in H . contortus . Also , in accordance with the phenotype of C . elegans acr-8 ( or lev-8 ) null mutants [26 , 33] , 72h incubation with double strand siRNA targeting Hco-acr-8 did not impact on the motility of H . contortus larvae ( S5 Video ) . In order to better define the motility deficiencies resulting from the H . contortus L-AChR subunit silencing , we developed an automated larval migration assay . Taking advantage of the natural auto-fluorescence of the H . contortus L2 larvae , we designed a spectrofluorometric-based approach to measure real-time fluorescence correlated with the migration rate of the worms ( S8 Fig ) . The correlation coefficient R2 showed a highly significant relationship between the fluorescence measured and the number of larvae that migrated through the sieve ( R2 = 0 . 9935 ) after 25 min . Therefore , we were able to automate the measurement of larval migration , and the effect of anthelmintics on this behaviour , by recording the increase in fluorescence against time in the absence or presence of drug . Paralysis assays performed on H . contortus L2 stage with Lev or Pyr confirmed the suitability of this approach to parameterize the effects on motility ( Fig 5 ) . Using this assay , we confirmed a reduction of the motility of H . contortus L2 larvae incubated with siRNA targeting Hco-unc-63 and Hco-unc-38 AChR subunit transcripts ( Fig 5 ) . In contrast , the motility of H . contortus L2 larvae incubated with siRNA targeting Hco-acr-8 was similar to control worms ( Fig 6 ) . Therefore , as previously reported for C . elegans , the AChR subunits UNC-63 and UNC-38 are involved in the control of motility in H . contortus , whereas ACR-8 is not required for wild type locomotion [33] . The potential modulation of Lev sensitivity associated with Hco-acr-8 gene silencing was investigated in H . contortus L2 larvae using Lev concentrations corresponding to the minimal efficient doses leading to 50 and 80% of motility reduction respectively ( i . e . Lev 0 . 3μM and Lev 0 . 6μM ) ( Fig 6 ) . Strikingly , for both Lev concentrations , Hco-acr-8 silenced larvae showed a reduction of Lev sensitivity in comparison with the control larvae not subjected to silencing . This provides the first in vivo evidence for a key role of ACR-8 in the Lev sensitivity of a parasitic nematode . Intriguingly , whilst Hco-acr-8 siRNA silenced larvae showed a reduced sensitivity to Lev , they were still subject to the inhibitory effects of Pyr . Indeed , using either Pyr 3μM or Pyr 10μM corresponding to the minimal efficient dose leading respectively to 50% or 80% of motility reduction in H . contortus L2 , the migration of Hco-acr-8 siRNA silenced larvae was similar to control worms ( Fig 6 ) . These data suggest that Lev and Pyr could mediate their inhibitory effects on H . contortus motility via two distinct pharmacological targets .
Screens for C . elegans mutants that resist to Lev have been instrumental to identify the 5 AChR subunits constituting the L-AChR ( i . e . UNC-38 , UNC-63 , UNC-29 , LEV-1 and LEV-8 ) [43] and laid a strong basis to investigate their counterpart in parasitic species . However , the absence of lev-8 homologs in closely related parasitic species for which Lev is widely used as an anthelmintic raised the question of an alternative L-AChR subunit composition in these nematodes . In the present study we demonstrate that ACR-8 , the closest homolog of the C . elegans LEV-8 subunit , can play a pivotal role in vitro and in vivo in the composition and pharmacological properties of L-AChR from parasitic nematodes . The analysis of genomic data from 68 nematode species raised the question of the origin of acr-8 and lev-8 homologs within the Nematoda . The phylogenetic analyses show that the acr-8 and lev-8 genes result from an early gene duplication event that occurred after divergence of the Clade I with the other nematode clades . It is noteworthy that Clade I species such as Trichinella spiralis , Trichuris suis or Trichuris muris are sensitive to Lev [44–46] providing evidence that in contrast with the C . elegans L-AChR , the LEV-8 subunit is not involved in the functional Lev receptors of these parasitic species . Since lev-8 must have been present in the common ancestor of Clade III , Clade IV and Clade V nematodes , the absence of lev-8 in species from these clades strongly suggests independent events where lev-8 was lost . Even though we cannot rule out that incomplete genomic data could explain the failure to identify lev-8 orthologs in some species from Clade III , Clade IV and Clade V , the systematic identification of lev-8 homolog in species harboring an acr-8 homolog as well as common amino acid signature shared by the C . elegans LEV-8 and ACR-8 sequences from parasitic species support the hypothesis that lev-8 loss was mediated by distinct evolutionary events . The substitution of the C . elegans LEV-8 subunit by ACR-8 from animal or plant parasitic nematode species in the recombinant C . elegans L-AChR ( Cel-L-AChR ) led to the robust expression of functional receptors . Interestingly , we showed that ACR-8 from A . suum and O . dentatum , species that also possess a lev-8 homolog , also functionally complement the Cel- L-AChR lacking the LEV-8 subunit . These results highlight the need for future research in order to investigate the putative functional redundancy between ACR-8 and LEV-8 in these two distantly related species . Interestingly , such redundancy is apparent in C . elegans as we demonstrated that the C . elegans ACR-8 subunit can functionally substitute the LEV-8 subunit in the recombinant Cel-L-AChR . In accordance with previous molecular and electrophysiological data that support the involvement of Cel-ACR-8 in a putative subset of L-AChR channels in C . elegans , here we report the first functional evidence for novel L-AChR subtypes from C . elegans ( Cel-L-AChR-2 . 1 and Cel-L-AChR-2 . 2 ) containing the ACR-8 subunit [33 , 47] . These results lay the basis to decipher the role of alternative Cel-L-AChR in the synaptic function and their potential interactions with accessory proteins specific to L-AChR such as MOLO-1 [48] . In previous studies performed on recombinant L-AChRs from H . contortus and O . dentatum expressed in Xenopus oocytes , we reported that ACR-8 is a critical subunit for the Lev response of Hco-L-AChR-1 and Ode ( 38-29-63-8 ) , respectively [34 , 36] . In the present work , when the C . elegans LEV-8 subunit was substituted by either Hco-ACR-8 or Ode-ACR-8 in the C . elegans L-AChR , Lev acted as a full agonist on the composite receptors as previously reported for Hco-L-AChR-1 and Ode ( 38-29-63-8 ) , respectively [34 , 36] . In contrast , on the C . elegans recombinant L-AChR , Lev acts only as a partial agonist . Indeed , Lev Imax values of the composite receptors containing Hco-ACR-8 ( 109 . 5±20 . 7 ) or Ode-ACR-8 ( 112 . 4±19 . 0 ) are within the same range as for Hco-L-AChR-1 ( 119 . 9±16 . 8 ) and Ode ( 38-29-63-8 ) ( 119±3 . 7 ) respectively [36] whereas Lev Imax for the Cel-L-AChR is 49 . 1±10 . 2 . Interestingly , when either Cel-UNC-63 or Cel-UNC-38 subunit was substituted by its H . contortus counterpart , the Lev Imax value was not modified in comparison with the “native” recombinant Cel-L-AChR . Similarly , the replacement of Cel-UNC-29 by UNC-29 paralogs from H . contortus in the Cel-L-AChR does not modulate the Lev response of the resulting composite receptors [35] . Therefore , it is tempting to speculate that ACR-8 from both parasitic nematode species could be directly involved in the Lev response of the composite receptor . Because ACR-8 subunits from parasitic species expressed in Cel-L-AChR lacking LEV-8 could potentially recapitulate the Lev sensitivity of their native L-AChR , this experimental platform could provide a basis for an original target-based drug screening tool . In order to test the relevancy of such an approach , functional reconstitution of L-AChRs from other parasitic nematode species is now urgently needed . Of note , is the particular case of A . suum for which co-expression in Xenopus oocyte of UNC-38 and UNC-29 subunits led to functional Lev-sensitive receptors [49] . In the present study , we demonstrate that ACR-8 from A . suum functionally complements the Cel-L-AChR lacking LEV-8 , resulting in a Lev-sensitive AChR . In A . suum , in vivo electrophysiological studies revealed three distinct AChR subtypes responsive to Lev [50] . In that respect , further investigation of A . suum L-AChRs that incorporate the ACR-8 subunit would be of particular interest in order to decipher the potential L-AChR subtype diversity for A . suum . Taken together , our results provide novel functional evidence for a critical role of the ACR-8 subunit in determining the Lev sensitivity of the parasitic nematode L-AChR and further validate the composite AChR approach to delineate the discrete role of parasitic nematode subunits in receptor function and pharmacology . In the present study , in order to investigate the specific function of ACR-8 subunit from parasitic nematodes , we decided to use C . elegans lev-8 null mutant genetic background for the following reasons: First , in C . elegans both lev-8 and acr-8 null mutants show no significant changes in thrashing rate with respect to N2 ( wild type ) worms [33] . However , whereas lev-8 null mutants are partially resistant to Lev , acr-8 mutants are not resistant to this drug [26 , 33] . Second , split tandem affinity purification performed in C . elegans with UNC-29/LEV-1 as baits resulted in the identification of ACR-8 as a co-assembled subunit [47] . Third , in the present study we demonstrated that substitution of LEV-8 by ACR-8 from parasitic nematodes in the C . elegans L-AChR expressed in Xenopus oocyte led to functional rescue of the receptor . In this context , we reasoned that a model hopping approach utilizing C . elegans lev-8 null mutants could represent a relevant model to investigate the functional role of ACR-8 subunit in parasitic nematodes . Even though we cannot exclude that Hco-ACR-8 could combine with a different set of native muscle AChR subunits , this result strongly supports the hypothesis that ACR-8 from H . contortus is able to assemble into a functional L-AChR in C . elegans . In C . elegans , L-AChR has been proposed as the pharmacological target of Pyr as unc-63 , unc-38 and unc-29 mutants are resistant to 1mM of this drug [40 , 41] . Supporting this hypothesis , we showed here that C . elegans is also resistant to 250μM Pyr , but only partially . This partial Pyr resistance in lev-8 null mutant could be explained by the expression of functional Cel-L-AChR-2 . 1/Cel-L-AChR-2 . 2 in the worms . Indeed , we demonstrated that both of these L-AChR subtypes ( that do not include the LEV-8 subunit ) are responsive to Pyr when expressed in the Xenopus oocyte . An analysis of Pyr sensitivity in a double null mutant , lev-8/acr-8 would be required to test this hypothesis . Surprisingly , lev-8 null mutants expressing the Hco-ACR-8 were more susceptible to Pyr than wild-type C . elegans . Because the recombinant C . elegans L-AChR expressed in Xenopus oocyte is less responsive to Pyr than ACh and Lev , we reasoned that the C . elegans L-AChR in which lev-8 was substituted by Hco-ACR-8 could be more responsive to Pyr , providing a potential explanation for the increased sensitivity to Pyr in the transgenic worms . However , we found that relative responses to Pyr were similar in both native and composite L-AChRs ( S5 Fig ) . Note that Pyr dose response was not established on C . elegans L-AChR or the composite receptor due to the small current amplitude . If these results suggest that Hco-ACR-8 does not modulate Pyr sensitivity in the composite receptor , they cannot support a rational explanation for the increased Pyr sensitivity in transgenic lev-8 null mutant expressing Hco-ACR-8 . In that respect , we could only speculate that when expressed in the C . elegans lev-8 null mutant , the parasitic nematode ACR-8 subunit can impact the pharmacological properties or expression of AChR subtypes for which subunit composition remains to be deciphered . Unlike C . elegans , RNAi approaches in plant and animal parasitic nematode appear capricious and less well controlled [51–53] . In 2015 , McCoy et al . reported the successful silencing of distinct L-AChR subunit genes unc-29 , unc-38 and unc-63 from the pig parasite A . suum [54] . However , these experiments performed on adult worms did not allow the identification of any phenotype associated with the AChR subunit gene silencing . More recently , the successful silencing of UNC-38 and UNC-29 associated with an impaired motility was reported in the filarial nematode Brugia malayi [18] . For trichostrongylid species such as H . contortus , T . colubriformis and T . circumcincta , the improvement of RNAi efficacy is therefore of critical importance to decipher the pharmacological target of anthelmintics . An important aspect that influences the tractability of the nematode to gene silencing is the ability to take up the siRNA constructs . Therefore , we optimized the gene silencing method in H . contortus , taking advantage of constitutive feeding in L2 larvae . After 72 h of incubation with siRNA targeting Hco-unc-63 or Hco-unc-38 , H . contortus L2 harbored a strong invalidated phenotype whereas incubation with siRNA targeting Hco-acr-8 did not induce motility deficiency . These observations are in accordance with the respective phenotype of the C . elegans , unc-63 , unc-38 and acr-8 null mutants [22 , 33] . Note that because of the functional redundancy of the four distinct unc-29 paralogs of H . contortus [35] , these L-AChR subunits were not investigated by RNAi in the present study . In order to quantify worm motility reduction resulting from either gene silencing or paralysis associated with drug application , we developed a novel larval migration assay that combines advantages of motility assays performed in filtration plates [55] and real-time video assisted monitoring [16] . This Automated Larval Migration Assay ( ALMA ) allows real time motility quantification on a large number of worms per set of experiments ( up to 10 000 ) and sees a significant reduction in experimental variability . Therefore , in addition to providing a relevant tool to quantify motility modulation associated with gene silencing , ALMA could also pave the way for a novel drug screening assay . Taking advantage of this assay , we quantified the phenotypes associated with Hco-unc-63 and Hco-unc-38 silencing , providing a first demonstration that L-AChR subunit transcripts from H . contortus are amenable targets for RNAi experiments . Single channel recordings performed on wild-type C . elegans muscle cells revealed one main L-AChR subtype sensitive to Lev , Pyr and Morantel ( Mor ) [56] . The systematic analysis of single L-AChR-channel properties from unc-38 , unc-63 , unc-29 , lev-1 , lev-8 and acr-8 null mutants suggested that the main functional L-AChR in wild type worms is made of UNC-38 , UNC-63 , UNC-29 , LEV-1 and LEV-8 [33] . Importantly , whereas in unc-63 , unc-29 and unc-38 mutants Lev-activated muscle currents are abolished , in lev-1 and lev-8 mutants they are only reduced [25 , 26 , 28 , 33 , 56] . This supports a potential plasticity of C elegans L-AChR subtype subunit composition including UNC-63/UNC-38/ UNC-29 as core components in combination with LEV-1 and/or LEV-8 . In addition , if single channel recording experiments indicated that Cel-ACR-8 might not be a component of the main L-AChR subtype , analysis of the double null mutant lev-8/acr-8 strongly suggested that ACR-8 is able to replace LEV-8 in the heteropentamer when this subunit is absent [33] . In accordance with this observation , in the present work we demonstrated that Cel-ACR-8 is able to associate with UNC-63 , UNC-38 , UNC-29 and LEV-1 to form functional L-AChRs when expressed in the Xenopus oocytes . These results highlight the need to further explore the diversity of C . elegans L-AChR subtypes as they could contribute to a better understanding of the synapse biology but also might be used as reference for comparative studies with parasitic nematode L-AChRs lacking LEV-8 and/or LEV-1 subunits . In comparison with wild-type C . elegans , electrophysiological experiments revealed an even greater functional and pharmacological diversity of L-AChRs in parasitic nematode species . For example , in O . dentatum ( Clade V ) single channel recordings revealed up to four distinct L-AChR conductance states [57] . The diversity of muscle AChRs has been investigated in H . contortus and O . dentatum [34 , 36] . For both species , using the Xenopus oocyte as an expression system , combination of UNC-38 , UNC-63 , UNC-29 and ACR-8 subunits led to the functional expression of L-AChRs preferentially activated by Lev ( Hco-L-AChR-1 for H . contortus , Ode ( 29-63-8-38 ) for O . dentatum ) whereas the same subunit combination lacking ACR-8 led to the functional expression of another AChR subtypes preferentially activated by Pyr ( Hco-L-AChR-2 for H . contortus , Ode ( 29-63-38 ) for O . dentatum ) . These recombinant experiments reinforce the potential for distinct subtypes of L-AChR assembled from distinct subunit combinations in animal parasitic nematodes . In accordance with this , the present work demonstrated that silencing of the Hco-acr-8 gene reduced Lev sensitivity of H . contortus L2 without impacting their Pyr sensitivity . This observation supports the notion of distinct subtypes of muscle L-AChR and the hypothesis that Lev and Pyr act on distinct AChR subtypes in H . contortus . Recently , we described a novel muscle AChR subtype made of ACR-26/ACR-27 subunits that is specific to parasitic nematodes . The co-expression of ACR-26/ACR-27 from H . contortus in C . elegans N2 conferred an increased sensitivity to both Pyr and Mor in the transgenic worms [38] . Thus , we could speculate that the absence of Pyr sensitivity in the face of Hco-acr-8 gene silencing is underpinned by the presence of alternative functional Pyr-sensitive-AChRs which might include the putative Hco-L-AChR-2 and/or Hco-26/27 receptors . In conclusion , whereas C . elegans remains a highly valuable model for deciphering the pharmacological targets of cholinergic anthelmintics , the differences between the prototypic L-AChR of C . elegans and its counterparts in parasitic nematodes highlight the need to further explore AChRs in target parasitic species . This is particularly pertinent in view of the pivotal role these targets play in drug treatment and resistance . In the present study , we used a set of complementary approaches to investigate specific functions of AChR subunits from nematodes and paved the way for a better understanding of anthelmintic mode of action in a wide range of species . Such information will be of major importance for the rational development of drug combination as well as the improvement of target-based drug screening .
All animal care and experimental procedures were conducted in strict accordance with the European guidelines for the care and use of laboratory animals and were approved by French ministry of teaching and research and the regional Val de Loire ethics committee ( no 19 ) as a protocol registered under the number 00219 . 02 in the experimental installations ( n° agreement: C371753 ) . For the purpose of this study , three-month-old sheep were infected with 6000 infective larvae ( L3 ) from Haemonchus contortus to extract nematode's eggs from fresh fecal material and collect adult worms after necropsy . For the three other animal parasitic nematode species used in the present study ( i . e . Ascaris suum , Dirofilaria immitis and Oesophagostomum dentatum ) , adult worms were obtained from already-existing collections . Haemonchus contortus experiments were performed on the Weybridge isolate as previously described [58] . Dirofilaria immitis adult male worms were supplied by the Filarial Research Reagent Resource Center , University of Georgia [59] . Meloidogyne incognita nematodes ( Morelos strain ) were cultured on greenhouse-grown tomato plants at the French National Institute for Agricultural Research ( UMR 1355 ISA , Sophia-Antipolis ) and collected as described previously [60] . The adult worms of Ascaris suum and Oesophagostomum dentatum levamisole-sensitive isolate ( SENS ) were obtained from the French National Institute for Agricultural Research; UMR 1282 ISP ( Nouzilly ) nematode collection [61] . Caenorhabditis elegans experiments were carried out on the Bristol N2 wild-type and lev-8 ( ok1519 ) strains obtained from the Caenorhabditis Genetics Center ( CGC ) . Total RNA was prepared from the distinct nematode species using: 10 adult males or 50μL of pelleted eggs , L2 or L3 larvae of H . contortus , cross-section ( 5mm thick ) from the mid body region of an individual adult worm of Ascaris suum , 5 adult males of D . immitis , 50μl of pelleted J2 stages of M . incognita , 10 adult males of O . dentatum or 100μL of pelleted mixed stages of C . elegans respectively . Frozen samples were ground in liquid nitrogen and homogenized in Trizol reagent ( Invitrogen , Carlsbad , CA , USA ) and total RNA was isolated according to the manufacturer’s recommendations . RNA pellets were dissolved in 25 μL of RNA secure resuspension solution ( Ambion , Austin , TX , USA ) and DNase-treated using the TURBO DNA-free kit ( Ambion ) . RNA concentrations were measured using a Nanodrop spectrophotometer ( Thermo Scientific , Waltham , MA , USA ) . First-strand cDNA synthesis was performed on 1μg of total RNA using the superscript III reverse transcriptase ( Invitrogen , Carlsbad , CA , USA ) according to the manufacturer’s recommendations . Complete coding sequences corresponding to Asu-acr-8 , Cel-acr-8 , Dim-acr-8 and Min-acr-8 , were obtained using first-strand cDNA as PCR template . Amplifications were performed with the Phusion High fidelity Polymerase ( New England Biolabs ) with a forward primer including the first ATG codon in combination with a reverse primer including the first stop codon . Amplicons were inserted in the transcription vector pTB207 [27] using the In-Fusion HD cloning kit ( Clontech ) . Primer sequences are reported in S2 Table . The novel complete coding sequences of Asu-acr-8 , Dim-acr-8 and Min-acr-8 were deposited to GenBank under the accession numbers: KY654347 , KY654349 and KY654350 , respectively . Database searches were performed with the tBLASTn or BLASTP algorithms [62] . Deduced protein sequences were aligned using the MUSCLE software [63] . Amino-acids homologies and identities were defined using the EMBOSS Needle program available at EMBL-EBI ( http://www . ebi . ac . uk/Tools/psa/emboss_needle/ ) . Signal peptide predictions were performed using the SignalP 4 . 1 server [64] and membrane-spanning regions were predicted using the SMART server [65] . Phylogenetic analyses were performed on coding sequences predicted from genomic data available in databases or cloned cDNA sequences when available . Sequences were aligned as codons using the MAFFT plugin ( v1 . 3 . 3 ) of Geneious ( v7 . 1 . 2 , Biomatters Ltd ) [66] . Regions corresponding to the signal peptide and the intracellular loop between TM3 and TM4 that could not be aligned unambiguously were removed . Maximal likelihood phylogeny reconstruction was performed using PhyML v20120412 ( https://github . com/stephaneguindon/phyml-downloads/releases ) as previously described in Duguet et al . 2016 [35] . Standard nomenclature of nematode species: Aca: Ancylostoma caninum; Ace: Ancylostoma ceylanicum; Acs: Angiostrongylus costaricensis; Asu: Ascaris suum; Avi: Acanthocheilonema viteae; Bma: Brugia malayi; Bxy: Bursaphelenchus xylophilus; Cbr: Caenorhabditis briggsae; Cel: Caenorhabditis elegans; Cjp: Caenorhabditis japonica; Cre: Caenorhabditis remanei; Dim: Dirofilaria immitis; Dme: Dracunculus medinensis; Dvi: Dictyocaulus viviparus; Eel: Elaeophora elaphi; Eve: Enterobius vermicularis; Gpu: Gongylonema pulchrum; Hba: Heterorhabditis bacteriophora; Hbk: Heligmosomoides polygyrus; Hco: Haemonchus contortus; Llo: Loa loa; Mfl: Meloidogyne floridensis; Mha: Meloidogyne hapla; Min: Meloidogyne incognita; Nam: Necator americanus; Nbr: Nippostrongylus brasiliensis; Ode: Oesophagostomum dentatum; Ovo: Onchocerca volvulus; Ppa: Pristionchus pacificus; Ptr: Parastrongyloides trichosuri; Rcu: Romanomermis culicivorax; Rhb: Rhabditophanes kr3021; Smu: Syphacia muris; Spa: Strongyloides papillosus; Sra: Strongyloides ratti; Sst: Strongyloides stercoralis; Svz: Strongyloides venezuelensis; Tca: Toxocara canis; Tci: Teladorsagia circumcincta; Tco: Trichostrongylus colubriformis; Tmu: Trichuris muris; Tna: Trichinella nativa; Tsp: Trichinella spiralis; Tsu: Trichuris suis; Ttr: Trichuris trichiura; Tzc: Thelazia callipaeda; Wba: Wuchereria bancrofti . PCR was carried out on H . contortus first strand cDNA prepared from eggs , L2 and L3 larvae . PCR reactions were carried out in a final volume of 20μl , containing 100ng of first strand cDNA , 1 unit of GoTaq polymerase ( Promega ) , 0 . 25mM dNTPs each and 0 . 3μM of each primer . The reaction mixture was denatured by heating to 94°C for 5 min , followed by 34 cycles of 94°C for 45 sec , 56°C for 45 sec , 72°C for 45 sec . A final extension step was performed at 72°C during 5 min . H . contortus GAPDH ( HM145749 ) was used as the reference transcript . Primer sequences are provided in S2 Table . A . suum , C . elegans , D . immitis , and M . incognita acr-8 cDNAs were PCR-amplified to be sub-cloned into the expression vector pTB207 that is suitable for in vitro transcription [27] . Subcloning experiments of H . contortus and O . dentatum acr-8 cDNA in pTB207 have been previously reported [34 , 36] . C . elegans unc-63 , unc-38 , unc-29 , lev-8 and lev-1 cDNA subcloned in pTB207 were kindly provided by Thomas Boulin [27] . The plasmids were linearized with the NheI restriction enzyme ( Fermantas ) and used as templates for cRNA synthesis using the T7 mMessage mMachine kit ( Ambion ) . Defolliculated Xenopus laevis oocytes were obtained from Ecocyte ( Germany ) . The oocytes were injected in the animal pole with a total volume of 36nL of cRNA mix containing 50ng/μL of each cRNA in RNase-free water using the Drummond Nanoject II microinjector . Microinjected oocytes were kept at 20°C in incubation medium ( 100mM NaCl , 2mM KCl 2 , 1 . 8mM CaCl2 . 2H2O , 1mM MgCl2 . 6H2O , 5mM HEPES , 2 . 5mM C3H3NaO3 , pH 7 . 5 , supplemented with penicillin 100 U/mL and streptomycin 100μg/mL ) for 4 days to allow the receptors expression . Two-electrode voltage-clamp recordings were carried out as previously described [38] . A 2 , 9 kb region upstream the initiation codon of Hco-acr-8 was PCR amplified on genomic DNA prepared from adult males of H . contortus using the NucleoSpin Tissue kit ( Macherey-Nagel , Duren , Germany ) following manufacturer recommendations . Primers were designed upon scaffold 5709 sequence available in H . contortus PRJEB506 Wormbase parasite database . Primer sequences are provided in S2 Table . The amplicon was subcloned upstream of the GFP coding sequence in the pPD95 . 75 ( Addgene ) expression vector and micro-injected into C . elegans N2 gonad ( 50 ng μl-1 ) . Expression of GFP was monitored on fixed wormed from the F1 progeny by fluorescence microscopy . Hco-acr-8 coding sequence was sub-cloned into the pPD96 . 52 ( Addgene ) vector containing a myosin promoter Pmyo3 , using the In-FusionHD cloning kit ( Clontech ) . Primer sequences are listed in S2 Table . C . elegans lev-8 ( ok1519 ) were injected with 30ng μl-1 of the resulting plasmid to drive the expression of H . contortus acr-8 in the body muscle cells . Transformed worms were identified by co-injecting pPD118 . 33 ( Pmyo-2::gfp ) plasmid at 50ng μl-1 ( Fire lab vector kit ) , which drives expression of green fluorescent protein ( GFP ) from the pharyngeal muscle promoter Pmyo-2 . The co-injected gfp transformation marker forms an extra-chromosomal array with the plasmids carrying the gene sequence and thus worms with fluorescent green pharyngeal muscle can be identified as carrying the plasmid of interest . For all the experiments , two independently transformed stable lines of transgenic C . elegans were assayed . Experiments were performed on age synchronized worms by picking L4 stage a day before the assay . The next day , one-day old adult hermaphrodite C . elegans were picked into 900 μL of M9 buffer containing 0 . 1% BSA in a 12 well plate . The worms were left to settle for 10 min . The number of thrashes was counted for 1 min . Then 100 μL of either M9 ( control ) , Lev or Pyr solution was added to the wells to give a final concentration of 10 , 25 , 50 , 100 or 150μM Lev or Pyr . The number of thrashes was counted after 10 , 20 , 30 and 40 min ( and 60 min for some concentrations ) of exposure to Lev . The data were plotted as a mean ± standard error of the mean . L2 larvae were obtained from in vitro culture of eggs using the standard procedure described by Rossanigo and Gruner ( 1991 ) [67] . Approximately 2000–3000 eggs/mL were cultured horizontally in tissue culture flasks at 20°C in a nutritive medium ( 0 . 1mL per mL of culture of 1X Earle's balanced salt solution ( Sigma-Aldrich ) and 0 . 5% ( w/v ) of yeast extract ) . The experimental procedures for soaking assays were performed on 7500 ( 2 days old ) L2 larvae in a final volume of 600μL in 5mL tubes . 1μM non-target ( gfp ) siRNA labeled with the Alexa 594 ( Eurogentec , Belgium ) was added to the culture medium . Each tube was incubated horizontally at 20°C on a rocking table . After 2 hours of incubation , L2 larvae were washed three times with tap water and ingestion was monitored under fluorescent microscopy . The absence of toxicity was confirmed by comparing the viability of dsRNA treated vs untreated L2 larvae after 72h of incubation . Double strand RNA targeting specifically Hco-unc-63 , Hco-unc-38 and Hco-acr-8 respectively were synthesized by Eurogentec ( Belgium ) . Soaking of larvae in 1μM dsRNA was performed for all the experiments as described for ingestion assays and putative phenotypes were visually checked on a daily basis ( up to 96h ) . The RNAi experiments were performed at least three times in independent replicates . The custom siRNA duplex sequences are provided in S2 Table . Control siRNA duplex targeting gfp were purchased from Eurogentec . Larval motility was estimated by measuring H . contortus L2 auto-fluorescence using a Quanta Master spectrofluorometer ( Horiba PTI , NJ , USA ) . Motility assays were performed using 7500 H . contortus L2 larvae . Worms were transferred into a 5mL glass tube and left for 15 min to concentrate by gravity . The supernatant was removed and replace by 2 mL of tap water or anthelmintic solution . After 5 min . , the tube was inverted on a 20μm sieve . After a stabilization time of 60 sec , the fluorescence accumulation ( correlated to the number of larvae migrating through the sieve ) was measured during 25 minutes . The recording rates were either one measure /sec ( Fig 5E , S8D Fig ) or one measure every 4 sec using a four positions sample holder ( Horiba PTI , NJ , USA ) allowing the synchronized recording of 4 distinct samples ( Figs 5A , 5C , 6A and 6B , S8E Fig ) . Each set of experiment was performed in triplicate . The final migration percentage relative to control was estimated using the mean data of the ten last fluorescence measures .
The accession numbers of annotated cDNA sequences mentioned in this article are: Ascaris suum: ACR-8 KY654347; Caenorhabditis elegans: ACR-8 NP_509745; ACR-12 NP_510262; ACR-14 NP_495716; ACR-16 NP_505207; LEV-1 NP_001255705; LEV-8 NP_509932; UNC-29 NP_492399; UNC-38 NP_491472; UNC-63 NP_491533 . Haemonchus contortus: ACR-8 EU006785; UNC-38 GU060984; UNC-63 GU060985; RIC3 . 1 HQ116823; UNC-74 HQ116821; UNC-50 HQ116822; GAPDH HM145749; Oesophagostomum dentatum: ACR-8 JX429921; Teladorsagia circumcincta: ACR-8 HQ215517; Trichostrongylus colubriformis: ACR-8 HQ215518; Meloidogyne incognita: ACR-8 KY654350 . Dirofilaria immitis: ACR-8 KY654349 . | Parasitic nematodes have global health and economic impacts . They infect animals , including livestock , humans , and plants including all major food crops . Their control in human and veterinary medicine is reliant on anthelmintic drugs but this is now challenged by resistant worms especially in livestock . Importantly , for anthelmintics such as levamisole and other cholinergic agonists , resistance appears to be less frequent stressing the need to investigate their molecular target in parasitic nematodes . The levamisole receptor was first identified in the free-living model nematode C . elegans but it is now becoming apparent that this is not a good predictor for many parasitic species . In particular we have found that the LEV-8 subunit which is involved in levamisole sensitivity in C . elegans , is not present in many levamisole-sensitive parasitic species . Here we used heterologous expression systems and gene silencing to provide the functional in vivo demonstration that the ACR-8 subunit , which is not an essential component of the levamisole receptor in C . elegans , has a critical role in the levamisole sensitivity of parasitic nematodes . This has important significance for understanding the molecular targets of cholinergic anthelmintics and addresses the increasing challenge of drug resistance . | [
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| 2018 | Deciphering the molecular determinants of cholinergic anthelmintic sensitivity in nematodes: When novel functional validation approaches highlight major differences between the model Caenorhabditis elegans and parasitic species |
New mode-of-action insecticides are sought to provide continued control of pesticide resistant arthropod vectors of neglected tropical diseases ( NTDs ) . We previously identified antagonists of the AaDOP2 D1-like dopamine receptor ( DAR ) from the yellow fever mosquito , Aedes aegypti , with toxicity to Ae . aegypti larvae as leads for novel insecticides . To extend DAR-based insecticide discovery , we evaluated the molecular and pharmacological characteristics of an orthologous DAR target , CqDOP2 , from Culex quinquefasciatus , the vector of lymphatic filariasis and West Nile virus . CqDOP2 has 94 . 7% amino acid identity to AaDOP2 and 28 . 3% identity to the human D1-like DAR , hD1 . CqDOP2 and AaDOP2 exhibited similar pharmacological responses to biogenic amines and DAR antagonists in cell-based assays . The antagonists amitriptyline , amperozide , asenapine , chlorpromazine and doxepin were between 35 to 227-fold more selective at inhibiting the response of CqDOP2 and AaDOP2 in comparison to hD1 . Antagonists were toxic to both C . quinquefasciatus and Ae . aegypti larvae , with LC50 values ranging from 41 to 208 μM 72 h post-exposure . Orthologous DOP2 receptors identified from the African malaria mosquito , Anopheles gambiae , the sand fly , Phlebotomus papatasi and the tsetse fly , Glossina morsitans , had high sequence similarity to CqDOP2 and AaDOP2 . DAR antagonists represent a putative new insecticide class with activity against C . quinquefasciatus and Ae . aegypti , the two most important mosquito vectors of NTDs . There has been limited change in the sequence and pharmacological properties of the DOP2 DARs of these species since divergence of the tribes Culicini and Aedini . We identified antagonists selective for mosquito versus human DARs and observed a correlation between DAR pharmacology and the in vivo larval toxicity of antagonists . These data demonstrate that sequence similarity can be predictive of target potential . On this basis , we propose expanded insecticide discovery around orthologous DOP2 targets from additional dipteran vectors .
Arthropod vectors transmit six of the 17 neglected tropical diseases ( NTDs ) currently recognized by the World Health Organization ( WHO ) . Of these , the causative agents of dengue virus and lymphatic filariasis are transmitted by mosquitoes in the subfamily Culicinae ( Phylum Arthropoda; Class Insecta; Family Culicidae ) and exact an enormous burden on human health in tropical and subtropical regions of the globe . Aedes aegypti is the principal vector of dengue , chikungunya , and yellow fever viruses , and Culex quinquefasciatus is the vector of West Nile virus and the nematode Wuchereria bancrofti , the causative agent of lymphatic filariasis . An estimated 50–100 million dengue infections occur annually [1] and approximately 120 million people are infected with W . bancrofti [2] with additional billions at risk of contracting these and other mosquito-borne diseases . Chikungunya is an ongoing threat in Africa and Southern Asia , and a recent outbreak could potentially lead to its establishment in the Americas [3] . The WHO has established a roadmap to eradicate multiple NTDs by 2020 , backed by the London Declaration on Neglected Tropical Diseases [4 , 5] . Achievement of this goal will require a multi-pronged , integrated approach involving new and existing vector control strategies , medicines , vaccines , and community outreach . Conventional insecticides will remain an important foundation of programs aimed at the control , elimination , and eradication of NTDs . Unfortunately the widespread development of insecticide resistant insect populations threatens continued control [6] . Vector control currently relies on a limited repertoire of active ingredients and the issue of insecticide cross-resistance is compounded by the fact that no new insecticides for insect vectors have become available for several decades [7] . In response , the Innovative Vector Control Consortium ( IVCC ) issued a call for three new insecticides with novel modes of action by 2023 to control malaria mosquitoes [8; http://www . ivcc . com] . The search for chemistries with unique and pest-specific modes of action with limited environmental impact necessitates new , rational design approaches [9] . G protein-coupled receptors ( GPCRs ) are successful pharmaceutical targets with over one third of human drugs acting on these receptors or their downstream signaling processes [10] . Invertebrate GPCRs have long been suggested as targets for the development of new classes of insecticides [11 , 12] . The Purdue Insecticide Discovery Pipeline ( PIDP ) [13] is a GPCR-based platform established for discovery and development of novel mode-of-action insecticides for vector control [11 , 13 , 14 , 15] . Initially the PIDP is pursuing small molecule antagonists and agonists of invertebrate dopamine receptors ( DARs ) ( Fig . 1 ) and has demonstrated proof of concept in the Ae . aegypti DAR system [11 , 13] . Vertebrate and invertebrate DARs are biogenic amine receptors in the Class A rhodopsin-like subfamily of GPCRs . DARs have been implicated in several neurological diseases of humans such as Parkinson's disease and schizophrenia . Scientific investment in human DAR pharmacology and associated therapeutic interventions [16 , 17] provides a much needed foundation to drive equivalent discovery work in arthropod systems . Dopamine has a role in locomotion , learning , courtship , development , and several other complex behaviors in invertebrates [see 18 , 19 , and 11 for an overview] . Several studies suggest that interference with dopaminergic processes may cause insect death or result in a variety of phenotypes such as incapacitation and disrupted development [11 , 13 , 15 , 20 , 21] that are highly attractive for insecticide development . The rational design of invertebrate DAR- and other GPCR-targeting chemistries could generate highly effective molecules for vector control . Invertebrates typically possess two D1-like receptors ( Gαs coupled ) and a single D2-like receptor ( Gαi/o coupled ) [19 , 22] . One of the D1-like DARs , hereafter referred to as DOP2 , exhibits high amino acid sequence identity among the arthropods Ae . aegypti , Anopheles gambiae ( African malaria mosquito ) , C . quinquefasciatus ( northern house mosquito ) , Ixodes scapularis ( Lyme disease tick ) , Drosophila melanogaster ( fruit fly ) , and Apis mellifera ( honey bee ) across the transmembrane ( TM ) spanning domains ( >70% ) but limited sequence identity to the human D1-like DARs , hD1 and hD5 ( <55% ) [13] . Two D1-like DAR sequences , AaDOP1 and AaDOP2 , were identified in the genome of the yellow fever mosquito , Ae . aegypti [23] . Assays using AaDOP1 or AaDOP2 expressing cells revealed elevated cAMP levels following exposure to dopamine in a concentration-dependent manner , providing support for the classification of these receptors [11] . Subsequently , the discovery of receptor antagonists with mosquitocidal properties was demonstrated by screening chemical libraries for AaDOP2 antagonists in cell-based assays [11 , 13] . Antagonists were evaluated in hit-to-lead studies that showed multiple compounds were selective for the AaDOP2 target versus hD1 and caused rapid and high mortality of Ae . aegypti larvae in vivo . Two D1-like DARs , CqDOP1 and CqDOP2 were identified from the assembled genome of C . quinquefasciatus [24] with CqDOP2 identified as the ortholog to AaDOP2 on the basis of amino acid similarity [13] . AaDOP2 and CqDOP2 provide an opportunity to address questions of relevance to insecticide discovery , namely ( 1 ) does sequence similarity between orthologous targets equate to conservation of pharmacological properties in vitro , ( 2 ) is sequence similarity predictive of the toxicity of target inhibitors in vivo , and ( 3 ) can differences in sequence between targets be exploited for development of taxon-selective chemistries ? Here we present the first study to assess conservation in the molecular and pharmacological properties of orthologous dopamine receptors from species representing two of the most important mosquito genera affecting human health , Culex and Aedes . The human receptor , hD1 , was evaluated in parallel to determine the relative potency and selectivity of DAR antagonists for mosquitoes versus humans . AaDOP2 antagonists were evaluated for toxicity to larvae of C . quinquefasciatus to explore the potential of extending the DAR antagonist-based insecticide discovery approach to this vector . Lastly , in silico analyses were conducted to evaluate DOP2 targets from three additional dipteran vectors of NTDs for inclusion in the PIDP; the An . gambiae mosquito , a vector of malaria , the Phlebotomus papatasi sand fly vector of leishmaniasis , and the Glossina morsitans tsetse fly vector of Human African Trypanosomiasis ( sleeping sickness ) .
Mosquito larvae of the Liverpool strain IB12 of Ae . aegypti and the Johannesburg strain of C . quinquefasciatus were reared in an insectary on a 12 h day/night cycle at 75% RH at 28°C in 25 x 40 cm plastic pans ( 150 larvae per pan ) on hamster pellets ( Ae . aegypti ) or ground flake fish food ( C . quinquefasciatus ) . Adult Ae . aegypti and C . quinquefasciatus that eclosed under this rearing regimen had an average wing length of 3 . 4 mm ( measured as in [25] ) and 3 . 2 mm ( measured as in [26] ) , respectively , suggesting diet was sufficient and larval crowding effects were minimal . The amino acid sequences of DARs from multiple arthropods were used to search the C . quinquefasciatus genome using the Basic Local Alignment Search Tool ( tBLASTn , [27] ) . Gene models were confirmed by sequencing of amplified RT-PCR products following procedures described in Meyer et al [11] . Briefly , total RNA was extracted from adult C . quinquefasciatus females and treated with RNase-free DNase . RT-PCR amplification was performed using the SuperScript One-Step RT-PCR kit ( Invitrogen , Carlsbad , CA ) and the CqDOP2-specific primers CqDOP2_1F: 5'-ATGATGACTACGAATGCAACTGATTAC-3' and CqDOP2_1R: 5'-CTAAATGTACGTCTGCTCGCAC-3' . RT-PCR products separated by electrophoresis on a 1% agarose gel were excised and cloned using the TOPO TA cloning kit ( Invitrogen , Carlsbad , CA ) . Purified plasmids from the resulting clones were sequenced at the Purdue Genomics Core Facility ( PGFC ) . DNA sequences were used to predict full-length coding regions and manual annotation was performed using Artemis software ( version 9 ) [28] . The CqDOP2 conceptual protein sequence was aligned to that of AaDOP2 using ClustalW [29] and used to identify conserved amino acid residues and predict protein structural features [22 , 30 , 31] . To determine receptor expression in different life stages , total RNA was isolated from C . quinquefasciatus eggs , L4 larvae , pupae , and 5-day old adults ( female and male ) using the RNeasy Mini Kit ( Qiagen ) and following kit protocols , including DNase treatment . Generation of cDNA was performed using the iScript cDNA Synthesis Kit ( Bio-Rad ) . Reverse transcription polymerase chain reaction ( RT-PCR ) was used with cDNA template , primers CqDOP2_1F and CqDOP2 . 2R: 5'-CCAGCAGTGGAAGATAGAACG-3' , Taq polymerase ( Phusion , New England Biolabs ) and the following thermo-cycling conditions: 35 cycles at 94°C , 45 s; 55°C , 45 s; 72°C , 2 min . Subsequent products were separated by gel electrophoresis and photographed ( EpiChemi II Darkroom , UVP Laboratory Products ) . Products of approximately 700 bp in length were excised , purified ( MinElute Gel Extraction Kit , Qiagen ) , and sequenced at the PGCF . A neighbor-joining phylogenetic analysis was conducted using amino acid sequences of arthropod and mammalian GPCRs retrieved from GenBank . MEGA6 [32] was used to align and perform tree construction according to the procedure of Hall [33] . Diuretic hormone 44 receptor 1 ( D . melanogaster ) was used as an outgroup . Bootstrap analysis ( 1000 replicates ) was performed as an estimate of branch reliability . To assess the potential of expanding the PIDP pipeline to orthologous DOP2 targets from a range of key dipteran vectors , additional tBLASTn searches of the assembled genomes of An . gambiae , P . papatasi ( www . vectorbase . org ) and G . morsitans [34] were performed using AaDOP2 and CqDOP2 sequences . The conceptual amino acid sequences for the resultant gene models , AgDOP2 , PpDOP2 , and GmDOP2 , were aligned with AaDOP2 and CqDOP2 using ClustalW [29] and conserved structural features were predicted as described above . For functional characterization of the mosquito receptors , CqDOP2 and AaDOP2 were synthesized by Genscript ( Piscataway , NJ ) and cloned into the expression vector pcDNA3 . 1+ ( Invitrogen , Carlsbad , CA ) . Stable cell lines expressing the receptors in HEK293 cells were generated as described previously for AaDOP2 [11] . The AaDOP2 expressing cells used here were from the same clone previously utilized for AaDOP2 characterization [11] . Briefly , HEK293-CRELuc cells were plated in Dulbecco's modified eagle's medium ( DMEM ) supplemented with 5% bovine calf serum , 5% fetal clone I ( Thermo Scientific , Waltham MA ) , 1% Antibiotic-Antimycotic ( Life Technologies , Grand Island NY ) and 2 μg/ml puromycin ( Sigma-Aldrich , St . Louis , MO ) , transfected , and then subjected to selection with G418 ( 600 μg/ml ) . G418-resistant clones were selected and screened for receptor expression in the cAMP activated luciferase ( CRELuc ) reporter cell line construct [11] . For pharmacological characterization of receptor activity , cryopreserved cells stably expressing AaDOP2 ( 10 , 000 cells/well ) , CqDOP2 ( 5 , 000 cells/well ) , or human D1 ( 5 , 000 cells/well ) were thawed , washed , and re-suspended in assay buffer ( Hank’s balanced salt solution , Hyclone , Logan , UT ) supplemented with 20 mM 2-[4- ( 2-hydroxyethyl ) piperazin-1-yl]ethanesulfonic acid ( HEPES , Hyclone , Logan , UT ) and 0 . 1% bovine serum albumin ( MP Biomedicals , Santa Ana , CA ) , seeded in white 384 well plates ( PerkinElmer , Waltham , MA ) , and incubated for 1 h at 37°C . Compounds were serially diluted in assay buffer containing 3-isobutyl-1-methylxanthine ( IBMX , final concentration 0 . 5 mM ) , added to the plates , and incubated for 1 h at 25°C to allow for cAMP accumulation . Reactions were stopped and cAMP was measured by a homologous time-resolved fluorescence ( HTRF ) assay according to the manufacturer’s recommendations ( Cisbio , Bedford , MA ) . Fluorescence was read on a Synergy4 plate reader ( BioTek , Winooski , VT ) . Dopamine hydrochloride , histamine dihydrochloride , 5-hydroxytryptamine hydrochloride ( serotonin ) , ( ± ) -octopamine hydrochloride , tyramine hydrochloride ( Sigma-Aldrich , St . Louis , MO ) , ( - ) -epinephrine bitartrate , and L ( - ) -norepinephrine bitartrate ( Research Biochemical International , Natick , MA ) were used for initial receptor characterization studies . Antagonist profiles were generated by adding serially diluted antagonists followed by dopamine ( 3 μM for AaDOP2- and CqDOP2- , and 0 . 5 μM for hD1-expressing cells ) . The antagonists amitriptyline hydrochloride , asenapine maleate , ( ± ) butaclamol hydrochloride , chlorpromazine hydrochloride , doxepin hydrochloride , cis- ( Z ) -flupenthixol dihydrochloride , SCH23390 hydrochloride ( Sigma-Aldrich , St . Louis , MO ) , and amperozide hydrochloride ( Tocris bioscience , Ellisville , MO ) were selected based on previous chemical screens against AaDOP2 and subsequent bioassays against Ae . aegypti larvae [11 , 13] . All serial dilutions were carried out using the Precision liquid handling station ( BioTek , Winooski , VT ) . Data were collected from a minimum of three independent experiments conducted in duplicate . Statistical analysis of data was conducted with GraphPad Prism 6 software ( GraphPad Software Inc . , San Diego , CA ) . A panel of nine AaDOP2 antagonists was selected based on toxicity determined from single-point dose , high-throughput screens against Ae . aegypti larvae [13 , 15] . Compounds were evaluated in parallel in concentration-response assays against third-instar larvae ( L3 ) of C . quinquefasciatus and Ae . aegypti at room temperature ( 23–25°C ) . Larvae were transferred using a plastic pipette to the wells of a 24-well tissue culture plate ( Corning Inc . , Corning NY ) ( five larvae per well ) containing 1 ml de-ionized water and 400 , 200 , 100 , 50 , or 25 μM test compound or water only as the control . Antagonists were diluted in water to the desired concentration immediately before transfer to tissue culture plates . Larval mortality was determined every 30 min for the first 3 h , then daily at 24 , 48 , and 72 h post-exposure . Plates were gently shaken and larvae were lightly touched with a sterile probe ( up to three times , as required ) and stringent criteria were established for scoring such that larvae that failed to respond to both stimuli were recorded as dead . Four technical replicates were performed per dose , and each bioassay was performed a minimum of three times . Calculations of lethal concentration 50 ( LC50 ) and lethal time 50 ( LT50 ) were made using GraphPad Prism 6 software ( GraphPad Software Inc . , San Diego , CA ) .
A 1 , 440 bp sequence encoding the predicted open reading frame of CqDOP2 was identified from cloned RT-PCR products ( Genbank ID KM262648 ) . Alignment of the conceptual CqDOP2 amino acid sequence with that of AaDOP2 showed high overall amino acid sequence identity ( 94 . 7% ) and 100% identity in the predicted TM-spanning domains ( Fig . 2 ) . The greatest divergence between the two sequences was observed in the third intracellular loop ( ILIII ) with nine amino acid differences and two additional amino acids in CqDOP2 , and in the N-terminus with seven differences , and two additional amino acids in CqDOP2 and one additional residue in AaDOP2 . The remaining amino acid differences were primarily conservative substitutions . CqDOP2 possesses key biochemical features considered essential for GPCR function and that were also identified in AaDOP2 ( Table 1 ) . Residues D140 , S225 , and S228 of CqDOP2 are predicted to interact directly with biogenic amines [19] while the "DRY" motif ( residues 157–159 ) , and aspartate in TMII ( D105 ) are required for receptor activation . CqDOP2 also possesses several putative palmitoylation and phosphorylation sites . Sequences from ~700 bp RT-PCR products amplified from C . quinquefasciatus eggs , L4 larvae , pupae or adult male or female cDNA ( S1 Fig ) matched that of the expected region of CqDOP2 , confirming the presence of CqDOP2 transcripts in the life stages examined . Neighbor-joining sequence analysis ( Fig . 3 ) placed CqDOP2 in a clade with other invertebrate D1-like DOP2 receptors from Ae . aegypti ( AaDOP2 ) , Ap . mellifera ( AmDOP2 ) , B . mori ( BmDOPR2 ) , D . melanogaster ( DmDAMB ) , and I . scapularis ( IsDOP2 ) . This group formed part of a larger clade comprising invertebrate octopamine receptors ( DmOAMB , BmOAR1 , and AmOA1 ) , but not human DARs . The analysis also revealed a second cluster comprising other invertebrate D1-like receptors , including IsDOP1 , AmDOP1 , BmDOPR1 , DmD-DOP1 , and AaDOP1 and the human DARs , hD1 and hD5 . The invertebrate D2-like receptor sequences from Ap . mellifera ( AmDOP3 ) , B . mori ( BmDOPR3 ) , and D . melanogaster ( DmDD2R ) were placed in a cluster with CqDOP3 and AaDOP3 and formed part of a larger clade with the human D2-like receptors hD2 , hD3 , and hD4 . Sequences containing putative DOP2 coding regions were identified from An . gambiae ( AgDOP2 ) , P . papatasi ( PpDOP2 ) , and G . morsitans ( GmDOP2 ) using tBLASTn searches . Percentage identity of amino acid sequences relative to AaDOP2 were as follows: AgDOP2 = 82 . 6%; PpDOP2 = 81 . 3%; GmDOP2 = 79 . 0% . Alignment revealed preservation of aspartate and serine residues predicted to bind biogenic amines and key aspartate residues and the DRY motif required for receptor activation ( S2 Fig ) . Putative palmitoylation and phosphorylation sites were also preserved with the exception of residue K426 in P . papatasi . Amino acid sequences were most divergent between species in the N-terminus and in ILIII . Although we have previously reported partial characterization of AaDOP2 using a luciferase-based system [11] , we evaluated CqDOP2 and AaDOP2 here in parallel using a HTRF-based cAMP assay ( Cisbio , Bedford , MA ) to avoid assay-induced bias . Both CqDOP2 and AaDOP2 responded to dopamine , with EC50 values of 2 . 3 and 1 . 7 μM , respectively ( Fig . 4 ) . For both receptors , epinephrine and norepinephrine elicited an increase in cAMP , however , these biogenic amines were much less potent having EC50 values at least 10-fold higher than that of dopamine ( Table 2 ) . Treatment with histamine , octopamine , serotonin , or tyramine did not cause a measurable response in activity for either receptor . The dopamine-stimulated activity of CqDOP2 and AaDOP2 in response to a subset of AaDOP2 and hD1 antagonists mirrored each other , but differed from those observed for hD1 ( Fig . 5 , Table 3 ) . Amitriptyline , asenapine , amperozide , chlorpromazine , and doxepin were markedly more potent at both mosquito receptors over the human receptor . In contrast , the D1 antagonists ( ± ) butaclamol and SCH23390 were approximately 60-fold and 500-fold more selective for the human receptor over the mosquito receptors , respectively . To test the in vivo activity of select antagonists , concentration-response assays were conducted against L3 larvae of C . quinquefasciatus and Ae . aegypti . All DAR antagonists elicited ≥70% mortality of Ae . aegypti and C . quinquefasciatus larvae by 72 h at 400 μM , the highest dose tested ( Fig . 6 ) . The LC50 values at 72 h ranged from 41 to 208 μM depending on antagonist and mosquito species ( Table 4 ) . Chlorprothixene was the most toxic compound , eliciting the lowest LC50 values in both species ( 41±7 μM for C . quinquefasciatus and 62±9 μM for Ae . aegypti ) and the lowest LT50 values ( 13 . 9±2 . 0 h for C . quinquefasciatus and 22 . 2±3 . 2 h for Ae . aegypti ) ( Table 4 ) . Mortality to antagonists in both species was similar , although C . quinquefasciatus larvae were slightly more susceptible than Ae . aegypti larvae , having lower LT50 and LC50 values ( Fig . 6 , Table 4 ) . Four compounds ( chlorprothixene , chlorpromazine , methiothepin , and mianserin ) caused >70% mortality in C . quinquefasciatus within the first 24 h ( S3 Fig ) , and all but amitriptyline caused >70% mortality by 48 h ( S4 Fig ) . In Ae . aegypti , >70% mortality was observed after 48 h for five compounds ( chlorprothixene , chlorpromazine , methiothepin , mianserin , and asenapine ) ( S4 Fig ) .
This study provides evidence for CqDOP2 as a D1-like DAR and supports an orthologous relationship to AaDOP2 . Within the mosquito subfamily Culicinae , tribes Aedini ( including Ae . aegypti ) and Culicini ( including C . quinquefasciatus ) are thought to have diverged in the early Cretaceous , an estimated 127 to 158 million years ago [41] , yet the degree of sequence and pharmacological similarity , coupled with the detection of transcripts in all life stages , suggests that CqDOP2 and AaDOP2 play a conserved role in mosquito neurological processes and may explain the similar toxic effects of DAR antagonists to the larvae of C . quinquefasciatus and Ae . aegypti . These findings are an important step towards understanding the relationship between sequence similarity , DAR pharmacology in vitro and antagonist toxicity in vivo . Future mutagenesis and modeling work with these receptors may reveal key DAR structural features required for activity that may be used to predict the pharmacology of orthologous targets . This information could be used to assess the value of de-orphanization and development of additional orthologous targets , which represents a considerable time and cost investment . DAR antagonist toxicity to C . quinquefasciatus suggests these molecules may have activity against other culicine species and possibly other arthropod vectors . The three additional DOP2 targets , AgDOP2 , PpDOP2 and GmDOP2 , identified in this study ( Figs . 1 , S2 ) provide a powerful research tool to investigate DAR antagonist potency to other dipteran vectors and the pharmacological consequences of divergence in amino acid sequences in these species . Importantly , our finding that CqDOP2 and AaDOP2 are pharmacologically distinct from the human D1-like DAR , hD1 , and exhibit a 35 to 227-fold range difference in response to select antagonists suggests that differences between mosquito and human DARs can be exploited for development of mosquito-selective compounds with low mammalian toxicity . The DAR antagonists tested in this study are tricyclic amines , with the exception of amperozide , a diphenylbutylpiperazine . Modeling of mosquito receptors may allow prediction of ligand binding sites and facilitate directed searches for small molecule inhibitors , potentially revealing new classes of DAR antagonists selective for mosquitoes . Agonists also have potential to disrupt AaDOP2 signaling [13] and should be examined against CqDOP2 and the DOP2 DAR targets of other dipteran vectors identified in this study . The rational , target-based insecticide discovery approach taken in this study provides further evidence that investment in small molecule antagonists of invertebrate GPCRs may deliver much needed novel mode-of-action products to the vector control market . | New mode-of-action insecticides are required to control arthropod vectors of neglected tropical diseases ( NTDs ) . Rational drug design approaches offer attractive methods to identify new insecticidal chemistries that are potent and selective for molecular targets of arthropod vectors . Previously identified antagonists of a D1-like dopamine receptor ( DAR ) from the yellow fever mosquito , Aedes aegypti were toxic to the larvae of this species and are candidate novel insecticide leads . Building on this work , here we evaluated the molecular and pharmacological characteristics of an orthologous DAR from Culex quinquefasciatus , the vector of lymphatic filariasis and West Nile virus . We show that orthologous mosquito DARs have similar pharmacological profiles in vitro and that Ae . aegypti-active DAR antagonists are toxic to C . quinquefasciatus larvae in vivo . Sequence similarity between orthologous targets can be indicative of DAR target potential for discovery of potent , selective inhibitors . These findings justify expansion of insecticide discovery efforts to orthologous DARs from additional dipteran vectors of NTDs and provide support for DAR antagonists as a new class of chemistries for taxon-selective insecticides for vector control . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
]
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| 2015 | Dopamine Receptor Antagonists as New Mode-of-Action Insecticide Leads for Control of Aedes and Culex Mosquito Vectors |
The C-type lectin receptor DCIR , which has been shown very recently to act as an attachment factor for HIV-1 in dendritic cells , is expressed predominantly on antigen-presenting cells . However , this concept was recently challenged by the discovery that DCIR can also be detected in CD4+ T cells found in the synovial tissue from rheumatoid arthritis ( RA ) patients . Given that RA and HIV-1 infections share common features such as a chronic inflammatory condition and polyclonal immune hyperactivation status , we hypothesized that HIV-1 could promote DCIR expression in CD4+ T cells . We report here that HIV-1 drives DCIR expression in human primary CD4+ T cells isolated from patients ( from both aviremic/treated and viremic/treatment naive persons ) and cells acutely infected in vitro ( seen in both virus-infected and uninfected cells ) . Soluble factors produced by virus-infected cells are responsible for the noticed DCIR up-regulation on uninfected cells . Infection studies with Vpr- or Nef-deleted viruses revealed that these two viral genes are not contributing to the mechanism of DCIR induction that is seen following acute infection of CD4+ T cells with HIV-1 . Moreover , we report that DCIR is linked to caspase-dependent ( induced by a mitochondria-mediated generation of free radicals ) and -independent intrinsic apoptotic pathways ( involving the death effector AIF ) . Finally , we demonstrate that the higher surface expression of DCIR in CD4+ T cells is accompanied by an enhancement of virus attachment/entry , replication and transfer . This study shows for the first time that HIV-1 induces DCIR membrane expression in CD4+ T cells , a process that might promote virus dissemination throughout the infected organism .
The Dendritic Cell ImmunoReceptor ( DCIR ) is a recently described member of the C-type lectin family . It is mainly expressed in cells of the myeloid lineage ( i . e . neutrophils , dendritic cells , monocytes and macrophages ) and also in B cells [1] . Its precise role and function are not completely understood but a recent work has suggested that DCIR might regulate expansion of dendritic cells ( DCs ) [2] . Moreover , it was previously established that DCIR can behave as an attachment factor for human immunodeficiency virus type-1 ( HIV-1 ) on DCs and contribute possibly to virus dissemination by promoting both cis- and trans-infection processes [3] . Interestingly , DCIR is expressed on the surface of CD4+ T cells in rheumatoid arthritis ( RA ) patients before glucocorticoid treatment and a decrease of DCIR expression was seen with disease improvement [4] . This study provides the first indication that DCIR expression in CD4+ T cells can be promoted by inflammatory and immune hyperactivated conditions since RA is considered as a chronic , systemic inflammatory disorder characterized by a chronic T-cell response that has escaped normal control mechanisms [4] , [5] . In addition , an increased surface expression of DCIR has been detected in patients suffering from a myocardial infarction [4] , which corroborates that this molecule is induced by an inflammatory environment . It is now well established that HIV-1 infection causes a slow but progressive impairment of the immune system , which is accompanied by a chronic hyperactivation of CD4+ and CD8+ T cells [6] , [7] , [8] . Consequently , infected patients display a heightened expression of various activation markers such as HLA-DR and CD38 in both CD4- and CD8-expressing T cells [9] . A relentless destruction of CD4+ T cells represents another hallmark of HIV-1 infection . The progressive loss of CD4+ T lymphocytes , either infected or uninfected ( also called bystander ) , occurs through several distinct mechanisms . For example , it has been proposed that cell death is resulting from direct killing of virus-infected cells [10] , elimination of HIV-1-infected CD4+ T cells by cytotoxic T lymphocytes ( CTL ) [11] , syncytia formation through a gp120-mediated cell-to-cell fusion process [10] , cytotoxic effects caused by some soluble viral proteins ( e . g . Tat and Vpr ) [10] and , finally , increased susceptibility to apoptosis in both infected and bystander cells that can be due , for example , to an interaction between the external envelope protein of HIV-1 ( i . e . gp120 ) and primary cellular receptor/coreceptor ( i . e . CD4 and CXCR4 or CCR5 ) [10] , [12] . Importantly , previous studies suggest a direct correlation between the magnitude of apoptosis in circulating CD4+ T cells and disease pathogenesis [13] , [14] . During evolution , the immune system has developed a number of strategies to fight viral infections , such as necrosis , autophagy and apoptosis . The last physiological mechanism is used by the body to eliminate overabundant cell populations and defective cells , and this form of cell death displays a propensity to be amplified and/or deregulated in various pathological processes [15] . Two major signalling pathways have been described to be involved in apoptosis induction , i . e . the intrinsic and extrinsic pathways . The first intracellular program is initiated by the disruption of the mitochondrial membrane and the release of mitochondrial proteins , such as cytochrome c , into the cytoplasm after developmental cues or severe cell stresses , such as DNA damage [16] . The extrinsic pathway is activated by the binding of ligands such as Fas ligand ( FasL ) ( also termed CD95L ) , tumor necrosis factor ( TNF ) , and TRAIL/Apo-2 ligand to their death receptors Fas/CD95/Apo-1 , TNFR1 and DR4/DR5 , respectively . These two pathways converge via activation of intracellular caspase-3 and -7 . The caspase biochemical cascade ultimately triggers cell death through the destruction of cellular proteins and induction of DNA fragmentation [16] . It is known that apoptosis can also result from a caspase-independent process , which relies on the apoptosis-inducing factor ( AIF ) . AIF represents the first mitochondrial protein shown to mediate cell death without the requirement for caspases . This protein is released from mitochondria and translocates to the nucleus , where it mediates nuclear features of apoptosis such as chromatin condensation and DNA degradation [17] . It has been shown that HIV-1 induces apoptosis in both infected and bystander immune effector cells [18] , [19] . It has been established that at least five different virus-encoded proteins can induce apoptosis ( i . e . Env , Tat , Nef , protease and Vpr ) and three of them share a capacity to induce cell surface expression of death ligands and receptors of the TNF family ( i . e . Env , Tat and Nef ) . Previous work indicates that HIV-1 induces a mitochondrial membrane permeabilization and release of AIF [20] . The regulatory protein Nef can confer protection against apoptosis but display also a converse capacity to induce apoptosis in neighbouring immune effector cells [21] . Cross-linking of CD4 molecules is the probable mechanism by which the virus-encoded gp120 can cause apoptosis in bystander CD4+ T cells [12] , [22] . Moreover , the viral protease , which inactivates anti-apoptotic Bcl-2 with a concomitant induction of the pro-apoptotic procaspase-8 , renders the cell more prone to mitochondrial dysfunctions in response to internal or external death signals [23] . It has been proposed that apoptosis of bystander cells in the context of HIV-1 infection is likely to be multifactorial . Possible mechanisms include soluble factors secreted by HIV-1-infected cells as well as virus-encoded proteins ( e . g . Env , Nef , TAT and Vpr ) [24] , [25] . For example , supernatants from HIV-1-infected DCs contain several heat labile soluble factors that mediate the killing of bystander thymocytes [26] and soluble factors were found to induce apoptosis in bystander cells [27] , [28] . In addition , the viral accessory protein Vpr mediates apoptosis of bystander cells by causing the release of AIF [24] . Therefore given that RA and HIV-1 infection are both characterized by inflammatory and immune hyperactivation conditions and considering the recently described link between RA and DCIR expression in CD4+ T cells , we hypothesized that HIV-1 can trigger DCIR expression in CD4+ T cells .
DCIR has been detected in CD4+ T cells originating from patients with active RA , a chronic disease characterized by a state of persistent inflammation and immune activation . Because a systemic inflammatory disorder and immune hyperactivation represent also key features of the HIV-1 infection , we first assessed DCIR expression in CD4+ T cells isolated from infected individuals . To this end , the level of ex vivo DCIR expression was evaluated by flow cytometry in peripheral blood CD4+ T cells from two HIV-1-infected aviremic/treated patients . Results depicted in Figure 1A clearly indicate that DCIR is expressed in this cell subset in the context of a natural infection as opposed to what is seen in cells from uninfected healthy donors . Flow cytometry analyses were also performed on circulating CD4+ T cells from additional seropositive individuals but who were this time viremic and treatment-naive . Again an up-regulation of DCIR expression was detected in such samples ( Figure 1B ) , which supports the concept that HIV-1 infection promotes expression of this C-type lectin receptor on the surface of circulating CD4+ T cells . A cell activation marker was also monitored as well ( i . e . HLA-DR ) and a positive correlation was found between DCIR and HLA-DR since both cell surface constituents were found to be increased in CD4+ T cells from viremic/treatment-naive persons compared to uninfected control samples ( data not shown ) . In an attempt to investigate further the capacity of HIV-1 to promote DCIR expression , in vitro studies were performed using human primary CD4+ T cells acutely infected with X4- and R5-using virus isolates ( i . e . NL4-3 and NL4-3/Balenv , respectively ) . Exposure of purified CD4+ T cells to NL4-3 for 3 days triggers DCIR expression on the cell surface ( Figure 1C ) . Similar observations were made when infection was carried out in parallel with the two tested viral isolates . For example , DCIR was detected in 9 . 0±1 . 5% and 8 . 6±0 . 8% of CD4+ T cells inoculated with NL4-3 and NL4-3/Balenv , respectively ( n = 3 ) ( data not shown ) . In some experiments , cells were first pre-treated with the antiretroviral drug efavirenz ( EFV ) before virus infection . This experimental strategy was used to decipher if the virus-mediated induction of DCIR requires a complete replicative cycle ( i . e . productive infection ) . Treatment of purified CD4+ T cells with EFV reduced significantly the percentage of DCIR-expressing cells , thus indicating that productive infection with HIV-1 is mandatory to lead to DCIR expression . Altogether these results suggest that HIV-1 drives DCIR expression in vivo and in vitro in CD4+ T cells , a cell population recognized as a major cellular reservoir for HIV-1 . Experiments were also performed with Vpr- or Nef-deleted mutant to define the possible contribution of each single gene in the virus-mediated induction of DCIR expression on the surface of CD4+ T cells . Induction of DCIR was similar when cells were acutely infected with wild-type and Vpr- or Nef-deleted mutant viruses ( data not shown ) . We next set out to determine whether induction of DCIR occurs in virus-infected and/or bystander cells . This fundamental question was addressed through the use of a novel HIV-1 reporter construct , NL4-3-IRES-HSA , which , unlike most of the previous reporter constructs , will lead to the production of fully competent virions [29] . This X4-tropic infectious molecular clone of HIV-1 codes for all viral genes , with no deletions in env , vpr , or nef . It also expresses a cell surface reporter molecule , the murine heat-stable antigen ( HSA ) , which permits the detection by flow cytometry of cells productively infected with HIV-1 through the surface expression of the HSA molecule . Briefly , human primary CD4+ T cells were exposed to NL4-3-IRES-HSA for 3 days and surface expression of HSA and DCIR was monitored by flow cytometry . Data shown in Table 1 demonstrate that 15 . 8±3 . 1% of cells are productively infected with HIV-1 ( i . e . HSA-positive ) , whereas DCIR is expressed in 5 . 0±0 . 8% of cells and 2 . 3±0 . 2% of cells express both HSA and DCIR ( n = 3 ) ( a representative donor is depicted in Figure 2 ) . Therefore , about 46% of DCIR-expressing cells are infected with HIV-1 and 56% of DCIR-positive cells are uninfected . It can be concluded that HIV-1 infection of CD4+ T cells promotes membrane expression of this C-type lectin surface receptor in both virus-infected and bystander cells . Our previous findings indicate that HIV-1 induces DCIR expression not only in virus-infected but also in bystander cells as well . Our next set of experiments was aimed at defining the possible involvement of soluble factors produced by infected cells in the up-regulation of DCIR seen in bystander cells . To this end , human primary CD4+ T cells were cultured with cell-free culture supernatants from HIV-1-infected cells and DCIR expression was monitored by flow cytometry . As shown in Figure 3 , exposure of CD4+ T cells to supernatants originating from cells acutely infected with HIV-1 is sufficient per se to drive DCIR expression in the three distinct donors studied . The HIV-1-mediated induction of apoptosis in both infected and bystander CD4+ T cells is a well-described phenomenon [30] , [31] , [32] . The peak of apoptosis is observed usually after 2 to 3 days [20] , the same time frame in which we detected the HIV-1-dependent induction of DCIR . Therefore , we next assessed whether there might be a connection between the virus-induced DCIR expression and apoptosis . We initially assessed the ability of NL4-3-IRES-HSA reporter virus to drive apoptosis in CD4+ T cells using FITC-VAD-FMK staining . This fluorochrome-labeled pan-caspase inhibitor is a specific and convenient-to-use marker of apoptotic cells , which can identify very early events of apoptosis associated with caspase activation ( i . e . pre-apoptotic cells ) [33] . Our studies indicate that NL4-3-IRES-HSA virions can potently mediate apoptosis in human primary CD4+ T cells ( data not shown ) . As expected , the percentages of apoptotic cells in both virus-infected ( i . e . HSA-positive ) and bystander cells ( HSA-negative ) were significantly reduced upon EFV treatment ( data not shown ) . To establish a link between DCIR expression and apoptosis following HIV-1 infection , we carried out a series of investigations with the broad-spectrum caspase inhibitor Z-VAD-FMK [34] . As illustrated in Figure 4 , the HIV-1-mediated DCIR up-regulation was partially reduced in presence of Z-VAD-FMK , thus suggesting that the virus-directed increased expression of DCIR is associated with both caspase-dependent and -independent apoptotic pathways . To shed light on the nature of the caspase-independent death mechanism , we studied the involvement of the apoptotic effector protein AIF based on the previous report showing that HIV-1 induces a mitochondrial-mediated but caspase-independent apoptosis controlled by AIF [35] . The possible contribution of AIF was investigated through the use of the inhibitor of apoptosis N-acetyl-L-cystein ( NAC ) , which blocks nuclear translocation of AIF . Our results demonstrate that the HIV-1-induced expression of DCIR on the surface of human primary CD4+ T cells is inhibited but not completely by a NAC treatment ( i . e . 16 . 2±3 . 4% in HIV-1-infected cells compared to 6 . 7±1 . 7% in virus-infected cells also treated with NAC ) ( n = 3 ) ( these three donors are depicted in Figure 5 ) . Experiments were performed also with both Z-VAD-FMK and NAC to see if this double treatment can totally inhibit the virus-mediated induction of DCIR expression . Unfortunately the concomitant use of the two compounds is leading to cell toxicity ( data not shown ) . It should be noted that no toxicity is seen when each compound are tested individually ( data not shown ) . Nevertheless , we provide evidence that there is a close connection between DCIR expression and apoptosis ( through caspase-dependent and -independent pathways ) after acute infection of CD4+ T cells with HIV-1 . In HIV-1-infected patients , the hyperactivation status is accompanied by an increased production of free radicals ( e . g . superoxide anion , hydroxyl radical and hydrogen peroxide ) . This excess of reactive oxygen species ( ROS ) damages cell membranes and generates apoptosis [36] . To establish a putative relationship between DCIR expression and apoptosis induced by free radicals after HIV-1 infection , we performed a double staining with anti-DCIR and FITC-VAD-FMK in virus-infected CD4+ T cells treated with catalase because this enzyme is a known scavenger of ROS ( including hydrogen peroxide ) . Results depicted in Figure 6 suggest that free radicals are indeed playing a functional role in the HIV-1-mediated induction of DCIR seen in apoptotic cells ( i . e . positive for both DCIR and FITC-VAD-FMK ) . Hydrogen peroxide ( H2O2 ) , a representative ROS , has been extensively used to study apoptosis following an oxidative stress [37] . Thus , additional experiments were performed in human primary CD4+ T cells using H2O2 as an inducer of an apoptotic-like cell death . Exposure of mitogen-stimulated CD4+ T cells to concentrations of H2O2 ranging from 20 to 60 µM led to a dose-dependent increased in DCIR expression ( Figure 7A ) . Cell viability was reduced when using doses of H2O2 ≥45 µM ( data not shown ) . Consequently , the subsequent experiments were performed using H2O2 at a final concentration of 30 µM . A time-course analysis indicated that the H2O2-mediated expression of DCIR is maximal at 16 h post-treatment and reached a plateau at a longer time period ( i . e . 32 h ) ( Figure 7B and data not shown ) . The specificity of the relation between DCIR expression and apoptosis was addressed by estimating surface expression of two other HIV-1 receptors , namely DC-SIGN ( used as a negative control ) and CD4 . Our data demonstrate that both cell surface molecules are not modulated upon induction of apoptosis by H2O2 ( data not shown ) . Importantly , DCIR was promoted as well by staurosporine ( data not shown ) , a well-known inducer of apoptosis in a wide range of cell lines [38] , which further confirms the connection between DCIR and apoptosis . Given that H2O2 induces also necrosis and mediates apoptosis primarily via a caspase-dependent pathway [39] , [40] , we performed experiments with Z-VAD-FMK . A pre-treatment with Z-VAD-FMK prevented DCIR expression in activated CD4+ T cells after H2O2 stimulation ( i . e . 21 . 4±3 . 4% in H2O2-treated cells compared to 1 . 0±0 . 2% in cells treated with both H2O2 and Z-VAD-FMK ) ( n = 3 ) ( a representative donor is depicted in Figure 8A ) . Experiments were repeated in quiescent CD4+ T cells and we made similar observations ( data not shown ) . Overall our results indicate that the H2O2-driven induction of DCIR is not due to necrosis and occurs through a caspase-mediated signal transduction pathway . Moreover , we estimated the percentages of apoptotic cells that express DCIR following H2O2 treatment . For this purpose , human primary CD4+ T cells were labelled with FITC-VAD-FMK and anti-DCIR . We found that 12 . 2±3 . 2% of apoptotic cells are also positive for DCIR ( n = 3 ) ( a representative donor is depicted in Figure 8B ) , which confirms the relationship between DCIR and apoptosis in CD4+ T cells . Taken together , our findings demonstrated that the HIV-1-mediated apoptosis promotes DCIR surface expression in CD4+ T cells . Previous results indicate that DCIR can capture HIV-1 on DCs , enhance de novo virus production by DCs ( i . e . infection in cis ) , and increase DC-mediated virus transmission to CD4+ T cells ( i . e . infection in trans ) [3] . Experiments were thus carried out to define first whether HIV-1 attachment/entry in CD4+ T cells can be affected by the H2O2-dependent increase in DCIR expression . As illustrated in Figure 9A , the early steps in the virus life cycle ( i . e . binding and entry ) are enhanced in CD4+ T cells following exposure to H2O2 ( i . e . 12 . 5±1 . 8 versus 3 . 8±0 . 8 ng/ml of p24 ) . We next set out to determine whether acute HIV-1 infection was also affected under these conditions . A statistically significant increase in virus production was seen in cells treated with H2O2 ( Figure 9B ) . Similarly , virus transfer was also enhanced when DCIR-expressing CD4+ T cells are used as transmitter cells ( Figure 9C ) . To further strengthen the contribution of DCIR in the virus trans-infection pathway , CD4+ T cells were first exposed to H2O2 to induce DCIR expression . Thereafter , DCIR-negative and DCIR-positive cells were isolated and used separately in HIV-1 transfer experiments . Data shown in Figure 9D demonstrate that HIV-1 transmission toward uninfected CD4+ T cells ( i . e . recipient cells ) is augmented when using , as transmitter cells , DCIR-positive CD4+ T cells . Finally , to substantiate the participation of DCIR in HIV-1 replication , H2O2-treated/virus-infected CD4+ T cells were subjected to a dual staining immunofluorescence method to detect both intracellular HIV-1 p24 and surface DCIR . An increase in virus binding/entry was detected in H2O2-treated cells expressing DCIR ( Figure 9E ) . A similar augmentation in cells expressing both DCIR and p24 was detected following acute virus infection ( Figure 9F ) .
It has been already recognized that neutrophils , monocytes , DCs , macrophages and B cells constitutively express a high level of DCIR , as opposed to CD4+ T cells that are negative for DCIR [1] . However , data from a recent work suggest that this type II membrane glycoprotein is expressed on CD4+ T cells in RA patients and the level of DCIR surface expression is higher in the rheumatic joint compared to peripheral blood [4] . Since DCIR expression is reduced following a local corticosteroid treatment , it was proposed that there is a potential connection between an inflammatory state and DCIR expression [4] . We established previously that DCIR can serve as an attachment factor for HIV-1 [3] , which is the causative agent of AIDS , another disease characterized by a chronic inflammatory state . Starting from these initial intriguing observations , we monitored DCIR expression on the surface of circulating CD4+ T cells isolated from HIV-1-carrying patients . We report here that DCIR is expressed on CD4+ T cells originating from aviremic/treated and viremic/untreated seropositive patients , whereas , no expression was detected in cells from healthy donors . These results suggest that DCIR expression on the surface of circulating CD4+ T cells seems to be a generalized phenomenon in the context of various inflammatory diseases . To acquire additional information about the ability of HIV-1 to induce DCIR expression in a cell subpopulation that is infected under physiological conditions , we performed in vitro experiments where human primary CD4+ T cells were acutely infected with X4- and R5-tropic virions and monitored DCIR expression . We showed that HIV-1 drives DCIR expression in both infected and bystander cells . Moreover , we monitored DCIR levels in the CD4+ T cell subpopulation following acute HIV-1 infection of unseparated peripheral blood mononuclear cells . Unfortunately , no conclusive data could be obtained because we detected a high mortality rate probably due to the presence of CD8+ T cells . In HIV-1 infection , disease progression correlates with elevated levels of apoptosis [13] . Therefore , we defined whether expression of the immunoreceptor DCIR on the surface of CD4+ T cells in the context of HIV-1 infection could perhaps be considered as a possible marker of apoptosis for these cells . We performed experiments and discovered effectively that there is a certain correlation between HIV-1 infection , DCIR expression and induction of apoptosis . We provide evidence that there is a connection between HIV-1-mediated induction of DCIR expression and apoptosis , the latter being caused by a caspase-dependent pathway in response possibly to a mitochondrial H2O2 generation by virus-infected cells and a caspase-independent process involving AIF . Our data are in agreement with published reports since Vpr has been shown to induce cell death via the mitochondrial caspase-independent death effector AIF [35] and Vpr can also induce a decrease of mitochondrial membrane potential along with the release of cytochrome c [41] . HIV-1 infection induces prolonged immune system activation that may cause local or systemic oxidative stress and thus result in oxidative damage . Oxidative stress occurs when the balance of antioxidant protection and the production of free radicals , primarily reactive oxygen and nitrogen molecules are disturbed [42] , [43] . Some viral proteins play a role in the intracellular increase of ROS ( e . g . superoxide anion , hydroxyl radical and hydrogen peroxide ) which in turn influence the increase in the apoptotic index causing a decrease of CD4+ T cells and more importantly increase in HIV-1 replication secondary to free radicals overproduction [42] . This excess of ROS damages cell membranes and generates apoptosis [36] . This may account for the loss of CD4+ T cells seen during progression of HIV-1 infection toward AIDS . Oxygen radicals produced under circumstances that occur during opportunistic infections mediate apoptosis and this effect is reversed by oxygen radical scavengers [44] . We corroborated that human primary CD4+ T cells are sensitive to apoptosis caused by H2O2 , a representative ROS that has been extensively used to study apoptosis following an oxidative stress [37] . Based on this information and the previous demonstration that free radicals are actively produced by CD4+ T cells from HIV-1-carrying patients [36] , we showed here that H2O2 induces also DCIR expression . The H2O2-mediated induction of apoptosis was not only detected in human primary CD4+ T cells but also in Raji and 293T cells ( data not shown ) . No increase in DCIR expression was seen when using a previously reported anti-Fas monoclonal antibody [45] ( data not shown ) , which is an effector of the extrinsic apoptosis pathway [46] . These results indicate that the DCIR induction in CD4+ T cells seen after HIV-1 infection is partly associated with a caspase-dependent intrinsic apoptotic process . An increased expression of DCIR was also observed in a proportion of bystander cells undergoing apoptosis . Experiments carried out with cell-free supernatants from HIV-1-infected cells revealed that soluble factors are sufficient to drive not only apoptosis but also DCIR expression . The phenomenon of bystander cell apoptosis is well described in the literature . Indeed , numerous viral proteins have been described as responsible for causing apoptosis in bystander cells ( e . g . Env , Nef , TAT and Vpr ) [24] . Supernatants from HIV-1-infected DCs contain several heat labile soluble factors that cause cell death in bystander thymocytes [26] and soluble factors were shown to induce apoptosis in bystander cells [27] , [28] . The transactivating protein Tat is released in the surrounding microenvironment and can be taken up by neighbouring bystander cells , which will ultimately undergo apoptosis [28] , [47] . Vpr has been detected in sera and cerebrospinal fluid from HIV-1-infected patients [48] , [49] and this protein of viral origin is recognized as a potent inducer of cell death via a caspase-independent mitochondrial pathway [24] , [35] . Recently , Lenassi and co-workers established that Nef induces the release of exosomes from T cells , which transport extracellular Nef and cause apoptosis of bystander CD4+ T cells [50] . Surprisingly , studies with Nef- or Vpr-deleted mutants suggest that these two viral genes are not involved in the HIV-1-mediated induction of DCIR expression in CD4+ T cells . Therefore , it can be proposed that the virus-directed induction of DCIR and apoptosis is caused by a multifactorial phenomenon that needs to be identified . More relevant to the pathogenesis of HIV-1 infection , we demonstrated that the H2O2-mediated induction of DCIR and apoptosis is coupled with an increased virus binding/entry and higher replication of HIV-1 in CD4+ T cells . Additionally , the noticed up-regulated DCIR expression is also leading to more efficient virus propagation . It can be proposed that DCIR , once expressed onto such CD4+ T cells , can participate actively to HIV-1 propagation . Although it might seem irrational that apoptotic cells would be more susceptible to productive HIV-1 infection , it should be stated that the fluorochrome-labeled pan-caspase inhibitor FITC-VAD-FMK , which was used to monitor the link between HIV-1-mediated DCIR expression and apoptosis , permits to identify the very early events of apoptosis ( i . e . pre-apoptotic cells ) . Thus it is possible that virus binding/entry and replication processes can still occur during a certain time period in CD4+ T cells that are in a pre-apoptotic state . It is known that HIV-1 exploits different strategies to escape the immune response including a rapid/high mutation rate , down-regulation of major histocompatibility complex class-I molecules , broad coreceptor usage and destruction of both CD4- and CD8-expressing T cells [51] . We suggest that HIV-1 can utilize DCIR as another tactic for escaping the immune system and/or increasing its infectivity . Different hypotheses may be formulated with respect to the role ( s ) played by DCIR once expressed on the surface of CD4+ T cells . It can be hypothesized that induction of apoptosis increases virus attachment/entry likely through DCIR expression on the surface of CD4+ T cells ( Figure 10 ) . This theory is supported by our results showing that the H2O2-mediated induction of apoptosis in CD4+ T cells and DCIR expression are not accompanied by a modulation of surface expression of two other attachment factors for HIV-1 , i . e . DC-SIGN and CD4 . DCIR carries an immunoreceptor tyrosine-based inhibitory motif ( ITIM ) in its cytoplasmic tail that is thought to be responsible for the immunoregulatory role played by this cell surface molecule . The intracellular ITIM motif of DCIR is involved in SHP-1 recruitment [52] , a tyrosine phosphatase known for its important role in maintaining cellular homeostasis [53] . The protein tyrosine phosphatase SHP-1 has also been shown to regulate HIV-1 transcription [54] and inhibit antigen-receptor-induced apoptosis [55] . Interestingly , DCIR-expressing cells following acute HIV-1 infection display a cell cycle arrest ( data not shown ) , which might permit virus attachment despite the appearance of a pre-apoptotic state . Studies are currently performed to address this possibility . Thus , the life cycle of HIV-1 can be affected in several ways by the newly expressed DCIR and recruited SHP-1 molecules . It can also be postulated that DCIR expression may lead to phagocytosis by macrophages and DCs of apoptotic CD4+ T cells also infected with HIV-1 , thereby promoting viral propagation and infection of such antigen-presenting cells . It is well established that macrophages play a central role in the pathogenesis of HIV-1 infection , functioning as stable viral reservoir due to their ability to resist HIV-1-mediated cytopathicity . Of importance to note is the previous report showing that phagocytosis of apoptotic cells induced an increase in HIV-1 replication in macrophages [56] . Similarly , we observed that HIV-1 replication in macrophages is enhanced when such cells are co-cultured with DCIR-positive apoptotic CD4+ T cells treated with H2O2 ( data not shown ) . Additionally , instead of inducing an inflammatory immune response , phagocytosis of DCIR-expressing apoptotic cells might promote the generation of suppressor macrophages as described previously for bacterial infections [57] and tumor cells [58] . This would allow microorganisms such as HIV-1 to escape the immune system . Alternatively , it is possible that DCIR is induced after HIV-1 infection because it acts as a death signal for the cell and/or as a sign to promote phagocytosis . It can also be proposed that DCIR facilitates HIV-1 attachment before cell death , a process leading to more extensive virus dissemination across the organism . Supplementary experiments are warranted to validate these non-mutually exclusive hypotheses . Together , our work represents the first evidence that DCIR can serve as a marker for apoptosis in the context of an HIV-1 infection . Additional studies are needed to define more firmly whether there is a connection between the chronic inflammatory state seen in HIV-1-infected persons and DCIR expression in CD4+ T cells . Importantly , the exact contribution of the immunoreceptor DCIR to HIV-1 pathogenesis needs to be delineated because it might provide novel therapeutic avenues .
Recombinant human interleukin-2 ( rhIL-2 ) and the non-nucleoside reverse transcriptase inhibitor EFV were obtained from the AIDS Repository Reagent Program ( Germantown , MD ) . The mitogenic agent phytohemagglutinin-L ( PHA-L ) was purchased from Sigma ( St-Louis , MO ) . The culture medium for human primary CD4+ T cells consisted of RPMI-1640 supplemented with 10% foetal bovine serum ( FBS ) , penicillin G ( 100 U/ml ) , streptomycin ( 100 U/ml ) , glutamine ( 2 mM ) , which were all purchased from Wisent ( St-Bruno , QC ) , and primocine , obtained from Amaxa Biosystems ( Gaithersburg , MD ) . The culture medium for 293T cells was made of Dulbecco's modified Eagle's medium ( DMEM ) supplemented with 10% FBS and penicillin G ( 100 U/ml ) , streptomycin ( 100 U/ml ) , and glutamine ( 2 mM ) ( Invitrogen , Burlington , Canada ) . R-Phycoerythrin ( R-PE ) -conjugated and fluorescein isothiocyanate ( FITC ) -labelled anti-DCIR monoclonal antibodies ( clone 216110 ) and the corresponding isotype-matched irrelevant control antibody ( Ab ) were purchased from R&D Systems ( Minneapolis , MN ) . The FITC-labelled anti-DC-SIGN monoclonal antibody ( Ab ) ( clone eB-h209 ) and the appropriate control Ab were purchased from eBioscience ( San Diego , CA ) . R-PE-labelled anti-HSA Ab ( clone M1/69 ) was purchased from Invitrogen ( Burlington , USA ) , whereas biotin-tagged anti-HSA ( clone M1/69 ) was purchased from BD Biosciences ( Mississauga , ON ) . PE-Cy5 anti-streptavidin Ab was obtained from eBioscience and the FITC-conjugated anti-p24 from Beckman Coulter ( Mississauga , ON ) . Virions were produced upon transient transfection of human embryonic kidney 293T cells as previously described [59] . The infectious molecular clones used in this study included pNL4-3/Balenv ( R5-tropic ) , pNL4-3 ( X4-tropic ) and its derivative pNL4-3-IRES-HSA ( X4-tropic ) . The pNL4-3-IRES-HSA molecular construct was obtained by replacing the enhanced green fluorescent protein ( eGFP ) gene in the NLENG1-IRES vector ( kindly supplied by D . N . Levy , New York University College of Dentistry , New York , NY ) [60] with the coding sequence for mouse heat stable antigen ( HSA ) [29] . Experiments were also carried out with NL4-3-based mutant deleted in Nef ( kindly supplied by S . Venkatesan , National Institute of Allergy and Infectious Diseases , Bethesda , MD ) or Vpr ( kindly provided by E . A . Cohen , Institut de Recherches Cliniques de Montréal , Montréal , QC ) . The virus-containing supernatants were filtered through a 0 . 22 µm cellulose acetate syringe filter , ultracentrifugated and normalized for virion content using an in-house sensitive double-antibody sandwich enzyme-linked immunosorbent assay ( ELISA ) specific for the viral p24 protein [61] . Purified human primary CD4+ T cells were isolated from peripheral blood mononuclear cells ( PBMCs ) using a negative selection kit according to the manufacturer's instructions ( StemCell Technologies , Vancouver , BC ) . Cells were obtained from anonymous and paid , healthy volunteer donors that were specifically solicited for provision of these samples . Healthy subjects signed an informed consent approved by the Centre Hospitalier de l'Université Laval Institutional Review Board . These cells were either left untreated ( to obtain quiescent cells ) or activated with PHA-L ( 1 µg/ml ) for 3 days prior their use ( to obtain mitogen-stimulated cells ) and maintained in complete RPMI-1640 culture medium supplemented with rhIL-2 ( 30 U/ml ) at a density of 2×106 cells/ml . Experiments were performed with cell preparations containing a minimal amount of contaminants as demonstrated previously ( i . e . CD4+ T cells: purity >98% ) [62] . Patient samples were obtained from two aviremic HIV-1-infected patients that were undergoing antiretroviral therapy ( kindly provided by Dr . Rafick-Pierre Sékaly , Université de Montréal , Montréal , QC ) and also from three additional viremic and treatment-naive patients ( kindly supplied by Dr . Jean-Pierre Routy at McGill University through the FRSQ - Réseau SIDA et Maladies Infectieuses ) [63] . Purification of CD4+ T cells was achieved using magnetic beads as described above . The first aviremic donor had a CD4+ T cell count of 463/mm3 and was undergoing a combined antiretroviral therapy consisting of 3TC , EFV and abacavir . The second aviremic donor had a CD4+ T cell count of 499/mm3 and was treated with D4T and atazanavir . In both individuals , the viral load was undetectable ( i . e . <50 copies/ml ) . The CD4+ T cell counts for viremic/untreated patients A , B and C were 290/mm3 , 520/mm3 and 420/mm3 respectively , whereas their respective plasma viral loads were 224×103 , 172×103 and 83×103 HIV-1 RNA per ml . Cells were obtained from anonymous and paid , healthy volunteer donors that were specifically solicited for provision of these samples . Healthy subjects signed an informed consent approved by the Centre Hospitalier de l'Université Laval Institutional Review Board . Patient samples were obtained from peripheral blood in accordance with the guidelines of the Institutional Bioethics Committee . All subjects signed an ethics board-approved informed consent form . In some experiments , purified CD4+ T cells ( 1×106 ) were incubated for 2 h with NL4-3 , Nef-deleted NL4-3 , Vpr-deleted NL4-3 , or NL4-3/Balenv ( 100 ng of p24 ) . After three extensive washes with phosphate-buffered saline ( PBS ) , the cells were cultured for 3 days in complete RPMI-1640 culture medium supplemented with rhIL-2 ( 30 U/ml ) , before staining and flow cytometry analysis . For other infection studies , CD4+ T cells were incubated for 2 or 3 days with NL4-3-IRES-HSA ( 100 ng p24/106 cells ) , in the absence or presence of EFV ( 50 nM ) . Mock-infected cells were used as negative controls . Purified CD4+ T cells ( 1×106 ) were initially infected for 3 days with NL4-3 . Next , supernatants from cells acutely infected with HIV-1 were filtrated and ultracentrifugated to eliminate cellular debris . Finally , cells were incubated with such cell-free supernatants and DCIR expression and apoptosis were monitored by flow cytometry . Controls consisted of cells incubated with cell-free supernatants from mock-infected cells . Purified cells ( 1×106 ) were incubated for 45 min at 4°C with a combination of antibodies made of either FITC-anti-DCIR ( 0 . 25 µg ) and R-PE-anti-HSA ( 1 µg ) , R-PE-anti-DCIR ( 0 . 25 µg ) and FITC-VAD-FMK ( 0 . 5 µg ) , or R-PE-anti-HSA ( 1 µg ) and FITC-VAD-FMK ( 0 . 5 µg ) . Non-specific staining was assessed by using an isotype-matched irrelevant control Ab for DCIR ( i . e . FITC- or R-PE-labeled IgG1 ) or mock-infected cells for HSA . Cells were then washed twice with PBS and 0 . 5% bovine serum albumin . Cells were fixed in 2% paraformaldehyde for 30 min at 4°C . Cell surface expression of DCIR and HSA was monitored using an Epics ELITE ESP apparatus ( Coulter Electronics , Burlington , ON ) . Single stained cells were used as controls for compensation adjustments . Purified CD4+ T cells ( 1×106 ) were first pretreated with PEG-catalase ( 200 U/ml ) ( Sigma ) for 10 min at 37°C . Next , cells were infected with NL4-3-IRES-HSA virions ( 100 ng of p24 ) and PEG-catalase was added to the culture medium every day . Flow cytometry analyses were performed to assess the percentage of cells positive for DCIR and FITC-VAD-FMK . Apoptosis was induced by incubating resting or PHA-activated CD4+ T cells ( 1×106 cells/ml ) with different concentrations of H2O2 for increasing time lengths . Where indicated , apoptosis was induced by a treatment for 16 h at 37°C with the protein kinase C inhibitor staurosporine ( 1 µg/ml ) . The cell-permeable , FITC-conjugated , pan-caspase inhibitor FITC-VAD-FMK ( R&D Systems ) was used to detect activated caspases in CD4+ T cells by flow cytometry . Briefly , in a 24-well culture plate , cells ( 1×106 ) in a final volume of 1 ml were stained directly with 10 µl of FITC-VAD-FMK and left at 37°C in the dark during the last 30 min of the apoptosis induction period . Cells were washed once in PBS to remove unbound reagent and fixed with paraformaldehyde or labeled with another Ab before flow cytometry analysis . Inhibition of apoptosis was achieved by pre-treating cells with Z-VAD-FMK ( 50 µM ) ( R&D Systems ) for 1 h before H2O2 stimulation or HIV-1 pulsing . It is known that Z-VAD-FMK is an irreversible caspase inhibitor that binds to the active site of activated proteases and displays low cytotoxicity . Experiments aimed at studying the contribution of caspase-independent apoptotic pathway were performed using NAC ( 5 mM ) from Sigma because this compound prevents nuclear translocation of AIF [64] . Purified CD4+ T cells were treated with H2O2 ( 30 µM ) during 16 h before performing the following experimental procedures . For the binding/entry assay , cells ( 1×106 ) were incubated for 60 min at 37°C with NL4-3 ( 100 ng of p24 ) . After three extensive washes with PBS to remove unabsorbed viruses , HIV-1 binding/entry was quantified by estimating the p24 content . For the infection assay , CD4+ T cells ( 1×106 ) were incubated with NL4-3 ( 100 ng of p24 ) for 2 h . After three extensive washes with PBS , the cells were cultured in complete RPMI-1640 culture medium supplemented with rhIL-2 ( 30 U/ml ) . Virus production was estimated by assessing the p24 levels in cell-free culture supernatants . For the transfer study , CD4+ T cells ( 1×106 ) were incubated with NL4-3 ( 100 ng of p24 ) for 2 h and after washes , autologous activated CD4+ T cells ( 1×106 ) were added ( ratio 1∶1 ) in complete RPMI-1640 culture medium supplemented with rhIL-2 ( 30 U/ml ) . Every two days , half of the medium was removed and kept frozen at −20°C and fresh medium was added to the culture . Virus production was estimated by measuring the p24 levels in cell-free culture supernatants . Virus transmission was also assessed using purified DCIR-negative and -positive cells . In brief , CD4+ T cells were first exposed to H2O2 to induce DCIR expression . Next , DCIR-negative and DCIR-positive cells were isolated and used separately in HIV-1 transfer experiments as described above . Cell isolation was achieved using the EasySep Biotin Selection kit according to the manufacturer's instructions with slight modifications ( StemCell Technologies Inc . , Vancouver , BC ) . The biotinylated anti-DCIR antibody ( clone 216110 from R&D Systems ) was used at a final concentration of 3 µg/ml . In some experiments , a dual staining technique was used to estimate the percentage of cells expressing surface DCIR and intracellular p24 by flow cytometry . Staining of the intracellular viral p24 core protein was achieved using the BD Cytofix/Cytoperm kit ( BD Biosciences ) and the monoclonal KC57 anti-p24 Ab ( Beckman Coulter ) . Statistical analyses were carried out according to the methods outlined in Zar [65] and Sokal and Rohlf [66] . Means were compared using Student's t test . P values of less than 0 . 05 were deemed statistically significant . Calculations were performed with the GraphPad Prism software . | The type II transmembrane protein DCIR belongs to the C-type lectin domain family receptor and is predominantly expressed in cells of the myeloid lineage . However recent evidence suggests that it can also be induced in CD4+ T cells placed under an inflammatory condition . We assessed the capacity of HIV-1 to promote DCIR expression in CD4+ T cells because the establishment of an inflammatory state is a hallmark of this retroviral infection in humans . We report here that a higher DCIR expression is detected not only in CD4+ T cells acutely infected with HIV-1 in vitro but also in clinical cell samples . Additional studies suggest a possible link between DCIR induction and apoptosis through both caspase-dependent and -independent intrinsic pathways . The greater expression of DCIR on the surface of CD4+ T cells results in more efficient virus attachment/entry , replication and transfer processes . | [
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| 2010 | HIV-1 Induces DCIR Expression in CD4+ T Cells |
Some studies suggest that complex arm movements in humans and monkeys may optimize several objective functions , while others claim that arm movements satisfy geometric constraints and are composed of elementary components . However , the ability to unify different constraints has remained an open question . The criterion for a maximally smooth ( minimizing jerk ) motion is satisfied for parabolic trajectories having constant equi-affine speed , which thus comply with the geometric constraint known as the two-thirds power law . Here we empirically test the hypothesis that parabolic segments provide a compact representation of spontaneous drawing movements . Monkey scribblings performed during a period of practice were recorded . Practiced hand paths could be approximated well by relatively long parabolic segments . Following practice , the orientations and spatial locations of the fitted parabolic segments could be drawn from only 2–4 clusters , and there was less discrepancy between the fitted parabolic segments and the executed paths . This enabled us to show that well-practiced spontaneous scribbling movements can be represented as sequences ( “words” ) of a small number of elementary parabolic primitives ( “letters” ) . A movement primitive can be defined as a movement entity that cannot be intentionally stopped before its completion . We found that in a well-trained monkey a movement was usually decelerated after receiving a reward , but it stopped only after the completion of a sequence composed of several parabolic segments . Piece-wise parabolic segments can be generated by applying affine geometric transformations to a single parabolic template . Thus , complex movements might be constructed by applying sequences of suitable geometric transformations to a few templates . Our findings therefore suggest that the motor system aims at achieving more parsimonious internal representations through practice , that parabolas serve as geometric primitives and that non-Euclidean variables are employed in internal movement representations ( due to the special role of parabolas in equi-affine geometry ) .
Despite decades of research on the formation of human hand trajectories , the basic mechanisms of neuromotor control underlying the generation of even the simplest drawing movements remain poorly understood [1] . Various studies have proposed that human movement preparation aims at optimizing either kinematic [2]–[4] or dynamic [5] criteria , or minimizing movement variance [6]–[9] . Studies in vertebrates have suggested that voluntary movements are composed of basic movement elements combined in parallel or sequentially [10]–[17] . Such modular organization can account for the versatility of animal and human movements and for their ability to acquire new skills . Geometrically invariant properties of drawing movements were formalized by the two-thirds power law [18] . These kinematic constraints were shown to hold both with respect to movement production [19] and perception [20] , [21] . Earlier studies also showed that the two-thirds power law is equivalent to moving at a constant equi-affine speed [22]–[24] and there is psychophysical and neurophysiological evidence for the significant role of the invariance of human motion with respect to equi-affine transformations [25]–[27] . We argue that geometric invariance may provide a more compact representation of complex movements composed of geometric primitives . Straight point-to-point movements show geometric invariance under dynamic perturbations involving the use of either elastic or viscous loads [15] , [28] . Point-to-point movements retain the invariance of their geometric properties even when subjects are required to control the movements of a cursor on a computer screen by moving their fingers in an instrumented data glove [29] . Recent studies in monkeys [25] , [27] , [30] and humans [31] have indicated that repeatable geometric ( curved ) shapes used in the construction of complex trajectories emerge after extensive practice in the generation of drawing and sequential movements . The ability to unify different kinds of movement constraints ( optimality , compositionality , geometric invariance ) in the modeling of human and animal movements could lead to further insights [4] , [27] . Parabolic movement primitives meet the demands of geometric invariance , kinematic optimality of movements and simplicity of movement representation , and may subserve as underlying building blocks in arm trajectory formation [25] , [27] . Here , the hypothesis that parabolic segments are geometric primitives in practiced movements was experimentally tested using spontaneous scribbling movements made by two monkeys . Our choice of the source of the data ( studying monkey rather than human drawings ) was motivated by the feasibility of subsequently analyzing the underlying activity of motor cortical neurons [27] . The predictions of both the two-thirds power law [18] and the constrained minimum-jerk model [4] are identical for a single parabolic stroke [27] , [30] . The fit of the recorded trajectories to the predictions of these two models was assessed ( based on modeling and analysis of equi-affine speed ) and is described in detail in Text S1 . Preliminary version of our findings was presented at the Tenth Biennial Conference of the International Graphonomics Society in 2001 ( URL of the proceedings paper: http://www . wisdom . weizmann . ac . il/~felix/texts/IGS2001 . pdf ) and at the Computational Motor Control Workshops at Ben-Gurion University in 2005 and 2006 .
All animals were handled in strict accordance with good animal practice as defined by the relevant national and local animal welfare bodies , and all animal work was approved by the appropriate committee Permit No . OPRR-A01-5011 . Parabolas play a special role in equi-affine geometry and motor control [27] . In particular , parabolas are the only equi-affine invariant curves for which predictions of the constrained minimum-jerk and the two-thirds power law coincide . Equi-affine invariant curves are considered because of the importance of equi-affine invariance in both human production and perception . Equi-affine transformations of curves differ from the widely known Euclidian transformations . Euclidian transformations preserve distances , whereas equi-affine transformations preserve only areas and parallelism of lines . Two important equi-affine invariant parameters are equi-affine length ( ) and equi-affine curvature . Time derivative of the equi-affine length of the trajectory ( ) called equi-affine velocity is exactly the piece-wise constant velocity gain factor from the two-thirds power law relating movement speed and Euclidian curvature [22] , [24] . Equi-affine transformations constitute the largest subgroup of affine geometric transformations that preserves the velocity gain factor of the two-thirds power law . Equi-affine curvature can be used to classify curves in equi-affine geometry: whenever two curves have the same equi-affine curvature , one curve can be obtained from the other by applying a unique equi-affine transformation . Parabolas have zero equi-affine curvature; therefore any two parabolic segments can be aligned by some affine transformation [27] . The notions of equi-affine geometry and the rationale for its application in motor control studies are described elsewhere [22]–[25] , [27] , [32] , [33] . We provide essential definitions , explanations , and methods of analysis of movements' kinematic parameters in the framework of equi-affine geometry in Text S1 . The subjects in this study were two monkeys , O and U ( female Macaca fascicularis , 2 . 6/3 . 5 kg , respectively ) . Animal handling procedures conformed to the NIH Guide for the Care and Use of Laboratory Animals ( 1996 ) , complied with Israeli law , and were approved by the Ethics Committee of the Hebrew University . During the experiments , each monkey sat in a primate chair with the left hand restrained and the right hand operating a two-joint low-friction manipulandum . Following a period of practice , the monkeys created smooth and continuous scribbling movements ( Figure 1A ) . During the entire recording session the monkeys saw nothing but a circular cursor ( diameter: 10/4 mm , monkey O/U respectively ) indicating the position of the hand . To motivate the monkey to generate continuous scribbling movements , the working plane was tiled with a grid of 19 possible targets ( monkey O circles with radius of 20 mm , monkey U hexagons with edge length of 20 mm ) . At the beginning of each session , a single target was randomly chosen . As soon as the cursor entered this invisible target , a short beep was produced , and a juice reward was released and delivered for 50 msec . The beep comes with the valve's release of the juice , but it takes a while before the juice actually starts dripping from the spout . We found that when the monkey does not protrude its lips and starts licking the juice spout , much of the reward-juice is spilled down creating a sticky mess on both the monkey's fur and the monkey's chair . Following a successful hit , another target ( also invisible ) was randomly selected . Whenever the monkey did not succeed in locating the target within 5 seconds , the target was randomly changed . The monkey had no knowledge of target location . It adopted a strategy of producing trajectories that covered the entire workspace . The monkey was not required to stop at any stage of the experiment . Figure 1 depicts the grid and task sequence . In a typical session , the monkeys worked for 1 . 5–2 hours and received 800–1500 rewards . During the first 4 days of practice for both monkeys , the average inter-reward time intervals within each session decreased from 4–5 seconds to 2–3 seconds simultaneously with the increase in the speed of drawing . There was nothing in the training to hint to the monkey that it should search for the target . The total length of monkey's U arm including the upper arm and forearm segments was 215 mm , and the length of its open hand was 75 mm; the corresponding measurements for monkey O were almost the same . The diameter of the working area was approximately 173 mm ( Figure 1A ) . Therefore , the production of hand movements within the workspace demanded movements of the shoulder and elbow . Hence , the monkeys indeed operated the manipulandum by moving their limbs and not only through wrist rotations ( power grip movements ) . Hand position in the two dimensional plane was sampled at 100 Hz and logged on a custom-designed data acquisition system . Coordinate data were smoothed using a Gaussian filter with a low-pass cutoff frequency of 8 Hz . Velocity , acceleration and jerk of the hand coordinates were estimated using finite difference approximations of the first- second- and third-order derivatives of position with respect to time: The equi-affine parameters ( equi-affine velocity and curvature ) were numerically estimated using a geometrical approximation method [34] which is based on fitting the position data with conics at 5 consecutive data points along the measured path . To analyze the strategy of the monkeys' drawings , we applied the notion of dwell distribution for the endpoint of the manipulandum position . It is defined here as the frequencies of visiting small parts of the workspace weighted by the movement tangential velocity . That is , visiting some location once with a tangential velocity of 450 mm/s makes the same contribution as visiting the same location 3 times with a tangential velocity of 150 mm/s . This weighting helps to avoid high contributions of slow movements or of periods of rest in the dwell distributions . The monkeys spontaneously switched between periods of rest , with no or very slow motion , and periods of active drawing . We analyzed data from movement segments of the drawings detected by the following procedure: An example of the tangential velocity profile for 3 movement segments is shown in Figure 2 . The least number of movement segments ( 57 ) was registered for monkey U's first practice session . After a period of practice , at least 500 movement segments were typically obtained from a recording session . It should be noted that movement segments are identified based on the values of the tangential velocity of the spontaneously generated movements and that the segmentation procedure did not consider rewarding the monkeys . A parabola is defined by 4 parameters: the focal parameter , two coordinates of the location of the vertex ( point of maximal curvature ) , and the orientation of the parabola ( defined by the direction of the normal vector at the vertex ) . Direct verification shows that the focal parameter p equals the radius of curvature ( reciprocal of the curvature ) at the vertex of a parabola . Every parabola can be transformed by rigid rotation and translation into the canonical coordinate system in which the orientation of a parabola is 270° , and its vertex is located at the point whose coordinates are ( 0 , 0 ) . In the canonical coordinate system , the parabola is described by a simple relationship with a single free parameter . Such a parabola is shown in Figure 3A . The three typical parabolas emerging from the fitting of the monkey drawing ( dots ) are given in Figure 3B . The mean of their R2-based estimate of the goodness of fit indicates a very good fit . Several typical examples of the fitted parabolas and estimates of the goodness of fit can be found in [27] . The focal parameters , orientations and locations of the vertices of these three fitted parabolas are all different . Note that for rest-to-rest movements through a single via-point , minimum-jerk trajectories can be very well approximated by parabolic segments , though they are not exact parabolas [27] . Such trajectories are characteristic of obstacle-avoidance human movements or curved movements through a single via-point [3] . The fitting of parabolic segments to the monkey hand trajectories was implemented in a consistent way using the greedy algorithm described in Text S2 . By consistent , we mean that the outcome of the fit is invariant under those equi-affine transformations of the path which preserve the location of the point of maximal curvature on the fitted parabolic segment . Thus , our procedure applied in the same manner to both “narrow” and “wide” path segments . Examples of “narrow” and “wide” parabolic segments are shown in Figure 3C . To quantitatively assess the amount of incongruence between any path segment and the corresponding parabolic segment fitted to it , a measure of discrepancy was defined . For each segment i of the recorded movements , this discrepancy measure was evaluated by calculating the value of , where and are the estimated equi-affine lengths of that path segment and of the corresponding fitted parabolic segment , respectively . The discrepancy measures ranged from 0 to 2 . 0 . The more similar the two equi-affine lengths are , the smaller is the discrepancy between the recorded path and the parabolic segment fitted to it . Higher discrepancy measures usually correspond to practically straight movement segments which contain inflection points causing larger errors in the numerical estimation of the equi-affine invariants . Equi-affine analysis is not appropriate near inflection points . Examples of movement segments and fitting parabolas corresponding to different discrepancy measures , having low ( 0 . 08 ) to high ( 1 . 5 ) values , are depicted in Figure S1 .
After both monkeys had practiced the drawing task , the parabolic segments that were fitted to the recorded movements fell into 2–4 clusters based on their orientation . The focal parameter p and orientation define a unique parabola up to translation ( see Figure 3 ) . Figure 5A shows typical histograms of the number of parabolic strokes tabulated according to the values of the quantized pairs of ( , p ) and according to the orientation parameter . In comparison to the lack of distinct clusters in the histograms for the parabolic segments derived from the beginning of practice , the practiced movements clearly showed convergence to well separated clusters , based on the orientation of the fitted parabolic strokes . Apart from the parabolas' focal parameter and orientation , we also examined the remaining two parameters that define a parabola , namely the and coordinates of the location of the vertex . Figure 5B shows the locations of the vertices of the fitted parabolic segments and their orientations for every tenth fitted parabola ( to make the data easier to visualize ) from the same recording sessions as in Figure 5A . The example shows that after a period of practice , the locations of vertices of similarly oriented parabolas were separable into distinct clusters as well . In Figure 5 , the clusters are labeled 1–3 , corresponding to the order of the performed trajectories . Note that monkeys O and U scribbled in opposite directions and therefore the orders of the clusters for the two monkeys are opposite . Figure 6 shows typical histograms of the equi-affine and Euclidian lengths of the recorded movement strokes and the corresponding parabolic strokes . Histograms of the equi-affine lengths of the monkey path strokes for all different practice periods are depicted in Figure 6A . Histograms of the equi-affine lengths of the parabolic strokes fitted to these path segments are shown in Figure 6B . Corresponding histograms in Figure 6A and 6B are more similar to each other for the sessions that followed a period of practice . This indicates that with practice the equi-affine lengths of the path strokes became more similar to those of the corresponding fitted parabolic strokes ( the similarity was assessed quantitatively using the discrepancy measure introduced in Methods ) . Typical distributions of the calculated discrepancy measures derived for different practice periods are depicted in Figure 6C . Indeed , practice led to a decrease in the values of the discrepancy measures . Euclidian lengths of the fitted parabolic strokes were all quite similar to the Euclidian lengths of the recorded parabolic-like paths ( which can be fit well with parabolas ) and therefore these lengths are not shown separately . Typical distributions of Euclidian lengths of the fitted parabolic strokes are depicted in Figure 6D . The data depicted in Figure 6 are summarized in Figure 7A–C for all analyzed recording sessions . Figure 7A shows the mean equi-affine arc lengths of the fitted parabolic segments and the parabolic-like path segments they fit for all the movement recording sessions together with 95% confidence interval . Figure 7B depicts the median values of the discrepancy measures for all the recording sessions analyzed and the corresponding 95% confidence intervals . The median values of the discrepancy measures decreased and the equi-affine lengths of the fitted parabolic segments increased ( i . e . the fitted parabolas became longer ) as both monkeys had more practice . Euclidian lengths of the fitted parabolic strokes and the corresponding path segments were very similar; they became larger with practice . The average values of the Euclidian lengths for all recording sessions and their 95% confidence intervals are shown in Figure 7C . The error in fitting parabolic strokes to movement paths was also estimated using the R2 measure as described in Text S2 . For every recording session analyzed , the median error was very small and in the range of 10−3–10−2 of data variance . Taking parabolic strokes from all recording sessions together , the resulting value of ( 1−R2 ) was 2 . 66×10−3 ( median; 95% confidence interval: [2 . 65 2 . 67]×10−3 ) . However an R2 based measure , unlike the discrepancy measure , is not sensitive to modifications of the drawn trajectories during practice . The goodness of fit of the monkeys' drawing movements to other ( non-parabolic ) curves was also evaluated . In particular , ellipses , higher order polynomials ( of orders 3–5 ) of the form and triplets of superimposed point-to-point movements ( which fit parabolic paths quite well , see Text S3 ) were fitted to the same movement parts that were fitted with parabolas . The R2 based measures of the deviation from the recorded paths for all the above-mentioned kinds of curves were small ( 1−R2<0 . 01 ) . To estimate the trade-off between goodness of fit and model simplicity ( number of parameters ) , the SIC score [35] was used ( see [27] for details of using the SIC score ) . This analysis indicated that out of all the different curves considered here , the parabolic model yielded the highest SIC score . That is , the parabolic model provided the best trade-off . The same conclusion was drawn in [27] for segmentation of the trajectories into parabolic segments using a different segmentation algorithm than the one used in the present manuscript . More specifically , in [27] curves were fitted to movement parts , where the end-points of the fitted curves were anchored at consecutive points of minima of the curve's Euclidian curvature . This fitting scheme did not aim at providing the longest possible path segments which can be well fitted with parabolic strokes by contrast to the fitting scheme used here . So far we have shown that parabolic segments grouped into clusters over the course of practice and therefore could capture the geometric regularities of the well-practiced movements . We also observed that about 60%–80% of trajectory durations recorded during each session could be well approximated by large parabolic-like segments . Hence , between rest periods and for movements generated within different parts of the workspace the monkeys completed motion sequences which were composed of several piece-wise parabolic segments . Furthermore , when the monkeys became well-practiced , they rarely reversed their movement direction which was either clockwise ( for monkey O ) or counterclockwise ( for monkey U ) . The fitted parabolic segments can be labeled according to the clusters to which they belong . Note that the order of clusters for each monkey is identified according to the direction of the drawing; i . e . , sequence ( 1-2-3 ) for monkey O would correspond to a clock-wise direction of motion , while for monkey U the same sequence of labels ( 1-2-3 ) would correspond to a counter-clockwise direction of motion . The clusters corresponding to different monkeys cannot be identically labeled because the monkeys generated movements in different directions . Therefore , as Figure 5 demonstrates , the clusters for monkey O are labeled differently than the clusters for monkey U . The alphabet of labels for practiced sessions of monkey O consisted of 3 labels because 3 parabolic clusters were found in that session ( Figure 5 ) . The sequence ( 1-2-3 ) represents a repeatable word because the orientation of the well-practiced drawings was mostly constant . Over the course of practice , the monkeys' drawings could be represented more and more precisely in terms of repeatable sequences of labels identifying parabolic clusters , where each series of labels constitutes a “word” . For example , the drawing of monkey O depicted in Figure 3B can be represented by the sequence ( 1-2-3 ) -1 , where , following the labeling shown in Figure 5 , 1 denotes upward oriented parabolas , 2 denotes downward oriented parabolas , and 3 denotes leftward oriented parabolas . Note that in Figure 3B , the parabola fitting the initial part of the drawing ( which corresponds to cluster 1 ) was not depicted ( to make the amount of information depicted in this plot comprehensible ) . Three parabolas fitted to the scribbling of monkey O ( in Figure 3B ) are associated with cluster sequence 2-3-1 . In Figure 7D1 and 7D2 we show typical examples of drawing patterns consisting of ordered sequences of parabolic-like strokes . These are quite characteristic patterns including relatively rare cases in which the direction of motion was reversed ( Figure 7D3–D5 ) . Nevertheless , in a few cases , elemental parabolic strokes identified as belonging to cluster 1 were not followed by other parabolic elements . This happened either when the movement was stopped or when reversing movement direction . Examples of paths partially composed of the fitted parabolic segments not belonging to any one of the three clusters are depicted in Figure 7D6–D8 . Parabolas are the only equi-affine invariant curves which provide identical predictions to the constrained minimum-jerk model and the two-thirds power law [25] , [27] . We therefore estimated the degree of fit of the monkeys' scribbling movements to these two models and the detailed description of this analysis is presented in Text S1 . In particular , an example of a movement segment , its corresponding equi-affine invariants and the predictions of the two models are presented in Figure S3 ( while the procedure of regularizing the equi-affine speed is demonstrated in Figure S2 ) . As Figure S4A and S4B show , monkey scribbling movements deviate to some extent from both models . Note that the degree of fit of the movements to the predictions of the two models was estimated for movement segments composed of several concatenated parabolic strokes , while the predictions of both models are identical only for a single parabolic segment , and not for sequences of parabolas . Drawing each separate parabolic segment within a sequence at a constant equi-affine speed would lead to very high values of jerk at the transitions between adjacent segments , resulting in non-smooth movements . This implies that although on the geometric level the movements were indeed shown to be approximately composed of simply concatenated parabolas , on a kinematic level constant equi-affine speed could not be maintained . Hence the spatial ( geometric ) aspects might be planned separately or even precede the temporal aspects of planning ( e . g . concatenation is observed only on the geometric level ) . Interestingly , the trajectories predicted by the constrained minimum-jerk model fit the two-thirds power law better for more practiced movement paths ( see Text S1 and Figure S4C and S4D ) . We also examined another kinematic optimality criterion , the minimum-acceleration model , according to which movements tend to minimize an integrated second derivative of the drawn trajectories ( rather than the third derivative as in case of the minimum-jerk model ) . Using the same approach as in the case of the minimum-jerk model in [27] , we derived an equation whose solutions define paths providing identical predictions for the two-thirds power law and the minimum-acceleration model:which is equivalent to for smooth enough curves . Here a prime denotes differentiation with respect to , and the numbers in brackets denote the corresponding higher order derivatives with respect to . Using the same approach as in [27] , it can be shown that parabolas constitute the only class of equi-affine invariant curves satisfying above equation . Nevertheless , there is no ambiguity as to which model provides a better fit for the data . An implementation of the minimum-acceleration modeling to the scribbling paths showed that the degree of deviation of the minimum-acceleration trajectories from the recorded movements was higher than that of the minimum-jerk trajectories . In this study of the compositional nature of movements , we attempt to go beyond analyzing separate movement components . In particular , we investigate the nature of the underlying movement primitives by examining modifications in scribbling strategies that were associated with well-identified behavioral events: receiving or not receiving a reward . The quantitative analysis was based on using the parabolic components of the recorded trajectories introduced above . The effect of rewards on the drawings was especially pronounced in the well-practiced movements of monkey U . After almost a year of practice , monkey U tended to decelerate and sometimes almost stop its arm movement after it had been rewarded . Rewards were obtained near the target boundary ( Figure 8A , upper plot ) . Hence , the locations rewarded within a session did not cover the workspace uniformly . To examine the kinematic differences between rewarded versus non-rewarded trajectories for the movements performed by the well-trained monkey U , in each session , 19 areas within the workspace where the reward density was high were selected based on the 19 targets where the reward was delivered . For convenience , every such area with a relatively high density of rewarded locations was represented by an ellipse whose main axes correspond to the principal components of the and coordinates of the rewarded locations within this area ( Figure 8A , lower plot ) . The lengths of ellipses' main axes are equal to the unbiased standard deviation along the corresponding directions: . Here , for rewarded locations corresponding to target , denotes a unit vector parallel to one of the two main axes of the ellipse , and corresponds to the vector connecting the ellipse's center with the rewarded location identified with this target . In the lower plot of Figure 8A , different colors were used to depict rewarded locations identified with the different targets as well as the corresponding ellipses . The scribbling strategy that the monkey tended to use consisted of initiating movements within the proximal part of the workspace with respect to the monkey and only then exploring the distal part of the workspace . The rewarded trajectories which passed through the areas of the designated ellipses were compared to movements that crossed these ellipses without being rewarded there . In general , both the rewarded and the non-rewarded trajectories were composed of several piece-wise parabolic segments . A careful analysis indicated clear differences between the rewarded versus the non-rewarded trajectories . Three examples of unrewarded trajectories are shown in Figure 8B . These movement patterns cross the ellipses centered on targets #18 and #16 without being rewarded there . In 2 of these 3 trajectories , marked in red and purple , the monkey completed the sequence which was composed of primitives ( 1–2 ) and continued to further generate parabolic segment 3 without stopping the movement after completing the sequence ( 1–2 ) . In the case of the trajectory marked in green , the monkey decelerated the movement after completing the sequence ( 1–2 ) . Plots in Figure 8C display the hand paths ( left panel , upper row ) and tangential velocity profiles ( left panel , lower row ) of the rewarded trajectories from one recording session , passing through the ellipse corresponding to 18th target . Figure 8C also shows the paths and tangential velocity profiles of the non-rewarded trajectories ( right panel ) , from the same recording session as in the left panel , passing through target #18 . Similar plots are shown in the corresponding panels of Figure 8D for both the rewarded and non-rewarded trajectories passing through the ellipse corresponding to the 16th target . In the case of the unrewarded trajectories passing through the 18th target , the monkey completed a movement sequence composed of two parabolas ( labeled 1–2 ) and continued the sequence by also completing the third parabolic element . By contrast , for the rewarded trajectories , after being rewarded at target #18 , the monkey completed parabolic segments 1 and 2 and then nearly stopped its movement ( see Figure 8C ) . The paths and tangential velocity profiles are clearly more variable for the non-rewarded trajectories . When rewarded at target #16 and not at target #18 , the monkey completed parabolic segment 2 , and only then the movement was decelerated and subsequently was nearly halted ( see Figure 8D ) . Hence , in general , after obtaining a reward at targets #18 or #16 ( at the beginning of either strokes 1 or 2 , respectively ) the monkey tended to decelerate and nearly stop its movement only after completing sequence ( 1–2 ) in the case of target #18 or element 2 in the case of target #16 . There were other targets which were followed by rewarded movement sequences composed of two parabolas . These targets corresponded to the stage following the initiation of drawing parabolas belonging to cluster 1 ( as depicted in Figure 8B ) . In particular , some of the sequences generated following reward delivery at target #14 consisted of two parabolic strokes . A more general observation follows from inspecting the lower-right plot depicted in Figure 4B which shows a typical path produced by the highly trained monkey U: a parabola belonging to cluster 1 is typically initiated at targets #13 , #14 or #17 . It typically crosses target #18 only after its initiation , that after crossing either of the targets #13 , #14 , #17 . Therefore trajectories following reward delivery occurring at targets #13 , #14 or #17 were typically more variable than those generated following reward delivery at target #18 . For the movements passing through the ellipse centered on target #16 ( Figure 8D ) , similarly to the case of target #18 , both rewarded paths and tangential velocity profiles were more stereotypical than non-rewarded paths and tangential velocity profiles . When plotted time is aligned on the event of receiving a reward at target #16 , or the mean time of crossing the boundary of the ellipse of target 16 for the non-rewarded trajectories , the rewarded trajectories showed a clear halt within about 0 . 8 sec following the reward with no such clear halt for the non-rewarded trajectories . Hence , based on these observations , we operationally define a movement primitive as a movement entity that cannot be intentionally stopped before its completion once it has been initiated . Furthermore , the above observations indicate the existence of “words” or “sentences” composed of several parabolic-like strokes ( e . g . sequences ( 1–2 ) ) which serve as higher level geometric primitives . Following the observations of the influence of drawing strategies on movement variability described above , for all 17 recording sessions with a well-trained behavior ( Monkey U ) , we then quantitatively examined the validity of the claim that the monkey indeed tended to slow down and almost stop movement after receiving a reward at targets #18 or #16 but only after either being able to complete the drawing of a parabolic element ( in the case of target 16 ) , or after completing the generation of a sequence composed of 2 parabolic segments ( in the case of target 18 ) . Since the monkey could complete a sequence of 2 parabolic-like segments within 1–2 s . , ( see the left panel in the lower part of Figure 8C ) it was assumed that the monkey nearly stopped its movement within a time interval of 1 to 2 s after receiving a reward as compared to simply passing through target ellipse #18 without being rewarded there . Similarly , for target #16 we assumed that the monkey nearly stopped its movement within a time interval of 0 . 5 to 1 s from the time it was rewarded ( see the left lower panel of Figure 8D ) . The speeds ( corresponding to the above-mentioned time intervals ) across rewarded and non-rewarded trajectories were further averaged . The graphs in Figure 9A show that for trajectories rewarded inside ellipses #16 , #18 , the average values of the hand speeds at their minima were always smaller than the velocity threshold which we used to mark periods of rest ( i . e . 150 mm/sec , Methods ) , thus indicating that the movements were nearly halted after receiving a reward . They were also always smaller than the minima of the average speed for the corresponding non-rewarded trajectories . The differences between the minimal tangential velocity values of the rewarded versus the non-rewarded trajectories averaged across all 17 sessions were significant ( Mann–Whitney U test , p = 0 . 05 ) . The ellipse corresponding to target #16 ( see Figure 8B and 8D ) was specifically chosen because it is positioned at the location which corresponded to the beginning of the parabolas constituting the last elements in the sequence ( 1–2 ) . This allowed us to use our parabolic fitting algorithm to estimate the degree to which a single movement primitive isolated from other movement elements was indeed stereotypical . For 10 of the 17 recording sessions ( 1–5 , 10 , 14–17 ) , we visually observed a greater variability in the non-rewarded trajectories that crossed ellipse #16 versus the trajectories that were rewarded inside this ellipse . We then defined a time interval of 0 . 05–0 . 4 seconds from the event ( of either getting a reward or the mean time of crossing the ellipse boundaries in case the reward was not obtained ) . As can be seen in the lower left plot of Figure 8D , this interval includes two local maxima and one local minima of the tangential velocity of the trajectories crossing the ellipse . This tangential velocity pattern usually corresponded to a single parabolic-like drawing . For each trajectory examined , we selected the point with the highest path curvature for the time interval of 0 . 05–0 . 4 seconds from the event . A parabola containing this point was then selected . All the rewarded trajectories shown in Figure 8D could be fitted with parabolas oriented at about 50° as compared to more variable orientations of the parabolas fitted to the non-rewarded trajectories . These parabolic elements from the rewarded and non-rewarded trajectories were then used to statistically demonstrate the greater variability of the trajectories that crossed the ellipse #16 without being rewarded there as compared to trajectories rewarded inside this ellipse . Figure 9B demonstrates that the orientations of the rewarded segments are concentrated within the interval [0° , 70°] . In fact , orientations of only 5 . 2% of the rewarded segments lay outside this interval , compared to 15 . 8% of the non-rewarded segments . This implies that parabolic strokes identified with the non-rewarded trajectories which crossed the ellipse corresponding to target #16 ( Figure 8D ) were more variable than the parabolic strokes fitted to the trajectories rewarded inside this ellipse ( binomial test , p = 0 . 01 ) . These parabolic elements of the rewarded trajectories may be less variable because the movement nearly stopped and was not followed by a consecutive movement element ( whose choice could be based on making a decision ) . The upper left plots in Figure 8C and 8D show that the locations of the termination of the parabolic stroke belonging to the 2nd cluster mainly correspond to the second quadrant of the workspace ( locations above and to the left of the central target ) ; i . e . , these locations are not uniformly distributed within the workspace . In order to quantitatively analyze this phenomenon , we define the location at which the movement stopped as the end of a movement segment having a speed threshold of 100 mm/s within 2 seconds after an event occurred . Such an event involved either getting a reward or corresponded to the mean time of crossing the ellipse boundary when the reward was not obtained . In cases when the end of a movement segment did not occur within 2 seconds after such an event , the location of a halt to movement was defined as the location at which the speed was the lowest within a 2 second interval after the event . Further analysis of the movements ( both rewarded and not rewarded ) which crossed all targets 1–19 , and not only targets 16 and 18 , showed that indeed the locations of movement halts were not uniformly distributed within the workspace . Taking all targets together , 40 . 36% of the rewarded trajectories stopped ( as defined in the paragraph above ) within the second quadrant of the workspace whereas only 29 . 8% of the non-rewarded trajectories stopped within this quadrant . The difference was significant ( binomial test , p = 0 . 01 ) . Therefore , frequent stopping within the second quadrant of the rewarded trajectories was related to getting a reward and not simply to the monkey's purported intention to stop there irrespective of the preceding movement history . Rather , stopping within the second quadrant of the workspace supports the notion that a post-rewarded sequence was halted after completion of the last element belonging to the 2nd cluster more often than a non-rewarded sequence . In some cases , although the monkey tended to decelerate its motion after receiving a reward , it did not entirely stop the movement . Because of the existence of such cases , there were post-rewarded movement sequences with more than 2 parabolic elements . However , the number of post-rewarded sequences rapidly decreased with the increase of the number of their elements and the sequences composed of 1 or 2 post-rewarded parabolic elements constituted 52 . 2% of all the post-rewarded sequences . Only one post-rewarded sequence contained 20 elements and this sequence was the longest one observed for all the recording sessions in the well-trained monkey U . Post-rewarded sequences with more than 2 elements mainly corresponded to the sessions with a higher speed of motion; e . g . sessions 15 and 16 as seen in the upper plot of Figure 9A .
Fitting parabolas to the scribbling movements robustly allowed us to determine the focal parameters and orientations of the fitted parabolic strokes . We also fitted parabolas to the paths which connect consecutive local minima of the Euclidian curvature . The orientations of these parabolic segments formed clusters which were similar to those derived based on the orientations of the parabolic strokes obtained through the application of the greedy algorithm applied here ( see Methods ) . Note that we fitted parabolas to the movement data and did not decompose the trajectories into the underlying strokes . That is , each shape was fitted independently of the others and an arbitrary amount of overlap between two consecutive fitted shapes was allowed . An important implication of fitting parabolas to the recorded movements is the dimensionality reduction of the data . In our fitting procedure , each parabola corresponds to a single local maximum of Euclidian curvature . No two maxima of Euclidian curvature correspond to the same parabola and no two parabolas correspond to the same maximum; that is , there is no ambiguity among movement elements . The existence of overlaps between consecutive parabolic elements allows for smooth transitions between these elements and strengthens our claim that the movements are well described by parabolic segments . However , there were also gaps between consecutive parabolic elements . The gaps corresponded either to occasional very slow motion within segments or to nearly straight motions . The case of occasional very slow motion within movement segments does not correspond to actively preplanned movements and therefore is not relevant to our analysis [27] . The case of nearly straight movements ( occurring near inflection points ) cannot be treated within the framework of equi-affine geometry because the equi-affine length of straight paths is zero and their equi-affine curvature is not defined . Straight movements are geodesics in Euclidian geometry and should be treated within the framework of Euclidian geometry , while parabolas are equi-affine geodesics [24] , [36] . Our study suggests the existence of a central representation of movements in terms of parabolic primitives . The emergence of the recorded parabolic-like patterns during practice cannot be described solely as a reflection of the generation of smoother movements per se [27] . Considering the fit with non-parabolic curves ( ellipses , polynomials of order 3–5 , and triplets of superimposed point-to-point movements ) , although the fit is very good and superior to the fit with parabolas , parabolas provide the best trade-off between goodness of fit and simplicity of the curve and are equi-affine invariant . As regards parsimony of representation , a complicated planar curve can be represented by means of an affine invariant model composed of parabolic polygons [37] . Hence , parabolic strokes cannot be considered as simply useful basis functions selected only because they provide a successful numerical approximation to the recorded movements . Our data were limited to the end-effector locations and therefore our analysis did not address the issue of what degree of motion smoothness combined with the biomechanical properties of the limb may have led to the observed piece-wise parabolic movement paths . Concerning the origin of the smoothness of hand trajectories , several empirical and modeling studies have proposed that muscle properties by themselves are sufficient to account for much of the observed smoothness and bell-shaped speed profiles which are characteristic of point-to-point movements , e . g . [38]–[40] . It has also been argued that the two-thirds power law also originates from low pass biomechanical properties of the muscles , e . g . [41] , or other peripheral factors such as the effects of the non-linearities of the forward kinematic transformations [42]–[44] , or the inherent noise present in the motor system [45] . Nevertheless , a number of studies have supported the central origin of the two-thirds power law . These include , for example , the demonstration that variations in the magnitudes and directions of the neural population vectors are consistent with the kinematic properties of monkey trajectories that obey the two-thirds power law [46] , [47] . Our analysis also showed that the firing rates of some of the cells in the motor cortical area ( recorded while the monkeys were performing the drawing movements reported here ) were more strongly correlated with the equi-affine movement speed rather than with Euclidian speed [25] , [27] . The two-thirds power law also affects the perception of motion . Movements performed according to the two-thirds power law are perceived as being more uniform [20] , [21] . Recently [26] , demonstrated that compliance with the two-thirds power law in motion perception is reflected in stronger fMRI activations of different cortical regions and in particular , of brain areas that subserve motor production , visual motion processing , and action observation functions . Interestingly , analysis of equi-affine invariant primitives in planar movements has been recently generalized to the spatial case in empirical and theoretical studies [25] , [27] , [48]–[50] . Earlier studies proposed the existence of movement primitives at different hierarchical levels , suggesting corresponding syntactical rules for combining them in order to compose complex movements; for more details see [51] . The use of geometric primitives in our study differs from other investigations , as we suggest a specific malleable geometric shape acted upon by a group of geometric transformations , without a vectorial basis of units of composition . This differs from vectorial superposition of elements from a fixed set of basic functions/force fields/muscle synergies , e . g . [11]–[13] , [52] , [53] . Our results suggest that parabolic movements identified in monkey scribblings may , through the process of practice , form a behavioral output of dynamically-switching cortical “attractors” ( states ) [27] . Convergence toward attractor-like neural activity during practice via Hebbian learning may underlie the superposition/co-articulation of sequences of point-to-point motion units into more compact and smoother parabola-like movement components . Rehearsal of a sequence of elementary planar point-to-point movements by human subjects leads to the formation of more complex smooth geometric primitives [31] , thus supporting this suggestion . Moreover , the smooth movements that emerged following practice were well approximated by minimum-jerk trajectories passing through a single via-point . Geometrically , such minimum-jerk trajectories have a parabolic-like shape; therefore the geometric primitives observed by [31] might be parabolic-like segments . A single parabolic drawing can be approximated with three superimposed point-to-point movements , each having a bell-shaped speed profile ( Figure S5A ) . Each bell-shaped speed profile can , in turn , be approximated with three smaller identical bell-shaped speed profiles , and so on ( Figure S5B ) . However , a triplet of point-to-point movements is defined by at least seven parameters while a parabola is defined by only four parameters , thus providing a more compact representation . One possibility is that in the hierarchy of geometric primitives , point-to-point movements constitute the lowest level which is below the hierarchy of curved movements; another possibility is that there is no hierarchical relationship between straight and curved movement primitives ( see also Text S3 ) . Elementary parabolic-like shapes ( “letters” of the “alphabet” used to achieve a compact representation ) constitute the lowest level in the hierarchy of curved movements , and sequences of parabolic-like shapes ( “words” ) belong to the next level above . Elementary parabolic primitives and their sequences are acquired during learning to achieve more efficient representation of complex well-trained movements ( in terms of complexity ) as the present study has demonstrated . The observed reduction in variability of the fitted parabolic components of the scribbling movements indicates a tendency of the CNS to increase the parsimony of movement representation through practice ( hypothesis of greater parsimony ) . The geometric reduction of dimensionality in drawing movements also has an equi-affine interpretation . Piece-wise parabolic trajectories can be generated based on affine transformations ( equi-affine transformations and spatial scaling ) of a single parabolic template; equi-affine curvature of monkey scribbling movements became closer to zero through practice [27] , [30] . Parabolas have a constant zero equi-affine curvature . Therefore , empirical evidence for greater parsimony implicitly imposes a geometric constraint on the movement path in terms of the equi-affine curvature . This role of the equi-affine curvature , in turn , suggests that equi-affine variables and geometry may play an important role in the representations employed by the primate motor system . Our demonstration of the dimensionality reduction that was achieved through practice also enabled us to represent spontaneous movements in terms of sequences of elementary primitives and finally to introduce a compact symbolic notation to describe the recorded ( continuous ) scribbling data based on a small ( discrete ) set of basic primitives . Moreover , mathematically , piece-wise parabolic movements can be viewed in this perspective as resulting from applying sequences of different affine geometric transformations to a single movement template ( parabolic ) , rather than constituting different movement elements . Hence , the experimental paradigm and the movement analysis approach described here may serve future studies that focus on human and primate acquisition of motor sequences ( see [54] , [55] for reviews of such studies ) . Earlier works studied the predictions of the minimum-jerk model for a number of geometric paths and compared corresponding predicted and recorded trajectories [4] , [19] , [56] . Here , the fit to the constrained minimum-jerk model and the two-thirds power law was estimated for monkey trajectories recorded at different stages of practice . Geometrically identified patterns were acquired by the monkey through practice but no ( or only a slight ) influence of practice on the fit to the constrained minimum-jerk model and to the geometric constraint formalized by the two-thirds power law model was detected ( Text S1 ) . This may follow from an underlying dissociation between the geometric and temporal aspects of motion planning . The existence of such dissociation was also proposed in earlier studies [57]–[59] . Human tracing movements [19] were found to fit the constrained minimum-jerk model better than monkey scribbling movements [30] . This difference may be due to task differences: the monkeys performed spontaneous scribbling movements , while the human subjects were instructed to repetitively follow prescribed geometric templates . For non-straight paths , zero jerk cost can be achieved only by drawing a single parabolic stroke and the corresponding minimum jerk trajectory also satisfies the two-thirds power law [27] , [30] . However , the constrained minimum-jerk model and the two-thirds power law are not simultaneously satisfied when considering a sequence of parabolic segments . Even so , for monkey scribbling movements , the trajectories predicted by the constrained minimum-jerk model for the recorded monkey paths obeyed the two-thirds power law better after a period of practice ( Text S1 ) . The variability ( deviation from being constant ) of the equi-affine speed was compared for the actual and predicted trajectories to estimate how well a geometric constraint is satisfied by the trajectories predicted by the minimum-jerk criterion versus the recorded trajectories . Future studies of motor learning may similarly apply the comparison of geometric invariants ( e . g . equi-affine speed ) for actual and modeled trajectories to detect acquisition of specific motor strategies ( in our case , modifications of the geometric properties of the movements being performed ( Text S1 ) ) . It has been suggested that the motor control signals used in movement generation are chosen such that the end-point variance should be minimized [6] . It was shown in [6] that the trajectories predicted by the minimum end-point variance for the drawing of ellipses fit the two-thirds power law well . Thus , the equi-affine speed of these predicted motions is close to being constant . Note as well that when drawing an ellipse according to the constrained minimum-jerk model , the predicted trajectories also fit the two-thirds power law quite well [56] , and that parabolas can be approximated arbitrarily well by ellipses with close to zero equi-affine curvatures [27] . Therefore , the predictions of the minimum-variance model for a parabolic path would yield trajectory which fits well the two-thirds power law , similarly to the case of the minimum-jerk model . Our observations , based on the comparison between the kinematic characteristics of the rewarded versus the non-rewarded trajectories have shown that receiving or not receiving a reward affected the motion sequences generated by a well-practiced monkey . Significantly smaller variability was observed for the parabolic strokes fitted to the trajectories rewarded at a specific spatial location ( corresponding to the start of the last element in the sequence ) versus parabolic strokes fitted to the non-rewarded trajectories . The last elements of the rewarded trajectories may be less variable due to the fact that when the movement is nearly halted , it is not followed by a consecutive movement element which is composed with the preceding one . Therefore , the observed differences in movement variability between the rewarded versus the non-rewarded trajectories lend support to our definition of a movement primitive . Our observations also indicated that parabolic segments constitute elementary motion primitives which are used in the construction of higher-level sequences , i . e . , “words” or “sentences” in well practiced scribbling movements . Consequently , we propose that the observed behavior of the well-trained monkey could imply that the monkey has applied a strategy of combining a few parabolic parameters into higher-level sequences . However , it is not entirely clear why the monkey tended to concatenate the parabolic elements belonging to clusters 1 and 2 into an indivisible sequence . That is , why didn't the monkey immediately halt its movement when it received a reward at target #18 , but tended , instead , to continue generating another parabolic element and then arresting the motion after its subsequent completion ? All trajectories which were rewarded at either targets #16 or 18 were stopped only after competing element 2 in the sequence . This finding , in turn , may imply that the monkey employed a movement generation strategy which involved automatic exploration of the distal part of the workspace ( parabolic cluster 2 ) in case no reward was obtained within its proximal part ( parabolic cluster 1 ) . The findings reported here indicate that complex movements may be generated by tuning the parameters of a small number of primitives and then concatenating them together to achieve the goals of complex movements . For example , a likely parameter to be tuned is the focal parameter of the parabolic-like segments which defines their “width” . Tuning of primitives in goal-directed movements may also be guided by decision-making and/or action selection based on ongoing feedback/reinforcement signals ( e . g . receiving or not receiving a reward ) . Therefore , paradigms involving decision-making could be advantageous in studies investigating movement construction based on the compositionality of a basic repertoire of motion primitives . In fact , a recent study involving the analysis of rapid pronation/supination wrist movements produced by monkeys during a 1D step-tracking task indicated that a decision-making process guided the initiation of corrective sub-movements [60] . Our preliminary observations also indicated that some of the motor cortical units may be responsive to receiving a reward and thus their activity may be related to decision-making processes [25] . Our monkey data were recorded in a paradigm in which the monkey did not have a clear motivation to stop its movements after reward delivery . However [61] , studied the performance of human subjects when generating free scribbling movements in which the subjects were looking for the location of an invisible target and were requested to unexpectedly impede their movements . Geometrical analysis of the recorded trajectories showed that the figural properties of the paths generated after the “stop” cue was given were part of a repetitive geometrical pattern and that the probability of completing a pattern after the “stop” cue was given was correlated with the relative advance in the geometrical plan rather than with the amount of time that had elapsed since the initiation . Thus the findings of [61] provide evidence in support of the existence of movement primitives which subserve the construction of sentence-like sequences in human trajectory formation . Their observations therefore support our claim that a primitive can indeed be defined as an entity that cannot be stopped before its completion . Is the convergence of monkey drawings to trajectory sequences composed of several parabolic-like segments an optimal strategy in terms of a sequential search for rewards ? Does it reflect the development of a geometric skill based on core knowledge [62] , or is it the outcome of the development of a dynamic internal model , or are both inherently related ? Further studies involving , for example , non-uniform or even non-stationary distributions of target locations and studies using dynamic perturbations of arm movements ( in both humans and monkeys ) should provide further answers to these questions . Such studies could also examine the possible existence of parabolic primitives and the degree of involvement of decision-making mechanisms in movement compositionality and variability . We also propose that the relative simplicity of movement data ( versus acoustic or semantic data , for example ) makes their analysis a useful tool in studies dealing with problems of binding and cognitive processing . In summary , different kinematical analysis and mathematical modeling approaches were combined in our study and indicated that with practice , monkey scribbling movements tend to be composed of parabolic elements drawn from a small number of directionally identified clusters . The observed piece-wise parabolicity of the movement segments is also compatible with our general definition of movement primitives and the notion that repeated practice of a given motor task leads to a more parsimonious motor representation . | Although our movements are flexible and versatile , they are nonetheless highly stereotypical . This versatility is similar to that of natural language sentences , which are composed of words which , in turn , are constructed from a small alphabet of elementary phonemes . Parabolic drawings are simple , smooth and remain parabolic even when undergoing a specific kind of geometric transformations . Smoothness , invariance and compactness of representation are important in motion planning and in visual feedback processing . Hence stereotypical parabolic sub-movements may serve as appropriate building blocks of complex movements . Given the similarities between motor organization in monkeys and humans and the greater opportunity to record brain activities in monkeys here we study the spontaneous emergence of stereotypical arm movements in monkeys following practice . We show that practice has indeed led to the emergence of a small alphabet of parabolic elements during spontaneous drawing movements . We further use this alphabet to study sequences of parabolic sub-movements with respect to possible decisions concerning the animal's choice of what elements to concatenate into words and sentences . We also propose that the relative simplicity of movement data compared , for example , to acoustic or semantic data makes their analysis a useful tool in studies of binding and cognitive processing . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
]
| [
"neuroscience/behavioral",
"neuroscience",
"neuroscience/cognitive",
"neuroscience",
"neuroscience/theoretical",
"neuroscience",
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| 2009 | A Compact Representation of Drawing Movements with Sequences of Parabolic Primitives |
Despite the crucial importance of Hox genes functions during animal development , the mechanisms that control their transcription in time and space are not yet fully understood . In this context , it was proposed that Hotair , a lncRNA transcribed from within the HoxC cluster regulates Hoxd gene expression in trans , through the targeting of Polycomb and consecutive transcriptional repression . This activity was recently supported by the skeletal phenotype of mice lacking Hotair function . However , other loss of function alleles at this locus did not elicit the same effects . Here , we re-analyze the molecular and phenotypic consequences of deleting the Hotair locus in vivo . In contrast with previous findings , we show that deleting Hotair has no detectable effect on Hoxd genes expression in vivo . In addition , we were unable to observe any significant morphological alteration in mice lacking the Hotair transcript . However , we find a subtle impact of deleting the Hotair locus upon the expression of the neighboring Hoxc11 and Hoxc12 genes in cis . Our results do not support any substantial role for Hotair during mammalian development in vivo . Instead , they argue in favor of a DNA-dependent effect of the Hotair deletion upon the transcriptional landscape in cis .
Hox genes encode transcription factors with crucial roles in the specification of regional identities along the body axes during development . Mutations affecting specific Hox genes typically lead to homeotic transformations , whereby a particular body part is transformed into the identity of another one [1–4] . In mammals , following the two rounds of genome duplication that occurred at the basis of the vertebrate lineage ( see [5] ) , four distinct clusters of Hox genes are found ( HoxA to HoxD ) ( ref . in [6] ) . During development , Hox genes are transcriptionally activated in a precise temporal and spatial sequence , which follows their chromosomal order [7 , 8] . These collinear patterns of transcription are regulated at multiple levels and studies focusing on the HoxA , HoxB and HoxD loci have revealed the importance of intricate combinations of local and long-range cis-regulatory elements . Also , studies using micro-dissected embryonic material have shown that the transcriptional activation of these genes , in different ontogenetic contexts , is accompanied by major changes in both the epigenetic modifications of the surrounding chromatin and its 3D spatial organization [9–11] . Long non-coding RNAs ( lncRNAs ) have been proposed to represent yet another layer of regulatory control at these important developmental loci ( e . g . [12–15] ) . Increasing evidence indeed suggests that lncRNAs can act as regulators of gene expression , for example by interacting with transcription factors and chromatin modifiers to modulate transcription during development [16] . Several lncRNAs associated with the mammalian Hox clusters have been identified , amongst which Hotair ( Hox transcript antisense intergenic RNA ) , a lncRNA transcribed from the intergenic region between Hoxc11 and Hoxc12 within the HoxC cluster and the founding member of this new class of RNAs . Hotair was proposed to help repress some 5’-located ( posterior ) Hoxd genes in trans , through its association with chromatin modification complexes such as PRC2 , LSD1 and CoREST/REST [13 , 17] . Accordingly , Hotair would recruit or enrich this part of the HoxD cluster with Polycomb ( Pc ) complex , thus contributing to its repressed state before transcription starts . This proposal was substantiated by the knockdown of Hotair in human fibroblasts , which led to a decreased binding of Pc repressive complexes in the HoxD cluster and to a concurrent increase in Hoxd genes expression [13] . This important function for a lncRNA in cultured human fibroblasts was however not supported by the analysis of a mouse line carrying a targeted deletion of the entire HoxC cluster [18] , i . e . including the mouse Hotair lncRNA . This deletion showed little effect in vivo , with no alteration of Hoxd genes expression . Also , the presence and enrichment of H3K27me3 repressive chromatin marks at the HoxD locus was not dramatically modified [19] . This lack of effect was tentatively explained by the concomitant in-cis deletion of all Hoxc genes , which may have masked or compensated a potential alteration caused by the absence of Hotair alone [20] . To alleviate this problem , three alleles were recently produced where the Hotair transcript was specifically targeted ( Fig 1 ) . The first allele is a targeted deletion of the two major exons of Hotair . Mice carrying this deletion were reported to display a malformation of the wrist and homeotic transformations of the spine , either from six lumbar vertebrae ( L6 ) to a L5 vertebral formula , or within the post-sacral region [20] . These phenotypes were associated with a de-repression of Hoxd genes and of a set of imprinted genes by modulation of their chromatin state [20] . These effects were scored in the absence of any change in the transcription of the neighboring Hoxc11 and Hoxc12 genes , supporting a function of Hotair in trans [20] . Two additional Hotair deletion mutant alleles combined with LacZ reporter knock-in were recently reported by Lai and colleagues [21] . The first allele deleted nearly the entire Hotair sequence and the second one comprised a smaller deletion starting in the second exon [21] . In both cases , while a subtle alteration of the 4th caudal vertebra was scored , the wrist and the spine appeared normally formed , without any sign of the lumbar homeotic transformation and wrist alterations previously reported for the deletion of both exons [20] . Due to our long-lasting interest in the transcriptional regulation of Hoxd genes during development ( e . g . [22] ) , we addressed these apparently conflicting results by re-assessing the effects of deleting the Hotair lncRNA during early mouse development , using the largest deletion allele previously described [20] . In agreement with earlier and more recent results [19 , 21] , we find that the deletion of Hotair has no substantial effect , neither on wrist morphology , nor on the vertebral formula at the lumbo-sacral level . In addition , transcriptome analyses reveal that the absence of Hotair does not impact upon Hoxd genes expression in trans , in any of the embryonic tissues analyzed . In contrast , we observe subtle yet significant changes in the expression of the neighboring Hoxc11 and Hoxc12 genes in the mutant mice , indicating an in-cis impact of modifying the genomic locus . Taken together , our results strongly suggest that the Hotair lncRNA has little effect–if any- on mouse embryonic development .
We extended the analysis of a mouse strain that includes a deletion of the two major Hotair exons ( Fig 1A ) [20] . Even though we concluded that this mutation is primarily an allele of the HoxC cluster ( see below ) and hence that it should be referred to as HoxCDel ( Hotair ) , we shall refer to it as Del ( Hotair ) throughout this study for sake of simplicity . We first confirmed the expression pattern of Hotair in vivo by whole mount in situ hybridization ( WISH ) using wild type mice of two distinct genetic backgrounds ( CD1 and CBA/C57/B6 ) as well as Del ( Hotair ) -/- mouse embryos at embryonic day 12 . 5 ( E12 . 5 ) ( Fig 1B ) . Staining of Del ( Hotair ) -/- embryos confirmed the specificity of the Hotair probe as no signal was detected in these embryos ( Fig 1B ) . In contrast , wild type embryos of both genetic backgrounds showed the presence of Hotair transcripts in the genital tubercle , the proximal part of the hindlimbs and in the caudal part of the embryo ( Fig 1B ) , confirming previously published data [19] and consistent with the LacZ staining reported for the Hotair knocked-in allele [21] . In both cases , staining was observed just posterior to the lumbar region and was not scored in developing forelimbs [21] . We quantified the expression levels of Hotair with high coverage RNA-sequencing ( RNA-seq ) ( Materials and Methods , S1 Table ) . Based on the spatial expression pattern of Hotair as determined with WISH and on the skeletal phenotypes reported in mice by Li et al . [20] , we micro-dissected both Del ( Hotair ) -/- and wild type E12 . 5 embryos into six distinct parts for comparative RNA-seq analyses ( Fig 1C ) . We thus separately collected the forelimbs ( FL ) , the hindlimbs ( HL ) , the genital tubercle ( GT ) , a piece of trunk corresponding to the lumbo-sacral region ( T1 ) ; a piece of trunk corresponding to the sacro-caudal region ( T2 ) and finally , a piece of trunk corresponding to the developing caudal region ( T3 , Fig 1C ) . As expected from the WISH experiments , Hotair transcripts were scored in the hindlimbs , the genital tubercle and the trunk samples T2 and T3 . The highest steady-state levels of Hotair RNAs were detected in the GT and the T3 embryonic tissues ( Fig 1D , S1 Dataset ) . As a control , Hotair transcripts were not detected in any tissues derived from homozygous Del ( Hotair ) -/- mutant embryos ( Fig 1D ) . To better compare this dataset with published results , we analyzed in parallel the RNA-seq data obtained from primary tail tip fibroblast ( TTF ) , derived from both wild type and Del ( Hotair ) -/- mice [20] . This analysis revealed that the expression level of Hotair in control TTF was very low when compared to its expression levels in the GT or the posterior T3 trunk sample ( Fig 1E , S2 Dataset ) . Hotair was reported to be important for both the proper establishment of the mouse vertebral column and for the formation of the forelimb mesopodial articulation: the wrist [20 , 21] . To confirm this phenotypic effect , we inter-crossed Del ( Hotair ) +/-heterozygous mice and examined the skeletons of F1 animals at postnatal day 22 ( P22 ) . We investigated in particular the three reported sites of observed alterations in mutant Del ( Hotair ) -/- mice [20 , 21] . We first searched for potential differences in vertebral formulae , as it was reported that 58% of Del ( Hotair ) -/- mice had five lumbar vertebrae , while 100% of wild type CBA/C57/BL6 mice had six lumbar vertebrae [20] . All our mutant alleles at Hox loci ( see e . g . [23] ) are systematically backcrossed onto mixed ( B6xCBA ) F1 animals to maintain heterogeneous but similar backgrounds when comparing experimental crosses . After bringing the Hotair mutant mice [20] onto this genetic background for some generations , we found that 80% of Del ( Hotair ) -/- mice displayed five lumbar vertebrae , similar to wild type littermates ( Chi-square test , p-value 0 . 97 , Fig 2A , Table 1 ) . In both wild type and homozygous mutant animals , the L6 formula was sporadically scored , as well as the mixed L6/S1 vertebral type , often observed in our stocks . Despite a limited number of specimens observed , but together with the fact that we were unable to detect specific transcripts in the embryonic trunk at this vertebral level by two independent methods , we conclude that this lncRNA is very unlikely to have a function in the organization of this very flexible morphological boundary ( see discussion ) . We next analyzed the morphology of caudal vertebrae in the post-sacral region . Previous analyses had concluded that mice with Hotair deletions had longer lateral processes on the fourth vertebra when compared to wild type animals , with full penetrance . In our case , we observed that three out of ten Del ( Hotair ) -/- mutant mice had longer processes on the fifth caudal vertebra , compared to wild type ( Fig 2B ) . This may indeed correspond to a very subtle morphological alteration in this region of the caudal spine , although the penetrance of this light phenotype is not 100% . Unlike the lumbo-sacral and wrist alterations [20] , this particular tail vertebral morphology was also scored by Lai and colleagues when analyzing another mutant allele of Hotair [21] . Finally , and even though we were unable to detect any Hotair transcripts in the forelimbs of E12 . 5 mice embryos , unlike for hindlimbs ( Fig 1B ) , we carefully examined both forelimb and hindlimb skeletons of wild type and Del ( Hotair ) -/- mutant mice . We did not detect any alteration in limb morphology ( Fig 2C and Table 1 ) , in particular in the anatomy of the wrist , where malformations due to the loss of Hotair had been previously reported ( Fig 2C ) . The same conclusion was reached concerning the hindlimbs , even though Hotair transcripts could clearly be scored in the proximal part . Altogether , we could not reproduce the reported phenotypic effects of Hotair deletion at two sites , the wrist and the lumbo-sacral region , where we were also unable to detect any Hotair transcripts . Regarding tail vertebrae , a slight effect could indeed be observed , poorly penetrant and likely dependent on the genetic background ( see below ) . To more globally evaluate the effect of Hotair deletion upon developmental gene regulation , we performed a principal component analysis ( PCA ) using the expression levels of all autosomal protein-coding genes detected in our RNA-seq experiments ( Materials and Methods ) . We observed a good separation of the data according to tissue type , although the T1 and T2 samples clustered together ( Fig 3 ) . Principal component 1 ( PC1 ) , which explained 61 . 6% of the total gene expression variance , separated the trunk segments ( T1 , T2 and T3 ) from the other embryonic tissues . Likewise , the differences between GT , HL and FL were resolved along PC2 , which accounted for 12 . 5% of the total variance ( Fig 3 ) . Part of the variance was also explained by the genotypes and we observed that wild type and Del ( Hotair ) -/- samples were separated along PC2 ( Fig 3 ) . Of note , the same separation between wild type and Del ( Hotair ) -/- on PC2 was observed in all tissues , even in T1 and FL where Hotair is not expressed ( Fig 3 ) . In agreement with the results from the clustering between samples , we observed high expression level correlations among biological replicates for all tissues ( S1 Fig ) . Furthermore , gene expression clustering based on pairwise Euclidean distances between samples ( see Materials and Methods ) showed a clear separation between four different groups: the limbs ( FL and HL ) , the GT , the T3 trunk segment and the remaining T1 and T2 trunk segments ( S2 Fig ) . Using this method , we observed a separation between genotypes only when the T3 sample was considered . Altogether , these results point to the reproducibility of replicates and illustrate the good separation between tissues , with the exception of the T1 and T2 trunk samples . The high similarity in global gene expression between T1 and T2 likely reflects the spatial proximity of these two tissues , even though we cannot exclude some variation in the positioning of the precise T1/T2 boundary during dissection , which is a challenging task in such young embryos . Since Hotair was proposed to act as a repressor of gene expression in cultured fibroblasts [13 , 20] , we conducted tissue-specific differential gene expression analyses between wild type and Del ( Hotair ) -/- samples to assess such a potential function under physiological conditions . We only considered as significant an absolute expression fold change greater than 1 . 5 and we set the false discovery rate ( FDR ) threshold at 5% . By using these parameters , we observed between 64 and 588 protein-coding genes differentially expressed in the various tissues analyzed ( S3 and S4 Datasets ) . We first compared all tissues that express Hotair in the wild type condition , i . e . the T2 , T3 , GT and HL samples , reasoning that potential differentially expressed Hotair targets should be identified in these contexts . However , we were not able to identify any common genes with altered expression between wild type and Del ( Hotair ) -/- samples ( S3 Fig ) , suggesting that the Hotair deletion does not affect the same set of genes in all tissues analyzed . We thus divided the differential expression analysis based on the global expression clustering results . First , we compared the trunk samples T1 ( lacking Hotair expression ) , T2 and T3 . We identified 62 down-regulated genes and 13 up-regulated genes between wild type and Del ( Hotair ) -/- samples , which are shared in all trunk sections ( Fig 4A and 4B ) . Of note , we observed a common trend in gene expression differences in all trunk samples , even though only some of them passed the established thresholds ( Fig 4A ) . Gene ontology ( GO ) analysis for either common down-regulated or common up-regulated genes showed no enrichment of functional terms and the majority of differentially expressed genes were down-regulated , which was unexpected given the previously proposed role of Hotair as a repressor [20] ( Fig 4A and 4B ) . GO analysis of down-regulated genes in distinct Del ( Hotair ) -/- trunk tissues revealed significant enrichment ( FDR<10% ) in functional terms related to organ development and multicellular organismal process for most tissues ( S5 Dataset ) . Up-regulated genes in T3 were enriched for functional terms related to metabolic and biosynthetic processes and a weak enrichment for neuron differentiation genes was observed for T2 ( S5 Dataset ) . Differential expression analysis for FL , HL and GT showed no common genes with altered expression ( Fig 4C and 4D ) . GO analyses for differentially expressed genes in these tissues showed enrichment for functional terms related to development ( S5 Dataset ) . We next asked whether Polycomb target genes were preferentially up or down-regulated upon Hotair deletion . We defined putative target genes using H3K27me3 ChIP-seq data from wild type tail tip fibroblasts [20] , selecting genes with a minimum 5-fold enrichment between H3K27me3 ChIP and input DNA in the gene promoter region ( Materials and Methods ) and thus obtained 861 putative target genes ( S6 Dataset ) . We analyzed their pattern of differential expression in the T3 trunk sample , which includes the fetal tail and thus likely has the cell type composition in vivo most related to tail fibroblasts . Out of the 485 putative Pc targets that were expressed in the T3 segment ( RPKM >1 in at least one wild type or Del ( Hotair ) -/- sample ) , 60 genes were significantly differentially expressed ( absolute fold change > 1 . 5 and FDR <10% ) , including 50 down-regulated and 10 up-regulated in the Del ( Hotair ) -/- samples ( S4 Fig ) . This indicates that only 17% of all differentially expressed Pc targets were up-regulated in Del ( Hotair ) -/- samples , which is slightly lower than the proportion of up-regulated genes among non-targets ( 25% up-regulated genes out of 564 differentially expressed non-target genes , Chi-square test p-value 0 . 18 ) . Thus , we could not detect any enrichment for up-regulation of putative Pc target genes when compared to all other expressed protein-coding genes . Therefore , under these physiological conditions , we could not find evidence supporting a role for Hotair in setting up , maintaining or re-enforcing the repression of this set of Polycomb target genes . While physiologically relevant , our analysis is however difficult to directly compare with the situation in tail fibroblasts , as Polycomb occupancy naturally depends on both the tissue-type and the developmental stage . Interestingly , a subset of imprinted genes including H19 and Meg3 was shown to be up-regulated upon deletion of Hotair in TTF [20] . We thus analyzed the expression status of known imprinted genes transcribed ( RPKM>1 ) in at least one sample ( S7 Dataset ) . To ensure maximum sensitivity , we lowered our FDR threshold to 10% while maintaining an absolute expression fold change greater than 1 . 5 . With these parameters , we observed a total of 21 imprinted genes differentially expressed in our samples ( S5 Fig , S7 Dataset ) . We found that 71% of differentially expressed imprinted genes were down-regulated , while only 29% were up-regulated . Notably , H19 and Meg3 were down-regulated in our samples , in contrast to what was observed in TTF . In conclusion , these global transcriptome analyses comparing Hotair deletion mutant and wild type micro-dissected tissues revealed changes in gene expression upon deletion of the Hotair locus . Noteworthy , we observed numerous expression changes not only in tissues that normally express Hotair at detectable levels , i . e . the T2 , T3 , GT and HL samples , but also in tissues like the anterior trunk ( T1 ) or the forelimb ( FL ) , where Hotair lncRNAs were not detected . This suggests that such observed differences in gene expression cannot be explained by a mere direct effect of the Hotair RNA . Potential explanations to these unexpected observations are discussed below . The original observation , which led Hotair to become the paradigm of lncRNAs acting in trans , was its capacity to regulate several genes members of the HoxD cluster by interacting with components of the Polycomb repressive complex 2 ( PRC2 ) [13] . In contrast , no effect was initially reported upon Hoxc genes expression levels [13 , 20] , despite the fact that Hotair is encoded from within the HoxC locus in both humans and mice [13 , 19] . We re-assessed this issue by analyzing the expression of all Hox genes across our various tissue samples ( Fig 5 ) . The global expression patterns of all four Hox clusters expectedly corresponded to previously described expression patterns for such embryonic stage and body levels ( e . g . [24 , 25] ) . In order to detect even subtle effects of Hotair upon Hox gene regulation , we lowered our FDR threshold to 10% for differential expression analyses . Under these conditions , we detected significant down-regulation of some anterior Hoxa genes ( Hoxa3 , Hoxa5 and Hoxa6 ) in the three trunk samples ( Fig 5 and S6 Fig ) . Interestingly , these differences were present in all trunk samples , including in T1 where Hotair is not expressed . We also observed a slight up-regulation of Hoxb9 in HL and GT ( Fig 5 and S6 Fig ) . Notably , in some of the tissues analyzed , we detected significant expression changes for both Hoxc11 and Hoxc12 , i . e . the two genes in the HoxC cluster that flank the Hotair locus ( see below ) . However , in contrast with previous reports using tail fibroblasts [13 , 20] , we did not detect any significant change in the steady-state levels of Hoxd genes RNAs , in any of the analyzed tissues ( Figs 5 and 6A ) . To clarify this contradictory observation , we re-analyzed the previously published RNA-seq data from both wild type and Del ( Hotair ) -/- TTF [20] . By implementing our analytical pipeline , we could not detect any significant difference in expression for any of the Hoxd genes ( S7 Fig , S4 Dataset ) . Noteworthy , the expression levels of posterior Hoxd genes in this TTF dataset are either barely detectable or not detected at all , as for the Hoxd12 gene , for example , suggesting that previous conclusions were raised based on particularly low transcript levels . The deletion of Hotair was claimed to alter both the expression levels and the spatial transcript distribution of the Hoxd10 and Hoxd11 genes in the trunk [20] . We performed whole mount in situ hybridization ( WISH ) on both wild type and Del ( Hotair ) -/- littermates to appreciate potential variations in the expression domains of these two genes . By using our well established protocol , we found that Hoxd10 and Hoxd11 transcripts showed wild type distributions in Del ( Hotair ) -/- mutant specimen ( Fig 6B ) . To more precisely evaluate any potential difference in these expression domains between homozygous mutant and control littermates , we carried out double WISH for the Hox gene of interest in combination with a probe specific for the MyoD gene , which allowed for unambiguous somite visualization [26] . In both wild type and Del ( Hotair ) -/- embryos , Hoxd10 was expressed in the future spine up to the level of somite 26 , whereas Hoxd11 was scored from somite level 29 and caudally , as previously reported [27] . Neither Hoxd10 , nor Hoxd11 showed any detectable increase in the intensity of the signal or in their spatial expression pattern , confirming the RNA-seq results ( Fig 6A and 6B ) . Taken together , these observations suggest that Hotair has no effect on the regulation of Hoxd genes , at least in the developmental context and at the stage where the function of Hox genes is critical for morphological development . Unlike for Hoxd genes , our differential expression analyses between Del ( Hotair ) -/- and wild type samples revealed modest but significant changes for both Hoxc11 and Hoxc12 , the two genes neighboring the Hotair locus and thus flanking the deletion breakpoint ( Fig 5 ) . To further verify this new observation , we carefully analyzed the expression levels of both Hoxc11 and Hoxc12 in all tissue samples from where RNA-seq datasets had been obtained . As expected from their collinear transcription [28] , Hoxc12 transcripts were mainly detected in the most posterior T3 trunk sample . In this sample , a significant reduction in the level of Hoxc12 RNAs was scored in Del ( Hotair ) -/- specimen ( Fig 7A ) . On the other hand , Hoxc11 transcripts were detected in the hindlimbs ( HL ) , the genital tubercle ( GT ) and the T2 and T3 trunk samples . In these tissues , we observed an up-regulation of Hoxc11 RNAs upon deletion of the Hotair locus , which was statistically significant for both HL and T3 ( Fig 7B ) . In addition , a strong positive correlation between the expression levels of Hotair and Hoxc11 was detected in wild type samples ( S8 Fig ) . The correlation was weaker between Hotair and Hoxc12 expression ( S8 Fig ) . We asked whether these changes in expression of Hoxc genes in some Del ( Hotair ) -/- samples were accompanied by alterations in their spatial expression patterns . We analyzed the expression of both Hoxc12 and Hoxc11 by WISH in Del ( Hotair ) -/- E12 . 5 embryos and wild type littermates . We did not observe any change for Hoxc12 expression , neither in the transcript domain , nor in the intensity of RNA signal ( Fig 7C ) . In contrast , Del ( Hotair ) -/- embryos showed a clear rostral expansion of the Hoxc11 transcript domain in the trunk , as well as an increased signal intensity in both the hindlimb buds and the trunk , in agreement with our RNA-seq data ( Fig 7D ) . To understand more precisely the reason why the deletion of the Hotair locus impacted the transcription of the flanking Hoxc genes , we analyzed in details the RNA-seq profiles of the region comprising Hoxc12 , Hotair and Hoxc11 . We first asked if all transcript isoforms derived from the Hotair locus were abrogated in the Del ( Hotair ) -/- allele and observed that the deleted region almost perfectly coincides with the annotated boundaries of the locus in the mouse . However , the inspection of the RNA-seq profiles in tissues that transcribe Hotair RNA revealed the existence of larger transcripts , extending over at least 2 . 4 kb upstream of the annotated promoter ( Fig 8A ) . Although we cannot determine the precise location of Hotair transcription start site ( s ) , the presence of continuous transcription upstream of the annotated gene boundaries indicates that at least one , and probably two of the possible Hotair promoters were not deleted . Indeed in Del ( Hotair ) -/- tissues , we detected transcripts initiating upstream of the annotated Hotair promoter , for instance within the Hoxc11 intron , and spanning over the deleted region ( Fig 8A ) . In contrast to its multiple start site ( s ) , Hotair displayed a very sharp transcription termination site . In wild type tissues , transcription of Hotair terminated at the annotated site , with virtually no RNA-seq reads mapped downstream of this position ( Fig 8A ) . However , in the Del ( Hotair ) -/- samples , we observed transcription downstream of the deleted locus terminating within 100bp of the Hoxc12 termination site ( Fig 8A ) . The presence of this extended transcript , which likely derives from one of the native Hotair promoters ( as predicted with a de novo transcript assembly procedure , S9 Fig ) , likely resulted from the deletion of the wild type Hotair termination signals ( Fig 8A ) . To quantify this gain of transcription , we counted RNA-seq reads mapping on the region between the annotated Hotair termination site and the Hoxc12 termination site , on the Hotair strand . We referred to this transcript , which only appears upon deletion of Hotair , as Ghost of Hotair ( Ghostair ) . We observed significant gains of Ghostair transcription in Del ( Hotair ) -/- samples , in all tissues that expressed Hotair in the wild type condition , i . e . the hindlimb buds , the genital bud and the two trunk samples T2 and T3 ( Fig 8B ) . Subsequent analyses of the RNA-seq profiles revealed an additional un-annotated promoter sequence , yet located on the Hox DNA strand . This promoter lies between Hoxc12 and Hoxc11 and overlaps with a CpG island ( Fig 8A ) . In the wild type situation , it generates a relatively short , poorly abundant and un-spliced transcript , ca 1 . 8kb in size . The estimated termination site for this transcript was found within the region deleted in Del ( Hotair ) , close to the termination of Hotair itself on the other strand . Accordingly , we refer to this short transcript as Anti-Hotair ( AHotair ) . In Del ( Hotair ) -/- samples , this CpG island promoter was still active , giving rise to a much longer AHotair transcript ( Fig 8A , long AHotair or LAHotair ) , consistent with the deletion of its termination site . We did not observe any clear boundaries between this extended transcript and Hoxc11 , suggesting that this AHotair RNA could leak onto the Hoxc11 transcription unit . This was confirmed by a de novo transcript assembly procedure ( see Materials and Methods , S9 Fig ) . To quantify this gain of transcription from the Hox strand , we further defined two transcribed regions; The first one largely corresponded to the short Anti-Hotair transcript detected in wild type samples , starting at the CpG island promoter and ending at the boundary of the deleted region ( Fig 8A ) . The second one , long AHotair , corresponded to the longer transcript observed in Del ( Hotair ) -/- samples , starting at the deleted region boundary and ending at the annotated Hoxc11 transcription start site ( Fig 8A ) . In agreement with our observations based on the RNA-seq profiles , we detected significant increases in expression for both AHotair and LAHotair in Del ( Hotair ) -/- mutant tissues ( Fig 8C and 8D ) . Therefore , the deletion of the two annotated exons of Hotair [20] had a previously ignored important impact in cis by generating two new transcripts , which may potentially interact with the transcription of neighboring Hoxc genes .
In this study , we have re-investigated the phenotypic and molecular effects of deleting the Hotair lncRNA on mouse development in vivo , as reported in [20] . In this previous study , three phenotypic differences between wild type and Del ( Hotair ) -/- mice were reported , namely wrist malformation , a posterior homeotic transformation from lumbar vertebra L6 to sacral vertebra S1 identity and a mild anterior homeotic transformation of the 4th caudal vertebra . We did not detect any wrist malformation , nor did we see any substantial homeotic phenotype in the lumbar region of mutant animals , thus contradicting two of the three reported phenotypic effects of the Hotair deletion . In Mus musculus , the lumbo-sacral transition shows great variability between L5 and L6 depending on the inbred strain considered and the total number of pre-sacral vertebrae . In fact , this number not only varies between inbred strains but also within the same strain and can even be biased by the sex of the animal [29] . Therefore , this region must be considered with great care before concluding on the presence of a homeotic transformation . We note that another study involving two distinct deletion alleles of Hotair–though of smaller extents- also failed to confirm these latter two phenotypic effects [21] . The lack of effect of Hotair deletion upon wrist morphology is consistent with the absence of any detectable Hotair transcripts in mouse embryonic forelimbs ( Fig 1 ) also reported previously [19] and by [21] using a sensitive lacZ reporter transgene system . Regarding the reported L5 to L6 transition , it is noteworthy that in wild type animals , detectable expression of Hotair in the paraxial mesoderm , i . e . in the mesodermal tissue that will generate the vertebrae , barely reaches the level of the lumbo-sacral transition , a transition labeled by its neighboring Hoxc11 gene [30 , 31] . This makes a more anterior ( at the L5 to L6 transition ) Hotair loss-of-function dependent gain of function phenotype due to Hoxd genes difficult to understand . Our analyses did nevertheless reveal a subtle difference between wild type and Del ( Hotair ) -/- mice in the morphology of the post-sacral caudal vertebrae . Although this observation is in agreement with one of the previously described morphological alterations [20 , 21] , we note that the penetrance of the mutant phenotype is much lower than the 100% reported by [20] . Also , such an anterior transformation should reflect a loss of function rather than the effect of de-repressed Hox genes [1 , 3] , as already scored in some instances , for example when abrogating the function of the nearby located Hoxc13 gene [32] . Moreover , we also observed variations in these vertebral morphologies amongst wild type animals . A potential explanation for the observed difference in phenotypic penetrance may reside in the genetic background of the animals . In this work , we used a mixed CBAxBL/6 strain , while previous studies used a BL/6 background . This relatively mild difference in genetic backgrounds may account , at least in part , for the discrepancy regarding the penetrance of this weak and physiologically poorly significant morphological transformation . Should this be the case , we would still have to conclude that the previously reported phenotypic effects of Hotair deletion are not only very mild but also inbred strain-specific , definitely arguing against a general role–even minor- of Hotair during mouse development . Accordingly , we would refer to these phenotypic alterations as homeopathic rather than homeotic [20] . Using sensitive RNA-seq measurements , we showed that the expression of hundreds of genes changed significantly upon deletion of the Hotair locus in vivo . However , none of these changes in gene expression could be reconciled with the suggested role for Hotair in silencing gene expression in vivo [20] . In particular , the initial proposal that Hotair RNA acts in trans to repress the expression of posterior Hoxd genes and of a subset of imprinted genes via the recruitment of the PRC2 complex [13 , 20] was not supported by our results . Indeed we did not note any significant change either in the levels , or in the spatial distribution of Hoxd transcripts , in any of the tissues analyzed . Also , when a larger set of putative Pc target genes was considered , the same conclusion was reached ( S4 Fig ) . Finally , the majority ( 71% ) of the differentially expressed imprinted genes including the reported Hotair targets H19 and Meg3 , were down- rather than up-regulated in Del ( Hotair ) -/- mutant samples , again in contradiction with previous results . Therefore , our results are at odds not only with the phenotypic outcome of the Hotair deletion , but also with its effects upon gene expression [20] . One potential explanation to these serious discrepancies may be that the regulatory effect of Hotair is highly specific for tail tip or foreskin fibroblasts , which were previously used for functional investigations [13 , 20] , whereas not at work in vivo , precisely in those embryonic tissues where Hotair is expressed at the highest levels . Indeed both Hotair and Hoxd genes transcripts are rather abundant in our tissue samples , while they are very weakly present in murine tail tip fibroblasts ( Fig 1 , S7 Fig ) . Unless Hotair would function more efficiently at low concentrations , we conclude that our in vivo system is better suited to reveal the role of Hotair , if any . Another possibility is that the function of Hotair might not be exerted at the developmental stage analyzed ( E12 . 5 ) but instead , at other time points . This explanation is nevertheless not compatible with the absence of phenotypic effects on skeletal morphology at P22 , which should still be scored , should the deletion of Hotair deregulate target genes at other developmental stages . Also , the various genetic backgrounds may influence the penetrance of the phenotype ( see above ) and , by genotyping through the Hotair deleted locus , one may select for one particular haplotype associated with the mutant allele , which may result in some differential gene expression . Finally , it remains possible that a few hours difference in the developmental timing may lead to substantial relative variations in amounts of transcripts for many genes , in particular at an embryological stage where many important differentiation events occur . In this context , it must be noted that the settings used for our transcriptome analyses overlap in sensitivity with the biological variations of the system itself , as seen for example with the variation in the level of Hotair in the GT replicate samples ( Fig 1 ) . Such differences can be due to intrinsic variations , yet most likely to slight variations in the micro-dissection plans or in the developmental stage of littermate embryos , or both . For example , a slight variation in the thickness of the piece in the trunk would elicit quantitative differences in Hox gene expression , whereas the depth of the piece ( trunk ) or the proximal level of the section ( limbs , genitalia ) may involve another presumptive tissue type , leading to large qualitative differences in transcripts . In fact , many of the strongest differentially expressed genes are clearly unrelated to those developmental processes involved in the potential morphological or molecular phenotypes under scrutiny ( S10 Fig ) . This would also explain that differences are seen even in those samples where neither Hotair , nor Hoxc11 are expressed . Accordingly , we do not interpret these results as reflecting changes in biological processes but , instead , as a sign of the sensitivity and intrinsic variations of our in embryo approach . When investigating the roles of lncRNAs by genetic approaches in vivo , it is often problematic to separate the lncRNA-dependent effects from those generated by the manipulation of the corresponding genomic locus [33] . Hotair is transcribed from within the HoxC cluster , a tightly packed and gene-dense locus , and its deletion was reported to have no consequence on the transcription of the neighboring Hoxc genes at the developmental stage and cell types examined [20] . Here again , our results in embryo contradict this view and showed that the expression levels of both Hoxc11 and Hoxc12 changed upon deleting the Hotair locus . We observed an extension in the spatial distribution of Hoxc11 transcripts in both the trunk and the hindlimbs of Del ( Hotair ) -/- mutant specimens . Upon examination of the datasets of [20] , we also found differences for Hoxc10 and Hoxc12 between wild type and Del ( Hotair ) -/- tail tip fibroblasts ( S7 Fig ) . Therefore , the deletion of the Hotair locus had a significant impact in cis on Hoxc gene expression , in both in vivo and in vitro systems . This was confirmed by the observation of Ghost of Hotair ( Ghostair ) , a novel RNA produced by the anti-Hox strand in the deletion mutant allele . This transcript initiates at one of the alternative Hotair promoters , which was not included into the deletion , and terminates close to the 3’ end of the Hoxc12 transcript on the opposite strand . Our analysis also revealed the existence of AntiHotair , a previously un-annotated transcript on the Hox strand , derived from a CpG island promoter located close to the 3’ end of Hotair . While in the wild type situation this transcript remains relatively short and ends within the region targeted by the Hotair deletion , a longer AntiHotair transcript was produced in Del ( Hotair ) -/- samples with no clear separation with Hoxc11 . As a consequence , this transcript could leak onto Hoxc11 , acting as an alternative 5’ un-translated region , which gives a mechanistic basis for the light gain of Hoxc11 expression in Del ( Hotair ) -/- tissues ( Fig 9 ) . Such in-cis effects on the local transcription landscape by deleting transcription termination signals on both strands are likely independent from any possible Hotair function . The Hotair lncRNA is transcribed from within the HoxC gene cluster [13] , i . e . one of the most gene-dense and GC-rich regions of mammalian genomes [34] . Due to the particular regulatory strategy at work on the four Hox gene clusters [8] , any endogenous or exogenous promoter present within such a gene cluster will be transcribed at the place and time where the neighboring Hox genes will be activated . The transcription of Hotair is no exception to this rule , for transcripts are found posteriorly , roughly matching the expression domains of Hoxc11 or Hoxc12 . While it is indeed possible that Hotair exerts a genuine function during development , for example by micro-tuning the transcription of Hoxc genes in cis , the question as to whether or not this RNA could be a mere by-product of the complex regulation occurring in the gene cluster remains open , in our opinion . In any case , a potential mis-regulation of Hoxc genes should be carefully considered when investigating Hotair functions . This is desirable not only when studying developmental phenotypes [18 , 19 , 35] , where it may represent a confounding factor due to the known roles of Hoxc genes there , but also when studying the roles of Hotair in other biological processes including human diseases . For instance , it was reported that Hotair is overexpressed in breast cancer and that this RNA regulates metastasis by reprogramming chromatin via Polycomb complexes [36] . Our analysis of expression data obtained from a cohort of cancer samples [37] revealed a strong positive correlation between Hotair and Hoxc11 expression ( S8 Fig ) , also observed in our mouse wild type samples ( S8 Fig ) . Therefore , while Hotair may indeed be involved in a variety of cancer conditions , it is likely that its over-expression in cancer cells is accompanied by Hoxc11 over-expression , which may again confound the observed phenotypes . Thus far , four different alleles have been studied , which partially or entirely removed the Hotair lncRNA and no consensus has been found regarding a potential function of this RNA during mouse development [19–21] . In our hands , Hotair has no function during mouse development , a fortiori when the regulation of Hoxd genes in trans is concerned . The deletion of the locus engineered by [20] induces modifications in the transcription of some Hoxc genes , through complex re-allocations of promoter and termination sites leading to novel RNA species . This mis-regulation of Hoxc gene transcription may have a slight effect upon some vertebral morphologies , yet this impact–if any- would be poorly penetrant and inbred strain-specific , i . e . of little interest for our understanding of developmental processes at large . Yet another allele would be necessary to solve these discrepancies , whereby the CRISPR-cas9 technology would help abrogate the Hotair transcription without substantially modifying the in-cis environment . At this point however , we do not see the urgency of increasing the number of mutant alleles at this locus , as confounds due to genetic background differences may always blur the resolution of such subtle effects .
The Del ( Hotair ) mouse strain was described in [20] and kindly provided by Dr . H . Chang . Heterozygous mice were crossed back onto a mixed CBAxC57/B6 background ( Charles River ) . Wild type , heterozygous and homozygous mutant embryos were obtained by inter-crossing heterozygous mice . Genotyping was performed by PCR analysis on individual yolk sac lysates using the following primers: Maintenance of , and experiments on animals were approved by the Geneva Canton ethical regulation authority ( authorization GE/81/14 to D . D . ) and performed according to Swiss law . Whole-mount in situ hybridizations ( WISH ) were performed according to standard protocols . Embryos were dissected in PBS and fixed from overnight in 4% paraformaldehyde ( PFA ) , washed in PBS , dehydrated and stored in 100% methanol at –20°C . Both Del ( Hotair ) -/- and control wild type E12 . 5 littermates embryos were processed in parallel to maintain identical conditions throughout the WISH procedure . DIG-labeled probes for in situ hybridizations were produced by in vitro transcription ( Promega ) and detection was carried out using an alkaline phosphatase conjugated anti-digoxigenin antibody ( Roche ) . WISH probes templates were previously described in: Hotair [19]; Hoxd10 and Hoxd11 [27]; Hoxc11 [35]; Hoxc12 [34]and MyoD [26] . Whole mount skeletal preparation of P22 animals was done with standard Alcian blue/Alizarin red staining protocols . Embryonic tissues were stored at -80°C in RNAlater stabilization reagent ( Ambion ) before genotyping . After genotyping and embryo sorting , total RNA was extracted from tissues using QIAGEN RNeasy Plus Micro Kit after disruption and homogenization . RNA quality was assessed using an Agilent 2100 Bioanalyser . Only samples with high RNA integrity number were used . Sequencing libraries were prepared according to TruSeq Stranded mRNA Illumina protocol , with polyA selection . RNA-seq libraries were sequenced on an Illumina HiSeq 2500 sequencer , as single-end reads ( read length 100 base pairs ) . We obtained between 36 and 54 millions of raw RNA-seq reads for each sample ( S1 Table ) . Raw RNA-seq reads were aligned on the mouse mm10 genome assembly using TopHat 2 . 0 . 9 [38] . Gene expression computations were performed using uniquely mapping reads extracted from TopHat alignments and genomic annotations from Ensembl release 82 [39] . We filtered the annotated transcript isoforms for protein-coding genes , keeping only transcripts annotated as ‘protein-coding’ , thus discarding transcripts flagged as ‘retained_intron’ , ‘nonsense-mediated decay’ etc . For Hox genes , we manually inspected annotated transcripts and retained only the canonical isoform for each gene , discarding read-through transcripts and retained introns . For non-coding genes , all annotated isoforms were kept . We then constructed ‘flattened’ gene models by combining the exon coordinates from all retained isoforms and counted the number of unique reads that aligned on these exons . We discarded reads that aligned on two or more overlapping genes on the same strand , as well as reads containing more than 2 mismatches or small insertions or deletions . We computed RPKM ( Read per Kilobase of Exon per Million mapped reads ) expression levels for each gene based on the unique read counts . The total number of mapped reads was computed on the entire nuclear genome , discarding reads that mapped on the mitochondria . RPKM expression levels were then further normalized across samples with a median scaling procedure , using as a standard the 100 genes with the least expression rank variation across samples , found in the 25%-75% range of expression levels [40] . As a control , we also computed expression levels using all TopHat mapped reads and the multi-read and fragment bias correction procedures implemented in Cufflinks [41] . The same procedure was applied for previously published tail tip fibroblast RNA-seq samples [20] . The RNA-seq data presented in this previous publication were also strand-specific and generated with a dUTP protocol that sequences the antisense mRNA strand like the TruSeq Stranded mRNA protocol . The principal component analysis ( PCA ) was performed using the dudi . pca function in the ade4 package in R [42] . The input table for the PCA consisted of log2-transformed RPKM expression levels , for all protein-coding genes that had RPKM >1 in at least one of our samples . The data was centered ( meaning that the mean expression levels were brought to a value of 0 for each gene , removing between-gene variations in expression levels ) but not scaled prior to the PCA analysis . Euclidean distances between samples were computed with the standard dist function in R and clustered using the hierarchical clustering method ( hclust ) . All statistics and graphical representations were done in R . We tested for differential gene expression using DESeq2 [43] in R . Specifically , we contrasted a generalized linear model that explains the variation in read counts for each gene , as a function of the genotype ( wild type or Del ( Hotair ) -/- ) with a null model that assumes no effect of the genotype . The analyses presented in the manuscript were performed with the likelihood ratio test ( LRT ) ; the Wald test was performed as a control and results are provided in the supplementary datasets . The tests were performed separately for each tissue . The p-values were corrected for multiple testing with the Benjamini-Hochberg approach , for all six tissues at the same time . The same procedure was applied for previously published tail tip fibroblast RNA-seq samples , which included wild type , heterozygous Del ( Hotair ) +/- and homozygous Del ( Hotair ) -/- samples . In this case , we performed three separate pairwise comparisons between the three genotypes . We tested for gene ontology ( GO ) enrichment in the sets of differentially expressed genes using the GOrilla webserver [44] . Each enrichment analysis compared two lists of genes , the focal list containing differentially expressed protein-coding genes ( up-regulated and down-regulated genes analyzed separately ) and the background list containing all protein-coding genes expressed in the corresponding samples . To construct the background list , we computed the minimum number of reads observed for differentially expressed protein-coding genes , summed across all relevant samples and selected all genes that had equivalent or higher read counts . To obtain a list of putative Polycomb target genes , we analyzed chromatin immuno-precipitation followed by sequencing ( ChIP-seq ) data for H3K27me3 and corresponding input data , from wild type tail tip fibroblasts [20] . We mapped the ChIP-seq data on the mm10 mouse genome using Bowtie 2 [45] . We removed identical ChIP-seq reads to avoid biases stemming from PCR duplication and we kept unambiguously mapped reads with at most two mismatches . We computed the average H3K27me3 and input read coverage in the promoter region ( defined as 2kb upstream the annotated transcription start site ) for each Ensembl-annotated transcript . The same conclusions were reached when defining promoter regions as 4kb regions centered on the TSS ( S6 Dataset ) . The read coverage was normalized by dividing by the total number of million mapped reads for each sample . We defined putative Polycomb targets as those genes for which the ratio between the H3K27me3 and input was at least 5 , and for which the absolute H3K27me3 normalized coverage was at least 0 . 1 . We discarded genes that had satellite repeats in the promoter regions as we observed that these repeats are enriched in H3K27me3 marks ( likely as an artifact ) . The list of known mouse imprinted genes was extracted from http://www . geneimprint . com . We used RNA-seq data from our Del ( Hotair ) -/- samples , combined across all six tissues , to predict transcript sequences for Ghostair and AntiHotair . To do this , we first split each RNA-seq reads into three segments and aligned them with Bowtie 2 on the DNA sequence delimited by Hoxc12 and Hoxc11 . We then extracted all RNA-seq reads that mapped at least partially onto this region and assembled transcripts de novo using Trinity [46] setting SS_lib_type = R since our data was strand-specific . We kept Trinity contigs with a minimum length of 1000bp and aligned them on the mouse chromosome 15 using BLAT [47] . We manually excluded small , repetitive BLAT hits . See also S8 Dataset . To study the correlation between Hotair expression and the expression of neighboring genes Hoxc11 and Hoxc12 , we analyzed gene expression levels ( RPM = reads per total million mapped reads ) for a cohort of cancer samples [37] . | During mammalian embryonic development , Hox genes must be tightly regulated . It was proposed earlier that part of this regulation relies upon Hotair , a long non-coding RNA that recruits repressive protein complexes onto the HoxD gene cluster to keep these genes silent before they become activated . A genetic deletion of Hotair in mice induced homeotic transformations , thus supporting this hypothesis . However , other alleles involving this locus gave controversial results and hence we re-assessed the effect of the full deletion of Hotair in vivo . In our genetic background and using our analytical conditions , we could not confirm the reported morphological alterations , nor could we detect any mis-regulation of Hoxd genes in those fetal tissues where Hotair is detected in control animals . However , the genomic deletion induces the mis-regulation in-cis of the neighboring Hoxc11 and Hoxc12 genes , a side-effect which may underlie a weakly penetrant alteration observed in the shape of some tail vertebrae . | [
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| 2016 | Hotair Is Dispensible for Mouse Development |
In 2006 , we reported a mariner ( Mos1 ) -transformed Aedes aegypti line , Carb77 , which was highly resistant to dengue-2 virus ( DENV2 ) . Carb77 mosquitoes expressed a DENV2-specific inverted-repeat ( IR ) RNA in midgut epithelial cells after ingesting an infectious bloodmeal . The IR-RNA formed double-stranded DENV2-derived RNA , initiating an intracellular antiviral RNA interference ( RNAi ) response . However , Carb77 mosquitoes stopped expressing the IR-RNA after 17 generations in culture and lost their DENV2-refractory phenotype . In the current study , we generated new transgenic lines having the identical transgene as Carb77 . One of these lines , Carb109M , has been genetically stable and refractory to DENV2 for >33 generations . Southern blot analysis identified two transgene integration sites in Carb109M . Northern blot analysis detected abundant , transient expression of the IR-RNA 24 h after a bloodmeal . Carb109M mosquitoes were refractory to different DENV2 genotypes but not to other DENV serotypes . To further test fitness and stability , we introgressed the Carb109M transgene into a genetically diverse laboratory strain ( GDLS ) by backcrossing for five generations and selecting individuals expressing the transgene's EGFP marker in each generation . Comparison of transgene stability in replicate backcross 5 ( BC5 ) lines versus BC1 control lines demonstrated that backcrossing dramatically increased transgene stability . We subjected six BC5 lines to five generations of selection based on EGFP marker expression to increase the frequency of the transgene prior to final family selection . Comparison of the observed transgene frequencies in the six replicate lines relative to expectations from Fisher's selection model demonstrated lingering fitness costs associated with either the transgene or linked deleterious genes . Although minimal fitness loss ( relative to GDLS ) was manifest in the final family selection stage , we were able to select homozygotes for the transgene in one family , Carb109M/GDLS . BC5 . HZ . This family has been genetically stable and DENV2 refractory for multiple generations . Carb109M/GDLS . BC5 . HZ represents an important line for testing proof-of-principle vector population replacement .
The four serotypes of dengue viruses ( DENV1-4; Flaviviridae; Flavivirus ) are considered the most important mosquito-transmitted arboviruses infecting humans . Epidemiologists have estimated 100–390 million people per year acquire DENV infections in tropical and subtropical regions of the world [1] , [2] . Dengue disease symptoms range from mild febrile illness , referred to as dengue fever ( DF ) , to severe disease dengue hemorrhagic fever ( DHF ) and dengue shock syndrome ( DSS ) [3] . DENV prevalence is increasing rapidly throughout South-East Asia , and Central-and South-America due to rapid urbanization , increased trade and human traffic . DENV in these regions can be hyper-endemic [4] , further increasing the risk of DHF . Furthermore , virulent strains have been introduced in the last decades from South-East Asia into Central-America and the Caribbean replacing endogenous DENV2 genotypes and causing more cases of DHF and DSS among local populations [5]–[7] . Currently , there are no vaccines or therapeutic drugs readily available to the more than two billion people at risk for DENV infection or the tens of millions manifesting some level of disease [2] , [8] . Thus , DENV prevention relies on vector control through indoor insecticide spraying , using insecticide treated door/window curtains and reducing the number of potential oviposition sites [9]–[11] . The principal vector of DENV is the peridomestic mosquito , Aedes aegypti ( L . ) , which is distributed widely in many regions of the world and is a major factor contributing to the global incidence of DEN disease . Novel alternative vector control strategies are now being tested that use genetically-modified Ae . aegypti carrying a dominant-lethal gene ( RIDL ) to reduce mosquito populations [12]–[14] . A second novel concept in DEN disease control is replacement of DENV-competent mosquito populations with DENV-refractory vectors [15]–[17] . The work presented here describes the generation of a new Ae . aegypti transgenic strain , Carb109M/GDLS . BC5 . HZ , which expresses an anti-DENV2 gene construct and is highly refractory to the virus after being introgressed into a genetically diverse laboratory strain ( GDLS ) . Ae . aegypti females acquire a DENV-containing bloodmeal from a viremic human host . DENV initially infects midgut epithelial cells and 4–5 days later disseminates to hemocytes , fat body , nervous system tissues , and salivary glands . The mosquito can transmit virus to a new host 10 to 14 days post-infection ( dpi ) depending on ambient conditions , virus strain and mosquito competence [18] , [19] . DENV is confronted inside the mosquito cell by the innate antiviral , exogenous small interfering RNA ( siRNA ) pathway [20] . The antiviral , siRNA arm of the RNAi pathway is a major defense used by mosquitoes early in infection with arboviruses [21] . The sequence-dependent RNAi pathway has been described in great detail [22] . During a typical mosquito infection with DENV , 21 nt virus-derived siRNAs ( or viRNAs ) are readily detectable , indicating that the RNAi machinery degrades viral genomes [23]–[25] . We showed that DENV titers increase in vectors when the RNAi pathway is impaired leading to significantly higher midgut infections and dissemination rates and shorter extrinsic incubation periods [23] , [26] , [27] . Thus , the RNAi pathway modulates DENV replication in the mosquito and may keep virus concentrations below a threshold that could become detrimental to insect fitness . Even though Ae . aegypti has a highly functional antiviral RNAi pathway , the vector remains an efficient transmitter of DENV . DENVs may have evolved mechanisms to counter the mosquito antiviral RNAi response . Schnettler and colleagues reported that the 3′UTR of the DENV genome generates a subgenomic flavivirus RNA ( sfRNA ) that modulates RNAi as part of a counter-defense [28] . Our strategy has been to initiate a DENV2-specific RNAi response in midgut epithelial cells within the first few hours following acquisition of a viremic bloodmeal and thereby prevent the virus from establishing infection foci in these cells . This RNAi-mediated midgut infection barrier should prevent further accumulation of sfRNAs or other RNAi-modulating factors the virus uses to counteract the RNAi response . We hypothesize that perturbing homeostasis between vector RNAi defense and virus counter-response and establishing a specific antiviral RNAi response coincident with infection will reduce vector competence and lower disease transmission . The successful use of genetically-modified mosquitoes for gene replacement in the field requires stable effector gene expression over many generations and across diverse genetic backgrounds to maintain the DENV refractory phenotype . Furthermore , effector genes need to be designed that target each of the four DENV serotypes and the many different DENV genotypes that arise among serotypes . Ideally , these transgenes should have minimal fitness costs relative to wild-type mosquitoes or the transgenic mosquito is unlikely to spread the effector gene through the population [29]–[31] . Earlier studies suggest evidence of a fitness load associated with transgenes [32]–[34] . However , these studies failed to assess this effect in different genetic backgrounds . Without this information the effect of the transgene cannot be distinguished from the generally low fitness imposed by deleterious recessive alleles as they become homozygous in inbred lines . Assessing transgene loads absolutely requires its analysis in diverse genetic backgrounds [30] . Previously , we described the generation of a transgenic Ae . aegypti line , Carb77 , which was engineered to be refractory to DENV2 by expressing an IR effector RNA in midgut tissue for a 24–48 h period after receiving a bloodmeal [16] . Carb77 mosquitoes exhibited a strong midgut infection barrier for DENV2 . However , Carb77 mosquitoes eventually lost their refractory phenotype for DENV2 [17] . The transgene sequence was fully intact in Carb77 G17 , but the IR RNA was no longer detected by Northern blot analysis [17] . We speculated that the loss of IR-RNA expression could have been due to chromatin/heterochromatin rearrangements that enabled regulatory elements of neighboring or even distant genes to silence the transgene [35] . In this study we engineered new transgenic Ae . aegypti lines harboring the same transgene as line Carb77 to further evaluate genetic and phenotypic stability associated with the transgene . One of these lines , Carb109M , was highly refractory to DENV2 infection . To evaluate the effect of genetic background on transgene stability , we performed five generations of backcrossing into a genetically-diverse laboratory strain ( GDLS ) derived from field collections of Ae . aegypti from southern Mexico [13] , [36] . We selected for the EGFP eye marker associated with the transgene in each of the five backcross generations to produce six BC5 lines . The BC5 lines were subject to five generations of selection for the EGFP eye marker to increase the frequency of the transgene prior to final family selection . We were able to select one family , Carb109M/GDLS . BC5 . HZ , that has maintained the DENV2 refractory phenotype for multiple intercrossed generations . Our results illustrate clearly the importance of outcrossing transgenes into genetically diverse , preferably recently-colonized , strains before attempting to assess transgene-associated fitness loads and certainly before using genetically modified strains in population cage or field experiments .
All Ae . aegypti colonies were maintained in a BSL2/Arthropod Containment Level 2 ( ACL2 ) insectary as described [16] . The temperature in the insectary was 28°C with 75–80% relative humidity and a 12 h light/12 h darkness cycle . Adults were generally maintained in 1ft3 cages and fed on raisins . For routine rearing and maintenance , mated females ( approximately 1-week post-eclosure ) received artificial bloodmeals consisting of citrated sheep blood . Females were allowed to oviposit eggs on paper towel strips inserted into small water-filled oviposition cups . The eggs deposited on papers ( or eggliners ) were viable for up to 3–4 months before hatching . Eggs were hatched in pans containing sterile , de-aerated water to synchronize the hatch and larvae were fed ground TetraMin ( Melle , Germany ) aquarium food . Pupae were separated and placed into water-filled cups . Pupae identified as females based upon size were mixed with male pupae at a ratio of ∼1∶20 . Two cups with up to 500 female pupae were placed in a 1ft3 cage prior to eclosion . The mariner Mos1-based IR effector gene construct ( pMos-carb/Mnp/i/Mnp/svA ) was used to transform pre-blastoderm embryos of the Higgs White Eye ( HWE ) strain of Ae . aegypti [37] . The Mos1-based DNA constructs were identical to those used to generate Carb77 mosquitoes earlier ( Fig . 1A ) [16] . The inverted-repeat effector RNA ( IR-RNA ) sequence was derived from nucleotide position nt 401–969 ( prM-M/E coding region of DENV2-Jamaica1409 RNA genome sequence; GenBank: M20558 . 1 ) . Microinjection of donor ( pMos-carb/Mnp/i/Mnp/svA ) and mariner Mos1 transposase helper plasmids has been described [16] , [38] . Methods used to screen for transformation and identify new transgenic lines were identical to those described previously [16] , [38] . Surviving G0 adults were outcrossed to the HWE recipient strain and pooled to reduce the overall number of subsequent bloodfeeds as described in Fig . 1B and Table 1 . Eleven separate colony lines containing transgenic G1 individuals were formed and designated Carb1M , Carb1F , Carb22M , Carb96M , Carb96F , Carb109M , Carb109F , Carb175M , Carb175F , Carb194M , and Carb203F ( Table 1 ) . Following generation G4 , mosquito lines were increased through sib-matings . In the Method descriptions , we generically refer to the HWE-based transgenic lines as HTLs . The Carb52 transgenic line has no anti-DENV effector gene but does express EGFP in the midgut after a bloodmeal [39] . Carb52 was used as a DENV2-susceptible , transgenic control in virus prevalence assays . Effector gene expression in HTLs was characterized by Northern blot analysis . Total RNA was extracted using TRIzol ( Invitrogen , Carlsbad , CA ) from a pool of 20 midguts originating from female mosquitoes given a non-infectious bloodmeal . RNA was analyzed at 20 and 48 h post-bloodmeal or from mosquitoes receiving only sugar . Approximately 3–5 µg of pooled midgut RNA was separated on a 1 . 2% agarose gel by electrophoresis and blotted onto a positively-charged nylon membrane ( Applied Biosystems ) . Blots were hybridized overnight at 52°C with a random-primed 32P-dCTP labeled DNA probe ( Megaprime DNA Labelling Kit , Amersham Biosciences , NJ ) , corresponding to the prM-M encoding region of the DENV2-Jamaica1409 RNA genome . Effector gene integration in HTL mosquitoes was characterized initially by Southern blot analysis [40] . Each sample of total DNA was extracted from three females using the Puregene kit ( Qiagen , Valencia , CA ) . DNA pellets were suspended in 50 µl Hydration Solution overnight at room temperature . Approximately 5 µg of total DNA were digested with restriction endonucleases KpnI or PstI for 4 h at 37°C . DNAs were denatured and separated by electrophoresis on a 0 . 8% agarose gel and transferred to a positively-charged nylon membrane ( ‘Brightstar’ , Applied Biosystems , CA ) . Blots were hybridized with a [α-32P]dCTP- ( 3 , 000 Ci/mmol ) labeled probe corresponding to the 354 bp left arm of mariner Mos1 DNA generated with the DECAprime II Random Primed Labeling kit ( Applied Biosystems ) . Hybridizations were performed overnight at 48°C . The integration site of the transgene in a selected HTLs was determined using the Clontech GenomeWalker Universal kit ( Takara Bio Company , Mountain View , CA ) and Advantage2 polymerase mix [17] , [26] , [39] . Total genomic DNA was extracted from transgenic males and females using the DNeasy Blood & Tissue Kit ( Qiagen , Valencia , CA ) . DNA was digested with Dra I , EcoR V , Pvu II , or Stu I , and ligated to the GenomeWalker adaptor provided with the kit . Amplification products were generated using the Advantage2 polymerase ( Takara Bio Company ) . An initial amplification reaction was conducted using the outer mariner Mos1-specific primers maLeft FWD: 5′caattatgacgctcaattcgcgccaaac3′ , maRight REV: 5′gagcagcgcttcgattcttacgaaagtgtg3′ , and the forward and reverse adaptor primers of the Genome Walker kit [17] . The resulting amplicons were used to generate nested PCR amplifications . The mariner Mos1-specific primers for the nested PCR reactions were: maRight_nested REV 5′gacgatgagttctactggcgtggaatcc3′ and maLeft_nested FWD 5′gtggttcgacagtcaaggttgacacttc3′and the forward- and reverse-nested adaptor primers of the Genome Walker kit . Amplicons were inserted into the TOPO-TA cloning vector ( Invitrogen , Carlsberg , CA ) and sequenced using TOPO-TA vector-specific primers . The transgene integration and orientation within the AAEL010318 gene ( VectorBase supercontig 1 . 470 ) were confirmed in a standard PCR reaction using 500 ng of total DNA of a HTL mosquito and primer pairs 10318 FWD ( 5′ctcacacggcattacatgaaatatgttagtatttaatc3′ ) /maRight REV and 10318 REV ( 5′aacagtagcttgtatgcttaggcatactaattgag3′ ) /maLeft FWD . Females from a selected HTL were offered a non-infectious bloodmeal . Total RNA was extracted 20 h post-bloodmeal from pools of 50 midguts using TRIzol reagent ( Invitrogen ) . Total RNA was size-fractionated using the FlashPAGE fractionator ( Applied Biosystems ) . Small RNA libraries were made using the SOLiD small RNA expression kit ( Applied Biosystems ) and sequenced on a SOLiD 3 sequencer ( Applied Biosystems ) . Sequence data were analyzed using the NextGENe software package ( Softgenetics , LLC , State College , PA ) , Version 1 . 96 . Potential siRNAs were aligned to the DENV2 genome using the transcriptome assembly function . The reference genome was a FASTA file of DENV2-Jamaica1409 RNA . DENV isolates used in this study were: Jamaica1409 , C-932/Acapulco 97 ( AY449678 ) , Mex96 Merida ( AY449677 . 1 ) , QR94 Quintana Roo ( AY449676; JX966379 ) , and 14757 Yucatan [7] , [19] , [41] representing Asian 2 , Cosmopolitan , American , and American-Asian genotypes of DENV2 , respectively , and the 6889/QR-MX/97 isolate of DENV3 ( DQ341205 ) . DENVs were propagated in C6/36 cells at 0 . 01 multiplicity of infection ( m . o . i . ) for 12 days using Dulbecco's Modified Eagle Medium ( DMEM ) complemented with 3% fetal bovine serum ( FBS ) . The cell culture medium was replaced after a 6 day incubation period at 28°C . Virus in cell culture media was collected at 12 dpi and mixed with defibrinated sheep blood at a 1∶1 ratio . Females were fed for 1 h with the cell culture-blood mixture at 37°C using a single glass feeder per carton [16] , [23] . Engorged females were selected after bloodfeeding , reared in 1 . 9 L ( 64 oz . ) cartons and offered sucrose and water until further analysis . Chikungunya virus ( CHIKV; Alphavirus ) 37997 ( GenBank accession: AY726732 . 1 ) was propagated in Vero cells for 36 h at an m . o . i . of 0 . 01 using Minimum Essential Medium Eagle ( MEM ) complemented with 7% FBS . Bloodfeeding with virus was performed as described above for mosquito infections with DENVs [16] . Virus titers from individual mosquitoes were determined by plaque assay at 7 and 14 dpi [16] . Samples were homogenized in 0 . 5 ml 7% FBS-complemented DMEM and MEM medium , respectively . Homogenized samples were filtered with Acrodisc HT Tuffryn 0 . 2 µm syringe filters ( Pall Life Sciences , East Hills , NY ) . Vero cells ( for CHIKV ) or LLC-MK2 cells ( for DENV ) were seeded into 24-well plates and left for three days to achieve confluence . Cells were infected with 10-fold serial dilutions of each mosquito homogenate . Cells were incubated for 1 h at 37°C before overlaying with an agarose nutrient mixture [1x Medium 199 ( Sigma-Aldrich , St . Louis , MO ) , 10% FBS , 4% NaHCO3 , 0 . 5% MEM vitamins , 0 . 5% MEM amino acids ( Mediatech Inc . , Manassas , VA ) ; 1% Hanks-DMEM medium ( only used for DENV ) ] . Plates were incubated at 37°C for 4 and 12 days for CHIKV and DENV , respectively . Cells then were stained with MTT ( 3-[4 , 5-dimethylthiazol-2-yl]-2 , 5-diphenyltetrazolium bromide ) ( Sigma-Aldrich , St . Louis , MO ) , incubated at 37°C for 24 h and the number of plaques counted for each sample . Viral titers of individual mosquitoes were calculated as plaque forming units per milliliter ( pfu/mL ) . GDLS mosquitoes were bred by mixing equal numbers of larvae from 10 separately-maintained Ae . aegypti populations from Chiapas State , Mexico [13] . Five larvae from each of the 10 strains were placed into 1 liter of tap water , poured into a 4 liter plastic box and fed as described above . The resulting adult females were mated to males from the same box and then bloodfed . The eggs arising from these matings were used in the initial HTL x GDLS intercrosses and in each of the subsequent five backcross generations . Virgin HTL females were placed in a cage with virgin GDLS males in each backcross , and reciprocally , virgin HTL males were placed in a separate cage with virgin GDLS females . Females were bloodfed and allowed to oviposit . Eggs were hatched , raised to third instar larvae and larvae lacking EGFP eye marker expression ( wild-type homozygotes ) were counted and culled . Two hundred larvae expressing EGFP were reared to adults and virgin adults then were mated to virgin GDLS . This backcross procedure was repeated four more times to yield HTL/GDLS . BC5 . Theoretically , five generations of backcrossing should generate mosquitoes in which 31 of every 32 alleles ( 97% ) are expected to have originated from the GDLS strain . However , this is only true for those alleles unlinked to the HTL transgene [42] . Proportions of EGFP-expressing larvae in both HTL/GDLS . BC1 through BC5 were computed and compared by estimating the 95% Highest Density Interval ( HDI ) with WinBUGS [43] and the Credible Intervals for Proportions script [44] . An additional backcross ( BC6 ) was generated for some analyzes described in the Results section . Due to the number of backcrosses and the selection scheme in this study we limited our analysis of transgene frequency to two HTL lines generically termed here as HTL1 and HTL2 . Selection of HTL1 and HTL2 was based on the two lines having the strongest , most consistent DENV2 refractory phenotype . After the first backcross , four groups of fifteen lines were initiated to assess whether backcrossing affected transgene stability . The first set of 15 lines was initiated by crossing a HTL1/GDLS . BC1 heterozygote with a GDLS parent to generate offspring having an initial HTL1allele frequency of 0 . 25 . The offspring were allowed to inter-mate randomly . The expected frequency of EGFP-positive offspring through five generations ( F1–F5 ) was 0 . 4375 , based on Hardy-Weinberg expectations ( 0 . 252 transgene homozygotes +2*0 . 25* ( 1–0 . 25 ) transgene heterozygotes ) . The second set of 15 lines was generated by intercrossing HTL1/GDLS . BC1 heterozygotes so that the initial frequency of the HTL1 allele was 0 . 5 . The expected frequency of EGFP-expressing larvae in F1–F5 was 0 . 75 ( 0 . 52 transgene homozygotes +2*0 . 5* ( 1–0 . 5 ) transgene heterozygotes ) . The third and fourth sets of 15 lines were the same as for the first and second sets except that HTL2/GDLS . BC1 were used . These 60 lines were maintained without selection of EGFP expressing larvae for five generations and the frequency of EGFP larvae was estimated in ∼150 larvae from each of the 15 lines in each generation . This same process was repeated for the BC5 offspring . The relative fitness loads of the transgene in homozygotes , heterozygotes and fitness of wild-type homozygotes were estimated by identifying the fitness coefficients in Fisher's Selection Model [45] that most closely fit the observed proportion of larvae expressing EGFP ( pEGFPt ) in each of the six generations . Fisher's Model is: ( 1 ) where: pt = transgene frequency in generation t , wAA = fitness of transgene homozygotes , wAa = fitness of transgene heterozygotes and waa = fitness of wild-type homozygotes . A FORTRAN program was written that generated a three-dimensional matrix containing all combinations of WAA , WAa and Waa each incremented by 0 . 01 from 0 . 0–1 . 0 . The matrix therefore contained 100×100×100 = 106 combinations . Fisher's model was run for five generations starting with a p0 ( starting allele frequency ) of either 0 . 5 or 0 . 25 for each combination . pt were transformed into proportion predicted EGFP expressing larvae in each generation t ( pEGFPt ) by: ( 2 ) All six values of pEGFPt were compared with observed proportions oEGFPt for t = 0… . 5 generations as: ( 3 ) The FORTRAN program identified the smallest diff and reported the associated wAA , wAa and waa values . Statistical comparisons among groups , generations and backcrosses were based on calculating Bayesian 95% Highest Density Intervals ( 95% HDI ) using WinBUGS [43] and the estimation of mean and variance script [44] . Proportions with non-overlapping 95% HDI were considered credibly different . Three crosses between HTL1/GDLS . BC5 heterozygotes ( 200 individuals/cross; F . 1 , F . 2 , F . 3 ) and three crosses between HTL2/GDLS . BC5 heterozygotes ( 200 individuals/cross; M . 1 , M . 2 , M . 3 ) were performed in six separate 1ft3 cages . One-week-old females received non-infectious bloodmeals as described above . All larvae from each cross were screened for EGFP expression and all wild-type larvae were culled . EGFP-expressing F1 larvae were reared to adults . Following random mating , females received bloodmeals and their F2 progeny were again screened for EGFP expression and wild-type larvae were culled . This procedure was followed for three more generations ( F3–F5 ) to increase the frequency of the HTL1 or HTL2 transgene in the population while minimizing inbreeding . Results were again compared to values expected under Fisher's Model ( eq . 1 ) . Only wAA and wAa were estimated because waa = 0 since all wild-type larvae were discarded during the selection process . Frequencies of EGFP-expressing larvae were recorded for five generations for each of the six lines . Mean observed-to-expected proportions were compared by estimating the 95% HDI with WinBUGS and the Estimating proportions script [44] . At the end of five generations of selection and assuming WAA = WAa = 1 , Fisher's model simplifies to ( 4 ) and predicts that 98 . 22% of larvae were expected to express EGFP , and the transgene frequency was expected to be 0 . 83 . Another 25 generations of selection would be required before nearly all larvae ( 99 . 9% ) could be expected to express EGFP . We therefore switched to a family based selection scheme . To generate HTL homozygous ( HZ ) families from the six lines ( HTL1/GDLS . BC5 F . 1 , F . 2 , F . 3 and HTL2/GDLS . BC5 M . 1 , M . 2 , M . 3 ) , 30 families were established each consisting of three F5 females placed in a cage with one male . Siblings from each of the thirty families were screened for EGFP expression and families with all siblings expressing EGFP were reared to adults and intercrossed . These offspring were reared to adults , intercrossed , bloodfed , eggs collected and hatched . These offspring were again screened for EGFP expression . Families , in which all siblings expressed EGFP were combined and maintained as homozygous ( HZ ) lines for further experiments . Ultimately this process yields one or more HTL/GDLS . BC5 . HZ lines which can be tested for vector competence to DENV2-Jamaica1409 .
Transgenic lines were generated using the transgene described previously [16] to test whether the RNAi-based DENV2 refractory phenotype in Carb77 could be repeated and this time be genetically- and phenotypically-stable over time . We co-injected 1 , 505 pre-blastoderm HWE embryos with the pMos-carb/Mnp/i/Mnp/svA donor and mariner Mos1 transposase helper plasmids ( Fig . 1A , B ) . We obtained 206 G0 larvae of which 191 developed into fertile adults . Each of the 111 G0 males was mated with 15–20 HWE females . Following a 2–3 day mating period , three of the crossings were combined into one pool each , reducing the number of 111 single crossings to 37 pools ( Fig . 1B ) . The 80 G0 females were combined into three pools , each containing three HWE males . Three eggliners were generated from each of the 40 pools . Larvae expressing the EGFP marker were observed among seven pools: P1 , P22 , P96 , P109 , P175 , P194 , and P203 ( Table 1 ) . All pools except P1 were based on male transgenic G0 founders ( originating from micro-injected embryos ) . Transgenic male ( M ) and/or female ( F ) G1 mosquitoes from each pool were outcrossed separately to HWE resulting in 11 lines ( Table 1; column 4 ) and their progeny were reared as separate colonies . Lines Carb96M and Carb175M were lost in a subsequent generation and before we could analyze their DENV2 refractory phenotypes ( Table 1 ) . The remaining nine transgenic lines were initially screened to identify which of them had a refractory phenotype for DENV2 infection . Mosquitoes from each line were offered bloodmeals containing 1 . 5×106–1 . 6×107 pfu/mL of DENV2-Jamaica1409 . Fully-engorged transgenic G3 females were analyzed for DENV2 at 7 and 14 dpi by performing plaque assays in LLC-MK2 cells ( Fig . S1A , B ) . DENV2 prevalence in Carb1M , Carb1F , Carb22M , and Carb96F at 7 and 14 dpi was not significantly different from that observed in the DENV2 competent control lines ( HWE and Carb52 ) and as a consequence these lines were not further used in this study ( Table 1 ) . DENV2 prevalence in lines Carb194M and Carb203F was significantly lower than prevalence in HWE and Carb52 controls ( Carb194M , p<0 . 01; Carb203F , p<0 . 02 ) , but these lines were lost in subsequent generations ( Table 1 ) . In contrast , transgenic lines Carb109M , Carb109F , and Carb175F were highly resistant to DENV2 infection and exhibited a significantly lower DENV2 prevalence than control mosquitoes ( Carb175F , p<0 . 004; Carb109M and Carb109F: p<0 . 0001 ) . Further , all three lines were readily maintained in colony . However , in later generations Carb175F mosquitoes displayed a significantly weaker DENV2 refractory phenotype than Carb109M and Carb109F ( data not shown; Table 1 ) . Consequently , Carb175F was not used in the introgression studies . Carb109F and Carb109M were maintained as separate lines throughout this study because the two lines arose from a G1 female ( F ) and male ( M ) of pool 109 , respectively . After further characterizations of the two lines having strong DENV2 refractory phenotypes , we selected Carb109F and Carb109M as HTL1 and HTL2 described in Methods for introgression and fitness analysis . Northern blot analysis detected a single hybridizing moiety in Carb109M and Carb109F total midgut RNA 20 h after mosquitoes had received a non-infectious bloodmeal . This corresponded to the expected 568 nt size of the mature IR-RNA ( Fig . S2A ) . The signal was not detected in RNA samples extracted at 48 h post- non-infectious bloodmeal or in RNA samples obtained from sugar-fed females . IR RNA also was not detected in the midguts of HWE or Carb52 lines [39] . Similarly to Carb109M and Carb109F , we detected IR RNA expression by northern blot analysis in Carb175F midguts at 20 h post bloodmeal ( Fig . S2B ) . However , the hybridization signal was faint even after long exposure times of the northern blot and consistent with the weaker virus resistant phenotype especially in later generations . Significantly , midgut-specific IR-RNA expression continued to be detectable in a GDLS genetic background after introgression of the Carb109F and Carb109M transgenes ( Fig . S2C ) . We performed NextGen sequencing with Carb109M to detect size and specificity of small RNAs generated from the IR-RNA of the effector gene . NextGen sequencing of size-fractionated , total RNA from Carb109M midguts at 20 h post-bloodmeal was performed to determine whether the expressed IR-RNA was processed as a double-stranded RNA by the vector's RNAi machinery . The DENV2-specific small RNA profile revealed that the IR RNA was processed into siRNAs within hours of Carb109M mosquitoes ingesting a bloodmeal containing no virus ( Fig . 2 ) . Alignment of the small RNA sequences to the whole genome of DENV2-Jamaica1409 revealed a cluster of small RNAs from nucleotide positions nt 450–1000 mapping to the prM-M coding region ( Fig . 2A ) . Alignment of the small RNA sequences to the 568 nt region of DENV2-Jamaica1409 RNA targeted by the IR effector sequence had significant overlap with sequences at nucleotide positions corresponding to the prM-M coding region ( Fig . 2B ) . Small regions of complementarity were concentrated at nucleotide positions 260–440 , with representation reaching up to 220 read counts per small RNA ( Fig . 2B ) . Size-distribution plotting of small RNAs showed that the predominant size was 21 nucleotides; the hallmark of the antiviral siRNA pathway ( Fig . 2C ) . Interestingly , there was a bias towards siRNAs in antisense orientation even though it was expected that RNAi degradation of the IR effector would have produced equal proportions of sense- and antisense-oriented siRNAs . Southern blot analysis with a [α-32P]dCTP DNA probe derived from mariner Mos1 left arm sequence ( Fig . 1A ) indicated that Carb109M had two transgene integrations ( Fig . 3A ) . If a single integration event occurred then a KpnI restriction endonuclease digestion of Carb109M DNA should detect a single junction fragment ( 1389 bp of transgene specific DNA plus vector DNA ) . A PstI digestion of Carb109M DNA should also detect a single junction fragment ( 1 , 476 bp of the transgene plus vector DNA ) . Instead , KpnI digestion and Southern blot analysis of Carb109M detected two junction fragments ( ∼5000 bp and ∼6000 bp ) supporting the conclusion that the transgene was integrated at two loci in Carb109M . PstI digestion and Southern blot analysis of Carb109M confirmed this finding by detecting two junction fragments ( ∼3500 bp and >10 , 000 bp ) . In contrast , Southern blot analysis of Carb175F DNA showed integration of the transgene at a single locus ( Fig . 3A ) . The transgene integration patterns of Carb109M and Carb109F were identical by Southern blot analysis and maintained the same pattern after introgression of the transgene into the GDLS genetic background ( Fig . 3B ) . Genome walking was performed with Carb109M and identified one of the two transgene integration loci in the 3′ UTR of gene AAEL010318 ( VectorBase supercontig 1 . 470 ) , encoding a polyadenylate binding protein , mapping terminally at 70 cM of chromosome 3 , and in physical division 3q4 . 4 as determined by Fluorescent in situ Hybridization ( FISH ) [46] . The integration of the TE occurred at a canonical TA recognition sequence motif resulting in target-site duplication . The second transgene integrated in a repetitive ( >50 copies ) sequence motif and was atypical by including a 920 bp portion of the pMos1 plasmid backbone extending from the left arm of the TE ( data not shown ) . The physical integration pattern of the second integration event extending from the right arm of the TE was not detectable by genome walking . The site of integration ( s ) in Carb109F has not been analyzed by genome walking . Carb109M ( G10–G33 ) mosquitoes were infected with ∼106 pfu/ml of DENV2-Jamaica1409 via artificial bloodfeeding . The prevalence at 7 and 14 dpi was only 0 . 5% ( 2/360 ) and 1 . 7% ( 6/356 ) , respectively ( Fig . 4 ) . The HWE control had >35% prevalence at either time point with mean DENV2 titers of 1000 and 9 , 000 pfu/ml at 7 and 14 days dpi , respectively . The absence of DENV2 titers in Carb109M mosquitoes demonstrates clearly that the refractory phenotype was stable over multiple generations even though occasionally , single Carb109M females had detectable DENV2 titers ( Fig . 4 ) . Carb109M mosquitoes also were resistant to different Mexican DENV2 isolates , C-932/Acapulco 97 , Mex96 Merida , QR94 , and 14757 Yucatan , representing Asian 2 , Cosmopolitan , American , and American-Asian genotypes , respectively ( Fig . 5 ) . The sequence of the IR gene derived from the DENV2-Jamaica1409 genome had >86% minimal nucleotide identity with isolates representing the four DENV2 genotypes isolated in Mexico ( Fig . S3 ) . Mean DENV2 titers in HWE at 14 dpi were 340 pfu/ml for C-932/Acapulco 97 ( 32% prevalence ) , 9100 pfu/ml for Mex96 ( 32% prevalence ) , 2700 pfu/ml for QR94 ( 30% prevalence ) , and 53 , 000 pfu/ml for 14757 ( 52 . 6% prevalence ) . Prevalence in Carb109M females never exceeded 8 . 3% as shown for DENV2 Mex96 . Carb109M mosquitoes challenged with DENV3 or with an unrelated arbovirus ( CHIKV ) did not show a refractory phenotype to these viruses ( Fig . S4 and S5 ) . At 7 days dpi , HWE mosquitoes had mean DENV3 6889/QR-MX/97 titers of 1300 pfu/ml and Carb109M had mean virus titers of 750 pfu/mL ( Fig . S4 ) . There is <60 % sequence identity in the 568 nt target region between the genome of DENV3 6889/QR-MX/97 and that of DENV2-Jamaica1409 , the source of the Mnp effector gene sequence . At 7 dpi , DENV3 prevalence was similar between HWE , Carb109M , Carb175F , and the Carb52 control . However , at 14 days dpi , DENV3 prevalence was significantly higher for HWE ( 80% ) than for Carb109M or Carb175F ( 55% ) ( Fisher's exact test , p = 0 . 0307 ) . Infecting the same mosquito lines with CHIKV 37997 showed no statistical differences in prevalence ( 90–100% ) ( Fig . S5 ) . All these data confirm the sequence-dependent nature of the resistance mechanism in Carb109M mosquitoes . We assessed whether the DENV2 refractory phenotype was maintained after introgression of the Carb109 transgene into a diverse genetic background . The GDLS strain is highly susceptible to DENV2-Jamaica1409 at 7 and 14 dpi ( Fig . 6 ) . Following five consecutive backcrosses to GDLS , Carb109F/GDLS . BC5 and Carb109M/GDLS . BC5 mosquitoes remained highly refractory to DENV2 infection at both time points , similar to line Carb109M , although Carb109F/GDLS . BC5 tended to be more susceptible to the virus at 14 dpi than Carb109M/GDLS . BC5 ( Fig . 6 ) . However , Carb109F/GDLS/BC5 and Carb109M/GDLS . BC5 mosquitoes had no significant difference with regard to DENV2 resistance . HWE , GDLS and BC5 Neg ( negative for the EGFP marker ) mosquitoes showed a significantly higher prevalence of DENV2 ( 59 . 6–67 . 5% ) than Carb109M , Carb109F , Carb109M/GDLS . BC5 , and Carb109F/GDLS . BC5 mosquitoes ( 1 . 8–7 . 4% ) at 7 and 14 dpi ( Fig . 6 ) . Furthermore , as stated earlier both transgene integrations were detected by Southern blot analysis from Carb109M/GDLS . BC5 and Carb109F/GDLS . BC5 mosquitoes ( Fig . 3B ) . Finally , homozygous line Carb109M/GDLS . BC5 . HZ was highly refractory to DENV2 with only 3/160 mosquitoes having detectable virus titers ( <200 pfu/mL ) at 7 or 14 dpi ( Fig . 7 ) . The 95% HDI for the proportion of larvae expressing EGFP overlapped among all of the five backcross generations of both Carb109F/GDLS and Carb109M/GDLS ( Fig . 8 ) . Furthermore , all 95% HDI contained the expected 0 . 5 proportion expected for heterozygotes . The frequencies of EGFP-expressing larvae were compared among Carb109F/GDLS . BC1 , Carb109M/GDLS . BC1 , Carb109F/GDLS . BC5 and Carb109M/GDLS . BC5 at starting frequencies of 0 . 25 and 0 . 5 . Frequencies were measured over five generations without selection for EGFP ( Fig . 9A , B ) . In all BC1 lines , the proportion of larvae expressing EGFP declined rapidly and reached zero by the fifth generation . This occurred whether p0 was 0 . 5 ( Fig . 9A ) or 0 . 25 ( Fig . 9B ) . Estimated fitness coefficients ( Table 2 ) among these four BC1 variants ( Carb109F/GDLS . BC1 , p0 = 0 . 25 , Carb109M/GDLS . BC1 , p0 = 0 . 25 , Carb109F/GDLS . BC1 , p0 = 0 . 5 , Carb109M/GDLS . BC1 , p0 = 0 . 5 ) were WAA = 0 . 01 in all four experiments while WAa varied from 0 . 11–0 . 21 and Waa varied between 0 . 94–1 . 00 , respectively . This supports the conclusion that either the transgene has a dominant fitness load or that a deleterious allele ( s ) was linked ( in cis ) to the transgene insertion site . In contrast , the proportions of EGFP-expressing larvae in Carb109M/GDLS . BC5 and Carb109F/GDLS . BC5 when initiated at p0 = 0 . 5 were 0 . 4881 and 0 . 4782 in F5 . However , the 95% HDI surrounding these frequencies did not contain the predicted 0 . 75 allele frequency . Again the predicted fitness coefficient for transgene homozygotes was WAA = 0 . 01 in all four experiments while WAa varied from 0 . 94–1 . 00 , which actually exceeded Waa ( 0 . 65–0 . 93 ) in the four BC5 experiments . The proportions of EGFP expressing larvae in Carb109F/GDLS . BC5 and Carb109M/GDLS . BC5 initiated at p0 = 0 . 25 ( Fig . 9B ) were 0 . 4538 and 0 . 2365 in F5 . However , only the 95% HDI surrounding frequencies in Carb109F/GDLS . BC5 ( BC5F in Fig . 9 ) contained the predicted 0 . 4375 frequency . The fact that the predicted fitness coefficient for transgene homozygotes was very low ( WAA = 0 . 01 ) , whereas fitness coefficients for transgene heterozygotes ( WAa = 0 . 94–0 . 95 ) and those for wildtypes ( Waa = 0 . 81–0 . 93 ) were significantly higher supports the interpretation that the wild-type allele had a dominant positive effect on fitness . Thus , five generations of backcrossing did not change the fitness of transgene homozygotes but greatly improved the fitness of transgene heterozygotes . Selection was applied to three lines each of Carb109F/GDLS . BC5 and Carb109M/GDLS . BC5 heterozygotes . In each generation wild-type larvae were culled and mosquitoes were allowed to inter-mate . This was repeated over four consecutive generations ( Fig . 10 ) . Observed values were compared with values predicted from equation 4 . Replicate F . 1 remained below predictions of Fisher's model from generations 2 through 5 ( Fig . 10A ) . Replicate F . 2 reached predictions for generation 2 but then remained lower than predictions from generations 3 through 5 . Replicate F . 3 reached model predictions for generation 4 but was lower than predicted in generation 5 . The 95% HDI surrounding proportions of EGFP-expressing larvae in generation 5 for all three F replicates did not cover the expected 0 . 9822 allele frequency . Replicate M . 1 started below the expected 0 . 75 value but exceeded predictions in generation 2 and overlapped predictions in generations 3–5 ( Fig . 10B ) . Replicate M . 2 fell below predictions for generation 2 and 3 but reached predictions in generations 4 and 5 . Only replicate M . 3 tracked model predictions in all generations . The 95% HDI surrounding proportions of EGFP-expressing larvae in generation 5 for all three M replicates contained the expected 0 . 9822 allele frequency . Among the three F replicated lines the fitness coefficients of transgene homozygotes ( WAA ) ranged from 0 . 32–0 . 51 , a dramatic improvement over the BC5 values of WAA = 0 . 01 ( Table 2 ) . Fitness of transgene heterozygotes ( WAa = 0 . 68–1 . 00 ) on the other hand overlapped the BC5 values ( WAa = 0 . 95 ) . Among the three M replicates the fitness coefficients of transgene homozygotes ( WAA ) ranged from 0 . 72–0 . 77 , far exceeding the BC5 values of WAA = 0 . 01 ( Table 2 ) . However , fitness of M replicate transgene heterozygotes ( WAa = 0 . 75–0 . 86 ) was lower than the BC5 values ( WAa = 0 . 94–1 . 00 ) . Family-based selection was conducted in 30 families in each of replicates M . 1–M . 3 and F . 1–F . 3 . However , we were successful in breeding only one homozygous family ( Carb109M/GDLS . BC5 . HZ ) by family-based selection on Replicate M . 3 .
We showed in an earlier proof of principle study that Ae . aegypti can be genetically modified for DENV resistance by establishing an RNAi-based infection barrier in the midgut [16] . However , Carb77 eventually lost its refractory phenotype to DENV2 before we completed introgression of the transgene into a more genetically diverse vector population . Here , we have developed and characterized a panel of new transgenic Ae . aegypti lines and have identified additional lines refractory to DENV2 and suitable for introgression studies . In previous , unpublished experiments ( Dr . Bill Black , CSU ) a transgene was introgressed into GDLS and after 5 backcrosses , 4 generations of selection and construction of the final strain from only 34 families there was only a very small decrease in genetic diversity ( a drop from 0 . 257 to 0 . 251 ) . Allele frequencies did not change at four of 10 loci . Given the very large numbers of genomes involved in these experiments , we considered it unlikely that these shifts arose via genetic drift but rather reflect laboratory adaptations . Our conclusion was that the transgene introgression strategy into GDLS was effective in maintaining genetic diversity . We report the development of a mosquito line ( Carb109M/GDLS . BC5 . HZ ) expressing an antiviral effector RNA in the midgut , and displaying a refractory DENV2 phenotype . This line should be suitable for analyzing and modeling transgene spread between mosquito populations using strategies such as Reduce and Replace [47] , [48] . The development and characterization of one transgenic line , Carb109M , resulted in five key findings . First , the transgenic genotype and DENV2 refractory phenotype of Carb109M mosquitoes in a HWE genetic background has remained stable over 33 generations . Second , the DENV2 refractory phenotype remained stable after five consecutive backcrosses into a genetically diverse background followed by four generations of selection and two generations of family-based selection . Third , backcrossing the transgene dramatically improved the fitness of the transgene heterozygotes from WAa = 0 . 11–0 . 21 after one backcross to WAa = 0 . 94–1 . 00 after five generations of backcrossing . However , the fitness of transgene homozygotes remained at WAA = 0 . 01 after five generations of backcrossing . Fourth , four generations of selection improved transgene homozygous fitness from WAA = 0 . 01 to 0 . 32–0 . 51 in the F replicates and from WAA = 0 . 01 to 0 . 72–0 . 77 in the M replicates . As discussed below , this is consistent with the presence of a deleterious recessive allele linked with the transgene but which gradually became unlinked with recombination . Fifth , family based selection involving 180 families yielded only one homozygous line ( Carb109M/GDLS . BC5 ) . HZ line . This line was highly refractory to DENV2 . We observed a few Carb109M/GDLS . BC5 . HZ individuals infected with DENV2 . This also was observed with Carb77 mosquitoes and may be a consequence of using artificial bloodmeals , which occasionally might lead to a leaky midgut phenomenon [49] . A concern is the possibility that the antiviral IR-RNA and siRNAs from the transgene promote selection for viral escape mutants , capable of evading our RNAi-based strategy . However , our results show that nucleotide sequence diversity over the 568 nt target region of the DENV2-specific RNAi trigger can reach 14% without compromising the sequence-dependent RNAi degradation mechanism . This presumably occurs because a number of siRNAs derived from the 568 bp effector RNA have exact matches with the sequence of each of the DENV2 genotypes . At this point we consider it unlikely that the effector RNA of Carb109M could select for a viable DENV2 mutant that has >14% sequence diversity in the prM-M encoding region of its viral RNA to escape the RNAi response in the transgenic mosquitoes . Nevertheless , the possible occurrence of RNAi escape mutants will be addressed in future experiments . The transgene integration pattern in Carb109M may account for the strong DENV2 resistance phenotype . In contrast to Carb77 , which had a single transgene integration event , Carb109M and Carb109F mosquitoes appear to have two independent insertions , which potentially result in an increased dose of the IR-RNA effector . If the quantity of IR-RNA produced in a cell is a rate-limiting factor determining the efficiency of the RNAi response , higher IR-RNA expression would degrade a proportionally higher number of target RNAs at any given time . The site of transgene integration cannot be targeted using a TE such as mariner Mos1 as the insertion vector , and this may lead to position-effects impacting transgene expression . A reason for the loss of the resistance phenotype in Carb77 mosquitoes could have been chromatin re-arrangements that led to silencing of the IR effector gene expression while leaving the transgene intact at the DNA level [33] , [35] . In contrast , Carb109M had an integration of the transgene in the 3′UTR of the polyadenylate binding protein gene ( AAEL010318 ) and another in a highly repetitive DNA motif . At this point we cannot explain why the integration pattern of the transgene in Carb109M mosquitoes produced more stable effector RNA expression than that observed in Carb77 mosquitoes . Two strategies can be employed to avoid transgene position effects in arthropods . One strategy is the use of a site-specific recombination system such as φ C31 [39] , [50] . Another strategy is the use of chromatin insulators such as those derived from the Drosophila gypsy retrotransposon [51] , [52] , which have not currently been used in Ae . aegypti . We propose that the initial rate and pattern of decline in the frequency of EGFP-expressing BC1 and BC5 larvae maintained without selection is consistent with the presence of deleterious or lethal recessive allele ( or allele ) originating from the HWE chromosome into which the transgene was originally inserted . This would explain why five generations of backcrossing increased the fitness of transgene heterozygotes but not homozygotes . Only a single wild-type allele would have been sufficient to cover the deleterious recessive allele linked to the transgene in heterozygotes . It is unlikely that the transgene itself was under negative selection due , for example , to overexpression of the EGFP reporter or the transgene disrupting expression of the polyadenylate-binding protein gene into which it is inserted . The insertion site is 679 bp 3′ from the stop codon in the 3′UTR . Further , if the transgene itself were under strong negative selection we would not have succeeded in breeding a homozygous Carb109M/GDLS . BC5 . HZ line . Nor would backcrossing and selection have improved the fitness of transgene homozygotes and heterozygotes ( Table 2 ) . Several studies have documented deleterious and lethal recessives in inbred lines of Ae . aegypti and several loci of this type have been mapped [53]–[55] . These are maintained as heterozygotes through balancing selection . Through rare recombination during backcrossing and selection , the deleterious allele of HWE origin might have recombined once or a few times with wild type alleles from the GDLS and thus become disassociated with the transgene . An additional factor may be that the GDLS strain was generally more fit than the HWE strain , which was inbred initially to generate homozygous white-eye mosquitoes and then maintained for over 20 years in the laboratory . In this case , a beneficial gene originating from the GDLS background would have become linked to the Carb109 transgene . If this is the case then we believe that crossover events between wild-type GDLS alleles and/or deleterious alleles from the HWE strain were rare ( i . e . the transgene and deleterious alleles were closely linked ) . This would explain why eventually only one of the six selected lines responded to selection in the predicted manner . It also would explain the discrepancy between the final allele frequencies in the BC5 F and M replicate strains ( Fig . 9B ) . Residual linkage between the transgene and deleterious allele would also explain why family based selection on 180 families yielded only one homozygous Carb109M/GDLS . BC5 . HZ line . Regardless of the mechanism , the results of the backcross and selection experiments emphasize the importance of outcrossing transgenes into genetically-diverse , recently-colonized strains before attempting to assess fitness loads imposed by the transgene and certainly before driving transgenes into natural or laboratory-maintained populations . Two additional components will be necessary to successfully implement a population replacement-based DENV control strategy in the field . ( 1 ) Design of an effector gene that targets and destroys simultaneously the genomes of all four DENV serotypes . Earlier attempts to design a tetravalent IR effector gene targeting the NS5 region of all four DENVs failed due to technical problems , although we are confident this problem can be overcome . ( 2 ) The anti-DENV effector gene may require linkage to an Ae . aegypti-specific gene drive system to enable fixation of the transgene in the target population . The Reduce and Replace strategies and killer-rescue based gene drive systems such as Medea are currently under development in Ae . aegypti [47] , [48] , [56]–[58] . | Expression of a DENV2 sequence-derived IR RNA in the mosquito midgut initiates an antiviral intracellular RNAi response that efficiently blocks DENV2 infection and profoundly impairs vector competence for that virus in Aedes aegypti . DENV2-specific IR RNA expression in the Carb109M strain has maintained the RNAi-based , refractory phenotype for 33 generations in laboratory culture . The two transgene integration sites were stable after multiple generations and following introgression into a genetically-diverse ( GDLS ) Ae . aegypti population . Introgression of the transgene into the GDLS genetic background changed GDLS from a DENV2 susceptible phenotype to a DENV2 refractory phenotype . The DENV2 refractory homozygous line , Carb109M/GDLS . BC5 . HZ , exhibits ( relative to GDLS ) minimal fitness loss associated with the transgene . This line could be a potential candidate for proof-of-principle field studies . | [
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| 2014 | Fitness Impact and Stability of a Transgene Conferring Resistance to Dengue-2 Virus following Introgression into a Genetically Diverse Aedes aegypti Strain |
Vascular development is a complex process regulated by dynamic biological networks that vary in topology and state across different tissues and developmental stages . Signals regulating de novo blood vessel formation ( vasculogenesis ) and remodeling ( angiogenesis ) come from a variety of biological pathways linked to endothelial cell ( EC ) behavior , extracellular matrix ( ECM ) remodeling and the local generation of chemokines and growth factors . Simulating these interactions at a systems level requires sufficient biological detail about the relevant molecular pathways and associated cellular behaviors , and tractable computational models that offset mathematical and biological complexity . Here , we describe a novel multicellular agent-based model of vasculogenesis using the CompuCell3D ( http://www . compucell3d . org/ ) modeling environment supplemented with semi-automatic knowledgebase creation . The model incorporates vascular endothelial growth factor signals , pro- and anti-angiogenic inflammatory chemokine signals , and the plasminogen activating system of enzymes and proteases linked to ECM interactions , to simulate nascent EC organization , growth and remodeling . The model was shown to recapitulate stereotypical capillary plexus formation and structural emergence of non-coded cellular behaviors , such as a heterologous bridging phenomenon linking endothelial tip cells together during formation of polygonal endothelial cords . Molecular targets in the computational model were mapped to signatures of vascular disruption derived from in vitro chemical profiling using the EPA's ToxCast high-throughput screening ( HTS ) dataset . Simulating the HTS data with the cell-agent based model of vascular development predicted adverse effects of a reference anti-angiogenic thalidomide analog , 5HPP-33 , on in vitro angiogenesis with respect to both concentration-response and morphological consequences . These findings support the utility of cell agent-based models for simulating a morphogenetic series of events and for the first time demonstrate the applicability of these models for predictive toxicology .
Vascular development is a complex process regulated by biological networks that vary in topology and state across different tissues and gestational stages . Initial stages of blood vessel development in the embryo encompass a morphogenetic series of events from angioblast differentiation into a self-organizing endothelial cell ( EC ) plexus [1] . This process requires coordinate regulation of complex cellular signals and behaviors such as mitosis , migration , differentiation , adhesion , contractility , apoptosis , and extracellular matrix ( ECM ) remodeling . A detailed computational model is therefore necessary to understanding both normal embryonic vascular development and how environmental or genetic factors may lead to a variety of developmental defects . Further , due to the significant overlap between developmental and pathological angiogenic signaling [2] , such a model could be potentially useful to a wide range of applications in wound healing and tumor angiogenesis , although that is beyond the scope of the current proof-of-concept study . The cardiovascular system is the first functional organ to develop in the mammalian embryo , reflecting the limits of oxygen diffusion at about 100–200 µm in size ( 3rd week of gestation in humans , 10th day of gestation in rats , 8th day of gestation in mouse ) [1]–[3] . The embryonic vasculature forms through a semi-autonomous process in which EC derived from migratory angioblasts assemble into a primitive multicellular network . This process , vasculogenesis , occurs at different times and locations centrally and peripherally in the embryo and is mediated by cellular processes such as differential migration , proliferation , and adhesion that may form polygonal ( roughly hexagonal ) whorls of endothelial cords . The endothelial cords undergo tubulogenesis and form a patent system of capillaries that eventually connect into a primitive vascular plexus . Examples include the Perineural Vascular Plexus ( PNVP ) , precursor to the blood-brain barrier , and the peripheral vascular plexus of the limb-bud mesenchyme [4]–[6] . Further growth and remodeling through angiogenesis supports the development of tissues and organ systems through growth and expansion of the primitive vasculature network via sprouting of new capillaries , vessel stabilization and maturation , and flow-based remodeling [7] . Perturbation of embryonic vascular development has the potential to disrupt embryogenesis , leading to adverse pregnancy outcomes such as low birth weight and birth defects [8] . For example , lack of PNVP invasion results in avascular neural tissue , neurodegeneration and embryolethality [9] , and inhibition of limb-bud vascularization may contribute directly or indirectly to the origins of phocomelia induced by thalidomide [10] , [11] . Analysis of the ToxCast Phase I high-throughput screening ( HTS ) dataset on 309 environmental compounds , largely pesticides with in vivo developmental toxicity information , revealed a strong in vitro signature for vascular disruption based upon chemical perturbation of multiple vascular targets and cell systems [12] . The potential molecular targets and key events were further elaborated as an ‘adverse outcome pathway’ framework based on a critical review of literature for embryonic vasculogenesis and angiogenesis [8] . A detailed computational model of critical pathways in vasculogenesis and angiogenesis can thus advance the science closer to predictive understanding of how putative vascular disruptor compounds ( pVDCs ) might perturb embryonic development [12] . Signals regulating de novo blood vessel formation ( vasculogenesis ) and remodeling ( angiogenesis ) come from a myriad of biological pathways linked to ECM biology and the local generation of chemokines and growth factors . Vasculogenesis and angiogenesis are regulated by these cell-cell and cell-matrix interactions . These interactions occur between ECs , as well as between EC and other cell types in the microenvironment that control the secretion and release of vascular growth factors and chemokines and contribute to vessel stabilization and sprouting behavior . Heterologous cell types may include inflammatory cells ( ICs ) such as macrophages and fibroblasts and mural cells ( MCs ) such as pericytes and vascular smooth muscle cells [2] , [5] . Understanding these complex interactions at a systems-level requires sufficient biological detail about the relevant molecular pathways and associated cellular behaviors , and tractable computational models that offset mathematical and biological complexity . The wide range of cell types , signaling molecules and pathways involved in vascular development necessitates a multi-scale modeling approach in which the key molecular pathways and cellular events can be integrated to simulate collective cell behaviors and higher order structure-function . Agent-based models ( ABMs ) provide one kind of computational modeling approach that has been used to mathematically describe key cellular events during vasculogenesis and angiogenesis . Previous models have been applied to both developmental and tumor angiogenesis , and have typically focused on one or two cell types ( EC , tumor cells ) , a specific behavior ( elongation , ECM interaction , tip cell selection ) and the influence of one major growth factor ( vascular endothelial growth factor ( VEGF ) ) via either paracrine or autocrine signaling [13]–[18] . Cellular ABMs can simulate discrete cellular behaviors in a multicellular field and solve systems of complex partial differential equations ( PDEs ) to mimic how cells interact with one another and their microenvironment [19] . In a cellular ABM , cells are represented as agents , i . e . , the smallest fundamental units capable of autonomous decisions . Each agent and its interactions are coded into the model based on biological knowledge and experimental data . Emergent properties and phenotypes are then evaluated for biological relevance and insight . One such model utilizing this approach is the Cellular Potts Model , also known as the Glazier-Graner-Hogeweg ( GGH ) model , implemented in CompuCell3D ( CC3D: http://www . CompuCell3D . org ) [20] . The GGH method has been extensively validated for multicellular morphogenesis modeling by numerous comparisons between experimental and simulated data for processes in developmental biology including gastrulation , limb-outgrowth , chondrogenesis , angiogenesis , and somitogenesis [16] , [21]–[26] . These models link the specific activities of cell signaling pathways to discrete morphogenetic events , and enable hypothesis generation concerning critical developmental driving factors and effects of specific perturbations . Dynamic cell ABMs have the ability to simulate complex developing systems to a degree of detail that recapitulates tissue-level observations and emergent behaviors [19] . Consequently , there exists the potential to simulate adverse effects that may emerge following exposure to environmental chemicals where there is information on perturbation of the model parameters and signaling networks controlling the simulation . This kind of information can be readily provided via HTS data and in vitro assays that are focused on key molecular targets and cellular processes . Here , we present a novel multi-cellular , multi-scale model of capillary plexus formation , implemented in CC3D , and demonstrate the potential application as an in silico testing platform for vascular disruption .
The model for embryonic vascular plexus formation was built in CompuCell3D v3 . 6 . 0 ( http://www . compucell3d . org ) . We used a two-dimensional ( 2D ) ABM lattice structure based on the observation that lateral splanchnic mesoderm may be represented as a planar surface upon which early embryonic blood vessels form from isolated angioblasts [27] . We therefore neglect critical effects of blood flow that influence arterial/venous specification and shear-based growth or regression later in angiogenic remodeling [28] . We also assume a heterogeneous cell population of endothelial cells ( ECs ) , inflammatory cells ( ICs ) and mural cells ( MCs ) . As such , we initialized the ABM to represent an approximate distribution of these critical cell types involved in vasculogenesis and angiogenesis in the mammalian embryo and placenta . An electronic library ( e-library ) for blood vessel development and remodeling ( AngioKB . v1 , provided as Supplemental Tables S1–S2 ) was built and curated semi-automatically from the open scientific literature . Relevant articles were retrieved from PubMed using ChemoText baseline version [29] and the keywords “Neovascularization , Pathologic” , “Neovascularization , Physiologic” , and “Blood Vessels” . These references were then assigned categories based on Medical Subject Headings ( MeSH ) . Those relating to development were parsed out by the following MeSH terms: “Embryonic and Fetal Development” , “Fetus” , “Gene Expression Regulation , Developmental” , “Embryonic Development” , “Growth and Development” , “Embryonic Induction” , “Embryonic Structures” , “[any MeSH heading]/embryology” . This body of literature was then used as a primary source of information to construct the multicellular agent-based model for normal vasculogenesis and for parameter determination . Because our goal was to predict outcome following perturbation , we adopted a general strategy to enable nascent formation of a vascular network by the major cell signals and responses known in embryonic vasculogenesis and angiogenesis ( RTK , GPCR and GPI pathways ) [8] . Proteins with MeSH terms appearing in these articles were automatically annotated , sorted by occurrence , and cross-referenced with ToxCast assay targets ( Table S2 ) . Articles were manually curated to extract relevant cellular biology and parameters such as secretion , diffusion , decay , adhesion , size and motility . CC3D represents cells as extended domains of pixels on a lattice , with cell index and cell type . The effective energy , or Hamiltonian ( HGGH ) , is computed for each cell in the model based on physical features such as volume , membrane area and cell adhesion , as well as dynamic inputs such as the response of a cell to a chemotactic gradient . At every Monte Carlo Step ( MCS ) or iteration , the effective energy is calculated for each lattice site . The physical inputs into the effective energy are represented in Equations 1–3 ( below ) where is the cell volume , represents the target volume , and denotes the inverse compressibility ( compliance ) of the cell . Similarly , is the surface area , is the target surface area and is the inverse membrane compressibility . The target volume and surface area are average values around which the cells will fluctuate based on experimental observations for each cell type , and are shown in Table 1 . In IC , MC , and EC-stalk cells , is calculated as . EC-tip cell filopodial extensions are reflected in the higher target surface area ( ) and increased as compared to EC-stalk cells . The presence of cellular adhesion molecules is represented by the boundary energy between cell types , shown in Table 2 , where a more negative value indicates a higher affinity . The dynamic inputs into the effective energy ( Equation 4 ) depend on chemotactic responses to continuously varying concentration fields , solved via reaction-diffusion systems of ordinary and partial differential equations and explained in a subsequent section . The total GGH effective energy ( Equation 5 ) is a summation of the physical and dynamic inputs . ( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 ) Cell motility is introduced into the simulation by allowing cells ( represented as groups of pixels ) from neighboring lattice sites to displace their neighbors ( known as an index copy attempt ) if it lowers the effective energy . If the effective energy is not reduced the displacement is accepted with a probability , P , that decays exponentially in proportion to the effective energy cost , incorporating a stochastic framework into the model known as Metropolis dynamics with Boltzmann acceptance [30] . This is represented in Equation 6 , where θ is the Heaviside step function and T ( σ ) is the cell-type specific “temperature” value that represents motility ( Table 2 ) . ( 6 ) In concordance with experimental observation , ICs such as macrophages exhibit the most exploratory behavior , followed by EC-tip cells and MCs , with EC-stalk cells being the least motile cell type . Apoptotic cells represent a special case where there is a designated motility value to prevent apoptotic bodies from being static as their volume decreases and they are processed by ICs . The model cell types are endothelial tip cells ( EC-tip cells ) , endothelial stalk cells ( EC-stalk cells ) , inflammatory cells ( ICs ) , mural cells ( MCs ) , and apoptotic cells as well as a representation of the ECM . There are 7 angiogenic signals ( VEGF165 , VEGF121 , CCL2 , CXCL10 , sVEGFR1 , Proteases , and TIE2 ) that are represented as biochemical fields whose spatial distribution is directed by a system of reaction-diffusion equations overlaid onto the cellular lattice . The remaining 5 angiogenic signals ( ANG1 , uPAR , PAI-1 , VCAM1 , and VEGFR2 ) are represented by their influence on specific cellular behaviors , such as adhesivity , motility and proliferation rate . Table 3 shows the modeling rules applied to the cell types , behaviors , and corresponding angiogenic signals represented in the computational model of early embryonic vascular plexus formation . ICs are considered to represent generalized macrophage-like cells , as defined by their migratory properties ( high motility value ) and secretion of inflammatory chemokines [31] . MCs are also represented as generalized cell types that associate with and stabilize the vascular endothelium , including pericytes ( e . g . , blood-brain barrier ) and vascular smooth muscle cells [2] , [32] . The ECM is modeled as an additional ‘cell type’ that remains static during the simulation , but can sequester/release growth factors based on protease and IC interaction . Apoptotic cells , regardless of their original cell type , assume a cell type that precedes their breakdown ( via incremental decreases in target volume ) into apoptotic bodies . There is basal rate of apoptosis represented via cell type switching based on a random number generator whose distribution is tailored to correspond to a 2 . 5% probability of becoming apoptotic ( Papop ( σ ) ) . This was the optimal rate of apoptosis derived from parameter sweeps between 0–25% probability of apoptosis in balancing cell density and overall VEGF concentration . Cytotoxicity may be represented in the model as an increase in the probability of apoptosis for a particular cell type . A basal rate of proliferation is implemented for each cell type , modeled as an incremental increase in the target volume of the cell . A cell reaching its doubling volume then undergoes mitosis into two daughter cells of the same cell type as the parent . In the case of ECs , there is a VEGF concentration threshold Cv:thr that must be exceeded to stimulate cell growth . Conversely , there is a CXCL10 concentration CCx:thr threshold that , once exceeded , counteracts VEGF-stimulated proliferation . ECs occur as either EC-tip cells or EC-stalk cells , defined in the model as separate cell types . The former are characterized by migratory behavior ( higher motility value and chemotactic strengths ) , exploratory filopodial extensions ( higher ) and minimal potential for proliferation , whereas the latter are non-exploratory and proliferative ( increase in target volume in response to VEGF ) [33] . Each cell in the computational model is able to keep track of its' shared surface area with other cells , and this data is used as an input to certain cellular behaviors . For example , EC-stalk cells adjacent to EC-tip cells express higher levels of soluble VEGFR1 ( sVEGFR1 ) , the decoy receptor that sequesters VEGF , creates a ligand corridor for vessel sprouting , and buffers the angiogenic response [34] , [35] . Similar to what has been observed experimentally , model ECs are able to switch cell type ( tip vs . stalk ) based on their local environment . An EC-stalk cell that has <80% shared surface ares with other cells may switch type and become an EC-tip cell based on exceeding a VEGF concentration threshold , leading to potential angiogenic sprout formation . An EC that shares >50% of its' surface area with an EC-tip cell , or is surrounded by other ECs ( no free surface area ) , cannot assume the EC-tip cell type [36] , [37] . These rules attempt to mimic the effects of Delta-Notch signaling without an explicit mathematical representation of the intra-cellular molecular networks . All cells in the model may differentially interact with the full range of signals , detailed in Table 3 , through a combination of mechanisms including receptor expression , secretion , chemotaxis , and uptake . VEGF is represented in the model as a freely diffusible isoform ( VEGF121 ) and as an isoform that contains a heparin sulfate binding domain ( VEGF165 ) . The latter isoform is liberated from the ECM by proteases secreted by ECs , and by ICs and MCs that interact with and break down the ECM . Sprouting EC-tip cells express higher levels of proteases such as MMP7 than quiescent EC-stalk cells [38] . Endothelial chemotaxis in response to VEGF gradients occurs via VEGFR2-mediated downstream activation of pathways leading to formation of filopodia , stress fibers , lamellipodia and focal adhesion complexes that facilitate migration along the ECM [39] . This is represented in the model by a higher chemotactic strength for EC-tip cells in response to a VEGF gradient . Previous CC3D models of early vascular development have assumed either exclusively paracrine [15] or autocrine [16] VEGF signaling as the driving factor for patterning . Here , we propose both autocrine and paracrine contributions to VEGF signaling whereby soluble VEGF121 is produced by EC-stalk cells , MCs and ICs , and bound VEGF165 is secreted by MCs and liberated from the ECM by ICs and EC-tip cell proteases . This scenario is supported by experimental evidence for cell-specific secretion of VEGF isoforms and by a demonstrated necessity for both the heparin-bound form and the freely diffusible form for correct spatial patterning [6] , [32] , [40]–[43] . The chemokine CCL2 is expressed at varying levels by all active cell types in the model and causes chemotaxis in EC-tip cells and ICs , and a mitogenic response in MCs [44] , [45] . An IC-MC interaction has been shown to synergistically amplify CCL2 production [46] , which is thus represented in the model as an increase in CCL2 secretion on contact between the two cell types . In addition to induction of a variety of angiogenic growth factors , receptors and adhesion molecules , CCL2 is also thought to enhance EC responsiveness to VEGF [47] . The anti-angiogenic chemokine CXCL10 is expressed by ICs and inhibits EC-stalk cell proliferation via a competitive mechanism with the heparin-binding site on VEGF165 , as well as via CXCR3-mediated apoptosis at higher concentrations [48] , [49] . This activity is represented in the model via a threshold-based approach where a shift in the relative concentrations of CXCL10 and VEGF165 causes first a decrease in incremental EC-stalk cell growth and subsequently an increase in EC-stalk cell apoptosis . Angiopoietin-1 ( ANG1 ) , a ligand for TIE2 expressed on the MC surface , binds its cognate receptor on EC-stalk cells , represented computationally via the boundary energy between these cell types . The association between MC and EC-stalk cells provides structural support for the nascent vessels , guidance cues from MC-derived growth factors , and promotes endothelial quiescence [2] . The latter effect is represented in the model by contact inhibition of EC-stalk cell proliferation based on shared surface area with MCs . The effects of the plasminogen activating system ( PAS ) are represented partly by the aforementioned proteases and partly by expression of the urokinase-type plasminogen receptor ( uPAR ) and Plasminogen Activator Inhibitor 1 ( PAI-1 ) . Through interaction with vitronectin ( VN ) , uPAR influences EC motility and migration along an ECM substratum [50] . PAI-1 has been shown to control MC motility by a similar VN-dependent mechanism [51] , [52] . PAI-1 also regulates protease secretion and thus may influence the proteolytic balance of the system . Finally , cell surface expression of the vascular cell adhesion molecule 1 ( VCAM1 ) by ECs , MCs , and ICs [53] , [54] is reflected in the model by differing contact energies between cell types . As shown in Equation 4 , chemotaxis is implemented as an energy bias in the direction of higher concentrations , allowing cells to preferentially move up or down a gradient based on the chemotactic field strength λchem ( Table 4 ) . The majority of the fields ( VEGF121 , CCL2 , CXCL10 , sVEGFR1 , Proteases , and TIE2 ) evolve according to the diffusion equation: ( 7 ) In Equation 7 , is the field concentration , is the diffusion constant , is the decay constant , and is the secretion rate . In the case of the cell-surface receptor TIE2 , the decay rate is greater than or equal to the secretion rate for each EC type , maximizing expression on the cell membrane and minimizing it elsewhere in the ABM lattice . The secretion rate can either assume a constant value for each relevant cell type ( sVEGFR1 , TIE2 , CXCL10 ) or it may include an additional dependence on contact between specific cell types ( VEGF121 , CCL2 , Proteases ) . Uptake of the growth factor VEGF121 and VEGF165 by EC-tip cells and EC-stalk cells is represented as a negative contribution to the secretion rate . To solve the concentration field for VEGF165 ( in Equation 8 ) , there are additional terms in the diffusion equation representing field coupling . ( 8 ) The terms nr and np denote coupling coefficients for the sVEGFR1 concentration and the protease concentration . The field coupling with sVEGFR1 is in the negative direction , representing growth factor sequestration , and the coupling with proteases is in the positive direction , representing breakdown of the ECM and release of bound VEGF165 . The initial configuration of our simulation is a heterogeneously seeded cell field on a hexagonal lattice of 200×200 pixels with periodic boundary conditions . There is a random seed number that ensures that the spatial distribution of the cells varies among simulations; however , the relative percentage of cell types is constant at the start of each run . The starting configuration ( ∼700 cells/mm2 ) and relative cell densities were estimated based on experimental observation , where ECs constitute 75% ( divided between 62 . 5% EC-stalk and 12 . 5% EC-tip cells ) and the remaining 25% are evenly divided between ICs and MCs [2] , [15] , [55] . Each pixel corresponds to ∼3 µm in biological space , and each MCS corresponds to ∼1 sec real time . All secretion/diffusion/uptake parameters are unique to each specific concentration field and were informed by the open scientific literature ( Table 4 ) . Parameters were converted from measured experimental values , as in the case of diffusion and secretion , or estimated based on observed behaviors as in the case of field coupling and chemotactic strength . When specific experimental values were not available , parameter values were estimated to achieve relative steady state concentrations that approximate measured serum levels in humans . The total simulated time over 10 , 000 MCS equates to roughly 3 hrs , which agrees with the estimated time scale over which the primitive embryonic capillary plexus forms in mammals in vivo [1] . ToxCast Phase I profiled the biological activities of 309 unique chemicals using over 600 HTS assays , including biochemical assays ( e . g . , nuclear receptor binding , enzyme inhibition ) , cell-based assays ( e . g . , cytotoxicity profiles , reporter gene assays ) , complex culture systems ( e . g . , embryonic stem cell differentiation , inflammatory/angiogenic signals ) , and chemical property information . The public in vitro dataset and relevant publications can be accessed at http://actor . epa . gov/actor/faces/ToxCastDB/Home . jsp and http://actor . epa . gov/toxrefdb/faces/Home . jsp . Several technology platforms in the ToxCast assay portfolio tested for chemical effects on molecular targets that map to key signaling pathways in vascular development [8] , [12] . The reference anti-angiogenic compound , 5-Hydroxy-2- ( 2 , 6-diisopropylphenyl ) -1H-isoindole-1 , 3-dione ( 5HPP-33 , ≥98% , Sigma-Aldrich , St . Louis , MO ) was identified in a previous structural development study on thalidomide analogs to identify anti-angiogenic compounds in a human umbilical vein endothelial cell ( HUVEC ) assay [56] . For the present study , this compound was tested in the following HTS assays as part of ToxCast Phase II ( >800 chemicals , >650 assays ) . The full ToxCast Phase II dataset will be available in mid-2013 ( http://www . epa . gov/ncct/toxcast/ ) .
The cellular ABM was run for 10 , 000 MCS to simulate the early stages of embryonic vascular plexus formation . Figure 1 displays the progression of the model as the simulation advances over six time points . The model can be viewed as a 2D cross section of a nascent capillary plexus comprising ECs ( Figure 1 , red cells ) supported by MC ( green cells ) and IC ( yellow cells ) . ECs initially elongate due to chemotactic forces leading to the formation of vascular cords . Under the mitotic influence of growth factors liberated from the ECM and secreted by the other cell types , EC-stalk cells proliferate while EC-tip cells extend exploratory filopodia into the microenvironment . This results in a rudimentary capillary plexus co-opting all ECs into the network by approximately 3000 MCS . At this stage , the model has achieved a steady state in which the overall distribution of cell types and general topology of the cellular network does not change significantly throughout the remainder of the simulation . However , the vascular network remains dynamic through 10 , 000 MCS and continues remodeling via random apoptosis as well as complex cell signal-response interactions leading to proliferative vessel thickening and stabilization by MC adhesion . As such , the capillary network is not programmed a priori but rather emerges from the system of complex interactions enabled by heterogeneous cellular properties and local environmental signals . The emergent capillary network is locally regulated by molecular signals in concentration fields that are determined by different rates of diffusion , decay , secretion and/or uptake , detailed in the Methods section and depicted graphically in Figure 2 . These signals may be freely diffusive ( e . g . , VEGF121 and sVEGFR1 ) or tightly bound to the ECM ( VEGF165 ) or cell membrane ( TIE2 ) . Some signals depend on the presence of a specific cell-type and are thus transitory in nature . Protease expression in EC-tip cells is , for example , more prominent earlier in the simulation when a significant amount of exploratory behavior and vessel sprouting occurs . The visual outcome of the simulation ( Figure 3 A , B ) recapitulated formation of the primitive capillary plexus in an early embryo [13] . These features included quasi-hexagonal lacunae formed by the vascular network , uniform vessel thickness of 2–3 cells , regular branch points , and angiogenic sprouts forming off the nascent vessels in response to local growth factor gradients . During nascent angiogenic sprout formation in the ABM , EC-tip cells continually explore their environment via filopodial extensions . This orients a tip cell chemotactically along a relevant growth factor gradient , such as VEGF . In contrast , EC-stalk cells follow and proliferate behind the exploratory EC-tip cells . In the computational model , EC-tip cells responded chemotactically to gradients of VEGF165 , VEGF121 and CCL2 ( see Table 4 ) . These molecules , plus the anti-angiogenic chemokine CXCL10 , are secreted by ICs; VEGF165 is also liberated from the ECM by secreted proteases . The CCL2 chemokine also has a chemotactic effect on IC . An interplay between concentration fields of these signal molecules and the differential adhesion strengths between cell types facilitated cell-cell interactions as the endothelial network forms ( Fig . 3C , D ) . This selective bridging of EC-tip cells , encouraged by macrophages , brought spatially seperated EC-tip cells into juxtaposition . This emergent property of the multicellular model mimics a ‘bridging phenomenon’ observed in vivo in mice during retinal angiogenesis , and in zebrafish vascular development [31] . To evaluate the performance of the cell ABM for predictive toxicology , the anti-angiogenic reference compound , 5HPP-33 was tested across a series of HTS and HCS assay systems . Results in the BioMAP system showed numerous targets relevant to inflammatory and vascular pathways ( Table 5 ) . The lowest effective concentration ( LEC ) is the in vitro test concentration at which a response was observed that was significantly different ( P≤0 . 01 ) from control . These are reported alongside the AC50 values , where available , based on the concentration response data . A maximum response ( Emax ) of ≥2-fold change was required to fit a curve and generate an AC50 . Examples of the concentration-response curves ( for inhibition of proliferation in several BioMAP vascular cell systems ) are shown in Figure 4 . 5HPP-33 invoked EC-specific inhibition of proliferation at low concentrations ( here defined as LEC ≤ 5 µM ) and affected other cell types to varying degrees at higher test concentrations ( here defined as 5 µM ≤ LEC ≤ 40 µM ) . There were a number of other targets affected by 5HPP-33 across the assay systems , including some that may be relevant to vascular development but have not yet been incorporated into the computational model . The full data set for 5HPP-33 is provided in Supplemental Table S3 . An unexpected result of the ToxCast HTS screening was the strong activity exhibited by 5HPP-33 against the estrogen receptor ( ER ) . When tested in NovaScreen cell-free biochemical assays , 5HPP-33 exhibited concentration-dependent binding to estrogen receptors from multiple species . The AC50 was 1 . 4 µM in mouse ER-alpha , 1 . 5 µM in human ER , and 1 . 8 µM in bovine ER binding assays ( Fig . 5A ) . In the Attagene reporter assays , 5HPP-33 affected ER-alpha transcription factor activity ( ERa_TRANS ) with an AC50 of 0 . 39 µM and the cis-regulatory estrogen response element ( ERE_CIS ) construct with an AC50 of 4 . 4 µM ( Fig . 5B ) . The effect of binding to the estrogen receptor is represented implicitly in the model via a surrogate effect on VEGF secretion ( SVf:ECs ) , based on the known transcriptional relationship between ER and VEGF [61] . An AOP framework anchoring molecular initiating events ( MIEs ) to adverse outcomes at the individual or the population level was previously developed for embryonic vascular disruption leading to developmental toxicity [8] . Based on that framework , the putative AOP for embryonic vascular disruption by 5HPP-33 is shown in Figure 6 . Examining the ToxCast HTS assay results represented in the computational model , 5HPP-33 affected the angiogenic switch via down-regulation of the VEGFR2 receptor and the chemokine pathway via downregulation of CCL2 , CXCL10 , IL1a , TNFa and other inflammatory signaling molecules . It also targeted vessel remodeling via down-regulation of TGFβ and ECM matrix interactions through a variety of MMPs and PAS targets , including uPAR , uPA and PAI-1 . An additional direct target in the biochemical cell-free assay platform was thromboxane A2 , which in addition to its' role in clot formation , regulates ECM gene and protein expression and migratory capabilities of various cell types [62] , [63] . Based on the estrogenic results across multiple assay types and platforms , we would also hypothesize that 5HPP-33 influences VEGF transcription and angiogenic growth factor signaling via an endocrine-regulated pathway . The ToxCast data for 5HPP-33 were translated into model parameter perturbations , as shown in Table 5 and detailed in the Methods section , based on the LECs for each target . Where possible , fold changes in protein levels from the concentration response curves ( Supplemental S3 ) were translated directly into parameter fold changes . For example , a 2-fold decrease in CCL2 levels resulted in a 2-fold decrease in the CCL2 secretion rate parameter for each corresponding cell type . Changes in proliferation were equivalently applied as adjustments to the change in target volume of the respective cell types . For example , at 40 µM 5HPP-33 caused ∼7-fold inhibition of endothelial cell proliferation ( Fig . 4 , 3C system ) vs . ∼3-fold inhibition of smooth muscle cells ( Fig . 4 , CASM3C system ) at the same concentration; equivalent decreases were directly applied to ΔVt ( ECs ) and ΔVt ( MC ) , respectively , as shown in Table 5 . Others were not as straightforward , such as VCAM1 , which influences cell-cell adhesion and transendothelial leukocyte migration and therefore is translated into contact energies between cells and cell type-specific motility , dimensionless computational parameters . In such cases there was no way to directly translate fold changes so a heuristic was applied in the form of incremental decreases in parameters . A 2-fold decrease in VCAM1 caused a drop in the contact energy between EC and IC from −5 to −4 , for example . The proposed decrease in VEGF secretion by ECs due to ER-binding and VEGFR2 inhibition ( a 10-fold drop in EC-specific VEGF secretion , without affecting secretion by the other cell types ) was implemented at the highest test concentration of 40 µM . The XML and Python configuration files used to parameterize and run each simulation are included in the Supplemental Material , and the parameters adjusted to mimic 5HPP-33 exposure were commented at the relevant places in the code . The simulations of 5HPP-33 exposure during early embryonic vascular patterning were compared to similar test concentrations with HUVEC cultures stimulated to undergo vasculogenesis [56] , to qualitatively assess the degree of vascular disruption and cellular pathophysiology . The experimental images were taken after 6 hours of chemical exposure ( 0 . 5% DMSO vehicle control , 3 µM 5HPP-33 , or 30 µM 5HPP-33 ) , a similar time scale to the simulations ( 10 , 000 MCS or ∼3 hours ) . Although the time points are not identical , and the computational model includes additional cell types ( IC and MC ) , the EC patterning ( red cells ) may be compared with HUVEC vascular network formation in order to identify similar features . The normal ( control ) simulation showed typical plexus formation and patterning , with a high degree of connectivity and regular branching ( Fig . 7A , D ) similar to what was observed in vitro . The low concentrations ( 3 µM in vitro , compared to 4 . 44 µM in silico ) showed partial disruption of plexus formation ( Fig . 7B , E ) that was more evident in the experimental images than the in silico results . There were isolated segments that form vessel networks but with a slightly lower degree of connectivity , and cellular clustering was observed in both cases . The high concentrations ( 30 µM in vitro , compared to 40 µM in silico ) showed little to no vessel formation and a high occurrence of cellular clustering ( Fig . 7C , F ) . The concordance between the simulated results and the experimental images suggests that the cell ABM had sufficient complexity and detail to learn new information about an important biological response . Vascular network images generated from multiple simulations ( n = 30 ) for each exposure scenario ( control , 5HPP-33_LC ( 4 . 44 µM ) , and 5HPP-33_HC ( 40 µM ) ) were analyzed using the automated image processing software AngioTool ( v0 . 5a ) . This tool was originally designed for use on experimental images such as allantois explants [60] and was adapted here for use with the in silico outputs from the cell ABM . The images were “stained” for ECs such that the simulated cell type colors were adjusted so that non-ECs appeared black to facilitate automated image processing . The first panel of Fig . 8A shows representative simulation outputs after they have been “stained” for ECs in silico , segmented and analyzed by AngioTool . Figure 8B shows a graphical representation of the distribution of various quantitative metrics of angiogenesis for 30 simulations in each exposure scenario ( control , 5HPP-33_LC , and 5HPP-33_HC ) . The coefficient of variation ( CV ) in almost all cases was <10% , except in the case of branching index for all three conditions and lacunarity and number of vessel segments at the simulated high test concentration . The total explant area showed a significant decrease ( p<0 . 0001 ) between the control model and the 5HPP33_LC , while the area occupied by the vessels was equivalent . This could be due to mild inhibition of proliferation of multiple cell types at the low test concentration , resulting in slightly stunted angiogenic outgrowth . The total explant area was increased for 5HPP33_HC , due to inhibition of cellular adhesion molecules and complete lack of vascular organization , as is evidenced by the large decrease in vessel density from ∼58% ( control ) to ∼29% ( 5HPP33_HC , p<<0 . 0001 ) . Both 5HPP33_LC and the control scenario usually resulted in a fully developed and interconnected plexus ( one vessel “segment” ) , whereas the number of vessel segments was significantly increased ( p<<0 . 0001 ) to >50 after simulated exposure to the high test concentration . The lacunarity , an index for vascular structural nonuniformity , was shown to increase significantly at the high concentration , consistent with what has been observed in the case of other known VDCs [60] , [64] . AngioTool analysis was also performed on representative experimental HUVEC images ( Fig . 8A , lower panel ) for control ( DMSO vehicle ) , 3 µM , and 30 µM 5HPP-33 exposure conditions . Because the computational model was not parameterized to match these experimental conditions and contains additional cell types , measured values such as explant area or branching index would be expected to differ . However , parameters such as lacunarity and vessel density show a similar concentration response trend , where the lacunarity was equivalent in the control and the 3 µM case ( 0 . 16 and 0 . 17 , respectively ) , and increased after exposure to 30 µM to 0 . 27 , similar to what was predicted in silico . The vessel density also changed very little after exposure to the low test concentration , going from 32% in the control image to 30% in the 3 µM image , while it dropped to 20% in the 30 µM case . While the vessel density was estimated to be higher in the computational model ( ∼60% for control and 5HPP33_LC ) , the concentration response trend was predicted where the vessel density remained almost unchanged at the low concentration and showed a strong decrease at the high concentration . The full set of AngioTool image data ( computational and experimental ) and statistical analyses are provided as Supplemental Datasets S1–S4 and Supplemental Table S4 .
Results from this study show that a cellular ABM with sufficient molecular complexity and mathematical detail can: ( a ) effectively simulate early embryonic vascular development including emergent properties such as macrophage bridging [13] , [31]; ( b ) recapitulate the topology of a functional angiogenesis assay in vitro [56]; ( c ) incorporate HTS data to quantitatively predict the higher-order effects on vascular network formation [12]; and ( d ) simulate key events from molecular perturbations to tissue disruption in an Adverse Outcome Pathway ( AOP ) for embryonic vascular disruption [8] . Taken together , these results for the first time demonstrate the translation of a mathematical model into predicted biological responses utilizing computer simulation and in vitro HTS data . The computational model simulated here includes a number of critical cell types and molecular signals needed for vasculogenesis and angiogenesis . Other studies have modeled vascular network formation using a cell agent-based strategy although have excluded control from the range of molecular signals possible in an embryological system . For example , previous models typically focused on one or two cell types ( ECs , tumor cells ) , detailed analysis of a specific behavior ( elongation , ECM interaction , tip cell selection ) and/or the influence of one major growth factor ( VEGF ) via either paracrine or autocrine signaling [14]–[16] , [18] , [21] , [65] . There are significant advantages and important insights gained from concentrating on a small number of cell types/signals/behaviors; however , for our purposes we strove to achieve a balance between sufficient degrees of biological complexity and simplifying assumptions that would allow for adequate recapitulation of embryonic vascular biology and subsequent prediction of chemical perturbation . This model expands upon a number of established models of plexus formation and incorporates the lessons learned and insights gained from previous approaches [14] , [15] , [16] , [18] . Previous work done also provided excellent starting points for parameter range-finding and sensitivity analysis , and assisted in choosing values that would minimize computational artifacts . The current model does not assume exclusively paracrine or autocrine signaling , but rather a combination of both , to best mimic an in vivo scenario . In addition , rather than one growth factor driving the patterning of a single cell type , here there were several molecular signals and cell types interacting to provide both pro- and anti-angiogenic cues . The net outcome is a stable capillary plexus that phenocopies what can be observed in the early embryo , but with subtle emergent features such as nascent vessel stabilization/remodeling and macrophage-tip cell bridging [31] , [32] that may be important to the timing and patterning of embryonic vascular development , and to the genetic or environmental determinants of susceptibility . Genetic studies have shown that perturbing vascular signals can lead to varying degrees of adverse consequences , ranging from congenital angiodysplasia to fetal malformations and embryolethality . Furthermore , evidence for chemical disruption of vascular developmental processes is available for thalidomide , estrogens , endothelins , dioxin , retinoids , cigarette smoke , and metals among other compounds . Exposure to these ‘Vascular Disruptor Compounds’ ( VDCs ) has been shown to cause a wide range of developmental adverse outcomes ( phocomelia , cleft palate , neural tube defects , preeclampsia , embryolethality , fetal weight reduction , etc . ) in a variety of in vivo animal models and human epidemiological data [8] . This provides compelling evidence for the value of computational models and simulations to predict effects of chemical exposure; however , their value should not be judged solely on the biological complexity and mathematical detail , but on what can be learned from them . Models that work , and that work for the right reason , offer the potential use as an in silico platform in predictive toxicology for assessing the potential consequences of drug or chemical exposure to embryonic vascular development . First-generation predictive models built from ToxCast HTS data and linked to apical in vivo endpoints include chronic liver cancer in rodents [66] , reproductive toxicity in rats [67] , prenatal developmental toxicity in rats and rabbits [68] , and multi-organ carcinogenesis [69] . We previously performed a biologically-based analysis of the data assisted by semi-automatic knowledgebase curation that revealed a number of angiogenic targets in inflammatory chemokine signaling , the VEGF pathway , and the PAS were strongly perturbed by some environmental compounds with positive correlations to developmental effects . This led to the development of a predictive model for ‘putative VDCs’ ( pVDCs ) based on ToxCast Phase I HTS data [12] and expansion of this model into a conceptual AOP framework for embryonic vascular disruption [8] . The cell types and molecular targets identified as critical to early embryonic vascular patterning were incorporated into the cellular ABM , and comprised a sufficient level of biological complexity to reproduce normal capillary formation and chemical disruption . Here , we followed this conceptual framework and developed an AOP for embryonic vascular disruption by the anti-angiogenic thalidomide analogue 5HPP-33 based on the ToxCast HTS data for targets in angiogenic growth factor signaling , chemokine pathways , ECM interactions , and vessel remodeling , and simulated it using the cellular ABM . The simulation predicted an initial effect at the low test concentration on EC proliferation , motility and adhesion . The multi-faceted impact on EC resulted in a more rigid cell shape , as shown in Figure 7B , which is consistent with another proposed mechanism of 5HPP-33 , microtubule stabilization [70] . At higher test concentrations , there were additional targets controlling ECM interactions , growth factor and chemokine signaling , and proliferation of other cell types . The presence of MC and IC in the simulation appeared to partially rescue early vascular cord formation , especially in the areas of high cell density , but this occurred over a much slower time scale than in the control model . Following image analysis by AngioTool , we were able to assess the variability in our model and make quantitative predictions showing significant changes in metrics of angiogenic disruption such as explant area , vessel density , number of segments and lacunarity , following exposure to increasing concentrations of 5HPP-33 . The branching index , measuring the number of junctions per unit area , did not show a significant concentration dependent response and was also the metric that exhibited the most variability . This may be because the image segmentation skeleton was not properly optimized for each exposure scenario ( the same analysis parameters were used for each case to minimize sources of uncertainty ) and the number of junctions was overestimated for the 5HPP33_HC outputs . Interestingly , at the low concentration the model predicted a slight decrease in lacunarity and increase in branching index ( due to a similar number of junctions over a smaller area ) , that is likely due to the inhibition of proliferation of various cell types observed in the ToxCast assays , but may also correlate with the mechanism of microtubule stabilization . This demonstrates the need for further targeted assays providing insight on how biological information flows from one cellular property to another , and how it is influenced by local factors such as cell density and heterogeneity . The disruptive effects of 5HPP-33 on EC proliferation and vascular network formation may also occur in part via estrogen receptor ( ER ) dependent signaling and a decrease in VEGF transcription . The AC50 values for the cell-free , biochemical ToxCast assays measuring ER binding and gene transcription were less than or equal to the concentrations at which significant changes in protein levels were observed . Hormonally controlled vascular changes are known to play a key role in endometrial development and blastocyst implantation , and estrogen-dependent VEGF is known to be a central regulator of uterine vasculature permeability , placentation and angiogenesis during the peri- and post-implantation period [71] . While there is a clear potential for adverse developmental outcomes that may follow from endocrine-mediated disruption of angiogenesis , the mechanisms are not yet known . Estradiol has been shown to promote EC migration , proliferation and inhibition of apoptosis through ERα-mediated pathways , while the estrogen metabolite 2-methoxyoestradiol has demonstrated potent anti-angiogenic properties mediated by cytoskeletal actions and an increase in EC apoptosis , and several non-steroidal anti-estrogens ( clomiphene , nafoxidine , tamoxifen ) ; pure ER antagonists inhibited angiogenesis in the chick chorioallantoic assay [61] . Estrogenic compounds have been shown to modulate VEGF transcription and secretion via ERα and ERβ in both a positive and negative direction in various cancer cell lines [72]–[74] , as well as in mesenchymal stem cells and murine embryonic lung cells [75] , [76] . Estrogens and selective ER modulators have been shown to inhibit vascular smooth muscle cell proliferation and endothelial VCAM1 expression [77] , [78] . In the present HTS study data , at test concentrations ≤5 µM , 5HPP-33 down-regulated VCAM1 expression in ECs and ICs , representing a possible downstream target of this partial/selective ER agonist . A postulated AOP for embryonic vascular disruption by 5HPP-33 therefore includes the molecular initiating event of ER binding leading to inhibition of growth factors and cell adhesion molecules . The computational model of early embryonic vascular development includes the key cell types and molecular signals that cooperate to promote initial capillary plexus formation . There are a number of extensions to the model under consideration , including the incorporation of intracellular signaling networks [79] and additional components such as the ER , identified here as a novel target of the thalidomide compound 5HPP-33 . Another limitation of the model that remains to be addressed is the lack of blood flow , which can be incorporated when the model is expanded into three dimensions . A comprehensive sensitivity analysis of every parameter was outside the scope of the present study; however , the vast body of vascular modeling literature that exists informed parameter range estimates for secretion rates , chemotaxis , motility and so forth . For stochastic cell-level behaviors such as cell growth and apoptosis , the present study performed parameter sweeps to optimize cell number and VEGF concentration for model development . An examination of varying patterns of chemical perturbations in future work will shed more light on which molecular targets , both uniquely and in combination , are predicted to have the most impact on plexus formation . However , while the current 2D model makes a number of simplifying assumptions , the degree of biological complexity was sufficient to reproduce key morphological features and emergent behaviors during vascular development . This in silico model also demonstrates a novel approach using in vitro assay data on a cellular and molecular scale to accurately predict qualitative phenotypic changes on a tissue scale with increasing concentrations of an anti-angiogenic compound . The quantitative outputs from the AngioTool analysis show a high degree of reproducibility across multiple simulations for each scenario , and , when combined with the qualitative comparisons to experimental results , provide new biological insight and quantifiable predictions relating molecular and cellular changes to disruption of vascular development . Although we have framed the current vascular model in the context of embryonic development , there is significant overlap between developmental and pathological angiogenic signaling [2] , and such a model could be potentially useful to a wide range of applications in wound healing and tumor angiogenesis . Traditional in vivo animal testing , usually at high test doses , is low-throughput and costly both in terms of financial and animal resources . These restrictions result in a relatively low number of compounds that have sufficient in vivo data to assess the potential for adverse effects on human development . Toxicity testing in the 21st century is moving toward using HTS assays to rapidly test thousands of chemicals against hundreds of molecular targets and biological pathways , to provide mechanistic information on chemical effects in human cells and small model organisms , and to construct predictive and mechanistic models [80] , [81] . Virtual tissue simulations may someday serve as a surrogate to animal testing by providing in silico testing platforms based on computational systems biology [82] . In conjunction with ToxCast HTS data , the model presented here has the potential to predict toxic effects caused by exposure to anti-angiogenic compounds , comparable to in vitro and in vivo angiogenesis assays . | We built a novel computational model of vascular development that includes multiple cell types responding to growth factor signaling , inflammatory chemokine pathways and extracellular matrix interactions . This model represents the normal biology of capillary plexus formation , both in terms of morphology and emergent behaviors . Based on in vitro high-throughput screening data from EPA's ToxCast program , we can simulate chemical exposures that disrupt blood vessel formation . Simulated results of an anti-angiogenic thalidomide compound were highly comparable to results in an endothelial tube formation assay . This model demonstrates the utility of computational approaches for simulating developmental biology and predicting chemical toxicity . | [
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| 2013 | A Computational Model Predicting Disruption of Blood Vessel Development |
An elegantly simple and probably ancient molecular mechanism of allostery is described for the Escherichia coli arginine repressor ArgR , the master feedback regulator of transcription in L-arginine metabolism . Molecular dynamics simulations with ArgRC , the hexameric domain that binds L-arginine with negative cooperativity , reveal that conserved arginine and aspartate residues in each ligand-binding pocket promote rotational oscillation of apoArgRC trimers by engagement and release of hydrogen-bonded salt bridges . Binding of exogenous L-arginine displaces resident arginine residues and arrests oscillation , shifting the equilibrium quaternary ensemble and promoting motions that maintain the configurational entropy of the system . A single L-arg ligand is necessary and sufficient to arrest oscillation , and enables formation of a cooperative hydrogen-bond network at the subunit interface . The results are used to construct a free-energy reaction coordinate that accounts for the negative cooperativity and distinctive thermodynamic signature of L-arginine binding detected by calorimetry . The symmetry of the hexamer is maintained as each ligand binds , despite the conceptual asymmetry of partially-liganded states . The results thus offer the first opportunity to describe in structural and thermodynamic terms the symmetric relaxed state predicted by the concerted allostery model of Monod , Wyman , and Changeux , revealing that this state is achieved by exploiting the dynamics of the assembly and the distributed nature of its cohesive free energy . The ArgR example reveals that symmetry can be maintained even when binding sites fill sequentially due to negative cooperativity , which was not anticipated by the Monod , Wyman , and Changeux model . The molecular mechanism identified here neither specifies nor requires a pathway for transmission of the allosteric signal through the protein , and it suggests the possibility that binding of free amino acids was an early innovation in the evolution of allostery .
Arginine repressor ( ArgR ) is the master regulator of the arginine regulon in a wide variety of bacteria [1] , acting as direct sensor and transcriptional transducer of intracellular L-arginine ( L-arg ) concentrations to provide feedback control over biosynthesis and catabolism of L-arg . The co-effector L-arg binds to a central hexamerization domain , altering DNA affinity and specificity [2] of peripheral domains ( Figure 1A ) . Thus , although its activation mechanism is unknown despite decades of study , ArgR is an apparent example of the action-at-a-distance principle embodied in the concept of allostery [3] . The structural organization of ArgR into N- ( ArgRN ) and C-terminal ( ArgRC ) domains , and the functional division of labor between them , are conserved even among distant orthologs that display an unexpected diversity of reported biochemical properties , notably the L-arg dependence of hexamerization and DNA-binding equilibria [4]–[8] . An allosteric mechanism was inferred by comparison of crystallized , intact , unliganded apoprotein from the thermophile Bacillus stearothermophilus ( apoBstArgR ) with its liganded C-terminal domain fragment ( holoBstArgRC ) , which differ by ∼15 degrees rotation about the trimer-trimer interface that was ascribed to L-arg binding and presumed to be transmitted to the DNA-binding domains [9] . However , rotation could reflect crystallization conditions and/or crystal packing , or the differential presence of the N-terminal domains , and no structure of apoBstArgRC has been reported . A similar degree of rotation was reported recently between apo- and holoArgRC of Mycobacterium tuberculosis ( MtArgRC ) [10] , but the relationship between rotation and the molecular mechanism of L-arg allostery is still unclear . Pursuit of the allosteric mechanism of E . coli K-12 ArgR ( EcArgR ) , the most thoroughly studied ArgR , is motivated by a wealth of genetic , biochemical , and biophysical knowledge [1] , [2] , [4] , [11]–[16] that is unavailable for any ortholog and is expected to constrain activation models . Unlike the Bacillus and Mycobacterium proteins , crystal structures of the E . coli ArgR C-terminal domain with ( holoEcArgRC ) and without ( apoEcArgRC ) bound L-arg are essentially identical [14] , with maximum local shifts of <∼0 . 5 Å except at poorly-ordered monomer termini , and Cα RMSD ∼0 . 76 Å for apo- and holo- hexamers , a value similar to the RMSD for monomers within holoEcArgRC . Crystalline apo- and holoEcArgRC hexamers are also entirely symmetric , a finding that is seemingly incongruent with the complex thermodynamics of L-arg binding . Isothermal titration calorimetry ( ITC ) confirms that EcArgR and EcArgRC hexamers bind six equivalents of L-arg , but with a negatively cooperative binding mechanism in which the first binding event has ∼100-fold greater affinity than the subsequent five events [16]; thus L-arg binding is itself allosteric , even in the absence of DNA binding or DNA-binding domains . Negative cooperativity with such different affinities creates two action levels in the protein's response to L-arg , although it is not yet known how these levels are exploited by multifunctional ArgR in carrying out its physiological roles . For both ArgR and ArgRC , binding of the first L-arg , though exothermic overall , is associated quantitatively with a slowly-evolving endothermic heat flow , as is binding of the analog L-canavanine despite almost 1000-fold weaker affinity than L-arg . Such slow , offsetting heat flows have been suggested [16] to be a hallmark of global conformational response that may underlie the allosteric transition . Further interpretation of this complex system requires an understanding of the protein's conformational landscape . Molecular dynamics ( MD ) simulations of the ∼50 , 000 Da ArgRC domain with and without bound L-arg provide a picture of populated conformations that identifies the molecular origins of its negative cooperativity and explains its ITC endotherm . The results reveal that , despite the conceptual asymmetry of partial ligation , the symmetric relaxed state predicted by the model of Monod , Wyman , and Changeux ( MWC; [17] ) can be achieved by exploiting the dynamic nature of the assembly and the distributed nature of its cohesive free energy . The allosteric mechanism discovered here neither specifies nor requires pathways or networks through the protein to transmit the allosteric signal among L-arg binding sites , and it represents a structurally and thermodynamically explicit and particularly simple example of the now-common view [18] , [19] that the emergent property of allostery derives from inherent , universal dynamics of proteins and that allosteric effectors redistribute protein conformational ensembles .
Molecular dynamics analysis used the modeling package GROMACS 3 . 3 . 1 [20] , [21] . ArgRC apo- and holoprotein crystal structures ( PDB entry 1XXC and 1XXA , respectively ) were prepared by standard methods in YASARA [22] including the following steps , and solvated in SPC water [23] . The simulation cell extended 10 Å beyond the protein , and periodic boundary conditions were applied . The system was neutralized with 36 sodium ions . For simulations the GROMOS87 force field [24] was employed with corrections [25] , [26] . Electrostatics were evaluated using the particle-mesh Ewald method [27] with a cutoff of 10 Å . van der Waals forces were evaluated with a Lennard-Jones potential having an 18 Å cutoff to permit L-arg ligands separated by 17 Å in adjacent binding sites to ‘feel’ each other; essentially identical results were obtained in trial calculations with the standard cutoff of 14 Å ( data not shown ) . Weak temperature and pressure coupling [28] were employed ( coupling constants 0 . 1 ps ) , with the protein and solvent atoms having separate baths maintained at 300 K , and pressure maintained at 1 bar with a compressibility of 4 . 6*10−5/bar . Virtual-site hydrogens [29] were employed to increase calculation speed by allowing for time steps of 5 fs . Bond lengths were constrained using LINCS [30] . The neighbor search list was updated every 20 fs . The solvated system was first energy minimized using steepest descent and the solvent was allowed to relax for 250 ps while keeping the protein restrained . Initial Boltzmann- weighted velocities were generated randomly and the system was further equilibrated for 500 ps . The MD production runs without constraints were carried out at least for 20ns and in some cases were continued for additional 50 ns . DynDom [31] , [32] was used to carry out a protein domain motion analysis that allows identification of moving domains , defines the screw axis , and measures the degree of rotation between two conformers . A sliding window length of 11 and an intra- to interdomain rotation ratio of 0 . 7 were used to compare the last frames of trajectories to the starting frames and to each other . Principal-components analysis was used to identify the global motions of the ArgRC hexamer . The elements of the covariance matrix are defined aswhere i and j are atom indices , are the coordinates and is the reference structure . The correlation of atomic displacements of Cα atoms was analyzed by calculating the atomic covariance matrix , defined as the sum ( Cxx + Cyy +Czz ) of atom pair covariances ( Cij ) in the X , Y and Z directions , respectively . The reference structure is represented by the mean values of the coordinates of each Cα atom over the analyzed trajectory . In the analyses of rotation from the crystal structure referenced to one trimer ( made up monomers ABC ) , the initial structure of each simulation was used as reference instead of the average structure . Eigenvectors representing molecular motions are extracted from the covariance matrix by diagonalization; the eigenvectors with largest amplitude represent the motions that describe global conformational changes . These motions are visualized by extracting coordinates representing the extreme conformations along the eigenvectors . Binding free energies were computed by the linear interaction energy method using g_lie [20] , with an electrostatic term for ligand/water interaction of −453 . 458 kJ/mol and a Lennard Jones term for ligand/water interaction of −4 . 63 kJ/mol that were calculated from 500 ps simulations of L-arg in SPC water utilizing the same force field parameters as for the whole system . The default values of the scaling factors for the Lennard-Jones term ( α = 0 . 18 ) and for the electrostatic term ( β = 0 . 50 ) were used as they are valid for small , charged molecules like L-arg [33]–[35] . Ligand binding enthalpies were calculated as the nonbonded interaction energy by g_energy [20] . The interaction energy between the free ligand and water is subtracted from the sum of the interaction energy terms calculated by the force field between protein and bound ligand and bound ligand and water . Root mean square deviations ( RMSD ) were calculated by g_rms [20] for the whole trajectory taking as a reference the coordinates at the start of the simulations . To calculate the atomic fluctuations ( RMSF ) , the trajectory was separated into six independent trajectories , one for each monomer , and the root mean square fluctuation was then calculated by g_rmsf from the last 10 ns of the simulations . Covariance matrices were calculated with g_covar and processed by g_anaeig [20] that performs a principal-components analysis to determine the major movements . The last 2 ns of the trajectories were compared to the mean structure of the last 2 ns . Entropies were computed from the mass-weighted covariance matrices produced by g_covar using a quasi-harmonic approximation [36] implemented in the calc_entropies . pl script from the Gromacs web-page ( www . gromacs . org ) . Differences of the conformational entropy were calculated by quasi-harmonic analysis from kB/2 ( det σa/det σb ) , where detσa and detσb are covariance matrices of atomic fluctuations , and kB is Boltzmann's constant . To gain higher accuracy the original Schlitter's approximation [37] is improved by removing the singularity of the covariance matrix in Cartesian coordinates [38] . Entropy calculations were carried out over the last 10 ns of the trajectories of three independent 20-ns simulations , permitting calculation of the standard deviation for each entropy value . Frames were sampled every 0 . 1 ps , well beyond the minimum frame number required for quasi-harmonic approximations [38] , yielding results that are independent of frame number . Each structure in the trajectory ( every frame ) was aligned to the average hexamer structure by mass-weighted fitting of heavy atoms to remove translational and rotational degrees of freedom , but not trimer rotation . All atoms ( 2850 ) were included in the analysis with the exception of the virtual sites , which do not contribute to the degrees of freedom as their position is reconstructed at each MD integration step . Radius of gyration was computed by g_gyrate [20] . The distances between selected atoms and between centers of mass were calculated by g_dist and graphs were prepared in Grace ( http://plasma-gate . weizmann . ac . il/Grace/ ) . The histograms of distances between selected atoms were calculated in Calc Spreadsheet included in the OpenOffice suite ( http://openoffice . org ) . For structure and trajectory visualization Yasara [22] and VMD [39] were used .
Multiple alignment of 500 intact ArgRs ( not shown ) suggests that the extremely variable sequence preceding the tandem ββα repeats of the ArgRC fold , which forms an extra helix in the apoBstArgRC crystal structure [10] that has no equivalent in the E . coli ArgR sequence , belongs not to the ArgRC fold as suggested for BstArgR but to a highly variable interdomain linker . Thus , E . coli apo- and holoArgRC crystal structures ( PDB IDs 1XXC and 1XXA , respectively [14] ) containing residues 80–156 of intact ArgR are inferred to contain the entire C-terminal domain of EcArgR . These PDB files were prepared as initial structures for simulation as described in Materials and Methods . Independent replicates of these starting structures were derived by removing all six L-arg ligands from 1XXA ( holoArgRC-6 ) and by adding six L-arg to 1XXC ( apoArgRC+6 ) in order to probe the reaction coordinate from opposite directions . Intermediate ligation states were prepared by adding one L-arg in turn to each monomer of 1XXC ( apoArgRC+1 ) to form six singly-liganded starting structures; six more were prepared by removing five L-arg from 1XXA in all permutations ( holoArgRC-5 ) ; and fifteen doubly-liganded starting structures were prepared by adding two L-arg to apoArgRC in every permutation . Simulations using GROMACS ( [20]; Materials and Methods ) ran for a minimum of 20 ns using steps of 5 fs , facilitated by using virtual-site hydrogens [29] . A Lennard-Jones potential having an 18 Å cutoff was used to permit L-arg ligands , which are separated by 17 Å in adjacent binding sites , to ‘feel’ each other; essentially identical results were obtained in trial calculations with the standard cutoff of 14 Å ( not shown ) , indicating that L-arg ligands do not experience direct pairwise interactions . Root-mean-square deviations ( RMSDs ) relative to the starting structures ( Figure S1 ) tend toward values of ∼2 Å , typical for equilibrated systems of this size [40] . Stable plateau values are reached by ∼10 ns except for holoArgRC-6 and apoArgRC+6 RMSDs that drift slightly through 70 ns . Monomer mass distributions and radii of gyration also equilibrate by ∼5 ns ( not shown ) . Cα root-mean-square fluctuations ( RMSFs ) relative to the hexamer structure averaged over the last 10 ns display maximum values of ∼1 . 5 Å for loop residues and minimum values of ∼0 . 3 Å for secondary structure segments ( not shown ) , corresponding well with the pattern of crystallographic B-factors in the PDB files . Manual inspection of the apoArgRC trajectory reveals that a dramatic shift away from the starting structure occurs very early in the equilibration phase . Quantitative analysis of domain motions as described in Materials and Methods indicates that this early shift comprises clockwise rotation of one apoArgRC trimer with respect to the other by ∼13° ( Figure 1B ) . Rotation in the counterclockwise direction does not occur; the clockwise direction of rotation is opposite of that observed in the B . stearothermophilus and M . tuberculosis crystal structures . Independently repeated runs using comparable preparation and equilibration steps with a wide range of force fields ( Gromos96 , GMX , OPLS-AA , Amber 96 , Amber99 , Yamber2 ) , two different water models ( SPC , TIP3 ) , and different randomly-assigned initial velocities all result in the same shift ( not shown ) . These findings , together with the fact that the shift was never observed in simulations with holoArgRC , argue strongly against artifactual causes of trimer rotation . ApoArgRC simulations equilibrate after the early conformational shift but the starting conformation is not visited again , suggesting that crystals trap a high-energy species that is rare in solution . Yet only minor differences are detected upon structural comparison of the rotated conformation with the starting conformation . Rotation alters the apposition of apoArgRC monomers across the inter-trimer interface so that Leu82 , Leu85 , Ala104 , and Leu107 face Leu107 , Pro102 , Ser101 , and Leu85 instead of the four symmetry-equivalent residues . Both interfaces are uniformly planar and similarly hydrophobic , and no structural changes are propagated beyond the interface . Only minor differences in the nature and extent of solvent-exposed surface area accompany rotation . Visual inspection of the trajectory indicates that the early rotational event is correlated with an altered conformation of the Arg110 sidechain in all six subunits ( Figure 1C ) . In the rotated conformation each Arg110 sidechain extends almost completely into each ligand-binding pocket , facilitated by rotation about dihedral angle C-Cα-Cβ-Cγ from ∼23±59° ( average±s . d . in apoArgRC crystal structure ) to ∼170±14° in rotated apoArgRC , and the Arg110 guanidino group makes a bidentate , doubly hydrogen-bonded salt bridge with the Asp128 carboxylate lying diagonally across the trimer interface . This interaction is equivalent to the interaction made by each L-arg guanidino group in holoArgRC crystals and simulations , where no Arg110-Asp128 interactions are observed and Arg110 residues face the solvent in random orientations . Although in apoArgRC crystals the distance between the Cα atoms of Arg110 and Asp128 would permit their terminal functional groups to form a hydrogen-bonded salt bridge , no such salt bridges are detected; instead both functional groups make no intramolecular interactions but are surrounded by solvent density . However , no steric clash can be detected that would prevent a dihedral angle change enabling Arg110 to reach Asp128 . These findings suggest that the high ion concentrations used in crystallization ( 50 mM NaHepes , 100 mM NaCl , 20 mM CaCl2; [14] ) may interfere with salt bridge formation , trapping a high-energy state in which Arg110-Asp128 interactions are disrupted . To clarify whether the Arg-Asp interaction is a cause or a consequence of apoArgRC rotation , an energy-minimized starting structure with Ala replacing all six Arg110 residues was created from the apoArgRC crystal structure , and equilibrated 20-ns simulations were analysed . The trajectories reveal no rotation of apoArgRC110Ala , implying that Arg110 promotes trimer rotation via interaction with Asp128 . Re-introduction of Arg in place of Ala110 in any single subunit of apoArgRC110Ala does not support rotation but instead introduces local conformational changes by orienting toward residues Asp128-Asp129 across the pocket ( not shown ) ; incremental re-introduction of additional Arg residues into apoArgRC110Ala has not been investigated . To examine the basis for the counterclockwise rotation reported in crystals from M . tuberculosis , 20-ns simulations were analysed for apoMtArgRC ( PDB ID 2ZFZ ) using parameters and preparation as for apoEcArgRC . Several simulations led to different , though equilibrated , states . Within some simulations two different conformations , rotated in opposite directions , are sampled ( Figures S2 and S3 ) ; this observation indicates that the failure to observe counterclockwise rotation of apoEcArgRC is not due to any inherent limitation in the simulations . Clockwise rotation in the apoMtArgRC simulations is correlated with formation of Arg133-Asp146 salt bridges , the three-dimensional equivalent of EcArgRC Arg110-Asp128 ( Figure 1D ) ; counterclockwise rotation as observed in the MtArgRC crystal is triggered by Arg118 , which salt-bridges from the opposite side of each pocket to Asp132 lying diagonally across . The EcArgRC residues equivalent to the Arg118-Asp132 pair of MtArgRC are His99 and Asp113 , which presumably cannot promote rotation in the counterclockwise direction . As with EcArgRC , the terminal functional groups of each Arg-Asp pair are within contact distance in MtArgRC crystals , but no salt bridges are detected in either the rotated or non-rotated crystal conformation , again suggesting that the high-salt crystallization conditions ( 0 . 1 M Hepes , 0 . 1 M NaCl [10] ) may interfere with salt-bridge formation . The finding that MtArgRC rotates in both directions using two Arg-Asp pairs whereas EcArgRC rotates in one direction using a single pair suggests a general functional role of Arg residues in ArgR rotational dynamics . This hypothesis predicts that a mutated apoEcArgRC with His99 replaced by Arg , mimicking MtArgRC Arg118 , should rotate in both directions . An energy-minimized His99Arg mutant structure was created from the apoEcArgRC crystal structure , and rotation in both directions was observed within one equilibrated 20-ns simulation , promoted by Arg99-Asp113 and Arg110-Asp128 salt bridges ( not shown ) . The accessibility of both rotational directions during apoEcArgRCHis99Arg simulations further rules out inherent limitations of the simulation , and indicates that the directionality of rotation is governed solely by the directionality of the salt bridges . The hypothesis also predicts that rotation of apoBstArgR in the counterclockwise direction uses Arg97-Asp111 , equivalent to MtArgRC Arg118-Asp132 , but that rotation in the clockwise direction does not occur because BstArgR presents Val108 at the position of EcArgR Arg110 and has no other Arg residue nearby . Thus , EcArgR , MtArgR , and BstArgR are inferred to share a common global dynamic process in which rotation of trimers is driven by Arg-Asp ion pairing , even though none of these salt bridges is detected in the crystal structures . Covariance analysis of Cα deviations during the equilibrated last 10 ns of each simulation reveals no clear pattern of correlated motions when the hexamer is the reference structure as defined in Materials and Methods ( Figure 2A ) . Considering that trimers are involved in the early conformational shift , covariance analysis was referenced as described in Materials and Methods to the ABC trimer defined in Figure 1A to determine whether correlated motions of trimers occur during the simulation . Trimer referencing reveals dramatically correlated motions between apoArgRC trimers and slightly correlated motions between holoArgRC trimers ( Figure 2B ) . Trimer referencing also unmasks an underlying pattern of traces reflecting the tertiary structure ( detectable as nine small blocks within each red block ) , indicating that monomers remain folded during the simulations , as observed also in manual inspection of the trajectories . Principal-components analysis reveals that the dominant motion of apoArgRC trimers during the trajectory is rotational oscillation across the inter-trimer interface , accounting for the intense positive correlation . Thus , after the early rotational event in which apoArgRC rotates by 13° relative to the crystal structure , apoArgRC undergoes continuing rotational oscillation about the new mean structure . The relative motion between trimers of holoArgRC has no rotational component according to the results of principal-components analysis; the weaker correlation instead reflects small variations in inter-trimer distance along the three-fold axis . The covariance patterns of apoArgRC110Ala strongly resemble those of holoArgRC ( Figure 2C ) , and those of apoMtArgRC resemble apoArgRC but reflect rotation in both directions ( Figure S3 ) . Residues lying along a line perpendicular to the axis of rotation were identified as metrics to quantify the conformational population distributions during each simulation . Gly103 of one monomer and Asp128 of the monomer diagonally across the binding pocket ( Figure 1B ) experience little local motion , but rotation of apoArgRC from the starting structure moves them apart by ∼1 . 6 Å . The six Gly103-Asp128 distances of each hexamer were measured during each trajectory ( Figure S2 ) and normalized by subtracting the holoArgRC crystal distance ( 9 . 8 Å ) to yield the distribution of distance deviations , Δδ , summarized in the frequency histograms of Figure 3 . The histograms enable precise distinction between the initial rotation ( the difference in the means of the distributions for apoArgRC and holoArgRC ) and repetitive motions ( the breadth of the distributions , reflecting rotational oscillation for apoArgRC and inter-trimer distance variation for holoArgRC ) . The holoArgRC ensemble with mean Δδ∼−0 . 1 Å samples mostly crystal-like distances , with a relatively narrow distribution . Similar results are found for apoArgRCArg110Ala , consistent with the results from covariance analysis ( Figure 2C ) and principal-components analysis that indicate the absence of rotational oscillation . Mean Δδ∼1 . 6 Å in the apoArgRC ensemble indicates that the rotated conformer is favored over the crystal-like conformer . During rotational oscillation apoArgRC samples a range of distances that at one extreme is equivalent to distances in the non-rotated starting state , but this extreme is sampled only rarely . ApoArgRC conformers interconvert freely with continuous change of energies , atom positions , and distances and a cycle time of ∼200–300 ps ( not shown ) , suggesting the observed rotational oscillation represents natural hexamer motion driven by thermal flux with no energy barrier between conformers , i . e . , motion about a local free energy minimum , with the rotated conformer lying at the bottom of the basin . Inspection of the apoArgRC trajectory indicates that the dominant and rare conformers differ significantly in the average number of inter-trimer hydrogen bonds . In the dominant conformer all six Arg-Asp salt bridges are intact during the simulation and remain almost fully hydrogen bonded on average , due to an optimal approach distance of their terminal functional groups . In the rare crystal-like conformer one salt bridge per hexamer is broken on average , with Arg110 flipped outward from the binding pocket , and one other salt bridge lacks one or both of its hydrogen bonds due to a slightly increased distance between the functional groups , although electrostatic interaction is preserved . As a consequence of these differences in distances and hydrogen bond number , totaling ∼7–8 in the rare conformer and ∼10–11 in the dominant conformer , access of the L-arg ligand to its binding pockets also differs , with the rare conformer presenting one open binding site on average and the dominant conformer offering little access . Thus , thermal flux drives rotational oscillation of apoArgRC to transiently sample a conformation with one open L-arg binding site per hexamer . Although the ligand might occasionally sample this open site , the rotated conformation is not a thermodynamic state but rather represents only one extreme in a local free energy minimum; thus , because binding can occur only from a thermodynamic state , another , binding-competent , state must also exist . The structure observed in apoArgRC crystals , with all Arg-Asp salt bridges broken but still within hydrogen-bonding distance , presumably represents a binding-competent state . Because it is never sampled again after the early rotational shift , even during long simulation times , this structure presumably lies at higher energy than the apoArgRC basin , but it must be accessible to apoArgRC even if it occurs rarely outside crystals , where lattice forces and/or crystallization conditions may favor it . However , apoArgRC crystals crack when L-arg is soaked in , apparently because the distance between Cα atoms of the salt-bridge pair is slightly larger in holoArgRC [14] . Thus an even higher-energy state of apoArgRC must also exist with Arg-Asp distances equal to those of holoArgRC; it is thus unclear if the binding-competent state resembles apo- or holoArgRC crystals . The finding that apoArgRC crystals crack upon addition of L-arg implies that crystal packing enforces the shorter Arg-Asp distance compatible with salt-bridge formation , suggesting that high-salt conditions alone interfere with salt-bridge formation . ApoEcArgRC simulations prepared with additional sodium and chloride ions to mimic the crystallization conditions are consistent with this suggestion ( not shown ) . At high concentrations sodium ions compete with Arg110 for interaction with Asp128 , randomly disrupting salt bridges and replacing rotational oscillation with random motions . Ions coordinate with Asp129 as well , altering the orientation of the Asp128-129 pair and displacing Arg110 in random orientations . On average one subunit of the hexamer becomes more mobile than the other five . The latter observation suggests an interpretation of a puzzling crystallographic observation: apoArgR hexamers from both B . stearothermophilus [9] and B . subtilis [41] present good electron density for only five of the six identical subunits , a seemingly unusual coincidence . Enhanced mobility of one subunit might favor crystallization by offering an additional degree of freedom for the hexamer in the lattice . However , the structural features observed in the high-salt simulations might be unresolvable in fitting the experimental electron density due to the motions of the sidechains and solvent and the rotational degeneracy of ArgR . Of 27 simulations initiated with artificial placement of ligands into apoArgR or their removal from holoArgR , L-arg maintained a crystal-like binding geometry after equilibration for two apoArgRC+1 ( a and b ) , three holoArgRC-5 ( a , b , and c ) , and three apoArgRC+2 simulations ( a , b , and c ) ; by this criterion the other 19 simulations were considered unsuccessful and were not analysed . None of the eight successful simulations experienced rotation or rotational oscillation in 20 ns , suggesting that binding of one L-arg per hexamer is sufficient to suppress rotational motions , consistent with its location spanning the inter-trimer interface . Covariance analysis , referenced therefore only to the hexamer to examine motions of individual monomers , reveals distinct patterns ( Figure 2D ) . ApoArgRC+1a displays large regions within most monomers with uncorrelated motion , interspersed with regions of negatively correlated motions , suggesting that subunit motions become more random when L-arg binds , particularly for subunits BCF that in this simulation do not contact L-arg . The prominent tertiary traces despite very different overall extents of correlation within monomers indicate that no unfolding occurs and that internal motions are correlated with monomer motion . ApoArgRC+1b displays an approximately uniform and equal distribution of correlated and uncorrelated motions over all monomers , indicating local motions less correlated with monomer motion . All three holoArgRC-5 simulations are very similar to apoArgRC+1b . All singly-liganded simulations thus indicate that this state presents intense , largely random , motions of folded monomers within the hexamer . Gly103-Asp128 distance histograms ( Figure 3 ) reveal fluctuation in the two apoArgRC+1 simulations about a common mean structure similar to , but distinct from , that of holoArgRC . The range of the six individual inter-subunit distances is as narrow as the tightly clustered distances measured in holoArgRC ( Figure S2 ) , with a slightly narrower range for apoArgRC+1a and a slightly broader range for +1b and the three holoArgRC-5 simulations , due partly to larger local fluctuations . The narrow distribution of distances indicates that the intense random motions detected by covariance analysis are not reflected in motions at the trimer interface , and that hexamer symmetry is unexpectedly high . Symmetry in the +1arg simulations is further indicated by the distances from the center of mass of the hexamer to the center of mass of each monomer , which vary randomly during the entire equilibrated part of each simulation by less than the length of a covalent bond ( mean distance ∼18±0 . 5 Å; Figure S2 and data not shown ) . Addition of a second L-arg ligand , regardless of its position relative to the first , completes the conversion to fully holo-like mean distances , with similarly narrow range ( not shown ) , indicating that the symmetry established in the +1arg state is maintained . Thus , binding of a single L-arg is necessary and sufficient to create an ensemble that is only slightly less holo-like than when all six sites are occupied , and in which symmetry is retained despite highly variable motions that reflect transfer of thermal flux to individual monomers . Importantly , the slightly greater Arg-Asp distances that are achieved only in the +2 state allow each of those residues to participate in electrostatic interactions with other surrounding residues , thus eliminating the directionality of motion . In all five single-ligand simulations , the L-arg guanidino group forms a salt bridge to Asp128 , replacing Arg110 that is displaced to make random motions; all other binding-site residues of the unliganded subunits maintain binding-competent conformations in all simulations . Four of the five single-ligand simulations present a common pattern , the +1a simulation being the exception . In those four , the unliganded binding pockets retain Arg110-Asp128 salt bridges , but their hydrogen bonding is frequently disrupted , and occasional opening of one further salt bridge is observed as well . The number of persistently populated inter-trimer hydrogen bonds ( those present >50% of the time ) is half or less of the total hydrogen bond number , indicating considerable flux in bonding partners . The ligand conformation is the same as in holoArgRC cocrystals , and it contributes one hydrogen bond . In contrast , in the +1a simulation the ligand conformation is fully extended , and it contributes two hydrogen bonds; however , the most striking feature of this simulation is that all inter-trimer hydrogen bonds are persistent , implying the existence of a cooperative hydrogen bonding network . The ability of L-arg to compete successfully for interaction with Asp128 appears surprising considering that the effective local concentration of the residue sidechain is expected to be much higher . L-arg apparently wins the competition despite this disadvantage because unlike residue Arg110 , ligand L-arg presents not only its guanidino group to Asp128 of one subunit , but its free α-amino and α-carboxylate substituents additionally form a complex mesh of interactions with eight more residues ( Figure 1C ) : Gln106 , Asp113 , Thr124 , and Ala126 in the same subunit that engages the guanidino group; Asp128 , Asp129 , and Thr130 in the subunit adjacent to the first in the same trimer; and Asp128 in a third subunit directly above the second in the other trimer; in the apoArgRC+1a simulation the ligand does not contact Gln106 but contacts Thr124 with higher frequency . Thus , each L-arg engages three subunits using common protein loop regions that cooperate to perform structurally distinct roles in the complex , creating a high-affinity site with few degrees of freedom for the ligand or the protein . The results indicate that L-arg ligands modulate global protein dynamics by competition with resident Arg residues for salt-bridge formation to Asp residues of the binding pockets . Per-ligand binding enthalpies and free energies , together with configurational entropy contributions to the total system free energy , were calculated as described in Materials and Methods from all simulations with zero , one , two , or six bound L-arg ligands ( Table S1 ) . Binding enthalpies per ligand are essentially the same for each single-ligand simulation as for each of the six ligands of holoArgRC , suggesting similar enthalpy increments for all six L-arg . Per-ligand binding affinities range widely for the singly-bound simulations , but all are significantly more favorable than the average value for holoArgRC , indicating that ligand affinity is substantially higher when only one L-arg is bound , consistent with ITC [16] . Entropies calculated from the covariance matrix are within error in all simulations; this finding is surprising considering that rotational oscillation is a major contributor to the entropy of apoArgRC but is absent in all states that include L-arg , indicating that these states must have other substantial sources of favorable entropy . Relative free energy levels for each state were estimated by combining these energetic contributions with the numbers of inter-trimer hydrogen bonds , and the states were ordered along a reaction coordinate that accounts for all available information ( Figure 4 ) . The resulting free energy landscape is quite rough on its left half . On the apoArgRC conformational coordinate , freely oscillating apoArgRC and the crystal-like high-energy , binding-competent state are represented by a double minimum separated by a barrier , reflecting the fact that the starting state is not sampled again after the initial shift . Singly-liganded states are represented by a manifold with multiple minima , reflecting differences in the energetic contributions determined from the two +1arg and three -5arg simulations . Ligation states beyond +1 lie at progressively lower energy levels , reflecting the cumulative free energy lowering of successive ligand additions , with equal increments after +2arg , the energy level of which cannot be set with presently available information . The +1a simulation reveals the apparently self-contradictory result that entropy is undiminished even though all inter-trimer hydrogen bonds are persistent , i . e . , the increased motions of individual subunits detected by covariance analysis are correlated with cooperative hydrogen bonding at the trimer interface . This result can be understood together with the other unexpected result for all +arg simulations: that hexamer symmetry is as high as in holoArgRC , as judged from the narrowly distributed Gly-Asp distances and essentially invariant center-of-mass distances . Such a seemingly paradoxical state , symmetric and with high entropy despite high hydrogen-bond occupancy , can be visualized as resulting from bonding constraints between the ligand and the subunits , as well as among subunits , that limit monomer motions close to the binding site but that transfer momentum to the peripheral parts of each subunit . This picture is confirmed by analysis of the root-mean-square displacement of each atom from its average position ( Figure 5 ) , showing that enhanced motion is confined to the surface , consistent with the patterns observed in covariance analysis . Although the differences between apoArgRC and +1arg states appear small in Figure 5 , they are amplified in the hexamer by the contributions from all six subunits . Thus , by exploiting the dynamics of the assembly , all interactions between the ligand and the subunits , as well as among subunits , can be optimized simultaneously , generating maximum affinity in the +1 state through favorable contributions to both enthalpy and entropy . The following picture emerges of the structural , kinetic , and energetic events and their manifestation in ITC as L-arg binds to a population of rotationally oscillating apoArgRC hexamers . Free L-arg occasionally encounters a hexamer in a high-energy , non-rotated conformation ( grey zone in Figure 4 ) with one open ligand-binding site and the remaining five salt bridges largely hydrogen-bonded . The highly charged L-arg ligand enters this site , where it may act similarly to high ion concentrations , promoting conversion to an even higher-energy , binding-competent state resembling crystalline apoArgRC , with Arg-Asp hydrogen bonds of the salt bridges mostly broken . Breaking of these hydrogen bonds constitutes an energy barrier between the apoArgRC basin and the binding-competent conformation , and is assigned to the slow ITC endotherm . The mechanism by which free L-arg promotes conversion to a binding-competent state , and the nature of this state , is under investigation to evaluate the interpretation suggested here . Binding of one L-arg to the binding-competent conformation of apoArgRC releases the constraint on Arg-Asp hydrogen bonding . Arg110 residues in the empty binding sites engage in directional interactions with Asp128 residues , but bound L-arg acts as a brake on oscillation by steric interference . These opposing effects result in intense , random monomer motions propagated to the periphery of each subunit . At the center of the hexamer a cooperative hydrogen bond network is established among subunits and between subunits and ligand , optimizing affinity while maintaining symmetry . Thus the singly-liganded state is conceptually , but not structurally , asymmetric . Addition of a second L-arg forces a compromise in the optimized hydrogen bond network of the singly-liganded state , reducing binding energy by an unknown amount ( dashed in Figure 4 ) . The second ligand completes the conversion to a fully holo-like state with all salt bridges too distant to promote directional interactions , but with no further endothermic heat flow . The structural symmetry established in the singly-liganded state is preserved regardless of the placement of the second ligand relative to the first , because this symmetry is rooted in the global dynamics of the system rather than in its structural features . The rate of L-arg dissociation from the singly-bound state is presumably slow relative to the time required for redistribution of the conformational ensemble; estimates from surface plasmon resonance [16] suggest an aggregate L-arg off-rate constant of ∼0 . 1 to 1 . 0 sec−1 . Thus , when free L-arg enters an open binding site , as during the early stages of the ITC titration , part of the apoArgRC population slowly crosses the barrier to the binding-competent state , giving rise to a slow endotherm , and becomes trapped by L-arg binding; additions of further aliquots of L-arg repeat the cycle of barrier-crossing , endotherm evolution , and trapping until binding of the first equivalent of L-arg per hexamer is complete . A single equivalent of L-arg is thus necessary and sufficient to accomplish the shift of the dynamic quaternary ensemble . An allosteric mechanism originating in oscillatory dynamics of the C-terminal domain could account for the fact that both ArgR and ArgRC display identical 1+5 ligand-binding behaviors in ITC [16] , suggesting that L-arg has the same global effect on the quaternary ensembles whether or not the DNA-binding domains are attached . The features of the +1arg state appear to correspond to the prediction of the MWC model [17] that constraints arising from subunit assembly are relaxed upon ligand binding , leading to a high-affinity , monomer-like state , but with maintenance of symmetry during the conformational transition . Despite this consistency , the negative cooperativity of L-arg binding is incompatible with the MWC model , which predicts only positive cooperativity . Thus , like many other allosteric systems [42] including hemoglobin [43] , ArgR appears to display features of both concerted and sequential models . However , the finding that symmetry can be preserved even during sequential filling of binding sites indicates the applicability of the symmetry principles of the MWC model to negative cooperativity , which its authors did not anticipate . At the time of the MWC model most cases of negative cooperativity were regarded as artifacts resulting from partial protein activity , as was later verified [44] for the controversial case of apparent negative cooperativity that had led to elaboration of the sequential allostery model [45] . Since that time , however , many carefully-documented examples of negative cooperativity including ArgR establish beyond doubt that both positive and negative cooperativity are common molecular strategies that serve complementary physiological purposes . Positive cooperativity enables a ligand to act as a switch by reducing the concentration of free ligand required to convert its target from the free to the bound state; negative cooperativity provides a buffer against changes in ligand concentration , requiring larger increases to convert the target from the free to the bound state . The key enabling feature that makes ArgR cooperativity negative is the fact that conversion to the holo-like state proceeds in at least two ligand-binding steps , with affinity optimized in the first step and compromised in the second . The first ligand-binding step achieves maximal affinity by exploiting the dynamic nature of the protein to form a symmetric assembly in which all inter-subunit and ligand-subunit interactions are optimized simultaneously . The second ligand-binding step forces compromise among the interactions established in the first step , reducing ligand affinity and thereby conferring negative cooperativity . No obvious constraint demands that in other cases the two steps of optimization occur in the order observed for ArgR . Depending whether ligand affinity can be maximized in the first step as in ArgR or in subsequent steps , cooperativity is predicted to be either negative or positive , respectively . The difference between positive and negative cooperativity presumably reflects the cohesiveness of the assembly at each ligand-binding step . Note that the free energy of a system is by definition a distributed property of the system , in which sources of cohesion arising from subunit-subunit or ligand-subunit interactions are not distinguished . Relatively weak subunit assemblies may be unable to optimize inter-subunit and ligand-subunit interactions in the first ligand-binding step if a single ligand makes an insufficient contribution to the cohesive free energy . In such cases subsequent ligands , rather than forcing a compromise as in the ArgR case , may take advantage of any partial relaxation promoted by the first ligand ( s ) to bind more strongly , yielding positive cooperativity . Thus , ligand-induced relaxation to a high-affinity , monomer-like state is limited by the cohesiveness of the assembly . As the MWC model points out , one of the advantageous properties associated with molecular symmetry [17] is that symmetric states allow equivalent interaction surfaces on all monomers , maximizing their cohesion . The ArgR example shows that symmetry can be maintained throughout the ligation process , even as optimized interactions are compromised . Optimization in a single ligand-binding step may in fact be rare , as examples of positive cooperativity appear to vastly outnumber bona fide instances of negative cooperativity . The apparent preponderance of positive cooperativity implies that most assemblies lack sufficient cohesive free energy to permit optimization of inter-subunit and ligand-subunit interactions in a single ligand-binding step . This suggestion is consistent with the relatively weak subunit affinity that is common among protein multimers [46] and which is likely to be under selection pressure in order to preserve allosteric modulation . In many cooperative systems the free energies of subunit interaction are of similar magnitude as those of ligand interactions [47] , indicating the two association processes are expected to exert mutual influence . Subunit affinities and their linkage to ligand binding has long been held to underlie the molecular mechanism of hemoglobin allostery [48] , [49] , although explicit correlation of its energetic and dynamic structural pictures has begun only recently [50] . Thus , the magnitude of inter-subunit affinity relative to ligand affinity is expected to predict whether a system exhibits positive or negative cooperativity . Subunit affinity is extremely high for apoArgR; for hexamer-trimer dissociation only an upper-limit value , Kd≤2 . 5 nM , is available from analytical ultracentrifugation data [2] . An archetypal ArgR presumably acquired residues that enabled a non-covalent ligand to substitute for its covalent counterpart , permitting feedback control by the regulon end-product . The work required to oscillate across the apoEcArgRC trimer interface is apparently small relative to the strength of Arg110-Asp128 interactions . However , the extremely high conservation of Arg-Asp pairs in the binding sites of ArgR orthologs contrasts with substantial differences in residues at their trimer interfaces identified by multiple sequence alignment ( not shown ) . This finding may account for the apparently divergent behaviors of ArgR orthologs with respect to the effects of L-arg binding . If competition between resident sidechains and L-arg ligands is general among ArgRs , the balance between oscillation work and strength of Arg-Asp pairs may be tuned differently in orthologs that occupy varied ecological niches . Covalent/non-covalent substitutions may be a general path to a useful response , and a particularly effective evolutionary driver of allostery among amino acid-binding proteins , as suggested by a second feedback regulator that uses the principle in the opposite sense . E . coli tryptophan repressor , TrpR , presents Gly85 instead of a hydrophobic residue , often Trp , found at the DNA-binding interface of other helix-turn-helix proteins [51] . The small size of Gly85 helps to accommodate the co-effector L-trp [52] . An archetypal TrpR presumably lost the aromatic residue at this position , creating the L-trp binding site and perhaps playing a role in evolution of domain-swapped TrpR _haract [53] that present a network of contacts from both subunits to each L-trp ligand to bring DNA binding under control of the regulon end product . Thus , amino acid binding by ArgR and TrpR recalls , and extends to allostery , the ambiguity of the boundary between covalent and non-covalent processes in proteins that is inherent in protein folding , which relies on cooperation between the covalent primary structure and the weak non-covalent interactions that couple the secondary structure to the tertiary structure [53] . Given its simplicity , amino acid binding may have been an early innovation in the evolution of allostery; similar covalent/non-covalent substitutions are known among nucleotide-binding RNAs [54] , [55] , suggesting that ligand/residue substitution , which requires the existence of only polymers and their constituent monomers , could predate the evolution of protein subunit assemblies that exploited the innovation for homotropic cooperativity . Indeed , the subtle modulation of protein activity by monomer binding , compared with the all-or-none effects on RNAs [54]–[56] , may have played a role in the ascendancy of proteins in the RNA world . The vast majority of allosteric ligands do not correspond to the monomeric constituents of their targets , and some proteins can respond even to non-biological ligands , representing an extreme example of gratuity as originally defined by Monod [3] , [57] , [58]: the concept that effectors need not resemble substrates , as exemplified by , e . g . , the inducer of the lac operon , isopropylthiogalactoside . A particularly dramatic example is bacteriophage T4 lysozyme , where single-residue substitutions produce binding sites for benzene [59] . Presumably , selection pressure for allosterically responsive targets can be created by any ligand exploiting any evolutionarily intermediate binding site on any macromolecule . Thus all binding species should probably be considered as potential allosteric effectors , reflecting the enormous capacity for allosteric response that is likely to be inherent in nearly all intermolecular interactions , just as allostery is understood to be a universal property of dynamic proteins [17] . | A controversial prediction of the famous allostery model of Monod , Wyman , and Changeux is that constraints imposed on protein subunits by multimerization are relaxed by ligand binding , but with conservation of symmetry in partially-liganded states . Interpretation of thermodynamic ligand-binding data through the lens of molecular dynamics simulation has led to structural and energetic description of such a state for the hexameric Escherichia coli arginine repressor , which displays strong negative cooperativity of L-arginine binding . The results indicate that partially-liganded states can be structurally symmetric despite their conceptual asymmetry . The symmetric relaxed state is visualized as a multimer with all subunits anchored near the center , and with motions transferred to the periphery of the assembly . Thus , even during sequential filling of binding sites , symmetry can be maintained by exploiting the dynamics of the assembly and the distributed nature of its cohesive free energy . | [
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| 2010 | Symmetric Allosteric Mechanism of Hexameric Escherichia coli Arginine Repressor Exploits Competition between L-Arginine Ligands and Resident Arginine Residues |
Chronic parasitic infections are associated with active immunomodulation which may include by-stander effects on unrelated antigens . It has been suggested that pre-natal exposure to parasitic infections in the mother impacts immunological development in the fetus and hence the offspring’s response to vaccines , and that control of parasitic infection among pregnant women will therefore be beneficial . We used new data from the Entebbe Mother and Baby Study , a trial of anthelminthic treatment during pregnancy conducted in Uganda , to further investigate this hypothesis . 2705 mothers were investigated for parasitic infections and then randomised to albendazole ( 400mg ) versus placebo and praziquantel ( 40mg/kg ) during pregnancy in a factorial design . All mothers received sulfadoxine/pyrimethamine for presumptive treatment of malaria . Offspring received Expanded Programme on Immunisation vaccines at birth , six , 10 and 14 weeks . New data on antibody levels to diphtheria toxin , three pertussis antigens , Haemophilus influenzae type B ( HiB ) and Hepatitis B , measured at one year ( April 2004 –May 2007 ) from 1379 infants were analysed for this report . Additional observational analyses relating maternal infections to infant vaccine responses were also conducted . Helminth infections were highly prevalent amongst mothers ( hookworm 43 . 1% , Mansonella 20 . 9% , Schistosoma mansoni 17 . 3% , Strongyloides 11 . 7% , Trichuris 8 . 1% ) and 9 . 4% had malaria at enrolment . In the trial analysis we found no overall effect of either anthelminthic intervention on the measured infant vaccine responses . In observational analyses , no species was associated with suppressed responses . Strongyloidiasis was associated with enhanced responses to pertussis toxin , HiB and Hep B vaccine antigens . Our results do not support the hypothesis that routine anthelminthic treatment during pregnancy has a benefit for the infant’s vaccine response , or that maternal helminth infection has a net suppressive effect on the offspring’s response to vaccines . ISRCTN . com ISRCTN32849447
There is substantial evidence that pre-natal exposures are important in shaping immunological development [1] . This includes strong evidence that prenatal exposure and sensitisation to parasite antigens determines susceptibility to the same parasite in the offspring [1] and that immunisation during pregnancy influences the infant response to the same vaccine [2] . There is also evidence that prenatal exposures may influence the offspring’s response to unrelated antigens [1] . It is important to better understand such effects since they are likely to be important in broadly determining susceptibility to infectious diseases , either directly or through responses to immunisation , as well as determining susceptibility to other immunologically mediated conditions ( such as allergy-related disease [3 , 4] ) . Vaccines provide an example of a standardised immunological challenge given at a standardised time and hence an opportunity to evaluate the effects of pre-natal exposures on infant immune responses . Recently , Malhotra and colleagues reported a study among children of mothers infected or uninfected with malaria and helminths in a coastal region of Kenya which suggested that infants of parasite-infected mothers had a reduced ability to develop antibody responses to Haemophilus influenzae type B ( HiB ) immunisation and diphtheria toxoid ( DT ) , but showed no effect on responses to hepatitis B ( Hep B ) immunisation or tetanus toxoid ( TT ) [5] . If there is a causal association between prenatal parasitic exposure and infant vaccine responses , then treatment of maternal parasitic infections might be expected to remove parasite-associated effects . We conducted a randomised controlled trial ( the Entebbe Mother and Baby Study , ISRCTN32849447 ) to investigate whether anthelmintic treatment of pregnant mothers improved the vaccine response amongst their children [6] . We have previously reported the effects of treatment on cellular responses following BCG and tetanus immunisation , and on tetanus and measles antibody concentrations: there were no overall effects but , in planned subgroup analyses , albendazole treatment of mothers with hookworm was associated with reduced T-helper 2 cytokine responses to TT in their infants , and ( unexpectedly ) albendazole treatment of mothers without hookworm resulted in increased interferon-γ ( IFN-γ ) responses to mycobacterial antigen; otherwise no effects of maternal treatment on responses to BCG , TT or measles were observed [7] . Here , we report on the effects of maternal anthelminthic treatment on a further six serological responses ( DT , pertussis [pertussis toxin ( PT ) , filamentous haemagglutinin ( FHA ) and pertactin] , Hep B and HiB ) . In addition to the trial results , we also present an observational analysis of associations between multiple maternal infections and infant immunological responses , analogous to the observational analyses reported by Malhotra and colleagues , using our existing dataset to investigate whether similar associations are also present in our cohort of mothers and babies from rural and urban areas of central Uganda .
Healthy pregnant mothers in their second or third trimester were enrolled as part of the Entebbe Mother and Baby Study ( EMaBS; ISRCTN32849447 ) between 2003 and 2005 , described elsewhere [6 , 7] . Briefly , this was a randomised , placebo-controlled , factorial study of the effect of single-dose albendazole ( 400 mg ) and praziquantel ( 40 mg/kg ) given during the second or third trimester of pregnancy on postnatal outcomes . Mothers were enrolled at their first antenatal visit unless they attended in the first trimester , in which case enrolment was postponed to minimise risk of teratogenicity . After enrolment they continued to receive standard antenatal care , including intermittent presumptive treatment for malaria with sulfadoxine/pyrimethamine and tetanus immunisation , and intrapartum and neonatal single-dose nevirapine for prevention of mother-to-child HIV transmission for the minority in whom it was required . Infants received the routine EPI ( Expanded Programme on Immunisation ) vaccines ( BCG and polio at birth; DT , pertussis toxin , TT , Hep B and HiB at age six , 10 and 14 weeks , measles at nine months ) . All mothers gave informed written consent on behalf on themselves and their children . We have previously reported on responses to vaccines against tuberculosis , TT and measles [7] . Here we assess six immunological responses amongst children at age one year: DT , Hep B , pertussis , FHA , pertactin , Hep B , and HiB . Only children who received all three doses of pentavalent vaccine are included in this analysis . Ethical consent was granted for the original trial and for subsequent analysis from UVRI ( GC/127/12/07/32 ) , the Uganda National Council for Science and Technology ( MV625 ) , London School of Hygiene & Tropical Medicine ( 790 , A340 ) , and the Oxford Tropical Research Ethics Committee ( 39–12 ) . At screening during pregnancy , and at delivery , or as soon as possible after delivery ( for those whose children were born outside hospital ) , blood samples were obtained from each woman to test for presence of malaria parasites by thick film and for microfilariae of Mansonella perstans using a modified Knott’s method [8]; a single stool sample was obtained for diagnosis of intestinal parasites including hookworm ( Necator americanus in this area [9] ) , Schistosoma mansoni , Trichuris trichiura and Ascaris lumbricoides using the Kato Katz technique [10] and for Strongyloides stercoralis by culture [11] . In case of multiple births just the first child was considered for inclusion in this analysis . Mother-baby pairs were excluded if the infant did not receive the standard three doses of EPI vaccines before samples were collected . At one-year of age a blood sample was obtained from infants . Plasma and serum were separated and stored at -80°C until processing . Plasma or serum were assessed for antibody concentrations against DT , pertussis antigens and HiB using a Luminex bead-based multiplex immunoassay described in detail elsewhere[12 , 13] . Antibody concentrations against Hep B were measured using the ABBOTT Architect i2000 with their anti-HBs kit ( Abbott Laboratories , Chicago IL , USA ) using the recommended protocol . We elected to measure the unstimulated serological response to vaccination in order to maintain consistency with other published reports of helminth-vaccine response associations . In the cases of DT and HiB these measures are likely correlated with protection against disease . In the case of pertussis it remains unclear which antigen is responsible for inducing protection and whether serological levels are sufficient correlates , whereas for hepatitis B , there is evidence that measuring peak response of antibody ( approximately 6 weeks post final vaccination ) is the optimal correlate of protection although practically this is very difficult to achieve [14] . Mothers were categorised by parasite exposure in three ways , following the approach of Malhotra 2015 for comparability [5] . First , mothers were grouped according to the total number of infections ( helminths and malaria: 0; 1; 2; ≥3 ) . Second , mothers were grouped as no infection; malaria only; malaria plus one helminth infection; malaria plus two or more helminth infections . Third , mothers without malaria were grouped as follows: no helminth infections; one infection and no malaria , two infections and no malaria; three or more infections and no malaria . The vaccines examined here were not the primary outcomes for this trial , so sample size calculations were not based on these responses . In a post-hoc evaluation of power , based upon the standard deviations we observed , we had 80% power to detect differences ranging from 1 . 18 ( FHA ) to 1 . 34 ( HiB ) . A complete case analysis was done where possible , and imputation of missing data was not performed . Vaccine responses from the included and excluded records were compared with t-tests or Mann-Whitney tests , as appropriate . As this was a factorial trial , comparisons were made between all those randomised to albendazole versus those randomised to matching placebo , and between praziquantel versus placebo . Linear regression was used to assess associations between exposures ( albendazole and praziquantel ) and outcomes ( infant vaccine responses ) . Outcome variables were transformed onto the log ( base 10 ) scale to reduce skew; hence reported coefficients represent geometric mean ratios ( GMR ) . Regression was performed with a bias-corrected bootstrap using 100 replicates . Covariates were selected a priori and included maternal baseline characteristics of age , parity ( 1; 2–4; ≥5 ) , education level ( none; primary; secondary; tertiary ) , and household socio-economic group ( on a six point scale , with six representing the highest group ) ; infant covariates were sex , infant malaria and time ( in days ) since the third EPI vaccination . Pre-planned sub-group analysis was carried out to examine the effect of albendazole on children of mothers who had a hookworm infection , and separately for the effect of praziquantel on the children of mothers with S . mansoni infection . This was performed using the same regression technique as described above and allowing an interaction between randomised treatment and infection . Interaction effects between the two randomised treatments were tested in a similar way . A similar approach was used to assess individual infections ( including binary indicator variables for malaria , hookworm , S . mansoni , M . perstans , Ascaris , Trichuris and strongyloidiasis in one linear regression model ) and the exposure categories defined above . In each case we also adjusted for randomised treatment . Exposure groups were treated as categorical variables designed to allow for a comparison with the previously published results from mothers in Kenya [5] . No adjustment was made for the numerous testing caused by assessing the effect of multiple exposures on six outcomes . Stata version 14 . 1 was used for all analyses .
A total of 2507 mothers were enrolled into the trial [7] . We had complete vaccine response data ( excluding hepatitis B ) for 1379 ( 55% ) mothers and first-born babies: 348 were randomised to albendazole + praziquantel , 346 to albendazole + placebo , 336 to praziquantel + placebo , and 349 to placebos only . Due to limited serum and plasma from infants , samples were unavailable for Hepatitis B assay for 374 of these infants ( Fig 1 ) . The demographic characteristics of these 1379 mothers were similar to those who were missing from our analysis . The biggest difference was in maternal malaria infection: this was 9 . 4% in those included in the analysis and 12 . 9% in those excluded . Other characteristics were broadly similar: average age was 23 . 9 years ( included ) and 23 . 4 years ( excluded ) ; education was “none” or “primary” in 54 . 7% in the included group and 54 . 5% in the excluded group . Hookworm infection was 43 . 2% and 45 . 3% , and S . mansoni infection was 17 . 3% and 19 . 7% in the included and excluded groups respectively . The sex of the babies was also similar between the included ( 49 . 6% female ) and excluded groups ( 47 . 3% female ) , as was the parity of the mothers ( mean 2 . 8 in both groups ) . Baseline characteristics were broadly balanced between the randomised arms ( Table 1 ) . The most common maternal infection among the mothers with vaccine response data was hookworm ( 43 . 1% ) , followed by Mansonella ( 20 . 9% ) , S . mansoni ( 17 . 3% ) , Strongyloides ( 11 . 7% ) , malaria ( 9 . 4% ) , Trichuris ( 8 . 1% ) and Ascaris ( 2 . 1% ) . We had stool samples for 1235 ( 89 . 6% ) of one-year olds . The most common parasites detected were Ascaris ( n = 16 ) and Trichuris ( n = 12 ) . We found very low levels of hookworm ( n = 4 ) and S . mansoni ( n = 1 ) . We found no evidence of an effect of randomised treatment on any of the infant vaccine responses ( Table 2 ) , nor any evidence for treatment interaction ( p>0 . 1 for all outcomes , S1 Table ) . In pre-planned sub-group analysis , the only evidence of a differential treatment effect was on DT response in children of mothers who received albendazole: in mothers with hookworm the adjusted geometric mean ratio ( aGMR ) for albendazole was 0 . 89 ( 95 CI% 0 . 74–1 . 08 ) and in mothers without hookworm it was 1 . 24 ( 95% CI 1 . 04–1 . 47 ) . The p-value for interaction was p = 0 . 01 ( Table 3 ) . For the observational analysis , a total of 1286 mothers-baby pairs had known status for all seven infections of interest and five of the six vaccines ( Hepatitis B , n = 940 ) . We found no evidence of different vaccine response results in the excluded records . Similar to the trial analysis , the most common maternal infection in this group was hookworm ( 43 . 1% ) , followed by Mansonella ( 21 . 2% ) , S . mansoni ( 16 . 7% ) , Strongyloidiasis ( 11 . 6% ) , malaria ( 9 . 2% ) , Trichuris ( 8 . 5% ) and Ascaris ( 1 . 9% ) . We found no evidence that maternal infections were associated with infant vaccine response except for maternal strongyloidiasis . The aGMR of HiB response for children of mothers with strongyloidiasis was 1 . 51 times greater ( 95% CI 1 . 11–2 . 01 ) than children of mothers who were uninfected . For Hep B the increase was by a factor of 1 . 47 ( 95% CI 1 . 11–1 . 94 ) and in pertussis response the aGMR was 1 . 41 ( 95% CI 1 . 06–1 . 88 ) . We found no evidence of an association between any of the maternal exposure groups ( number of infections , number of infections alongside malaria , number of infections among mothers without malaria ) and any infant vaccine response at one year . Full results are in Table 4 .
We found no evidence of enhanced vaccine responses among infants of infected mothers who were treated for helminths during pregnancy , nor evidence of a suppressive effect of prenatal exposure to maternal parasitic infections on infant vaccine responses , in this cohort of mothers and infants in Uganda . Such possible effects as were observed were indicative of enhanced responses for a number of vaccines in the infants of mothers identified as having strongyloidiasis , and of reduced DT responses in the infants of mothers with detectable hookworm infection who were treated with albendazole . The primary strength of our study is the randomised , controlled intervention during pregnancy . Hookworm infection was treated effectively by albendazole ( declining from over 40% prevalence before treatment to 5% after delivery among albendazole treated women ) and schistosomiasis was treated effectively by praziquantel ( declining from about 18% to 5% ) while Mansonella and Strongyloides were unaffected by the treatment [15] . From a simplistic perspective , the lack of effect of maternal treatment on vaccine responses among infants of women infected with hookworm and S . mansoni implies either that prenatal exposure to these helminth species has no important effect on the infant response to unrelated vaccines ( and hence perhaps to unrelated infections ) , or that the impact of prenatal exposure is established prior to the second trimester and cannot be reversed thereafter . Of note , Malhotra and colleagues treated all mothers with albendazole ( for nematodes ) during pregnancy; we , and Malhotra and colleagues , treated all mothers with sulfadoxine / pyrimethamine ( for malaria ) ; the effects described in each study were necessarily those that occur despite , or in the context of , these interventions [5] . However , the truth of the matter seems to be that the impact of prenatal parasitic infections on infant vaccine responses is complex and depends at least on characteristics of both the parasitic infection and the vaccine , and on the nature of the desired , protective vaccine response . A study from Ecuador , in accord with our results , showed no association between maternal geohelminths and infant IgG responses to DT , TT , PT , measles , rubella or HiB [16] , but several studies have now indicated a net enhancement of infant vaccine responses following exposure to certain maternal infections , including Trypanosoma cruzi for BCG , DT , TT and Hep B [17] , maternal intestinal helminth infections and the IgA response to rotavirus and polio ( in the Ecuador study [16] ) , as well as our result for strongyloidiasis and PT , HiB and Hep B . Meanwhile , Malhotra and colleagues have shown that , for malaria and lymphatic filariasis ( and , in an earlier study , schistosomiasis [18] ) , the impact of maternal infection on infant vaccine response depends upon whether or not the infant was sensitised to the parasitic infection in utero: compared to unexposed infants , malaria sensitised infants showed an increase , and malaria tolerised infants a decrease , in the response to DT [5]–this may contribute to a neutral net effect in studies which do not make the same distinction . Like us , Malhotra and colleagues observed no effect of pre-natal exposure to parasitic infections on infant responses to most of the EPI vaccines . The principal exception was HiB and , interestingly , although individual maternal infections were associated with reduced responses , additional infections tended to reverse this effect . Our findings for HiB followed a similar pattern , although the associations were not statistically significant . Our observation of an enhanced response to DT amongst infants of mothers without hookworm who received albendazole was surprising , and may be a chance finding given that subgroup analyses were conducted and multiple comparisons were made , with no formal adjustment in statistical interpretation . However , this result accords with our previous findings of an enhanced IFN-γ response to BCG , an enhanced IL-13 response to TT , and an enhanced risk of infantile eczema in the same group [7 , 19] and suggests a pro-inflammatory effect of albendazole , in the absence of maternal hookworm , which may be a direct effect of the drug , or mediated by effects on other co-infections . We think it unlikely that these results represent an effect of albendazole on light , undetected hookworm infections: as we have previously reported , a proportion of mothers in the albendazole placebo group had three samples examined before treatment was given post-delivery , evaluation of which increased the prevalence of hookworm in this group by only 6% ( from 45% to 51% ) [19] . A net adverse effect of prenatal exposure to maternal parasitic infections on the induction of immune responses by vaccines given to the offspring would imply a net adverse effect on the infant’s ability to respond to pathogens , also . This would be expected to result in increased neonatal or infant mortality . However , an initial result suggesting that anthelminthic treatment during pregnancy had benefits for infant mortality [20] has not been substantiated in controlled trials [7 , 21] . By contrast , there is considerable evidence that intervention against malaria during pregnancy has benefits for infant mortality [22] . While this may be mediated largely by prevention of the major effects of malaria on placental function and fetal growth , and by effects on infant susceptibility to malaria itself [23 , 24] , an impact on vaccine responses and on susceptibility to heterologous infections may contribute . This accords with the relatively prominent suppressive effect of maternal malaria described in Malhotra’s study , with a reported suppressive effect of prenatal exposure to malaria on the infant response to BCG , described in The Gambia [25] and with our own previous finding of an association between maternal malaria and reduced infant antibody response to measles immunisation [26] . A limitation of our study was that we included just 55% of eligible infants due to incomplete data . However , the mother-baby pairs that were excluded were similar in known characteristics to those included in the analysis . Further infants were missing from the Hep B analysis . A limitation of the observational component of our study was the classification of maternal infection status based on a single blood or stool sample . This would substantially underestimate and misclassify malaria exposure , which is best assessed by placental histology , and more sensitively assessed by polymerase chain reaction ( PCR ) assays . The use of a single stool result would also result in misclassification for hookworm or S . mansoni exposure [27 , 28] and hence may have obscured relevant associations . We have previously reported that in this study the sensitivity of one stool sample compared with three stool samples was 89% for hookworm infection and 66% for schistosomiasis [7] . This limitation does not apply to evaluation of Mansonella exposure which showed 96% agreement between samples taken in pregnancy and after delivery in this cohort ( 2077 of 2162 mothers for whom samples were available at both time points ) . Our classification of exposure differed markedly from the classification used by Malhotra and colleagues who included microscopy and PCR on placental and cord blood for malaria , and assays of circulating antigen and IgG4 for Schistosoma haematobium and Wuchereria bancrofti . The use of IgG4 detection as a marker of active infection is of possible concern as levels may be higher among individuals with a regulatory bias in their immune response [29] . Contrasting with the time-course described by Malhotra and colleagues , we had data on vaccine-specific antibody responses only at age one year , but this was a time point at which many of the effects observed by Malhotra and colleagues were evident , so comparable results might have been expected . Although other markers or measured timepoints may be more desirable in terms of assessing true protection against disease ( such as neutralisation or functional opsonophagocytic assays [14] ) , many of these other assays are more prone to inter-observer error . Our analysis of the unstimulated antibody concentrations maintains consistency between studies and provides observations relating to immunological responses rather than chances of protection . Furthermore , we had data on a different set of helminth infections to those used by Malhotra . It could be that particular helminths are more important in determining vaccine responses of infants , or that there are interaction effects which we have not explored . This paper contains a large number of estimates , confidence intervals and hypothesis tests . Due to these multiple comparisons , it is possible that some associations which appear statistically significant are in fact due to chance alone . These results should therefore not be considered definitive , but should instead be seen as evidence to be considered alongside other studies in this field . From a public health perspective , the additional results that we contribute here accord with our previous findings [7 , 26] and suggest that , whatever its benefits , routine anthelminthic treatment during pregnancy is not likely to result in improved infant vaccine responses . | Parasitic infections , such as worms and malaria , have potent effects on the human immune system . These effects include modification of immune responses in the fetus and infant if a mother has a parasitic infection during pregnancy . These immunological changes can influence the way a child responds to the same infection when exposed in later life . It has been suggested that the immunological changes might also influence how the child responds to the vaccines given in infancy , and that treating mothers for parasitic infections when they are pregnant might be helpful . In this study we compared responses to vaccines between infants of mothers who had , or had not , been treated for worms while they were pregnant . We found no overall differences . We also compared vaccine responses between groups of mothers with and without parasitic infections . We found no evidence that the parasitic infections were associated with reduced responses in the children . This means that , although treating worms during pregnancy may have some benefits , improvements in the children’s responses to vaccines are not likely to be among them . | [
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| 2017 | The impact of prenatal exposure to parasitic infections and to anthelminthic treatment on antibody responses to routine immunisations given in infancy: Secondary analysis of a randomised controlled trial |
Down Syndrome ( DS ) is caused by trisomy of chromosome 21 ( Hsa21 ) and results in a spectrum of phenotypes including learning and memory deficits , and motor dysfunction . It has been hypothesized that an additional copy of a few Hsa21 dosage-sensitive genes causes these phenotypes , but this has been challenged by observations that aneuploidy can cause phenotypes by the mass action of large numbers of genes , with undetectable contributions from individual sequences . The motor abnormalities in DS are relatively understudied—the identity of causative dosage-sensitive genes and the mechanism underpinning the phenotypes are unknown . Using a panel of mouse strains with duplications of regions of mouse chromosomes orthologous to Hsa21 we show that increased dosage of small numbers of genes causes locomotor dysfunction and , moreover , that the Dyrk1a gene is required in three copies to cause the phenotype . Furthermore , we show for the first time a new DS phenotype: loss of motor neurons both in mouse models and , importantly , in humans with DS , that may contribute to locomotor dysfunction .
Down Syndrome ( DS ) , trisomy 21 , is characterized by a wide range of phenotypes including cognitive deficits , early-onset Alzheimer’s disease , locomotor dysfunction and congenital heart defects [1 , 2] . These diverse phenotypes could be caused by small numbers of dosage-sensitive genes on Hsa21 whose increased copy number leads to increased expression and hence phenotypic effects [3] . Alternatively , phenotypes may result from increased dosage and thus expression of large numbers of sequences on Hsa21 . Such a possibility was recently reported for aneuploidy in yeast , where the deleterious effects of an extra chromosome on cell proliferation were shown to be due to the combined action of large numbers of genes rather than small numbers of dosage-sensitive genes , potentially resulting in proteotoxic stress [4 , 5] . Finally , it is possible that aneuploidy per se , rather than additional copies of genes may cause phenotypes; and any combination of these phenomena may be important for individual phenotypes . To understand the genetic basis of DS , we and others have used mouse genetics to model the syndrome by generating a series of strains containing either Hsa21 or duplications of mouse chromosome regions orthologous to Hsa21 located on mouse chromosome 16 ( Mmu16 ) , Mmu17 and Mmu10 ( Fig 1 ) [1 , 6–13] . Some of these strains are also aneuploid–the Tc1 mouse strain carries a freely segregating Hsa21 and has increased dosage of genes on Hsa21 [9] . While Tc1 mice have a number of phenotypes that model aspects of DS , the mice are mosaic for Hsa21 and this chromosome is rearranged such that only ~200 Hsa21 protein-coding genes are present ( ~75% of all Hsa21 genes ) [14] . The Ts65Dn strain is also aneuploid , containing an extra chromosome carrying a portion of Mmu16 orthologous to Hsa21 and has been used extensively for the study of DS phenotypes [7] . However , this additional chromosome also includes ~10 Mb of Mmu17 containing 60 mouse genes that are not orthologous to Hsa21 , thus limiting the utility of this model [15] . Chromosome engineering has allowed the generation of strains with carefully designed gene dosage increases . In particular , three strains known as Dp ( 16 ) 1Yey , Dp ( 17 ) 1Yey and Dp ( 10 ) 1Yey , have been generated carrying tandem duplications of the entire Hsa21-orthologous regions of Mmu16 , Mmu17 and Mmu10 respectively ( Fig 1 ) [10 , 11] . The intercross of these three mutations ( triple trisomic mouse ) gives the most complete mouse model of DS to date because it carries an extra copy of every mouse gene orthologous to Hsa21 . We have also generated the Dp1Tyb strain with a tandem duplication of the entire region of Mmu16 orthologous to Hsa21 , the largest of the three syntenic regions [16] . Furthermore , to allow an unbiased mapping of dosage-sensitive genes that may cause DS phenotypes we constructed another 6 strains ( Dp2Tyb , Dp3Tyb , Dp4Tyb , Dp5Tyb , Dp6Tyb and Dp9Tyb ) that have duplications of shorter regions of Mmu16 nested within the region duplicated in Dp1Tyb ( Fig 1 ) [16] . Thus , between them , these strains can be used to establish if phenotypes are caused by small or large numbers of genes , or they if they require aneuploidy . People with DS have deficits in motor function , showing alterations in balance , postural control and fine motor skills [17–22] . This understudied DS phenotype has been suggested to arise in part because of defects in cerebellar anatomy [23 , 24] . The Ts65Dn mouse model has reduced cerebellar size and decreased numbers of granule cells , and the same defects were found in cerebella of humans with DS [24] . The defect in this model is likely caused by decreased Sonic Hedgehog-induced proliferation of granule cell precursors [25] . However , we do not know the identity of any dosage-sensitive genes whose increased copy number is required for motor defects , or indeed if these defects are caused by small numbers of dosage-sensitive genes , or by the mass action of increased dosage of large numbers of genes on Hsa21 , or by aneuploidy . In this study , we aimed to identify dosage-sensitive genes that cause motor defects and to examine possible pathological changes that might underpin them . In particular , we investigated changes in cerebellar anatomy as well as sensory and motor neuron function . Using our genetic mapping panel of mouse strains , we show that motor dysfunction can be caused by increased dosage of a region with a small number of genes , and within these we demonstrate that the Dyrk1a gene is required in three copies to cause the phenotype . Furthermore , we show that , surprisingly , there is no alteration in cerebellar anatomy in mice that have increased dosage only of genes orthologous to Hsa21 . However , we identified an entirely novel form of neurodegeneration in DS , the progressive loss of motor neurons , a phenotype that , importantly , is recapitulated in human samples with DS and may contribute to the locomotor dysfunction . Our results support the hypothesis that some DS phenotypes are caused by increased copy number of small numbers of dosage-sensitive genes , and broaden the neurodegenerative phenotypes in DS .
Previously , we showed that Tc1 mice have defects in locomotor function using both a static rod and a rotating rod ( Rotarod ) test [26] . To identify whether locomotor defects can be modeled by increased dosage of mouse genes orthologous to Hsa21 , we examined locomotor function in Dp ( 16 ) 1Yey , Dp ( 17 ) 1Yey and Dp ( 10 ) 1Yey mice that between them carry duplications of all three regions of mouse chromosomes orthologous to Hsa21 ( Fig 1 ) . We chose to use a Rotarod paradigm in which mice are placed onto an accelerating rod , recording the speed of the rod at which the mouse falls . Each mouse was tested 3 times during one day , and then a further 3 times on the second and third day of testing–a protocol in which control mice usually show improved performance over the 3 days , demonstrating motor learning . We found Dp ( 16 ) 1Yey mice performed significantly less well than wild-type littermates , whereas Dp ( 17 ) 1Yey mice showed no defects , and Dp ( 10 ) 1Yey mice had improved performance , demonstrating that duplication of the orthologous region on Mmu16 was sufficient to cause locomotor defects ( Fig 2A ) . To evaluate whether the orthologous regions on Mmu17 and Mmu10 may contribute to the phenotype when combined with the duplication on Mmu16 , we intercrossed the three mutant strains and analyzed the progeny . We found that the Dp ( 17 ) 1Yey and Dp ( 10 ) 1Yey mutations alone or together did not exacerbate the phenotype of the Dp ( 16 ) 1Yey mice , with the triple trisomic mice performing no worse than Dp ( 16 ) 1Yey ( Fig 2A ) . Thus , the region of Hsa21 orthology on Mmu16 is both required and sufficient to cause locomotor defects . It is possible that the mice performed poorly in the Rotarod test because of reduced motivation rather than locomotor dysfunction . To address this , we tested Dp1Tyb mice ( which bear a duplication of the same Mmu16 genes as Dp ( 16 ) 1Yey mice ) using the Locotronic apparatus ( Intellibio ) . In this assay , mice traverse a horizontal ladder with evenly spaced rungs , and the number of errors in foot placement ( missed rungs ) was recorded . Mice were tested 2–3 times , and trials where mice took > 60 s to traverse the ladder were excluded from the analysis in order to eliminate trials where the mice were insufficiently motivated . Dp1Tyb mice showed a significant increase in errors , supporting the conclusion that duplication of the Hsa21-orthologous region of Mmu16 results in locomotor defects ( Fig 2B ) . To narrow down the location of potential dosage-sensitive genes causing this defect , we examined Dp2Tyb , Dp3Tyb and Dp9Tyb mice which contain duplications that between them cover the entire region duplicated in Dp ( 16 ) 1Yey ( Fig 1 ) . We found that only Dp3Tyb mice had a significant defect in the Rotarod assay , though we noted that the extent of the defect was smaller than that seen in Dp ( 16 ) 1Yey ( Fig 2A ) . A similar defect was also seen in Ts1Rhr mice that contain a duplication of 33 genes that is entirely contained within the Dp3Tyb region , but is smaller by 8 genes [27] . Lastly , we examined Dp4Tyb , Dp5Tyb and Dp6Tyb mice that break down the region duplicated in Dp3Tyb into three smaller regions . We found both Dp4Tyb and Dp5Tyb showed defects in the Rotarod assay . Thus , the regions duplicated in Dp4Tyb and Dp5Tyb , spanning a total of 3 . 3Mb and containing 15 and 12 genes respectively are each sufficient to cause some locomotor dysfunction , although genes in other areas also contribute to the full phenotype . DYRK1A is protein kinase encoded on Hsa21 , whose overexpression has been implicated in neuronal phenotypes in DS , including brain development and synaptic plasticity [28] . Transgenic mice overexpressing DYRK1A have motor defects [29–32] , and since Dyrk1a is located within the region duplicated in Dp ( 16 ) 1Yey , Dp3Tyb , Ts1Rhr and Dp5Tyb mice , we tested whether three copies of Dyrk1a are required for the locomotor defects . We crossed Dp ( 16 ) 1Yey and Ts1Rhr mice to mice heterozygous for a null allele of Dyrk1a ( Dyrk1a+/- ) . The phenotype was rescued in both the Dp ( 16 ) 1Yey/Dyrk1a+/- and Ts1Rhr/Dyrk1a+/- progeny ( 2 copies of Dyrk1a ) , which showed no defect in the Rotarod assay , thus demonstrating that three copies of the Dyrk1a gene are required for the locomotor deficit ( Fig 2A ) . If an increased gene dosage of Dyrk1a is required for the locomotor defects , there should be increased Dyrk1a mRNA expression in mice with a duplication that includes this gene . We found significantly increased Dyrk1a mRNA in the cerebellum of Dp ( 16 ) 1Yey mice at both 6 days and 10 weeks of age and in 10 week old Dp3Tyb mice ( Fig 3A–3C ) . We also saw a trend for increased Dyrk1a expression in Dp5Tyb mice ( Fig 3D ) . Interestingly , the upregulation of Dyrk1a was larger in young Dp ( 16 ) 1Yey mice at 6 days of age ( 1 . 64-fold ) compared to adult 10 week old mice ( 1 . 25-fold ) . This is similar to a previous report on cerebellar DYRK1A levels in Ts1Cje mice that have an additional copy of 87 Mmu16 genes including Dyrk1a [33] . This study shows that the increase in cerebellar DYRK1A was larger in young compared to old Ts1Cje mice . It has been previously described that increased Dyrk1a expression in transgenic mBACtgDyrk1a mice leads to increased levels of Gad1 and Gad2 in several brain regions including the cerebellum [34] . GAD1 and GAD2 are isoforms of glutamate decarboxylase , an enzyme that synthesizes the gamma-aminobutyric acid ( GABA ) neurotransmitter , and are expressed in inhibitory interneurons . The increased levels of GAD1 and GAD2 in mice overexpressing Dyrk1a have been postulated to indicate elevated numbers of inhibitory neurons , a phenotype that could contribute directly to behavioral and cognitive changes [34] . Analysis of cerebellar mRNA in Dp ( 16 ) 1Yey mice at 6 days and 10 weeks of age and in 10-week old Dp3Tyb and Dp5Tyb mice showed no significant change in the expression of either Gad1 or Gad2 ( Fig 3A–3D ) . This result shows that the upregulation of Dyrk1a in these strains ( 1 . 25- to 1 . 64-fold ) is insufficient to perturb expression of Gad1 or Gad2 , and suggests that numbers of interneurons may not be altered in the cerebellum of these mice . Since defects in cerebellar anatomy have been described in both humans with DS and in the aneuploid Ts65Dn and Tc1 mouse models [9 , 24] , and these have been proposed to contribute to motor defects in DS [23] , we investigated the anatomy of the cerebellum in Dp ( 16 ) 1Yey mice , which have motor defects but are not aneuploid . We analyzed cerebella at two ages , 6 days after birth ( P6 ) and in adult mice at 6 months of age , since previous studies had documented changes at these ages in Ts65Dn mice [24 , 25] . At P6 we found no significant changes in cerebellar area , width of the external granule cell layer , or in the density of Purkinje cells or granule cells in Dp ( 16 ) 1Yey mice , either in individual lobules or averaged over the whole cerebellum ( Fig 4A–4E ) . Similarly , in adult mice there was no change in the width of the granule cell and molecular layers or in the density of Purkinje cells , granule cells or interneurons , again over individual lobules or averaged over the whole structure ( Fig 4F–4L ) . Thus , in contrast to Ts65Dn mice , Dp ( 16 ) 1Yey mice have no obvious defect in cerebellar anatomy , and this cannot contribute to their locomotor dysfunction . These results do not rule out that there may be functional abnormalities in the cerebellum despite the absence of anatomical changes . Given the changes in motor function we also screened for sensory deficits across a range of modalities , since these can result in locomotor defects [35 , 36] , and have not been examined in DS . Using assays measuring nocifensive responses to cold , heat , mechanical stimulation and formalin injection , we compared Tc1 mice with controls and found no differences between the groups ( Fig 5A–5D ) . We also analyzed the dorsal root ganglia ( DRG ) by histology . Sensory neuron cell bodies located in the DRG can be classified based on their neurochemical characteristics . Large and medium diameter sensory neurons with myelinated axons were identified by expression of NF200 . Peptidergic or non-peptidergic small diameter sensory neurons with unmyelinated axons ( C-fibers ) were identified by expression of calcitonin gene related peptide ( CGRP ) or by binding the isolectin Griffonia simplicifolia I-B4 ( IB4 ) respectively [37] . We found no change in the fraction of neurons expressing NF200 or CGRP , but a significant decrease in the fraction of neurons binding IB4 in Tc1 mice ( Fig 5E ) . We extended these studies to Dp1Tyb mice that , similar to Dp ( 16 ) 1Yey mice , contain a duplication of the entire Hsa21 orthologous region on Mmu16 ( Fig 1 ) . Once again we found no changes in nocifensive responses to heat , mechanical stimulation and formalin injection , but the response to cold was partially impaired ( Fig 5F–5I ) . Importantly , Dp1Tyb mice showed no defect in a beam walk test , a measure of proprioception ( Fig 5J ) , but , interestingly , we again found a decrease in IB4-binding neurons in the DRGs of Dp1Tyb mice , similar to that seen in Tc1 mice ( Fig 5K ) . These IB4+ neurons respond to high threshold noxious mechanical stimuli and thus their reduction is unlikely to contribute to the locomotor defects [38] . In summary , we found little evidence of a broad sensory defect in mouse models of DS that could account for the motor defects , but discovered a specific reduction in one class of nociceptive sensory neurons , IB4+ afferents , in Tc1 and Dp1Tyb mice , a phenotype that would merit further investigation in both mice and humans . We undertook a series of studies to investigate if defects in muscle function or its innervation contribute to the locomotor defects . We previously showed that Tc1 mice have no defect in muscle strength as measured by a grip test [26] . To extend these studies we measured the maximum force produced by the tibialis anterior ( TA ) , extensor digitorum longus ( EDL ) and soleus muscles of the mouse hindlimb in response to a tetanic stimulation of the sciatic nerve in live anaesthetized mice . We found no change in muscle force in Tc1 mice at either 4 or 19 months of age ( Fig 6A–6C ) . However , analysis of the number of physiological motor units innervating the EDL muscle showed a significant 8% decrease in Tc1 mice at 4 months of age , rising to a 11% decrease at 19 months of age ( Fig 6D and 6E ) . In agreement with this , analysis of motor neuron numbers in the sciatic motor pool showed an 18% and a 23% decrease in Tc1 mice at 6 and 19 months of age respectively ( Fig 6F and 6G and S1 Table ) . Histology of the TA muscles confirmed this observation , showing changes characteristic of denervation and subsequent re-innervation , resulting in characteristic fiber type grouping of oxidative fiber types ( Fig 6H ) . Interestingly , there was no decrease in motor neuron numbers in young Tc1 mice aged 22 days , indicating that there is no developmental deficit in the generation of these cells ( Fig 6I ) . Thus , Tc1 mice show a progressive loss of motor neurons and motor unit function , which could contribute to the locomotor dysfunction . To investigate the genetic basis of the motor neuron loss , we counted motor neurons in models with duplications of mouse regions orthologous to Hsa21 . Analysis of Dp ( 16 ) 1Yey , Dp ( 17 ) 1Yey and Dp ( 10 ) 1Yey mice at 6 months of age showed that only Dp ( 16 ) 1Yey mice had decreased numbers of motor neurons ( 20% reduction ) , similar to that seen in Tc1 mice ( Fig 6J and 6K and S1 Table ) . Furthermore , mice with duplications of all three regions ( triple trisomic ) showed a loss of motor neurons that was no greater than that seen in the single mutant Dp ( 16 ) 1Yey mice ( Fig 6J ) . Thus , duplication of the orthologous region of Mmu16 is both necessary and sufficient to cause a reduction of motor neurons , and the Hsa21 orthologous regions on Mmu10 and Mmu17 do not contribute to the phenotype . In contrast we found no change in motor neuron numbers in Dp ( 16 ) 1Yey mice at P6 ( Fig 6L ) , once again showing that the loss of motor neurons is progressive neurodegeneration and not a developmental deficit . To map the location of potential dosage-sensitive genes that cause this neurodegeneration , we analyzed motor neuron numbers in Dp2Tyb , Dp3Tyb , and Dp9Tyb mice at 6 months of age ( Fig 1 ) . We saw no change in motor neuron numbers in any of these three strains ( Fig 6M and S1 Table ) . Thus , the motor neuron loss is caused by an additional copy of at least 2 genes and these are located in 2 or more of the Mmu16 regions duplicated in Dp2Tyb , Dp3Tyb or Dp9Tyb . The loss of motor neurons observed in Tc1 and Dp ( 16 ) 1Yey mice led us to investigate spinal cord motor neuron numbers in humans with DS , since these have not been previously reported . Strikingly , we found decreased numbers of motor neurons in humans with DS compared to non-DS controls ( Fig 7A and 7B and S2 Table ) . We compared our results to motor neuron counts in spinal cord sections of people with the motor neuron disease amyotrophic lateral sclerosis ( ALS ) and found the decrease in DS was less than in ALS . Thus , observation of reduced numbers of motor neurons in a mouse model of DS has led us to discover a novel phenotype in humans with DS .
Using a combination of mouse strains that contain Hsa21 genes or additional copies of their mouse orthologues that are also aneuploid ( Tc1 ) or not ( Dp strains ) we were able to show that locomotor defects resulted from an additional copy of small numbers of genes , and that aneuploidy was not required for this phenotype . This supports the hypothesis that at least some DS phenotypes are due to increased dosage of small numbers of dosage-sensitive genes , rather than mass action of large numbers of additional genes or aneuploidy . This has important implications for driving forward future investigations into mitigating the effects of a few or single dosage-sensitive genes in DS . Nonetheless we noted that the locomotor defects became weaker as we reduced the size of the duplications , implying that in addition to the small regions containing dosage-sensitive genes that are sufficient on their own to cause phenotypes , there are additional genes outside these regions that also contribute . The presence of defects in two different locomotor assays ( Rotarod and Locotronic ) supports our conclusion that the mutant mice performed poorly because they have locomotor defects rather than lacking motivation . In particular , in the Locotronic test we eliminated trials in which the mice took >60 s to traverse the ladder , thereby excluding trials where the mice were insufficiently motivated . However , it is possible that the poorer performance in these tests was due to other causes . For example Dp ( 16 ) 1Yey have been shown to have disrupted sleep , which could impair performance [39] . The locomotor defect was evident in both Dp4Tyb and Dp5Tyb mice , showing that increased dosage of two separate regions is sufficient to cause this phenotype , and implies that at least two different dosage-sensitive genes contribute to this defect . It is likely that the more severe phenotype in Dp ( 16 ) 1Yey mice is caused by additive effects of the Dp4Tyb and Dp5Tyb regions , together with genes outside these regions . Furthermore , since Ts1Rhr mice also have defects , the dosage-sensitive genes are most likely to be within the 25 genes that are duplicated in both Ts1Rhr and Dp4Tyb and Dp5Tyb ( Fig 8 ) . One of these is Dyrk1a and we have shown that three copies of this gene are required for the locomotor defect in Dp ( 16 ) 1Yey mice , in agreement with previous studies showing that overexpression of DYRK1A leads to motor defects [29–32] . However , our results also show that the situation is complex , since Dp4Tyb mice show a phenotype whereas Dp ( 16 ) 1Yey/Dyrk1a+/- do not , despite having an extra copy of all the genes that are also duplicated in Dp4Tyb . Thus , there must be genes outside the Dp4Tyb region that suppress the effects of increased dosage of gene ( s ) in Dp4Tyb , and suggests that DS phenotypes may result from the interplay of dosage-sensitive genes that both enhance and suppress phenotypes . DYRK1A is a protein kinase whose overexpression has been proposed to lead to defects in brain development , synaptic plasticity and in learning and memory [28] , however , the mechanism by which this happens is not understood . Recently published studies proposed that increased dosage of regions between Hspa13 and App on Mmu16 and Abcg1 and U2af1 on Mmu17 contribute to locomotor defects [13 , 40] . These regions are duplicated in Dp9Tyb and Dp ( 17 ) 1Yey mice respectively and we showed that in three copies they are not sufficient to cause locomotor defects , though we cannot rule out that they could contribute to the phenotype when combined with duplication of the region in Ts1Rhr . Indeed the stronger phenotype in Dp ( 16 ) 1Yey mice compared to Ts1Rhr may be due to an increased dosage of genes in the Dp9Tyb region . Previous studies had shown that both Ts65Dn and Tc1 mice have defects in cerebellar anatomy , but despite a more extensive analysis than was carried out in these strains , defects were not observed in Dp ( 16 ) 1Yey mice . There are a number of differences between these models , including a different complement of duplicated genes , but we note that both Ts65Dn and Tc1 are aneuploid and this may possibly contribute to the phenotype . A recent study reported that Dp ( 16 ) 1Yey mice have a reduced density of Purkinje cells and granule cells in the cerebellum [41] . It is unclear why our findings are different , but the two studies were carried out on different genetic backgrounds , which may have an effect . Our results show that both Tc1 and Dp ( 16 ) 1Yey mice have progressive degenerative loss of motor neurons in the spinal cord . This unexpected and novel phenotype led us to examine spinal cords from humans with DS; importantly , we found that humans also show reduced numbers of motor neurons . Since we only analyzed older adults , we are unable to distinguish if the loss is degenerative or a consequence of developmental abnormalities . To establish this would require analysis of spinal cords from young people with DS; to our knowledge such samples are not currently available . A previous study showed that people with DS have defective peripheral and central nervous system conduction parameters , consistent with axonal degeneration [42] . Interestingly , genetic mapping showed that the Hsa21 orthologous region of Mmu16 was both required and sufficient to cause the motor neuron loss , that the orthologous regions on Mmu10 and Mmu17 did not contribute , and that aneuploidy was not required . However , breaking up the Mmu16 region into three smaller regions caused the phenotype to disappear . Thus , the neuronal loss is caused by at least 2 genes and these must reside in 2 or more of these 3 regions . It is possible that this phenotype is caused by the mass action by an increased dosage of a large number of genes–there are 148 duplicated genes in Dp ( 16 ) 1Yey–or by a small number of dosage-sensitive genes . Further mapping studies would be needed to distinguish these possibilities . The extent of motor neuron loss ( around 20% ) is not large enough to explain the locomotor defects . Furthermore , genetic mapping showed that the locomotor defects and motor neuron loss do not map to the same region , again suggesting that the locomotor defects are not caused by motor neuron loss at the ages we tested . Nonetheless it is possible that the loss of these neurons may contribute to the defects in combination with other pathological mechanisms , and may account for why the locomotor phenotype of Dp ( 16 ) 1Yey mice is stronger than that of Ts1Rhr mice . In summary , we have been able to map the genomic location of causative genes underlying DS phenotypes . We have identified that three copies of the Dyrk1a gene are required for locomotor dysfunction , a relatively understudied phenotype , and using mouse models of DS we have discovered a decreased number of motor neurons , a novel phenotype which we have shown is also present in humans with DS .
Mice carrying the Dp ( 16Lipi-Zbtb21 ) 1TybEmcf ( Dp1Tyb ) , Dp ( 16Mis18a-Runx1 ) 2TybEmcf ( Dp2Tyb ) , Dp ( 16Mir802-Zbtb21 ) 3TybEmcf ( Dp3Tyb ) , Dp ( 16Mir802-Dscr3 ) 4TybEmcf ( Dp4Tyb ) , Dp ( 16Dyrk1a-B3galt5 ) 5TybEmcf ( Dp5Tyb ) , Dp ( 16Igsf5-Zbtb21 ) 6TybEmcf ( Dp6Tyb ) , Dp ( 16Lipi-Hunk ) 9TybEmcf ( Dp9Tyb ) , Dp ( 16Lipi-Zbtb21 ) 1Yey ( Dp ( 16 ) 1Yey ) , Dp ( 17Abcg1-Rrp1b ) 1Yey ( Dp ( 17 ) 1Yey ) , Dp ( 10Prmt2-Pdxk ) 1Yey ( Dp ( 10 ) 1Yey ) , Dp ( 16Cbr1-Fam3b ) 1Rhr ( Ts1Rhr ) , Tc ( HSA21 ) 1TybEmcf ( Tc1 ) and Dyrk1atm1Mla ( Dyrk1a+/- ) alleles have been described [9–11 , 16 , 27 , 43] . Triple trisomic mice were generated by intercrossing Dp ( 16 ) 1Yey , Dp ( 10 ) 1Yey and Dp ( 17 ) 1Yey mice to generate Dp ( 16 ) 1Yey/Dp ( 10 ) 1Yey/ Dp ( 17 ) 1Yey mice with all three duplications . All strains were maintained by backcrossing to C57BL/6JNimr , except for mice bearing Tc1 , Ts1Rhr and Dyrk1a+/- alleles , which were maintained by crossing to ( C57BL/6J x 129S8 ) F1 . All mice on the C57BL/6JNimr background that were used for experiments had been backcrossed for at least 5 generations . The intercross of Dp ( 16 ) 1Yey and Dyrk1a+/- mice was backcrossed to C57BL/6JNimr for two generations . All mice were bred and maintained at the MRC National Institute for Medical Research ( now The Francis Crick Institute ) , except for Dp1Tyb mice used in the Locotronic assay which were bred at the MRC Harwell Institute , and 10-week old Dp ( 16 ) 1Yey mice whose cerebella were used for Q-RTPCR studies which were bred at Institut de Génétique et de Biologie Moléculaire et Cellulaire , Illkirch , France and provided by Veronique Brault . All experiments were carried out on males , using age-matched littermate controls , except for analysis of Dp ( 16 ) 1Yey mice at P6 where both genders were used . No randomization was used , but in all cases analyses were carried out by experimenters who were blind to genotype . The Rotarod test was used to evaluate locomotor function . 12-week old male mice were habituated to the RotaRod Advanced apparatus ( Letica Scientific Instruments ) the day before testing commenced by placing them on the rotating rod at a constant speed of 4 rpm . Motor performance was tested by placing the mouse onto the accelerating rod ( 4 to 40 rpm over 5 min ) and then recording the speed of the rod at which the mouse fell off . The trial was repeated three times per day with an hour inter-trial interval . The average RPM of the three trials for each day for each animal was then calculated . Trials were then repeated on a second and third day to evaluate learning . The Locotronic test ( Intellibio , France ) is a test of locomotor function . Mice traverse a horizontal ladder with evenly spaced rungs , along a narrow corridor to reach the exit . The number of rungs that the mouse stepped on and/or missed was recorded automatically , thereby determining how many rungs were missed . Each animal ( n = 13 WT , 12 Dp1Tyb ) was tested 2–3 times . Trials where mice took more than 60 s to traverse the ladder were excluded from the analysis; a total of 10 out of 69 trials were excluded , 5 from WT mice and 5 from Dp1Tyb mice , indicating no difference in motivation between genotypes . Cerebella from 10-week or 6-day old mice were dissected and snap frozen . Each sample was homogenized in Buffer RLT ( Qiagen ) with 143mM 2-mercaptoethanol using a Pellet Pestle cordless motor ( Kimble ) . Tissue lysates were loaded onto the QIAshredder ( Qiagen ) according to manufacturer’s instructions . Total RNA was isolated using RNeasy Mini Kit ( Qiagen ) and quantified using NanoDrop1000 ( Thermo Scientific ) . RNA was reverse transcribed into cDNA using a MEGAscript T7 Kit ( Invitrogen ) , and analysed by quantitative real-time PCR on a Quant Studio3 Real-Time PCR Machine ( Thermo Fisher Scientific ) using TaqMan gene expression assays ( Thermo Fisher Scientific ) . Expression of test genes ( Dyrk1a , Gad1 and Gad2 ) was normalised to the expression Gapdh and then to expression in the wild-type control samples . P6 and 6-month old mice were sacrificed and brains were extracted and immersion fixed in 10% Formalin ( VWR ) . After fixation , brains were embedded in paraffin and 5 μm sections were taken at the midline . Sections were stained with hematoxylin and eosin and images were acquired using an Olympus VS120 slide scanner . Measurements were performed in FIJI software ( ImageJ ) and , except where indicated , all enumeration was performed manually using the cell counter tool . Purkinje cell counts were performed along the whole length of the indicated lobule and density derived by dividing cell counts by the length of the Purkinje cell layer . Cerebellar granule cells were counted in identical locations at the tip of each lobule and density calculated by dividing cell counts by the area . Width measurements of the granule cell layer and molecular layer were all performed at identical locations within each lobule . Interneuron numbers were analyzed by drawing regions of interest around the molecular layer of lobule IX and dividing this region into three further regions of interest , then using the automated cell counter with the spot detector plugin in Icy ( http://icy . bioimageanalysis . org/ ) . Similarly the cerebellar granule cell numbers at P6 were counted using the automated cell counter in Icy . Tissue samples in this and all other histological analyses were excluded if quality or integrity of the sample was diminished or damaged . Mice were sacrificed and the L3-L5 DRG were dissected and fixed for 90 min in 4% paraformaldehyde in 0 . 1 M phosphate buffer and mounted in OCT embedding compound . DRG blocks were sectioned on a cryostat at 10 μm thickness . Sections were washed in Phosphate Buffered Saline ( PBS ) and blocked using 10% normal goat serum ( Vector Laboratories ) for 30 min . Primary antibodies were incubated overnight and secondary antibodies were incubated for 2 h at room temperature in the dark . After each incubation step , the sections were washed three times with PBS . All reagents were diluted in PBS containing 0 . 2% Triton X-100 and 0 . 1% sodium azide . Slides were sealed with coverslips mounted with Vectashield medium . Immunofluorescence was visualized using a Zeiss Imager . ZI fluorescence microscope and pictures acquired with Axiovision software . Primary antibodies or lectins used: polyclonal rabbit anti-mouse CGRP ( Biomolecular , CA1137 , 1:500 ) , biotinylated isolectin B4 ( Sigma , L2140 , 1:100 ) , mouse anti-mouse NF200 ( Millipore , MAB5266 , 1:200 ) , polyclonal rabbit anti-human PGP9 . 5 ( Ultraclone , RA95101 , 1:800 ) . Secondary antibodies used: anti-rabbit-Cy3 ( Stratech Scientific Ltd . 711-166-152 , 1:500 ) , extra-avidin FITC ( Invitrogen , 1:400 ) , anti-mouse FITC ( Jackson Immunoresearch , 1:500 ) . All antibodies listed here have been validated by their suppliers and references can be found on their website or on the online validation databases Antibodypedia and 1DegreeBio . Quantification was performed by manually counting the total number of DRG cell profiles identified by positive staining for PGP9 . 5 as well as the numbers of profiles positive for NF200 , CGRP or IB4 in a representative region of interest in a DRG , from which a percentage was calculated . For each DRG at least three sections were quantified . For the assessment of muscle function in vivo , mice were examined as previously described [48] . Following the physiological assessment of muscle function , the TA , EDL and soleus muscles were dissected and snap frozen in isopentane cooled in liquid nitrogen and stored at -80°C until processing . Frozen muscle samples were cut on a cryostat at 12 μm . Serial cross sections were collected on glass slides and stained for succinate dehydrogenase ( SDH ) activity to determine the oxidative capacity of the muscle fibres , as previously described[48] . In all mouse strains except Tc1 mice at 6 and 19 months , motor neurons were counted using a rapid dissection method . Animals were sacrificed and the L4 and L5 spinal segment was identified externally by distance from the most caudal rib and the iliac crest bone . The whole spinal column was extracted and placed in 10% Formalin before being decalcified by immersion in 5% formic acid ( Immunocal , Decal Chemical Corporation , NY , USA ) overnight , processed for paraffin embedding and sectioned at 5 μm thickness . Every eighth section was collected , thus giving a 35 μm interval between representative sections and 40 sections in total were collected per animal . Sections were stained with Cresyl Violet for Nissl bodies and images acquired with an Olympus VS120 Slide Scanner . All sections were assessed for correct anatomical location before counting . Motor neurons in the sciatic motor pool were located and counted based on the following criteria: a visible nucleolus , a soma rich in Nissl bodies , a diameter >15 μm and multipolar morphology with visible dendritic branching . Spinal cords of Tc1 mice at 6 and 19 months were removed , fixed in 4% paraformaldehyde and cryopreserved in 30% sucrose overnight . The lumbar L2-L6 region was sectioned on a cryostat and 20 μm transverse sections were collected serially onto glass slides and stained for Nissl bodies with gallocyanin . The number of Nissl-stained large motor neurons in every third L3-L6 lumbar section was established , using the same criteria as described above , with counted sections being at least 60 μm apart . When counting motor neurons at different levels of the spinal cord , the number of sections included in the counts was standardized . Thus , a total of 5 sections from L3 , 10 sections from L4 , 20 sections from L5 and 5 sections from L6 region were analyzed . 40 sections were counted for each animal . Cervical spinal cord autopsy samples from people with Down syndrome ( n = 3 , 58–70 years old ) , amyotrophic lateral sclerosis ( n = 3 , 33–70 years old ) and controls with no neurological disease ( n = 4 , 59–71 years old ) were obtained from the Netherlands Brain Bank , Netherlands Institute for Neuroscience , Amsterdam , Netherlands ( 8 samples ) , and the Thomas Willis Oxford Brain Collection , John Radcliffe Hospital , Oxford UK ( 3 samples ) . From the samples , 3 were paraffin-embedded tissues ( 2 controls and one DS ) , which were serially cut at 6–14 μm , whereas 8 specimens were frozen tissues which were cut at 20 μm serially . Sections were stained with Luxol Fast Blue , which stains the myelin sheaths blue and combined with Cresyl Violet staining that stains Nissl granules ( RNA in rough endoplasmic reticulum ) pink . For each sample a total of 20 sections each 60 μm apart , were assessed . The total number of large motor neurons with intense Nissl staining was counted in each section . Results are shown as number of motor neurons per hemisection for each disease group . The work on human tissues was approved by a UK Research Ethics Committee , specifically the NRES Committee London—Queen Square ( REC approval number 09/H0716/57 ) . This ethics approval covers the procurement and use of linked-anonymised and anonymised human tissue and samples and data from banks , established collections and collaborators in the UK and internationally . All patient consent , consent from a legal representative , an opinion from a consultee or consent from an individual in a qualifying relationship to the deceased was obtained directly by the providing bank in line with their national legislation and institution policy . In order to maintain donor or consentee privacy , details of personal identifiable information about the patient/donor is held confidentially by the providing bank ( s ) and was not shared with the research team . Mouse experiments were approved by the Animal Welfare and Ethical Review Panel ( AWERP ) of the Francis Crick Institute and were carried out under the authority of a Project Licence ( PPL70/8843 ) granted by the UK Home Office under the regulations of the Animals ( Scientific Procedures ) Act 1986 . | Down Syndrome is caused by an extra copy of chromosome 21 and results in many different phenotypes including learning difficulties , Alzheimer’s disease and problems with motor function such as abnormal gait and poor fine motor skills . These different phenotypes are thought to result from an increased copy of one more of the genes on chromosome 21 , but it is not known which gene or genes cause the phenotypes . Using a panel of mouse strains with an extra copy of different sets of mouse genes that are equivalent to the human genes on chromosome 21 we were able to show that an extra copy of a small number of genes was sufficient to cause motor abnormalities in the mice , and that one of these genes , Dyrk1a , was required in three copies for this phenotype . Furthermore , we found that these mouse models of Down Syndrome showed loss of motor neurons , which could account in part for the motor dysfunction . This result led us to look in spinal cords from humans with DS , where we also found decreased numbers of motor neurons , a phenotype that has never been previously reported . | [
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| 2018 | Analysis of motor dysfunction in Down Syndrome reveals motor neuron degeneration |
Sensing viruses by pattern recognition receptors ( PRR ) triggers the innate immune system of the host cell and activates immune signaling cascades such as the RIG-I/IRF3 pathway . Mitochondrial antiviral-signaling protein ( MAVS , also known as IPS-1 , Cardif , and VISA ) is the crucial adaptor protein of this pathway localized on mitochondria , peroxisomes and mitochondria-associated membranes of the endoplasmic reticulum . Activation of MAVS leads to the production of type I and type III interferons ( IFN ) as well as IFN stimulated genes ( ISGs ) . To refine the role of MAVS subcellular localization for the induction of type I and III IFN responses in hepatocytes and its counteraction by the hepatitis C virus ( HCV ) , we generated various functional and genetic knock-out cell systems that were reconstituted to express mitochondrial ( mito ) or peroxisomal ( pex ) MAVS , exclusively . Upon infection with diverse RNA viruses we found that cells exclusively expressing pexMAVS mounted sustained expression of type I and III IFNs to levels comparable to cells exclusively expressing mitoMAVS . To determine whether viral counteraction of MAVS is affected by its subcellular localization we employed infection of cells with HCV , a major causative agent of chronic liver disease with a high propensity to establish persistence . This virus efficiently cleaves MAVS via a viral protease residing in its nonstructural protein 3 ( NS3 ) and this strategy is thought to contribute to the high persistence of this virus . We found that both mito- and pexMAVS were efficiently cleaved by NS3 and this cleavage was required to suppress activation of the IFN response . Taken together , our findings indicate comparable activation of the IFN response by pex- and mitoMAVS in hepatocytes and efficient counteraction of both MAVS species by the HCV NS3 protease .
Vertebrates developed several defense mechanisms against invading pathogens . Upon viral infection foreign RNA or DNA is introduced into the host cell where it is detected by highly conserved pattern recognition receptors ( PRRs ) sensing distinct non-self motifs [1–7] . Well known examples of PRRs are RIG-I ( Retinoic acid-Induced Gene I ) and MDA5 ( Melanoma Differentiation-Associated protein 5 ) that both are cytosolic RNA helicases recognizing primarily 5’-triphosphorylated and long ( i . e . >2 , 000 nucleotides ) double stranded ( ds ) RNA , respectively [1 , 3 , 8] . Upon interaction with RNA , both RIG-I-like receptors ( RLR ) induce a signaling cascade which leads to the production of type I and III Interferon ( IFN ) as well as IFN stimulated genes ( ISGs ) . In the case of RIG-I , RNA interaction induces conformational changes , rendering the CARD ( Caspase Activation and Recruitment Domain ) accessible for ubiquitination by TRIM25 and subsequent interaction with the signal adaptor protein MAVS ( Mitochondrial Antiviral-Signaling protein; also known as IPS-1 , Cardif or VISA ) [9–13] . MAVS is a ubiquitously expressed protein consisting of an N-terminal CARD , a proline-rich region and a single transmembrane domain at the very C-terminus . Activation of MAVS induces a prion-like oligomerization of the protein , forming large signaling platforms [14] . These platforms lead to the activation of NFκB and IFN regulatory factor 3 and 7 ( IRF3 , IRF7 ) , which upon nuclear translocation drive the expression of IFNs and other cytokine genes [9–12 , 15] . In principle the same pathway is utilized upon activation of MDA5 [14 , 16 , 17] . The IFN system consists of three classes that are grouped according to the receptor to which they bind . Type II IFN is primarily produced by T cells and natural killer cells in response to the recognition of infected cells . Type I IFNs include IFN-β , which is encoded by a single gene and synthesized by most cell types , especially fibroblasts , and IFN-α , which is encoded by a gene cluster of 13 genes and predominantly synthesized by leukocytes . Type III IFN has only been described recently and includes three members: IFN-λ1 , IFN-λ2 and IFN-λ3 that are also known as IL-29 , IL-28A and IL-28B , respectively [18 , 19] . Recently a new member of this group was discovered , designated IFN-λ4 , which is a frameshift variant arising from a peculiar polymorphism in the IL28 gene locus that is predictive for the outcome of therapy of chronic hepatitis C [20] . IFN-λ is produced by several cell types including hepatocytes [21 , 22] where it contributes to the control of hepatitis C virus infection ( HCV ) [23 , 24] Moreover , type I and III IFNs use different receptors , with type III receptors being composed of two chains: the ligand binding chain IFNλR1 and the accessory IL-10R2 subunit that is shared with the receptor for cytokines of the IL10 family [24] . Tissue distribution of the type III IFN receptor is by and large restricted to epithelial cells and hepatocytes in humans [25 , 26] . Although type I and type III IFN use different receptors , it is believed that they both signal via the JAK/STAT pathway to induce a very similar set of more than 300 ISGs [23 , 27] . HCV is a member of the genus Hepacivirus within the family Flaviviridae . A hallmark of HCV is its high propensity to persist . In fact , around 80% of HCV infections become persistent and persistently infected individuals have a high risk to develop chronic liver disease , including liver cirrhosis and hepatocellular carcinoma [28 , 29] . The HCV particle is enveloped and contains a single-strand RNA genome of positive polarity . This ~9 , 600 nucleotides long RNA encodes a polyprotein that is cleaved into 10 products . Processing of the polyprotein is mediated , in part , by a viral serine-type protease that forms the N-terminal domain of nonstructural protein ( NS ) 3 . Activation of this protease requires the viral cofactor NS4A that intercalates into the protease domain and , in addition , anchors the NS3/4A complex to intracellular membranes [30] . Apart from polyprotein cleavage , NS3/4A was found to counteract the activation of the IFN response by efficient cleavage of MAVS at amino acid 508 [10] . Consistent with the localization of the NS3 protease active site , the cleavage site in MAVS resides close to the membrane surface , thus liberating MAVS from the membrane and blocking RLR signaling function , both in vitro and in vivo [10 , 31–33] . While these studies focused on MAVS localizing to mitochondria and mitochondria-associated membranes ( MAMs ) , a distinct membrane compartment that links this organelle to the endoplasmic reticulum [32] , MAVS was also reported to localize to peroxisomes to induce a rapid type I IFN-independent ISG response [34] . Moreover , it was reported that mitochondrial ( mito ) MAVS induces IFN-β and IFN-λ whereas peroxisomal ( pex ) MAVS only triggers an IFN-λ response [35] . However , the relative contribution of mito- and pexMAVS to the induction of the antiviral state and counteraction of the MAVS variants by viral infection remains largely unexplored . In this study we evaluated the relative contribution of mito and pexMAVS to the activation of the type I and III IFN response in hepatocytes upon infection with various RNA viruses . In addition , we determined the control of either MAVS species by HCV . We found that mito- and pexMAVS activated type I and III IFN response with comparable efficiency and kinetics and both MAVS variants were potently counteracted by HCV .
With the aim to characterize the activation of the interferon ( IFN ) response after viral infection by pex- and mitoMAVS we first determined the subcellular localization of endogenous MAVS on both organelles in the HCV-permissive human hepatoma cell line Huh7 . Consistent with earlier reports endogenous MAVS primarily co-localized with mitochondria and , to a lesser extent , with peroxisomes as determined by co-staining with the marker protein PMP70 [9 , 32 , 34] ( Fig 1A ) . Quantification of the MAVS distribution revealed that ~80% of the protein was localized on mitochondria and ~20% on peroxisomes ( Fig 1B ) . However , ~17% of peroxisomes resided in close proximity to mitochondria , precluding the precise allocation of MAVS to either organelle . Thus , the total amount of pexMAVS might range between 3–20% . To study the impact of pex- and mitoMAVS separately , we utilized a strategy reported earlier [34] to generate MAVS variants exclusively localizing to either organelle . This was achieved by replacing the C-terminal transmembrane ( TM ) region of wild type MAVS ( wtMAVS ) with either the TM region of PEX13 ( peroxisomal location; pexMAVS ) or the TM region of Bcl-Xl ( mitochondrial location; mitoMAVS ) . ( Fig 1C ) . By using quantitative immunofluorescence , we confirmed the organelle-specific subcellular localization of these MAVS variants ( Fig 1D ) . To avoid competition between endogenous and ectopically expressed MAVS variants we attempted to reduce the levels of endogenous MAVS by using siRNA- and shRNA-mediated knock-down approaches; however , expression levels were at best reduced by 70% , which we considered to be insufficient for functional assays . To overcome this limitation we stably expressed the HCV NS3/4A protease , which is known to efficiently cleave MAVS off the mitochondria [10 , 36] , thus rendering MAVS inactive and interrupting signaling . Cleavage of MAVS occurs at the membrane-proximal cysteine residue 508 , which we replaced by an arginine residue to render MAVS cleavage resistant [10 , 37] . Variants containing this C508R point mutation are designated MAVSCR throughout this report . Combining MAVSCR with the organelle-specific TMs thus offered a possibility to study pex- and mitoMAVS in diverse human cell lines expressing the NS3/4A protease . To this end , we utilized Huh7 and A549 cells where MAVS localization to the respective organelle was confirmed by quantitative immunofluorescence analyses ( Fig 1D and 1E and S1 Fig , respectively ) . In both cell lines we observed highly significant co-localization of pexMAVSCR with peroxisomes and mitoMAVSCR with mitochondria , while wtMAVSCR mainly localized to mitochondria , with a small proportion localizing to peroxisomes , consistent with the subcellular distribution of endogenous MAVS ( cf . Fig 1A and 1B ) . Prior to conducting functional assays with these cell lines , we first determined whether wild type ( i . e . C508 ) pexMAVS would be depleted from our cells by NS3/4A-mediated cleavage , because only then we would be able to measure IFN activation exclusively by exogenous organelle-specific MAVS . To allow measurement with high sensitivity we constructed a fusion protein composed of enhanced green fluorescent protein ( eGFP ) fused with a nuclear localization signal ( NLS ) and an organelle-specific targeting sequence ( MAVS-C-terminal domain , MAVS-CTD ) ( S2A and S2B Fig ) . Analogous to an earlier report [38] , upon cleavage of MAVS-eGFP the cleavage product is liberated from the membrane and translocated into the nucleus , which could be easily monitored by fluorescence imaging . Cells stably expressing NS3/4A or the non-functional protease mutant NS3/4A-S139A ( S2C Fig ) [39] were transfected with either one of the eGFP-NLS-MAVS-CTD constructs and subcellular eGFP distribution was determined . First , we confirmed localization of eGFP-NLS-tagged wt- and pexMAVS-CTD at peroxisomes in naïve Huh7 cells ( S2B Fig ) . As expected , in cells expressing NS3/4A-S139A the eGFP distribution pattern of the MAVS variants was consistent with mitochondrial and peroxisomal localization ( S2D Fig , left panel ) . In contrast , in NS3/4A-expressing cells transfected with eGFP-NLS-wtMAVS-CTD , the eGFP signal was exclusively detected in the nucleus reflecting highly efficient cleavage as reported earlier ( S2D Fig , upper right panel ) [10 , 37 , 38] . Importantly , analogous cleavage efficiency was also found with eGFP-NLS-pexMAVS-CTD ( S2D Fig , lower right panel ) . These results demonstrate that HCV NS3/4A efficiently cleaves MAVS irrespective of its subcellular localization on mitochondria or peroxisomes . Having confirmed that NS3/4A expressing cells are a valid model system for functional MAVS depletion and are suited to determine the capacity of organelle-specific MAVSCR variants to induce the IFN response , we transduced NS3/4A overexpressing Huh7 cells with lentiviral vectors encoding organelle-specific MAVSCR ( Fig 2A ) . Consistent with earlier reports , we observed an intrinsic activation of the IFN response upon overexpression of MAVS as revealed by increased levels of IFN-λ in the supernatant of transduced cells [9 , 40] ( Fig 2B ) . This was not due to the lentiviral particles themselves since lentiviruses encoding the unrelated peroxisomal protein Pex14-HA did not induce IFN-λ expression . Apart from the activation of IFNL , we also observed an activation of IRF3 as determined by luciferase-based IFIT1 , IFNB and IFNL1 promoter reporter assays ( Fig 2C ) . Importantly , this activation was found with each MAVS variant and both , in the reporter assay and when measuring mRNA levels of endogenous ISG56 or various IFNs ( Fig 2D ) . Moreover , analogous results were obtained with the IFN-competent cell line A549 ( Fig 2E and S3 Fig ) and with 293T cells ( S4 Fig ) demonstrating that the observed phenotypes are not specific to human hepatoma cells . To exclude possible confounding effects exerted by NS3/4A in a MAVS cleaveage-independent manner [41 , 42] , we transiently transduced naïve A549 cells and A549-MAVSKO cells with pex- or mitoMAVS variants . Of note , we observed a comparable induction of type I and type III IFN production , arguing that NS3/4A does not interfere with IFN activation , at least in this experimental setup ( S5A and S5B Fig ) . We also determined the kinetics of IFN response activation by using microarrays to determine the transcriptome of wt- , mito- and pexMAVS-expressing cells . While most ISGs were upregulated to a similar degree , we observed subtle differences in induction kinetics for a few ISGs ( S5C Fig ) . Although these differences were not consistent throughout the time course , it is possible that pex- and mitoMAVS have slightly different IFN induction capabilities . Having shown that expression of pexMAVS and mitoMAVS were able to induce type I and III IFN responses , we next compared these MAVS variants for their capacity to induce the IFN response upon viral infection . We reconstituted MAVS activity in 293T cells overexpressing HCV NS3/4A ( 293T-NS3/4A ) by lentiviral transduction of MAVSCR variants . As shown in Fig 3A , expression levels of the MAVS proteins in the cell pools were comparable , although wtMAVS abundance was ~20% higher as compared to the variants . Importantly , we only used cell pools that were not pre-activated by MAVS transduction , which we achieved by infecting the cells with MAVS-encoding lentiviruses at a very low MOI of 0 . 1 and passaging them under selective pressure . To investigate the IFN response we used the single stranded RNA virus Sendai Virus ( SeV ) , which is specifically sensed by RIG-I [3 , 7] . When we infected 293T-NS3/4A cells transduced with the empty vector with SeV or with other RNA viruses , we detected only a minor IFN response , which might be due to residual amounts of non-cleaved MAVS ( 0 . 2% ) ( Fig 3B , 3C and S6 Fig , respectively ) . However , in cells expressing mitochondrial or peroxisomal MAVS , IFN-λ expression was potently induced and amounts released into the culture supernatant of SeV-infected cells were well comparable between pexMAVSCR and mitoMAVSCR cells ( Fig 3B ) . Consistent with this result we observed induction of endogenous ISG56 , IFN-β and type III IFN expression upon SeV infection and these responses barely differed between the MAVS variants ( Fig 3C ) . As reported earlier [43 , 44] , IFN-β expression was rapidly and very transiently induced and this also applied to IFN-λ1 . In contrast , activation of IFN-λ2/3 expression was much more sustained and did not decline throughout the 48 h observation period . Activation of both type I and III IFN expression as well as ISG56 was also found when we infected the cells with other RNA viruses: Vesicular Stomatitis Virus ( VSV ) and Newcastle Disease Virus ( NDV ) , which are both negative strand ssRNA viruses , and the dsRNA virus reovirus ( S6 Fig ) . We note that kinetics and magnitudes of activation differed between these viruses , likely reflecting their diverse replication strategies . Nevertheless , comparable activation by mito- and pexMAVS was detected in all cases . Response levels were higher in case of wtMAVS , which might be due to the higher expression of this protein as compared to the MAVS variants ( Fig 3A ) . Alternatively , highest IFN response might require both mito- and pexMAVS as suggested earlier [34] . Although we observed minor differences in ISG and IFN response in 293T cells between pex- and mitoMAVS we note that these differences were not consistent between different experiments . Taken together , we observed efficient MAVS-dependent activation of the type I and III IFN response upon virus infection , independent of the subcellular localization of MAVS . In the experiments described so far , we degraded endogenous MAVS from the mitochondria and peroxisomes by using the HCV NS3/4A protease . Under these conditions we were not able to detect an IFN response in these cells unless we reconstituted with MAVSCR variants , demonstrating efficient blockade of MAVS-dependent signaling via mitochondria and peroxisomes . However , we could not exclude undesired effects exerted by either HCV NS3/4A overexpression or cleaved cytoplasmic MAVS . Therefore , we made use of mouse embryonic fibroblasts ( MEFs ) derived from a mouse line lacking endogenous MAVS ( MAVS-/- ) and reconstituted these cells by stable expression of wtMAVS , pexMAVS or mitoMAVS ( Fig 4A ) . These cell pools were infected with SeV or reovirus , and analyzed for type I IFN production . At different time points after infection supernatants were harvested and secreted IFN-β was quantified by ELISA . While in MEFs lacking MAVS no IFN could be detected , in cells ectopically expressing organelle-specific MAVS similar levels of IFN-β were determined at 16 h post infection ( Fig 4B ) . These results were further confirmed by the IFN reporter cell line L929-ISRE-Firefly-Luciferase , which is a reliable type I IFN bioassay as reported earlier [45] . Supernatants taken from virus-stimulated cells were UV-inactivated prior to adding to the reporter cell line . As expected , in MAVS-/- MEFs without reconstituted MAVS no type I IFN was produced upon infection with SeV or reovirus ( Fig 4C ) . However , consistent with our results obtained in NS3/4A-expressing cells , we detected comparable amounts of type I IFN in the supernatants of MEFs expressing wt- , pex- or mitoMAVS . Analogous results were obtained when we quantified the mRNA amounts of endogenous IFN-β in infected and functionally reconstituted MAVS-/- MEFs ( Fig 4D ) . A rapid and profound activation was observed in cells infected with either virus and no difference was detected between the MAVS variants . In contrast , induction of IFN-λ2/3 expression could not be detected in this cell system , arguing that the antiviral activity released into the culture supernatant was exclusively mediated by type I IFN ( Fig 4E ) . In summary , these results corroborate the conclusion that mito and pexMAVS can induce the type I IFN response with comparable efficiency . The results described so far were based on the use of engineered MAVS variants with defined subcellular localization . Since we could not exclude that a small fraction of MAVS , undetectable by our assays , might have been mislocalized , we took advantage of cells lacking peroxisomes . By definition , these cells are unable to mount MAVS signaling platforms at peroxisomes , thus offering an alternative approach to study the activation of the IFN response in the absence of pexMAVS . This is the case with the patient-derived human fibroblast cell line ΔPex19 [46 , 47] , which naturally lacks the peroxisomal factor Pex19 required for budding of peroxisomes from the ER membrane [48] . These cells do not have peroxisomes but the peroxisomes can be restored by stably expressing recombinant Pex19 [47] . Indeed , upon ectopic expression of Pex19 , we could detect peroxisomes , as revealed by staining with the peroxisomal marker PMP70 ( cell line ΔPex19+Pex19 ) , which were absent in non-reconstituted cells ( Fig 5A ) . To determine activation of the type I and III IFN response in the absence or presence of peroxisomes we infected the two cell lines with SeV and monitored the kinetics of MxA protein accumulation that is induced by type I and III IFN . As shown in Fig 5B , activation of this ISG did not require peroxisomes and induction kinetics were virtually identical . Moreover , SeV infection also induced robust type III IFN production as measured by ELISA in culture supernatants and by a luciferase reporter assay based on the IFNL1 promoter ( Fig 5C and 5D ) . Consistently , mRNA levels of ISG56 were induced in both cell lines already 6 h post infection and increased over time ( Fig 5E ) . Importantly , both type III and type I IFN expression was induced with IFN-β showing a rapid response kinetic whereas activation of IFN-λ1 and IFN-λ2/3 was slower . Having established the comparable activation of type I and III IFN response by mito- and pexMAVS , we wanted to clarify the relative impact of either MAVS species on the IFN response during infection with HCV . To this end we generated Huh7 MAVS knockout cell lines ( Huh7 MAVSKO ) by targeting the first exon of the coding sequence of MAVS with the CRISPR/Cas9 system [49] . Since in only a fraction of cells knock-out is achieved , we generated three independent knock-out cell clones , whereby each clone was generated by using each time the same sgRNA . For each of these cell clones cell pools were established expressing NS3/4A protease cleavage resistant MAVSCR variants localizing to the mitochondria or peroxisomes as well as wild type MAVSCR ( Fig 6A; results obtained for only one cell clone are shown ) . In the initial set of experiments we determined HCV permissiveness of the cell lines by infecting them with an HCV reporter virus encoding Renilla luciferase ( JcR2a ) . In all tested cell pools we observed robust HCV replication as determined by luciferase assay ( Fig 6B , left panel ) and analysis of cells by immunofluorescence staining revealed an infection efficiency of 60–80% ( Fig 6B , right panel , and S7 Fig ) . Interestingly , after introducing the MAVSCR variants , the relative number of infected cells showed a minimal , yet statistically significant reduction , but there was no difference between organelle-specific MAVS variants ( Fig 6B , right panel ) . Consistent with our earlier report [50] , upon HCV and SeV infection there was no type I IFN production detectable in these Huh7 cells , ( S8 Fig ) . In contrast , a robust type III IFN response was observed after infection with HCV . This response was first detected 48 h after infection ( ~55 pg/ml IFN-λ ) and increased massively in the subsequent 24 h reaching ~250 pg/ml with pexMAVSCR cells and 305 pg/ml with mitoMAVSCR cells ( Fig 6C ) . These quantitative differences corresponded well to the variations of expression levels of the MAVS variants ( cf . Fig 6A ) . Consistently , mRNA levels of endogenous ISG56 , IFN-λ1 and IFN-λ2/3 increased 48 h after HCV infection and reached maximum levels 72 h after infection ( Fig 6D ) . These results were not a cell clone specific effect , because well comparable results were obtained with ( a ) two independent MAVSKO cell clones , each reconstituted with MAVSCR variants and analyzed in parallel and ( b ) Huh7 cells stably expressing NS3/4A to purge endogenous MAVS from intracellular membranes and reconstituted with MAVSCR variants ( S9 Fig ) . With the aim to characterize control of pexMAVS in HCV infected cells , we utilized the eGFP-NLS-MAVS-CTD fusion proteins described above ( cf . S2A Fig ) , which were expressed in Huh7 cells 36 h after HCV infection by transient transfection . As expected , in uninfected cells we observed predominantly mitochondrial localization of the wtMAVS marker protein ( Fig 6E , left panel ) . However , upon HCV infection cleaved wtMAVS-eGFP translocated into the nucleus , demonstrating efficient cleavage ( Fig 6E , right panel ) . Importantly , pexMAVS was also cleaved quantitatively in HCV-infected Huh7 cells as revealed by the exclusive nuclear localization of the marker protein . Taking advantage of this cell system , we next determined the impact of MAVS cleavage and MAVS subcellular localization on activation of the IFN response by HCV in an infection-based system . This was possible because the used cells were highly permissive for HCV , mounted a robust IFN response , but did not suppress HCV replication , thus excluding confounding effects on activation kinetics as a result of impaired virus replication . To this end we constructed Huh7 MAVSKO cells expressing exclusively HCV cleavable , i . e . wild type full-length MAVS variants localizing specifically to peroxisomes or mitochondria . All cell lines responded comparably to SeV infection by upregulation of ISG56 and IFN-λ1 , thus validating their functionality ( Fig 7A ) . Of note , the ISG response induced by SeV and Reo virus was only detectable at late time points after infection ( > 4 hours; S10 Fig ) . As expected , upon HCV infection all MAVS variants were efficiently cleaved , yet also non-cleaved MAVS was detected by Western blot ( Fig 7B ) . Since uncleaved MAVS might be produced by non-infected cells , we utilized an immunofluorescence-based single cell readout . We found that in HCV-infected cells MAVS was not detectable and this was independent from the subcellular localization of MAVS ( S11 Fig ) . Consistent with this efficient MAVS cleavage in HCV-infected cells was the almost complete block of IFN-λ production and ISG56 response ( Fig 7C and 7D ) . This inhibition was specific and not observed in cells reconstituted with the cleavage-resistant MAVS ( wtMAVSCR ) . Taken together we demonstrate that both mitochondrially and peroxisomally localized MAVS induce a robust type III IFN response in Huh7 cells with comparable efficiency . Importantly , both MAVS variants are efficiently controlled in HCV-infected cells by the NS3/4A protease , thus blocking IFN response irrespective of MAVS localization .
Activation of the IFN response by the RLRs RIG-I and MDA5 critically depends on MAVS that relays the signal via several protein kinases to NFκB and IRF3 , which in turn promote the transcription of IFN-β , IFN-λ and several ISGs [9–12] . MAVS was originally found to localize to mitochondria , but more recent studies also described MAVS on MAMs and peroxisomes [9 , 32 , 34] . Although in this study we could confirm peroxisomal localization of MAVS in the human hepatoma cell line Huh7 , it is still unclear how MAVS is targeted to this organelle . While the transmembrane region in the C-terminal part of MAVS appears to be responsible for targeting to mitochondria [9] and peroxisomes [34] , the molecular details remain to be clarified . Three possibilities can be envisaged how MAVS is targeted to peroxisomes . First , regarding mitochondria , the majority of peroxisomal proteins is translated by free polyribosomes in the cytosol [51–53] and they are post-translationally incorporated into newly formed peroxisomes via Pex19p [53] . Second , MAVS is present on ER membranes [32] . Since peroxisomes are derived from the ER , MAVS might be incorporated into new peroxisomal membranes by chance when they bud off [54] . Third , MAVS might shuttle from mitochondria to peroxisomes via a specialized transport system called mitochondrial-derived vesicles as described for other proteins [55] . It remains to be determined by which route MAVS is targeted to peroxisomes or whether several of these routes are used . In any case , we observed efficient cleavage of pexMAVS by the HCV protease . This might occur at the ER , prior to MAVS “loading” onto peroxisomes . NS3/4A might also be recruited to peroxisomes where cleavage could occur . Consistent with the latter assumption it has been reported that a fraction of NS3/4A co-localizes with peroxisomes [32] . We and others observed that overexpression of MAVS per se was sufficient to activate the RLR pathway [9 , 40] . However , there was no consistent difference between organelle-specific MAVS variants and this we observed with several cell lines and by using direct ( ELISA , qRT-PCR and Western blotting ) and indirect detection methods ( luciferase-based promoter assays ) . Moreover , after RLR stimulation by viral infection , comparable responses were induced by pexMAVS or mitoMAVS . Of note , efficient antiviral signaling was also detected in the human fibroblast cell line ΔPex19 , which lacks peroxisomes altogether , and no difference in activation kinetics or magnitude was found in this cell line after functional reconstitution of peroxisomes . It has been reported that peroxisomal proteins , in the absence of peroxisomes , either stay in the cytosol , are degraded or mistargeted to other organelles such as mitochondria [56–59] . Consistently , immunofluorescence staining of MAVS in ΔPex19 cells revealed a typical mitochondrial distribution and the abundance of MAVS in cells with or without peroxisomes was well comparable . Therefore , we assume that mitochondrial MAVS might compensate for peroxisomal MAVS in cells lacking peroxisomes . In any case , the activation of type I and III IFN in ΔPex19 cells shows that peroxisomes are dispensable for induction of this antiviral response . To corroborate this assumption we attempted stable knock-down of Pex19 in Huh7 cells , but this was not possible because of cytotoxicity . Moreover , transient knock-down was limited because peroxisomes not only renew from the ER , but also renew from already existing peroxisomes ( G . Dodt , Thübingen University , personal communication ) , thus requiring extended periods of Pex19 depletion which could not be achieved by transient knock-down . Although we did not observe a difference in activation of the IFN response by mito- or pexMAVS in various cell lines and experimental approaches , Dixit and colleagues described a rapid , type I IFN-independent signaling of pexMAVS in MAVS-/- MEFs after reovirus infection [34] . Furthermore , the same group recently reported that upon viral infection pexMAVS induces IFN-λ1 , but not IFN-β in Huh7 cells after knockdown of endogenous MAVS expression [35] . Consistent with this data , we could confirm IFN-λ1 activation by pexMAVS in MAVS knock-out Huh7 cells as well as IFN-λ2/3 production as determined by ELISA , luciferase reporter assay and qRT-PCR analysis . Moreover , for each condition and approach we detected comparable induction of type III IFNs by wt- , pex- and mitoMAVS . However , we did observe an induction of type I IFN by peroxisomally localized MAVS in i ) MAVS-/- MEFs after stimulation with reovirus or SeV , ii ) Huh7 and A549 cells after ectopic expression of MAVS variants and iii ) 293T cells with a functional ( NS3/4A-mediated ) block of endogenous MAVS and expressing organelle-specific cleavage-resistant MAVS variants . These results are at odds with the earlier studies that concluded a selective activation of IFNL by pexMAVS . Currently , we can only speculate about possible reasons for the discrepancy . One possibility is the use of different experimental set ups , most notably the MAVS constructs . Common to the present and the earlier studies is the replacement of the C-terminal TM domain of MAVS by pex- and mito-targeting sequences ( Pex13 and Bcl-XI , respectively; Fig 1C ) . However , while we fused MAVS with the targeting sequence at amino acid position 514 of MAVS , the MAVS fusion constructs employed in the earlier studies retained MAVS only up to amino acid residue 500 , which might affect signaling capacity of the MAVS fusion proteins . Indeed , activation of the IFN response by pex- and mitoMAVS truncated at position 500 seems rather low as compared to wtMAVS [34 , 35] . An alternative explanation for the discrepant results reported here and in the two earlier studies might be differences in sensitivities of used read-out systems to measure type I IFN . In fact , we were unable to detect IFN-β in Huh7 cells by ELISA , but could detect low endogenous IFN-β activation by qRT-PCR and by promoter assay . This appears to be a specific property of Huh7 cells , because in several other cell lines we could detect robust IFN-β by pex and mitoMAVS . Currently , we cannot exclude that a very small fraction of pexMAVS is localized on mitochondria . To address this possibility we attempted to separate peroxisomes , MAMs and mitochondria biochemically , but consistent with an earlier report [32] we found that subcellular fractionation was not sufficient for that purpose . However , we did find that gradual knockdown of mitoMAVS to a level still detectable by fluorescence microscopy profoundly reduced the ISG response after SeV infection ( S12 Fig ) . Given that pex- and mitoMAVS efficiently induced the IFN response to comparable levels , the degree of activation observed with pexMAVS cannot be ascribed to small amounts of pexMAVS mislocalized to mitochondria and not detectable by fluorescence microscopy . Thus we conclude that the activation observed with pexMAVS is indeed mediated by MAVS residing at peroxisomes . We also note that mitoMAVS might not only localize on mitochondria , but as reported earlier , Bcl-Xl also localizes to the ER [60–62] . However , ER or MAM localization of mitoMAVS would not affect our conclusions . By using Huh7-MAVSKO cells expressing organelle-specific MAVSCR variants , we observed a profound activation and secretion of type III IFN upon HCV infection ( Fig 6 ) . Surprisingly , while this IFN is biologically active , HCV replication in infected cells was barely affected . This might be due on one hand to impaired responsiveness of Huh7-MAVSKO cells to IFN ( S13 Fig ) , on the other hand to the limited IFN response of HCV in Huh7 cells [63] . Although this precludes studying the impact of IFN released from HCV-infected cells on viral replication and spread , it offers the possibility to characterize activation of the IFN response without confounding effects caused by a reduction in replication resulting from the antiviral state induced by IFN . It has been proposed that HCV might induce type III IFN via non-cleaved pexMAVS [64] . However , we demonstrate that pexMAVS is efficiently cleaved in HCV-infected cells . While this result verifies the reported co-localization of NS3/4A with peroxisomes [32] , we cannot exclude that a fraction of MAVS remains uncleaved . At the single cell level , despite Western Blot analysis indicating incomplete cleavage , MAVS was virtually non-detectable in HCV-infected cells . Thus , the apparent incomplete MAVS cleavage as detected by Western blot probably reflects incomplete infection of the used Huh7 cell cultures with uncleaved MAVS being produced by non-infected cells . The analogous might apply to MAVS cleavage as it was observed in the liver of HCV-infected patients [33] . Thus , the type III IFN response observed by our group and others [65–68] in primary human hepatocytes after HCV infection might result from a small fraction of non-cleaved MAVS or induction of other innate signaling pathways such as TLR3 , rather than reflecting a specific role of peroxisomal MAVS . The functional and biological differences of type I and III IFN remain poorly understood . Both classes of IFNs are produced after activation of RIG-I , MDA5 and TLR3 and both induce a very similar spectrum of ISGs via the JAK/STAT signaling pathway [69 , 70] . However , the two IFN classes are produced in different tissues and bind to different receptors , IFNAR and IL28R , respectively , [18 , 19] both of which also have distinct tissue distribution . Restriction of the IFN-λ receptor complex to epithelial cells and hepatocytes indicates a high importance of IFN-λ for control of infections with viruses of distinct tropism such as influenza or hepatitis viruses , respectively [65 , 66 , 71 , 72] . Furthermore , stimulation of cells with IFN-β or IFN-λ1–3 induces a long-lasting ISG response , while IFN-α induces a much faster and less sustained ISG response [69] . Consistent with these reports we found that cells expressing only IFN-β and IFN-λ mount a similar ISG56 response , which was comparably induced by mitoMAVS and pexMAVS . The observation that cleavage of MAVS by the NS3/4A protease completely blocks activation of the IFN response suggests that MAVS , which is not tethered to the membrane , loses its signaling capacity . Although purified MAVS protein forms prion-like structures even in solution in vitro and therefore independent of its transmembrane domain [15] , we and others never detected an IFN response by cleaved MAVS inside cells . This might be due to the lower effective concentration of cleaved MAVS in the cytoplasm as compared to the in vitro conditions and to a rapid degradation of MAVS that has a half-life of only ~4 hours in HCV replicon-containing cells [9 , 37] . In summary , our data indicate that peroxisomes appear to be dispensable in terms of signaling platforms in the type I and III IFN activation pathway , arguing for a redundant role of pex- and mitoMAVS . We observed comparable induction of type I and type III IFN by peroxisomal and mitochondrial MAVS upon viral infection and show that both , mitochondrial as well as peroxisomal MAVS are efficiently cleaved and inactivated in HCV-infected cells . Thus , HCV has evolved strategies to counteract this important IFN signaling molecule independent from its subcellular localization .
All cell lines were cultured in Dulbecco’s modified Eagle medium ( DMEM , Life Technologies , Germany ) supplemented with 10% fetal calf serum ( GE Healthcare , Germany ) , 100 μg/ml penicillin , 100 μg/ml streptomycin ( Sigma Aldrich , Germany ) , non-essential amino acids ( Life Technologies , Germany ) and maintained in a humidified incubator with 5% CO2 at 37°C . Stable cell lines were created by lentiviral transduction as described below and cultured in selection medium in the presence of 1 mg/ml G418 ( Life Technologies , Germany ) , 5 μg/ml puromycin ( Sigma Aldrich , Germany ) and/or 5 μg/ml blasticidin ( MP Biomedicals , USA ) . MAVS-/- mouse embryonic fibroblasts , human fibroblasts deficient for Pex19 ( ΔPex19 ) and mouse L929-ISRE-Firefly-Luciferase cells were kindly provided by Ulrich Kalinke ( Hannover , Germany ) , Gabrielle Dodt ( Tübingen , Germany ) and Steeve Boulant , ( Heidelberg , Germany ) , respectively . Jc1 wild type HCV and the Renilla Luciferase reporter virus JcR2a were generated as described recently by using the plasmids pFKJFH1/J6/C846ΔG and pFKJFH1/J6/C846-JcR2a , respectively [73 , 74] . Briefly , in vitro transcripts were purified by phenol/chloroform extraction and precipitated at room temperature using isopropanol . Ten microgram RNA was transfected by electroporation into 3 . 5x106 Huh7 . 5 cells using the GenePulser system ( Bio-Rad , Germany ) . Cell culture supernatant was harvested 24 , 48 and 72 hours post electroporation , pooled , filtered through a 0 . 45 μm SteriCap ( Millipore , Germany ) and precipitated with polyethylene glycol ( PEG ) 800 ( Applichem , Germany ) in PBS for 72 hours at 4°C . After centrifugation for two hours at 8 , 000x g , the pellet was resuspended in DMEM and the titer was determined by limiting dilution assay using the TCID50 method ( http://www . klinikum . uni-heidelberg . de/Downloads . 126386 . 0 . html ) . Vesicular Stomatitis Virus ( a kind gift from Gert Zimmer , Mittelhäusern , Switzerland ) was produced by infecting DF1 chicken embryo fibroblasts ( MOI = 0 . 0001 ) and harvesting culture supernatant 48 hours later . Virus stocks were titrated by standard plaque assay . Sendai virus ( kindly provided by Rainer Zawatzki , Heidelberg , Germany ) and New Castle Disease Virus ( a kind gift from Georg Kochs , Freiburg , Germany ) were propagated in LSL Valo SPF embryonic chicken eggs ( Lohmann Tierzucht , Germany ) . Shortly , eggs were incubated at 37 . 8°C with 60% humidity and turned every 24 hours . Eggs were infected at embryonic development day 12 with 103 plaque forming units diluted in OptiMEM ( Life Technologies , Germany ) . Allantoic fluid was harvested from 48 to 72 hours post infection , cellular debris was removed by centrifugation and the virus titer was determined by using the TCID50 method . Reo virus was kindly provided by Steeve Boulant ( Heidelberg , Germany ) . The mouse monoclonal antibody 9E10 detecting NS5A domain III of HCV was kindly provided by Charles Rice ( New York , USA ) . The Pex19-specific monoclonal antibody was a kind gift of Gabrielle Dodt ( Tübingen , Germany ) . The mouse monoclonal NS3/4A antibody recognizing the NS3 helicase ( 2E3 ) was generated in cooperation with Hengli Tang ( San Diego , USA ) . The mouse anti-MxA antibody was purchased from Georg Kochs ( Freiburg , Germany ) . Other commercially available antibodies used in this study were mouse monoclonal antibodies recognizing β-actin ( Sigma Aldrich , Germany ) , GAPDH ( Santa Cruz Biotechnology , USA ) and the HA-tag ( Sigma Aldrich , Germany ) as well as rabbit polyclonal antibodies recognizing MAVS ( Enzo Life science , Switzerland ) , PMP70 ( Abcam , United Kingdom ) and CoxIV ( Cell Signaling , Netherlands ) . Goat-anti-rabbit-Alexa-488 , goat-anti-mouse-Alexa-488 , donkey-anti-mouse-Alexa-568 , goat-anti-rabbit-Alexa-568 , chicken-anti-rabbit-Alexa-647 ( all from Life Technologies , Germany ) were used as secondary antibodies . For Western blot analysis anti-mouse and anti-rabbit antibodies , each coupled with horseradish peroxidase were used ( Sigma , Germany ) . Unless otherwise stated , plasmids were taken from the Orfeome cDNA library collection ( Life Technologies , Germany ) that is based on the pENTR221 vector . Organelle-specific targeting of MAVS was achieved by replacing the wild-type-transmembrane ( TM ) region of MAVS with the TM region of either Bcl-Xl ( clone BC019307; aa 203–233 ) [75] or Pex13 ( isolated from cDNA derived from Huh7 cells; aa 136–233 ) [76] ) by using PCR-based mutagenesis . Cleavage resistant MAVS containing the C508R substitution was generated by QuickChange site directed mutagenesis ( Agilent Technologies , Germany ) as recommended by the manufacturer . To construct the organelle-specific MAVS-eGFP-NLS reporter , the C-terminal domain ( CTD ) of pex- and wtMAVS were inserted into the pCDNA6 . 2 vector ( Life Technologies , Germany ) . All the other constructs were generated by inserting the respective gene into the pDONR207 vector and shuttling of the insert into the pWPI lentiviral transduction vector by using the Gateway cloning technology ( Life Technologies , Germany ) according to the instruction of the manufacturer . The type III IFN reporter pGL3-IFNλ1-Firefly-Lucifease was generated as previously described [77] . Stable cell lines were generated as described earlier [78] . In brief , 1 . 2x106 293T cells were seeded into a 6 cm-diameter dish one day before transfection . Plasmids pMD . G ( encoding the VSV G glycoprotein ) , pCMVΔR8 . 91 ( encoding HIV gag-pol ) and pWPI ( encoding the gene of interest and a selection marker ) were transfected in a 1:3:3 ratio into the cells by using the CaPhos Mammalian Transfection kit ( Clontech Laboratories , France ) according the manufacturer´s protocol . Supernatant was harvested 36 , 48 and 72 hours post transfection and passed through a 0 . 45 μm filter . Target cells were seeded at the density of 8x104 cells per well of a six-well plate and 24 hours later inoculated with different amounts of supernatant containing lentiviral particles The selection was started 36 hours post transduction . For MAVS transduction experiments lentiviral particles were purified by sedimentation onto a 20% sucrose cushion using a SW28 Ti rotor ( Beckman Coulter , Germany ) and two hours centrifugation at 20 , 000 rpm . RIG-I signaling in Huh7 , 293T and A549 cells with organelle-specific MAVS was measured indirectly by using dual luciferase reporter constructs as previously described [79] . Briefly , cells were seeded into 96 well plates 24 hours before co-transfection with either IFIT1 , IFNB , or IFNL1 promotor constructs pGL3B or p125-Firefly-Luciferase ( kindly provided by Ganes Sen , Cleveland and Takashi Fujita , Tokyo , respectively [79 , 80] ) . The co-transfected Renilla-Luciferase reporter plasmid pRL-SV40 ( Promega , Germany ) served as transfection control . Plasmid transfection was conducted with the Effecten transfection reagent ( Qiagen , Germany ) according to the instructions of the manufacturer . Cells were infected with different viruses 24 hours post transfection . For some experiments specified in the results section , cells were transfected with reporter plasmids 24 hours after lentiviral transduction and lysed 16 hours later . For mouse type I IFN bioassays , MEFs expressing organelle-specific MAVS variants were infected with either Sendai or Reo virus . Supernatant was harvested at given time points and stored at -80°C . Mouse L929-ISRE-Firefly-luciferase reporter cells [45] were seeded 24 hours prior to treatment . Reporter cells were stimulated with UV-inactivated supernatant and eight hours later firefly luciferase activity contained in cell lysates was determined . Total cellular RNA was isolated from single wells of a 24 well plate using the NucleoSpin RNA extraction kit ( Macherey-Nagel , Germany ) according to the manufacturer´s protocol . RNA analysis was carried out using the two-step qRT-PCR approach . Total RNA was reverse transcribed into cDNA and DNA amplicons were measured with SYBR Green ( Bio-Rad , Germany ) . The following primers were used: human GAPDH: forward 5’-GAAGGTGAAGGTCGGAGTC-3’ , reverse 5’- GAAGATGGTGATGGGATTTC-3’; human IFN-β: forward 5’- CGCCGCATTGACCATCTA-3’ , reverse 5’-GACATTAGCCAGGAGGTTCTC-3’; human IFN-λ1: forward 5’-GCAGGTTCAAATCTCTGTCACC-3’ , reverse 5’-AAGACAGGAGAGCTGCAACTC-3’; human IFN-λ2/3 forward 5’-CAGCTGCAGGTGAGGGA-3’ , reverse 5’-GCGGTGGCCTCCAGAACCTT-3’; human ISG56: forward 5’-GAAGCAGGCAATCACAGAAA-3’ , reverse 5’-TGAAACCGACCATAGTGGAA-3’; mouse GAPDH: forward 5’-AGGTCGGTGTGAACGGATTTG-3’ , reverse 5’-TGTAGACCATGTAGTTGAGGTCA-3’; mouse IFN-β: forward 5’-CAGCTCCAAGAAAGGACGAAC-3’ , reverse 5’-GGCAGTGTAACTCTTCTGCAT-3’; mouse IFN-λ2/3: forward 5’-AGGGTGCCATCGAGAAGAG-3’ , reverse 5’-GTGGTCAGGGCTGAGTCATT-3’ . Data were analyzed by using the ΔΔct method as described recently [81] . IFN-α pan ( Mabtech , Sweden ) , IFN-β as part of VeriPlex Human Cytokine 16-Plex ELISA Kit ( PBL-Interferon source , USA ) , IFN-λ1–3 ( PBL-Interferon source , USA ) and mouse IFN-β ( BioLegend , USA ) contained in the supernatant of cells were quantified according to the instructions of the manufacturers . Cells were seeded on cover slips in a 24-well plate . In case of mitotracker staining ( Life technologies , Germany ) , the dye was added 20 minutes before fixation with 4% paraformaldehyde ( PFA ) for 20 minutes . Cells were washed with PBS and permeabilized using 0 . 5% Triton X-100 for 5 minutes . After blocking of non-specific binding with 3% bovine serum albumin ( BSA ) in PBS for at least 20 minutes , cells were incubated with primary antibodies in 1% BSA for 60 minutes . After washing with PBS , cells were stained with secondary antibodies in 1% BSA . Nuclear DNA was stained with 4' , 6-diamidino-2-phenylindole ( DAPI , MoBiTec , Germany ) . Cells were mounted on glass slides using Fluoromount-G ( SouthernBiotech , USA ) . Pictures were taken on either a Leica SP2 confocal laser scanning microscope ( Leica , Germany ) or a Keyence BZ 9000 Imager ( Keyence , Germany ) . Image processing including cropping , automatic contrast , merge , cell counting and Mander’s coefficient analysis were performed using the Fiji software package [82] . Knock-out of MAVS in Huh7 cells was achieved by using the CRISPR/Cas9 system as previously described [49] . In brief , three different single-guide RNAs ( sgRNAs ) were designed targeting the first and second exon of the coding region of MAVS with the help of the open source tool [83] and inserted into the lentiviral vector lentiCRISPR v2 ( Addgene #52961 ) also encoding the Cas9 nuclease . The following sgRNAs were used: 5’ TCAGCCCTCTGACCTCCAGCG 3’ ( #1 ) , 5’ CGCTGGAGGTCAGAGGGCTGG 3’ ( #2 ) , 5’ AGGGGCTGCAGAGGGTAAACG 3’ ( #3 ) . Lentiviruses were produced as described above and 8x104 cells were transduced three times using undiluted stocks of lentiviral particles encoding sgRNA #1 , 2 or 3 . All shown data were obtained by using a cell clone treated with sgRNA #1 , but analogous results were obtained with cell clones generated with the two other sgRNAs . To establish MAVS knock-out cells , clonal selection was performed using single cell dilution in a 96-well plate . Knock-out was confirmed by Western blotting , immunofluorescence and functional tests . Cells were seeded with a density of 3x104 cells per well of a 24-well plate one day prior to transfection . Transfection was performed with Lipofectamine RNAiMax ( Life technologies , Germany ) according to the instruction of the manufacturer using a siRNA targeting MAVS ( 5’ CCCACAGGGUCAGUUGUAU 3’ ) and a control siRNA . Forty-eight hours post transfection cells were infected with Sendai virus . Knockdown efficiency was determined by Western blot analysis . Cells were lysed in 2x Laemmli buffer ( 200 mM Tris-HCl [pH8 . 8] , 5 mM EDTA , 0 . 1% ( w/v ) Bromophenol Blue , 10% ( w/v ) Sucrose , 3% ( w/v ) SDS , 2% β-Mercaptoethanol ) and heated to 98°C for 10 minutes . Proteins were separated by SDS-PAGE and blotted onto a PVDF membrane by either semi-dry or wet-blot ( Bio-Rad , Germany ) . Membranes were blocked with 5% milk in PBS containing 0 . 1% Tween 20 for one hour at room temperature or overnight at 4°C . Membranes were incubated with primary antibodies overnight at 4°C or for 90 minutes at room temperature . Secondary antibodies tagged with horseradish peroxidase were incubated for one hour at room temperature . After washing with PBS containing 0 . 1% Tween 20 , proteins were visualized by using the Western Lightning Plus-ECL reagent ( PerkinElmer , USA ) and an INTAS Advanced Fluorescence and ECL imager ( INTAS , Germany ) or a Curix 60 Developer device ( AGFA , Germany ) . The Lab Image 1D software package ( Kapelan BioImaging Solutions , Germany ) was used for quantification of specific signals that were normalized to β-actin or GAPDH or wild type MAVS expression as specified in the results section . Total RNA was purified as described above and analyzed using the Illumina HumanHT-12 v4 Expression BeadChip technology ( Illuminia , USA ) according to the protocols of the manufacturer . Data were processed using the software package R ( http://www . r-project . org ) . Statistical analysis of obtained data was performed using the GraphPad Prism software package ( GraphPad software , USA ) . Unpaired t-tests were performed as described in the results section . Asterisks indicate *** , P-value ≤0 . 0005; ** , P-value ≤0 . 005; * , P-value ≤0 . 05; ns , non-significant . | Mammalian cells developed several defense mechanisms against viral infection . One major strategy involves pattern recognition receptors ( PRRs ) recognizing non-self motifs in viral RNA and triggering the production of type I and III interferon ( IFN ) that induce an antiviral state . One central signaling molecule in this cascade is MAVS ( Mitochondrial Antiviral Signaling protein ) , residing on mitochondria , mitochondria-associated membranes of the endoplasmic reticulum , and peroxisomes . Here we characterized the role of mitochondrial and peroxisomal MAVS for the activation of the IFN response and their counteraction by the hepatitis C virus ( HCV ) , a major causative agent of chronic liver disease with a high propensity to establish persistence . By using various functional and genetic knock-out cell systems reconstituted to express exclusively mitochondrial or peroxisomal MAVS , we observed comparable activation of type I and III IFN response by either MAVS species . In addition , we found that the HCV protease residing in nonstructural protein 3 ( NS3 ) efficiently cleaves MAVS independent from its subcellular localization . This cleavage is required for suppression of the IFN response and might contribute to HCV persistence . Our results indicate a largely localization-independent activation of the IFN response by MAVS in hepatocytes and its efficient counteraction by the HCV NS3 protease . | [
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| 2015 | Activation of Type I and III Interferon Response by Mitochondrial and Peroxisomal MAVS and Inhibition by Hepatitis C Virus |
Several intracellular pathogens arrest the phagosome maturation in the host cells to avoid transport to lysosomes . In contrast , the Leishmania containing parasitophorous vacuole ( PV ) is shown to recruit lysosomal markers and thus Leishmania is postulated to be residing in the phagolysosomes in macrophages . Here , we report that Leishmania donovani specifically upregulates the expression of Rab5a by degrading c-Jun via their metalloprotease gp63 to downregulate the expression of miR-494 in THP-1 differentiated human macrophages . Our results also show that miR-494 negatively regulates the expression of Rab5a in cells . Subsequently , L . donovani recruits and retains Rab5a and EEA1 on PV to reside in early endosomes and inhibits transport to lysosomes in human macrophages . Similarly , we have also observed that Leishmania PV also recruits Rab5a by upregulating its expression in human PBMC differentiated macrophages . However , the parasite modulates the endosome by recruiting Lamp1 and inactive pro-CathepsinD on PV via the overexpression of Rab5a in infected cells . Furthermore , siRNA knockdown of Rab5a or overexpression of miR-494 in human macrophages significantly inhibits the survival of the parasites . These results provide the first mechanistic insights of parasite-mediated remodeling of endo-lysosomal trafficking to reside in a specialized early endocytic compartment .
Leishmania donovani ( Ld ) is an obligate intracellular parasite which causes visceral leishmaniasis in the mammalian hosts that affects annually about 12 million people worldwide [1] . This parasite is postulated to reside and replicate in a phagolysosomal compartment in mouse macrophages as the parasites acquire lysosomal markers such as Lysosome Associated Membrane Protein 1 ( Lamp1 ) , Lamp2 and CathepsinD on PV [2 , 3] . However , not much is known about how Leishmania is surviving in such detrimental compartment . Interestingly , it has been shown that Leishmania excludes Vesicular Proton-ATPase on PV by inhibiting the recruitment of Synaptotagmin V to prevent the acidification in mouse macrophages [4] . Consequently , some recent studies have shown that Leishmania also modulates the recruitment of Rab7 [5] and ER markers [6] on PV in mouse macrophages . Thus , the Leishmania possibly resides in a hybrid compartment , but the nature of the compartment is not well characterized [7] . Since Rab GTPases are the central regulators of membrane trafficking pathways [8 , 9 , 10] , targeting the function of Rab proteins is one of the commonly used mechanisms exploited by intracellular pathogens to subvert their lysosomal targeting [11 , 12 , 13 , 14 , 15 , 16] . Moreover , successful intracellular pathogens are also shown to modulate the expression of host cytokines to establish a safe niche inside the host cells [17 , 18 , 19] . Consequently , we have shown that cytokines can specifically modulate the expression of different Rabs [20] . Thus , it is possible that intracellular pathogens might also modulate the expression of Rab GTPases in the host cells . In addition , recent studies have shown that several intracellular pathogens such as S . typhimurium , M . tuberculosis and L . monocytogenes target the host microRNAs ( miRNA ) to modulate the expression of host proteins for their successful infection and survival [21 , 22 , 23] . Thus , it is tempting to speculate that Leishmania might target the host miRNA ( s ) to modulate the expression of endocytic Rab GTPases in infected macrophages . Here , we have shown that L . donovani upregulates the expression of Rab5a in human macrophages by inhibiting the expression of miR-494 and retains Rab5a and EEA1 on PV to survive in an early endocytic ( EE ) compartment . Thus , in contrast to previous perception , our results demonstrate that L . donovani modulates endo-lysosomal pathway to reside in a modified early endocytic compartment and inhibits lysosomal transport in macrophages .
To determine whether Leishmania infection modulates the expression of different Rab GTPases , human THP-1 monocytic cell line were differentiated into macrophages ( human macrophages ) and were infected with L . donovani promastigotes ( MOI 1:20 ) . Subsequently , the cellular contents of various Rabs were determined by Western blot analysis using specific antibodies at indicated time points of infection . About 4-fold and 2-fold increase in the cellular content of Rab5 and Rab11 , respectively , were observed after 24 h of infection ( Fig 1A ) in comparison to uninfected control cells . No significant change in the levels of expression of Rab4 and Rab7 were observed in infected and uninfected cells . To determine whether higher level of Rab5 expression was due to transcriptional activation , we checked transcript levels of different endocytic Rab GTPases by Real Time PCR ( qPCR ) in infected human macrophages . Similar to protein expression , our results showed about 3-fold and 2-fold induction in the levels of Rab5a and Rab11 mRNA expression , respectively , after 24 h of L . donovani infection in comparison to uninfected control cells ( Fig 1B ) . Previous studies demonstrated that Rab5 has three isoforms namely , Rab5a , Rab5b and Rab5c in mammalian cells [24] . Interestingly , we observed that L . donovani infection does not significantly alter the expression of Rab5b and Rab5c ( Fig 1B ) . These results were further confirmed by Western blot analysis using isoform-specific antibodies ( Fig 1C ) and limited dilution semi-quantitative RT-PCR ( S1 Fig ) . To understand the mechanism of upregulation of Rab5a expression in Leishmania infected human macrophages , we compared the miRNA profiles of uninfected and infected macrophages . Our results showed that expressions of 29 miRNAs are upregulated by more than 2-fold in L . donovani infected macrophages whereas expressions of 19 miRNAs are found to be downregulated by about 2-fold in infected cells in comparison to uninfected cells ( Fig 2A ) . As miRNA negatively regulates the expression of their target gene , we screened those miRNAs which were downregulated in Leishmania infected human macrophages to identify their putative target sites in 3/-regulatory region of Rab5a using TargetScan prediction algorithms . This analysis predicted that 3/-UTR of Rab5a mRNA of human cells contains an 8-mer target site ( 5/-AUGUUUCA-3/ ) located between 191–198 nucleotides that precisely matches the seed region ( positions 1–8 ) of miR-494 ( Fig 2B ) . Interestingly , we observed that 8-mer target site of miR-494 is well conserved in the 3/-UTR of Rab5a in hamster whereas it is completely absent in the 3/-UTR of Rab5a in mice ( Fig 2B ) . Our analysis also predicted ( S2 Fig ) that miR-494 has highest affinity to its complimentary target site for Rab5a ( mirSVR score: -1 . 2494 ) compared to Rab5b ( mirSVR score: -0 . 0086 ) and Rab5c ( mirSVR score: -0 . 1579 ) in human cells . Consequently , we determined the level of expression of miR-494 in L . donovani infected human macrophage ( MOI 1:20 ) at different time periods of infection . Our results showed about 60% inhibition of the expression of miR-494 in L . donovani infected macrophages after 24 h of infection in comparison to uninfected cells ( Fig 2C ) . We also found that infection with MOI ( MOI 1:40 ) led to higher suppression of miR-494 than with MOI ( MOI 1:20 ) after 12 h of infection ( Fig 2D ) . These results indicated that inhibition of miR-494 expression in infected macrophages is also dependent on the extent of infection . To determine how L . donovani infection downregulates the expression of miR-494 in macrophages , we checked for the level of c-Jun subunit of AP-1 transcription factor in infected cells as it was previously reported that miR-494 expression is regulated by AP-1 transcription factor [25] . Previous studies demonstrated that AP-1 is composed of c-Jun , c-Fos family of proteins . Interestingly , we found that c-Jun subunit level is significantly lower ( ~50% ) in human macrophages after 6 h of infection with L . donovani in comparison to uninfected cells ( Fig 2E ) . It was reported earlier that Leishmania gp63 is a metalloprotease which is secreted into host cells and degrades several host proteins including some transcription factors like NF-kB , STAT1 and AP-1 to alter gene expression [26 , 27] . Therefore , to determine how Leishmania infection lowers the c-Jun level in infected macrophages , we infected the human macrophages with L . donovani overexpressing LdRab1:S22N , a gp63 secretion deficient parasite [28] . Interestingly , our results showed ( Fig 2F ) that degradation of c-Jun is significantly lower in LdRab1:S22N infected cells in comparison to infection with Ld:WT cells . These results were substantiated by the fact that overexpression of gp63 in Raw 264 . 7 mouse macrophages significantly degrades c-Jun ( S3 Fig ) . Thus , Leishmania degrades c-Jun in infected human macrophages via their gp63 to inhibit the expression of miR-494 . In order to validate the regulation of Rab5a expression by miR-494 , we prepared chimeric construct by ligating 3/-UTR of human Rab5a with luciferase as heterologous reporter . First , the human 3/-UTR of Rab5a was identified from human genome database and amplified ( 1350 bp ) from cDNA prepared from THP-1 macrophage using appropriate primers . Subsequently , amplified product ( 1350 bp ) was digested and cloned in pmir-GLO dual luciferase vector at SacI/XhoI restriction sites . To determine the specificity , we also made another chimeric construct of 3/-UTR of Rab5a containing mutation in miR-494 recognition element ( 5/-CGACACGG-3/ ) . The chimeric construct containing 3/-UTR region of Rab5a or its mutant was co-transfected with miR-494 mimic into HeLa cells and firefly luciferase reporter activity was measured after 48 h of transfection . Co-transfection of 3/-UTR region of human Rab5a and a nonspecific miR mimic into HeLa cells was used as a control . The result presented in the Fig 3A showed that transfection with the miR-494 ( 40 nM ) reduces about 50% luciferase activity of Rab5a 3/-UTR reporter , whereas about 20% inhibition of mutant Rab5a 3/-UTR reporter was detected . These results indicated that miR-494 binds with miR-494 recognition element present in the 3/-UTR of human Rab5a to repress the expression of Rab5a . To demonstrate that miR-494 specifically regulates the expression of Rab5a , HeLa ( Fig 3B ) and THP-1 ( Fig 3C ) cells were transfected with miR-494 mimic ( 40 nM ) or control mimic and levels of Rab5 isoforms were determined by qPCR using specific TaqMan probes . We observed that miR-494 specifically inhibits about 50% expression of Rab5a mRNA compared to the control mimic in both cells types . No significant changes were observed in Rab5b and Rab5c levels in miR-494 transfected cells . Finally , we checked the expression of Rab5a protein after 48 h of transfection of indicated concentration of miR-494 in THP-1 macrophages by Western blot analysis using specific antibody . Our results demonstrated that overexpression of miR-494 inhibits Rab5a expression in a concentration dependent manner . More than 80% inhibition of Rab5a protein expression was observed in 100 nM miR-494 transfected human macrophages in comparison to the control miR transfected cells ( Fig 3D ) . Similar results were also observed in HeLa cells overexpressing miR-494 ( S4 Fig ) . These results demonstrated that miR-494 regulates the expression of Rab5a in human cell . Subsequently , we determined whether L . donovani recruits Rab5a on Leishmania containing parasitophorous vacuole ( Leishmania-PV ) in macrophages . Indeed , we found that Rab5a is specifically recruited on Leishmania-PV ( Fig 4A ) after 24 h of infection in human macrophages . In contrast , cells infected with dead parasites were unable to recruit Rab5a and it was found to be localized on discrete small punctate vesicular structures as observed in uninfected cells . Among the different isoforms of Rab5 , interestingly our results showed that Rab5a is predominantly recruited ( S5A Fig ) and retained on Leishmania-PV for at least 48 h ( S5B Fig ) . Moreover , we also found that Leishmania-PV also recruits ( Fig 4B ) and retains ( S5C Fig ) Early Endosome Associated Antigen ( EEA1 ) , a Rab5 effector . Moreover , we also observed higher recruitment of Rab5a and EEA1 on PV at 24 h in comparison to 6 h . In addition , results presented in the Fig 4C showed that Leishmania-PV does not recruit Rab7 possibly to block transport to lysosomes . We also used anti-Rab8 and anti-Rab9 antibodies as control and found that Leishmania-PV does not recruit Rab9 and Rab8 indicating that Rab5a recruitment on PV is specific ( S5D Fig ) . Further quantitation revealed that more than 90% of Leishmania-PV recruits Rab5a and EEA1 whereas less than 10% PV recruits Rab7 ( Fig 4D ) . In addition , our results also showed that L . donovani infection overexpresses Rab5a in human PBMCs ( Fig 5A ) by downregulating the expression of miR-494 ( Fig 5B ) . Consequently , Rab5a was found to be recruited on Leishmania-PV in Leishmania infected human PBMCs ( Fig 5C ) . In contrast , we observed that Leishmania infection in Raw 264 . 7 mouse macrophages neither induces the expression of Rab5a ( S6A Fig ) nor recruits Rab5a on Leishmania-PV ( S6B Fig ) . Taken together , these results demonstrated that Leishmania-PV specifically recruits and retains Rab5a and EEA1 in human macrophages to reside in an early endocytic compartment . In order to unequivocally prove that Leishmania inhibits its transport to lysosomes by recruiting Rab5a and EEA1 on PV , lysosomes of the human macrophages were labeled with the internalization of latex beads and infected with Leishmania . Accordingly , THP-1 differentiated human macrophages were incubated with Fluoresbrite-YG-latex beads ( 2 μm ) for 3 h and chased for 24 h at 37°C and subsequently cells were immuno-stained with anti-Lamp1 or anti-CathepsinD antibody . As expected , our results showed that latex bead containing phagosomes after 24 h of internalization are positive for both Lamp1 and CathepsinD ( Fig 6A ) . In addition , latex beads after 24 h of internalization in human macrophages were found to be transported to dextran-Texas Red prelabeled lysosomes ( Fig 6A ) . These results clearly demonstrated that lysosomes in macrophages can be labeled with internalization of latex beads for 24 h . Therefore , THP-1 differentiated macrophages were co-infected with latex beads and live Leishmania for 3 h at 37°C , washed and chased for additional 24 h at 37°C . Our results showed that Leishmania-PVs are clearly separated from latex beads containing lysosomes in human macrophages after 24 h of incubation ( Fig 6A , lower panel ) . In addition , we also found that latex beads containing phagosomes are clearly separated from Rab5a and EEA1 positive Leishmania-PV in macrophages after 24 h of infection ( S7 Fig ) . Further quantitation revealed that more than 90% of the latex beads containing phagosomes are positive for Dextran-Texas Red , Lamp1 and CathepsinD whereas Rab5a and EEA1 were almost not detected on these phagosomes after 24 h of internalization in macrophages . Most interestingly , more than 95% of Leishmania-PVs were found to be separated from Latex beads containing lysosomes under same conditions ( Fig 6B ) . To determine the proteolytic activity of the lysosomes of the L . donovani infected and uninfected human macrophages , cells were labeled with DQ-BSA Red which induces strong fluorescence upon hydrolysis by proteases [29] . Our results showed that most of the internalized latex beads in human macrophages are colocalized with DQ-BSA Red labeled proteolytically active lysosomes . Whereas , Leishmania failed to colocalize with DQ-BSA Red labeled proteolytically active lysosomes ( Fig 6C ) . Most importantly , a significant reduction in the fluorescence of DQ-BSA Red and numbers of red puncta per cell were observed in Leishmania infected human macrophages in comparison to uninfected and latex beads infected cells ( Fig 6C ) . These results indicated that Leishmania not only inhibits transport to lysosomes but also blocks the proteolytic activity of the lysosomes in infected macrophages . Previous studies demonstrated that Leishmania resides in the phagolysosomal compartment in mouse macrophages [30 , 31] . However , we found that Leishmania resides in Rab5a and EEA1 positive early endocytic compartment in human macrophages . Therefore , we tried to determine the localization of CathepsinD and Lamp1 on Leishmania-PV after 24 h of infection in human macrophages . Interestingly , we also found that Leishmania-PV acquires Lamp1 and CathepsinD after 24 h of infection in human macrophages ( Fig 7A ) as observed previously in mouse macrophages . This observation was puzzling; therefore , we tried to determine how Leishmania-PV recruits lysosomal markers like CathepsinD and Lamp1 when parasites reside in an early compartment positive for Rab5a and EEA1 . Previous studies demonstrated that lysosomal enzymes like CathepsinD and Lamp1 are trafficked via early endosomes to their final destination of lysosomes [32] . Thus , we speculated that induced expression of Rab5a in Leishmania infected macrophages might promote the fusion of Golgi derived Lamp1 or CathepsinD containing vesicles with Leishmania PV and thereby , blocks the transport of lysosomal proteins in early endosomes . To test this hypothesis , we overexpressed GFP-Rab5a or its mutants in HeLa cells and determined the distribution of CathepsinD and Lamp1 by immune-staining . Our results showed that both CathepsinD ( Fig 7B ) and Lamp1 ( Fig 7C ) are localized into perinuclear lysosomal compartment in untransfected control cells as reported earlier [33] . In contrast , majority of CathepsinD ( Fig 7B ) and Lamp1 ( Fig 7C ) containing vesicles were found to be colocalized with GFP-Rab5a positive early endosomal compartments in GFP-Rab5a:WT and GFP-Rab5a:Q79L overexpressed cells . No apparent change in the distribution CathepsinD and Lamp1 was observed in GFP-Rab5a:S34N overexpressing HeLa cells in comparison to control cells . These results demonstrated that overexpression of Rab5a inhibits the transport of CathepsinD and Lamp1 to the lysosomes and retains them in the early endocytic compartment . Previous studies demonstrated that the lysosomal hydrolase CathepsinD is synthesized as a 52-kDa precursor protein which is cleaved from amino-terminus in acidified early endosome resulting in a 48-kDa intermediate enzyme form . Subsequently , further proteolytic cleavage of the protein in low pH of the lysosomes produced the mature active CathepsinD composed of heavy ( 34-kDa ) and light ( 14-kDa ) chains linked by non-covalent interactions [34] . Thus , size of CathepsinD was used as an indicator for their processing as well as for their localization in previous studies . Therefore , we determined the size of the CathepsinD in Leishmania infected human macrophages . We found significantly lower levels of the mature/activated form of CathepsinD in live parasite infected macrophages in comparison to dead parasite infected cells ( Fig 7D ) . Further quantitation revealed that live Leishmania infection inhibits about 70% mature form of CathepsinD in infected human macrophages than dead parasite infected cells . Similar block in the processing of CathepsinD was found in Rab5a:WT , Rab5a:Q79L and Rab5a:S34N overexpressing HeLa cells ( Fig 7E ) . Taken together , these results clearly demonstrated that Leishmania induces the expression of Rab5a in infected human macrophages and thereby blocks the trafficking of lysosomal proteins and retains them as inactive precursor form in early endosomes . To determine the function of Rab5a in the survival of L . donovani in human macrophages , Rab5a was knockdown in THP-1 differentiated macrophages by specific siRNA and these cells were infected with Leishmania . First , THP-1 differentiated macrophages were transfected with 50 nM of Rab5a specific siRNA and cells were incubated for 48 h at 37°C . Subsequently , levels of Rab5 isoforms in these cells were determined by Western blot analysis . The results presented in the Fig 8A showed that siRNA specifically downregulates the expression of Rab5a in macrophages . Finally , Rab5a knockdown cells were infected with Leishmania and the parasite load was determined at indicated time points . Our results showed that infection in Rab5a knockdown macrophages is not compromised as similar numbers of parasites are observed in both Rab5a knockdown and control cells at 0 h . However , more than 74% inhibition of parasite load was observed in Rab5a knockdown human macrophages in comparison to control cells after 96 h of infection ( Fig 8B and 8C ) . Similar results were also obtained in miR-494 ( 50 nM ) overexpressed macrophages under identical conditions ( Fig 8B and 8C ) . Subsequently , our results showed that this is due to significant less recruitment of Rab5a on Leishmania-PV in miR-494 overexpressed human macrophages ( Fig 8D ) . These results demonstrated that knocking down of Rab5a by siRNA or inhibiting the expression of Rab5a by miR-494 significantly inhibits the growth of parasite in macrophages .
One general strategy used by several intracellular pathogens is to avoid transport to lysosomes by modulating the intracellular trafficking pathway in the host cells to survive in modified phagosomes [11 , 12 , 13] . In contrast , previous studies using mainly mouse macrophages have shown that Coxiella [35 , 36] and Leishmania [2 , 3] survive in a phagolysosomal compartment decorated with Lamp1 , vacuolar ATPase , and CathepsinD . As phagolysosomal compartment is detrimental for invading pathogen , it is puzzling how Coxiella and Leishmania are surviving in such a degradative compartment in the cell . Consequently , recent studies have shown that pathogenic C . burnetii blocks the recruitment of Vps41 and inhibits its transport to phagolysosomes [37 , 38] . However , not much is known about how Leishmania modulates the trafficking pathway in the host cells and the nature of the compartment they survive . Interestingly , we have found that Leishmania donovani infection specifically upregulates the expression of Rab5a in human macrophages . Subsequently , we have shown that upregulation of Rab5a expression is due to transcriptional activation as enhanced level of Rab5a transcript is detected in infected macrophages . To understand how Leishmania induces the expression of Rab5a in infected human macrophages , we have compared the miRNA profile of uninfected and infected macrophages as some of the current studies have shown that miRNA can also modulate the expression of Rab GTPases [39 , 40] . In addition , it has been shown that Leishmania infection downregulates miR-122 expression to lower serum cholesterol [41] and significantly enhances the miR-30A-3p expression to modulate autophagic pathway in macrophages [42] . Interestingly , we have also found that L . donovani infection modulates the expression of various miRNA in human macrophages . Subsequently , we have identified that miR-494 has a target site in 3/-regulatory region of Rab5a of human and hamster but not in mouse . As not much work has been done about regulation of expression of Rab GTPases , we have first validated that miR-494 specifically regulates the expression of Rab5a using a chimeric construct containing 3/-UTR of Rab5a with luciferase as heterologous reporter in mammalian cells . Our results have demonstrated that miR-494 binds with miR-494 recognition element present in the 3/-UTR of Rab5a to repress the expression of Rab5a in human cells . Most importantly , we have found that transfection of miR-494 specifically inhibits the expression of Rab5a protein in human macrophages and HeLa cells compared to the control mimic . This is the first demonstration that miR-494 negatively regulates the expression of Rab5a in HeLa cells and macrophages . Subsequently , we have shown that L . donovani infection downregulates the expression of miR-494 . To determine how L . donovani infection downregulates the expression of miR-494 in macrophages , we have checked the expression of AP-1 transcription factor in infected human macrophages as it has been previously reported that AP-1 transcription factor is involved in the synthesis of miR-494 [25] . Interestingly , we have found that Leishmania degrades the c-Jun subunit of AP-1 transcription factor by gp63 in infected human macrophages using gp63 secretion deficient LdRab1:S22N transgenic parasites [28] . These results are supported by the fact that Leishmania major infection degrades AP-1 complex in infected mouse macrophages via their metalloprotease gp63 [26] . Consequently , we have also found that gp63 overexpression degrades c-Jun in Raw 264 . 7 mouse macrophages . Taken together , our results have demonstrated that L . donovani infection degrades c-Jun by their metalloprotease , gp63 to inhibit the synthesis of miR-494 and thereby upregulates the expression of Rab5a in infected human macrophages . In contrast , Leishmania infection does not induce the expression of Rab5a in mouse macrophages as miR-494 target site is absent in the 3/-UTR of mouse Rab5a . In order to understand the significance of Rab5a upregulation in L . donovani infected human macrophages , we have checked the recruitment of Rab5a on Leishmania-PV . We have found that Leishmania not only specifically recruits Rab5a on PV but also retains it throughout the experimental period of 48 h . Similarly , Leishmania recruits and retains EEA1 on its PV which is a Rab5 effector molecule present in early endosomal compartment [43] . However , Leishmania containing PV does not acquire Rab7 , Rab8 and Rab9 in human macrophages . It is also pertinent to mention that antibodies against several mammalian Rabs like Rab5 , Rab7 and Rab1 do not cross react even with respective Leishmania proteins as it has been shown previously [28 , 44 , 45 , 46] . Therefore , our results unequivocally prove that Leishmania resides in the early endocytic compartment in human macrophages by selectively recruiting Rab5 and EEA1 as observed for Mycobacteriun and Salmonella -containing phagosomes [15 , 47 , 48] . As Rab5 is an early endocytic GTPase [49 , 50] , retention of Rab5a on PV might promote the constitutive fusion of Leishmania-PV with early endosomes to inhibit its trafficking to lysosomes as it has been shown previously with Salmonella-containing phagosomes [51] . Therefore , we have checked whether Leishmania inhibits its transport to lysosomes . Indeed , we have found that Leishmania inhibits its transport to latex beads containing lysosomes even after 24 h of infection in human macrophages . Consequently , we have found that Leishmania-PV does not recruit Rab7 which is required for lysosomal targeting . Taken together , these results unambiguously prove that Leishmania inhibits its transport to the lysosomes to survive in human macrophages . However , our results have also shown that Leishmania containing PV in mouse macrophages does not recruit and retain Rab5a after 24 h of infection whereas Leishmania containing PV in mouse macrophages is shown to recruit Rab5 in early time point of infection [52] . This is possibly due to the fact that Leishmania infection does not overexpress Rab5a in mouse macrophages . Conversely , our results also suggest that Leishmania infection in hamster macrophages might induce the Rab5a expression to reside in early endosomes to produce persistent infection . Thus , it is tempting to speculate why among the two animal models of leishmaniasis [53] , hamster model mimic human infection whereas Leishmania infection is self-healing in mouse . However , it has been previously shown that Leishmania-PV recruits lysosomal markers like CathepsinD and Lamp1 predominantly in mouse macrophages [30 , 31] . We have confirmed these observations and have also found that Leishmania-PVs are positive for CathepsinD and Lamp1 in human macrophages . This is puzzling how Leishmania-PV recruits lysosomal markers like CathepsinD and Lamp1 when they reside in an early compartment positive for Rab5a and EEA1 . Thus , we have evaluated the trafficking of CathepsinD and Lamp1 in Rab5a overexpressed cells as Leishmania infection induces the expression of Rab5a in the infected cells . Our results have shown that both Lamp1 and CathepsinD are predominantly colocalized with GFP-Rab5a positive early endosomal compartments in GFP-Rab5a:WT and GFP-Rab5a:Q79L overexpressed cells in comparison to their perinuclear localization in control cells as reported earlier [33] . Thus , overexpression of Rab5a might promote the fusion of Golgi derived Lamp1 or CathepsinD containing vesicles with early endosomes and thereby , retain them in early endosomes as these proteins are trafficked via early endosomes to lysosomes [54] . Our results are also supported by the fact that overexpression of dominant active mutant of Rab5a redistributes the lysosomal enzymes in early endosomes and disturbs the lysosome biogenesis [55] . These results suggest that higher expression of Rab5a in L . donovani infected human macrophages blocks the trafficking of lysosomal enzymes and retains them in early endocytic compartment . Moreover , it has been shown that size of CathepsinD present in early endosomal compartment is 48kDa whereas the size of the mature protein in the lysosome is found to be around 34 kDa and 14 kDa [56] . Consequently , we have found that size of the CathepsinD is predominantly 48 kDa in L . donovani infected human macrophages as well as Rab5a and its mutants overexpressed cells indicating that Leishmania infection retains lysosomal enzymes in early compartment in an immature and inactive form via the overexpression of Rab5a . Though overexpression of Rab5a:S34N does not apparently alter the distribution of CathepsinD in HeLa cells , but our results have shown that overexpression of this mutant also blocks the processing of this enzyme . This might be due to the essential role of Rab5 in the biogenesis of the endolysosomal system [57] . In addition , we have shown that L . donovani infection blocks proteolytic activity of the lysosomes in infected human macrophages using DQ-BSA Red as a fluorogenic substrate for proteases [29] . Thus , Leishmania-PV recruits Lamp1 and CathepsinD as it has been shown earlier but these proteins are localized in immature and inactive form in early endosomes . Finally , we have shown that selective depletion of Rab5a in human macrophages by specific siRNA significantly inhibits the growth of the parasites . Similar results are also obtained in miR-494 overexpressed macrophages under identical conditions . These results suggest that knocking down of Rab5a by siRNA or inhibiting the expression of Rab5a by miR-494 possibly targets internalized parasites to lysosomes as they will not be able to promote Rab5a-mediated constitutive fusion with early endosomes . Thus , our results demonstrate that Rab5a function is essential for the survival of Leishmania in human macrophages . In conclusion , this is the first demonstration that Leishmania resides in Rab5a and EEA1 positive early endocytic compartment in human macrophages . To delineate the mechanism , we have shown that Leishmania upregulates the expression of Rab5a in infected macrophages by downregulating the synthesis of miR-494 by degrading c-Jun via gp63 . Subsequently , parasites recruit Rab5a on PV and inhibit transport to lysosomes ( Fig 9 ) . However , Leishmania residing in early compartment also recruits lysosomal enzymes in immature and inactive form in human macrophages . Thus , blocking the processing of the lysosomal enzymes to the mature active form might also help the parasites to survive in macrophages . These results also indicate the possibility of modulating endo-lysosomal pathway in parasite infected cells by miR-494 or small molecules to divert trafficking of Leishmania probably to lysosome which might be useful for developing future therapeutic intervention .
Unless otherwise stated , all reagents were obtained from Sigma Chemical Co . ( St . Louis , MO ) . Tissue culture supplies were obtained from the Grand Island Biological Co . ( Grand Island , NY ) . Lipofectamine 2000 and Lipofectamine RNAi max reagent were purchased from Thermo Fisher Scientific . pmirGLO Dual-Luciferase miRNA Target Expression Vector and Dual-Luciferase Reporter Assay System were purchased from Promega Life Science ( Madison , WI ) . microRNA mimics were obtained from Sigma Aldrich ( St . Louis , MO ) . Antibodies against Rab5a and Rab5c were purchased from Abcam ( Cambridge , England ) whereas anti-Rab5b antibody was obtained from Santa Cruz Biotechnology ( Santa Cruz , CA ) . Antibodies against Rab5 , Rab7 , Rab9 , c-Jun , Lamp1 were obtained from Cell Signaling Technologies ( Danver , MA ) . Anti-Rab8 and anti-Rab11 antibodies were purchased from BD Biosciences , USA . Anti-EEA1 antibody was received as kind gift from Dr . Marino Zerial ( Max Planck Institute , Dresden , Germany ) . All HRP-conjugated secondary antibodies were purchased from Jackson ImmunoResearch Laboratory ( West Grove , PA ) and ECL was obtained from Amersham Biosciences , UK . All secondary antibodies used for immunofluorescence studies were purchased from Molecular Probes ( Eugene , OR ) . All other reagents used were of analytical grade . | Leishmania donovani causes visceral leishmaniasis in human . This parasite is thought to reside and replicate in a phagolysosomal compartment in macrophages . But , how Leishmania survives in such a detrimental compartment in macrophages is not known . In contrast , most of the intracellular pathogens avoid targeting to lysosomes in the host cells . We have found that Leishmania upregulates the expression of Rab5a , an early endosomal protein , by downregulating the expression of miR-494 in infected human macrophage as a consequence of gp63-dependent degradation of c-Jun . Subsequently , parasites recruit and retain Rab5a and EEA1 on their parasitophorous vacuoles demonstrating that Leishmania resides in the early endosomal compartment and consequently inhibits their transport to the lysosomes . However , we have found that parasites also recruit Lamp1 and inactive pro-CathepsinD on PV which led to previous conclusion that Leishmania resides in the phagolysosomal compartment . We have also shown that parasites block the processing of the lysosomal enzymes in the early endosomal compartment by overexpressing Rab5a which also helps the parasites to survive in human macrophages . Finally , we have shown that overexpression of Rab5a by downregulating miR-494 in macrophages is essential for parasite survival . These results unequivocally prove that L . donovani resides in a modified early compartment in human macrophages . | [
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| 2017 | Leishmania donovani resides in modified early endosomes by upregulating Rab5a expression via the downregulation of miR-494 |
It is well known that iron is an essential element for life but is toxic when in excess or in certain forms . Accordingly there are many diseases that result directly from either lack or excess of iron . Yet many molecular and physiological aspects of iron regulation have only been discovered recently and others are still elusive . There is still no good quantitative and dynamic description of iron absorption , distribution , storage and mobilization that agrees with the wide array of phenotypes presented in several iron-related diseases . The present work addresses this issue by developing a mathematical model of iron distribution in mice calibrated with ferrokinetic data and subsequently validated against data from mouse models of iron disorders , such as hemochromatosis , β-thalassemia , atransferrinemia and anemia of inflammation . To adequately fit the ferrokinetic data required inclusion of the following mechanisms: a ) transferrin-mediated iron delivery to tissues , b ) induction of hepcidin by transferrin-bound iron , c ) ferroportin-dependent iron export regulated by hepcidin , d ) erythropoietin regulation of erythropoiesis , and e ) liver uptake of NTBI . The utility of the model to simulate disease interventions was demonstrated by using it to investigate the outcome of different schedules of transferrin treatment in β-thalassemia .
Iron is an essential metal in living organisms , which is required as a co-factor for many proteins with roles in electron transport and oxygen binding . In mammals the majority of the body’s iron is used for oxygen transport in the form of hemoglobin in red blood cells ( RBC ) . Anemia arises whenever there is an impairment of functional hemoglobin , as is the case with low iron availability for RBC production , and results in debilitating low energy due to decreased oxygen delivery . Anemia can be caused by iron deficient diets or from dysregulation of the body’s iron distribution . Iron can also be extremely toxic if present in labile forms ( i . e . free or weakly bound ) as it can catalyze synthesis of hydroxyl radical , one of the most potent oxidants known , possibly the cause of iron being associated with a large number of apparently unrelated diseases , such as cardiovascular , neurodegenerative , cancer , and many others [1] . Thus mechanisms have evolved to control the total iron content in the body , its distribution across organs , and to limit its presence in labile forms . Some of these mechanisms have been known for decades , but some important aspects are only now being unraveled ( e . g . [2] ) . The present understanding of mammalian iron regulation reveals an intricate network containing many feedback loops [3 , 4] . Dietary iron is absorbed by duodenal enterocytes which release it to the blood where it binds to transferrin ( Tf ) , a transport protein with a very strong affinity for ferric iron at neutral pH . Transferrin-bound iron is delivered to cells through binding of this protein to specific receptors with subsequent internalization , acidification-driven iron release , and recycling of the apo-transferrin back to blood . In normal conditions the amount of plasma non-transferrin bound iron ( NTBI ) is extremely low . NTBI is a classification that includes all plasma iron that is not bound to transferrin [5]; this includes iron complexed to citrate , acetate , or sugars , and non-specifically bound to albumin . NTBI is considered toxic as part of its iron content is not fully coordinated ( labile iron [6] ) and is able to enter oxidation-reduction reactions , including catalysis of the Fenton reaction generating hydroxyl radicals . Iron is required by all tissues for its cofactor role in several enzymes , though quantitatively this is a very small proportion of the total iron in adult animals . The majority of absorbed iron is used for hemoglobin and circulates between erythroid precursor cells in the bone marrow , RBC , and macrophages in the spleen . When there is iron overload , either due to excessive dietary intake or as a consequence of disease , the excess iron accumulates predominantly in the liver . There , as in other tissues , most iron is stored inside ferritin nanoparticles , where it is less dangerous than if it was in labile form . Systemic iron regulation happens primarily through the control of iron export from cells , a process mediated solely by ferroportin [7] and regulated by the peptide hormone hepcidin [8] . Hepcidin inhibits ferroportin activity resulting in iron immobilization inside cells , and this also inhibits iron acquisition from the diet because it impedes enterocytes from delivering newly acquired iron to the plasma . Hepcidin secretion by the liver is transcriptionally stimulated by high plasma transferrin-bound iron and , independently , also by interleukin-6 . The latter mediates an important defense mechanism against microbial growth by reducing the circulating iron , trapping it in organs through maintenance of high levels of hepcidin . As the plasma iron increases , so does the concentration of hepcidin , and this forms a negative feedback loop on dietary iron acquisition . Mutations that impair sensing of plasma iron by the liver or the activity of hepcidin result in hemochromatosis , a disease characterized by very high levels of total body iron , with a strong accumulation in the liver . Untreated , hemochromatosis can lead to liver disease , cancer , and cardiovascular disease [9] . Chronic inflammation upregulates hepcidin production resulting in a type of anemia where , even though the total body iron is not low , its availability for production of new RBC is impaired [10] . In this type of anemia of inflammation , iron is trapped in the liver and spleen , and absorption of dietary iron is inhibited , resulting in inadequate iron supply to the bone marrow and consequently low levels of hemoglobin . Since one of the largest uses of iron in the body is in the production of hemoglobin it is relevant to consider the regulation of erythropoiesis . The hormone erythropoietin ( EPO ) , secreted by the kidneys , stimulates the proliferation of erythroid progenitor cells and therefore increases the consumption of iron by the bone marrow , and consequently its level in RBC . EPO is increased under hypoxia and abrogated when oxygen is plentiful . A recently discovered hormone , erythroferrone [2] , couples EPO to hepcidin signaling . Erythroferrone secretion by erythroid precursors is stimulated by elevated EPO levels , and erythroferrone inhibits the secretion of hepcidin–resulting in a negative regulation of hepcidin by EPO . EPO is elevated in conditions that require increased production of RBC , such as after hemorrhage or in anemia , resulting in increased erythroferrone and finally in suppression of hepcidin [11–13] , allowing iron to be mobilized from the liver and spleen to the plasma , and also increasing dietary iron absorption . This provides the required increased levels of iron for increased RBC production . Complex biological phenomena are difficult to understand due to the large number of molecular interactions that often have counter-intuitive outcomes . Their understanding requires a systems-level and quantitative analysis such as aided by computational models [14] . Computational models are therefore useful tools for understanding the mechanisms contributing to homeostasis and disease [15 , 16] . Iron regulation , as briefly described above , is sufficiently complex to warrant such an analysis . Several models have been previously developed that focus on various aspects of mouse iron homeostasis at the whole-body level [17–19] . However , these models are not capable of adequately explaining the iron distribution in conditions of iron overload with the same set of parameters as for iron deficiency . A recent model focused on chronic kidney disease [20] , while useful for the purpose intended , is not applicable to many other iron-related diseases ( and does not include the processes of iron absorption and excretion ) . Our previous work revealed two major problems with model predictions in high iron conditions: a ) an overestimation of iron levels in the RBC , and b ) did not predict iron accumulation in the liver [19] . However , data show that under dietary iron overload mice do accumulate the excess iron in the liver , not in the RBC . This suggested to us that the regulation by hepcidin alone is not sufficient and that other mechanisms play a role in regulating iron distribution in excess iron conditions . The present work presents an improved model of systemic mouse iron homeostasis that is capable of explaining iron distribution for a wide range of total body iron concentration . The results communicated here suggest that the regulation of iron distribution depends on the combined action of hepcidin and erythropoietin ( and implicitly also erythroferrone ) , with an additional important role of the direct import of NTBI into the liver . Since models can be deceiving , particularly when used for extrapolations , it is important to assess their validity by applying them to a domain different from that used to construct them [21 , 22] . We carried out validation of this new model by using it to successfully predict the phenotype of a number of iron disorders , several of which result in iron accumulation . This is a clear improvement over previous systemic iron distribution models . The results show that the model can be used to aid our understanding of the pathophysiology of various iron disorders and different dietary regimes . The model was also applied to predict the outcome of a proposed treatment of beta-thalassemia with transferrin injections , and the difference between intravenous and intragastric iron supplementation .
Hereditary hemochromatosis ( HH ) is a very common genetic defect among Caucasians that leads to iron overload . HH is caused by genetic disruption of the hepcidin-ferroportin regulatory mechanism . HH can originate from mutations in genes involved in the signaling pathway that induces hepcidin expression as a function of transferrin-bound iron , the most common being HFE [29] , but also TFR2 [30] and HJV [31] . Mutations in the hepcidin gene ( HAMP ) cause a rare type of juvenile hemochromatosis [32] . All of these mutations lead to low levels of hepcidin activity and therefore higher ferroportin activity , resulting in higher absorption of dietary iron through the duodenal enterocytes . The predominant phenotypic alteration in HH is high level of total body iron with accumulation in various tissues , but predominantly in the liver . The severity of iron overload increases in the order from HFE-/- to TFR2-/- to HJV-/- or HAMP-/- . Here we use the computational model to simulate hemochromatosis; this is achieved by setting the rate of synthesis of hepcidin ( ksHepcidin ) to zero . This is similar to an induced knockout of the HAMP gene in a normal mouse ( e . g . by action of a siRNA , or a drug that would block hepcidin activity ) . We compare the mutant phenotype with the control after 365 days ( Table 1 ) . With the HAMP mutation the model does not achieve a steady state and accumulated unlimited amount of iron in the liver compartment ( and consequently the total body iron ) , reflecting the large liver iron accumulation observed in untreated HH [33–35] . In the model transferrin is essentially fully saturated , which is in agreement with observations in animal models of this disease [33–35] , and the level of NTBI is significantly elevated [35] . Spleen iron is lower than normal , also seen in animal models [34 , 35] . No major change was observed in the RBC iron content despite the high plasma iron level , which is also in agreement with experimental observations [33] . In summary the model reflects all of the experimental observations of mouse models of hemochromatosis . Craven et al . [37] observed that the clearance of intravenously given 59Fe from plasma was much faster in animals that had their transferrin previously saturated with iron compared with controls with a normal level of transferrin saturation . Additionally , 59Fe accumulated mainly in the liver and pancreas when transferrin was fully saturated , otherwise it appeared primarily in the bone marrow , spleen , and RBC [35 , 37] . These experiments were simulated with the model by first bringing the transferrin saturation to near 100% through the addition of an appropriate amount of NTBI . This was then followed by an addition of radioactive tracer in the form NTBI* . The simulated tracer time course was then compared to a control simulation where the radioactive tracer is injected without first saturating transferrin . The simulation results ( Fig 3A and 3B ) show a faster clearance of the plasma 59Fe when transferrin is pre-saturated than without it , in line with the experimental results of Craven et al . [37] ( see their Fig 3 ) . The simulation also shows that the liver acquires a larger proportion of the tracer when transferrin is pre-saturated than in the control case , which is also in line with the experimental data [37] . We now investigate how the model behaves if the direct NTBI uptake by the liver is removed . Thus we simulate the experiments carried out by Jenkitkasemwong et al . [35] where they knocked out the gene for the ZIP14 transporter . They observed that the liver accumulation of 59Fe under conditions of saturated transferrin could be prevented by disruption of the ZIP14 transporter . They observed that the kidney and spleen acquired more iron in ZIP14-/- mice compared to controls when transferrin was saturated . This behavior can indeed be reproduced with the present model by setting the rate parameter for liver uptake of NTBI to zero ( simulating ZIP14-/- ) . Fig 3C shows that liver iron accumulation is essentially eliminated , and indeed we also observed a higher iron accumulation in the spleen and “rest of body” compartments . There was no effect of removing NTBI uptake on the transferrin-bound 59Fe distribution in agreement with the experiments . Jenkitkasemwong et al . also investigated the effect of the ZIP14 knockout in mouse models of HH ( HFE-/-:ZIP14-/- and HJV-/-:ZIP14-/- ) which greatly reduced the accumulation of iron in the liver [35] . To reproduce this result with the model , we set the rate of liver uptake of NTBI to zero ( ZIP-/- ) while simultaneously setting the rate of hepcidin synthesis to zero ( HAMP-/- , causing HH ) . The results in Table 2 show that this prevented iron accumulation in the liver , consistent with the experimental observations [35] . However , the model did not reproduce the significant iron accumulation in the spleen and the “rest of body” ( which is where the model includes kidney , heart and other organs ) as was observed in the experiments . Instead the model showed a very large increase of NTBI . This is because in the present model we have not included transport of NTBI into any other organ , and thus all the excess iron stays in the plasma . Together with the experimental results , this suggests that there are likely also other transporters that allow NTBI uptake in other organs [38] . β-thalassemia is a hereditary disease caused by reduced or absent production of hemoglobin’s β-globin chain . This results in a defective erythropoiesis where α-globin tetramers precipitate and cause oxidative membrane damage leading to apoptosis of a large proportion of RBC precursor cells [43] . It also results in shorter lifespan of mature RBC due to their increased catabolism by splenic macrophages [44–46] . Partial loss of β-globin synthesis results in a milder form of the disease known as thalassemia intermedia while its complete loss results in an extreme form , thalassemia major . The phenotype includes reduced hemoglobin level and RBC count [47–49] , tissue iron overload especially in the liver and spleen [48 , 50] , increased plasma iron and transferrin saturation [48 , 50 , 51] , decreased hepcidin level [50 , 52] and increased EPO level [52] . While the present computational model does not represent hemoglobin explicitly , it is still possible to use it to simulate the phenotype of thalassemic mouse models in terms of the iron distribution . To achieve this the computational model was modified to have a reduced rate of iron transfer from plasma to the bone marrow ( parameter kInBM ) , representing the impaired erythropoiesis . In addition the rate of transfer of iron from RBC to spleen ( kRBCspleen ) was increased , representing the increased destruction of RBC by spleenic macrophages with resulting shorter RBC lifespan . The parameter kRBCspleen was increased four-fold given the experimental observations of Gelderman et al . [52] that found the RBC half-life four-fold lower in thalassemic mice compared to wild type . By modulating the degree of change in kInBM ( rate constant for transfer of iron from transferrin to bone marrow ) the model could simulate thalassemia intermedia and thalassemia major ( Fig 5 ) . This parameter change reflects the efficiency of erythropoiesis in the model , and thus by decreasing it the model is able to reproduce the main features of the thalassemia phenotypes . Fig 5 depicts the changes in phenotype as this parameter is reduced . At around 60% of kInBM’s original value there is a qualitative change in the phenotype , suggesting that this may reflect the transition between the two types of thalassemia; this qualitative change seems to depend on the full saturation of transferrin ( Fig 5A ) . For a small decrease of paramater kInBM , e . g . 80% of its original value corresponding to mild impaired erythropoiesis , the model shows iron accumulation in the spleen but not in the liver . This pattern of iron accumulation is in good agreement with experimental data pertaining to the thalassemia intermedia mouse model ( th3/+ ) [48] . The trends in other variables , such as increased NTBI , Tf saturation , and EPO and lower hepcidin are also in good agreement with experimental observations in a thalassemia mouse model [48 , 50 , 52] . By further reducing the parameter kInBM , e . g . down to 50% of its original value , the model predicts high iron accumulation in the liver however the spleen iron is back to wild type levels . This is also supported by experimental observations in the thalassemia major mouse model ( th3/th3tp ) at 2 months [48] . Other marked changes are a large increase of NTBI and EPO , and a severe reduction of hepcidin , which are also characteristic of the thalassemia major phenotype [48] . The simulation suggests that the most important qualitative change between thalassemia minor and major is the full saturation of transferrin , leading to a complete dysregulation of iron homeostasis with extremely high NTBI levels and very low hepcidin . The model is able to simulate these qualitative changes even though the change in the efficiency of erythropoiesis decreases gradually from 80% to 50% of wild type level ( Fig 5A ) . Note that normally high transferrin saturation would tend to induce hepcidin secretion and this would control the iron level , but the inhibitory effect of EPO on hepcidin ( through erythroferrone ) overcomes the transferrin-bound iron induction leading to a very low hepcidin level in the thalassemia major phenotype . This in turn increases iron absorption , which further increases NTBI and liver iron accumulation . An important role for computational models is that they allow us to examine the individual contribution of each parameter that may be difficult to elicit independently in experiments . The etiology of β-thalassemia includes two associated parameter changes that are not easily separated in experiments: the degree of erythropoiesis impairment and the extent of RBC lifspan shortnening ( both are caused by the excess α-globin in erythroid cells ) . This , however , is easy to analyze with the model , where each of these perturbations can be applied independently . Thus , Fig 6 shows the independent role of each of these two contributions , as embodied by the kInBM and kRBCSpleen model parameters . Interestingly it becomes apparent that the RBC lifespan ( kRBCSpleen ) alone is not able to push the phenotype into the thalassemia major domain ( as judged by 100% Tf saturation ) . On the other hand it is always possible to push the phenotype into that range by decreasing the efficiency of erythropoiesis ( kInBM ) . This suggests that the inefficient erythropoiesis is a more determinant cause of the disease than the RBC half-life . Transferrin treatment has been shown to reduce ineffective erythropoiesis , improve hemoglobin level , elevate hepcidin expression , and normalize the iron content of liver and spleen in β-thalassemic mice [52 , 53] . We investigated if the computational model could simulate the effect of this treatment accurately . For this purpose , we simulated the experimental protocol where thalessemic and wild type mice were injected with 10 mg of transferrin daily for 60 days [52 , 53]; accordingly the simulation adds 10 mg apotransferrin ( equivalent to increasing the Tf plasma concentration by 102 μM ) for the same period of 60 days . In the model this periodic Tf treatment applied to the “wild type” model did not affect the iron content of most compartments , which is consistent with the experimental results of Li et al . [53] , the only significant changes observed were a decrease in [NTBI] ( about 10x ) with an associated decrease in transferrin saturation ( down to 10% ) . Then we simulated applying the same treatment to a thalassemia major version of the model ( kInBM at 25% and kRBCSpleen at 400% their WT values , see previous section ) and this resulted in higher iron level in RBC , lower [NTBI] , a normalized Tf saturation , normalized liver iron ( i . e . similar to WT ) as well as lower total body iron , compared to the values before treatment . Additionally the concentration of EPO decreased while that of hepcidin increased . All these indicators become closer to their values in the wild type , demonstrating that the model predicts a good response from transferrin treatment as did the experiments [52 , 53] . These results are depicted in Fig 7 , where it is also evident that these beneficial effects only last while the transferrin treatment is applied ( shaded area ) ; once treatment ceases those variables go back to their diseased levels , suggesting that to be effective this treatment would likely need to be life-long . The only major discrepancy between the simulation and in vivo observations is that in the former the spleen iron content increases during the transferrin treatment ( Fig 7F ) while in vivo this has been observed to decrease [52 , 53] . This increase in the model is likely caused by the increase in hepcidin concentration ( compare Fig 7F and 7G ) causing retention of iron in the macrophages . On the other hand , the liver , which is similarly responsive to hepcidin , does not increase its iron level , but rather has it reduced to wild type levels during the treatment ( as also seen in the experiments ) . Analysis of the model strongly suggests that this reduction in the liver is due to to a much lower influx of NTBI through ZIP14 caused by the drastic fall of [NTBI] ( Fig 7C ) . The discrepancy between the simulation and experiments relative to spleen iron is likely due to the fact that in vivo the RBC half-life increases with the transferrin treatment [53] . This has an effect on the spleen iron level opposite to that of the hepcidin increase , the former likely overcoming the latter . Li et al . [53] attribute the increased RBC half-life to a decrease in α-globin , but since the current model has no representation of hemoglobin it cannot reproduce this effect , other than by changing the value of the kRBCSpleen parameter , which directly determines the RBC half-life . Apart from this discrepancy on spleen iron , every other aspect of iron distribution in this proposed thalassemia treatment with transferrin injections is replicated in the model . Finally the model also shows that while daily injections of Tf are very effective ( Fig 7 ) , lower frequencies of injection may be sufficient to increase the RBC iron level to a similar extent . However , these lower frequencies will lead to oscillatory patterns in the other variables , driven by the half-life of transferrin ( S6 Fig ) . When the injections are applied daily , these oscillations are only really detectable in the total amount of transferrin and NTBI ( Fig 7A and 7B ) Kautz et al . [11] showed that an ablation of expression of erythroferrone in thalassemic mice restored the hepcidin level and improved iron overload , however it did not improve the hemoglobin level . In our present model erythroferrone is not represented explicitly but its role is included as an inhibition of hepcidin synthesis by EPO ( see Methods , Eq 6 ) . Simulation of a decreased expression of erythroferrone can be achieved by weakening that inhibition effect ( increasing the inhibition constant KEPOhepcidin ) . Thus we compared the model variables when this inhibition has been lowered in the thalassemia simulation . Fig 8 depicts a comparison between the simulation results of the wild type , thalassemia major , and thalassemia major with decreased EPO inhibition of hepcidin . The simulation behaved essentially like the experimental observations , except what concerns the spleen iron level . As in the experiments , decreasing the action of erythroferrone in the simulation causes a normalization of liver iron , NTBI , transferrin saturation and hepcidin levels , and it does not improve the iron content of RBC ( or bone marrow ) . However , contrary to the experiments the model increased the spleen iron content , which is similar to the observations of the previous section , pointing to a part of the computational model that could be improved . Since the hepcidin level was restored from a low value , this model increase of spleen iron is due to the consequent ferroportin blocking effect; since this effect is not observed in vivo it suggests that the spleen iron traffic is likely more complicated than represented in the model ( where iron can only enter through transferrin or erythrophagocytosis ) . Hypotransferrinemia is a rare iron overload anemia due transferrin deficiency resulting from mutations in the transferrin gene ( TF ) . Key phenotypic changes include substantial iron overload in non-erythropoietic organs , diminished hepcidin levels , and severe iron deficiency anemia [54 , 55] . This phenotype is very similar between humans and the mouse model [55] . As a further validation of the computational model , we simulate hypotransferrinemia by reducing Tf concentration to 2% of the normal level ( achieved by reducing its rate of synthesis ) . Fig 9 shows the differences between wild type model and the model of the hypotransferrinemia ( Hpx ) . In agreement with the experimental mouse data [54 , 56] the Hpx simulation displays reduced iron in the RBC and spleen , decreased hepcidin , and significant accumulation of iron in the liver relative to the WT simulation . Bartnikas et al . [56] proposed that the reduced hepcidin level in Hpx mice was associated with two signals , one from the loss of Tf-mediated activating signal and the other is the inhibitory signal from erythroid regulators . To determine which signal is dominant in suppressing hepcidin , we also created a version of the computational model where in addition to the reduced Tf we removed the ( erythroferrone-mediated ) negative feedback of EPO on hepcidin ( denominated Hpx ERFE-/- ) . This resulted in a two-fold increase in the hepcidin level , though still far from the WT level but this did not affect the liver iron accumulation nor the other effects of the Hpx mutation . Thus our simulation results indicate that the signal from erythroid regulators ( erythroferrone ) does not have an influence in the observed phenotype and the major effect is transmitted through abrogated Tf-mediated signaling . We also wanted to follow the dynamics of plasma iron in the Hpx mutation . This was achieved through a simulation of an injection of a bolus of 59Fe tracer , following its distribution through 365 days . Fig 10 displays the simulations obtained with the Hpx and wild type models . In Hpx the majority of the tracer goes to the liver and is retained there for the entire period of time , while in the wild type only a small proportion of tracer appears in the liver and rapidly decreases . In the wild type a large proportion of the tracer quickly enters the RBC ( circa 60% ) , while in the Hpx it only reaches 20% . Overall our model is able to qualitatively simulate the Hpx phenotype and displays an appropriate organ distribution of radioactive iron under this condition . This provides further validation of the present model . Kim et al . [57] investigated the differences between uptake and distribution of iron when administered through intragastric ( IG ) and intravenous ( IV ) routes . They observed that when 59Fe was administered by IG route , the amount of iron accumulated in the blood of hemochromatosis ( HFE-/- ) mice was significantly higher compared to that of wild type mice , while the opposite was observed when 59Fe was given by IV route . The present model was also used to simulate these experiments successfully . In this case the simulation of hemochromatosis is achieved by silencing hepcidin synthesis at the transcription level ( HAMP-/- ) instead of the HFE-/- mutation . The model simulations behave in the same way as the experimental results , also showing that IG administration results in higher blood iron accumulation in HAMP-/- than in WT; while the opposite pattern is observed for the IV route ( see Fig 11A and 11B and compare with Fig 2 in [57] ) . The measurements reported by Kim et al . [57] were for total blood radioactivity , which includes NTBI , transferrin-bound iron , and RBC iron , and this does not easily allow for a clear explanation of the phenomenon , but those authors suggested that it could be due to an increased clearance of NTBI from the plasma in hemochromatosis , compared with the wild type [57] . This is where a model can be uniquely useful to aid in the interpretation of results since in the model all variables are readily available and it is possible to carefully inspect the fate of the tracer . Fig 11C and 11D depicts the simulated dynamics of the administered 59Fe in the first four hours in the three components of blood ( NTBI , Tf , and RBC ) , as well as the liver compartment . It is clear that the major difference is that in the HAMP-/- the liver is rapidly accumulating a large proportion of the tracer . This explains the differences observed in the case of IV administration: in HAMP-/- the tracer is moving from NTBI to the liver at a fast rate , leaving less tracer in the blood than in the WT . But this is not the reason why there is more blood iron in the HAMP-/- when the tracer is administered through IG . To explain this we have to look at the duodenal rate of absorption of the tracer ( i . e . the flux of tracer from the duodenum into the plasma ) . Fortunately the model also readily provides the fluxes ( S7 Fig ) , and from those data it becomes clear that HAMP-/- absorbs the tracer at a much higher rate than the wild type , and that flux is higher than the flux of tracer binding to transferrin . This stems from the absence of hepcidin , which results in a higher activity of duodenal ferroportin and consequently higher iron absorption . While the simulation differs slightly from the experiment , by causing hemochromatosis through mutation in the HAMP gene ( hemochromatosis type 2B ) rather than through the HFE gene ( hemochromatosis type 1 ) , the results are qualitatively similar to the experiments . The major difference between the two is the complete absence of hepcidin in the model versus a low level of hepcidin level in the experiment , though clearly both result in a similar phenotype . This example illustrates the benefits of using computational modeling and simulation with a validated model to aid the interpretation of experimental results .
A mathematical model that integrates our current knowledge on iron metabolism would provide valuable information to better understand its homeostatic regulation , and it could be an aid to experimental design and interpretation of results . Previously , we developed such a model centered on hepcidin regulation that successfully reproduced iron dynamics under iron-adequate and iron-deficient diets but not the dynamics under an iron-rich diet [19] , failing to reproduce iron accumulation in the liver and overestimating RBC iron under iron-rich conditions . While that model could simulate anemia of chronic disease , it was unable to reproduce the phenotype of hereditary hemochromatosis appropriately . The results from that earlier model indicated that hepcidin regulation alone was not sufficient to explain phenotypes under high iron conditions , and suggested that other mechanisms must play a significant role . Another recent modeling study on iron regulation under inflammation [18] also fails to explain high iron conditions and overestimates RBC iron in the simulation of hemochromatosis , unlike well-established experimental results that show RBC iron to be essentially unaltered in that condition [33] . Here , we presented a new mathematical model that can accurately explain the iron distribution under low and high iron conditions . The model required the addition of two important mechanisms: 1 ) NTBI uptake by the liver , and 2 ) the regulation of erythropoiesis by erythropoietin . First , we calibrated the model using the same ferrokinetic time-series data [17 , 23] used to calibrate previous models [18 , 19] . Then the model was validated by simulating several mouse experiments related to pathological conditions that affect iron dynamics and regulation . This included hemochromatosis , β-thalassemia , anemia of inflammation , and hypotransferrinemia . Importantly these simulations did not attempt to fit parameter values to the data , they simply replicated an independent set of experiments still using the same parameter set . This extensive validation provides strong confidence in the predictive powers of the model . One aspect where computational models can be very helpful is in asking certain what if ? type questions that are impossible to execute experimentally . Here we used the model to investigate the relative contribution of the two major mechanisms causing the β-thalassemia pathophysiology: inefficient erythropoiesis and lower RBC half-life . Experimentally these are both consequences of an unbalanced composition of hemoglobin types and are not easily separated . In the model they can be manipulated independently and doing so suggested that the inefficiency of erythropoiesis is the largest contributor to the pathophysiology . Lower RBC half-life alone would never lead to the observations typical of thalassemia major , but impaired erythropoiesis could . We believe that this use of the model to better understand the underlying causative biology is perhaps its most important role , functioning as a kind of “executable” and quantitative review of the underlying biology . The application of the model to simulate transferrin treatment in thalassemia provides an example of how such a model could eventually be used to plan and optimize therapies and interventions . The model was used to investigate the effect of the frequency of Tf injections from daily to every four days; the results in S6 Fig show that injections every other day would have almost the same effect as daily injections . In terms of just decreasing the liver iron content , even injections every four days would have a similar efficiency . The present model can then be useful to help design certain mouse experiments . Obviously , for therapeutic use the model would have to be adapted and validated with human data . If such a human model could also be calibrated for specific subjects it would then become a useful tool for personalized medicine . The model replicated the experimental observation that when Tf saturation is high the organ distribution of injected iron is different than when it is lower ( Fig 3 ) . Future human personalized models would allow one to design better interventions for anemic patients where the amount of injected iron would not exceed that person’s transferrin capacity ( calculated from the model ) , maybe allowing for an optimized multiple injection schedule . This would maximize the transfer of the injected iron to the erythropoietic system instead of its accumulation in the liver or other organs causing side effects . The strategy behind the development of this model is that it ought be as simple as possible to explain the iron distribution in the whole body focusing on the most relevant compartments . This meant selecting the important compartments: liver , bone marrow , spleen , duodenum and all components of blood , namely red blood cells , transferrin-bound iron and non-transferrin bound iron . The rest of the body is still accounted for in quantitative terms , but aggregated in a single compartment . Further models could benefit from increased compartmental resolution ( e . g . separating the brain , pancreas or kidneys from the “rest of body” compartment ) . Another important simplifying criterion was to limit intracellular regulation mechanisms to a minimum . Thus the iron regulatory protein-iron regulatory elements system ( IRP-IRE , [58] ) was not included here , nor was there a distinction made between different intracellular iron states ( labile iron pool , ferritin-bound , enzyme-bound , etc . ) Interestingly , the model is still able to largely follow iron distribution dynamics at the organ level , thus showing that those intracellular molecular details may not be so important at the whole body level–a finding that is perhaps surprising . But some molecular details did have to be included: a saturable ferroportin-mediated export of iron and its inhibition by hepcidin [7] , and liver import of NTBI through Zip14 [35 , 59] revealed to be crucial molecular details that impact whole-body iron distribution . The case of Zip14 is well illustrated in the result sections on hemochromatosis , thalassemia , and the differential dynamics of iron upon intravenous injection or intragastric gavage . We had previously ignored the effect of erythropoietin regulation [19] , but this erythropoiesis regulatory system also proved to be essential to explain the physiology of high iron conditions . While the model does not explicitly consider the recently discovered hormone erythroferrone [2] , its action is included as an inhibition term between the level of EPO and the synthesis of hepcidin ( see Methods , Eq 6 ) . The extensive validation passed by this model gives a very good level of confidence in its predictive powers , but it is also important to discuss its limitations . When applying the model to conditions where NTBI import to the liver becomes significant , we noted that the model would not reach a steady state , instead accumulating unlimited amounts of iron in the liver . A steady state would likely be reached if the model included ferritin and its regulation by the IRP system . On the other hand , it is unknown how much iron can be accumulated in the liver . It so happens that the most common complication from untreated hemochromatosis is liver disease caused by severe iron accumulation , so perhaps this lack of a steady state is not so problematic and does reflect the pathology . The model only considers direct import of NTBI to the liver , however there is evidence that this happens also for other organs like the heart and pancreas [35 , 37] . Finally , the model does not fit the spleen data very well ( see S3 and S4 Figs ) and some of the validation were also less successful for the spleen than for the other organs . This is likely due to the fact that the model does not consider the erythropoietic function of the spleen , nor does it consider that , in addition to spleen macrophages , the RBC are also degraded by liver Kupffer cells . To overcome this would require adding more detail in these two organs , leading to a multi-scale model that considers different cell types in each organ . Note , however , that this deficiency is quantitatively small . Given the strong emphasis on the ability of this model to reproduce high iron conditions , it is perhaps opportune to speculate about the role of the liver in taking up iron . While developing the model to support high iron conditions it became obvious to us the direct import of NTBI into the liver was required in high plasma iron concentrations but should not operate under lower plasma iron concentrations . This means that the liver only takes in NTBI when it is in excess in the plasma , and this has been confirmed experimentally [35] and replicated by the model ( Fig 3 ) . An interesting question is what mechanism leads to an increased activity of Zip14 in high iron conditions ? In the model this was simplified by adopting a kinetic rate law for Zip14 with substrate activation , thus its activity only becomes significant at high substrate concentration ( [NTBI] ) . But in reality Zip14 is known to be induced by iron overload [60] , but it is not clear how . The mechanism must depend either on NTBI or transferrin-bound iron because the Hpx mice lacking transferrin accumulate large amounts of iron in the liver [54 , 56] , supposedly due to high expression level of Zip14 . So what is the pathway for Zip14 induction under iron overload ? What is the signal ? What is the receptor ? These are interesting questions that ultimately need be addressed with new experiments but the present model could also contribute by allowing to test hypotheses prior to experimentation . In summary we presented a whole-body model of iron dynamics for the mouse that is consistent with a wide range of dietary iron intake . The model was subsequently validated with a series of independent experiments showing that it behaves very closely to the majority of their results . We demonstrate how the model can be applied to better elucidate the operation of mechanisms , applying it to the comparative contribution of erythropoiesis and RBC half-life to thalassemia symptoms . The model was also used to show how it could have a role in planning interventions ( transferrin treatment in thalassemia ) . We discussed aspects of the model that can still be improved and speculate on how a human specific model could have a role in personalized medicine .
The differential equation-based model was constructed based on a previous version [19] with important modifications as described below . Full details of the model are included in the supplementary information: equations , parameter values ( S1 Table ) , and initial conditions ( S2 Table ) . A sketch of the model is presented in Fig 12 , while a complete diagram of the reaction network using the SBGN standard [63] is depicted in S1 Fig ( constructed with the aid of CellDesigner [64] ) . Simulation files in the COPASI format and in the SBML standard [65] are also included in the supplementary information . Two versions of the model ( with and without radioactive iron tracer species ) have been submitted to the BioModels database [66] where they are available with identifiers MODEL1805140002 and MODEL1805140003 . Following our previous work , we estimate parameters based on radioactive iron tracer data with a version of the model where each reaction or transport step can happen with radioactive or non-radioactive iron species; all kinetic constants are assumed to be the same irrespective of iron isotope . The model parameters were adjusted simultaneously to match data from three tracer experiments with mice subjected to high , low , and adequate iron diets . Since the tracer data are only important for estimation of parameter values , the model version with radioactive species was only used for parameter estimation; model validation and other results were obtained with the version without tracer , except where noted . The model considers seven compartments with fixed volumes ( determined as in [19] ) , namely: duodenum , plasma , liver , bone marrow , red blood cells ( RBC ) , spleen , and rest of body ( accounting for the remaining parts of a mouse body ) . The iron content inside each of the organs is considered with a single variable , thus the model does not distinguish between labile pools and ferritin-bound iron in the organs . The only exception is the plasma compartment where transferrin-bound and NTBI are both considered explicitly . Below we discuss the specific new features that were crucial to making the present model pass a wide array of validation data . To estimate the values of the parameters , the model was set up to simulate the same protocol followed in the ferrokinetic experiments that generated the calibration data [17 , 23] . Briefly these consisted of three mice groups which were put on different iron diets ( iron-deficient , adequate , and iron-rich ) for 5 weeks , then injected with a tracer and followed for a 28 day time course , with the radioactive tracer quantified in all their organs . To replicate this protocol the simulation happened in two phases: 1 ) phase one starts by setting the value of the vDiet parameter to simulate the three iron diets; this phase lasts 5 weeks as in the experiments . 2 ) The second phase starts with the addition of a bolus of radioactive iron ( setting the total radioactivity in the variable NTBI* with a discrete event ) and is simulated for another 28 days . It is only in phase two that the tracer data is compared between simulation and experiment and their distance minimized using least squares . The regression was carried out by minimization of the sum of squares of the residuals using COPASI’s parameter estimation task . This was effected first by application of a global optimizer ( SRES [67] ) followed by a local optimizer [68] . The values of kinetic constants obtained from this procedure are those that best fit the tracer time courses for all three mice groups simultaneously ( adequate , deficient , and rich diets ) . To ensure that the initial conditions of the model correspond to a steady state ( as the mice are expected to have been in a quasi steady state ) , the model includes specific constraints consisting of algebraic expressions relating parameter values and the initial concentrations that were derived from the right-hand side of the set of ODEs . The calibrated model is then put to test against a variety of experiments carried out with mutant or diseased mice previously reported in the literature . These simulations are carried out with the calibrated parameter set , and no more fitting is carried out . Some parameter values are changed corresponding to the specific mutations or their known physiological outcomes , and these are indicated in the text in each specific case . Therefore the model is being tested against independent experiments that form a robust validation procedure demonstrating the wide applicability of the model [21] and a low degree of overfitting . The simulation protocols are specific for each case and are described together with the results . | Iron is an essential nutrient in almost all life forms . In humans and animals iron is used for respiration and for transporting oxygen inside red blood cells . But in excess iron can be toxic and therefore the body regulates its distribution and absortion through the action of hormones , which is not yet completely understood . Here we created a computational model of the regulation of iron distribution in the body of a mouse based on experimental data . The model can accurately simulate many iron diseases such as anemia , hemochromatosis , and thalassemia . This computational model is helpful to understand the basis of these diseases and plan therapies to address them . | [
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| 2019 | A computational model to understand mouse iron physiology and disease |
Dengue is an arthropod-borne virus of great public health importance , and control of its mosquito vectors is currently the only available method for prevention . Previous research has suggested that insecticide treated curtains ( ITCs ) can lower dengue vector infestations in houses . This observational study investigated individual and household-level socio-demographic factors associated with correct and consistent use of ITCs in Iquitos , Peru . A baseline knowledge , attitudes , and practices ( KAP ) survey was administered to 1 , 333 study participants , and ITCs were then distributed to 593 households as part of a cluster-randomized trial . Follow up KAP surveys and ITC-monitoring checklists were conducted at 9 , 18 , and 27 months post-ITC distribution . At 9 months post-distribution , almost 70% of ITCs were hanging properly ( e . g . hanging fully extended or tied up ) , particularly those hung on walls compared to other locations . Proper ITC hanging dropped at 18 months to 45 . 7% . The odds of hanging ITCs correctly and consistently were significantly greater among those participants who were housewives , knew three or more correct symptoms of dengue and at least one correct treatment for dengue , knew a relative or close friend who had had dengue , had children sleeping under a mosquito net , or perceived a change in the amount of mosquitoes in the home . Additionally , the odds of recommending ITCs in the future were significantly greater among those who perceived a change in the amount of mosquitoes in the home ( e . g . perceived the ITCs to be effective ) . Despite various challenges associated with the sustained effectiveness of the selected ITCs , almost half of the ITCs were still hanging at 18 months , suggesting a feasible vector control strategy for sustained community use .
Dengue viruses , transmitted primarily by the diurnal-biting mosquito , Aedes aegypti , are the cause of more human morbidity and mortality than any other arthropod-borne virus , with an estimated 96 million apparent infections and an additional 294 million inapparent infections occurring globally in 2010 [1] . The Americas are one of the most affected areas in the world , and in 2013 had the largest number of cases ever reported in that region—2 . 3 million—of which 37 , 692 were severe and 1 , 208 resulted in death [2] Peru bears a substantial portion of the disease burden in the Americas , with 11 , 816 cases and 16 deaths reported in 2013 [3] . In particular , the Department of Loreto , where this study was conducted , experienced nearly a third of the country’s cases in 2013 , with 3 , 542 cases reported [3] . While significant advances are underway in dengue vaccine development , vector control remains the only current option for prevention of dengue infection [4 , 5] , and it is likely to remain necessary once vaccines become available both in the context of integrated dengue control programs and to prevent other Aedes aegypti transmitted infections such as the rapidly increasing chikungunya virus . Vector control strategies most commonly target the immature stages of the mosquito , as Ae . aegypti preferentially breed in containers in close proximity to human habitations . Container control interventions at most reduce vector density and do not impact adult mosquito longevity , which limits their potential to reduce dengue transmission . Interventions targeting the adult stage of the mosquito are often only used in outbreak situations due to their limited efficacy and residuality , as well as their comparatively labor intensive requirements [6–9] . For this reason , long-lasting interventions targeting the adult stage of the mosquito are attractive , and the use of insecticide-treated curtains ( ITCs ) offers one promising household-level strategy to achieve this . The use of ITCs combined with insecticide-treated container covers has been demonstrated to decrease significantly both the Breteau Index ( BI ) ( number of positive containers/100 households ) and pupae per person index ( PPI ) , with evidence that a protective spillover effect from the intervention occurs in neighboring untreated areas [10–15] . The effectiveness of any intervention relies on more than just its availability . Successful implementation requires full acceptance by key community leaders and members , as well as correct and consistent use of the intervention [16–18] . Numerous studies have been conducted about the use of insecticide-treated materials ( ITMs ) as vector control tools for malaria prevention [19–27] , but the use ITCs for dengue prevention has been evaluated in only a few contexts . Those studies reported high acceptability of ITCs [13 , 15] , particularly among those families who had previous experience with dengue infection [13] and who perceived the ITCs to be effective [28] . Continued ITC use was reported to be higher among families who had been resident in their home for more than five years and families that had previously used decorative curtains in their homes [28] . An attractive aspect of using ITCs to suppress dengue vector populations is that they comprise a largely passive intervention: the ITCs simply hang inside a house and beyond acceptance of their presence they require minimal behavior change on the part of the householders . Understanding the socio-demographic factors and knowledge , attitudes and practices associated with correct and consistent ITC use can shed light on how to maximize the effectiveness of future ITC intervention programs . A systematic review of the effectiveness of 21 dengue fever prevention programs from around the world revealed that an understanding of the link between human behavior and correct and effective vector control programs was missing [16] . Additionally , a more recent study focusing on the individual determinants of insecticide-treated bednets use—a similar vector control strategy—identified a gap in understanding which behavioral mechanisms and educational tools were associated with successful ITC program implementation [27] . This study aims to fill these gaps with respect to the effectiveness of ITC interventions for dengue control . As part of a cluster-randomized trial examining whether ITCs could reduce dengue transmission , the objective of this study was to determine the individual and household-level socio-demographic factors that are associated with the correct and consistent use of ITCs in Iquitos , Peru . Correct and consistent use was defined for this study as 1 ) ITCs observed as hanging properly—loosely and extended—at the time of visit , 2 ) ITCs observed to be tied up , but still in their place , at the time of visit , and 3 ) ITCs reported as washed correctly–with only water and/or mild soap and hung to dry in the shade .
This observational study was conducted in an urban part of the San Juan district population ~102 , 000 ) of Iquitos , Peru ( population ~430 , 000 ) —a large , geographically isolated city in the middle of the Amazon rainforest , accessible only by boat or plane [29] . Dengue epidemiology has been extensively studied in Iquitos since 1999 by the University of California at Davis/U . S . Naval Medical Research Unit 6-Iquitos group [30–39] . This study is part of a larger cluster-randomized controlled trial , initiated in October 2009 , to measure whether ITCs can reduce dengue transmission and dengue vector activity in 10 treatment clusters compared to 10 control clusters of approximately 70 households each ( 2–3 city blocks ) . ITCs were an official WHO-recognized brand of long-lasting insecticide ( deltamethrin ) treated material . Based on formative research conducted prior to the study , we estimated that each household would request approximately 5 ITCs , but participants could request as many as they wanted . There were three colors to choose from–pink , sky blue and dark blue–and the ITCs were lacey , since there was consensus at the formative stage that this is what individuals preferred . Study staff hung the ITCs in locations suggested by householders , which were documented ( i . e . , doorways , walls , windows , as room dividers ) . A member of the study team distributed an information sheet about the ITCs and discussed their correct use ( i . e . , hanging loosely ) and upkeep ( i . e . , not washed with bleach , kept out of direct sunlight when being dried , mend holes if holes form ) with the householders . Due to an unexpected problem with ITC losing bioefficacy after 3–6 months , the research team removed all ITCs from houses 12 months after distribution and re-treated them with deltamethrin , and then returned them to their locations in the houses . One month prior to ITC distribution , a baseline survey was administered to 1 , 333 individuals in intervention and control households , assessing knowledge , attitudes , and practices ( KAP ) associated with dengue and mosquito control [40] . A total of 3 , 178 ITCs were then distributed to the 593 intervention households between November and December 2009 , with an additional 1 , 049 ITCs distributed to the intervention homes that requested more 9 months later . Follow up KAP surveys were conducted at 9 and 27 months post-ITC distribution with the same household respondent , when questions regarding barriers and motivators to regular ITC use were included ( although there were no KAP related interventions , we expected possible knowledge improvement from the regular presence of our research team in the community monitoring ITCs and answering questions ) . ITC-monitoring checklists were also conducted at 9 and 18 months post-ITC distribution , when “correct and consistent use” of the ITCs , as has been defined for this study , was verified by direct observation by a 7-membered research team . If individuals wanted more ITCs for their homes at the 18 months , more ITCs were distributed at that time . Additionally , focus group discussions with a small sub-sample were conducted at 6 and 12 months to understand more about the participants’ experience using the ITCs–findings from the focus groups are being published separately . For the KAP surveys and ITC-monitoring checklists , we sought to interview the same individual at the home who was responsible for the ITCs–which was determined by asking who makes the decisions in the home ( most commonly the person who managed all aspects of the household , e . g . housewife ) . If the person identified as responsible for the ITCs was not at home during follow-up visits , additional visits were made in the morning , afternoon , and evening hours a minimum of 3 times and a maximum of 8 times . If repeated visits were unsuccessful in locating the person responsible , then the home was removed from the analysis . All tables report whether data came at 9 , 18 or 27 months , unless it was socio-demographic and dengue-related knowledge or preventive practices which all come from the baseline KAP . The hanging , condition and upkeep status of the ITCs was observed at 9 and 18 months after distribution using a monitoring checklist based on direct observation . To assess hanging status , our trained research team members directly observed , for each ITC , whether it was: 1 ) hanging correctly , 2 ) hanging in place but tied up , 3 ) being washed , 4 ) being stored , 5 ) other location , or 6 ) missing ( see Table 1 for a full list of variables and their definitions ) . The condition of the ITC was also directly observed by our team , and recorded as excellent , good , poor , or missing . To ensure consistency in the responses recorded by our research team members , during training the team practiced recording their observations about ITCs in different conditions until their reports were consistent with each other . To assess the upkeep status , the householder was asked how the ITC had been washed , and who had washed it , as well as whether it had required mending–and if so , it was checked for having been mended by our research team . Other relevant variables and their definitions are also included in Table 1 . STATA 11 . 0 was used to estimate the means ( for continuous variables ) and frequencies ( if categorical ) of social , economic , and demographic variables , as well as the main variables of interest ( hanging , condition and upkeep status ) . Principal component analysis ( PCA ) was used to create the socioeconomic status ( SES ) variable , where 50% above the mean was defined as higher SES and less than or equal to 50% was defined as lower SES . Components of the PCA included: exterior wall material , interior wall material , roof material , floor material , window material , cooking material , has fixed telephone line , has electricity , and number of refrigerators , televisions , DVD , computer , radio , washing machine , cars , rooms in the home . Because each home had more than one ITC , the dependent variables of interest—ITCs hanging correctly , tying ITCs up , and washing ITCs correctly—were made dichotomous by classifying those homes with 50 percent or more ITCs hanging , tied up or washed correctly as ‘correct’ , whereas those with 49 percent or less were classified as ‘not correct’ . Chi-square tests were conducted to determine whether differences observed in the categorical variables of knowledge , perception of risk , prevention practices , and perceived effectiveness were statistically different between those households: 1 ) that had ITCs hanging fully extended vs . tied up or not in place , 2 ) that had ITCs hanging fully extended and/or tied up vs . not in place , and 3 ) that had washed ITCs correctly or not ( see precise definition of these measures in Table 1 ) . Additionally , we looked to see if there were significant differences amongst participants who reported that they would recommend ITCs to friends or family in the future versus those who would not . A logistic regression model was used to estimate the association between the predictor ( independent ) variables ( socio-demographic variables , as well as variables associated to dengue knowledge , perception of risk , prevention practices , and perceived ITC effectiveness , as described in Table 1 ) to the dichotomous criterion ( dependent ) variables of interest associated with correct and consistent use of ITCs . The independent variables for these models included: age , education , occupation , number of children 3 years old and under living at home , wealth index , knows a relative or close friend who had dengue , knows three or more correct symptoms of dengue , knows three or more correct protection methods , knows one or more correct treatment methods , children sleep under a mosquito net , and saw a change in the amount of mosquitoes . The odds ratios and 95% confidence intervals were reported for the adjusted associations estimated through these models . Our study design received approval from the Institutional Review Boards ( IRBs ) at the Liverpool School of Tropical Medicine , the Tulane School of Public Health and Tropical Medicine , and the U . S . Naval Medical Research Center Detachment ( now Naval Medical Research Unit-6 or NAMRU-6 ) in Peru . The latter had inter-institutional-IRB agreements with the Tulane School of Public Health and Tropical Medicine and the University of California at Davis . The Regional Health Authority ( DIRESA ) , the local branch of the Ministry of Health , also provided approval . The trial was registered with the International Standard Randomized Controlled Trial Number Register: ISRCTN08474420 . This manuscript does not report the outcome of the cluster randomized trial . The investigation was carried out only on the treatment group; the control group in the study received no treatment . Therefore this study is not an analysis that is dependent on randomized control trial ( RCT ) design , but instead measures properties of the intervention tool ( e . g . , ITCs ) . All subjects provided written informed consent . The experiments reported herein were conducted in compliance with the Animal Welfare Act and in accordance with the principles set forth in the "Guide for the Care and Use of Laboratory Animals , " Institute of Laboratory Animals Resources , National Research Council , National Academy Press , 1996 .
A total of 1 , 742 lots were part of the intervention study area , of which 1 , 512 ( 86 . 8% ) were houses . The other lots were vacant houses ( 188 , 10 . 8% ) , non-residential ( 25 , 1 . 4%; e . g . , churches , nursery schools , and warehouses ) , or simply empty lots ( 17 , 1 . 0% ) . Of the 1 , 512 houses , 1 , 345 ( 89 . 0% ) wanted ITCs and were considered “study participants;” of the study participants , most ( 1 , 333 , 99 . 1% ) agreed to participate in the KAP survey . Eighty-two households ( 4 . 7% ) refused to participate and 85 ( 4 . 9% ) were not found at their home despite multiple visits . Since this study focuses on correct use of the ITCs , our sample is the intervention homes that completed the KAP survey ( 593 ) . The general socio-demographic characteristics of the study population are described in Table 2 . Over three quarters of participants were female , and also had less than or equal to 11 years of education . The predominant occupation of respondents was housewife ( 48 . 7% ) . Of the 593 households , 6 . 2% had one or more pregnant woman living at home , and 20 . 8% had children 3 years of age or younger living in the home . Overall , households had a median of 5 people ( adults and children ) living in the home , and a median of 2 children ( 18 years or under ) living in the home . A total of 3 , 178 ITCs were distributed to 593 homes in the study area in November and December of 2009 . A mean of 5 ITCs ( range 1–15 ITCs ) were distributed to each home ( Table 3 ) . Of these , more than half of the ITCs were hung to cover doorways ( n = 1 , 720 ) and the remainder were utilized as room dividers ( n = 1 , 127 ) , window coverings ( n = 293 ) , or as wall hangings ( n = 38 ) ( Fig 1 ) . Examples of ITCs hanging correctly in a doorway can be seen in Fig 2 . At 9 months after distribution , approximately 9 . 7% were missing . Of the 2870 ITCs still present in the home 9 months after distribution , 49% were hanging correctly ( i . e . fully extended ) , 25 . 2% were hanging in place but tied up , 15 . 1% were being washed ( and our research team had to confirm they saw them in water or hanging to dry ) , 10 . 3% were stored somewhere ( but observed to be in the home ) , and 0 . 5% were being used in a different way than expected ( i . e . , mosquito net ) ( Table 3 ) . The fact that 15 . 1% were being washed indicates that the ITCs were being cared for , although we were unable to verify if the ITCs had been hanging correctly when they were not being washed . A majority of the ITCs , 59 . 5% ( n = 1 , 707 ) , were reportedly being washed correctly . The average number of hours that ITCs were tied up per day was 10 . 6 hours with a range of 1–24 hours ( 18 ITCs were reportedly tied up 24 hours/day ) . The ITCs placed on walls were most likely to be hanging correctly , compared to those used as room dividers , in doorways , or in windows ( Fig 1 ) . A third ( n = 1 , 049 ) of all ITCs , regardless of original location , were not in place , meaning they were being washed , had been stored , moved to another location , or were missing entirely from the home ( Fig 1 ) . All except for 5 . 5% ( n = 159 ) of the ITCs that were still in use were in either excellent or good condition . An additional 1 , 049 ITCs were distributed at 9 months , to replace ITCs that had been lost or damaged ( noted as “missing” in the data set , and this included intervention households that took ITCs when they moved or , in some cases , had taken them to another home for protection there ) , supplement existing ITCs at the request of householders , and offer ITCs to new residents who had recently moved into the study area; this brought the total number of ITCs in the field to 4 , 227 . By 18 months after the original ITC distribution , the percentage of the ITCs hanging correctly or tied up for a portion of the day dropped to 45 . 8% ( n = 1 , 932 ) for all locations combined ( Fig 3 ) . More than half of the ITCs ( 51 . 4% ) were missing , and 2 . 9% of the houses could not be accessed for the final visit . Perceived effectiveness of the ITCs in our study was high at 9 and 27 months , with 90 . 2% of respondents at 9 months and 90 . 6% at 27 months saying they saw a change in the amount of mosquitoes in their home , although for many of these individuals , the change was only seen for a few months ( 34 . 9% reported ITCs only worked for a few months at 9 months and 55 . 7% reported this at 27 months ) . In addition , the percent that would recommend ITCs to family or friends in the future at 9 months and 27 months was high , at 92 . 7% at 9 months and 94 . 6% at 27 months . We examined whether there were significant differences in knowledge , perception of risk , prevention practices , and perceived effectiveness between those households that had ITCs hanging correctly and consistently versus those that did not at 9 months after ITC distribution . A significantly greater ( p<0 . 05 ) proportion of households that had ITCs hanging properly reported knowing three or more correct symptoms of dengue , knowing someone who had dengue , having children sleep under a mosquito net , and seeing a change in the amount of mosquitoes in the home compared to those that did not have ITCs hanging properly ( Table 4 ) . Compared to those who did not , a significantly greater ( p<0 . 05 ) proportion of households that had ITCs hanging properly and/or tied up for a portion of the day reported knowing at least one correct treatment method for dengue and significantly greater proportion ( p<0 . 01 ) reported seeing a change in the amount of mosquitoes in the home . Among those households that washed ITCs correctly , a significantly greater ( p<0 . 01 ) proportion reported being a housewife , knowing three or more correct symptoms of dengue , having children sleep under a mosquito net , and perceiving a change in the amount of mosquitoes compared to those that did not wash correctly . Additionally , a significantly greater ( p<0 . 05 ) proportion among this same group reported having children ≤3 years at home . Finally , 27 months after distribution , a significantly greater ( p<0 . 01 ) proportion of those who would recommend ITCs in the future reported having children sleep under a mosquito net and perceiving a change in the amount of mosquitoes in the home compared to those who would not recommend ITCs . Examining the adjusted associations , we found the odds of ITCs hanging correctly ( i . e . , fully extended ) was significantly greater if respondents knew a relative or close friend who has had dengue ( OR: 1 . 66 , 95% CI: 1 . 09 , 2 . 53 ) , or had children that slept under a mosquito net ( OR: 1 . 53 , 95% CI: 1 . 00 , 2 . 33 ) ( Table 5 ) . Those respondents who had their ITCs hung up and/or tied up for a portion of the day also had significantly greater odds of knowing a relative or close friend who had dengue ( OR: 1 . 77 , 95% CI: 1 . 06 , 2 . 96 ) , as well as of knowing at least one correct treatment ( OR: 1 . 65 , 95% CI: 1 . 08 , 2 . 54 ) and perceiving a change in the amount of mosquitoes in the home ( OR: 2 . 11 , 95% CI: 1 . 19 , 3 . 74 ) . The odds of washing ITCs correctly was significantly greater if respondents were housewives ( OR: 1 . 94 , 95% CI: 1 . 34 , 2 . 81 ) , knew three or more correct symptoms of dengue ( OR: 1 . 94 , 95% CI: 1 . 28 , 2 . 93 ) , had children sleep under a mosquito net ( OR: 1 . 75 , 95% OR: 1 . 15 , 2 . 69 ) , or perceived a change in the amount of mosquitoes in the home ( OR: 2 . 16 , 95% CI: 1 . 22 , 3 . 85 ) . Finally , the odds of recommending ITCs in the future was significantly greater among those respondents who perceived a change in the amount of mosquitoes in the home ( OR: 19 . 78 , 95% CI: 7 . 62 , 51 . 30 ) .
There were several limitations to this study . First , we were unable to track any changes in the location of individual ITCs inside the home . For example , were ITCs placed in the doorway more likely to be moved to other locations compared to those placed on the wall ? These data would have enabled us to determine the most successful long-term placement for ITCs within the home . Second , it would have been useful to know more about maintenance behaviors: how often did individuals report washing the ITCs ? How often did these need to get mended ? Though we perceive the ITCs to require minimal upkeep , it would be useful to find out what type of upkeep effort was required . Third , it would have been interesting to quantify how much of an impact was attributable to a barrier effect and how much was attributable to an insecticide effect . However , the trial did not aim to measure the impact of insecticide vs . non-insecticide treated curtains; rather , the aim was to assess the impact of ITCs as a single intervention vs . the status quo which was no ITCs at all . Fourth , “correct and consistent use” was a subjective measure used for this study and based on only two observation visits . It is possible that ITCs that were coded as “hanging loosely and extended” were actually tied up at other times of the day , and vice versa . Despite this limitation , we felt it was important to assess use in the most objective way we could: observation of use ( vs . self-reporting ) . We also expect that if ITCs were hanging—fully extended or not—that they were hanging there most of the time , since participants did not know when we were coming to monitor the use of ITCs and we would not expect them to put them in place just for us . Finally , due to social desirability bias ( wanting to seem like they were using them correctly most of the time ) , respondents might be more likely to underestimate the time the ITCs were tied up . ITCs are an attractive vector control intervention due to the minimal upkeep and attention they require . The findings reported here add to the body of literature regarding the effectiveness of ITCs as a vector control tool in dengue endemic areas . The long-term use of ITCs in this study decreased significantly over time; we believe this was primarily the result of a reduction in perceived effectiveness of the insecticide . In fact , this perception was correct: when we learned that people were perceiving the ITCs to no longer be working , we removed several ITCs from the field , tested them using standard WHO cone bioassays [42] , and found that indeed , the ITCs were not killing mosquitoes as they should have been . As such , we then decided to remove all ITCs from the field to re-impregnate them , which might have validated people’s perceptions that the ITCs lost their effectiveness . The odds of using ITCs correctly and consistently and recommending ITCs in the future were significantly greater among those who did perceive the ITCs to be effective . Hence , importantly , despite the fact that the effectiveness of the ITCs did decline noticeably , it is encouraging that 18 months after distribution , ITCs were still hanging in almost half of the houses . It is important for future ITC intervention programs to note that , although all participants had a choice as to how many and where to place their ITCs , a higher percentage of ITCs that were hung on the wall were hanging properly at 9 months as compared to windows , doors , or room dividers ( Fig 3 ) . This study supports previous findings that perception of ITC effectiveness was associated with correct and consistent use . Additionally , this study reports that certain participant characteristics and behaviors—especially knowing someone who has had dengue or having children sleep under a mosquito net—were significantly positively associated with different measures of correct and consistent use of ITCs . These findings reveal that perception of risk for dengue ( by knowing someone who has had dengue ) and already practicing preventive behaviors are key factors associated in using ITCs correctly and consistently , and that vector control health promotion should include messages stressing the high risk for all to dengue in dengue endemic regions like Iquitos . Finally , findings from this study could guide future ITC-related interventions regarding locations in the house where ITCs were more likely to be used correctly and remain over time . | Dengue is an arthropod-borne virus of great public health importance . Vector control is currently the only available method for dengue prevention . This cluster-randomized trial investigated individual and household-level socio-demographic factors associated with correct and consistent use of insecticide-treated curtains ( ITCs ) —one promising vector control method—in Iquitos , Peru . Most people preferred to hang the ITCs in doorways and as room dividers , but also hung them as curtains on windows and on their walls . We assessed who still had their ITCs hanging or tied up at 9 months and 18 months after distribution , and found that use of the ITCs decreased over time to about half . When we explored who was more likely to be using the ITCs correctly ( having them hanging in place , or tied up in place , or washed without bleach and avoiding direct sunlight ) , we found that those who knew more about dengue , knew someone who had dengue , had young children in their homes sleeping under an insecticide treated mosquito net , or who perceived the ITCs to work well , were more likely to be using their ITCs than others . Despite various challenges in sustained ITC effectiveness in this study , the fact that almost half of the homes still had the ITCs hanging at 18 months suggests this vector control strategy is feasible for long term community use . | [
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| 2016 | Factors Associated with Correct and Consistent Insecticide Treated Curtain Use in Iquitos, Peru |
The eukaryotic cytoskeleton is essential for structural support and intracellular transport , and is therefore a common target of animal pathogens . However , no phytopathogenic effector has yet been demonstrated to specifically target the plant cytoskeleton . Here we show that the Pseudomonas syringae type III secreted effector HopZ1a interacts with tubulin and polymerized microtubules . We demonstrate that HopZ1a is an acetyltransferase activated by the eukaryotic co-factor phytic acid . Activated HopZ1a acetylates itself and tubulin . The conserved autoacetylation site of the YopJ / HopZ superfamily , K289 , plays a critical role in both the avirulence and virulence function of HopZ1a . Furthermore , HopZ1a requires its acetyltransferase activity to cause a dramatic decrease in Arabidopsis thaliana microtubule networks , disrupt the plant secretory pathway and suppress cell wall-mediated defense . Together , this study supports the hypothesis that HopZ1a promotes virulence through cytoskeletal and secretory disruption .
The disruption of critical host cellular structures and processes is an important virulence tactic employed by bacterial pathogens of both plants and animals [1] , [2] . Many Gram-negative bacterial pathogens accomplish this goal using the type III secretion system ( T3SS ) to inject virulence proteins known as type III secreted effectors ( T3SEs ) directly into the host cytosol [3] . One of the major virulence functions of phytopathogen T3SEs is to block host immune responses [4] , [5] . These T3SEs employ a range of biochemical activities to modify host cell proteins and promote the infection process [6] , [7] . However plants have evolved resistance ( R ) proteins that can recognize specific T3SE proteins to induce an effector-triggered immunity ( ETI ) , which is often accompanied by localized cell death response called the hypersensitive response ( HR ) [8]–[10] . In some cases , plant resistance proteins directly bind T3SE proteins and induce ETI , but more typically , the actions of T3SEs on their host targets are monitored by plant resistance proteins as a trigger for ETI [11] . Therefore , T3SEs from phytopathogenic bacteria can act as either virulence or avirulence factors that promote bacterial growth or induce host immunity , respectively . The YopJ superfamily of T3SEs is widely distributed in animal- and plant-pathogenic bacteria [12] . All members of this superfamily share a conserved catalytic triad consisting of the amino acids histidine , glutamic/aspartic acid , and cysteine that are characteristic of cysteine proteases [13] , [14] . Despite sharing these catalytic residues , members of the YopJ superfamily display an array of biochemical activities . The archetypical member of this family , YopJ , displays de-sumoylating , de-ubiquitinating and acetyltransferase activity [13] , [15]–[17] . The acetyltransferase activity of YopJ requires the eukaryotic cofactor phytic acid for full activation [18] . Phytic acid is also required for the full activation of AvrA , a YopJ homolog in Salmonella [18] . YopJ plays a crucial role in suppressing host immune responses , inhibiting cytokine production and inducing macrophage apoptosis [19] , [20] . YopJ exerts its inhibitory activity by acetylating serine and threonine residues in the activation loop of mitogen-activated protein kinase ( MAPK ) superfamily , preventing their activation by phosphorylation and inhibiting downstream defense signaling pathways [15] . From plant pathogens , YopJ family members PopP2 from Ralstonia solanacearum displays acetyltransferase activity [21] , AvrXv4 from Xanthomonas campestris pv . vesicatoria ( Xcv ) displays de-sumoylating activity [22] and members of the HopZ family from Pseudomonas syringae display protease activity [12] . PopP2 has been shown to autoacetylate residue Lys383 , which is required for its avirulence activity in Arabidopsis [21] . Another YopJ homolog in Xcv , AvrBsT , has been shown to suppress AvrBs1-specific HR by interacting with SNF1-related kinase 1 ( SnRK1 ) , a putative regulator of sugar metabolism [23] . Lastly , HopZ1a and HopZ1b have been shown to interact with soybean GmHID1 ( 2-hydroxyisoflavanone dehydratase ) , which leads to suppression of daidzein biosynthesis . However , the direct mechanisms by which AvrBsT modify SnRK1 or HopZ1 modify GmHID1 are currently unknown as these effectors do not show proteolytic or acetyltransferase activity towards their targets [24] , [25] . Thus , the enzymatic activities of many YopJ homologs in phytopathogens remain to be addressed . The HopZ1 family of P . syringae T3SEs has diversified into three allelic forms ( HopZ1a , HopZ1b and HopZ1c ) with HopZ1a predicted to be most similar to the ancestral HopZ allele in P . syringae [12] . All members of the HopZ1 family contain a consensus myristoylation site ( G2 ) required for membrane localization , suggesting that their host targets are membrane localized [26] , [27] . In Arabidopsis , HopZ1a is recognized by the ZAR1 resistance protein [28] . This recognition requires the catalytic cysteine residue ( C216 ) as well as the consensus myristoylation site ( G2 ) , suggesting that ZAR1 recognizes the membrane-localized enzymatic activity of HopZ1a [12] , [26] , [28] . In Arabidopsis plants lacking ZAR1 , HopZ1a can promote P . syringae virulence suggesting that ZAR1 evolved to counter the ancestral virulence function of HopZ1a [28] . In this study , we show that HopZ1a , like YopJ , is an acetyltransferase that requires the eukaryotic co-factor , phytic acid , for full activation of its enzymatic activity . HopZ1a binds directly to tubulin and activated HopZ1a acetylates itself as well as tubulin . In planta , active HopZ1a causes a dramatic decrease in plant microtubule networks , inhibition of protein secretion and suppression of cell wall-mediated defense , supporting a novel mode-of-action for the YopJ superfamily .
In an effort to identify conserved host targets of phytopathogen T3SEs , we utilized a heterologous in vivo screen whereby we expressed tandem-affinity-purification ( TAP ) -tagged phytopathogen T3SEs in human embryonic kidney ( HEK293T ) cells [29] . We hypothesized that those phytopathogen T3SEs that cause sick or lethal phenotypes in HEK293T cells are targeting conserved eukaryotic proteins . One T3SE identified from this screen was HopZ1a from P . syringae pv . syringae A2 . HEK293T cells transiently expressing HopZ1a exhibited altered cell morphology compared to control HEK293T cells carrying the empty vector ( Figure 1A ) , indicating a possible perturbation of the host cytoskeleton . The observed rounding and decrease in cytoplasmic projections of HEK293T cells expressing HopZ1a was dependent on the catalytic residue as it was not observed in cells expressing HopZ1a ( C216A ) ( Figure 1 ) . We used TAP-tagged HopZ1a and liquid chromatography-tandem-mass spectrometry ( LC-MS/MS ) to recover eukaryotic proteins in complex with HopZ1a in HEK293T cells [29] . Tubulin was identified as an interacting protein based on its atypically large recovery and high sequence coverage ( Table S1 ) . To determine if purified HopZ1a directly interacts with tubulin heterodimers in vitro we used surface plasmon resonance technology to monitor real-time changes in the refractive index of bound HopZ1a associated with the presence of tubulin heterodimers . Tubulin is highly conserved across all eukaryotes ( Arabidopsis and humans share ∼85% amino acid identity for both α-tubulin and β-tubulin ) . We therefore used commercially available purified bovine brain tubulin for this assay as high purity plant tubulin is not available . Both wild type HIS-HopZ1a and HIS-HopZ1a ( C216A ) catalytic mutant bound tubulin heterodimers , suggesting that HopZ1a has a tubulin-binding domain ( TBD ) that is independent of its enzymatic activity ( Figure 2A ) . Additionally , tubulin heterodimers interacted with the purified HIS-HopZ1a proteins in a dosage-dependent manner ( Figure 2B ) . The interaction between tubulin and HopZ1a was specific , as glutathione S-transferase ( GST , Figures 2A , S1B and S1C ) or bovine serum albumin ( BSA , Figure S1A ) did not bind to tubulin heterodimers . GST-HopZ1a also bound tubulin heterodimers , indicating that the fusion tags were not responsible for the interaction between tubulin and HopZ1a ( Figures S1B and S1C ) . Lastly , HopZ1a bound to soybean tubulin ( Figure S1C ) . However , due to the higher levels of impurities found in soybean tubulin , we conducted all subsequent experiments using bovine brain tubulin . We next determined whether purified HopZ1a could bind to intact microtubules ( as opposed to tubulin heterodimers ) in vitro using a microtubule co-sedimentation assay [30] in which microtubule-binding proteins sediment with microtubules . We found that both purified HIS-HopZ1a and HIS-HopZ1a ( C216A ) co-sedimented with taxol-stabilized microtubules in the pellet fraction . In the absence of taxol-stabilized microtubules , HopZ1a and HopZ1a ( C216A ) remained in the supernatant ( Figure 2C ) . Thus , as seen with the interaction between HopZ1a and tubulin heterodimers , HopZ1a also bound microtubules independent of its catalytic residue , C216 . Moreover , this interaction is specific as the unrelated P . syringae type III effector , HopF2 , remained predominantly in the supernatant fraction ( Figure S2 ) . Given that HopZ1a binds to human and bovine tubulin , we next determined whether HopZ1a can bind to plant tubulin using co-immunoprecipitation ( CoIP ) on Arabidopsis transgenic plants expressing HopZ1a ( C216A ) -HA ( Figure 2D ) . Despite a low level of HopZ1a ( C216A ) -HA immunoprecipitation ( Figure 2D , top panel ) , a considerable amount of tubulin proteins were detected in the HopZ1a fractions ( Figure 2D , bottom panel ) . Given that tubulin is an abundant protein that can be pulled down non-specifically in CoIP experiments , we used HopF2-HA transgenic plants as a negative control since HopF2 is not expected to bind to tubulin specifically ( see Figure S2 ) and , like HopZ1a , is membrane localized via myristoylation . Although some tubulin was detected from the HopF2-HA eluted fractions , quantification of the band intensities of the α-tubulin and α-HA blots showed that the ratio of tubulin to HA-tagged effector was significantly lower than in the HopZ1a fractions . In fact , HopZ1a pulled down 30–200 times more tubulin than HopF2 demonstrating that HopZ1a binds to plant tubulin specifically . Since HopZ1a binds tubulin and microtubules , we next investigated whether active HopZ1a could modify tubulin . Based on the acetyltransferase activity of YopJ , we tested whether HopZ1a could perform a similar function via a 14C-labelled acetyl-coenzyme A ( acetyl-CoA ) transferase reaction [15] , [31] . HIS-HopZ1a showed strong autoacetylation activity in the presence of tubulin and also weakly acetylated tubulin ( Figures 3 and S3A , S3B and S3D ) . This activity was dependent on the conserved catalytic residue , C216 , as the HIS-HopZ1a ( C216A ) mutant did not autoacetylate or acetylate tubulin ( Figures 3 and S3D ) . Phytic acid ( also known as phytate , inositol hexakisphosphate and IP6 ) is an abundant eukaryotic cofactor that has been shown to be required for the full activation of number of YopJ family effectors [18] . Phytic acid is also a likely contaminant of purified bovine brain tubulin due to its abundance in this tissue [32] . Consequently , we asked whether HopZ1a was actually activated by phytic acid rather than tubulin . Acetylation assays showed that phytic acid activated the acetyltransferase activity of HopZ1a to the same extent as tubulin ( Figure 3 ) . Further , the removal of tubulin by Proteinase K treatment confirmed that HopZ1a's acetyltransferase activity was likely activated by contaminating phytic acid in the bovine tubulin ( Figure S3C ) . Finally , mass spectrometry analysis of the bovine tubulin confirmed the presence of contaminating phytic acid ( data not shown ) . Using the molecular weight differences between GST-HopZ1a ( ∼70 kDa ) and HIS-HopZ1a ( ∼41 kDa ) , we next investigated whether the autoacetylation on HopZ1a occurred in cis or in trans . Purified GST-HopZ1a showed a weak basal level of acetyltransferase activity in the absence of tubulin , possibly due to the GST tag stabilizing HopZ1a protein ( Figure S3A ) . However , the acetyltransferase activity of GST-HopZ1a , like HIS-HopZ1a , was strongly activated by phytic acid ( Figure S3A ) . GST-HopZ1a and HIS-HopZ1a migrate at different rates during electrophoresis; therefore , we could show that HopZ1a autoacetylated in cis since GST-HopZ1a strongly acetylated itself ( in cis ) and only weakly acetylated HIS-HopZ1a ( C216A ) ( Figure 3 ) . The reciprocal experiment showed that active HIS-HopZ1a strongly acetylated itself in cis and did not acetylate GST-HopZ1a ( C216A ) ( Figure S3D ) . Taken together , our data show that ( i ) HopZ1a acetylates tubulin [33] , HopZ1a requires eukaryotic co-factor phytic acid for full activation of its acetyltransferase activity and ( iii ) the HopZ1a autoacetylation activity occurs predominantly in cis . To understand the function of HopZ1a autoacetylation , we identified a potential autoacetylation site by aligning various effector proteins from the HopZ family ( HopZ1a , HopZ1b and HopZ2 ) to their homolog in R . solanacearum , PopP2 ( Figure 4A ) . Work by Tasset et al [21] has shown that K383 in PopP2 ( which is homologous to K289 in HopZ1a ) is autoacetylated and a mutation in K383 blocks R-protein ( RRS1 ) - mediated recognition of PopP2 . Thus , to address whether K289 is also required for HopZ1a function , we assayed the acetyltransferase activity of HopZ1a ( K289R ) . In the presence of tubulin , GST-HopZ1a ( K289R ) , like the catalytic-null GST-HopZ1a ( C216A ) , did not display any acetyltransferase activity either towards itself or tubulin ( Figure 4B ) . HopZ1a ( K289R ) tubulin-binding was similar to wild-type HopZ1a indicating that this mutation does not significantly affect the overall structure of the protein ( Figure S4A ) . Additionally , in the absence of tubulin , GST-HopZ1a ( K289R ) did not show even the weak autoacetylation activity seen with the wild type GST-HopZ1a ( Figure S4B ) . Therefore , the putative autoacetylation site , K289 , is required for HopZ1a acetyltransferase activity . Previous work has shown that HopZ1a induces a strong HR in A . thaliana ecotype Col-0 as a result of recognition by the ZAR1 resistance protein [12] , [26] , [28] . We monitored whether K289 of HopZ1a plays a role in host recognition by monitoring macroscopic HR symptoms ( Figure 4C ) as well as changes in conductivity due to HR-associated ion leakage ( a quantitative measure for the HR; Figure 4D ) of Arabidopsis leaves infiltrated with P . syringae pv . tomato DC3000 ( PtoDC3000 ) expressing HopZ1a WT , HopZ1a ( C216A ) , or HopZ1a ( K289R ) . As previously shown , HopZ1a requires the catalytic C216 residue to induce a strong macroscopic HR and a corresponding increase in ion leakage in Arabidopsis ecotype Col-0 [12] , [26] , [28] . Interestingly , HopZ1a ( K289R ) phenocopied the catalytic-inactive HopZ1a ( C216A ) and did not elicit a macroscopic HR ( Figure 4C ) . Furthermore , HopZ1a ( K289R ) induced conductivity similar to both PtoDC3000 carrying empty vector ( EV ) or the inactive HopZ1a ( C216A ) ( Figure 4D ) . Thus , HopZ1a ( K289R ) plays a critical role in host recognition and the avirulence function of HopZ1a . In the absence of the Arabidopsis resistance protein ZAR1 , HopZ1a confers a virulence function to both PtoDC3000 and P . syringae pv . cilantro 0788-9 ( Pci ) , in a catalytic ( C216 ) dependent manner [28] . Like PtoDC3000 , Pci strain does not carry an endogenous HopZ allele [12] . However , Pci carrying HopZ1a displayed a more significant growth increase compared to empty vector than what was observed in PtoDC3000 [28] . Therefore , we tested whether the K289 residue also played a role in HopZ1a virulence function by conducting in planta growth assays using Pci carrying the empty vector , wild type HopZ1a , and HopZ1a ( K289R ) . In zar1-1 plants , Pci expressing wild type HopZ1a promoted bacterial growth compared to Pci carrying the empty vector ( Figure 4E ) , indicating that HopZ1a has a virulence function as previously shown [28] . However , the K289R mutation abolished HopZ1a virulence function ( Figure 4E ) . Together , our data demonstrate that K289 contributes to HopZ1a: ( i ) acetyltransferase activity [33] , avirulence function mediated by ZAR1 recognition , and ( iii ) virulence function . Since HopZ1a bound tubulin in vitro and in vivo , we examined whether HopZ1a can induce structural changes to the host microtubule network . In order to minimize ETI-associated changes due to recognition of HopZ1a by ZAR1 , we utilized a liquid assay where many features of ETI are suppressed [34] , [35] . In brief , Arabidopsis seedlings expressing GFP-labeled microtubule markers were grown in liquid plant media and infected with PtoDC3000 expressing the empty vector [26] , HopZ1a [26] , HopZ1a ( C216A ) [26] , or the unrelated P . syringae T3SE AvrRpt2 [36] . Since AvrRpt2 induces an HR in Arabidopsis within the same time frame as HopZ1a , it served as an additional control to ensure that any observed changes to the microtubule networks were HopZ1a-specific . We used confocal microscopy to observe changes in the 3-dimensional microtubule architecture of the epidermal layer at ∼16 hours post-infection with PtoDC3000 . Two GFP-labeled microtubule markers were visualized: MAP4 [37] , which associates with microtubules , and EB1 [38] , which labels the growing ends of microtubules . At sixteen-hours post infection , PtoDC3000 carrying HopZ1a destroyed 50% of the cortical microtubule networks of Arabidopsis GFP-MAP4 , whereas infection with PtoDC3000 carrying the empty vector , the HopZ1a ( C216A ) catalytic mutant , or AvrRpt2 induced no change in the cortical microtubule networks ( Figure 5A ) . However , at later time points ( ∼22 hours post-infection ) , we observed microtubule destruction in all the treatments indicating that PtoDC3000 may alter the cytoskeleton at later time points ( data not shown ) . At sixteen hours post infection , PtoDC3000 carrying wild type HopZ1a also reduced the number of EB1 spots by 50% , while the other constructs showed no effect ( Figure 5B ) . Note that the faint green haze in EB1 images were due to the fact that GFP-AtEB1 not only associates with the ends of microtubules , but also with the endomembrane system [38] . The microtubule-specific destruction in the presence of HopZ1a was not a result of general perturbation to the plant cytoskeleton , since PtoDC3000 carrying HopZ1a did not disrupt the actin cytoskeleton at the same time point ( Figure S5 ) . Thus , HopZ1a required its catalytic residue to specifically cause destruction of Arabidopsis microtubule networks . As mentioned above , HopZ1a promotes P . syringae ( PtoDC3000 ) growth in Arabidopsis plants lacking the ZAR1 R protein [28] . Given that the microtubule networks may play a crucial role in plant defense , we hypothesized that HopZ1a may promote the virulence of P . syringae via the destruction of microtubule networks . We used a pharmacological approach to determine whether the disruption of microtubules could account for HopZ1a-mediated enhanced virulence . When we co-infiltrated PtoDC3000 with the well-characterized disruptor of microtubules , oryzalin ( 100 µM in 1% ethanol ) , we observed significantly higher growth than PtoDC3000 co-infiltrated with 1% ethanol alone ( Figure 6 ) . A mutant lacking a functional T3SS , PtoDC3000 ΔhrcC , did not grow better in the presence of oryzalin , indicating that the virulence advantage of microtubule destruction requires a functional T3SS ( Figure 6 ) . Importantly , oryzalin did not provide a further virulence advantage to PtoDC3000 expressing HopZ1a , demonstrating that oryzalin can phenocopy HopZ1a virulence activity ( Figure S6 ) . Together , our results demonstrated that microtubule destruction promoted phytopathogen virulence . Microtubules play an important role in regulating vesicle trafficking and polarized secretion at the cortex [39] , [40] . Given that plant microtubule networks were disrupted by catalytically active HopZ1a , we also investigated whether HopZ1a can block in planta secretion using a secreted green fluorescent protein ( secGFP ) assay . This assay has recently been used to demonstrate that the YopJ / HopZ family member , XopJ , can inhibit secretion in planta [41] . Normally , secGFP is secreted into the extracellular space ( also known as the apoplast ) of Nicotiana benthamiana leaves via a constitutive secretion pathway [41] , [42] . However , accumulation of secGFP in apoplastic fluids from plants expressing wild type HopZ1a-myc was significantly reduced , demonstrating that HopZ1a blocked secretion to the same extent as the known secretion inhibitor , AtSYP121-SP2-myc ( Figure 7 ) . Furthermore , an unrelated T3SE , XopB , did not inhibit the secretion of secGFP to the apoplast ( S . Sonnewald , personal communication ) , indicating that inhibition of secretion is not a general feature of type III effectors . The decreased accumulation of secGFP in the apoplastic fluids of plants expressing HopZ1a-myc was not due to decreased expression of secGFP or the induction of HR , as GFP was detected in the remaining tissues after apoplastic fluid extraction . The ability of HopZ1a to inhibit secretion was dependent upon the catalytic residue , C216 , as secGFP was detected in the apoplastic fluids from plants expressing HopZ1a ( C216A ) -myc at similar levels to untransfected samples ( Figure 7 ) . Given that HopZ1a caused microtubule destruction , inhibited secretion , and the fact that plant microtubules are largely associated with the cell cortex [43] , [44] , we next investigated whether HopZ1a can interfere with cell wall-based defenses such as callose deposition . Callose , a key component of cell wall papillae , is composed of β- ( 1 , 3 ) -glucan polymers . These callose-containing papillae form important barriers at the sites of pathogen attack and are typically induced by the recognition of conserved pathogen- or microbe- associated molecular patterns ( PAMPs/MAMPs ) [45] , [46] . However , P . syringae can counteract these cell wall-based defenses and other components of the PAMP-triggered immunity ( PTI ) by injecting virulence factors such as T3SEs into the plant [5] , [47] . To determine whether HopZ1a can suppress cell wall-based defenses , we treated Arabidopsis leaves with a well-known PAMP , flg22 , followed by staining with Aniline blue for callose ( Figure 8 ) . As mentioned above , HopZ1a triggers a strong HR due to the recognition by the ZAR1 R protein [28] . Therefore , transgenic plants expressing HopZ1a were generated in the zar1-1 background . As expected , flg22 elicited strong callose deposition in the zar1-1 plants ( Figure S7 ) . However , in zar1-1 plants expressing HopZ1a flg22-induced callose deposition was suppressed demonstrating that HopZ1a blocked cell wall-defenses ( Figure 8 ) . Importantly , HopZ1a required the catalytic C216 residue to block flg22-induced callose deposition ( Figure 8 ) .
Bacterial pathogens use the type III secretion system to inject virulence proteins directly into the host cell . While we know the identity and general activity ( e . g . suppression of defense signaling ) of many phytopathogen T3SEs , relatively few have well-characterized enzymatic activities or host interactors . We used a heterologous screen to identify conserved host proteins that interact with phytopathogen T3SEs . We demonstrated that P . syringae T3SE , HopZ1a , is an acetyltransferase that interacts with tubulin . Furthermore , active HopZ1a alters microtubule networks , inhibits secretion and blocks cell wall-based defenses . Plant microtubule networks are tightly associated with the cell membrane and this cortical association is important for maintaining the stability and organization of microtubule architecture in the plant cells [43] , [44] . Unlike animal cells , plant microtubule arrays are non-centrosomal and nucleated by membrane-associated gamma-tubulin [48] , [49] . A number of membrane-associated proteins , such as phospholipase D , provide additional tethering of microtubules to the membrane [50] . Given the importance of cortical association to plant microtubule stability and dynamics , it is not surprising that the membrane-localized HopZ1a can cause a dramatic destruction to the plant microtubule network [26] . We show that HopZ1a acetylates tubulin in vitro , however , this activity is notably weaker than the autoacetylation activity ( Figures 3 and S3 ) . It is possible that the in vivo microtubule context may be more readily acetylated by HopZ1a or that weak acetylation of tubulin in vivo may be sufficient to alter the assembly or disassembly dynamics of microtubules [51] . Recent work by Chu et al ( 2010 ) [52] has shown that acetylation on lysine 252 ( K252 ) of beta-tubulin prevents incorporation of acetylated tubulin into the microtubule and alters microtubule polymerization , thus providing a potential mechanism by which HopZ1a alters plant microtubule networks . The acetylation of a subpopulation of tubulin by HopZ1a may be sufficient for lowering the critical concentration necessary for microtubule assembly , leading to the dramatic microtubule destruction that we observed . Alternatively , HopZ1a may cause microtubule destruction by acetylating as yet unidentified host protein ( s ) , such as MAPKs , that are involved in regulating the stability of cortical microtubule networks [53] . Lastly , we cannot rule out that HopZ1a alters the microtubule networks as a consequence of ZAR1-mediated recognition . Experiments with zar1/GFP-MAP4 plants will address whether HopZ1a-mediated microtubule destruction is ZAR1-dependent . Interestingly , the acetyltransferase activity of HopZ1a is activated by phytic acid ( Figure 3 ) , which plays an important role in defense signaling in addition to functioning as phosphate reserve in plants [54] . Phytic acid has been previously shown to activate the acetyltransferase activities of YopJ and AvrA , both highly divergent homologs of HopZ1a from animal pathogens [18] . Upon binding to phytic acid , AvrA undergoes a conformational change , which Mittal et al speculate leads to the allosteric activation of the acetyltransferase activity of AvrA [18] . Given that the catalytic inactive AvrA mutant also undergoes a similar conformational change upon binding to phytic acid , the phytic acid-binding site of AvrA is likely to be distant from the active site . It remains to be determined whether phytic acid induces similar conformational changes in HopZ1a and whether this putative conformational change is responsible for the activation of its enzymatic activity . However , the requirement for eukaryotic co-factor to fully activate HopZ1a's acetyltransferase activity suggests that this virulence protein is only ‘armed’ after injection into the host . This activation is reminiscent of the P . syringae T3SE AvrRpt2 , which requires the eukaryotic cofactor cyclophilin to activate its cysteine protease activity , keeping this T3SE inactive until after delivery into the eukaryotic host cell [55] , [56] . Recent work by Zhou et al [25] has identified an isoflavone biosynthesis enzyme GmHID1 as a target of HopZ1a and the closely related HopZ1b in soybean . Both HopZ1a and HopZ1b interact with the soybean GmHID1 protein in vivo and in vitro . However , Zhou et al did not observe any acetyltransferase activity of HopZ1 in the presence of GmHID1 and 14C-labeled acetyl CoA [25] . We speculate that the lack of acetyltransferase activity of HopZ1a is most likely due to the absence of the appropriate eukaryotic co-factor , or that while HopZ1 may bind to GmHID1 , it is not a direct target of its enzymatic activity . Previously we showed that the HopZ family has low-level in vitro protease activity using fluorescently-labeled casein as a generic substrate [12] . While our current confirmation of acetyltransferase activity is apparently at odds with these original findings , Mukherjee et al . [31] describe a “ping-pong” acetylation mechanism in which acetyltransferases and proteases use the same catalytic mechanism on different substrates . Consequently , it is possible that the acetyltransferase catalytic triad of the HopZ T3SEs could induce the weak fluorescence observed in our original assay , although the target of protease activity remains to be identified . Upon activation by phytic acid , HopZ1a strongly autoacetylates in cis . Like its R . solanacearum homolog PopP2 [21] , a mutation in the conserved lysine residue ( K289 in HopZ1a ) abrogates HopZ1a's autoacetylation activity and disrupts host recognition as evident by the lack of HR ( Figures 3A–C ) . These results further support that it is the activity of HopZ1a that is recognized by the ZAR1 resistance protein in Arabidopsis [12] , [26] , [28] . In addition , the HopZ1a ( K289R ) mutant loses its virulence function and cannot promote bacterial growth in plants lacking the resistance protein ZAR1 ( Figure 3D ) . These data indicate that the K289 autoacetylation site is conserved among members of the YopJ / HopZ superfamily and contributes to both virulence and avirulence functions . Microtubules play an essential role in intracellular vesicle transport , structural support , cell division , developmental processes , and potentially host defense against fungal and oomycete pathogens [39] , [57]–[60] . However , the role of microtubules in plant immunity against bacterial pathogens is unknown . Our data provide evidence that microtubules play an important role in defense against bacterial pathogens as the microtubule inhibitor oryzalin promotes P . syringae growth . However , the relatively weak growth advantage conferred to PtoDC3000 by oryzalin treatment may suggest that this pathovar possesses type III effectors that can target microtubules and/or possess activities that are functionally equivalent to microtubule destruction . Furthermore , the observation that oryzalin treatment did not promote PtoDC3000 ΔhrcC suggests that microtubule destruction is not sufficient to disrupt basal resistance/PTI and that microtubule destruction is just one component of the net virulence activity used by PtoDC3000 to promote in planta growth . We have also demonstrated that HopZ1a can block secretion , which may be partially as a result of microtubule destruction . Components of the secretory pathway have been demonstrated to play a role in resistance to P . syringae in Arabidopsis [61] , [62] , as well as in other pathosystems [63] . Most importantly , HopZ1a inhibits cell wall-based defenses by suppressing callose deposition in response to PAMP . Given that the cytoskeleton is important for trafficking of the flagellin receptor FLS2 [64] , [65] and potentially the proper positioning of callose synthase , we speculate that a component of HopZ1a PTI-suppression is a consequence of microtubule destruction . Thus , by altering cortical microtubules with HopZ1a , P . syringae could affect intracellular transport and secretion thereby uncoupling defense signaling and cell wall-based defenses [45] , [59] , [60] , [66] .
Promoter-less hopZ1a without the stop codon was PCR amplified from P . syringae pv . syringae A2 and cloned into the C-terminal TAP fusion vector [29] . Promoter-less hopZ1a was PCR amplified and cloned into the Gateway pENTR vector ( Invitrogen , USA ) using the BP reaction , followed by subcloning into the pDEST15 GST purification vector using the LR reaction . The HIS-HopZ1a and HIS-HopZ1a ( C216A ) purification constructs in pET14b ( Novagen , USA ) were described by Ma et al . [12] . All catalytic hopZ1a ( C216A ) mutants were generated using the QuikChange site-directed mutagenesis kit ( Agilent Technologies , USA ) . To make hopZ1a/zar1-1 transgenic plants , hopZ1a with an in-frame HA tag was PCR amplified and cloned into the dexamethasone-inducible pBD vector ( a gift from Dr . Jeff Dangl , University of North Carolina , Chapel Hill , NC , USA ) . Transient transfections of HEK293T cells were performed as previously described [29] . In brief , 5 µg of plasmid DNA and 5 µg of carrier DNA were added to 50 µl of 2 . 5 M CaCl2 , 400 µl of sterile ddH2O and 500 µl of 2× HEPES-buffered saline ( HBS ) [50 mM HEPES pH 7 , 10 mM KCl , 12 mM dextrose , 280 mM NaCl and 1 . 5 mM Na2PO4] . The calcium phosphate-DNA suspension was added drop-wise to 10 cm-plates of HEK293T cells . Cell culture media was changed 16 hours post transfection . Images of transfected and untransfected cells were taken 24 hours after transfection at 40× magnification , using Nikon Eclipse TS100 microscope and Canon DS6041 camera . HEK293T cells expressing the TAP-tagged HopZ1a wild-type construct were used for tandem affinity purification and subsequent LC-MS/MS analysis as previously established [29] . HEK293T cells expressing HopZ1a-TAP or HopZ1a ( C216A ) -TAP were lysed in TAP-lysis buffer [0 . 1% Igepal CA 630 , 10% glycerol , 50 mM HEPES; pH 8 . 0 , 150 mM NaCl , 2 mM EDTA , 2 mM DTT , 10 mM NaF , 0 . 25 mM NaOVO3 , 50 mM beta-glycerophosphate , and protease inhibitor cocktail ( Sigma , USA ) ] for 15 min at 4°C . The cleared lysates were separated by 12% SDS-PAGE , transferred to nitrocellulose membrane and detected by anti-HA antibodies ( Sigma , USA ) via chemiluminescence ( Amersham , USA ) . The His-tagged HopZ1a and HopZ1a ( C216A ) proteins were overexpressed in Escherichia coli BL21-Gold ( DE3 ) cells ( Stratagene , USA ) [12] , purified using High Trap chelating columns ( GE Healthcare , USA ) preloaded with nickel , followed by size-exclusion chromatography using the AKTA FPLC with the Superdex S-200 column ( GE Healthcare , USA ) . The GST-tagged HopZ1a and GST proteins were purified using glutathione Sepharose 4B column ( GE Healthcare , USA ) . CM5 research grade sensor chips were docked into the Biacore 3000 biosensor ( GE Healthcare , USA ) and preconditioned in HPS-EP running buffer [ ( 0 . 01 M HEPES pH 7 . 4 , 0 . 15 M NaCl , 3 mM EDTA , 0 . 005% Surfactant P20 ) ( GE Healthcare , USA ) ] with 3 consecutive pulses of 10 ul each of 50 mM HCl , 50 mM NaOH , 0 . 5% SDS , and water at a flow rate of 100 ul/min . Injection of 200 mM 1-Ethyl-3- ( 3-dimethylaminopropyl ) carbodiimide hydrochloride , ( EDC ) and 50 mM N-Hydroxysuccinimide ( NHS ) activated the CM5 sensor chip surface . 100 µg of purified recombinant HopZ1a , HopZ1a ( C216A ) and GST proteins were immobilized on CM5 chips in Biacore 3000 ( GE Healthcare , USA ) at a flow rate of 10 µl/min at 25°C by amine coupling using the Biacore Surface Preparation Wizard , followed by injection of 1 M Ethanolamine ( GE Healthcare , USA ) to block any remaining activated ester groups . Binding experiments were conducted at 25°C in HPS-EP running buffer in HPS-EP running buffer . 500 µg/ml of bovine brain tubulin or soybean tubulin ( Cytoskeleton Inc . , USA ) was injected over the sensor chip surface BIAcore Quickinject protocol . Taxol-stabilized microtubules were prepared as previously described [30] . In brief , 2 . 5 mg/ml of bovine brain tubulin ( Cytoskeleton Inc . , USA ) was resuspended in 1× BRB80 ( 80 mM PIPES pH 6 . 8 , 1 mM MgCl2 , 1 mM EGTA ) plus 1 mM DTT , 1 mM GTP and 5% DMSO , followed by 30 min incubation at 37°C . Polymerized microtubules were centrifuged at 55 , 000×g for 15 minutes at 25°C . The microtubule pellet was resuspended in warm 1× BRB80 with 10 µM Taxol to generate Taxol-stabilized microtubules . Purified recombinant HIS-HopZ1a , HIS-HopZ1a ( C216A ) and HIS-HopF proteins were pre-cleared of protein aggregates by centrifugation at 100 , 000×g for 10 minutes at 4°C . 1 µg each of pre-cleared HopZ1a , HopZ1a ( C216A ) and HopF2 , in the presence or absence of 0 . 25 mg/ml of Taxol-stabilized microtubules , were incubated for 30 minutes at 25°C . Microtubules were recovered from the solution by centrifugation at 55 , 000×g for 15 minutes at 25°C . The resulting pellet ( P ) and supernatant ( S ) fractions were separated by SDS-PAGE , transferred to nitrocellulose membrane and detected with Ponceau X stain followed by α-His antibodies ( Cell Signaling Technology , USA ) via chemiluminescence ( Amersham , USA ) . HopZ1A ( C216A ) -HA and HopF2-HA transgenic Arabidopsis leaves were snap frozen in liquid nitrogen and then 1 g of tissue was homogenized in 2 ml of ice-cold extraction buffer composed of HEPES-KOH ( pH 7 . 5 ) , 50 mM KCl , 2 mM EDTA , 1 mM DTT , 0 . 2% Triton-X 100 , 0 . 1 mg/ml dextran and 1∶100 ( v/v ) plant protease inhibitor ( Sigma #P9599 , USA ) . Homogenates were centrifuged at 4°C and 10 , 000×g for 10 min , and the supernatants reserved as clarified extract . Final concentrations of 1 mM GTP and 10 µM taxol were added to clarified extracts , followed by incubation at room temperature for 30 min to stabilize microtubules . Subsequently , 1 . 5 ml of stabilized extracts were subjected to co-immunoprecipitation by the addition of 30 µl α-HA IgG antibodies conjugated to agarose beads ( Sigma; A2095 ) and incubated overnight at 4°C with gentle inversion . Agarose beads were collected by centrifugation at 4°C and 1000×g for 2 min and washed twice with 500 µl ice cold extraction buffer , followed by two washes with 1 ml RIPA buffer ( 50 mM Tris-HCL ( pH 7 . 4 ) , 150 mM NaCl , 1% NP-40 , 0 . 25% Deoxycholate ) . Proteins were eluted from α -HA IgG-agarose beads by incubating 30 µl beads in 100 µl Laemmli sample buffer for 5 min at 95°C . 5 µl , 2 . 5 µl and 1 . 25 µl of samples ( corresponding to 5% , 2 . 5% and 1 . 25% of eluates ) were resolved by 10% SDS-PAGE and transferred to nitrocellulose membranes . Blots were probed with either 1∶20 , 000 α-HA antibodies ( Roche ) or 1∶2000 α-tubulin antibodies ( Sigma #T9028 , USA ) . Immunoreactive bands were visualized using horse radish peroxidase-conjugated secondary antibodies and detected via chemiluminescence ( Amersham , USA ) . Band intensities were quantified using the Gel Analyzer tool in ImageJ ( NIH ) . 1 , 2 , 5 , 10 or 20 µg of purified bovin e brain tubulin ( Cytoskeleton Inc . , USA ) or 100 nM of phytic acid ( Sigma #P5681 , USA ) was mixed with 2 µl of 14C-acetyl CoA ( 56 µCi/µM ) in the absence or presence of 1 µg of HopZ1a , HopZ1a ( C216A ) , or HopZ1a ( K289R ) in a 20 µl reaction containing 50 mM HEPES ( pH 8 . 0 ) , 10% glycerol , 1 mM DTT and was incubated for 1 hour at 30°C [15] . For Proteinase K-treated tubulin or phytic acid , 2 µg of tubulin or 100 nM of phytic acid was pre-incubated with 0 . 04 µg/µl ( 1/500 ) or 0 . 005 µg/µl ( 1/5000 ) of Proteinase K for 20 minutes at 37°C , and heat-inactivated for 10 minutes at 95°C . The reaction mixtures were loaded onto 12% SDS-PAGE gels and ran at 120 V for 90 minutes . The radioactive gels were fixed with 50% methanol and 10% glacial acetic acid for 30 minutes , followed by 15-minute incubation in the Amplify ( Amersham , USA ) enhancing solution . Gels were dried and placed in a phosphorimager cassette ( Bio-Rad , USA ) at −20°C for at least two weeks . Leaves of four-week-old Arabidopsis Col-0 plants were syringe-infiltrated with PtoDC3000 at OD600 = 0 . 04 ( ∼2×107 CFU/ml ) . Following inoculation , four leaf discs ( 1 . 5 cm2 ) were harvested from each plant , soaked in distilled water ( dH2O ) for 45 min , and transferred to 6 ml of dH2O . Conductivity readings were taken using an Orion 3 Star conductivity meter ( Thermo Electron Corporation , USA ) . Arabidopsis zar1-1 was syringe-infiltrated with P . syringae pv . cilantro 0788-9 ( Pci0788-9 ) carrying pUCP20 , pUCP20-hopZ1a-HA or pUCP20-hopZ1a ( K289R ) -HA at ∼1×105 CFU/ml ( OD600 = 0 . 0002 ) . For the oryzalin experiments , Arabidopsis Col-0 or zar1-1 plants were syringe-infiltrated with PtoDC3000 , P . syringae pv . maculicola ES4326 , PtoDC3000 carrying pUCP20 or pUCP20-hopZ1a-HA in the presence of 1% ethanol or 100 µM oryzalin . Four leaf-discs ( 1 cm2 ) were collected from each plant and colony counts were performed on Day 0 and Day 3 using established methods [26] . The experiment was performed at least three times . Surface-sterilized A . thaliana GFP-MAP4 [37] and GFP-AtEB1 [38] seeds were grown as previously described [34] . Five-day-old seedlings were inoculated with PtoDC3000 carrying pUCP20 , pUCP20-hopZ1a-HA , pUCP20-hopZ1a ( C216A ) -HA , or pDSK519-avrRpt2 to a final concentration of 107 CFU/mL for ∼16 hours . Epidermal layers of five- or six-day-old seedlings were visualized with a Leica DMI 6000B fluorescence microscope ( Quorum Technologies , Canada ) . Z-stacks of 80 confocal images separated by 0 . 2 µm were taken and compressed into a 2D-image . Images were analyzed and quantified using the Volocity Quantification software ( Quorum Technologies , Canada ) . For quantification of microtubules , regions in the images that correspond to guard cells were removed and fluorescence signals corresponding to microtubules from the pavement cells were quantified by Volocity Quantification module . The ends of microtubules were similarly quantified after removing regions in the images that correspond to guard cells . HopZ1a-myc , HopZ1a ( C216A ) -myc , and AtSYP121-Sp2-myc were transiently expressed in leaves of Nicotiana benthamiana expressing secGFP using Agrobacterium infiltration . Extracellular ( apoplastic ) fluid was isolated from infiltrated leaves at 24 h post-inoculation using established protocols [41] . Intracellular proteins from the remaining leaf tissue were separated by SDS-PAGE followed by immunoblot analysis using an anti-GFP serum . To verify expression of the effector proteins , the blot was stripped and then probed with an anti-myc antibody . zar1-1 plants [28] were transformed with pBD::hopZ1a-HA and pBD::hopZ1a ( C216A ) -HA [26] using the floral dip method . Transgenic plants were selected by Basta resistance and confirmed by PCR and sequencing . The zar1-1 genotype was confirmed by PCR and by loss of the HopZ1a-induced hypersensitive response [28] . Homozygosity of T3 lines was determined by their segregation ratios on plates containing half-strength Murashige and Skoog ( MS ) media and 6 mg/L bialophos . Three-week-old zar1-1 or zar1-1/Dex:hopZ1a transgenic line ( 2D ) plants were sprayed with 30 µM dexamethasone to induce HopZ1a protein expression for 24 h . Leaves were then syringe-infiltrated with 10 µM of purified flg22 peptides and harvested 24 h post flg22-infiltration , cleared , and stained with 0 . 01% aniline blue for callose as previously described [47] . Leaves were visualized with a fluorescence microscope and the callose deposits were calculated using Image J software . 100 µM of oryzalin ( in 1% ethanol ) or 1% ethanol alone was co-infiltrated with 1×105 CFU/mL of PtoDC3000 or PtoDC3000 ΔhrcC into four-week old A . thaliana leaves . Four leaf-discs ( 1 cm2 ) were collected from each plant and colony counts were performed on Day 0 and Day 3 following standard growth assay methods [26] . | Many bacterial pathogens disrupt key components of host physiology by injecting virulence proteins ( or “effectors” ) via a needle-like structure , called the type III secretion system , directly into eukaryotic cells . The YopJ / HopZ superfamily of type III secreted effector proteins is found in pathogens of both animals and plants providing an excellent opportunity to address how a family of type III secreted effectors can promote pathogenesis in hosts from two kingdoms . YopJ from the animal pathogen Yersinia pestis is an acetyltransferase that targets signaling components of innate immunity and prevents their activation . Here we show that HopZ1a , from the phytopathogen Pseudomonas syringae is an acetyltransferase that binds plant tubulin . Like YopJ , the eukaryotic cofactor phytic acid activates the acetyltransferase activity of HopZ1a . In addition , we demonstrate that activated HopZ1a can acetylate tubulin , a major constituent of the eukaryotic cytoskeleton . In plants , activated HopZ1a causes a dramatic destruction of microtubule networks , inhibits protein secretion , and ultimately suppresses cell wall-mediated defense . Our study emphasizes the functional diversification of this important type III effector family in plant and animal hosts using a conserved acetyltransferase activity . | [
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| 2012 | A Bacterial Acetyltransferase Destroys Plant Microtubule Networks and Blocks Secretion |
The rapid invasion and spread of Aedes albopictus ( Skuse , 1894 ) within new continents and climatic ranges has created favorable conditions for the emergence of tropical arboviral diseases in the invaded areas . We used mosquito abundance data from 2014 collected across ten sites in northern Italy to calibrate a population model for Aedes albopictus and estimate the potential of imported human cases of chikungunya or dengue to generate the condition for their autochthonous transmission in the absence of control interventions . The model captured intra-year seasonality and heterogeneity across sites in mosquito abundance , based on local temperature patterns and the estimated site-specific mosquito habitat suitability . A robust negative correlation was found between the latter and local late spring precipitations , indicating a possible washout effect on larval breeding sites . The model predicts a significant risk of chikungunya outbreaks in most sites if a case is imported between the beginning of summer and up to mid-November , with an average outbreak probability between 4 . 9% and 25% , depending on the site . A lower risk is predicted for dengue , with an average probability between 4 . 2% and 10 . 8% for cases imported between mid-July and mid-September . This study shows the importance of an integrated entomological and medical surveillance for the evaluation of arboviral disease risk , which is a precondition for designing cost-effective vector control programs .
The invasive mosquito species Aedes ( Stegomyia ) albopictus ( Skuse , 1894 ) ( syn . Stegomyia albopicta ) has spread widely in Europe over the last twenty years , especially in the Mediterranean region [1 , 2] . Aedes albopictus is a highly competent vector for many arboviruses including chikungunya [3 , 4] , dengue [3 , 4] and Zika virus [5] . Therefore , its broad diffusion provides favorable conditions for the potential spread of diseases that have been until now confined to tropical regions [6] , and which are continuously imported into temperate regions by infected international travelers . A major outbreak of chikungunya occurred in Italy in 2007 , with over 200 cases [7] , and sporadic local transmission has been recorded elsewhere in Europe [8 , 9] . A small outbreak of dengue ( 15 cases ) took place in Croatia in 2010 [10] , followed by a much larger one , mainly transmitted by Aedes aegypti L . , in the Portuguese island of Madeira in 2012 [11] , with over 2000 cases; autochthonous dengue transmission has been repeatedly reported in France as well [12 , 13] . In Italy , chikungunya and dengue are included in the mandatory medical surveillance system [14] to apply timely interventions aimed at limiting the impact of imported tropical infections [9] . In addition to medical surveillance , entomological surveillance is crucial to evaluate potential risks and identify optimal preventive and control strategies targeting the vector population . Aedes albopictus populations are highly variable across different sites , depending among other things on local temperature patterns , which strongly influence their life cycle [15] , and on the amount and quality of breeding sites available for egg hatching and development of immature stages . The amount of breeding sites also depends directly on many complex factors , including precipitation patterns , air humidity , availability of natural or artificial containers , presence of shaded areas , and indirectly on land use ( vegetation index , urbanization level ) . In particular , precipitations fill natural and artificial containers , creating potential habitats for the aquatic stages; on the other hand , excessive rainfalls may flush larval habitats reducing the overall population of adult mosquitoes [16] . Temperature and air humidity influence the evaporation of water , which is detrimental to the productivity of the breeding site [17] and may lead to complete drying out of the container . The suitability of a breeding site for oviposition may depend , among other factors , on: water surface and depth [18] which vary with evaporation and meteoric water input; nutrient concentration [19] , depending on the presence , density and type of vegetation surrounding the container; and shading [20] which depends on land use and vegetation . The combination of all these dynamic variables determines a high spatial and temporal heterogeneity of mosquito population densities . The purpose of this study is to estimate the potential risk of outbreaks of chikungunya and dengue in northern Italy related to the abundance of Ae . albopictus mosquitoes . To this aim , we estimated vector abundance by fitting a mathematical model for mosquito population dynamics to novel data obtained from entomological surveillance .
The study was carried out in the provinces of Belluno ( 46°08’27”N , 12°12’56”E ) and Trento ( 46°04’00”N , 11°07’00”E ) , northern Italy . This mountainous area covers a large part of the Dolomites and the Southern Alps . More than 70% of the territory lies over 1 , 000 m a . s . l . and about 55% is covered by coniferous and deciduous forests [21] . The climate is temperate-oceanic with four main areas: sub- Mediterranean ( close to Lake Garda with mild winters ) , subcontinental ( the main valleys with more severe winters ) , continental ( the alpine valleys ) and alpine ( the areas above the tree line ) [21] . The human population as of 2014 is around 208 , 000 in the Belluno Province and 537 , 000 in the Trento Province with the majority of the population concentrated in the valley floors . An entomological surveillance has been initiated in northeastern Italy since the introduction and establishment of Ae . albopictus in 1991 [22] . Furthermore , Aedes koreicus ( Edwards , 1917 ) , another invasive mosquito species , has been detected in the Belluno province in 2011 and in the Trento province in 2013 [23 , 24] . Therefore , as part of the LExEM project ( Laboratory of Excellence for Epidemiology and Modeling , www . lexem . eu ) , a mosquito monitoring focused on these two invasive Aedes species has been carried out in several localities of the provinces of Belluno and Trento ranging from 74m a . s . l . to 650m a . s . l . ( see S1 Text ) . Seventy trapping locations in ten municipalities ( Feltre , Povo , Riva del Garda , Santa Giustina , Strigno , Tenno , Tezze , Trento , Belluno and Rovereto ) were selected by skilled entomologists in different places such as houses , schools , cemeteries , garden centers , public buildings , farms and tyre storage ( Fig 1 ) . Mosquitoes were collected using Biogents Sentinel traps ( Biogents AG , Regensburg , Germany ) baited with BG lures and CO2 from dry ice . Traps were placed in shaded positions sheltered from wind and rainfall as recommended by the manufacturer . All the collections were conducted in 2014 . Each trap was set fortnightly for 24h from mid-April to the beginning of November except the trap of Rovereto which was set monthly from the end of June to the end of September . After each trapping session , mosquitoes were killed by freezing at -20°C and identified using taxonomic keys [25 , 26] and confirmed by PCR if found in a location for the first time [26] . The MODIS sensors onboard the sun-synchronous polar-orbiting NASA satellites AQUA and TERRA are designed for global environmental monitoring , providing four records per day with a spatial resolution of 250 m to 1000 m . Most MODIS land products are accompanied by a detailed pixel-wise quality assessment , which facilitates further automated processing such as filtering and enhancement . Land surface temperature ( LST ) data for 2014 were obtained from the MODIS version 5 LST products MOD11A1 and MYD11A1 with four records per day and a spatial resolution of 1000 m . We applied outlier filtering and gap filling to these data and enhanced the spatial resolution to 250 m [27] . Here we used daily averages calculated from the four records per day of the reconstructed dataset . We developed a mathematical model representing the dynamics of Ae . albopictus populations throughout the whole developmental cycle ( eggs , larvae , pupae , female adults ) within a single mosquito season ( April to November ) . We adopted a compartmental structure similar to other models commonly used for Ae . albopictus [28–30] and other related species [31] . We used previous estimates of temperature-dependent rates of mortality , development from one stage to the next , and gonotrophic cycle [28] based on experimental data specific for Ae . albopictus [15] . The model is initialized at April 1st 2014 with a fixed number of 10 , 000 mosquito eggs . The model robustness with respect to this assumption was verified by means of a sensitivity analysis . Abundance variability across sites was captured by local temperatures as well as by the estimation of a site-specific parameter summarizing the quantity and quality of breeding sites and representing the carrying capacity of the larval population . In other words , larval carrying capacity represents a measure of habitat suitability in our analysis . The process of capture of adults was modeled with a common capture rate across sites that was also estimated during the calibration process . We do not explicitly consider mosquito dispersal , because the average flight range of Ae . albopictus is much smaller ( a few hundreds of meters [32] ) than the scale of our risk predictions , which are given at the municipality level . However , the mosquito flight range was implicitly considered in the modeling of the capture process ( see S1 Text ) . The number of captures estimated by the model was calibrated to experimental data by a Monte Carlo Markov Chain approach based on a Poisson likelihood combined over the capture sessions and ten study sites . All details are reported in the S1 Text . We investigated the relationship between site-specific habitat suitability ( encoded by values of the larval carrying capacity estimated by the population model ) and two precipitation variables: the average daily rainfall ( rA ) and the number of rainy days ( dr ) , calculated for each study site . We sampled 10 , 000 sets of values for the carrying capacities of the ten sites from their posterior distributions , and computed the precipitations occurring at the corresponding study sites within a given time window . We performed a correlation test with Spearman rank method between each set of carrying capacity values and the two precipitation variables . If the correlation was statistically significant ( p<0 . 05 ) , the value of Spearman’s rank correlation coefficient ( ρ ) was stored for that test; otherwise we considered the correlation coefficient to be zero . We then computed the 95% credible interval ( CI ) of the distribution of 10 , 000 correlation coefficients and considered the correlation to be robust if both boundaries resulted to have the same sign ( i . e . either strictly positive or strictly negative ) . This procedure was iterated over 340 different temporal windows , starting on 34 different dates ( between January 1st and August 20th 2014 , with interval of one week ) and with ten different window lengths ( between 4 and 13 weeks ) . For instance , the first temporal window of 4 weeks starts on January 1st and ends on January 28th while the last temporal window of ten weeks starts on August 20th and ends on November 10th . We assessed the potential risk of outbreaks of chikungunya and dengue caused by the introduction in the study sites of a single infectious individual , in the absence of control interventions . To this aim , we used mathematical formulae previously derived by the analysis of host-vector transmission dynamic models [33 , 34] . These models assume the same SEI-SEIR epidemiological structure for both chikungunya and dengue: Ae . albopictus female adults may become infected after biting an infectious human host and start transmitting to other hosts after an incubation period and throughout the rest of their life ( SEI ) . Human hosts may be infected by a bite of an infectious mosquito , develop symptoms after a latent period and may transmit to other mosquitoes for a given time , called infectious period ( SEIR ) . The average number of mosquitoes infected by a single infectious human host in a population of fully susceptible mosquitoes and hosts is given by: RHV=k βVτVHωVωV+μV where k is the biting rate of mosquitoes , βV is the probability of transmission to mosquitoes per bite , τ is the infectious period of a human host , V is the density of female adult vectors , H is the density of human hosts , ωV is the inverse of the mosquito incubation period and μV is the mosquito mortality rate . Similarly , the average number of hosts infected by a single infectious mosquito introduced in a population of fully susceptible mosquitoes and hosts is given by: RVH=k βHμV where βH is the probability of transmission to humans per bite . The basic reproduction number ( i . e . the average number of secondary human infections caused by the introduction of a single infectious host in a population of fully susceptible mosquitoes and hosts ) can be simply computed as the product of these two quantities: R0=RHVRVH When a single infectious host is introduced in a population , the transmission process is stochastic and the host can heal before transmitting the infection even for values of R0 much above the epidemic threshold , purely because of chance . In this case , transmission dies out early without causing an epidemic outbreak . The probability that an outbreak will actually take place can thus be defined , and for a SEI-SEIR model is given by [35]: pO=1−RVH+1RVH ( RHV+1 ) We compute the reproduction numbers and probabilities of outbreak for both chikungunya and dengue in the ten study sites at different times of introduction of the index case . In particular , the values for the transmission probabilities , host infectious period and mosquito incubation period were taken from [28] for chikungunya , and from [36] for dengue . The mosquito incubation period and the transmission probabilities for dengue ( but not for chikungunya ) have been shown to depend on temperature [36] , according to formulae initially estimated by Focks and colleagues on Ae . aegypti [37] . We used these formulae to compute those parameter values over time based on the daily temperature recorded at each trap locations . For both chikungunya and dengue , we assumed the same biting rate of 0 . 09 bites per mosquito per day estimated for the chikungunya outbreak occurred in 2007 in Italy [28] . The density of human hosts was estimated by dividing the census population of municipalities considered in the study by the corresponding urban surface . Finally , the density of female adult mosquitoes was obtained by dividing model estimates of the total population by the surface of the modeled capture area ( about 56 ha , see S1 Text ) . Differences across sites in human density , local temperatures influencing epidemiological parameters , and the estimated vector abundance translate into a geographical heterogeneity in the risk of outbreaks .
In Fig 2 , we show the density of female adults over time as estimated by the model for the different study sites . The curves are substantially synchronized across sites due to the relative homogeneity of temperature patterns over time , although quantitative differences in average temperatures ( see S1 Text ) contribute to the geographical heterogeneity in mosquito abundance . The average peak density ranges from 17 . 0 ( 95% CI: 10 . 3–25 . 5 ) adult female mosquitoes per hectare in Strigno to 365 ( 95% CI: 264–562 ) in Riva del Garda . These estimates were quite robust with respect to the model assumption on the initial number of eggs ( see S1 Text ) . Overall , the general seasonal pattern is well reproduced both qualitatively and quantitatively by the model ( see S1 Text ) . Fig 3 shows boxplots of the distribution of errors between the observed number of captured females and corresponding model estimates ( with 95% CI ) for the ten study sites , normalized by the corresponding maximum number of captured females during the season to allow comparability across sites . In Table 1 , we reported two metrics to assess the goodness of fit: the value of R2 between predicted and observed values and the root mean squared error , normalized by the maximum number of captured females ( nRMSE ) . Both metrics are obtained by averaging over the 10 , 000 stochastic simulations . Two sites , Strigno and Belluno , are characterized by a lower R2 and higher nRMSE . The poorer fit at these two sites can be explained with the low estimated local mosquito abundance ( see Fig 2 ) , resulting in a stronger variability driven by stochastic effects in the capture process . Posterior means and 95% CI of estimates for the capture rate ( α0 ) and the site-specific larval carrying capacities ( as ) , as estimated by the MCMC procedures , are reported in Table 2 . A robust association between the average daily amount of precipitations and the estimated carrying capacities was found only in one time window , covering the period May 1st—June 5th , with a strong negative correlation ( average Spearman’s ρ = -0 . 76; 95% CI: -0 . 69 –-0 . 82 ) . When considering the number of rainy days , a negative correlation was also found for 17 time windows , covering periods between April 17th and July 17th ( average ρ ranging between -0 . 70 and -0 . 84 , depending on the time window , see S1 Text ) . Thus , late spring/early summer precipitations contribute negatively to local mosquito abundance and therefore to transmissibility of arboviruses . Figs 4 and 5 show the predicted peak value of R0 in the ten study sites for chikungunya and dengue respectively , against four site-specific variables: local altitude ( computed as an average between the altitudes of active traps ) , average daily rainfall between May 1st and June 5th , average temperature between April 1st and October 31st , and urban density . These figures summarize the effects of different local environmental factors on the geographic heterogeneity in the transmissibility of arboviral diseases . A Spearman rank correlation test between the peak value of R0 for each disease and each variable was computed , and the results are summarized in Table 3 . Lower altitudes and higher temperatures significantly increase R0 by creating favorable conditions for the vector population . Conversely , an excess of precipitations in late spring is associated with a reduction in the larval carrying capacity and , therefore , in the amount of mosquitoes available for transmission . Finally , urban population density does not seem to have a strong effect on the transmissibility of disease , despite the fact that this variable appears directly in the denominator of R0 . This is explained by considering that , in a mountainous region such as the one under study , densely populated areas are concentrated at the bottom of the valleys , characterized by lower altitudes and warmer temperatures . Therefore , the negative effect of higher urban density on transmission is offset by the presence in the same sites of higher vector densities . We estimated the basic reproduction number of a potential chikungunya outbreak originated by a single imported case . The peak value of R0 is highly heterogeneous across sites , ranging from 0 . 25 ( 95%CI: 0 . 15–0 . 38 ) in Strigno to 2 . 9 ( 95% CI: 2 . 0–4 . 2 ) in Feltre and occurring , depending on the site , between mid-August and mid-September . The latter compares well with the R0 estimated for the chikungunya outbreak occurred in the Italian region of Emilia-Romagna in 2007 [28] and suggests the existence of further areas where similar outbreaks are likely to occur . Fig 6 reports the corresponding probability of observing an outbreak for different times of importation of the index case in the ten study sites . The three sites characterized by lowest estimated mosquito abundance ( Strigno , Tenno and Belluno ) are , according to the model , virtually safe from possible outbreaks . For all other sites , we can define a “chikungunya season” as the period over which the probability of observing an outbreak is higher than zero . The probability of an outbreak , averaged within each site’s chikungunya season , ranges from 4 . 9% in Tezze over a period of just below two months ( end July to mid-September ) , to 25% in Feltre for a duration of 4 . 5 months covering the whole summer and up to mid-November . The peak probability within these seasons ranges from 8 . 6% ( 95% CI: 0 . 0–23 . 2% ) in Tezze to 38 . 3% in Feltre ( 95% CI: 25 . 0–51 . 2% ) . Equivalent model predictions for dengue suggest generally lower values of R0 than for chikungunya . This is due to lower average transmissibility ( confirmed experimentally in Ae . albopictus from northern Italy [38] and France [3] ) and longer incubation periods [37 , 38] , resulting in longer generation times , so that a higher proportion of mosquitoes die while in the exposed ( non-infectious ) phase . Peak values of R0 range from 0 . 15 ( 95% CI: 0 . 09–0 . 22 ) in Strigno to 2 . 3 ( 95% CI: 1 . 7–3 . 3 ) in Riva del Garda and occur generally at the end of July , i . e . earlier than chikungunya . The anticipated peak of dengue transmissibility depends on the steady decrease of temperatures registered in 2014 after the end of July across all study sites ( see S1 Text ) , which has reduced the estimated transmission probability and increased the incubation period [37] , thereby offsetting the continued growth of vector abundance throughout August . Fig 7 shows the predicted probability of observing a dengue outbreak at different times of importation . Predictions are much more erratic over time than for chikungunya due to the strong temperature dependence assumed for dengue epidemiological parameters . Tezze adds to the list of virtually outbreak-free study sites , and the probability of outbreaks averaged over dengue seasons ranges from 4 . 2% in Trento , with a season length of one month across the end of July , to 10 . 8% in Riva del Garda over a two-month season that extends up to mid-September . The peak probability ranges from 12 . 1% ( 95% CI: 1 . 2–24 . 8% ) in Trento to 27 . 8% ( 95% CI: 16 . 5–40 . 9% ) in Riva del Garda . A spatial interpolation of R0 is not possible , given the small number of data points ( ten , i . e . one for each study site ) . For the same reason , a multivariate analysis to assess the contribution of environmental factors to local mosquito abundance or transmissibility would be statistically underpowered . Therefore , to provide spatial estimates of possible ranges of the outbreak risk , we applied our model to predict vector densities within the provinces of Trento and Belluno , based on local temperature records from satellite data and considering spatially constant values of the larval carrying capacity ( see S1 Text ) . Afterwards , we computed the corresponding outbreak risks using population density maps derived from high-resolution census data . In Fig 8 , we show the peak values of the outbreak risks for both infections in the worst-case scenario , i . e . using the maximum value of the larval carrying capacity estimated among our ten study sites . Fig 8 shows that areas with highest risk are rural villages placed at the bottom of the main valleys , where vector populations are predicted to be most abundant while human density is relatively lower . Minor valleys may be considered potentially free from the risk of outbreaks because of their lower mosquito abundance , which is in turn due to lower average temperatures . In the worst-case scenario , the fraction of the population living in areas with peak outbreak risk higher than zero is up to 74% of the total for chikungunya and 50% for dengue . However , the importation of a case in the study area at the exact time of the peak has a limited probability of generating an outbreak: on average 33% for chikungunya and 16% for dengue . The fraction of the population living in areas with a risk of outbreak higher than 50% after importation of a case at the time of highest risk is 25% for chikungunya and 6% for dengue . These results are in agreement with the specific findings obtained for the 10 study sites where entomological surveillance was carried out .
The diffusion of Ae . albopictus in temperate climates has created the conditions for local transmission of typically tropical pathogens , such as dengue and chikungunya viruses . These pathogens are continually imported into Europe through returning infected travelers from endemic areas , and may occasionally cause outbreaks of autochthonous transmission [7 , 8 , 10 , 11] . In this work , we explore the probability of occurrence of dengue and chikungunya outbreaks in ten different sites from northern Italy , using a mosquito population model calibrated to capture data collected throughout 2014 . The model was able to reproduce seasonal patterns in mosquito abundance , as well as variability across study sites . This geographic heterogeneity was represented in the model by local temperatures driving mosquito life cycles and by site-specific habitat suitability , summarized by a parameter encoding the larval carrying capacity . Increased habitat suitability was strongly associated with reduced precipitations in late spring and early summer . This negative correlation can be interpreted as a washout effect of rainfalls on breeding sites , previously observed in field experiments [39] and entomological observations [40] , although other studies did not find any association between precipitations and Ae . albopictus abundance [41] . Given the strong inter-annual variability of precipitation patterns , our result will need to be validated by further studies . Our analysis also provides estimates on the transmission potential and on the probability of outbreaks of chikungunya and dengue in the absence of control interventions , for different dates of importation of the index case . Based on model estimates of site-specific mosquito abundance and previous estimates for parameters driving host-vector transmission dynamics [28 , 36] , results suggest that six sites out of ten ( Feltre , Povo , Riva del Garda , Santa Giustina , Trento and Rovereto ) are affected by a high and sustained risk of chikungunya outbreak , as well as by a comparatively lower risk of dengue that extends over a shorter period . One study site ( Tezze ) had a negligible risk of a dengue outbreak but a significant risk of a chikungunya outbreak; the remaining three sites ( Belluno , Strigno , and Tenno ) were characterized by lowest observed abundances and had a negligible risk of outbreak for both diseases independently of the time of importation . Based on risk maps of potential outbreak in the study region , results suggest that the density of mosquitoes is expected to be higher in the main valleys . Within these areas , medium-sized towns ( 5 , 000–20 , 000 inhabitants ) may be the local hotspots of an outbreak risk due to their lower urban densities compared to larger towns and cities . The limitation of these risk maps is the use of spatially-constant values for the parameter encoding the local availability and productivity of breeding sites . A punctual estimation of the actual outbreak risk in a given location would require additional entomological surveillance data similar to those collected from our 10 study sites . However , these maps are a useful tool for providing scenario predictions of the potential risk for Chikungunya and Dengue in our study region . For what concerns the temporal variability of the risk of outbreak , it is interesting to compare our predictions with the observed distribution of the dates of importation in Italy in 2006–2014 ( [42] and C . Rizzo , personal communication ) , which shows a clear seasonal pattern . For chikungunya , imported cases occur with equal probability throughout the year , except between June and September , which account for over 60% of imported cases overall . The peak of importations overlaps significantly with the peak of the outbreak risk for chikungunya ( August and September , see Fig 6 ) . Similarly , the rate of imported cases for dengue is about constant throughout the year , except for the months of August and September , representing alone about one sixth and one fourth of all imported cases respectively . In this case the peak of importations occurs slightly late with respect to the peak in outbreak risk ( mid-July to mid-August , Fig 7 ) , meaning that only a small fraction of imported cases occur when the risk of dengue is sufficiently high for an outbreak . This may explain why , despite the much higher number of imported cases of dengue ( 502 between 2006 and 2014 ) relatively to chikungunya ( 95 in the same period ) , Italy has already experienced a large chikungunya outbreak but only sporadic cases of autochthonous dengue transmission . However , these conclusions need to be taken with caution: the timing of importation of cases depends on international travels and on the timing of epidemics in countries from which the infection is imported . In addition , model predictions are strongly dependent on the recorded temperature patterns in 2014 in terms of the estimated mosquito abundance and , for dengue , also in terms of epidemiological parameters . Yearly variations in temperature records over time may provide different results for both the estimated risk and its timing . In the S1 Text , we propose a sensitivity analysis where a constant temperature variation between -2°C and +2°C is applied to the observed daily temperature . The analysis shows that model predictions are robust in terms of sites considered at risk of an outbreak , but the probability of outbreak and season length may vary considerably for sustained daily temperature variations higher than ±0 . 5°C . Another key source of uncertainty for model estimates is the value of the biting rate . We assumed it to be equal to that estimated during the 2007 chikungunya outbreak [28] and performed a sensitivity analysis using boundaries of the estimated credible interval in the same study [28] . Resulting predictions range from complete absence of outbreak risk for both chikungunya and dengue in all sites , to a doubling of estimates of the peak probability of outbreak for both diseases with respect to the main analysis . However , even in the high biting rate scenario , sites with lowest abundances maintained a very low or negligible risk of chikungunya or dengue outbreaks . Full results of the sensitivity analysis are reported in the S1 Text . In temperate climates , the population dynamics of Ae . albopictus during winters ( overwintering ) remains poorly understood [43] . Shortening photoperiods ( duration of daylight ) induce diapause in female adults , leading to generation of eggs that are particularly resistant to desiccation and cold [44] . It is thought that the hatching of diapausing eggs at low temperatures is slowed down , until warmer temperatures in spring allow survival of the larvae . However , other mechanisms have been shown to contribute to overwintering of Ae . albopictus , such as the availability of refuges with warmer microclimates for both adults and eggs [45] , persisting oviposition at very low temperatures [43] and mosquito dispersal [46] . Lack of data on the abundance of vectors during winter in our study area prevents us from trying to reproduce the mechanism of persistence; therefore , the dynamics of mosquito abundance is simulated only within a single season . Although the population model for Ae . albopictus was able to reproduce well the mosquito capture data from the ten sites , further specifications on some of the biological modifications of the mosquito life cycle at low temperatures and short photoperiods may improve model accuracy . For example , the model has a general tendency to underestimate the sharp drop in the observed number of captured females between the end of October and the start of November . One reason for this may be that the model neglects the reduced developmental rates [47] and increased durations of gonotrophic cycles [48] caused by diapause . In addition , we may be underestimating adult mortality rates at temperatures below 10°C [49] , since our mortality curves are based on data collected at a minimum temperature of 15°C [15] . Finally , a general limitation of population dynamics models applied to arbovirus vectors is the lack of biological data for the endemic mosquito strain under local , possibly natural , environmental conditions [50] . This limitation applies also to this work , where parameters for the mosquito life cycle were estimated from laboratory experiments on Ae . albopictus from Reunion [15] . Collection of analogous data from strains adapted to temperate areas is therefore needed to improve the accuracy of model estimates on vector abundance . As for epidemiological parameters , we used estimates from the 2007 chikungunya outbreak in Italy [28] and from the 2014 dengue outbreak in Madeira [36] . Although the latter epidemics was spread by Ae . aegypti , there are no available estimates for actual outbreaks caused by Ae . albopictus . We preferred to avoid using data from laboratory experiments on Ae . albopictus [3] , given the uncertainties in extending these findings to natural conditions . The competence of Ae . albopictus for transmitting dengue is generally considered lower than that of Ae . aegypti , the dominant vector of these diseases in endemic areas [51] . However , recent experimental results on mosquitoes from southern France [3] found a transmission efficiency for dengue of up to 16–28% in Ae . albopictus and up to 15–18% in Ae . aegypti; however the shorter extrinsic incubation period in the latter species may compensate the lower transmission efficiency in terms of epidemic potential [3] . The high vectorial competence of Ae . albopictus from temperate climates suggests the possibility that a rapid evolutionary adaptation towards a higher transmissibility is ongoing [3 , 4]; in this case the risk of arboviral diseases would be higher than estimated . Furthermore , the Madeira outbreak demonstrated that Ae . aegypti poses an additional serious threat to public health [36] in Europe . Aedes aegypti has recently re-established also on the European mainland ( namely in territories surrounding the Black Sea ) after an absence of several decades [52 , 53] . International travel of persons and goods increases episodes of introduction of Ae . aegypti in other areas , as documented in the Netherlands [52] . Entomological surveillance and mosquito control are therefore critical to prevent its re-establishment , and the ensuing costs to health systems [44] , in other European countries with a suitable climate [54] . Medical surveillance against arboviral diseases has been introduced in Italy since the chikungunya outbreak in the Emilia-Romagna region in 2007 [7] . When a case is diagnosed during the mosquito season , a number of preventive measures are applied , including quarantine of the infected individuals and vector control interventions ( e . g . adulticide spraying in combination with larvicide treatment of public catch basins ) in the neighborhood of the imported case’s residence . Furthermore , many local authorities in areas heavily infested by Ae . albopictus have introduced routine vector control to reduce the transmission risk even in the absence of imported cases . Thanks to preventive and reactive interventions , autochthonous transmission of arboviral diseases has not been observed in Italy since the 2007 outbreak , despite the high risk estimated by our model and the high importation rates ( 48 chikungunya and 78 dengue cases between May and October 2014 alone ) . This public health success highlights once again the importance of surveillance and preventive actions against arboviral diseases in temperate climates . There are other arbovirus infections for which Ae . albopictus is competent and which may be of great public health interest . Aedes albopictus can enhance the spread of West Nile Virus [55] , which is mainly transmitted by endemic mosquito species such as Culex pipiens L . [56] . Importation of Zika virus infection [57] raises concerns as the pathogen , which generally causes mild symptoms , is suspected of being associated with Guillain-Barre’ syndrome , a severe autoimmune neurologic disease [58] , and with congenital microcephaly , a disabling and potentially lethal condition in newborns [59] . We did not include in our analysis the risk of outbreak for West Nile Virus because of the significant complications in the transmission dynamics [55] due to the presence of other animal hosts such as avian species and of other vector species such as Cx . pipiens . Furthermore , we did not consider the potential risk of Zika virus outbreaks , given that estimates of epidemiological parameters from actual outbreaks [59–61] are not yet available for this infection . The continued expansion and environmental adaptation of Ae . albopictus is making chikungunya , dengue and other tropical diseases a growing threat for public health authorities in temperate climates . Expected trends in climate and land use and more frequent pathogen introductions due to worldwide intensification of travels will exacerbate this problem in the years to come . For example , the “explosive epidemic” of Zika virus ongoing in Brazil [59] poses specific concerns related to the massive inflow of international tourists expected during the upcoming Olympics in Rio de Janeiro in the second half of August . The return of infected travelers to countries in the northern hemisphere , where the mosquito season will be at its peak , may facilitate the geographical spread of Zika virus . Enhanced entomological and medical surveillance will be especially important to prevent such a scenario; population and transmission dynamic models can assist surveillance policies by quantifying the risks of arboviral infections , designing effective intervention strategies and optimizing resource allocation for the control of Ae . albopictus populations . | The tropical mosquito Aedes albopictus is a vector for several viruses that can be transmitted to human hosts through infectious bites . In the last 20 years , Aedes albopictus has rapidly expanded its habitat to temperate climates and it is now widespread in a large part of southern Europe and the United States . Here , it may cause outbreaks of local transmission of exotic diseases , such as chikungunya and dengue , after the arrival of infected patients through international travels . In this study , we estimate Ae . albopictus abundance in a subalpine region of Italy , by means of a mathematical model calibrated to data from adult mosquito captures throughout 2014; we then proceed to estimate the probability that an outbreak of local transmission of chikungunya and dengue will occur , following the importation from abroad of a single infectious case at different times in the year . These estimates are useful for planning preventive actions , such as programs of vector population control , as well as for increasing the preparedness of public health authorities in responding to an outbreak . | [
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| 2016 | Potential Risk of Dengue and Chikungunya Outbreaks in Northern Italy Based on a Population Model of Aedes albopictus (Diptera: Culicidae) |
The treatment for leishmaniasis is currently based on pentavalent antimonials and amphotericin B; however , these drugs result in numerous adverse side effects . The lack of affordable therapy has necessitated the urgent development of new drugs that are efficacious , safe , and more accessible to patients . Natural products are a major source for the discovery of new and selective molecules for neglected diseases . In this paper , we evaluated the effect of apigenin on Leishmania amazonensis in vitro and in vivo and described the mechanism of action against intracellular amastigotes of L . amazonensis . Apigenin reduced the infection index in a dose-dependent manner , with IC50 values of 4 . 3 μM and a selectivity index of 18 . 2 . Apigenin induced ROS production in the L . amazonensis-infected macrophage , and the effects were reversed by NAC and GSH . Additionally , apigenin induced an increase in the number of macrophages autophagosomes after the infection , surrounding the parasitophorous vacuole , suggestive of the involvement of host autophagy probably due to ROS generation induced by apigenin . Furthermore , apigenin treatment was also effective in vivo , demonstrating oral bioavailability and reduced parasitic loads without altering serological toxicity markers . In conclusion , our study suggests that apigenin exhibits leishmanicidal effects against L . amazonensis-infected macrophages . ROS production , as part of the mechanism of action , could occur through the increase in host autophagy and thereby promoting parasite death . Furthermore , our data suggest that apigenin is effective in the treatment of L . amazonensis-infected BALB/c mice by oral administration , without altering serological toxicity markers . The selective in vitro activity of apigenin , together with excellent theoretical predictions of oral availability , clear decreases in parasite load and lesion size , and no observed compromises to the overall health of the infected mice encourage us to supports further studies of apigenin as a candidate for the chemotherapeutic treatment of leishmaniasis .
Leishmaniasis is a parasitic disease endemic in 98 countries , affecting more than 12 million people worldwide . Cutaneous leishmaniasis has an incidence of approximately 1 . 2 million cases per year [1] . Leishmania amazonensis is the etiological agent of cutaneous or diffuse cutaneous lesions . Originally described in the Amazon region , L . amazonensis occurs in many parts of Brazil [2] . Pentavalent antimonials , the first-line compounds , and amphotericin B , second-line drugs , have been used for decades to treat leishmaniasis , saving thousands of lives . However , these treatments require intra-muscular administration and long periods of internalization have several side effects and contribute to parasite resistance , reducing the efficacy of treatment [3 , 4] . The lack of affordable therapy has necessitated the urgent development of new drugs that are efficacious , safe , and more accessible to patients . Natural products are a major source for the discovery of new and selective molecules for neglected diseases [5 , 6] . Compounds isolated from plants , including some flavonoids , have been reported to possess significant antiprotozoal properties [7–13] . Apigenin ( 5 , 7 , 4'-trihydroxyflavone ) is a natural flavone that is abundantly found in fruits and vegetables such as parsley , lemons and berries . It has been recognized as a bioactive flavonoid with a wide range of reported biological effects , including antioxidant , cancer chemopreventive , antihypertensive , anti-inflammatory , antimicrobial and antiprotozoal activities . [14–20] It has been shown that apigenin induces mitochondrial damage and the production of reactive oxygen species ( ROS ) [16–20] . However , the precise molecular mechanisms underlying its antiprotozoal activity remain unknown . In this study , we describe the possible mechanism of action for apigenin , demonstrating that antileishmanial activity in vitro against intracellular amastigotes of L . amazonensis and in vivo , using a murine-model of cutaneous leishmaniasis . Apigenin reduced the infection index in a concentration-dependent manner . Additionally , apigenin demonstrated to be non-cytotoxic to murine macrophages at a potent leishmanicidal concentration , with activity that was determined to be ROS-dependent . Leishmania-infected macrophages treated with apigenin exhibited an increase in double-membrane vesicles and myelin-like membrane inclusions , characteristic of autophagosomes . Apigenin treatment was also effective in a murine model of L . amazonensis infection , demonstrating oral bioavailability , as it decreased parasitic load without altering serological toxicology markers .
Apigenin ( ≥97% purity; lot 081M1457V ) , Schneider's Drosophila medium , fetal calf serum , RPMI-1640 medium , penicillin , streptomycin , JC-1 ( 5 , 5′ , 6 , 6′-tetrachloro-1 , 1′ , 3 , 3′-tetraethyl-imidacarbocyanine iodide ) , NAC ( N-acetyl-L-cysteine ) , GSH ( reduced glutathione ) , glutaraldehyde , sodium cacodylate , osmium tetroxide , potassium ferricyanide , uranyl acetate and FCCP [carbonyl cyanide 4- ( trifluoromethoxy ) phenylhydrazone] were obtained from Sigma-Aldrich ( St . Louis , MO , USA ) . H2DCFDA ( 2' , 7'-dichlorodihydrofluorescein diacetate ) was obtained from Invitrogen Molecular Probes ( Leiden , The Netherlands ) . All other reagents were purchased from Merck ( São Paulo , Brazil ) . Deionized distilled water obtained using a Milli-Q system ( Millipore Corp . , Bedford , MA , USA ) was used to prepare all solutions . Endotoxin-free sterile disposable supplies were used in all experiments . Apigenin was prepared in dimethylsulfoxide ( DMSO ) and diluted in culture medium such that the solvent concentration did not exceed 0 . 2% ( v/v ) in the final solution . In the control samples ( absence of apigenin ) , a similar volume of vehicle ( DMSO 0 . 2% v/v ) was added to the cells . The MHOM/BR/75/LTB0016 strain of L . amazonensis was used throughout this study . This strain was isolated from a human case of cutaneous leishmaniasis in Brazil . Promastigotes were cultivated at 26°C in Schneider medium ( pH 7 . 2 ) supplemented with 100 U/mL penicillin , 100 μg/mL streptomycin and 10% ( v/v ) heat-inactivated fetal calf serum . L . amazonensis promastigotes were washed with phosphate-buffered saline ( PBS ) , counted using a Neubauer chamber and added to peritoneal macrophages at a multiplicity of infection ( MOI ) of 3 . 0 . The macrophages were collected from Swiss mice ( 6–8 weeks old ) , plated in Roswell Park Memorial Institute ( RPMI ) medium at 2 × 106 cells/mL ( 0 . 4 mL/well ) in Lab-Tek eight-chamber slides and then incubated for 3 h at 37°C in an atmosphere of 5% CO2 . The free parasites were removed by successive washes with RPMI medium . L . amazonensis-infected macrophages were then incubated in the absence or in the presence of apigenin ( 3 μM , 6 μM and 12 μM ) for 72 h . The percentage of infected macrophages was determined using light microscopy; at least 300 cells on each coverslip were counted randomly in duplicate . The results were expressed as the infection index ( % of infected macrophages × number of amastigotes/total number of macrophages ) . The IC50 value was determined by logarithmic regression analysis using GraphPad Prism 6 . In the control samples ( absence of apigenin ) , a similar volume of vehicle ( DMSO 0 . 2% v/v ) was added to the cells . The experiments were performed thrice . Peritoneal macrophages ( 2 × 106 cells/mL ) were allowed to adhere to 96-well tissue culture plates for 1 h at 37°C in an atmosphere of 5% CO2 . Non-adherent cells were removed by washing with RPMI-1640 medium . Then , the adherent macrophages were incubated with the indicated concentrations of apigenin ( 3 to 96 μM ) for 72 h . The medium was then discarded , and the macrophages were washed with RPMI-1640 , after which time they were incubated with Alamar blue ( 10% v/v ) for 12 h at 37°C in an atmosphere of 5% CO2 . The absorbance was measured at 570 nm using a spectrophotometer , and the IC50 value was determined by logarithmic regression analysis using GraphPad Prism 6 . The selectivity index was determined as macrophage IC50/intracellular amastigote IC50 . Untreated peritoneal macrophages were lysed by the addition of 0 . 1% Triton X-100 as a positive control . Peritoneal macrophages ( 2 × 106 cells/mL ) were allowed to adhere to black 96-well tissue culture plates for 1 h at 37°C in an atmosphere of 5% CO2 . Non-adherent cells were removed by washing with RPMI-1640 medium . Next , the adherent macrophages were incubated with the indicated concentrations of apigenin ( 3 μM , 6 μM and 12 μM ) for 72 h . Cells were harvested and resuspended in Hank's Balanced Salt Solution ( HBSS ) and incubated with JC-1 ( 10 μg/mL ) for 30 min at 37°C in an atmosphere of 5% CO2 . After washing twice with HBSS , fluorescence was measured spectrofluorometrically at 530 nm and 590 nm using an excitation wavelength of 480 nm . The ratio of values obtained at 590 nm and 530 nm was plotted as the relative ΔΨm . The mitochondrial uncoupling agent carbonyl cyanide p- ( trifluoromethoxy ) phenylhydrazone ( FCCP; 200 μM ) was used as a positive control . Intracellular ROS levels in uninfected macrophages and in L . amazonensis-infected macrophages that were treated with apigenin or untreated were measured using the cell-permeable dye H2DCFDA . L . amazonensis promastigotes were added to the peritoneal macrophages at an MOI of 3 . 0 . The cells were then plated in black 96-well tissue culture plates in RPMI-1640 medium at a density of 2 × 106 macrophages/mL and incubated for 3 h at 37°C in an atmosphere of 5% CO2 . For the uninfected macrophages , peritoneal macrophages were plated in black 96-well tissue culture plates at a density of 2 × 106 macrophages/mL and incubated for 3 h at 37°C in the presence of 5% CO2 . Uninfected macrophages and L . amazonensis-infected macrophages were incubated in the absence or presence of apigenin ( 3 μM , 6 μM and 12 μM ) for 72 h . The medium was then discarded , the macrophages were washed with HBSS , and the cells were incubated with H2DCFDA ( 20 μM ) for 30 min at 37°C . Fluorescence was measured spectrofluorometrically using an excitation wavelength of 507 nm and an emission wavelength of 530 nm . For all measurements , basal fluorescence was subtracted . L . amazonensis promastigotes were added to the peritoneal macrophages at an MOI of 3 . 0 . Next , L . amazonensis-infected macrophages were incubated in the absence or in the presence of apigenin ( 12 μM ) for 72 h . After washing with PBS , the infected macrophages were fixed in 2 . 5% glutaraldehyde in 0 . 1 M sodium cacodylate buffer ( pH 7 . 2 ) at room temperature for 40 min and post-fixed in a solution of 1% osmium tetroxide , 0 . 8% potassium ferricyanide and 2 . 5 mM CaCl2 for 20 min . The cells were dehydrated in an acetone series and embedded in PolyBed 812 resin . [21] Ultrathin sections were stained with uranyl acetate and lead citrate and examined using a JEOL JEM1011 transmission electron microscope ( Tokyo , Japan ) in the Plataforma de Microscopia Eletrônica , IOC , FIOCRUZ . BALB/c mice ( 5/group ) were maintained under specific pathogen-free conditions and inoculated with stationary-phase L . amazonensis promastigotes ( 2 x 106 cells in 10 μl of PBS ) intradermally in the right ear using a 27 . 5-gauge needle . The method of treatment was similar to previously described methods [9] and was initiated seven days following infection . Apigenin ( 1 mg/kg and 2 mg/kg ) was diluted in DMSO ( 1% v/v ) , incorporated in an oral suspension and administered orally through an orogastric tube once daily seven times per week until the end of the experiment ( day 45 ) , when the animals were euthanized . The control group was treated orally with an oral suspension with DMSO ( 1% v/v ) in the absence of apigenin ( vehicle of apigenin ) . The positive control was treated with intraperitoneal injections of meglumine antimoniate ( pentavalent antimonial; 100 mg/kg/day ) once daily seven times per week until the end of the experiment ( day 45 ) . The lesion sizes were measured twice per week using a dial caliper . The parasite load was determined 45 days post-infection using a quantitative limiting dilution assay as described previously [9 , 13] . The infected ears were excised , weighed and minced in Schneider's medium with 20% fetal calf serum . The resulting cell suspension was serially diluted . The number of viable parasites in each ear was estimated from the highest dilution that promoted promastigote growth after seven days of incubation at 26°C . Serum levels of toxicological markers ( aspartate aminotransferase ( AST ) , alanine aminotransferase ( ALT ) , creatinine ( CREA ) , urea , total protein ( TP ) , globulin ( GLO ) , albumin ( ALB ) and creatine kinase ( CK ) in the infected BALB/c mice treated as described above were measured by the Program of Technological Development in Tools for Health-PDTIS-FIOCRUZ . This study was performed in strict accordance with the recommendations of the Guide for the Care and Use of Laboratory Animals of the Brazilian National Council of Animal Experimentation ( COBEA ) . The protocol was approved by the Committee on the Ethics of Animal Experiments of the Fundação Oswaldo Cruz ( CEUA-FIOCRUZ , License Number: LW-7/10 ) . All experiments were performed in three independent trials . The data were analyzed using Student’s t-test or analysis of variance ( ANOVA ) followed by Bonferroni's post-test in GraphPad Prism 6 ( GraphPad Software , La Jolla , CA , USA ) . The results were considered significant when p≤ 0 . 05 . The data are expressed as the mean ± standard error .
The activity of apigenin against the promastigote form of L . amazonensis has been described [17] . To determine the effects of apigenin on the interaction of L . amazonensis with macrophages after parasite invasion , untreated promastigotes were allowed to interact with macrophages for 3 h . Leishmania-infected macrophages were then incubated in the absence or presence of apigenin ( 3–12 μM ) for 72 h ( Fig 1 ) . Apigenin reduced the infection index in a dose-dependent manner ( p < 0 . 001 ) with an IC50 value of 4 . 3 μM , and inhibited the growth of L . amazonensis by 71% after 72 h at the highest dose tested ( 12 μM ) . An evaluation of the cytotoxic effect of apigenin in murine macrophages revealed a lack of toxicity and maintenance of the mitochondrial membrane potential ( S1 Fig ) . The IC50 value of apigenin against murine macrophages was 78 . 7 μM , which corresponds to a selectivity index of 18 . 2 . The biological efficacy of a drug is not attributed to cytotoxicity when the selectivity index is greater than or equal to 10 [22 , 23] . These results demonstrate the antileishmanial activity of apigenin against L . amazonensis amastigotes . ROS are produced as a response to pathogen infection of macrophages and result in the destruction of cellular and macromolecular components . ROS can also be generated in response to the administration of some drugs; this mechanism is the basis of various antiprotozoal medications used to combat parasites in infected cells [24] . Although apigenin is known to exhibit antioxidant properties , some studies have demonstrated pro-oxidant activities , resulting in cytotoxicity in some cancer cells [16 , 19 , 20] . To investigate whether the leishmanicidal effect of apigenin is due to ROS production , ROS levels were measured using the cell-permeable dye H2DCFDA [12 , 13] . Apigenin induced ROS production in Leishmania-infected macrophages in a dose-dependent manner ( p < 0 . 01 ) ( Fig 2A ) ; however , it did not induce an increase in ROS production in non-infected macrophages , suggesting that such increase is specific to infected cells . These data suggest that apigenin-induced leishmanicidal activity occurs at least in part through the production of ROS . The linear correlation ( R2 = 0 . 9306 ) observed between the percent inhibition of the infection index and ROS production by apigenin reinforces this hypothesis ( Fig 2B ) . ROS production in a concentration-dependent manner has also been reported for the exposure of L . amazonensis-infected macrophages to quercetin [12] , which induces a severe reduction in the number of parasites . ROS levels were 1 . 5-fold higher after treatment with 12μM quercetin at 72 h , similar to observations following treatment with apigenin . In animal cells , reduced glutathione ( GSH ) is the most abundant non-protein sulfhydryl-containing ( thiol ) tripeptide . It serves as a cellular defense mechanism against oxidative injury and maintains a reduced cellular environment in many cell types [25] . ROS are often targeted by GSH in both spontaneous and catalytic reactions . N-acetyl-L-cysteine ( NAC ) is a thiol compound that is known to promote GSH synthesis and has been used in conditions characterized by decreased GSH or oxidative stress [26] . To confirm that the inhibitory effects of apigenin are mediated by ROS production , L . amazonensis-infected macrophages were preincubated with GSH or NAC ( 300 μM ) . As demonstrated in Fig 3 , GSH and NAC protected L . amazonensis from apigenin-mediated inhibition ( panel A ) ( p < 0 . 05 ) , corroborating ROS production as a possible mechanism for the induction of L . amazonensis amastigote death . Treatment with apigenin inhibited the parasite intracellular proliferation without any apparent host cytotoxicity , as evidenced by the intact cell morphology ( Fig 3B–3I ) . Autophagy is another mechanism of defense against intracellular pathogens . ROS have been shown to activate autophagy to protect cells from invading pathogens such as Leishmania [27] . Transmission electron microscopy analyses of untreated L . amazonensis-infected macrophages are shown in Fig 4 . The macrophages displayed typical morphology with a preserved nucleus ( N ) , endoplasmic reticulum ( ER ) and parasitophorous vacuoles containing amastigotes ( A ) . Concentric membranous structures ( white asterisk ) and cytosolic vacuolization ( V ) were also observed ( panels A–D ) . In addition , several autophagosomes ( AP ) were observed surrounding the parasites ( panels E and F ) . In contrast , when Leishmania-infected macrophages were incubated in the presence of apigenin ( 12 μM ) for 72 h , the treatment induced a remarkable increase in the number of double-membrane vesicles and myelin-like membrane inclusions within macrophages , characteristics of autophagic pathway ( Fig 5A and 5B ) ; additionally , these structures were found to be co-localized with L . amazonensis amastigotes ( Fig 5; panel B and panel E ) . Fusion between autophagosomes-like structures and the parasitophorous vacuole was also observed ( Fig 5C; black arrow ) . Treated macrophages displayed both an intact nucleus ( N ) and endoplasmic reticulum ( ER ) ( Fig 5F ) . Increases in pro-oxidant states , promoted by xenobiotic exposure , have been associated with the stimulation of the autophagic pathway , [28] and ROS have been demonstrated as signaling molecules in starvation-induced autophagy [29] . Several sources of ROS exist in phagocytic cells , the most prominent of which is NADPH oxidase ( NOX ) . [30] It has been demonstrated that NADPH oxidase—generated ROS contribute to autophagic induction [27] . Appropriate signaling promotes NOX attachment to the phagosomal membrane and generates superoxide by transferring electrons from cytosolic NADPH to oxygen in the phagosome lumen [31 , 32] . Increased NOX activation enhances the ability of the infected macrophage to kill L . amazonensis [33]; conversely , inhibition of NOX activation appears to be a strategy of L . amazonensis infection [34] . Therefore , it can be postulated that the effects observed following treatment of L . amazonensis-infected macrophages with apigenin occur through the activation of NOX , generating ROS and leading to an increase in autophagy . This hypothesis is reinforced by the following observations: ( a ) ROS production occurred only in L . amazonensis-infected macrophages treated with apigenin , which exhibited a linear correlation between the percent inhibition of the infection index and ROS production; ( b ) GSH and NAC significantly reduced apigenin-induced intracellular amastigote death; and ( c ) Apigenin clearly induced a significant increase in autophagosomesco-localized with L . amazonensis amastigotes in apigenin-treated macrophages without apparent cytotoxicity . Accordingly , it has been demonstrated that in HepG2 human hepatoma cells , activation of NOX leads to ROS generation following treatment with apigenin [16] . The current lack of reasonable therapeutics necessitates the development of novel antileishmanial compounds . A compound is classified as orally effective when it demonstrates good absorption . To determine the possible oral effectiveness of apigenin prior to in vivo testing , the ADMET ( absorption , distribution , metabolism , excretion and toxicity ) properties were evaluated using the admetSAR tool [35] ( Table 1 ) . Apigenin presented great probabilities ( 98 . 9% and 85 . 4% ) for human intestinal absorption ( HIA ) and Caco-2 cell permeability , respectively . In terms of metabolism , a series of cytochrome P450 were evaluated . Toxicity was also analyzed , and apigenin demonstrated the absence of mutagenic toxicity and carcinogenic effects . Apigenin is also predicted as a class III risk for acute toxicity ( compounds with an LD50 greater than 500 mg/kg ) [35 , 36] . Taken together , these data suggest that apigenin is safe and orally absorbed . Furthermore , Lipinski’s rule of five was calculated [37] . As observed in Table 1 , apigenin has five hydrogen bond acceptors , three hydrogen donors , and a molecular weight of 270 . 2 and a clogP of 2 . 58 , thus fulfilling the Lipinski rule of five . Taking into consideration the above results , the efficacy of apigenin in a murine model of cutaneous leishmaniasis was evaluated using oral administration . Ears of BALB/c mice were intradermally infected with 2x106L . amazonensis promastigotes , and the mice were subsequently treated orally with apigenin ( 1 mg/kg/day and 2 mg/kg/day ) . As shown in Fig 6A , the oral administration of apigenin reduced the lesion ( p < 0 . 05 ) . Interestingly , oral treatment also significantly reduced the parasite burden ( p < 0 . 001 ) , with ED50 and ED90 values of 0 . 73 and 1 . 2 mg/kg/day , respectively . This reduction was equal to 73 . 4% and 94 . 3% with 1 and 2 mg/kg/day , respectively ( Fig 6B ) . Furthermore , significant differences in lesion size and parasite load were observed between the infected mice treated with apigenin ( 2 mg/kg/day ) and a pentavalent antimonial ( meglumine antimoniate ) . In addition , no significant differences were observed in serum alanine aminotransferase , aspartate aminotransferase , creatinine , albumin , globulin , total protein , urea or creatine kinase levels between mice treated with apigenin and the control group ( S1 Table ) . All serological toxicology markers were within reference values , suggesting the absence of kidney and liver toxicity . However , further specific toxicity studies , such as genotoxicity , should be performed . In conclusion , our study suggests that apigenin exhibits leishmanicidal effects against L . amazonensis-infected macrophages . ROS production , as part of the mechanism of action , could occur through the increase in host autophagy and thereby promoting parasite death . Furthermore , our data suggest that apigenin is effective in the treatment of L . amazonensis-infected BALB/c mice by oral administration , without noticeable kidney or liver toxicity . The selective in vitro activity of apigenin , together with excellent theoretical predictions of oral availability , clear decreases in parasite load and lesion size , and no observed compromises to the overall health of the infected mice encourage us to supports further studies of apigenin as a candidate for the chemotherapeutic treatment of leishmaniasis . | Leishmaniasis is an important neglected disease caused by protozoa of the genus Leishmania and affects more than 12 million people worldwide . Pentavalent antimonials and amphotericin B have been used for decades to treat leishmaniasis; however , these drugs result in numerous adverse side effects , have variable efficacy and are subject to parasite resistance . The lack of suitable therapy necessitates the development of novel antileishmanial compounds . In this study , we investigated the antileishmanial activity of apigenin in vitro and in vivo and described the mechanism of action against intracellular amastigotes of Leishmania amazonensis . Apigenin reduced the infection index in a dose-dependent manner and increased reactive oxygen species ( ROS ) generation . Additionally , apigenin induced an increase in the number of macrophages autophagosomes after the infection , surrounding the parasitophorous vacuole , suggestive of the involvement of host autophagy probably due to ROS generation induced by apigenin . Furthermore , treatment with apigenin was also effective in vivo , showing oral bioavailability and significantly reducing lesion sizes and parasite burden without altering serological toxicity markers . | [
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| 2016 | Oral Efficacy of Apigenin against Cutaneous Leishmaniasis: Involvement of Reactive Oxygen Species and Autophagy as a Mechanism of Action |
Infants born to dengue immune mothers acquire maternal antibodies to dengue . These antibodies , though initially protective , decline during the first year of life to levels thought to be disease enhancing , before reaching undetectable levels . Infants have long been studied to understand the interaction between infection and disease on an individual level . Considering infants ( cases <1 year old ) as a unique group , we analyzed serotype specific dengue case data from patients admitted to a pediatric hospital in Bangkok , Thailand . We show differences in the propensity of serotypes to cause disease in individuals with dengue antibodies ( infants and post-primary cases ) and in individuals without dengue antibodies ( primary cases ) . The mean age of infant cases differed among serotypes , consistent with previously observed differential waning of maternal antibody titers by serotype . We show that trends over time in epidemiology of infant cases are consistent with those observed in the whole population , and therefore with trends in the force of infection . Infants with dengue are informative about the interaction between antibody and the dengue serotypes , confirming that in this population DENV-2 and DENV-4 almost exclusively cause disease in the presence of dengue antibody despite infections occurring in others . We also observe differences between the serotypes in the mean age in infant cases , informative about the interaction between waning immunity and disease for the different serotypes in infants . In addition , we show that the mean age of infant cases over time is informative about transmission in the whole population . Therefore , ongoing surveillance for dengue in infants could provide useful insights into dengue epidemiology , particularly after the introduction of a dengue vaccine targeting adults and older children .
DENV is a flavivirus that exists as four serotypes . Infection with one serotype leads to long-term immunity to that serotype . There is also a short-term period of cross-protection to other serotypes [1 , 2] followed by an indeterminate period during which infection by another serotype may lead to more severe disease [3] . One theory for this increased severity is antibody dependent enhancement , whereby non-neutralizing antibodies bind to the virus and facilitate viral entry into cells and increased viral replication [4] . The overwhelming majority of hospitalized cases in regions where all four serotypes circulate are due to post-primary infections [5] . Infants born to dengue-immune mothers receive dengue antibodies , and , over the first year of life , experience an accelerated version of the susceptibility pattern that individuals experience during a lifetime in endemic areas: there is a short period of universal protection lasting a few months after birth , followed by a period also lasting a few months in which infections are more likely to be severe possibly through the action of antibody dependent enhancement [6] . Infant cases of dengue have been an important group for studying dengue immunopathogenesis . Previous studies have described the disease presentation and age distributions of infants in Thailand , Vietnam , Indonesia and the Philippines [7–9] , as well as considering the interaction between antibody titres and disease [6 , 10–12] . Infant cases may also be an important group for understanding other aspects of the epidemiology of dengue at population scales . There are two main advantages to evaluating infant cases for studying the interaction between immunity and disease . First , at a population scale and even at individual scales , infants have fairly uniform antibody titers across serotypes and , thus , eliminate the uncertainty of timing and nature of past exposures that exists when considering serotype differences in disease severity among older children . Second , the time period that infants are at high risk of infection with severe outcome is relatively short , thus providing information on forces of infection in the population at this time . In the current study , we analyzed dengue case data from Queen Sirikit National Institute of Child Health ( QSNICH ) from 1973–2012 to investigate dengue in infants ( cases <1 year old ) . We sought to elucidate intrinsic differences in the propensity for different DENV serotypes to cause disease among patients with pre-existing antibodies by examining serotype distributions in hospitalized infants , compared to other age and immunity groups . We also examined possible relationships between antibody levels and disease outcome by examining the age of severe cases among infants . Finally , we considered changes in dengue case numbers and mean age of infant cases over time and what these changes revealed about the force of infection ( FOI ) of dengue and population level transmission . This work is important for the study of dengue pathogenesis and epidemiology and is particularly relevant to the development of vaccines . An understanding of potentially protective antibody titers could inform vaccine immunogenicity targets and could clarify the interactions among serotype , immunity and disease outcome when interpreting population level vaccine trial results . Since no vaccine approaching licensure currently plans to target those under 1 year of age , this group could also be an important resource in Phase 4 studies as they will be easily identified as non-vaccinees , and can be used to characterize indirect effects of vaccine campaigns as well as characterize temporal patterns in population level transmission after the introduction of vaccines .
The analyzed data were obtained from public health samples collected during passive surveillance of hospitalized dengue cases from 1973 to 2012 at QSNICH , a 420-bed tertiary care pediatric hospital located in Bangkok , Thailand , that serves as a Thailand Ministry of Public Health ( MOPH ) dengue referral center for Bangkok . Ninety-nine percent of the dengue cases were ≤15 years of age . Acute and convalescent blood samples from clinically suspected dengue inpatients at QSNICH were tested for evidence of DENV infection at the Armed Forces Research Institute of Medical Sciences ( AFRIMS ) laboratory in Bangkok . The case data up until 1999 have been presented previously [13] and an updated analysis is in submission ( Nisalak et al . , submitted to AJTMH ) . Techniques used for measuring antibody titers and detecting virus have changed over the years ( see [13] Table 2 ) . In brief , acute blood samples were tested by viral isolation and/or hemi-nested reverse transcriptase polymerase chain reaction ( RT-PCR ) as previously described [14–18] . Acute and convalescent blood samples were tested by dengue serological assays as previously described [19–22] . Primary infection refers to the first DENV infection in an individual and was determined serologically by dengue hemagglutination inhibition assay ( HAI ) and/or dengue IgM/IgG capture enzyme-linked immunosorbent assay ( ELISA ) according to published criteria [22] . Post-primary infection refers to any DENV infection subsequent to primary infection and was also determined serologically [22] . The retrieval and analysis of coded pre-existing data in this study was approved by the QSNICH and Walter Reed Army Institute of Research Institutional Review Boards . Blood samples from passive surveillance were originally collected at QSNICH for public health purposes . All data analyzed were anonymized . For the purpose of this analysis , cases were grouped into three groups: 1 ) primary cases aged less than 1 year old , which we refer to as infant primary cases , 2 ) primary cases aged ≥1 year old which we refer to as non-infant primary cases , and 3 ) post-primary cases of all ages . Only 40 of 21 , 090 post-primary cases were <1 year of age; these cases were included in the post-primary group , but their inclusion in this or the infant primary group did not alter the results . For each group , we calculated the proportion of cases that were of each serotype , and for each serotype , the proportion of cases that were in each group . Using Pearson correlations , the correlation between the annual numbers of cases in each group for each serotype was assessed . For infant primary cases , we calculated the mean age for cases of each serotype over all years and the mean age for all serotypes for each year . Using generalized linear models , we assessed trends over time and the relationship between annual mean age and annual proportion of all cases in infants . Analysis was performed using R version 3 . 0 . 2 ( R Foundation for Statistical Computing , Vienna , Austria ) [23] .
The serotype distribution in primary infant cases was more similar to the post-primary cases than to primary cases in non-infants ( Table 1 and Fig 1 ) . For both post-primary and infant primary cases , around 35% of cases were DENV-1 and and 31% of cases were DENV-2 . This is significantly different to primary non-infant cases where a much greater 57% of cases were DENV-1 and only 5% were DENV-2 . For DENV-3 there are slight , non-significant differences between post-primary and infant primary with 22% of post-primary and 27% of primary infant cases due to DENV-3 , and these are both significantly less than the 37% of primary non-infant cases that were due to DENV-3 . For DENV-4 there are significant differences across all 3 groups with 12% of post-primary cases , 4% of infant primary cases and only 1% of primary non-infant cases due to DENV-4 ( Table 1 and Fig 1 ) . Although disease was still more common in post-primary compared to primary cases for DENV-1 and DENV-3 ( 73% of cases were post-primary ) , primary infections with these two serotypes caused a substantial amount of disease in dengue naïve individuals . For DENV-2 and DENV-4 however , almost all of the cases of these serotypes were post-primary cases ( 92% and 96% , respectively ) . The percentage of cases that were untyped was the same for the primary infant and non-infant cases ( both 40% ) , compared to post-primary cases ( 53% ) ( Table 2 and Fig 2 ) . There were positive , significant correlations within each serotype each year between the number of infant primary cases , non-infant primary cases , and post-primary cases in each year , i . e . , the number of primary non-infant cases of a serotype each year was associated with the number of infant primary cases and , separately , associated with the number of post-primary cases of that serotype each year ( see Fig 2 and Table 3 ) . For DENV-1 and DENV-3 , the correlations between the numbers in each group were between 0 . 78 and 0 . 95 , while for DENV-2 and DENV-4 , these correlations were lower between 0 . 40 and 0 . 60 . Finally , we considered the trends over time in the mean age of infant primary cases and the infant primary cases as a proportion of all cases ( Fig 4 ) . There was a significant positive correlation between mean age and year ( correlation 0 . 31 , p-value: < 0 . 05 ) . This increase in mean age was clear from 1990 to around 2007 , but there was a suggestion of a decrease after 2007 . Age distributions by decade are shown in Fig 3B . Using a linear model , there was a significant relationship between proportion of cases that were in infants and the mean age , year and the interaction between mean age and year ( p-values all <0 . 01 , coefficients: mean age: 18 , year: 0 . 005 and mean age*year: -0 . 009 ) . This relationship showed that during the years of increasing mean age of infant cases ( 1990–2007 ) , there was a decrease in the proportion of all cases that were in infants; and during the years of decreasing mean age ( after 2007 ) , there was an increase in the proportion of cases that were in infants ( Fig 4 ) .
The serotype distributions of hospitalized dengue cases in different immune groups , as presented in this paper , add to the evidence that differences in the outcome of infection by each serotype depends on immune status . The paucity of primary cases of DENV-2 and DENV-4 has been shown in previous studies in Thailand [5] . The presence of infant primary cases with these serotypes , suggests that dengue naive individuals ≥1 year of age are exposed to these serotypes , but that these exposures do not result in hospitalized disease . There are two non-mutually exclusive ways to interpret these findings: ( 1 ) DENV-1 and DENV-3 were more likely to cause disease in non-immune individuals compared to DENV-2 and DENV-4 , or ( 2 ) DENV-2 and DENV-4 were more likely to cause disease in an enhanced post-primary infection than DENV-1 and DENV-3 . These results from the infants suggest that the former is the most likely explanation , with the correlations between the annual case numbers in each group ( infant primary , primary non-infant and post-primary ) being lower for DENV-2 and DENV-4 , than for DENV-1 and DENV-3 , suggesting the immune status of the population plays a larger role in the dynamics of DENV-2 and DENV-4 . These differences may also explain previously seemingly contradictory results where a relationship was shown between infant DHF and increased levels of enhancing activity in sera for DENV2 [6] , but not DENV3 [10] . In addition , there is a suggestion that DENV-4 is under represented in the infant primary cases compared to the secondary cases . This could be explained by a lower force of infection for DENV-4 ( consistent with the fewer observed cases ) leading to fewer exposures in this early time period . Differences between the serotypes in the propensity to cause disease in immune and non-immune individuals should be considered in the context of vaccination trial results with seemingly differential efficacy across serotypes , as with the Sanofi Phase 2b results [24] . Could the observed effect of the vaccination for DENV2 be because of the differential outcomes of DENV2 exposure in naïve and non-naïve individuals ? The mean age of infant primary cases was similar to previous studies [7 , 8] . The observed differences in the mean age in infants by serotype ( highest for DENV-1 and DENV-3 followed by DENV-2 then DENV-4 ) could be due to two non-mutually exclusive reasons . Firstly , the force of infection could have been higher for the serotypes with the lower mean ages ( so infections occurred on average earlier ) . Secondly , differential waning of antibody titers by serotype could lead to potential enhancement occuring at different ages for the different serotypes . The first explanation would suggest a higher force of infection for DENV-2 and DENV-4 compared to DENV-1 and DENV-3 . Previous work has suggested R0 and thus the FOI may be higher for DENV-2 and the considerable numbers of DENV-2 cases [25] would be consistent with a high FOI for DENV-2 . However , DENV-4 had lower incidence overall and serological studies do not suggest that DENV-4 has a higher R0 than other serotypes [25] . For antibody waning , previous work by van Panhuis et al . [26] indicated the fastest antibody waning to be for DENV-4 ( with a mean titer at 6 months of 17 [95% CI: 12 , 25] ) . The next fastest waning serotype was DENV-2 ( with a mean titre at 6 months of 25 ( 95% CI: 21 , 31 ) , followed by DENV-3 and DENV-1 ( mean titer at 6 months of 35 ( 95% CI: 29 , 43 ) ) . This order of antibody waning is consistent with the observed serotype-specific infant mean ages , though these results may be specific to the assays performed . There could , of course , also be differences between the serotypes in the antibody response required for protection or to lead to enhanced infections , as observed in older individuals in a study in Kamphaeng Phet , Thailand [27] . With a population such as Bangkok , we would expect broad antibody responses in maternal antibody , however further study of maternal over time and in multiple populations would be of great interest . The small but significant increase in mean age of infant cases over time ( particularly in the 1990s to 2007 ) is consistent with the increase in mean age of dengue seen in the general population in Thailand [28] . One of the leading hypotheses for this increase is a reduction in the force of infection [28] . Both the trends in mean age of infant primary cases and the relative proportion of all cases that were in infants , are consistent with a decrease in FOI during this period ( as the FOI drops we would expect to see fewer cases in infants as the chance of being infected in this first year of life drops ) . It is interesting that after 2007 , a decrease in the mean age of infant cases , and an increase in the proportion of cases that were in infants was observed , suggesting an increase in FOI in this period . This decrease in mean age was also observed in primary cases of older age groups ( Nisalak et al . , submitted to AJTMH ) . Further years of data will be needed to determine whether this a persistent trend or a transient fluctation . We show a change in the mean age of infants over time and by serotype , however the numbers of cases in each year are too small to determine the changes over time for each serotype . Our study suggests that infants , in addition to being informative about immune-mediated pathogenesis , could act as a sentinel population for understanding population-level transmission . Vaccines currently in development are unlikely to be given to infants [29 , 30] , and therefore , this age group will still be largely susceptible to infection even after vaccine introduction . With this lack of change in susceptibility , surveillance of this population could help determine whether transmission or simply disease presentation has altered due to vaccination . Whether infant susceptibility changes due to vaccine-derived maternal immunity will also be an important question to address in follow up studies . One would hope that ultimately , infants will be protected by herd immunity , however , if protection is suboptimal for some serotypes , is greater against severe disease as opposed to just infection , or vaccine coverage is low , the force of infection could be maintained at close to pre-vaccination levels . Therefore , infants will still be at substantial risk for infection and be an important population to observe to understand immunity and transmission . | Infants born to dengue immune mothers acquire maternal dengue antibodies . These antibodies , though initially protective , decline during the first year of life to levels thought to be disease enhancing , before reaching undetectable levels . We show that in this population , DENV-2 and DENV-4 almost exclusively cause disease in the presence of dengue antibody , despite infections occurring in others . We also observe serotype-specificity in the mean age of infant cases , consistent with differential waning of antibody to each serotype . These results highlight serotype-specificity in the way the immune response interacts with infection to cause disease . In addition , we show that the mean age of infant cases over time is informative about transmission in the whole population . Therefore , ongoing surveillance for dengue in infants could provide useful insights into dengue epidemiology , particularly after the introduction of a dengue vaccine targeting adults and older children . | [
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| 2015 | Epidemiology of Infant Dengue Cases Illuminates Serotype-Specificity in the Interaction between Immunity and Disease, and Changes in Transmission Dynamics |
Scabies is highly prevalent in socially disadvantaged communities such as indigenous populations and in developing countries . Generalized itching causes discomfort to the patient; however , serious complications can occur as a result of secondary bacterial pyoderma , commonly caused by Streptococcus pyogenes ( GAS ) or Staphylococcus aureus . In the tropics , skin damage due to scabies mite infestations has been postulated to be an important link in the pathogenesis of disease associated with acute rheumatic fever and heart disease , poststreptococcal glomerulonephritis and systemic sepsis . Treatment of scabies decreases the prevalence of infections by bacteria . This study aims to identify the molecular mechanisms underlying the link between scabies and GAS infections . GAS bacteria were pre-incubated with blood containing active complement , phagocytes and antibodies against the bacteria , and subsequently tested for viability by plate counts . Initial experiments were done with serum from an individual previously exposed to GAS with naturally acquired anti-GAS antibodies . The protocol was optimized for large-scale testing of low-opsonic whole blood from non-exposed human donors by supplementing with a standard dose of heat inactivated human sera previously exposed to GAS . This allowed an extension of the dataset to two additional donors and four proteins tested at a range of concentrations . Shown first is the effect of scabies mite complement inhibitors on human complement using ELISA-based complement activation assays . Six purified recombinant mite proteins tested at a concentration of 50 µg/ml blocked all three complement activation pathways . Further we demonstrate in human whole blood assays that each of four scabies mite complement inhibitors tested increased GAS survival rates by 2–15 fold . We propose that local complement inhibition plays an important role in the development of pyoderma in scabies infested skin . This molecular link between scabies and bacterial infections may provide new avenues to develop alternative treatment options against this neglected disease .
The global prevalence of pyoderma from various bacterial infections has been estimated to exceed 111 million children , making it one of the most common skin afflictions along with scabies and tinea [5] . In tropical climates , scabies predisposes to secondary bacterial skin infections in particular by Streptococcus pyogenes ( group A streptococci , GAS ) , the causal agent of acute rheumatic fever and rheumatic heart disease ( ARF/RHD ) . This association between scabies and pyoderma caused by GAS has been well established [4] . Globally , GAS associated diseases , such as RHD , acute post-streptococcus glomerulonephritis ( APSGN ) and severe invasive diseases , affect an estimated 18 million individuals and account for over 0 . 5 million deaths per year [6] . In Australian Aboriginal communities RF/RHD prevalence has steadily risen to almost 2% in 2008 [7] , translating to the highest incidences reported globally . Scabies and pyoderma have also been linked with outbreaks of APSGN [4] . In remote Aboriginal communities more than 70% of the children below two years of age have presented with scabies and skin sores , respectively [8] . Community-wide treatment of scabies decreases pyoderma [9] , [10] , which suggests a key role of the burrowing mite . Mechanical disruption of the stratum corneum caused by mites and host scratching may be considered a primary prerequisite promoting secondary skin infections; however contributing molecular interactions between host , parasites and bacteria have not been investigated . An immediate , non-specific epidermal host defense mechanism against microbes is the local activation of the complement system [11] . Phagocytes migrate to the site of infection and attempt to engulf and dispose of the invading organisms with the help of available antibodies and complement , both present in the host's serum , [12] . We have recently established that scabies mites express extensive complement evasion machinery disrupting the three complement pathways at several levels . Two members of a large family of catalytically inactive serine protease paralogues termed SMIPP-Ss [13] , [14] inhibit all three pathways of human complement [15] . Furthermore two scabies mite serpins ( SMSs ) also function as effective complement inhibitors , each binding to a range of complement factors on several levels of the three complement pathways [16] . While the in vivo concentrations of these mite complement inhibitors excreted into the confined space of the burrows have not been determined , cumulative effects of multiple anti-complement activities can be expected . A logical question to ask is whether this increased level of anti-complement activity has an effect on the bacteria that colonize the burrows . Here we present a set of in vitro data focusing on two SMIPP-Ss and two SMSs , which indicate that under physiological conditions there is indeed a considerable effect on the growth of Streptococcus pyogenes .
Normal human serum for complement activation assays and whole blood samples for bactericidal assays were prepared from blood donated by healthy volunteers after informed consent and in accordance with the principles in ethical conduct as stated in the “National Statement on Ethical Conduct in Human Research” , documented by the Australian National Health and Medical Research Council . Five scabies mite inactive serine protease paralogues ( SMIPP-S D1 , GenBank accession no . AY333085 , SMIPP-S I1 , AY333081; SMIPP-S B2 , AY333073; SMIPP-S G2 , JN167504 and SMIPP-S G4 , AY333078; ) were cloned into Pichia pastoris KM71H using vector pPICZαA ( Invitrogen ) as described earlier [15] . Two scabies mite serpins ( SMSs; SMSB3a , cDNA clone Yv7088B02; GenBank accession no . JF317220; SMSB4 , cDNA clone Yv5004A04 , GenBank accession no . JF317222 ) were amplified on cDNA libraries made from human scabies mites Sarcoptes scabiei [17] , [18] and cloned into the pQE9 expression vector ( Qiagen ) [16] . Recombinant SMIPP-S proteins were expressed in P . pastoris as secreted proteins and purified as described earlier [15] . Briefly , mature SMIPP-S protein secreted from P . pastoris was purified from the expression culture supernatant by hydrophobic interaction chromatography on a 5 ml HiTrap phenyl-Sepharose column ( GE Healthcare ) followed by dialysis and ion chromatography on a 5 ml HiTrap SP Sepharose FF column ( GE Healthcare ) . Recombinant SMS proteins were expressed in E . coli and purified under denaturing conditions from thoroughly washed and solubilised inclusion bodies by nickel immobilized metal affinity chromatography ( Qiagen ) . Purified SMS proteins were refolded for 3 hours in 300 mM L- arginine , 50 mM Tris , 50 mM NaCl and 5 mM DTT at pH 8 . 0 for SMSB3 and pH 10 . 5 for SMSB4 . Refolded SMS proteins were concentrated using an Ultrasette Lab Tangential Flow Device ( 10 kDa cut off; PALL Life Sciences ) , followed by further concentration in centrifugal filters ( Amicon Ultra , Millipore ) . Molecular masses and purity of the purified recombinant mite proteins were confirmed using SDS-PAGE analysis with silver and Coomassie blue R-250 staining . Protein concentrations were determined according to the Bradford method [19] . Prior to the phagocytosis assays , the recombinant mite proteins were buffer exchanged into the corresponding assay buffers using Zeba Desalt Spin columns ( Pierce ) . Human serum complement levels were assessed using a Wieslab Complement System Screen kit ( EuroDiagnostica ) according to the manufacturer's instructions . Normal human serum was prepared from blood of eight healthy volunteers after informed consent . Inhibition of complement by five SMIPP-Ss and one SMS was measured in a total volume of 100 µl at serum concentrations of 1% , 1% and 5 . 5% for the classical , lectin and alternative complement pathways , respectively . These serum concentrations represent recommended conditions , under which each assay is most sensitive to changes . Normal human serum was pre-incubated for 30 min at room temperature with 50 µg/ml of purified scabies mite protein before addition to the ELISA microtiter plate and immunodetection of the terminal membrane attack complex ( MAC , C5b-9 ) . Absorbance was measured at a wavelength of 405 nm on a POLARstar Optima fluorescent microtiter plate reader ( BMG Labtech , Melbourne , Australia ) . The absorbance obtained in the absence of SMIPP-Ss was defined as 100% . S . pyogenes ( GAS strain 2967 , emm-type emm 1 ) was originally isolated from a patient with APSGN in Townsville , Queensland , Australia . Informed consent was obtained from all blood donors . The initial set of phagocytosis assays was performed using blood from an individual previously exposed to GAS ( D1 ) with a robust type-specific immune response to the GAS strain 2967 . The standardized phagocytosis assays used blood from “nonimmune” human donors who did not exhibit type-specific immunity to GAS 2967 . Among several donors tested in preliminary experiments , blood from most donors allowed growth of the GAS strain , and blood from two such donors was used in each set of phagocytosis experiments ( D2 and D3 ) . To reduce differences in bacterial growth based on differences in anti-GAS antibody levels of individual whole blood donors , bactericidal assays were standardized for donors without or with low levels of naïve anti-GAS IgG by addition of antibodies from a donor previously exposed to GAS ( D1 ) . Immediately prior to use added antibody sera were heat-inactivated at 56°C for 15 min to abolish complement activity . The initial set of bactericidal assays was performed with human whole blood collected in a standard BD vacutainer ( Becton , Dickinson and Company ) , using sodium heparin as anticoagulant at a concentration of 15 USP Units/ml . Comparison of heparin- versus hirudin-treated blood in bactericidal assays confirmed earlier findings [20] that the anticoagulant heparin can alter complement activation , thereby affecting bacterial survival . Thus , further bactericidal assays were carried out using hirudin ( lepirudin ) as anticoagulant , at a concentration of 25 µg/ml ( Dynabyte Informationssysteme GmbH , Munich , Germany ) . The assays were performed as described previously [21] with modifications . Bacteria were grown overnight without agitation at 37°C in 5 ml Todd-Hewitt Broth ( THB ) . A dilution of this pre-culture was grown in THB with agitation to early exponential growth phase ( OD600 0 . 1 ) , and then diluted in PBS to 10−2 or 10−3 , representing on average 6×103 colony forming units ( CFU ) per ml . Per assay 100 µl human venous blood , 12 . 5 µl antibody serum , which was heat-inactivated for 15 min 56°C in a water bath , 15 µl of scabies mite protein as complement inhibitor or BSA as negative control ( final concentration 25–400 µg/µl ) in GVB2+ buffer ( 5 mM veronal , pH 7 . 35 , 140 mM NaCl , 0 . 1% ( w/v ) gelatin , 1 mM MgCl2 , 0 . 15 mM CaCl2 ) and 12 . 5 µl bacteria ( containing on average approximately 75 CFU ) were added to a total volume of 140 µl . Assays were placed on a rotisserie and incubated by end over end mixing for 3 h at 37°C . Subsequently 50 µl aliquots from each tube were plated in duplicate on 2 . 5% ( v/v ) defibrinated horseblood THB agar ( Equicell , Australia ) using the pour plate method and incubated overnight at 37°C for enumeration of CFU . Bacteria growth may vary between assays performed on different occasions and between different donors . Hence the percentage difference in bacterial growth was calculated by comparing CFU recovered after addition of scabies mite proteins against CFU recovered from buffer controls at the same time points in individual experiments . Individual assays were performed in duplicates and repeated independently between 4 and 12 times . Statistical significance was determined using t tests ( GraphPad Prism software , version 5 . 0; GraphPad Software Inc . USA ) . Values of p<0 . 05 were considered significant .
A simple method comprising three ELISAs , originally developed to screen for complement deficiencies [22] , was employed to confirm the complement-inhibitory properties of the recombinant mite molecules investigated here . The assays were performed with normal human serum and detection of activation was determined as the incorporation of C9 into the terminal membrane attack complex . In this system six purified recombinant mite proteins ( five SMIPP-Ss and one SMS ) at a final concentration of 50 µg/ml inhibited all three complement activation pathways ( Figure 1 ) . Prior to phagocytosis assays samples from three individuals ( D1 , D2 , D3 ) were assessed for activation of the classical and alternative pathways by ELISA for detection of the deposition of C5b-C9 , i . e . the terminal complement membrane attack complex . The complement activation levels from the three donors were similar to those of pooled normal human serum ( Figure 2a ) , thereby validating the suitability of the donors . In whole blood bactericidal assays heparin treated blood samples resulted in recovery of fewer GAS colonies than samples from the same donor treated with hirudin ( Figure 2b ) . This confirmed earlier findings that the anticoagulant heparin can alter complement activation , while hirudin ( lepirudin ) generally preserved the complement reactivity , making it more suited for in-vitro studies [20] , [23] . Thus , further bactericidal assay analysis was carried out using hirudin . We aimed to investigate the effect of mite complement inhibitors on bacterial growth in blood from several donors , however most individuals tested did not show a type-specific immune response to the GAS strain 2967 ( data not shown ) . To determine whether blood from “non-immune” donors ( i . e . blood that did not contain type-specific opsonizing antibodies ) was suitable , assays were conducted with or without the addition of such antibodies . Assays testing one SMIPP-S and one SMS at a concentration of 200 µg/ml generally resulted in increased bacterial growth; however , the most striking effects of mite proteins were seen in the presence of strain specific antibodies . One exemplary set of these results is shown in Figure 2c . Initial experiments were performed with plasma from an individual ( D1 ) previously exposed to GAS and thus with a robust type-specific immune response to the GAS strain . A dramatic increase in the bacterial growth ranging from over 200 to almost 1500% was seen in the presence of SMSB3 ( 200 µg/ml ) , SMSB4 ( 25 µg/ml ) and SMIPP-S I1 ( 200 µg/ml ) , compared to no effect of BSA ( 200 µg/ml ) , indicating that the mite proteins efficiently interfered with bacterial uptake by human phagocytes ( Figure 3a ) . As it was difficult to recruit further blood donors with high antibody titers , whole blood from non-exposed human donors was used and supplemented with a standard dose of heat-inactivated human sera previously exposed to GAS ( D1 ) . This allowed an extension of the dataset to two additional donors ( D2 and D3 ) and four proteins , tested at a range of concentrations . Similarly to what was observed in the immune-competent donor , the presence of each scabies mite complement inhibitor increased bacterial survival rates considerably in a dose dependent manner ( Figure 3b ) . The highest increases ranged from 200–300% for SMSB3 , 400–600% for SMIPP-S I1 to over 1000% for SMSB4 and SMIPP-S D1 , while bacterial growth was unchanged when the same amount of BSA bovine serum albumin was added instead .
We have previously demonstrated that two SMIPP-Ss are potent inhibitors of the human complement system , interfering with all three pathways of the complement cascade [15] . To assess the effect of additional mite molecules on complement , a microtiter plate-based deposition assay was performed in which complement activation was initiated by specific ligands for each pathway . After addition of human serum , pre-treated with the purified recombinant mite proteins , deposited complement proteins were detected using specific Abs against the terminal membrane attack complex ( MAC , C5b-9 ) . We showed that three further SMIPP-Ss from additional clades within the phylogenetic tree of the SMIPP-S family [14] and one scabies mite serpin expand the set of complement-inhibiting mite proteins , as these also prevent activation of all complement pathways in these ELISA-based functional assays . All SMIPP-Ss and SMSs investigated to date were previously localized by immunohistology [16] , [24] . All are secreted into the mite gut and subsequently excreted as components of feces into the confined space of the mite burrows within the upper epidermal layers of the human skin . Taken together , the mite produces an astonishing repertoire of complement inhibitors to prevent complement activation within the mite gut and in its vicinity . While complement factors C1q and C9 are localized within the mite digestive system , the terminal complement MAC formation was not detectable in the mite gut , indicating that this anti-complement machinery may be very efficient in vivo [25] . By inference , such a situation favors secondary infections by bacterial pathogens and indeed , bacterial lawns are found to coat the mite burrows and gram-positive cocci have been isolated from mite fecal pellets [26] . We now argue that effective inactivation of complement by these scabies derived complement inhibitors may aid in efficient growth of GAS in the microenvironment of the burrows . We tested this in whole blood bactericidal assays employing functional human phagocytes and complement . We found that the presence of each of the four representatively chosen scabies proteins enhanced growth of GAS in these assays . Three individuals with normal activation levels of the two dominant complement pathways ( CP and AP , Figure 2a ) , which were previously shown to be required for innate immunity to S . pyogenes [27] , were recruited as human blood donors . Generally , mite complement inhibitors enhanced bacterial growth in GAS bactericidal assays using naïve donor blood , with or without added antibodies specific to the GAS strain used . However , the most striking effects of mite molecules were seen when strain specific antibodies were present . Growth of GAS in the presence of blood from donor D1 , with a robust type-specific immune response to the GAS strain 2967 , was significantly increased in the presence of SMIPP-S I1 , SMSB3 and SMSB4 . These results were similar with blood from donors ( D2 and D3 ) , who did not exhibit type-specific immunity to GAS 2967 . A standardized assay , using naïve blood from donors D2 and D3 allowed testing of four mite proteins at a range of concentrations . These experiments showed dose dependent bacterial growth . Notably , the effects of some of the mite complement inhibitors tested in this system were most dramatic at concentrations below 100 µg/ml , which may be more relevant in a physiological context . Moreover , additive effects of multiple mite proteins accumulated in fecal pellets within the human skin burrows would be expected to cause strong local complement inhibition and may play an important role in vivo . Under physiological conditions the increase in bacterial survival may occur at relatively low concentrations of individual mite complement inhibitors . This is the first molecular study suggesting a mechanism that may contribute to the positive association between scabies and GAS skin infection . We propose that the collective complement-inhibitory function of multiple scabies mite excretory proteins in combination with complement inhibitors produced by GAS [28] promote the survival of bacterial pathogens in the microenvironment of the epidermal burrows ( Figure 3c ) . Their co-localization and our demonstration of their interactions clearly establish the potential worth of a concerted intervention against scabies in the control of secondary bacterial skin infections . These scabies mite proteins may present themselves as new targets for protective intervention at the onset of disease , both scabies and associated pyoderma . As pyoderma is a condition caused by a combination of bacteria species , extending the ground-breaking studies presented here to further pathogens is imperative . Similar studies on Staphyococcus aureus are currently underway and show comparable preliminary data ( not shown ) . Another important step will be to demonstrate the effect of mite molecules in an in vivo setting . Our group has developed a tractable experimental scabies porcine model [29] . We aim to study pyoderma development in vivo and to investigate the synergism between scabies mites and pathogenic bacteria in complement inhibition . Our results strongly suggest that the misnomer “itch-mite” trivializes an important component of an increasingly urgent public health issue worldwide . More clinical emphasis should be given to the scabies component in controlling pyoderma in tropical settings . Understanding the biological relationship between host , mites and bacteria in vivo will promote the development of novel preventive and therapeutic strategies to control scabies and associated bacterial disease , likely translating into changes of policy and practice . | Australian Aborigines experience streptococcal invasive diseases at a five times greater rate than the general Australian population [1] , contributing to an estimated life expectancy gap of currently 13 years [2] with infectious diseases being the major cause in remote areas . Alternative approaches to control skin infection and associated complications in Aboriginal communities are imperative [3] . A clear link between scabies and bacterial pyoderma has been recognised as an underlying factor of Aboriginal cases of rheumatic fever and heart disease ( RF/RHD ) , skin sepsis and renal disease [4] . Aiming to elucidate the causal molecular mechanisms we identified multiple scabies intestinal protein families functioning as inhibitors of human complement pathways , thereby preventing complement-mediated gut damage . The work presented here is at the forefront of a new agenda , looking at the interactions between scabies mites , bacteria and the host . We show that scabies mite proteins act as complement inhibitors and enhance GAS growth in whole blood assays , presumably by inhibition of host innate immunity . Based on this groundwork data we hypothesize that the complement-inhibitory functions of excreted gut molecules promote the growth of bacterial pathogens in the microenvironment of the epidermal burrows . | [
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| 2012 | Complement Inhibitors from Scabies Mites Promote Streptococcal Growth – A Novel Mechanism in Infected Epidermis? |
Leptospirosis , a spirochaetal zoonosis , occurs in diverse epidemiological settings and affects vulnerable populations , such as rural subsistence farmers and urban slum dwellers . Although leptospirosis is a life-threatening disease and recognized as an important cause of pulmonary haemorrhage syndrome , the lack of global estimates for morbidity and mortality has contributed to its neglected disease status . We conducted a systematic review of published morbidity and mortality studies and databases to extract information on disease incidence and case fatality ratios . Linear regression and Monte Carlo modelling were used to obtain age and gender-adjusted estimates of disease morbidity for countries and Global Burden of Disease ( GBD ) and WHO regions . We estimated mortality using models that incorporated age and gender-adjusted disease morbidity and case fatality ratios . The review identified 80 studies on disease incidence from 34 countries that met quality criteria . In certain regions , such as Africa , few quality assured studies were identified . The regression model , which incorporated country-specific variables of population structure , life expectancy at birth , distance from the equator , tropical island , and urbanization , accounted for a significant proportion ( R2 = 0 . 60 ) of the variation in observed disease incidence . We estimate that there were annually 1 . 03 million cases ( 95% CI 434 , 000–1 , 750 , 000 ) and 58 , 900 deaths ( 95% CI 23 , 800–95 , 900 ) due to leptospirosis worldwide . A large proportion of cases ( 48% , 95% CI 40–61% ) and deaths ( 42% , 95% CI 34–53% ) were estimated to occur in adult males with age of 20–49 years . Highest estimates of disease morbidity and mortality were observed in GBD regions of South and Southeast Asia , Oceania , Caribbean , Andean , Central , and Tropical Latin America , and East Sub-Saharan Africa . Leptospirosis is among the leading zoonotic causes of morbidity worldwide and accounts for numbers of deaths , which approach or exceed those for other causes of haemorrhagic fever . Highest morbidity and mortality were estimated to occur in resource-poor countries , which include regions where the burden of leptospirosis has been underappreciated .
Leptospirosis is a zoonotic bacterial disease that occurs in diverse epidemiological settings but imparts its greatest burden on resource-poor populations [1–6] . The disease has a broad geographical distribution due to the large spectrum of mammalian hosts that harbour and excrete the spirochete agent from their renal tubules [1 , 3 , 7] . Leptospirosis affects risk groups that are exposed to animal reservoirs or contaminated environments , such as abattoir and sewage workers , military personnel , and individuals partaking in water sports and recreation [8–12] . However , leptospirosis has a broader health impact as a disease of impoverished subsistence farmers [13–15] , cash croppers , and pastoralists [16] from tropical regions . Furthermore , leptospirosis has emerged as a health threat in new settings due the influence of globalization and climate . Disasters and extreme weather events are now recognized to precipitate epidemics [6] . The emergence of leptospirosis in Thailand [17] and Sri Lanka [18] highlight the potential for the disease to rapidly spread and cause large unexplained nationwide outbreaks . Finally , the expansion of urban slums worldwide has created conditions for rat-borne transmission [19–24] . Urban epidemics are reported in cities throughout the developing world [6 , 19 , 25] and will likely intensify as the world’s slum population doubles to two billion by 2030 [26] . The major burden attributed to leptospirosis has been its severe life-threatening manifestations . Leptospirosis has emerged as an important cause of pulmonary haemorrhage syndrome [27–30] and acute kidney injury due to Weil’s disease [31] in many regions where transmission is endemic . Case fatality for pulmonary haemorrhage syndrome and Weil’s disease is more than 10% and 70% respectively [14] . In addition , leptospirosis is increasingly recognized as an important cause of undifferentiated fever [16 , 32–38] . The majority of leptospirosis patients are not recognized or misdiagnosed as malaria [16] , dengue [39–41] , and other causes of an acute febrile illness . The lack of an adequate diagnostic test [42 , 43] has further contributed to under-reporting of cases [44 , 45] , as well as deaths [39] . Underestimation of the morbidity and mortality due to leptospirosis is therefore common [44] and has directly contributed to its neglected disease status . The lack of reliable estimates of the leptospirosis burden has hampered efforts to formulate the investment case to address key barriers , such as improved diagnostics , and identify effective prevention and control measures . Leptospirosis is amenable to One Health approaches to intervention [46] , since it is an animal health problem and a cause of economic loss in the same impoverished settings where the human disease burden is high . However , current estimates of cases and deaths rely on national surveillance data compiled from selected countries [47] . Pappas et al performed a review of reports and published literature , which identified regions with high endemicity [7] . Attempts have not been made to systematically estimate the global and regional disease burden , as has been done for other neglected diseases in the Global Burden of Disease ( GBD ) Study 2010 [48] . The World Health Organization ( WHO ) convened the Leptospirosis Epidemiology Reference Group ( LERG ) to guide this task [44] . Herein , we present the findings of a study that aimed to perform a systematic literature review of the data on leptospirosis morbidity and mortality , estimate the annual burden of cases and deaths , and identify GBD and WHO regions with the highest burden to inform local decision making and policy .
The systematic review covered published reports and grey literature on leptospirosis morbidity and mortality from January 1970 to October 2008 . We performed a systematic review of published literature by screening 32 electronic databases ( Fig 1 ) , for search terms ( S1 Protocol p . 2 ) , without language limitations , according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses ( PRISMA ) guidelines [51] . We defined all variables for which data were extracted ( S2 Protocol ) . In addition , the LERG requested public health officials and researchers to provide supplementary information from published studies as well as grey literature . Studies that fulfilled the selection criteria ( S1 Protocol pp . 3–5 ) were evaluated for methodology and study design and assigned to four quality assurance categories by two independent raters ( S1 Protocol p . 5 , S1 and S2 Tables ) . For studies that met the study quality criteria ( S3 Table ) , we applied LERG-approved definitions ( S1 Protocol p . 5 ) for confirmed leptospirosis cases and deaths and extracted information on crude disease incidence and case fatality ratio . The systematic review also identified case series of leptospirosis patients among the quality assured incidence studies , and extracted information on age and gender-stratified proportions of cases and deaths ( S1 Protocol pp . 4–5; S5 Table ) . Since standard serologic confirmation of leptospirosis requires evaluation of paired acute and convalescent-phase sera [52] , we reviewed laboratory confirmation procedures and extracted data on proportions of suspected cases that had incomplete diagnostic evaluation ( S1 Protocol p . 5 ) and ratios of clinically-suspected to laboratory-confirmed cases and deaths ( S1 Protocol p . 5 , S7 Table ) . We evaluated quality-assured studies for sources of heterogeneity due to study design , epidemiological setting , time , and geographic region . When multiple data sources were available for a country , mean estimates of crude country-specific morbidity were calculated , weighted by the size of the study population . Since information on mortality and case fatality was sparse ( S4 Table and Fig 2 ) , we calculated the mean case fatality ratio using all reported data , weighted by study population , and used this estimate together with crude country-specific morbidity to calculate crude country-specific mortality ( Fig 3 , S10 Table , equation 7 and S1 Protocol p . 6 ) . The majority of studies did not report age and gender-specific incidences . We therefore used the crude country-specific morbidity and mortality estimates , together with data on age and gender-specific risk for disease and death identified from case series reports ( S10 Table , equation 2; and S5 and S6 Tables , Fig 3 and S1 Protocol p . 5 ) , to obtain estimates for age and gender-specific morbidity and mortality for countries and territories that had quality-assured data ( S10 Table , equation 3 , Fig 3 and S1 Protocol pp . 5–6 ) . Because data were not available for every sub-region , a multivariable regression model was developed to estimate leptospirosis incidence and mortality for each country and territory . We estimated the age and gender-specific morbidity and mortality and their 95% confidence intervals for each of 222 of the world’s countries and territories , ( S12 Table ) based on a model that was developed with data on age and gender-specific incidences from quality-assured studies ( S1 Protocol pp . 6–9 ) . After a range of multivariable regression approaches and candidate variables were evaluated to select a country-level prediction model for age and gender-specific morbidity , we used a linear regression model approach to predict the log-transformation of leptospirosis morbidity based on country-specific indicator variables ( S10 Table , equations 4 and 5 , S9 Table and Fig 3 ) . This model aimed to derive estimates based on the relationship between the mean reported leptospirosis incidence and country-level characteristics such climate , sociodemographic indicators and health indicators . Variables were screened based on plausibility , availability for all countries and territories , and univariable relationship with leptospirosis incidence . The final variables in the prediction model were selected to produce the highest adjusted R2 in order to yield the smallest prediction error: 1 ) whether the country is a tropical island , 2 ) percent urbanization of the population , 3 ) Distance from the equator in degrees latitude , and 4 ) the mean years of life expectancy at birth . Since crude mortality was calculated directly from disease incidence estimates , we used the same variables to model age and gender-specific mortality at the country level . We used a Monte Carlo model , which incorporated age and gender-specific incidence estimates and 95% CI for each country and territory as inputs , to obtain country , region , and global estimates and 95% CI of leptospirosis morbidity , mortality , cases and deaths ( S10 Table equation 6 , Fig 3 and S1 Protocol pp . 7–8 ) . These estimates were used to create probability distributions of age and gender-specific incidence and mortality from which random samples were drawn . The ratios of clinically-suspected to laboratory-confirmed cases and deaths and their 95% CI , obtained from case series reports ( Fig 3 and S10 Table equation 1 ) , were used to create normal distributions for the estimated under-reporting ratio for cases and deaths . A random draw from these normal distributions was multiplied by each random sample from estimated incidence and mortality distributions in order to obtain estimates adjusted for incomplete diagnostic testing ( S1 Protocol p . 7 ) . Population estimates for 2010 were obtained from United Nations Population Division [53] . Morbidity and mortality estimates were calculated for both GBD [48] and WHO [54] geographical regions , described on S1 Protocol p . 2 , so that these figures can be compared with information on other neglected diseases . Estimates were rounded to three significant figures , with a precision limit of 100 cases or deaths . Modelling was performed using the R statistical language [55] , and Monte Carlo simulation was performed using the Poptools plug-in for Microsoft Excel 2007 [56] . Maps were created to illustrate estimated morbidity using the rworldmap package for R [57] . Country-specific estimates of leptospirosis mortality and morbidity were shared with each country in compliance with WHO guidelines .
The search strategy and quality assessment and data extraction process yielded eight high-quality and 72 medium-quality studies , including seven grey literature studies ( S3 Table and Fig 1 ) . Inter-evaluator agreement for the quality assessment was high ( Kappa 0 . 93 , 95% CI 0 . 80–1 . 00 ) . The majority of studies reported data that were published after 1989 ( 66% ) and obtained from five regions , Western Europe ( n = 15; 19% ) , Caribbean ( n = 14; 18% ) , South-East Asia ( n = 10; 13% ) , Tropical Latin America ( n = 10; 13% ) , and Oceania ( n = 8; 10% ) . Among studies , 96% used hospital-based surveillance to identify leptospirosis cases , while 4% performed case ascertainment in community-based outpatient facilities . Reported disease incidence ranged from 0 . 10 to 975 . 00 annual cases per 100 , 000 population ( S3 and S8 Tables ) . We did not identify significant temporal trends in morbidity or mortality ( considering 10-year periods ) , but found differences in reported morbidity and mortality based on study design and population ( Table 1 ) . Studies that used active surveillance to identify leptospirosis cases reported significantly higher morbidity than passive surveillance studies ( 12 . 09 vs . 2 . 13 per 100 000 population , p<0 . 01 ) . Morbidity was also significantly higher in studies of rural populations and tropical regions compared to urban settings . Among the 35 studies that reported information on case fatality ratios ( S3 Table ) ; the mean case fatality ratio was 6 . 85% ( 95% CI 5 . 66–8 . 03 ) . Ten studies reported age- and gender-stratified data for leptospirosis cases ( listed in S5 Table ) . Adults and males had a greater risk for leptospirosis than children and females ( S6 Table and Fig 4A ) , with highest risk ( RR , 2 . 4 , 95% CI 0 . 7–4 . 1 ) occurring among adult males with 20–29 years of age . Among three studies with age- and gender-stratified data for deaths from leptospirosis ( S5 Table ) , the age-specific risk for death was different from that for disease ( S6 Table and Fig 4B ) , and the highest risk for death occurred in an older age group of males with 50–59 years of age ( RR , 3 . 7 , 95% CI 2 . 6–4 . 8 ) . Among 10 studies that reported information on the completeness of laboratory confirmation procedures , paired samples were obtained from a mean of 53% of cases ( range , 20–88% ) . A total of 19 and four studies reported data on both clinically-suspected and laboratory-confirmed cases and deaths , respectively , due to leptospirosis ( S7 Table ) . Among these studies , the mean ratio of clinically-suspected to laboratory-confirmed cases and deaths was 3 . 1 ( 95% CI 1 . 2–5 . 1 ) and 2 . 2 ( 95% CI , 0 . 9–3 . 3 ) , respectively . The model selection process screened 147 candidate variables for entry in a multivariable regression model of leptospirosis morbidity ( S1 Protocol pp . 6 and 7 ) . Eight variables met statistical , plausibility , and availability screening criteria and were evaluated in multivariable regression models . A linear regression model yielded the best fit multivariable prediction model ( Tables 2 , S9 and S10 , equation 4 ) . This model predicted the natural logarithm of leptospirosis morbidity based on four country-specific variables related to geography and climate ( distance from the equator , location on a tropical island ) , indicators of the population’s overall socioeconomic and health status ( life expectancy at birth ) , and urbanization . The adjusted R2 of the prediction model was 0 . 600 . This model was used to estimate age and gender-specific morbidity and mortality for 222 countries . A Monte Carlo model incorporated age and gender-specific incidence and mortality at the country level to obtain country-specific , regional , and global estimates for incidence and mortality that were adjusted for incomplete diagnostic testing ( Table 3 , S11 and S12 Tables ) . The annual morbidity and mortality due to leptospirosis worldwide was estimated to be 14 . 77 cases per 100 , 000 population ( 95% CI 4 . 38–25 . 03 ) and 0 . 84 deaths per 100 , 000 population ( 95% CI 0 . 34–1 . 37 ) , respectively . Highest disease incidences were estimated in GBD regions of Oceania ( 150 . 68 cases per 100 , 000 , 95% CI 40 . 32–272 . 29 ) , South-East Asia ( 55 . 54 , 95% CI 20 . 32–99 . 53 ) , Caribbean ( 50 . 68 , 95% CI 14 . 93–87 . 58 ) , and East Sub-Saharan Africa ( 25 . 65 , 95% CI 9 . 29–43 . 31 ) ( Fig 2 and Table 3 ) . Small tropical islands had high estimated incidence of leptospirosis; however , in several cases there was also significant uncertainty associated with those predictions . Morbidity and mortality by WHO sub-region ( S11 Table ) by country , ( S12 Table ) and stratified by age and gender ( S13 Table ) are detailed in the S1 Protocol . The model estimated that worldwide there are 1 , 030 , 000 cases ( 95% CI , 434 , 000–1 , 750 , 000 ) and 58 , 900 deaths ( 95% CI , 23 , 800–95 , 900 ) due to leptospirosis annually ( Table 3 ) . The majority of leptospirosis cases and deaths occur in tropical regions; 73% of the world’s leptospirosis cases and deaths occur in countries situated between the Tropics of Cancer and Capricorn . Highest morbidity occurred among males with 20–29 years of age ( 35 . 27 cases per 100 , 000 , 95% CI 13 . 79–63 . 89 ) , while highest estimated mortality occurred in older males with 50–59 years of age ( 2 . 89 deaths per 100 , 000 , 95% CI 1 . 22–4 . 95 ) . A significant proportion of global burden of cases and deaths due to leptospirosis occurred in the demographic group of males with 20–49 years of age ( 48% [95% CI 40–61%] and 42% [95% CI 34–53%] , respectively ) .
We estimated that leptospirosis causes 1 . 03 ( 95% CI 0 . 43–1 . 75 ) million cases worldwide each year . These estimates place the disease among the leading zoonotic causes of morbidity and mortality . Furthermore , the number of estimated deaths ( 58 , 900; 95% CI 23 . 800–95 , 900 ) attributable to leptospirosis approaches or exceeds those for causes of haemorrhagic fever which were investigated in the Global Burden of Disease Study 2010 [48] and other studies [58] . The large majority of the estimated disease burden occurred in tropical regions and the world’s poorest countries . The systematic literature review also found that adult males were the principal risk group for leptospirosis . Based on model predictions , morbidity and mortality was estimated to be high in regions , such as South and Southeast Asia , where leptospirosis is an under-recognized public health problem . Our approach had to address key challenges in the estimation of leptospirosis burden . First , the available data was sparsely distributed and not representative of all world regions . We therefore developed a model to estimate morbidity and mortality in regions with limited or no information and identified a final model that captured a significant amount of the variability ( R2 , 0 . 600 ) in the data from quality-assured studies . Although 95% confidence intervals for estimates were calculated to account for the variability in our assumptions , we may not have accounted for all potential uncertainties . Leptospirosis is an environmentally-transmitted disease [1–3 , 6]; disease risk may therefore vary significantly within a region , which in turn would contribute to spatial uncertainty . We applied criteria , accepted by an independent panel of experts ( LERG ) , to select studies that employed appropriate methodologies with respect to case definitions , case ascertainment and case confirmation . Yet regional differences in access to health care facilities and laboratory testing , which are not explained by country-specific indicators of health and socioeconomic wealth , may have contributed to unaccounted variation . The true uncertainty may thus be greater than indicated by the confidence intervals for our estimates . Lastly , because specific countries had atypical characteristics , their model-predicted morbidity and mortality had high uncertainty which resulted in inflated estimates due to exponentiation from the log scale , which incorporates the standard error into the estimate . Estimates are therefore most reliable at the regional and global level , and caution should be taken when interpreting individual country estimates . The second challenge related to incomplete laboratory testing of suspected cases . This is a widespread problem for leptospirosis since case confirmation relies primarily on identifying seroconversion of agglutinating antibodies between acute and convalescent-phase samples [52] . Among studies with information on laboratory confirmation procedures , complete laboratory testing for leptospirosis was not performed in almost 50% of the suspected cases . In order to address this source of under-reporting , we adjusted estimates of morbidity and mortality for the effect of incomplete diagnostic testing . Similar barriers with respect to sparse data and reliance on antiquated serologic tests are shared among many of the neglected diseases [59] , and as with leptospirosis , have directly contributed to their neglected disease status . Although our modelling approach has limitations , it may have a more generalizable application in estimating the disease burden for neglected diseases . Our estimates likely underestimate the morbidity of leptospirosis , since disease incidence data was obtained from hospital-based surveillance studies ( S3 Table ) , the majority ( 65% ) of which used passive case ascertainment . Similarly our estimates of mortality represent an underestimation since these were highly sensitive to estimates of morbidity . We obtained information on case fatality ratios from 35 studies , which included 20 ( 57% ) conducted in World Bank upper income countries . The mean case fatality ratio ( 6 . 85% ) that we used in modelling mortality is likely a significant underestimation of the ratios that occur in resource-poor regions . Worldwide case fatality ratio , based on estimated cases and deaths , was even lower ( 5 . 72% ) due to the influence of the worldwide age and sex population structure . We opted to use a conservative assumption when faced with uncertainty , rather than attempt to model regional differences in case fatality ratio , or use ad-hoc adjustments . Our estimates of annual leptospirosis cases are higher than the approximately 500 , 000 cases estimated based on a survey of national surveillance data [47] . The higher estimates obtained from our study are plausible since this survey was conducted among a convenience sample of Ministries of Health . The study’s morbidity estimates reflect the incidence of severe leptospirosis , rather than rates for clinical or symptomatic illness , since selected studies used case definitions that relied on detection of severe manifestations [60] . Severe leptospirosis is generally believed to account for a small fraction ( 5–15% ) of all clinical infections [1 , 14 , 61] . There is a growing recognition that leptospirosis is an important cause of an acute febrile illness: leptospirosis has been shown to be the cause of 5–69% of acute undifferentiated or non-malarial fever cases in different parts of the world [16 , 18 , 32–38 , 62] . Leptospirosis , as in the case of dengue [58] , may therefore account for a much greater burden than indicated by morbidity estimates of severe disease . The study’s findings highlight the contribution of geography , climate , and poverty in the worldwide distribution of leptospirosis . Countries situated in the tropics had the highest estimated disease incidence and accounted for 73% of the world’s estimated cases . This pattern is attributable to environmental and social conditions which promote the abundance of reservoir animals , survival of the bacterium in soil and surface water , and risk of human exposures with these sources of infection [3 , 22 , 23] . Tropical climate also favours transmission of leptospirosis , which is often seasonal and increases during periods of heavy rainfall [6 , 19] . The disease is well-recognized as a health problem of impoverished rural-subsistence farmers [13 , 15] , pastoralists [16 , 45 , 63] , and urban slum dwellers [19–22] . We found that life expectancy , which serves in part as a proxy for poverty , was an independent predictor of country-specific disease incidence ( Table 2 ) . Finally , although urban slum environments are an emerging and increasingly important setting for leptospirosis transmission [19–22] , in our model , country percent urbanization was inversely associated with leptospirosis incidence , reflecting in part the high burden of leptospirosis in rural settings , but also the well-recognized association between lower aggregated country-level percent urbanization and poverty . The study identified regions within the developing world where the burden of leptospirosis may be significantly under-recognized . The annual morbidity of leptospirosis was estimated to be high in countries of South and Southeast Asia with large populations , such as India ( 19 . 7 cases [95% CI 6 . 8–36 . 8] per 100 , 000 population , S12 Table ) and Indonesia ( 39 . 2 [12 . 8–78 . 0] per 100 , 000 population , S12 Table ) . Although transmission is endemic and large outbreaks have been reported in these countries [15 , 62 , 64] , surveillance for leptospirosis has not been routinely performed . An important limitation of the study was the scarce data on disease burden in specific geographical regions . This was particularly evident for regions within Africa , where information on morbidity and mortality rates was available from two studies . Although the burden estimates may not be reliable for this region , there is increasing evidence suggesting their plausibility . A large spectrum of sylvatic and domestic animals are reservoirs for Leptospira in Africa [65 , 66]; leptospirosis is a recognized animal health problem in the region [4] . A recent systematic review found high seroprevalence among human populations in different settings across the continent [4] . Furthermore , surveys of patient populations have found leptospirosis to be a prevalent cause of acute febrile illness [32 , 63] . A recent population-based study reported an annual morbidity for leptospirosis of 75–102 cases per 100 , 000 population in northern Tanzania [45] . Additional locally representative data will be key to validate our estimates for the African continent and other regions with sparse data , though these efforts will require resources and time and may delay decision making with respect to strengthening surveillance and implementing control measures . Our study was also limited by the lack of studies that reported age and gender-specific incidence for morbidity and mortality . We extracted data from case series of representative patient populations in order to estimate the age and gender-specific risk for leptospirosis , which in turn was incorporated as an input in our models ( S1 Protocol pp . 5–8 and Fig 3 ) . We found that the risk of acquiring leptospirosis was higher in adults than children and higher in males than females , and highest among adult males with 20 to 29 years of age ( S6 Table and Fig 4A ) . Male gender preference is a well-recognized phenomenon in leptospirosis and due to the gender-specific occupational and peridomicilary risk activities [22–24 , 67] . The age and gender-specific risk for death had a different pattern: the risk for death increases with increasing age ( S6 Table and Fig 4B ) , a finding which has been observed in a range of epidemiological settings [14] . Since these estimates were based on small number of case series , additional studies of well-characterized patients from representative sites would therefore improve these estimates . Our systematic review did not include more recent literature from 2008; however there have been few reports on population-based mortality and morbidity rates during this interval . Leptospirosis is caused by a large number of serovar and serogroup agents which vary across regions . We could not evaluate the contribution of such agents on mortality and morbidity estimates , since less than 20% of the studies reported serologic or culture identification of these agents . Our study provides a baseline estimate to evaluate trends , as processes of climate and land use evolve in the future [6] . By 2037 , the majority of the world’s population will be inhabitants of urban centres in developing countries . A large proportion of this population will reside in slum settlements , where poor sanitation has created the conditions for annual rainfall-associated epidemics [22 , 68] . Extreme weather events and flood-related disasters [6] are predicted to escalate with global climate change [69 , 70] . As deforestation and agricultural expansion intensify in tropical regions [71] , rural-based farming populations may be increasingly exposed to leptospirosis . A formal burden of disease calculation will need to be performed to provide estimates based disability-adjusted life years ( DALYs ) . As a caveat , the health outcomes of leptospirosis have been traditionally associated with its acute disease . The disease causes sub-acute and chronic complications , such uveitis [72] , and persistent complaints [73] . However , the frequency and magnitude of long-term sequelae have not been rigorously quantified . Although the disease is life-threatening , the overall DALYs attributable to leptospirosis may be relatively low . Considering the annual number of deaths worldwide , the impact of leptospirosis equals that of canine rabies ( 59 , 000 annual deaths ) [74] . The burden of leptospirosis , with respect to morbidity , is higher than some other important neglected tropical diseases , including visceral leishmaniasis and severe dengue , and is similar to others , including echinococcosis and cysticercosis [75] . The study provides decision makers with an evidence base to implement effective policy and responses to leptospirosis . As identified in this study and cited in previous reviews [42 , 43] the lack of an adequate diagnostic test remains a foremost barrier . The demand for improved diagnostics will be greater than indicated by cases estimated in this study , since these estimates reflect the burden of severe leptospirosis and represent a lower boundary for the actual number . The distribution of the leptospirosis burden ( Fig 2 ) overlaps significantly with that for malaria [48] , dengue [48 , 58] , and enteric fever [48] . Misdiagnosis between these diseases is common [16 , 39–41] and in the case of leptospirosis , leads to delayed treatment of severe complications and poor outcomes [40] . Development and roll-out of diagnostic protocols could be leveraged and implemented synergistically that aim to address the multiple causes of acute fever in resource-poor , high-burden regions . Finally , the estimation of global burden of leptospirosis now provides the opportunity to evaluate One Health strategies for prevention and control . The lack of recognition of leptospirosis as an important zoonotic disease had previously hampered consideration of such approaches . Our estimates support the assertion that leptospirosis is a leading zoonotic cause of morbidity and mortality in humans . The majority of the estimated morbidity and mortality occurs in regions which have large subsistence farming and pastoral populations and where the disease is a veterinary health problem and cause of lost productivity . Additional work is needed to quantify the economic burden of leptospirosis , which incorporates an assessment of its impact on animal health . Vaccines for leptospirosis are routinely used in livestock and domestic animals , although they do not appear to be transmission-blocking [5] . Investment towards identifying interventions , such as vaccines , may therefore yield synergistic health and societal benefits for poor populations in developing countries . Moreover , more sustainable practices considering ecosystems [76 , 77] are needed for disease prevention . Finally , leptospirosis is a social-ecological problem , which often occurs in the context of social inequity . Therefore there is a critical need to evaluate and address the investment case for interventions that target the underlying environmental conditions and infrastructure deficiencies , such as open sewers in urban slum communities [22–24] , in order to make sustainable progress against this neglected disease . | Leptospirosis is a zoonotic bacterial disease that affects vulnerable populations such as rural subsistence farmers and urban slum dwellers . Although leptospirosis causes life-threatening clinical manifestations , such as pulmonary hemorrhage syndrome , and has a worldwide distribution , the key barrier to addressing this neglected disease has been insufficient data on its disease burden . We searched published literature and grey literature studies on leptospirosis and using information collected on disease incidence and case fatality , estimated leptospirosis incidence and mortality at country , regional and global level . Overall leptospirosis was estimated to cause 1 . 03 million cases and 58 , 900 deaths each year . These estimates place leptospirosis as a leading zoonotic cause of morbidity and mortality . In addition , morbidity and mortality was greatest in the poorest regions of the world and in areas where surveillance is not routinely performed . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
]
| []
| 2015 | Global Morbidity and Mortality of Leptospirosis: A Systematic Review |
Over the last few years , momentum has gathered around the feasibility and opportunity of eliminating gambiense human African trypanosomiasis ( g-HAT ) . Under the leadership of the World Health Organization ( WHO ) , a large coalition of stakeholders is now committed to achieving this goal . A roadmap has been laid out , and indicators and milestones have been defined to monitor the progress of the elimination of g-HAT as a public health problem by 2020 . Subsequently , a more ambitious objective was set for 2030: to stop disease transmission . This paper provides a situational update to 2012 for a number of indicators of elimination: number of cases annually reported , geographic distribution of the disease and areas and populations at different levels of risk . Comparing the 5-year periods 2003-2007 and 2008-2012 , the area at high or very high risk of g-HAT shrank by 60% , while the area at moderate risk decreased by 22% . These are the areas where g-HAT is still to be considered a public health problem ( i . e . > 1 HAT reported case per 10 , 000 people per annum ) . This contraction of at-risk areas corresponds to a reduction of 57% for the population at high or very high risk ( from 4 . 1 to 1 . 8 million ) , and 20% for moderate risk ( from 14 . 0 to 11 . 3 million ) . Improved data completeness and accuracy of the Atlas of HAT enhanced our capacity to monitor the progress towards the elimination of g-HAT . The trends in the selected indicators suggest that , in recent years , progress has been steady and in line with the elimination goal laid out in the WHO roadmap on neglected tropical diseases .
Thanks to the efforts of a wide range of stakeholders , as well as to the commitment of countless field workers in affected countries , the elimination of gambiense human African trypanosomiasis ( g-HAT ) seems achievable . In 2001 , when the number of infected people was reaching alarming levels [1] , the World Health Organization ( WHO ) and its partners launched a public-private partnership that , combined with the efforts of NGOs and bilateral cooperation , resulted in enhanced disease control and a sizable reduction in the number of cases . In 2003 and 2004 , high-level political willingness led the World Health Assembly to pass resolutions ( WHA56 . 7 and WHA57 . 2 ) that urged to strengthen disease control and to aim at elimination . In subsequent years , further progress was made in decreasing the number of reported cases , until in 2011 the WHO Strategic and Technical Advisory Group on Neglected Tropical Diseases ( NTDs ) judged that the elimination goal was achievable . A roadmap on NTDs was produced , which targets , among others , the elimination of HAT as a public health problem by 2020 [2] . Over the last three years momentum has kept building . On 30 January 2012 , a diverse and committed gathering of actors from the public and private sectors aligned itself with WHO objectives and issued the ‘London Declaration on Neglected Tropical Diseases’ . The Declaration focused on 10 infections that affect the world’s poorest populations , and HAT was targeted for elimination . A meeting organized by WHO in late 2012 with g-HAT-affected countries , cemented this commitment . A more ambitious goal was also set for 2030: the complete interruption of transmission [3] . In April 2013 , a WHO Expert Committee on control and surveillance of HAT was convened [4] . The experts recognized the feasibility of g-HAT elimination , and they identified and endorsed two primary indicators to measure the progress towards elimination: ( i ) the number of cases annually reported , and ( ii ) the number of foci validated as eliminated ( i . e . reporting less than 1 case per 10 , 000 inhabitants per annum—p . a . ) . Both indicators will be monitored annually . However , for the latter indicator , monitoring is planned to start in 2015 and , as such , it is not addressed in this paper any further . Secondary indicators were also defined , which are intended to assess the intensity and effectiveness of the elimination activities . These include: ( i ) the geographical distribution of the disease , ( ii ) the areas and populations at different levels of risk , and ( iii ) the proportion of the population at risk covered by control and surveillance activities . The latter indicator is not discussed further in this paper as the coverage of passive surveillance has already been analyzed elsewhere [5] , and the coverage of active surveillance will be the object of a separate , dedicated publication . In the present paper , two 5-year periods are analyzed ( i . e . 2003–2007 and 2008–2012 ) , with a view towards assessing trends and measuring progress . The geographical extent of the disease and the areas and populations at risk herewith presented also provide an update of previously published estimates for the period 2000–2009 [6 , 7] .
This indicator is based on annual figures reported to WHO by National Sleeping Sickness Control Programmes ( NSSCPs ) and NGOs . The tally is complemented by cases detected in non-endemic countries , concerning essentially travellers and migrants . These are linked to the location where they have most probably been infected [8] . The geographic distribution of g-HAT presented in this paper is based on the data contained in the database of the Atlas of HAT for the 10-year period 2003–2012 [4] , for a total of 115 , 368 cases . Mapping was carried out using methodologies already described [6 , 9] . The risk of g-HAT infection was estimated using previously developed methods [7 , 10] . In a nutshell , risk is expressed as the ratio of two surfaces: disease intensity and population intensity . The former is based on g-HAT occurrence data as assembled in the Atlas of HAT , the latter relies on estimations of human population density as provided by Landscan databases [11] . Both intensity surfaces are calculated through kernel smoothing [12] , by using a search radius of 30 km . On the basis of the number of HAT cases p . a . , risk is subsequently categorized as ‘very low’ ( ≥ 1 per 106 people and < 1 per 105 people ) , low ( ≥ 1 per 105 people and < 1 per 104 people ) , moderate ( ≥ 1 per 104 people and < 1 per 103 people ) , high ( ≥ 1 per 103 people and < 1 per 102 people ) and ‘very high’ ( ≥ 1 per 102 people ) [10] . Below the threshold of 1 HAT case per 1 million people p . a . risk is considered ‘marginal’ . Importantly , risk categories below the level of ‘moderate’ ( i . e . ‘low’ and ‘very low’ ) meet the objective set by WHO for the elimination as a public health problem . While for previous studies disease distribution and risk were estimated for the 10-year period 2000–2009 [7 , 10] , in the present paper we look at the period 2003–2012 . This is because , as we write , the database of the Atlas of HAT provides consolidated data up to 2012 . Data for 2013 onwards are still under processing and verification . The trend is explored by splitting the analysis into two 5-year periods ( 2003–2007 and 2008–2012 ) . For both periods , the estimation of the risk layer is made by using as denominator the mean of the five years’ population ( Landscan datasets ) , while the calculation of the population at risk , as a standalone indicator , is based on the Landscan population at the end of each period ( i . e . 2007 and 2012 respectively ) [7 , 10] . For the present run of risk estimation the Atlas of HAT provided village-level mapping for 92 . 1% of g-HAT reported cases . For the remaining 7 . 9% of the cases for which village-level information was not available , focus-level information was used . In particular , cases unmapped at the village-level but with known area of occurrence ( e . g . focus , parish , health zone , etc . ) were allocated proportionally to the endemic villages of the same area using methods already described [10] .
The first primary indicator for g-HAT elimination ( i . e . number of reported cases by year ) is illustrated in Fig 1 , which also shows the expected progression of the indicator according to the scenario of elimination as a public health problem by 2020 [3] . A general decreasing trend was observed over the years . In 2012 , an 18% excess of reported cases over the expected value was observed . That year , the renewed active screenings in the foci of Oriental province ( DRC ) and Ouham ( Central African Republic ) contributed to this surplus . In these areas , security constraints had prevented access for a few years , leading to detect infections in 2012 which had cumulated over the previous years when appropriate surveillance was lacking . The geographic extent of the disease has been identified as one of the secondary indicators of g-HAT elimination . G-HAT distribution maps are shown separately for the periods 2003–2007 and 2008–2012 ( Fig 2 ) . Regional distribution maps for West and Central Africa for the same periods are provided in S1 File . A detailed status of mapping of g-HAT reported cases and geographical locations , as well as mapping accuracy , is presented in S2 File . In summary , 92% of g-HAT cases reported between 2003 and 2012 were localized at the village level , and 88% of the locations of epidemiological interest were geo-referenced . Using a methodology previously described [6] , the average mapping accuracy for g-HAT cases is estimated at ≈ 1 , 000 m . Looking at West Africa , in Guinea disease transmission has not declined significantly ( 436 reported cases in 2003–2007 versus 364 in 2008–2012 ) , despite sustained active case-finding surveys in the costal foci characterized by a mangrove biotope . In response to this stagnant situation , in December 2011 vector control activities have been initiated in these areas with a view to complementing and synergizing with medical surveys . In Côte d'Ivoire , a decrease in the number of cases has been recorded ( 226 in 2003–2007 versus 49 in 2008–2012 ) . Reduced active surveillance in the country due to civil unrest may have resulted in under-detection , thus showing this declining trend . However , when active case finding was resumed in 2009 , no increase in the number of cases was noted . Active screening surveys targeted at all foci of Côte d'Ivoire are in the pipeline , aiming to improve our knowledge of the disease status . A similar drop in the number of g-HAT reported cases has been observed in Nigeria ( 65 in 2003–2007 versus 7 in 2008–2012 ) , but a lack of sufficiently extensive passive and active screening activities should be taken into account . Active case-finding surveys have continued to detect no cases in such countries as Burkina Faso , Benin , Ghana , Mali and Togo , thus underlining how mobile teams no longer offer a cost-effective approach to g-HAT surveillance in such epidemiological settings . Consequently , integrated passive surveillance in the health system has been initiated in 2010 . In Central Africa , social stability facilitated access to g-HAT foci in Cameroon , Congo , Equatorial Guinea and Gabon , where sustained active screening activities were carried out . In these countries , a significant and reliable decrease in the number of cases was observed ( 2 , 831 in 2003–2007 versus 646 in 2008–2012 ) . In Chad , despite regular active screening in the most active focus ( i . e . Mandoul ) , the number of reported cases did not show the expected decrease ( 1 , 268 in 2003–2007 versus 1 , 411 in 2008–2012 ) . More systematic and extensive vector control to complement and synergize with medical surveys started in November 2013 . In Central African Republic , reported cases ( 3 , 057 in 2003–2007 versus 3 , 156 in 2008–2012 ) are unlikely to reflect the reality of transmission on the ground because logistic and security constraints hamper access to the foci in Haut Mbomou and Ouham Prefectures . In South Sudan and Uganda a substantial decrease in the number of cases was reported ( from 7 , 914 to 1 , 784 and from 1 , 616 to 462 respectively between 2003–2007 and 2008–2012 ) . In Uganda the decrease was accompanied by extensive active and passive screening activities , while in South Sudan surveillance needs to be reinforced to ascertain the situation of disease transmission [13] . In Angola , the reported decrease in the number of cases was important ( from 8 , 875 in 2003–2007 to 1 , 199 in 2008–2012 ) , although there was also a slowdown in active case-finding activities , while maintaining passive screening capacities . Therefore , although an assessment in some areas could be required , there is a sense that the figures reported from Angola do reflect a real abatement . The Democratic Republic of the Congo ( DRC ) also displayed a marked decrease in the number of reported cases ( from 48 , 304 in 2003–2007 to 31 , 716 in 2008–2012 ) , while remaining the country burdened by the vast majority of g-HAT cases . The area and population at different levels of risk represent an additional secondary indicator to assess the impact of the elimination activities . With regard to areas at different levels of risk , the progress between the two 5-year study periods ( 2003–2007 and 2008–2012 ) is mapped in Fig 3 , summarized at continental level in Fig 4 , and presented on a country-by-country basis in Table 1 . Regional risk maps for West and Central Africa are provided in S3 File . We note that , between the two periods , the total area at high or very high risk of g-HAT was reduced by 60% ( from 211 to 85 thousand km2 ) , while the area at moderate risk decreased by 22% ( from 475 to 371 thousand km2 ) . The reduction in these categories resulted in an increase of 25% ( from 586 to 731 thousand km2 ) in the area at low or very low risk . Overall , the area at risk of g-HAT shrank by 6% ( from 1 . 27 to 1 . 19 million km2 ) . A few salient risk patterns and trends can be highlighted at the national level . In Equatorial Guinea , Uganda and Angola we observe the virtual disappearance of the areas at high and very high risk . The reduction in these risk categories was of 89% in South Sudan , 73% in Congo , 30% in CAR , and 32% in DRC , which by itself accounts for over half of the total risk area . Only Chad has experienced an increase of area at high and very high risk . The pattern observed for the areas at risk is mirrored by the population at risk . Fig 5 summarizes the trends at continental level of the total population at different levels of g-HAT risk , while Table 2 provides country-level details comparing the two 5-year study periods ( 2003–2007 and 2008–2012 ) . It is observed a 57% decrease for the categories very high and high ( from 4 . 1 million to 1 . 8 million ) and a 20% decrease for the population at moderate risk ( from 14 . 0 million to 11 . 3 million people ) . People living at low or very low risk of g-HAT have increased by 27% ( from 34 . 3 million to 43 . 4 million ) . The latter increase is not only due to the passage of certain populations from higher to lower risk status , but also to population growth . The net effect of these combined factors is an increase in the total population at risk from 52 . 4 to 56 . 4 million . Nevertheless , it is noteworthy that according to the criteria defined by the WHO Expert Committee on HAT [4] , over three quarters of the total population at risk of g-HAT ( i . e . 43 . 4 million ) have reached a status where the disease is no longer considered a public health problem ( i . e . low and very low risk , i . e . < 1 case per 10 , 000 inhabitants p . a . ) [4] . CAR and Chad appear to be the only notable exceptions to this declining pattern . CAR shows an increase of 33% for the categories very high and high risk , whereas Chad shows a 31% increase , albeit over a relatively circumscribed area ( i . e . 3 , 100 km2 ) .
The number of new cases reported by year is the first primary indicator to monitor the elimination of g-HAT . The data presented in this paper show for 2012 an excess of 1 , 106 cases as compared to the target . When looking at this indicator it is important to stress that small fluctuations in the number of reported cases may reflect circumstances of surveillance rather than epidemiological trends . Therefore , great caution must be taken when interpreting trends in reported cases , especially when unexpected increases or decreases occur . The steadily decreasing trend resumed in 2013 [14] , with a narrowing of the gap between reported cases and the target ( i . e . an excess of 728 cases ) . Discussing in detail the reasons for the observed decrease in the number of cases is beyond the scope of this paper . Suffice it say that the results were achieved through outstanding efforts in disease surveillance and control by NSSCP , with the support of international organizations led by WHO and the financial assistance of the private sector and of major international donors [15] . The database of the Atlas of HAT is progressively improving in completeness and accuracy , thus enhancing its reliability . The Atlas represents a key tool to follow the geographic distribution of the disease , and to monitor g-HAT control and elimination . The robustness of the Atlas of HAT enables the estimation of populations and areas at risk , which in turn provides information on two secondary indicators that assess the intensity and effectiveness of the elimination activities ( i . e . ( i ) the geographical extent of the disease and ( ii ) the populations at different levels of risk ) . All risk estimations presented in this paper are solely based on g-HAT cases detected and reported from field actors involved in control and surveillance . As such , the estimates are affected by under-detection and underreporting at a degree that is presently difficult to quantify . To address this gap , a spatial modelling framework is being developed . This modelling exercise is expected to shed some light into the magnitude and distribution of under-detection . Arguably , as g-HAT elimination progresses and reported cases decrease , efforts will have to be made to improve our knowledge of the factors that may influence the risk of disease re-emergence and re-introduction . Modelling exercises could contribute to shed some light on these factors . Gaps and limitations notwithstanding , there is sufficient evidence to support the notion of a real overall decline of transmission of g-HAT . In West Africa , the combined effect of public health interventions and climatic and demographic changes [16] have provoked a dramatic decline of disease transmission rendering active case-finding surveys no longer cost effective in the majority of the foci . As a first step to respond to this epidemiological status of the disease , in 2010 a control and surveillance strategy integrated in the health system was piloted in Benin and Togo , followed in 2014 by Côte d’Ivoire , Ghana and Guinea . Burkina Faso , Guinea Bissau , Mali , Niger , Senegal and Sierra Leone are planned for 2015 . A similar decline in transmission is apparent in Central Africa , with the exception of areas affected by insecurity such as the foci in northern CAR—connected to the foci in southern Chad—as well as the Oriental province in DRC . In DRC logistical hurdles compound security constraints in making it difficult to draw an accurate picture of the extent of the disease in some parts of the country . Overall , it would appear that the shrinking number of reported cases in DRC reflect a real decline in disease transmission . This notion is corroborated by the increasing difficulties in recruiting patients for clinical trials [17] . In this epidemiological context , as of 2014 a control and surveillance strategy integrated in the health system has been already deployed in Cameroon , Chad , Congo , Equatorial Guinea , Gabon and Uganda , while DRC and South Sudan are planned for 2015 . Concerning the population at risk , the challenge for the future is twofold . First , to prevent the 43 . 4 million people presently living in low and very low risk areas from sliding back into a situation of higher risk through effective surveillance and response . Second , to setup appropriate and sustainable control strategies to reduce transmission in the areas where 13 million of people are still living at moderate to very high risk of infection . If met , these targets will enable to reach the 2020 goal of eliminating g-HAT as a public health problem [2] . In the process , additional steps will have to be planned to reach the complete interruption of transmission by 2030 [3] . The availability of new control tools , i . e . individual screening test [18 , 19] , oral treatments [20 , 21] , and simpler and cheaper devices for vector control [22] will facilitate sustainable control and surveillance based on the involvement of the regular health system , including the peripheral level . Technical advances are coupled with a positive political momentum [23 , 24] and the commitment of strong stakeholders [25] . At the same time , risk factors still exist that could jeopardize the achievement of the set goals; these include possible donor fatigue , competing health priorities at the national level , social unrest [26] , gaps in the coverage of population at risk [5] , challenges in the progressive integration of vertical programmes into often weak health systems , and dwindling resources for a still needed operational research [4] . Also , to tackle the risk of duplication of efforts and competition for prominence that can jeopardize the process , a coordinated and unequivocal support of g-HAT stakeholders to disease endemic countries must be articulated . In this regard , the WHO network for the elimination of HAT , set up in March 2014 , provides an opportunity and a tool to synergize efforts and to overcome , in a coordinated manner , the expected and unexpected obstacles we are bound to face in this exciting and challenging endeavour [15] . As already stated [27] , the journey towards elimination of g-HAT is not far , nor easy . | Control activities conducted over the last 15 years against gambiense human African trypanosomiasis ( g-HAT ) have had a tremendous impact on disease transmission , and the elimination of g-HAT now appears achievable . In this context , accurate monitoring is crucial . This paper analyzes g-HAT epidemiological trends by comparing two periods: 2003–2007 and 2008–2012 . The number of reported cases decreased from 19 , 963 in 2003 to 7 , 106 in 2012 . The areas at high or very high risk shrank by 60% between the two study periods . For 2008–2012 , 43 . 4 million people out of a total of 56 . 4 million at risk lived in areas at low or very low risk of infection , and they have therefore met the criterion of elimination as a public health problem ( i . e . < 1 case per 10 , 000 inhabitants per year ) . The challenge for the future is twofold . First , to prevent these 43 . 4 million people from sliding back into a situation of higher risk through effective surveillance . Second , to develop sustainable and adapted strategies to curb transmission in the areas where people are still living at moderate to very high risk . The WHO network for g-HAT elimination provides an opportunity to synergize efforts and to overcome the hurdles in this challenging endeavour . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
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| []
| 2015 | Monitoring the Progress towards the Elimination of Gambiense Human African Trypanosomiasis |
There is currently no licensed antiviral drug for treatment of dengue . Chloroquine ( CQ ) inhibits the replication of dengue virus ( DENV ) in vitro . A double-blind , randomized , placebo-controlled trial of CQ in 307 adults hospitalized for suspected DENV infection was conducted at the Hospital for Tropical Diseases ( Ho Chi Minh City , Vietnam ) between May 2007 and July 2008 . Patients with illness histories of 72 hours or less were randomized to a 3-day course of CQ ( n = 153 ) or placebo ( n = 154 ) . Laboratory-confirmation of DENV infection was made in 257 ( 84% ) patients . The primary endpoints were time to resolution of DENV viraemia and time to resolution of DENV NS1 antigenaemia . In patients treated with CQ there was a trend toward a longer duration of DENV viraemia ( hazard ratio ( HR ) = 0 . 80 , 95% CI 0 . 62–1 . 05 ) , but we did not find any difference for the time to resolution of NS1 antigenaemia ( HR = 1 . 07 , 95% CI 0 . 76–1 . 51 ) . Interestingly , CQ was associated with a significant reduction in fever clearance time in the intention-to-treat population ( HR = 1 . 37 , 95% CI 1 . 08–1 . 74 ) but not in the per-protocol population . There was also a trend towards a lower incidence of dengue hemorrhagic fever ( odds ratio = 0 . 60 , PP 95% CI 0 . 34–1 . 04 ) in patients treated with CQ . Differences in levels of T cell activation or pro- or anti-inflammatory plasma cytokine concentrations between CQ- and placebo-treated patients did not explain the trend towards less dengue hemorrhagic fever in the CQ arm . CQ was associated with significantly more adverse events , primarily vomiting . CQ does not reduce the durations of viraemia and NS1 antigenaemia in dengue patients . Further trials , with appropriate endpoints , would be required to determine if CQ treatment has any clinical benefit in dengue . Current Controlled Trials number ISRCTN38002730 .
Dengue is a globally important public health problem . This mosquito-borne viral infection results in an estimated 50 million cases of symptomatic illness each year in over 100 affected countries [1] . There are no licensed vaccines to prevent dengue and no specific therapies to stop or limit viral replication or modulate the severity of symptoms in patients . Infection with any of the four dengue virus serotypes can cause clinically apparent disease . A measurable viraemia is typically present for the duration of the febrile period , with the first 48–72hrs characterized by relatively high viraemia levels that then rapidly decline as acquired humoral and cellular immune responses resolve infection [2] . NS1 , a non-structural protein secreted by virus-infected cells , can be detected in the peripheral blood in some , but not all , symptomatic individuals [3] , [4] . Both viraemia and NS1 levels are higher in patients with more severe clinical patterns of disease [5] . The majority of symptomatic infections manifest as an acute systemic febrile illness that is clinically uncomplicated and lasts for 3–7 days . For reasons not fully elucidated , some DENV infections result in severe dengue , a syndrome usually characterized by transiently increased capillary permeability and a hemorrhagic diathesis . Parenteral fluids are used to replenish the intravascular volume and maintain cardiovascular stability during the period of maximum capillary permeability . Mortality in severe dengue can be reduced to less than 1% in experienced settings . Previous randomized controlled trials in dengue have focused on supportive management and to our knowledge , there has never been a trial directed towards reducing the virus burden . Chloroquine ( CQ ) is a cheap , widely available and well-tolerated lysosomotropic 4-amino-quinoline derivative . In vitro , CQ has modest anti-viral effects on replication of viruses from diverse taxonomic families ( reviewed in [6] ) . This has led to speculation that CQ could have a therapeutic role in the treatment of viral diseases where there are limited or no other therapies [6] , [7] . In the context of DENV , the lysosomotropic and weak base properties of CQ could exert anti-viral activity by interfering with endosomal fusion and furin-dependent virus maturation , which both require low pH environments in late endosomes and the lumen of the trans-Golgi network respectively [8] , [9] . Indeed , treatment of mammalian-expressing cells with chloroquine inhibits DENV infection [10] . Furthermore , treatment of DENV-2 infected mammalian cells with chloroquine reduces the infectivity of the produced virus by six- to eightfold , possibly by reducing the efficiency of the virus maturation process [11] . The 50% inhibitory concentration of chloroquine for DENV [10] is achievable inside human cells following ingestion of standard doses of CQ [12] , [13] . CQ could also modulate the host response to virus infection . Recognition of viral products by plasmacytoid dendritic cells ( pDCs ) occurs through a TLR-dependent pathway that requires endosomes acidification [14] , [15]; chloroquine-mediated blocking of this process partially inhibited West Nile virus-induced IFN-α production by pDC cultures [16] . CQ could also modulate antigen processing via an increased export of soluble antigens into the cytosol of DCs [17] . CQ also attenuates inflammatory cytokine responses [18] , [19] and this may in part explain why CQ is used as a 2nd line therapy in the treatment of inflammatory disorders such as rheumatoid arthritis and systemic lupus erythematosus [20] , [21] , [22] , [23] . Possibly related to its anti-inflammatory properties , CQ exerts an antipyretic effect equal to paracetamol during treatment of uncomplicated P . falciparum malaria [24] . Against a backdrop of interest in CQ as a therapeutic for acute viral infections [6] , [7] , the purpose of this study was to evaluate CQ as potential anti-viral therapy in a randomized , double-blind placebo-controlled trial of adolescents and adults with dengue .
We performed a randomized ( allocation ratio 1∶1 ) , double blind , placebo-controlled parallel-group study in 307 adults hospitalized for suspected DENV infection . Study participants were recruited from the Hospital for Tropical Diseases ( HTD ) in Ho Chi Minh City , Vietnam . Patients were eligible if they were ≥15 yrs , had a self-reported illness history of 72 hrs or less and were suspected of having dengue . Patients were excluded if they were pregnant or receiving therapy for other chronic disorders , had a history of hypersensitivity to CQ , or written consent from either the patient or a parent was not obtained . Physicians in the Hospital for Tropical Diseases were responsible for enrolment . Patients were randomly assigned to receive CQ ( Mekophar Chemical-Pharmaceutical Joint-Stock Company , Ho Chi Minh City , Viet Nam ) or placebo . The regimen for CQ was 600mg base ( 4×150mg tablets ) on enrolment to the study , then 600mg on day 2 and 300mg on day 3 ( following the World Health Organization recommended treatment regimen for CQ susceptible P . vivax ) [25] . Patients in the placebo arm received the same regimen of tablets ( identical color and size ) . All treatment courses were contained in identical pre-packed bottles that were randomly assigned to patients via a computer-generated sequence of random numbers in blocks of 20 patients . A pharmacist generated the random sequence and was the only person who knew the content of each bottle . All patients , care providers and study investigators were blinded to treatment assignments . Physicians in the Hospital for Tropical Diseases were responsible for ensuring that the correct sequence of study codes , and therefore the treatment allocation , was followed . The study medication was given within 1 hr of a baseline blood sample being collected . Clinical care , including other treatments such as parenteral fluid therapy was at the discretion of the attending physician and following hospital guidelines . Case classification was according to 1997 WHO classification criteria and was applied to each case after review of study notes [26] . The Scientific and Ethical committee of the HTD and the Oxford Tropical Research Ethical Committee approved the study protocol and all patients gave written informed consent . The trial was registered with the ISRCTN Register ( ISRCTN38002730 ) . To determine the optimal time point for cytokine measurement , levels of IL-1β , IL-6 , IL-8 , IL-10 , IL-12p70 , and TNF-α were measured on serial plasma samples from 39 patients by using a CBA Human Inflammatory Cytokines kit ( Becton Dickinson , San Jose , CA ) according to the manufacturer's instructions ( except that all samples were fixed in 4% paraformaldehyde before being analyzed ) . Subsequently , a luminex-based Bio-Plex system ( Bio-Rad Laboratories , Hercules , CA ) was used according to the manufacturer's instructions to measure simultaneous plasma levels of IL-2 , IL-4 , IL-6 , IL-8 , IL-10 , granulocyte macrophage colony stimulating factor ( GM-CSF ) , INF- γ , and TNF-α in 1 plasma sample from each patient . Flow-cytometric analysis of whole-blood samples stained with fluorochrome-conjugated monoclonal antibodies ( CD3-Cy , CD4-PE-Cy7 , CD8-PE , CD38-FITC , HLA-DR-PerCP and Ki67FITC ) was performed by use of a FACScalibur flow cytometer ( Becton Dickinson ( BD ) ) . Cell-surface staining was routinely performed on 150µL of fresh whole blood . All antibodies were purchased from BD . Whole-blood samples from healthy volunteer subjects were used as group control . Assuming a median time from enrolment to resolution of viraemia or NS1 antigenaemia in the placebo group of 72 hours and a reduction of this time by 24 hours due to CQ treatment ( corresponding to a hazard ratio of 0 . 67 assuming an exponential distribution of the resolution times ) , we would need to observe viraemia or NS1 antigenaemia resolution in 191 patients to show such an effect with 80% power at the two-sided 5% significance level . Assuming sufficient follow-up to observe viraemia or NS1 antigenaemia resolution in 90% of patients , we would need to include at least 213 patients with confirmed dengue . The statistician was unblinded for the data analysis . Data stayed blinded until the database was cleaned and locked ready for data analysis . All statistical analyses were performed using Intercooled STATA version 9 . 2 ( StataCorp , TX ) . A two-sided p-value ≤0 . 05 was considered significant for all parameters . The intention-to-treat ( ITT ) population was defined as all subjects who were randomized regardless of whether or not they began the treatment regimen . All laboratory confirmed dengue patients completing the expected number of days of treatment who fulfilled the inclusion/exclusion criteria of the protocol and who did not leave before the end of the study drug course formed the per-protocol ( PP ) population . Secondary endpoints ( except the FCT ) were compared between the 2 groups and analyzed using the Kruskal-Wallis test for continuous variables and the Fisher's exact test for categorical variables . For the primary endpoints and the FCT , the null hypothesis is that CQ has no effect on duration of DENV viraemia , NS1 antigenaemia and fever . Survival analysis using the Kaplan-Meier ( KM ) method and log-rank test was used for all time-to-event outcomes . Cox regression was used to quantify the difference in risk between treatment groups and to adjust for all the following baseline variables: time since illness onset at enrolment , serological status , serotype ( DENV-1 vs other ) , viraemia and temperature . Because these covariates were thought to influence the time to resolution of viraemia , the time to resolution of NS1 antigenaemia and the FCT , all were retained in the final adjusted models . The proportional hazards assumptions were checked using a test based on Schoenfeld residuals .
Between May 2007 and July 2008 , 307 adults with suspected dengue were randomized to CQ or placebo ( Fig . 1 ) . Of these 307 patients , 257 had laboratory confirmed dengue and 50 had no evidence of recent or acute dengue . All patients recovered fully . The baseline characteristics of the study population are summarized in Table 1 . Baseline characteristics were generally well-balanced between the two groups except for baseline viraemia which tended to be higher in the CQ group ( median 9 . 04 vs 8 . 52 Log10 copies/mL ) and the proportion of DENV3 infected patients , which was lower in the CQ arm ( 11 . 3% CQ vs 21 . 8% placebo ) . Thirty two patients ( 21 in CQ arm and 11 in placebo arm ) required parenteral crystalloid fluid therapy during their hospitalization ( for rehydration , and/or maintenance ) but none required blood transfusion ( Table 2 ) . There was no significant difference between the 2 groups in the need for fluid therapy ( p-value = 0 . 11 in the PP population and 0 . 06 in the ITT population ) . Given the clinical experience of using CQ therapy in inflammatory autoimmune disorders , we investigated whether CQ was associated with a measurable attenuation of the T cell response . To this end , the activation state of peripheral blood CD3+CD4+ and CD3+CD8+ T cells was assessed in fresh whole-blood at the time of enrolment , on illness day 6 , and again at follow-up in 172 consecutive patients enrolled in the study between September 07 and June 08 ( 85 in CQ arm , 87 in placebo arm ) , amongst whom there were 147 laboratory-confirmed dengue patients . The activation markers used were CD38 , HLA-DR and Ki-67 . As a reference , we also phenotyped T cells in fresh whole blood from 9 healthy adult volunteers . Strikingly , in dengue patients we observed a large population of surface-activated ( CD38+ or HLA-DR+ ) and proliferating ( Ki-67+ ) CD8+ T cells at early convalescence that were mostly absent at the time of enrolment and follow-up ( Fig . 5 ) . There was no evidence however of a significant difference in the proportion of activated T cells in patients treated with CQ or placebo . To understand if CQ modulated the cytokine response to DENV infection , plasma concentrations of IL-2 , IL-4 , IL-6 , IL-8 , IL-10 , GM-CSF , INF-γ , and TNF-α were measured in plasma from 234 laboratory-confirmed dengue patients ( 121 in CQ arm , 113 in placebo arm ) 2 or 3 days after randomization ( Fig . 6 ) . However , there was no significant difference in plasma concentrations of any of these cytokines between CQ or placebo treated patients ( Mann-Whitney P>0 . 1 ) .
There are no specific therapies for treating dengue . This controlled trial was conducted to determine if CQ could reduce the viral burden in dengue patients . We found no evidence that CQ reduced the duration of viraemia or NS1 antigenaemia in adult dengue patients , but did observe a modest anti-pyretic activity of CQ in the intention to treat population , but not in dengue laboratory-confirmed cases . CQ was associated with a higher frequency of adverse events compared to placebo , but these were generally mild . There was no evidence that CQ reduced the magnitude of cytokine or T cell responses to DENV infection . To our knowledge the only previous therapeutic trial of CQ for an acute viral infection has been in a small number of patients with Chikungunya virus infection [29] , in which CQ had no impact on either duration of febrile arthralgia or viraemia . Several possible reasons could explain the lack of measurable activity in this study of CQ against virological markers of DENV infection in vivo . Although the Cmax of CQ inside cells approximates the IC50 value of CQ against DENV in vitro , it is possible that CQ does not achieve inhibitory concentrations inside the reticuloendothelial cells where DENV replication is believed to occur [30] . Furthermore , it may not achieve the same pH modulation in vivo that is postulated to explain its activity on cultured virus in vitro . Alternative trial designs and protocols , such as increasing the therapeutic dose , dosing patients earlier in their illness or increasing the sample size substantially might increase the chances of observing an in vivo effect by CQ on the duration of DENV viraemia and NS1 antigenaemia . The importance of treating early is highlighted by the fact that in this trial the median duration of illness prior to treatment was relatively short ( ∼48 hrs ) and the median viraemia clearance times after treatment were ∼3 . 75 days in the CQ arm and ∼3 days in the placebo arm . Strikingly however , the duration of NS1 antigenaemia was relatively long , with as many as 92/243 ( 38% ) of dengue patients still NS1 positive at the time of discharge from hospital , although most of this antigen is probably generated in the first few days of illness and its prolonged clearance simply reflects its large , oligomeric structure [31] , [32] . The time to resolution of NS1 antigenaemia may therefore not be an optimal endpoint and an alternative approach could have been to compare the proportion of patients that were positive at a single post-therapy timepoint ( e . g . study day 5 ) . Collectively , these data underscore that there is only a brief therapeutic window of opportunity to improve upon the host's virus-eliminating immune response . Encouragingly however , strategies to diagnose patients very early in their illness are available in the form of NS1 rapid diagnostic tests [27] , [33] , [34] , [35] and these could in principal guide rational treatment with an anti-viral or other intervention as early as 24–48hrs into the illness course . Of additional value , but not yet identified , would be early prognostic markers of severe outcome , so that interventions can be delivered to those patients at higher risk . A CQ-mediated anti-pyretic effect equal to paracetamol has been shown during treatment of uncomplicated P . falciparum malaria [24] , [36] , [37] . This effect may be explained by CQ's anti-inflammatory properties , including CQ effects on TLR signaling [38] , [39] . Fever during an infection is thought to be initiated by virtually immediate cyclooxygenase-2 , prostaglandin E2 ( PGE2 ) production , activation of hypothalamic PGE2 receptors and then cytokines and TLR ligand activity [40] . It is reasonable to believe that CQ mediates an anti-pyretic effect by altering the levels and balance of these pyretic mediators during infection . Accordingly , we found a small reduction in fever clearance median times ( ∼6 hrs ) amongst CQ patients in the intention-to-treat patient population , and whilst a similar trend was observed amongst the dengue confirmed patients , it was not statistically significant . CQ might be a better anti-pyretic in non-dengue patients in this study because these patients had milder infections , albeit of unknown origin . Fewer patients receiving CQ developed DHF . The intriguing possibility that CQ mediated an anti-disease effect , but not a measurable anti-viral effect in this trial is plausible given the literature on CQ as a pleotropic immune-modulatory drug . To find support for this possibility we measured pro- and anti-inflammatory plasma cytokine concentrations and T cell activation markers in dengue patients . Of particular interest were vasodilatory and pyretic cytokines such as TNF-α that have been identified as susceptible to CQ modulation [18] , [41] and important in the pathogenesis of the dengue capillary leak syndrome [42] . Similarly , the magnitude of T cell activation has been postulated to be associated with dengue severity [43] . Whilst robust T cell activation and cellular proliferation was indeed present around the time of defervescence , there was no evidence of a difference between CQ and placebo arms for the cellular markers we investigated nor in the cytokines that were measured . The absence of a measurable impact by CQ on these elements of the host response might suggest any trend towards less DHF in the CQ arm is simply chance or reflects our inability to identify and measure true immunological correlates of disease . Only further large trials , with clinical endpoints , will determine if CQ has a disease modulating effect . Our study had several limitations . The study was hospital-based and therefore the patient population , although presenting early in their illness , may not reflect that seen in primary health care settings where milder infections might be expected . The study was performed in adults , who generally compensate well for capillary permeability , and it's plausible that different findings might be observed in children , who in most endemic settings carry much of the disease burden . We measured viraemia by quantitative RT-PCR as a surrogate and well characterised marker of infection though we recognise this is not that same as a quantitative biological assay of infectious virus . Finally , we did not formally conduct pharmacokinetic analysis of CQ in treated patients and this could have aided the interpretation of the final outcomes . There is growing interest in the potential for anti-viral therapies for dengue [44] , [45] . This study illuminated several important issues in the design of anti-viral interventions trials . Most striking is the rapid decline in the DENV viraemia beginning ∼72hrs into the illness , highlighting the fact that anti-viral interventions will likely need to be delivered very early and aggressively , preferably guided by cheap , sensitive and specific diagnostics . NS1 is a useful and easily assayed biomarker of DENV infection and in the context of a trial it conceivably provides a slightly different insight into virus infection than is given by measurement of viral RNA in plasma . In early phase trials , measurement of virological and clinical markers at multiple time-points per day is strongly recommended given the speed of viral clearance and evolution of disease . In later phase trials , the choice of clinical endpoints will depend on the target patient population and the setting . In children , single or combination endpoints around dengue shock syndrome , the most common life-threatening complication in children , should be considered . In adults , other complications such as severe bleeding may also be relevant . In summary , this study suggests CQ has no measurable impact on virological or immunological parameters of DENV infection in young adults . We also found no convincing evidence that CQ reduces the time to fever resolution in adults with dengue . Interventions with either more potent anti-viral molecules and/or immunomodulatory drugs are needed to improve clinical outcomes for patients in endemic settings . | There is no available drug or vaccine against dengue , an acute viral disease that affects ∼50 million people annually in tropical and sub-tropical countries . Chloroquine ( CQ ) , a cheap and well-tolerated drug , inhibits the growth of dengue viruses in the laboratory with concentrations achievable in the body . To measure the antiviral efficacy of CQ in dengue , we conducted a study involving 307 adults with suspected dengue . Patients received a 3-day oral dosage of placebo or CQ early in their illness . Unfortunately , we did not see an effect of CQ on the duration of viral infection . We did , however , observe that CQ had a modest anti-fever effect . In patients treated with CQ , we observed a trend towards a lower incidence of dengue hemorrhagic fever , a severe form of dengue . We did not find any differences in the immune response that can explain this trend . We also found more adverse events , primarily vomiting , with CQ . This trial provides valuable new information on how to perform trials of antiviral drugs for dengue . | [
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| 2010 | A Randomized Controlled Trial of Chloroquine for the Treatment of Dengue in Vietnamese Adults |
Murine gammaherpesvirus 68 ( MHV68 ) establishes long-term latency in memory B cells similar to the human gammaherpesvirus Epstein Barr Virus ( EBV ) . EBV encodes an interleukin-10 ( IL-10 ) homolog and modulates cellular IL-10 expression; however , the role of IL-10 in the establishment and/or maintenance of chronic EBV infection remains unclear . Notably , MHV68 does not encode an IL-10 homolog , but virus infection has been shown to result in elevated serum IL-10 levels in wild-type mice , and IL-10 deficiency results in decreased establishment of virus latency . Here we show that a unique MHV68 latency-associated gene product , the M2 protein , is required for the elevated serum IL-10 levels observed at 2 weeks post-infection . Furthermore , M2 protein expression in primary murine B cells drives high level IL-10 expression along with increased secretion of IL-2 , IL-6 , and MIP-1α . M2 expression was also shown to significantly augment LPS driven survival and proliferation of primary murine B cells . The latter was dependent on IL-10 expression as demonstrated by the failure of IL10−/− B cells to proliferate in response to M2 protein expression and rescue of M2-associated proliferation by addition of recombinant murine IL-10 . M2 protein expression in primary B cells also led to upregulated surface expression of the high affinity IL-2 receptor ( CD25 ) and the activation marker GL7 , along with down-regulated surface expression of B220 , MHC II , and sIgD . The cells retained CD19 and sIgG expression , suggesting differentiation to a pre-plasma memory B cell phenotype . These observations are consistent with previous analyses of M2-null MHV68 mutants that have suggested a role for the M2 protein in expansion and differentiation of MHV68 latently infected B cells—perhaps facilitating the establishment of virus latency in memory B cells . Thus , while the M2 protein is unique to MHV68 , analysis of M2 function has revealed an important role for IL-10 in MHV68 pathogenesis—identifying a strategy that appears to be conserved between at least EBV and MHV68 .
Herpesviruses establish life-long , latent infections characterized by episodic virus reactivation and subsequent virus shedding . Chronic infections with the lymphotropic gammaherpesviruses are associated with a variety of lymphomas and carcinomas which in humans includes Burkitt's lymphoma , nasopharyngeal carcinoma , Hodgkin's disease and Kaposi's sarcoma . The narrow host range of the gammaherpesviruses that infect humans , Epstein-Barr Virus ( EBV ) and Kaposi's sarcoma-associated herpesvirus ( KSHV ) , has severely hindered detailed pathogenesis studies . Murine gammaherpesvirus 68 ( MHV68; also known as γHV68 and murine herpesvirus 4 ) shares extensive genetic homology and biological similarity with both EBV and KSHV and is a natural pathogen of wild murid rodents . As such , MHV68 infection of inbred strains of mice has gained favor as a small animal model in which to evaluate viral and host determinants of gammaherpesvirus pathogenesis in vivo . Upon intranasal infection , MHV68 infection results in acute viremia in the lung that is later resolved into a latent infection of B cells , dendritic cells , and macrophages [1] . B cells are necessary for trafficking of virally infected cells to the spleen , leading to the establishment of splenic latency [2] , [3] . The CD8+ T cell response is critical for control of lytic infection in the lung as well as establishment of latent viral load in the spleen [4] . MHV68 infection results in a CD4+ T cell-dependent expansion of splenic B cells and both virus-specific and non-specific hypergammaglobulinemia [5] , [6] . Similar to EBV pathogenesis , memory B cells are the primary long-term reservoir of latent MHV68 in mice [7] , [8] , [9] . All herpesviruses manipulate the host's immune system to establish and maintain a long-term , latent infection , and many of these immunomodulatory mechanisms are conserved among the members of the gammaherpesvirus family . Both KSHV and MHV68 encode proteins , K3 and mK3 , respectively , that downregulate MHC I [10] . MHV68 also encodes a viral bcl-2 homolog , a viral cyclin , and a chemokine-binding protein , M3 [11] , [12] , [13] . The EBV proteins LMP1 and LMP2a mimic CD40 and tonic BCR signals , respectively , to manipulate B cell development and are believed to enable the virus to gain access to the memory B cell compartment independent of antigenic stimulation of the host B cell [14] , [15] . KSHV encodes both a viral IL-6 and a viral MIP-1α ortholog , while EBV encodes a viral IL-10 homolog , BCRF1 ( or vIL-10 ) [16] , [17] . Interleukin-10 ( IL-10 ) was first noted as a cytokine synthesis inhibitory factor ( CSIF ) that is secreted by TH2 cells and suppresses the activity of TH1 cells [18] . IL-10 enhances murine B cell viability and can activate human B cell proliferation and class switching in culture [17] , [19] . In addition , IL-10 suppresses TH1 responses through modulation of macrophage function by downregulation of MHC II and costimulatory molecules as well as inhibition of cytokine production and macrophage effector functions [20] , [21] . Dendritic cells exposed to IL-10 do not down-regulate costimulatory molecules but do secrete lower levels of IL-12 , impairing their ability to induce a TH1 response [22] . The M2 protein , unique to MHV68 , has been shown to play a critical role in both the establishment of latency as well as reactivation from latency [23] , [24] , [25] . A M2-null strain of MHV68 ( MHV68/M2 . Stop ) replicates with wild-type efficiency in mice following intranasal inoculation but exhibits a dose-dependent defect in the establishment of latency at day 16 post-infection [23] , [24] . Under conditions in which the MHV68/M2 . Stop mutant can efficiently establish a latent infection ( high dose intranasal inoculation or low dose intraperitoneal inoculation ) , the M2-null virus exhibits a profound reactivation defect , revealing dual roles for the M2 protein in the viral life-cycle [23] , [24] . Additionally , efficient transition of latently-infected B cells from the germinal center reaction to the memory B cell reservoir appears to be stalled in the absence of M2 , suggesting M2 may manipulate B cell signaling or differentiation to facilitate establishment of long-term latency in the memory B cell pool [24] , [26] . Numerous candidate SH3 binding motifs throughout M2 suggest the protein may function as a molecular scaffold that may modulate specific cellular signal transduction pathways . Consistent with this hypothesis , M2 has been shown to interact with a number of cellular proteins in vitro . M2 co-immunoprecipitates with Vav1 in S11 B cells , a MHV68 latently infected cell line , and M2 and Vav1 overexpression in A20 B cells leads to Vav1 phosphorylation , trimerization with Fyn , and downstream activation of Rac1 [27] . In fibroblast cultures , M2 interacts with DDB1/COP9/cullin repair complex and ATM to suppress DNA-damage induced apoptosis [28] . In addition , M2 can suppress STAT1/2 expression , leading to inhibition of the interferon response [29] . However , to date the impact of M2 expression in primary murine B cells has not been reported . Here we show that one function of the M2 protein is to induce expression of IL-10 in primary B cells , demonstrating a common immunomodulatory strategy utilized by those gammaherpesviruses encoding a viral IL-10 homolog and MHV68 .
The MHV68 M2 protein has been shown to be critical for both establishment and reactivation from B cell latency . M2 has no known homologous proteins , viral or cellular , and contains numerous SH3 binding motifs through which it can potentially manipulate B cell biology . Proliferating B cells harbor the majority of latent MHV68 genomes , and splenic B cell activation is associated with MHV68 infection at the onset of latency [30] , [31] . We asked whether expression of M2 in primary murine B cells in vitro altered proliferation or activation . B cells were purified by negative selection from mouse splenocytes , stimulated overnight with LPS , and transduced with either an M2 protein expressing recombinant murine stem cell virus ( MSCV ) retrovirus , MSCV-M2-IRES-Thy1 . 1 , or a control retrovirus , MSCV-M2 . Stop-IRES-Thy1 . 1 , which harbors a translation termination codon near the 5′ end of the M2 open reading frame at amino acid 13 ( Figure 1A ) . LPS stimulation is necessary for efficient retroviral transduction in this system because MSCV infection requires the cells to be in cycle [32] . The presence of an IRES-Thy1 . 1 cassette readily allowed retroviral transduction efficiency to be monitored by flow cytometry for surface expression of Thy1 . 1 . Notably , expression of the M2 protein from the retroviral construct could be detected as demonstrated by immunoprecipitation and immunoblotting of whole cell lysates harvested from primary B cells transduced with MSCV-M2-IRES-Thy1 . 1 at four days post-transduction ( Figure 1B ) . It should be noted that detection of M2 expression in the transduced primary murine B cells required immunoprecipitation with a chicken anti-M2 antisera raised against two M2 peptides , followed by immunoblotting with a rabbit polyclonal antiserum raised against a bacterially expression recombinant M2 protein . In contrast , M2 expression in the MHV68 latently infected B lymphoma cell line S11 can be detected by immunoblotting S11 lysates with the rabbit polyclonal anti-M2 antiserum ( data not shown ) . Thus , it does not appear that M2 is “over-expressed” in transduced primary murine B cells . Two days post-transduction , there were similar frequencies of transduced , Thy1 . 1+ B cells in the control and M2-transduced B cell cultures . However , by 5–6 days post-transduction , nearly 100% of the B cell culture transduced with MSCV-M2-IRES-Thy1 . 1 was Thy1 . 1+ as compared to ca . 20% of the culture transduced with the control vector , MSCV-M2 . Stop-IRES-Thy1 . 1 ( Figure 1C ) . Notably , the dominance of M2-expressing , Thy1 . 1+ B cells in the M2-transduced cultures was observed repeatedly . The increase in the percentage of Thy1 . 1+ cells in the M2-transduced culture was gradual , and it did not correspond to an increase in overall cell number in the cultures or a decrease in cell death ( Figure 1D ) . The latter result suggests that in a mixed culture ( M2 expressing and non-expressing cells ) , the non-transduced primary B cells are actively selected against . This could either be due to the secretion of a “toxic” factor by the M2 expressing cells or competition for a limiting factor necessary for cell survival . Upon observing the M2-transduced cells dominating the culture , we asked whether M2 was influencing B cell survival , proliferation , or both . To directly assess B cell survival in M2-transduced and control retrovirus cultures ( M2 . Stop ) , cells were stained with anti-Thy1 . 1 , Annexin V , and 7-AAD and analyzed by flow cytometry . In contrast to the results obtained by trypan blue exclusion which measured the live/dead ratio in the entire culture ( Figure 1D ) , flow cytometry of the transduced and untransduced populations within the culture revealed a survival advantage of the M2-transduced B cells . At day 2 post-transduction , 20% more of the M2-transduced B cells were alive ( AnnexinV− 7-AAD− ) than the untransduced cells in the same culture ( Figure 2B ) . At day 3 post-transduction , there was a four-fold higher frequency of live cells in the M2-transduced population as compared to the untransduced cells in culture ( Figure 2 , panels A & B ) . M2-transduced cells continued to survive better than the untransduced cells in the population , despite an equal frequency of cells entering apoptosis ( data not shown ) . At day 2 post-transduction , the M2-transduced cells have equal frequencies of live cells as the control M2 . Stop retrovirus transduced cells ( Figure 2B ) . Analysis at day 3 post-transduction revealed a 20% increase in the frequency of live cells in the M2-transduced population as compared to the cells transduced with the control retrovirus ( Figure 2B ) . This trend continued until the end of the time-course , with a higher frequency of live cells found in the M2-transduced population versus the M2 . Stop retrovirus control ( Figure 2B ) . The increased frequency of live cells in the M2-transduced population versus both the untransduced cells within the culture as well as the control retrovirus transduced population reveals a pro-survival effect of M2 protein expression in B cells . We next addressed whether M2 protein expression altered proliferation in the B cell cultures thereby contributing to the expansion of transduced cells . To directly assess B cell proliferation in M2-transduced and control retrovirus cultures , cells were pulsed with bromodeoxyuradine ( BrdU ) for 24 hours at different time points post-transduction . Cells were surface stained for Thy1 . 1 and proliferation was measured by intracellular staining for incorporation of BrdU . The time course analyses revealed that B cells transduced with either the M2 or control retrovirus exhibited equivalent frequencies ( 84–90% ) of proliferating cells 2 days post-transduction ( Figure 2D ) . However , 80–90% of M2-transduced B cells continued to proliferate 3 and 4 days post-transduction as compared to 40–50% of the cells transduced with the control retrovirus ( Figures 2 , panel C & D ) . By 5–6 days post-transduction there was a significant drop in the proliferation of M2-transduced B cells ( Figure 2D ) . These results indicate that M2 protein expression is able to transiently augment murine B cell proliferation . Thus , these analyses indicated that the M2 protein contributes to both enhanced B cell survival as well as promoting continued B cell proliferation post-LPS stimulation – which together leads to dominance of M2-transduced B cells in the primary murine B cell cultures over the time-course analyzed . The transition from the germinal center B cell population to the long-lived memory B cell compartment is critical for establishment of MHV68 latency [8] , [9] . Latent genomes in mice infected with M2-deficent MHV68 accumulate in the germinal center compartment late in infection , leading to the hypothesis that M2 is capable of manipulating B cell differentiation [26] . To determine whether M2-transduction leads to differentiation of B cells , surface expression of B cell differentiation markers was analyzed by flow cytometry . At four days post-transduction , B cells expressing M2 were CD19+ , CD25high , GL7high , B220low , I-Ab low , surface IgD− ( sIgD ) , sIgG+ , and CD138low when compared to untransduced cells within the culture ( Figure 3A ) . Strikingly , M2-transduced cells expressed higher levels of CD25 as compared to cells transduced with the control retrovirus , although the MFI of CD25 was similar between the two populations ( Figure 3A ) . Both transduced populations ( M2 and M2 . Stop ) became surface IgG positive , likely due to LPS stimulation coupled with retrovirus infection selecting for the LPS-driven proliferating B cell population . However , the M2 expressing B cells expressed higher levels of surface sIgG than the control M2 . Stop retrovirus transduced cells . Similarly , the M2 and M2 . Stop transduced populations both upregulated CD138 , although the presence of M2 did not lead to the high levels of CD138 indicative of plasma cell differentiation . Notably , the other changes observed in B cell differentiation were unique to the M2-transduced B cell population versus the cells transduced with the M2 . Stop control retrovirus . In addition , the M2-transduced B cells secreted significantly higher levels of IgG on days 4–6 post-transduction than the cells transduced with the control retrovirus ( Figure 3B ) . Secreted IgM levels remained similar throughout the time-course for M2 and control retrovirus B cell cultures ( Figure 3C ) . Importantly , M2-transduced cells express surface IgG and remain CD138low , indicating that they have not fully differentiated into plasma cells . Together , these data provide strong evidence that M2 expression leads to B cell activation and differentiation similar to a functional activated , pre-plasma memory B cell phenotype , namely CD19+ , sIgG+ , sIgD− , B220low , CD138low [33] , [34] . However , we cannot formally rule out that M2 expression leads to differential survival and expansion of a population of pre-plasma memory B cells present in the transduced culture – although this seems unlikely based on the very low frequency of this population in the purified naïve splenic B cells used for these studies . To further investigate the proliferative effects of M2 protein expression in primary murine B cells , the supernatants of the transduced B cells were screened for a variety of cytokines using a mouse cytokine antibody array ( see Materials and Methods ) . Supernatants of B cell cultures transduced with M2 and control retrovirus were compared at four days post-transduction ( Figure 4A ) . Cytokine arrays performed in duplicate time-course experiments revealed substantial increases in IL-10 , IL-2 , IL-6 , and MIP-1α in the culture supernatants of B cells expressing M2 compared to the control retrovirus transduced B cell cultures ( Figure 4A ) . Cytokine levels throughout the time-course analyses were subsequently quantitated by ELISA . IL-2 levels in the M2-transduced cultures peaked at 50 pg/mL of supernatant at day 4 and waned by day 6 post-transduction , while only 1–2 pg/mL of IL-2 were detected in the control retroviral supernatants ( Figure 4B ) . From 3 days post-transduction until the end of the time-course , the supernatants from M2-expressing B cells contained levels of IL-6 twice as high as those of B cells transduced with the control retrovirus ( 780 pg/mL vs . 400 pg/mL ) at day 6 post-transduction ( Figure 4C ) . There was also a 10-fold increase in the level of MIP-1α with M2-transduced cultures containing an average of 1845 pg/mL of MIP-1α versus 139 pg/mL in the control retrovirus supernatant at the end of the time course ( Figure 4D ) . Notably , we observed a 20-fold increase in IL-10 levels in the B cell cultures transduced with M2 with 17 . 5 ng/mL of IL-10 in the M2-transduced cultures as compared to 0 . 9 ng/mL in the control retroviral supernatants at day 6 post-transduction ( Figure 4E ) . Notably , the number of cells in the M2 protein expressing and control B cell cultures were not significantly different , and thus the observed differences in cytokine levels cannot be explain by an increase in cell number . These data demonstrate that M2 expression in primary murine B cells leads to enhanced secretion of several cytokines , most notably IL-10 . Finally , to further assess the ability of M2 expression to up-regulate IL-10 secretion from B cells , we transfected the murine A20 B cell line with either a control expression vector ( pIRES-EGFP ) or an M2 expression vector ( pM2-IEGFP ) and assessed IL-10 secretion by ELISA ( Figure 4F ) . Untreated A20 cells secrete significant levels of IL-10 , which were only modestly enhanced by LPS treatment ( Figure 4F ) . In addition , transfection of the control expression vector had no impact of the levels of IL-10 secreted by A20 cells ( Figure 4F ) . However , transfection with the M2 expression vector lead to a substantial increase in the levels of IL-10 secretion ( Figure 4F ) . The latter result provides further evidence that M2 is able to increase IL-10 secretion by B cells – independent of LPS stimulation . IL-10 has been demonstrated to be involved in the establishment of a latent MHV68 infection , and we asked whether IL-10 played a role in M2-driven B cell proliferation [35] , [36] . To address the role of IL-10 in M2-driven proliferation , B cells were isolated from wild-type and IL-10−/− mice , transduced with M2 or the control retrovirus , and surface Thy1 . 1+ expression was monitored over a six day time course . Although the percentage of M2-transduced C57Bl/6 B cells increased from 40% to 85% of the culture , as previously observed ( see Figure 1C ) , there was only a modest expansion of the Thy 1 . 1+ population from 37% to 49% in the IL-10−/− cultures transduced with M2 expressing MSCV retrovirus ( Figure 5A ) . ELISAs of the supernatants from the transduced cultures confirmed that the IL-10−/− B cells do not secrete detectable levels of IL-10 ( Figure 5B ) . We noted an approximately 10% increase in the percentage of IL-10−/− M2 protein expressing B cells over the time course experiments , and we hypothesize that this small increase might be due to the ability of the M2 protein to manipulate proliferation and/or survival pathways independent of IL-10 . Notably , IL-10−/− mice have been shown to have 20-fold higher levels of serum IL-6 than IL-10-sufficient mice [37] , and indeed we observed a two-fold increase in IL-6 in the IL-10−/− B cell supernatants of the untransduced population at day 2 ( Figure 5C ) . Expression of the M2 protein led to a four-fold increase in the levels of IL-6 in the culture supernatants of IL-10−/− B cells ( Figure 5C ) . In addition , MIP-1α levels were four-fold higher in the IL-10−/− B cell cultures at day 2 post-transduction , and this increase was observed throughout the time-course ( data not shown ) . However , the increased levels of IL-6 and MIP-1α observed in the M2-transduced IL-10−/− cultures could not compliment the loss of IL-10 in the cultures , leading us to hypothesize that IL-10 secretion is required for the expansion of the M2 protein-expressing B cells . To more directly assess the role of IL-10 in M2 protein-mediated B cell proliferation and survival , we tested the ability of the cytokine enriched supernatants from transduced wild-type and IL-10−/− B cells to compliment loss of M2 and IL-10 expression in culture . After analysis of transduction efficiency at two days post-transduction , one third of the supernatant from the WT MSCV-M2 . Stop and IL-10−/− MSCV-M2 transduced cultures was replaced with an equal volume of supernatant from C57BL6 MSCV-M2 cultures from the respective days post-transduction . B cells were analyzed for Thy1 . 1 expression for the remainder of the time-course , and IL-10 levels were measured by ELISA ( data not shown ) . Interestingly , addition of culture supernatants from C57BL6 M2 protein expressing B cells to C57BL6 B cells transduced with MSCV-M2 . Stop failed to induce significant proliferation of the transduced B cells ( Figure 5D ) . In contrast , addition of IL-10 containing culture supernatants to IL-10−/− B cells expressing the M2 protein led to a steady proliferation nearly equivalent to that of C57BL6 B cell cultures expressing the M2 protein ( Figure 5D ) . Finally , to formally demonstrate that IL-10 is required for the observed phenotype , we transduced IL10−/− B cells with either the M2 or M2 . Stop control recombinant MSCV viruses and assayed the frequency of Thy 1 . 1 . cells in the culture over time in the presence and absence of recombinant IL-10 ( Figure 5E ) . As expected , the addition of recombinant IL-10 had no discernable effect on M2 expressing IL-10-sufficient B cells recovered from C57Bl/6 mice . However , addition of IL-10 to the M2 transduced IL-10−/− B cells ( but not the M2 . Stop transduced IL-10−/− B cells ) rescued the dominance phenotype ( Figure 5E ) . These results demonstrate that both intracellular M2 expression and IL-10 secretion are necessary for the observed proliferative expansion of the transduced B cell population , and that neither one alone is sufficient to induce this expansion . These data suggest that M2 manipulates intracellular signaling pathways which enhance the response to IL-10 signaling as well as induce IL-10 secretion . Previous studies have shown that in the absence of a functional M2 gene , establishment of MHV68 latency following intranasal inoculation is severely reduced [24] . Similarly , inoculation of IL-10−/− mice with wild-type MHV68 leads to a decrease in the establishment of latency [35] , [36] . To determine whether M2 expression leads to IL-10 secretion in vivo , C57Bl/6 mice were infected ( either 1 , 000 pfu via intranasal inoculation or 100 pfu via intraperitoneal inoculation ) with either a recombinant MHV68 harboring the same translation termination codon near the 5′ end of the M2 open reading frame as used in control retrovirus construction ( MHV68/M2 . Stop ) or with a genetically repaired marker rescue isolate of the same locus ( MHV68/M2 . MR ) . Both intranasal and intraperitoneal inoculation of the M2-null mutant were assessed , since we have previously reported that route of inoculation impacts the latency phenotype observed [24] . Serum IL-10 was measured by in vivo cytokine capture and ELISA ( Figure 6 , panels A & B ) . Notably , mice infected with MHV68/M2 . Stop had serum IL-10 levels that were only slightly elevated over the levels present in naïve mice and were 2- to 3-fold lower than the levels observed in mice infected with the marker rescue virus ( MHV68/M2 . MR ) . Notably , this phenotype was independent of the route of inoculation ( Figure 6 , panels A & B ) . As we have previously reported [24] , we observed defects in both establishment of latency ( which was accentuated following intranasal inoculation ) , as well as reactivation from latency with the M2-null mutant MHV68 ( Figure 6 , panels C & D ) . Intraperitoneal infection with MHV68/M2 . Stop increased the establishment of latency eight-fold over intranasal inoculation , yet serum IL-10 levels were very similar to those observed following intranasal inoculation ( Figure 6 ) . Importantly , the serum levels of IL-10 we observed in MHV68/M2 . MR infected mice were similar to those previously observed [35] . These results provide strong evidence that M2 induction of IL-10 secretion , either from latently infected B cells or some other latency reservoir ( e . g . , infected macrophages or dendritic cells ) , contributes significantly to the serum levels of IL-10 observed during MHV68 infection following either intranasal or intraperitoneal virus inoculation . We next examined whether loss of M2 expression and the concomitant reduction in IL-10 expression might alter the CD8 T cell response to MHV68 since IL-10 is known to suppress T cell responses [18] . Thus , we examined the MHV68-specific CD8+ T cell response following infection of mice with either MHV68/M2 . Stop or MHV68/M2 . MR . Mice were infected intraperitoneally with 100 pfu of MHV68/M2 . Stop or MHV68/M2 . MR and splenocytes were harvested at day 16 post-infection , a time at which lytic virus has been cleared and latency established . As previously reported , there was a ten-fold decrease in establishment of latency with a 20-fold decrease in reactivation ( Figure 6D ) . Splenocytes from individual mice were stained for activated , tetramer positive CD8+ T cells using tetramers specific to two MHV68 antigens encoded by ORF6 and ORF61 ( Figures 7 , panels A–C ) . Both tetramers used in this analysis were specific for viral antigens expressed during the virus lytic replication cycle . In two independent experiments , tetramer staining for two different lytic antigens revealed a statistically significant increase in the frequency of tetramer-specific , activated CD8+ T cells in mice infected with MHV68/M2 . Stop compared to MHV68/M2 . MR ( Figure 7A–C ) . In contrast , there was no global change in overall CD8 activation as determined by the percentage of CD8+ CD11ahigh T cells in the spleens of infected mice ( Figure 7D ) . CD4+ T cell activation , as well as the percentage of CD44high CD62Llow CD4+ and CD8+ T cells , was the same in the two groups of infected mice ( data not shown ) . These data indicate that the loss of M2 during MHV68 infection specifically enhanced the MHV68-specific CD8+ T cell response , despite a significant decrease in viral latency and reactivation ( see Figure 6D ) . Overall , the immune response in the absence of M2 protein expression during infection is unique in that the MHV68-specific T cell response is increased correlating with a decrease in serum IL-10 levels . These data point to a potential role of M2 protein-mediated IL-10 secretion in the quiescence of the virus-specific T cell response in vivo which may facilitate both the efficient establishment of latency as well as reactivation from latency .
The latency-associated M2 protein is critical for establishing splenic latency following low dose intranasal inoculation and for virus reactivation from latency following low dose intraperitoneal inoculation [23] , [24] . In the absence of M2 , infected B cells are unable to efficiently transition from the germinal center to the follicles [26] . Early in latency , there is an accumulation of latently infected naïve B cells in the absence of the M2 protein , indicating a role for the M2 protein in manipulating B cell development during infection [24] . Epstein-Barr virus is hypothesized to drive naïve B cells to enter the germinal center reaction in order to establish latency in the memory B cell pool [7] , [38] , [39] . In long-term EBV carriers , lytic EBV gene transcripts are preferentially found in the plasma cell population , leading to a model whereby reactivation from latency is associated with differentiation from memory to plasma cell [40] . B cell proliferation is necessary for the establishment of MHV68 latency , and , similar to EBV , memory B cells are the primary long-term latency reservoir [8] , [9] , [30] . Reactivation is hypothesized to be needed for efficient seeding of the spleen during the establishment phase of MHV68 infection , and , as such , the M2-associated defects in establishment of latency and reactivation from latency may , in fact , be functionally linked . In this study we explored the impact of M2 protein expression in primary murine B cells - a system capable of differentiation . M2 expression in primary B cells led to proliferation of transduced B cells , driving a rapid expansion of transduced cells within the culture , regardless of initial transduction efficiency . Although both the transduced ( Thy1 . 1+ ) and untransduced ( Thy1 . 1− ) B cell populations could be shown to be proliferating ( by BrdU incorporation ) , the enhanced proliferation and survival of the M2-transduced B cells rapidly led to this population dominating the mixed culture . In primary murine B cells , M2-driven proliferation was dependent on the B cell's ability to secrete IL-10 and respond to IL-10 signaling . Notably , transfer of culture supernatants from M2 expressing C57Bl/6 B cells , or addition of recombinant murine IL-10 , did not result in dominance of the M2 . Stop retrovirus transduced ( i . e . , Thy1 . 1+ ) population in the absence of M2 protein expression , leading us to hypothesize that some other function ( s ) of the M2 protein augments IL-10 signaling . Culturing stimulated human memory B cells with IL-10 or IL-2 and IL-6 leads to plasma cell differentiation [41] , [42] . Also , an increase in MIP-1α transcription is associated with differentiation to a plasma cell phenotype [43] . Human germinal center B cells can be induced to differentiate into plasma cells rather than memory B cells in the presence of IL-10 [44] . In contrast to human B cells , IL-10 enhances murine B cell viability but does not drive proliferation [19] . Our data suggest that the MHV68 M2 protein uniquely increases the murine B cell proliferative response to IL-10 , mimicking the role of IL-10 signaling in human B cells . M2-transduced B cells were B220low , I-Ab low , sIgD− , yet retained surface expression of CD19 and IgG , and remained CD138low , indicating that they did not fully differentiated into plasma cells . Instead , the surface phenotype of the B cells expressing M2 most closely resembled that of a pre-plasma memory B cell , an intermediate stage in development between the memory and plasma cell phenotypes [33] , [45] . Together , this data supports a model wherein infection of naïve B cells in the lung with MHV68 leads to M2 expression , B cell proliferation and activation , and differentiation to a pre-plasma memory B cell phenotype . Depending on other cytokines and signals in the area , M2-expressing B cells may further differentiate into memory B cells , establishing long-term latency , or plasma B cells , potentiating virus reactivation . Thus , in this model of MHV68 pathogenesis , M2 protein manipulation of B cell differentiation to an intermediate pre-plasma memory B cell phenotype could facilitate both virus reactivation as well as establishment of viral latency . IL-10 has potent immunoregulatory activity , suppressing proinflammatory cytokine secretion and activation of antigen-presenting cells - functions which result in suppressed NK cell and T cell activity [46] . IL-10 plays an important role in MHV68 pathogenesis , but prior to our analyses of M2 protein function no specific viral antigen had previously been shown to stimulate cellular IL-10 production . It has been shown that ex vivo stimulation of MHV68 latently infected splenocytes with MHV68-infected antigen presenting cells resulted in IL-10 secretion peaking at the onset of splenic latency , and B cells were shown to be responsible for a significant portion of the IL-10 secreted [47] . Dendritic cells isolated from MHV68 infected mice express IL-10 transcripts , and dendritic cells infected ex vivo secrete IL-10 only when concurrently stimulated with LPS [36] . Interestingly , these investigators showed that M2 is transcribed by infected dendritic cells , although they did not demonstrate that IL-10 secretion was mediated by M2 [36] . In the absence of IL-10 , establishment of MHV68 latency is decreased concurrent with an increase in serum IL-12 p70 and splenomegaly , demonstrating a role for IL-10 in both establishment of latency as well as immunosuppression [1] , [35] , [47] . We observed a significant decrease in serum IL-10 in mice infected with an M2-null MHV68 mutant . Notably , the decreased serum IL-10 levels correlated with an increase in the percentage of MHV68-specific CD8+ T cells . Furthermore , it is important to note that this increased CD8+ T cell response was in the setting of an infection where virus reactivation was severely attenuated . Therefore , increased persistent virus replication cannot explain the increase in the tetramer-specific response . Thus , we hypothesize that during M2-mediated reactivation there is concurrent IL-10 secretion , locally dampening the ability of the MHV68-specific CD8+ T cells to clear the infected cells , leading to enhanced establishment and reactivation from latency . Manipulation of the IL-10 signaling pathway appears to be a conserved mechanism used by a number of herpesviruses . EBV encodes a viral IL-10 homolog , BCRF1 ( or vIL-10 ) , that has been shown to increase human B cell proliferation following surface immunoglobulin crosslinkinking and induce B cells to secrete increased levels of IgM , IgG , and IgA in a similar manner to cellular IL-10 [17] . In the absence of vIL-10 , EBV can still efficiently establish latent , long-term lymphoblastoid lines ( LCLs ) [48] . Exogenous vIL-10 added during the transformation of B cells by EBV enhanced both the rate and frequency of growth transformation , and antisense oligonucleotides to vIL-10 could negate this enhancement [49] , [50] . Human IL-10 could complement the loss of vIL-10 during EBV infection of B cells , demonstrating that it is IL-10 mediated signaling that augments B cell transformation following EBV infection [50] . LMP1 , a functional CD40 ortholog encoded by EBV , is both IL-10 responsive and induces secretion of cellular IL-10 in stimulated Burkitt's lymphoma cells [51] , [52] . Patients with EBV-associated post-transplant lymphoproliferative disease also have elevated serum IL-10 , but not IL-6 [53] . However , in vivo , whether the primary role of vIL-10 is to suppress the immune response , trigger B cell proliferation and differentiation or both is unclear . Serum cellular IL-10 levels are elevated both during primary EBV infection as well as during EBV reactivation from latency , implying that IL-10 plays a role in both the establishment and reactivation from latency [54] . Human cytomegalovirus ( HCMV ) , a beta herpesvirus , encodes a viral IL-10 ( cmvIL-10 ) that has only 27% homology to cellular IL-10 , but is nevertheless capable of binding the IL-10 receptor and mediating downstream STAT1/STAT3 signaling [55] . Human cmvIL-10 is capable of downregulating MHC I and II , suppressing PBMC proliferation , and decreasing IFNγ , IL-1α , GM-CSF , IL-6 , and TNF-α secretion in response to stimulation [56] . Transcripts encoding cmvIL-10 have been detected in the bone marrow and mobilized peripheral blood during natural HCMV latency , indicating that cmvIL-10 may play a role in either establishment , maintenance , or reactivation from latency [57] . Murine CMV ( MCMV ) does not encode an IL-10 homolog , although , parallel to the studies of Flano et al . on dendritic cells infected with MHV68 [36] , in vitro MCMV infection of primary macrophages results in secretion of cellular IL-10 and downregulation of MHC II [58] . Recently , IL-10 production by CD4 T cells has been shown to be of key importance in regulating MCMV persistence in the salivary glands . Blockade of the IL-10R resulted in a significant decrease in the titer of MCMV in the salivary glands with a concurrent increase in the frequency of IFNγ-producing CD4 T cells , indicating a role for IL-10 in the maintenance of a MCMV infection , possibly through reactivation from latency [59] . It seems reasonable to speculate that suppression of the host response may help these viruses both establish latency as well as reactivate from latency , reseeding the latency reservoir without clearance by memory T cell responses . IL-10 has been implicated in the pathogenesis of both autoimmune as well as viral diseases , and the fact that many viruses carry IL-10 orthologs speaks to the potency of IL-10 in manipulating the host immune system [60] , [61] . The parapoxvirus , ORF virus , encodes a viral IL-10 homolog with 80% homology to ovine IL-10 and is capable of inhibiting T cell proliferation [62] . Finally , two independent reports have demonstrated that blockade of the IL-10 receptor during chronic lymphocytic choriomeningitis virus infection led to clearance of the infection with enhanced IL-10 production by dendritic cells [63] , [64] . We also observed a significant increase in IL-6 and MIP-1α in the B cell cultures transduced with M2 . Interestingly , KSHV encodes homologs of both IL-6 and MIP-1α , suggesting that these cytokines have key roles in gammaherpesvirus immunomodulation that we have yet to appreciate [16] . MIP-1α can be detected in the BAL and lung homogenate of MHV68-infected mice at the peak of lytic replication , but the contribution of this cytokine to latency and reactivation has not been studied directly [65] , [66] . During MHV68 infection , MIP-1α secretion in the lungs may attract B cells to the area of acute replication , facilitating viral infection and trafficking to the spleen . There is no significant difference in pathology or viral latency in IL-6−/− mice , despite the fact that upon ex vivo stimulation infected splenocytes secrete IL-6 [67] . Further study is necessary to explore the links between M2 and these cytokines . Does M2-driven IL-10 secretion play a critical role in MHV68 latency ? The studies presented here provide an indication that the M2 protein has multiple functions – some of which are necessary for primary murine B cells to respond to IL-10 signaling in the retroviral transduction assays we have described . Our attempts to neutralize IL-10 in the B cell culture system have been unsatisfactory ( data not shown ) – perhaps owing to the difficulty of neutralizing the autocrine activity of IL-10 expressed from primary murine B cells . Thus , we anticipate that attempting to neutralize IL-10 in vivo during MHV68 infection will be difficult . As a distinct approach , we have recently published the analysis of a panel of point mutations in candidate functional motifs in the M2 protein [68] . The latter studies have also provided evidence for the presence of multiple functionally important domains in M2 [68] . With respect to M2-driven B cell proliferation and IL-10 secretion , we analyzed in primary B cells three M2 mutants which , in the context of virus infection , were severely attenuated in establishment and reactivation from MHV68 latency . Notably , two of these mutations ( Y129F/P7 and P8 ) ablated the IL-10 dependent proliferative dominance phenotype in primary B cell cultures while the other mutation ( P9 ) was similar to wild type M2 [68] . The latter result underscores that M2 is a multifunctional protein . In addition , these studies link M2 functional domains that play a critical role in MHV68 latency in vivo to M2-driven IL-10 secretion . Further studies will be required to assess the contribution of M2-driven IL-10 expression to chronic MHV68 infection . In summary , the analysis of M2 protein function provides a unique insight into an immunomodulatory mechanism that is employed by many viruses , particularly the herpesvirus family . Our work demonstrates that the M2 protein , a unique viral protein , manipulates B cell signaling to induce cellular IL-10 secretion and make cells more responsive to IL-10 signaling , leading to proliferation and enhanced survival of M2-expressing primary B cells in culture . M2 expression in primary murine B cells results in differentiation to a pre-plasma memory B cell phenotype , an intermediate in mature B cell development . In addition , M2 protein expression correlates with high serum IL-10 levels and an increased frequency of virus-specific CD8+ T cells during MHV68 infection . We conclude that driving B cell proliferation , survival and differentiation , while simultaneously dampening the host immune response to the virus , is an elegant immunomodulatory mechanism used by MHV68 to both facilitate establishment of latency and subsequent episodic virus reactivation from latency .
Female C57Bl/6 and IL-10−/− mice 6 to 8 weeks of age were purchased from the Jackson Laboratory . Mice were sterile housed and treated according to the guidelines at Emory University School of Medicine ( Atlanta , GA ) . Following sedation , mice were infected intranasally with 1000 pfu of either MHV68/M2 . Stop or MHV68/M2 . MR in 20 µL of cMEM . Mice were infected with 100 pfu of MHV68/M2 . Stop or MHV68/M2 . MR in 500 µL of cMEM intraperitoneally . Mice were allowed to recover from anesthesia before being returned to their cages . Spleens were homogenized and erythrocytes removed by hypotonic lysis . B cells were enriched using negative selection by magnetic cell separation with the mouse B Cell Isolation Kit ( Miltenyi Biotech ) . Purity was confirmed by staining for CD19 , and B cells used in experiments were 93–97% pure . Cells were cultured in RPMI-1640 supplemented with 10% FCS , 100 U/mL penicillin , 100 mg/mL streptomycin , 2 mM L-glutamine , 10 mM HEPES , 1 mM sodium pyruvate , 10 mM non-essential amino acids , and 25 µg/mL of LPS ( Sigma ) overnight before retroviral transduction . BglII sites were cloned flanking the M2 ORF with primers 5′ CAG CTC AGA TCT ATG GCC CCA ACA CCC 3′ and 5′ CAG CTC AGA TCT TTA CTC CTC GCC CCA 3′ and cloned into pCR-Blunt ( Invitrogen ) . Positive clones were sequenced , digested with BglII , and cloned into the pMSCV-IRES-Thy1 . 1 vector ( a gift from Philippa Marrack ) to construct pMSCV-M2-IRES-Thy1 . 1 . pMSCV-M2Stop-IRES-Thy1 . 1 was constructed in a similar manner . Retroviruses were produced using the BOSC23 producer cell line ( ATCC ) . 2×106 BOSC23 cells were plated on 60 mM Collagen II coated plates . The following day , 10 ug of pMSCV vector was transfected into the BOSC23 cells using the LT-293T reagent from Mirus Biotech . Retroviral supernatants were harvested 48 to 72 hours post-transfection , centrifuged at 2000 rpm for 10 minutes to clear cell debris , and supplemented with 5 µg/mL of polybrene . B cells were transduced by removing 700 µL of media and replacing it with 1 mL of retroviral supernatant/polybrene . Cells were spun at 2500 rpm at 30°C for one hour . 750 uL of retroviral supernatant was removed and replaced with fresh , complete RPMI . Cells were rested for 48 hours before analysis . In some analyses recombinant IL-10 was added back to transduced primary B cell cultures , as follows . Primary murine B cells were harvested from C57Bl6 and IL-10−/− mice as previously described . Transduction efficiencies were measured 48 hours post-transduction by flow cytometry . Following day 2 analysis , indicated cultures received 20 ng/mL of murine recombinant IL-10 ( Peprotech ) . B cell populations were analyzed on days 3–5 post-transduction by flow cytometry . Cells were lysed in a suitable volume of ELB buffer on ice for 20 minutes . Lysates were pre-cleared with pre-immune chicken IgY , and M2 precipitated with chicken anti-M2 IgY followed by capture by agarose anti-IgY beads ( Aves Labs , Inc . ) . Precipitates were run on a 15% acrylamide gel , transferred to nitrocellulose membranes , and blotted with rabbit anti-M2 antisera followed by donkey anti-rabbit HRP . Protein was detected using chemiluminescence on Kodak X-Omat Blue XB-1 film . Rat anti-mouse CD16/32 ( Fc block ) was used prior to staining in most experiments . Cells were stained with the following antibodies: Thy1 . 1-FITC , -PE , or –APC ( eBiosciences ) , CD44-FITC ( Caltag ) , CD62L-PE ( Caltag ) , GL7-FITC , IgG1 , 2a , 2b , 3-FITC , CD25-PE , CD138-PE , I-Ab-PE , CD4-PerCP , CD11a-PE-Cy7 , CD19-APC , B220-APC , CD8-PacficBlue ( BD Pharmigen except where noted ) . Tetramers were synthesized at the NIH Tetramer Core Facility at Emory University and conjugated to streptavidin-APC ( Molecular Probes ) according to core protocol . Intracellular bromodeoxyuradine incorporation was measured using BrdU-APC according to the manufacturer's protocol ( BD Pharmigen ) . AnnexinV-PacificBlue and 7-AAD reagents were purchased in the Vybrant® Apoptosis Assay Kit #14 ( V35124 ) from Molecular Probes and used per manufacturer's protocol . Cells were analyzed on FACScalibur or LSR II flow cytometer . Data was analyzed using FlowJo software ( TreeStar , Inc . , San Carols , CA ) . TranSignalTM Mouse Cytokine Antibody Arrays 1 . 0 ( Panomics , Inc . ) were used to screen for secreted cytokines as per manufacturer's instructions . Membranes were blocked in Blocking Buffer for two hours , washed , and then incubated for two hours at room temperature with day four supernatants from B cells transduced with MSCV-M2 or MSCV-M2 . Stop . Membranes were washed and incubated with Biotin Conjugated Anti-Cytokine Mix as per protocol . Membranes were washed and incubated with Streptavidin-HRP . After a final wash , bound cytokine was detected using chemiluminescence on Kodak X-Omat Blue XB-1 film . Cytokines were quantitated by ELISA . IL-6 and IL-10 were detected with reagents from BD Biosciences , and IL-2 and MIP-1α were detected with reagents from R&D Biosystems . IgM and IgG were detected with reagents from Bethyl Biosciences . Triplicate cultures of 1×106 A20 B cells were nucleofected ( Amaxa Biosystems ) with 4 ug of pIRES2-EGFP ( BD Biosciences Clontech ) , pM2-IRES-EGFP [68] , or pBluescriptIISK ( Stratagene ) using Solution T with setting T-01 on an Amaxa Nucleofector I ( Amaxa Biosystems ) . Cells transduced with pBluescriptIISK were stimulated with 100 ng/mL of LPS following nucleofection as indicated . 48 hours post-nucleofection , supernatants were harvested and secreted IL-10 measured by ELISA ( BD Biosciences ) . A MHV68 genomic fragment containing the region from bp 2403 to bp 6262 ( WUMS sequence ) [69] was cloned into the Litmus-38 plasmid ( Lit38-M2 ) as previously described [23] . With Lit38-M2 as a template , a stop codon was introduced into the M2 ORF using the following oligonucleotides: Oligo1 ( 5′ CCA CCA GGC CGA AGC TTA CGG ATT GGG AAT C ) and Oligo2 ( 5′ CCA ATC CGT AAG CTT CGG CCT GGT GGA TG ) generating a translational stop codon at bp 4566 and introducing a Hind III restriction site . The resultant product was ligated into the pCR Blunt plasmid ( Invitrogen ) . In addition , an M2 marker rescue pCR Blunt plasmid was generated by PCR using Lit38-M2 as a template and designated as M2 . MR . M2 . Stop pCR Blunt plasmid and M2 . MR were sequenced to verify the introduction of the site directed point mutations and the absence of unwanted mutations . Recombinant viruses were generated by allelic exchange in E . coli , as described by Smith and Enquist [70] , [71] . Briefly , the Not I and Bam HI restriction sites within pCR Blunt were used to liberate the MHV68 genomic region contained within the plasmid . This fragment was cloned into the suicide vector pGS284 which harbors an ampicillin gene and a levansucrase cassette for positive and negative selection , respectively . The resulting plasmid was transformed into S17λpir E . coli cells and mated to GS500 E . coli ( RecA+ ) containing wt MHV68 BAC . Cointegrants were selected on Luria-Bertani ( LB ) agar plates containing chloramphenicol ( Cam ) and ampicillin ( Amp ) and were resolved following overnight growth in LB medium with Cam . Next , bacteria were plated on LB agar plates containing Cam and 7% sucrose to select for loss of pGS284 vector sequence . Individual colonies harboring site specific point mutations within M2 were identified by colony PCR followed by restriction digest . Positive clones were grown in LB medium with Cam , and BAC DNA was purified with a Midi Prep Kit ( Qiagen , Hilden , Germany ) as described by the modified manufacturer's protocol . The presence of site specific point mutations and the absence of unwanted mutations within the region of homologous recombination were confirmed by sequencing and southern blot . Virus stocks were generated by Superfect ( Qiagen , Hilden , Germany ) transfection of recombinant MHV68 BAC DNA into Vero-Cre cells as previously described [70] . In wells showing cytopathic effect ( CPE ) , virus was harvested , cleared of cell debris , and used to infect Vero-Cre cells in order to generate high-titer stocks . Following the presence of CPE in Vero-Cre cells , samples were harvested , homogenized , clarified , and aliquoted for storage at −80°C . Virus stock titers were determined by plaque assay as previously described [23] , [72] . Limiting dilution assays for frequency of latent were performed as previously described [23] , [24] . To determine the frequency of cells harboring latent viral genomes , single-copy-sensitive nested PCR was performed . Frozen samples were thawed , washed in isotonic buffer , counted , and plated in three-fold serial dilutions in a background of 104 NIH 3T12 cells in 96 well plates . Cells were lysed by protease K digestion for six hours at 56°C . Two rounds of nested PCR were performed per sample with twelve samples per dilution , and the products were resolved on 2% agarose gels . In order to measure the frequency of reactivating splenocytes , bulk splenocytes were resuspended in cMEM and plated in serial two-fold dilutions on mouse embryonic fibroblast ( MEF ) monolayers in 96-well tissue culture plates . Parallel samples of mechanically disrupted cells were plated to detect preformed infectious virus . Wells were scored for cytopathic effect 14 to 21 days post-explant . The Mouse IL-10 In Vivo Capture Assay Set ( BD Biosciences ) was used to detect IL-10 in vivo during infections . On day 14–15 p . i . , parallel groups of five mice were injected with 10 µg of biotinylated rat anti-mouse IL-10 antibody in 200 µl of sterile PBS . Mice were bled on day 15–16 p . i . and serum collected . Samples were prepared and assayed by ELISA as per protocol . The limit of detection of this assay is 31 . 3 pg IL-10/mL of serum . Data analysis was conducted using GraphPad Prism software . Error bars in all graphs depict standard error of the mean . For limiting-dilution analysis , data was subjected to nonlinear regression analysis with a sigmoidal dose-response algorithm for best-fit . Poisson distribution predicts that the frequency at which 63 . 2% of wells are positive for an event ( PCR or reactivation ) is the frequency at which there is at least one event present in the population . Statistical significance of the flow cytometry and ELISA data was determined by two-tailed , unpaired Student's T test with a confidence level of 95% . | Gammaherpesviruses are able to maintain life-long , quiescent infections ( latency ) in lymphocytes characterized by intermittent production of infectious progeny virus ( reactivation ) . The murine gammaherpesvirus 68 ( MHV68 ) has extensive genetic and phenotypic similarities to the human gammaherpesviruses Epstein-Barr virus ( EBV ) and Kaposi's sarcoma associated herpesvirus ( KSHV ) . Similar to EBV pathogenesis , MHV68 establishes long-term latency in memory B cells . A unique MHV68 protein designated M2 is known to play an important role in both establishment of latency and reactivation from latency . Efficient transition of MHV68 latency to the memory B cell population is hindered in the absence of M2 , leading to the hypothesis that M2 may be involved in MHV68-driven B cell differentiation . In this study we show that M2 expression enhanced primary murine B cell survival , proliferation , and differentiation in culture . M2 expressing B cells secreted of high levels of IL-10 that is necessary for the observed expansion of the M2-expressing B cell population in vitro . Mice infected with a M2-null MHV68 mutant had a significant decrease in serum IL-10 , and this correlated with an increased frequency of MHV68-specific CD8+ T cells in these animals . Thus , M2 manipulation of IL-10 signaling appears to both drive expansion of the major latency reservoir ( B cells ) , as well as dampen the immune response to the virus facilitating both viral latency and reactivation . | [
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| 2008 | The MHV68 M2 Protein Drives IL-10 Dependent B Cell Proliferation and Differentiation |
Regulation of mitochondrial DNA ( mtDNA ) expression is critical for the control of oxidative phosphorylation in response to physiological demand , and this regulation is often impaired in disease and aging . We have previously shown that mitochondrial transcription termination factor 3 ( MTERF3 ) is a key regulator that represses mtDNA transcription in the mouse , but its molecular mode of action has remained elusive . Based on the hypothesis that key regulatory mechanisms for mtDNA expression are conserved in metazoans , we analyzed Mterf3 knockout and knockdown flies . We demonstrate here that decreased expression of MTERF3 not only leads to activation of mtDNA transcription , but also impairs assembly of the large mitochondrial ribosomal subunit . This novel function of MTERF3 in mitochondrial ribosomal biogenesis is conserved in the mouse , thus we identify a novel and unexpected role for MTERF3 in coordinating the crosstalk between transcription and translation for the regulation of mammalian mtDNA gene expression .
There is a growing interest in molecular mechanisms regulating oxidative phosphorylation capacity because of the increasing number of diseases associated with mitochondrial dysfunction [1] and as aging is associated with mitochondrial functional decline [2] , [3] . Regulation of mitochondrial gene expression has an important role in fine-tuning oxidative phosphorylation capacity because critical subunits of the respiratory chain and the ATP synthase are encoded by mitochondrial DNA ( mtDNA ) [4] , [5] . The regulation of mtDNA expression is completely dependent on nuclear genes but the mechanisms are not fully understood [4] . The expression of mtDNA could , in principle , be controlled at many different levels , e . g . by regulation of mtDNA copy number , transcription initiation , mRNA stability , translation or stability of respiratory chain subunits . Mitochondrial transcription factor A ( TFAM ) is essential both for transcription initiation [6] , [7] , [8] , [9] and mtDNA copy number control [10] . TFAM packages mtDNA into a compact protein-DNA structure termed the nucleoid [11] , [12] . There is a good correlation between TFAM levels and mtDNA levels in eukaryotic cells and mtDNA cannot be stably maintained if not coated by TFAM . However , there are a large number of mtDNA molecules in any given cell and changes in copy number are a slow process that is unlikely to have a main regulatory importance . In support of this notion , experimental manipulation of TFAM expression has been used to create mouse models with moderate decrease or increase of mtDNA copy number , with no or only minor effects on oxidative phosphorylation capacity [10] , [13] . The basal mitochondrial transcription machinery consists of the nuclear-encoded mitochondrial RNA polymerase ( POLRMT ) , TFAM and mitochondrial transcription factor B2 ( TFB2M ) , which together are sufficient and necessary for transcription initiation in vitro [6] , [14] , [15] , [16] . A large number of nucleus-encoded proteins have been reported to directly interact with and modulate the activity of the basal mitochondrial transcription machinery [17] , [18] , but this whole area is lacking a consensus for the role of these putative intramitochondrial transcription factors [14] . We have recently demonstrated that the mammalian mitochondrial leucine-rich pentatricopeptide repeat containing ( LRPPRC ) protein [19] and its fly homolog the bicoid stability factor ( BSF ) protein [20] are essential and have very similar roles in controlling mRNA stability , mRNA polyadenylation and coordination of translation in metazoan mitochondria [19] , [20] . Regulation of mitochondrial translation not only involves mRNA maturation and stability , but also factors regulating translation and ribosomal biogenesis [21] , [22] . An example of these factors is the adenine dimethyltransferase TFB1M , which modifies the 12S rRNA of the small ribosomal subunit and is necessary for the stability of the small ribosomal subunit and ribosomal biogenesis [23] . The role of the MTERF ( mitochondrial transcription termination factor ) -family of proteins [24] in regulation of mtDNA expression is of particular interest , because its members have been reported to influence mtDNA expression at different levels . MTERF1 has been suggested to play a role in mitochondrial transcription termination , by binding mtDNA downstream of the two mitochondrial rRNA genes to regulate the ratio between transcription of the upstream rRNA genes and the downstream mRNA genes [25] , [26] , [27] , [28] , [29] . In addition , MTERF1 has been reported to have a role in activating mtDNA transcription [30] . Mice lacking the Mterf2 gene are viable , but have been reported to develop myopathy and memory deficits [31] . The exact molecular mechanisms of MTERF2 function remain unclear , but it has been reported to bind the mitochondrial promoter region and to stimulate transcription initiation [31] , whereas another report showed that MTERF2 associates with nucleoids without displaying sequence-specific DNA binding [32] . MTERF3 and MTERF4 are both essential for embryonic survival [5] , [33] . Characterization of conditional knockout mice has shown that MTERF3 functions as a negative regulator of mtDNA transcription initiation by interacting with the control region to inhibit activation of the two mitochondrial promoters [5] . Loss of MTERF3 in the mouse heart leads to a massive activation of mtDNA transcription and a severe respiratory chain deficiency , possibly caused by imbalanced amounts of mtDNA transcripts [5] . MTERF4 forms a heterodimer with the cytosine methyltransferase NSUN4 and targets this enzyme to the large ribosomal subunit [33] , [34] , where it likely modifies 16S rRNA to regulate mitochondrial ribosomal biogenesis . The mitochondrial genomes of flies and mammals have the same gene content although there are differences in gene order and expression patterns [35] , [36] . This high level of conservation of metazoan mtDNA suggests that important regulators of mtDNA expression also may be conserved . We therefore decided to use a cross-species comparison approach to further study the in vivo role of MTERF3 . We demonstrate here that knockout and knockdown of the Mterf3 gene expression in Drosophila melanogaster leads to activation of mtDNA transcription and impaired mitochondrial translation . We further show that imbalanced transcription is not the only cause of the altered mtDNA expression because also the 16S rRNA levels are reduced and the assembly of the large ( 39S ) mitochondrial ribosomal subunit is impaired . These findings prompted us to reinvestigate the role for MTERF3 in the mouse , where we also found a reduction in levels of the 39S mitochondrial ribosomal subunit and impaired ribosomal assembly in the absence of MTERF3 . These findings identify a novel role for MTERF3 in the biogenesis of metazoan mitochondrial ribosomes and point to a close crosstalk between transcription initiation and ribosomal biogenesis in control of mtDNA expression and regulation of oxidative phosphorylation capacity .
We performed an extensive phylogenetic analysis of Mterf3 and found a single gene ortholog in Drosophila melanogaster , which we denoted DmMterf3 ( Figure S1A ) . We used algorithms to predict the subcellular localization for the DmMTERF3 protein and found a high probability for mitochondrial localization using either Mitoprot ( 0 . 986 ) or TargetP ( 0 . 875 ) softwares . Next , we performed live imaging of cells expressing GFP-tagged DmMTERF3 after counterstaining with MitoTracker Deep Red ( Figure S1B ) and found a co-localization rate of 94 . 9±1 . 4% in Schneider ( S2R+ ) cells ( n = 8 analyzed cells ) and 98 . 3±0 . 4% in HeLa cells ( n = 10 ) , thus experimentally verifying the predicted mitochondrial localization of DmMTERF3 . In order to analyze the in vivo function of DmMterf3 we generated knockout flies by ends-out homologous recombination [37] to replace the complete coding sequence for DmMterf3 with an attP-site and a loxP-flanked marker gene denoted white ( Figure 1A ) . Heterozygous knockout flies ( genotype DmMterf3+;white ) were crossed to cre-recombinase expressing flies to remove the white gene and thereafter the third chromosome balancer Tubby ( TM6B ) was introduced . This balancer chromosome causes the Tubby larval phenotype , which will segregate with the wild-type DmMterf3 allele in our crosses . The homozygous removal of DmMterf3 as well as the excision of white was confirmed by PCR analysis of genomic DNA with gene-specific primers ( Figure 1B ) . DmMterf3 knockout ( DmMterf3−/− ) larvae have a profoundly reduced body size and die in the third instar larval stage , whereas heterozygous DmMterf3+/− larvae pupate and develop into flies in a similar manner as wild-type larvae ( Figure 1C ) . Quantitative reverse transcription ( qRT ) -PCR from DmMterf3−/− larvae showed ∼90% reduction of the DmMterf3 transcript levels at 3 days after egg-laying ( ael ) and ∼95% reduction at 6 days ael ( Figure 1D ) . The residual levels of DmMterf3 transcript found in DmMterf3−/− larvae at 3 days ael are most likely due to a persisting maternal contribution because we saw further reduction of DmMterf3 transcript levels in older DmMterf3−/− larvae ( Figure 1D ) . Loss of DmMTERF3 resulted in increased mtDNA levels in knockout larvae at 3 and 6 days ael ( Figure 1E ) and an increase of ND1 , ND2 and ND6 steady-state transcript levels , whereas the steady-state levels of the COXIII and 12S rRNA transcripts were unchanged and levels of the 16S rRNA profoundly reduced ( Figure 1F ) . To summarize , there are important phenotypic similarities between DmMterf3 knockout flies and Mterf3 knockout mice [5] , because in both cases the gene is essential and its inactivation leads to increased steady-state levels of most mitochondrial transcripts , as well as reduction of 16S rRNA transcript levels . The early death of DmMterf3−/− larvae prevented a detailed molecular characterization of the phenotype and we therefore proceeded to use DmMterf3 RNAi flies for the subsequent studies . We proceeded to use a UAS-GAL4 based strategy to knock down DmMterf3 expression in flies . We first tested the RNAi construct by using it in conjunction with the eye-specific eyeless-GAL4 driver and found a massive phenotype with reduced eye-size and disorganized head structure consistent with efficient silencing of DmMterf3 expression ( Figure S2A , S2B ) . Next , we proceeded with the ubiquitous knockdown ( KD ) of DmMterf3 expression using the daughterless-GAL4 driver ( da-GAL4 ) to produce a KD line containing transgenes encoding both the da-GAL4 transactivator and the inducible UAS-RNAi construct w;;UAS-DmMterf3-RNAi/da-GAL4 . We also generated two control lines , the first line w;;da-GAL4/+ only containing the da-GAL4 transgene and the second line w;;UAS-DmMterf3-RNAi/+ only containing the transgene encoding the inducible RNAi construct . The KD line and the two control lines were analyzed in parallel for all subsequent experiments . Ubiquitous knockdown of DmMterf3 expression led to ∼80–90% reduction of DmMterf3 transcript levels in KD larvae at 3 , 5 and 6 days ael ( Figure 2A ) . The DmMterf3 KD larvae were visibly smaller from 4 days ael and onwards , as documented by a reduced body weight in comparison with controls ( Figure 2B ) . Eventually , DmMterf3 KD larvae displayed delayed larval development and died at the pupal stage . In order to rule out off-target RNAi effects , we generated a transgenic fly line expressing Drosophila pseudoobscura ( Dp ) MTERF3 . DpMTERF3 has ∼80% similarity to DmMTERF3 at the amino acid level , whereas the nucleotide sequence of the corresponding genes differs substantially ( Figure S3A , S3B ) . We therefore hypothesized that the RNAi construct directed against DmMterf3 expression would have no effect on DpMterf3 expression and that the DpMTERF3 protein therefore would be expressed to rescue the lethal DmMterf3 KD phenotype . This prediction was indeed confirmed and qRT-PCR analysis showed loss of DmMterf3 transcripts and the presence of the DpMterf3 transcript in rescued flies ( Figure 2C ) . Importantly , expression of DpMTERF3 fully rescued the growth phenotype of DmMterf3 KD larvae ( Figure 2D ) , which indicates the absence of off-target effects of the RNAi construct we are using . We proceeded to investigate the biochemical consequences of reduced DmMterf3 expression by measuring mitochondrial respiratory chain capacity in permeabilized tissue extracts from larvae . DmMterf3 KD larvae at 3–6 days ael showed a major reduction in the oxygen consumption rates in the presence of substrates that are metabolized to deliver electrons to the respiratory chain at the level of complex I ( CPI ) or complex I and II ( CPI-SUCC-G3P ) ( Figure 3A ) . In contrast , substrates metabolized to deliver electrons at the level of complex II or glycerol-3-phosphate dehydrogenase , thereby eliciting electron transport by-passing complex I , had no major effect on oxygen consumption ( Figure 3A ) . We also measured the activities of individual respiratory chain complexes in larvae at 6 days ael and found severely decreased enzyme activities of all complexes containing mtDNA-encoded subunits in KD larvae , whereas the exclusively nucleus-encoded complex II was unaffected ( Figure 3B ) . Additionally , we assessed the levels of assembled respiratory chain enzyme complexes by Blue-Native polyacrylamide gel electrophoresis ( BN-PAGE ) . Assembled complex I and IV were markedly reduced in DmMterf3 KD larvae at 6 days ael , as indicated by a reduction of complex I and IV in-gel activity ( Figure 3C and Figure S4B ) . In addition , a smaller and partially assembled form of complex I was present in DmMterf3 KD larvae at 6 days ael , again indicating a problem with complex I ( Figure 3C , asterisk ) . Western blot analysis showed low levels of the NDUFS3 subunit of complex I , whereas the levels of the ATP5A subunit of complex V ( ATP synthase ) were unaffected in KD larvae at 6 days ael ( Figure 3D ) and in knockout larvae at 3 days ael ( Figure S4A ) . Taken together , these results show that complex I and IV are the most affected of the oxidative phosphorylation complexes in the absence of DmMTERF3 . The progressive respiratory chain dysfunction induced by loss of DmMTERF3 ( Figure 3 ) led us to investigate mtDNA levels and mtDNA expression ( Figure 4 and Figure S5A ) . Similar to what we observed in DmMterf3 knockout embryos ( Figure 1E ) , we found an increase of mtDNA levels in DmMterf3 KD larvae at 6 days ael ( Figure S5A ) , possibly caused by a compensatory activation of mitochondrial biogenesis as previously observed in respiratory chain deficient flies [20] and mice [23] . We proceeded to use qRT-PCR to analyze levels of mtDNA-encoded transcripts ( Figure 4A , 4B ) in KD larvae at 3 , 5 and 6 days ael . We observed a progressive increase in levels of the ND1 , ND2 and ND6 transcripts , whereas there were no changes in the levels of ND4L , COXIII and 12S rRNA transcripts ( Figure 4B ) . We also used Northern blots to analyze transcript steady-state levels in larvae at 6 days ael and found an increase of the ND2 , ND4 and Cytb transcripts , whereas the levels of COXI , COXII and 12S rRNA transcripts were unaltered ( Figure 4C , 4E ) . We observed decreased levels of the 16S rRNA ( Figure 4C , 4E and Figure S5B , S5C ) . In contrast , all tRNAs analyzed , regardless of the location of the corresponding genes in the genome , showed a progressive increase of their steady-state levels ( Figure 4D , 4F and Figure S5D , S5E ) . Interestingly , the mtDNA transcript profiles in Mterf3 knockout mice [5] and DmMterf3 KD fly larvae show many similarities , including increased levels of many , but not all , mRNAs , increased levels of tRNAs and decreased levels of the 16S rRNA . We have previously observed that the steady-state levels of tRNAs , but not those of mRNAs , tend to correlate well with increased de novo transcription in flies [20] and mice [33] . We performed in organello transcription assays in larvae at 3–5 days ael and found no clear difference at 3 days ael , whereas there was an increase of de novo transcription of mtDNA in KD larvae at 4 and 5 days ael ( Figure 5A ) . The combination of a respiratory chain deficiency ( Figure 3 ) and transcription activation ( Figure 4C–4F and Figure 5A ) suggested that either the activation of de novo transcription leads to a respiratory chain deficiency , e . g . by causing the observed imbalance in steady-state levels of transcripts , as previously suggested for the Mterf3 knockout mouse [5] , or , alternatively , that the transcriptional activation is a secondary response to respiratory chain deficiency . The observation of decreased 16S rRNA levels ( Figure 4C , 4E and Figure S5B , S5C ) were interesting in this respect because the Mterf3 knockout mouse also displays such a decrease [5] . We therefore continued with a more detailed characterization of mitochondrial translation in the DmMterf3 KD larvae ( Figure 5B–5D ) . First , we determined the assembly states of the mitochondrial ribosomes by sedimentation gradient centrifugation of mitochondrial extracts isolated from larvae at 3 and 5 days ael ( Figure 5B , 5C and Figure S6A ) . Fractions were collected across the gradient and analyzed for absorption at 260 nm to determine the RNA content in each fraction . Equal loading was assessed by analyzing an aliquot of the total protein extract , to be loaded on the gradient , on a SDS-PAGE gel followed by Coomassie staining ( Figure S6B ) . Fractions were thereafter analyzed by qRT-PCR to measure levels of 12S and 16S rRNA . Control samples confirmed that we indeed were able to separate the small ( 28S ) subunit , the large ( 39S ) subunit and the assembled ( 55S ) ribosome ( Figure 5B , 5C ) . Already in DmMterf3 KD larvae at 3 days ael , we observed a reduction in levels of the assembled 55S ribosome ( Figure 5B ) . This decrease of assembled ribosomes was even more pronounced in DmMterf3 KD larvae at 5 days ael and was , at this time point , accompanied by a marked increase of the 28S ribosomal subunit and a marked decrease of the 39S ribosomal subunit ( Figure 5C ) . To further study the consequences of reduced levels of the assembled ribosome , we performed assays to determine the de novo translation activity in isolated mitochondria and found a clear decrease in DmMterf3 KD larvae at 3 days ael and onwards ( Figure 5D ) . Our results suggest that the reduced mitochondrial translation is caused by a problem with ribosome assembly and that the concomitant transcriptional response with imbalanced steady-state transcript levels may be a contributing factor , thus suggesting a link between these processes . The suggestion that DmMTERF3 might play a direct role in mitochondrial ribosome biogenesis prompted us to re-investigate the Mterf3 knockout mice . We previously created Mterf3 heart knockout mice by crossing Mterf3loxP mice to transgenic mice expressing cre-recombinase under the control of the muscle creatine kinase promoter ( Ckmm-cre ) [5] . Deletion of MTERF3 in the heart leads to a severe respiratory chain deficiency , progressive increase in steady-state levels of most mitochondrial transcripts and profound increase of de novo mtDNA transcription [5] . In the knockout hearts , MTERF3 protein levels are severely reduced already at 4 weeks of age ( Figure S7A ) , concomitant with a dramatic increase of de novo transcription ( Figure S7B and [5] ) . We proceeded to investigate the assembly of the mitochondrial ribosomal subunits in Mterf3 knockout mouse heart mitochondria , by gradient sedimentation and Western blot analysis . The 28S and 39S ribosomal subunits , as well as the fully assembled 55S ribosome were clearly resolved in control samples , as determined by the migration of the ribosomal subunit markers MRPS15 and MRPL13 ( Figure 6A , 6B ) . In contrast , in Mterf3 heart knockout samples the amount of MRPL13 co-migrating with MRPS15 was severely reduced already at 4 weeks of age , suggesting a reduction of fully assembled ribosomes ( Figure 6A ) . Concomitant with the reduction of 55S ribosomes , we observed increased levels of the free 28S ribosomal subunit ( Figure 6A ) . The MRPL13 protein steady-state levels progressively decreased ( Figure S7C ) and by 13 weeks of age , no fully assembled ribosomes were detectable ( Figure 6B ) . These results clearly suggest that the assembly of the mitochondrial ribosome is impaired in the absence of MTERF3 . We performed a set of confirmatory experiments , where we used qRT-PCR to assess presence of 12S and 16S rRNA in the different fractions . As predicted , the relative levels of 16S rRNA were reduced in the fraction corresponding to the 39S ribosomal subunit already at 4 weeks of age in Mterf3 heart knockout mitochondria ( Figure 6C ) . This reduction became even more pronounced at later stages and there was eventually a complete loss of the fully assembled ribosomes ( Figure 6D ) . The partial co-migration of MTERF3 and MRPL13 on sucrose gradients ( Figure S7D ) suggests that MTERF3 is involved in the maturation of the 39S subunit . Next , we investigated whether the impaired ribosomal assembly affected mitochondrial translation by performing in organello de novo translation experiments , which showed no change at the age of 4 weeks and severely decreased translation in 13-week-old Mterf3 knockout heart mitochondria ( Figure 6E ) . A similar global decrease in mitochondrial translation has also been reported in Mterf3 knockdown Drosophila cell lines [38] . We observed that loss of MTERF3 leads to reduced 39S ribosomal subunit assembly and a concomitant decrease of levels of the fully assembled 55S ribosome , in both flies and mice . These results are somewhat reminiscent of the findings in Mterf4 heart knockouts [33] , which show accumulation of apparently normal 28S and 39S ribosomal subunits , but a severe reduction in levels of the fully assembled 55S ribosome . Loss of MTERF3 is associated with a drastic reduction of 16S rRNA and impaired assembly of the 39S large ribosomal subunit , suggesting that the 16S rRNA may be interacting with MTERF3 . We therefore performed electrophoretic gel mobility shift assays ( EMSA ) by incubating constant amounts of DNA and RNA templates with increasing amounts of recombinant human MTERF3 protein . Non-specific double- ( ds ) and single-stranded ( ss ) DNA or RNA templates with an identical 28 bases long arbitrary sequence only interacted weakly with MTERF3 ( Figure S8A , S8B ) , whereas mitochondrial ribosomal RNA templates showed a stronger binding ( Figure S8C , S8D ) . These binding assays support the prediction that MTERF3 preferentially binds mitochondrial ribosomal RNA . To further characterize MTERF3 interactions with mitochondrial rRNA in vivo , we performed RNA-immuno-precipitation ( RNA-IP ) in mitochondrial preparations from wild-type mouse heart and fly larvae . The lack of a suitable DmMTERF3 antibody prompted us to generate a transgenic fly line expressing a Flag-tagged form of DmMTERF3 under the inducible UAS-GAL4 system . Expressing DmMTERF3 with a Flag tag directly at the C-terminus leads to an unstable protein not detectable by Western blotting . We therefore introduced a linker sequence between the C-terminus of DmMTERF3 and the Flag tag , and confirmed the expression of this tagged protein by Western blot ( Figure S9A ) . Control experiments demonstrated that we were able to efficiently immuno-precipitate endogenous mouse MTERF3 or Flag-tagged DmMTERF3 proteins ( Figure S9B , S9C ) . RNA-IP clearly demonstrated a very specific interaction between MTERF3 and 16S rRNA both in mouse and fly samples ( Figure 7A , 7B ) .
Regulation of mtDNA expression is important to fine-tune oxidative phosphorylation in response to physiological demand and pathological states . This regulation may occur at many different levels and mitochondria are in essence a prokaryotic system where transcription and translation occur within the same compartment , the mitochondrial matrix , and therefore likely directly interact in a molecular crosstalk . The roles of many of the involved factors are poorly understood . The 39S ribosomal subunit MRPL12 and the posttranscriptional regulator LRPPRC have both been implicated in activation of transcription [18] , [39] , but these proposed roles are not supported by other studies [14] , [19] . MTERF3 has previously been identified as a negative regulator of mtDNA transcription in mammals [5] , but its molecular mode of action has remained difficult to assess . Based on the knowledge that the mtDNA gene content is conserved among metazoans , we hypothesized that key regulatory processes controlling mtDNA expression also should be conserved . We therefore decided to take a novel approach to investigate MTERF3 function by creating knockout and knockdown fruit flies with abolished or reduced DmMTERF3 expression . Similar to the mouse , we found that loss of DmMTERF3 results in lethality and activation of mtDNA transcription . Unexpectedly , we could also identify a novel role for DmMTERF3 in mitochondrial ribosome assembly by regulation of the biogenesis of the 39S ribosomal subunit . The biogenesis of the 28S ribosomal subunit was not affected in the absence of DmMTERF3 and instead this subunit accumulated as it could not be assembled into a functional ribosome in the absence of the 39S subunit . Reinvestigation of the Mterf3 knockout phenotype in the mouse showed a similar assembly defect of the 39S subunit , which was present already in early stage knockout animals . In summary , MTERF3 has a novel function in regulation of ribosomal biogenesis and loss of MTERF3 expression does not only impair translation but also causes activation of mtDNA transcription in both flies and mice . The crystal structure of MTERF3 has been solved at 1 . 6 Å resolution [40] and predicts that the protein binds nucleic acids . The crystal structure of MTERF1 bound to mtDNA has given novel mechanistic insights how such a binding can occur [28] , [29] . On chromatin immuno-precipitation analysis , MTERF3 binds the promoter region of mammalian mtDNA and depletion of MTERF3 from a human mitochondrial extract leads to activation of mtDNA transcription [5] . These effects of MTERF3 on transcription may be due to direct interaction with mtDNA , but it is also possible that MTERF3 modulates transcription by binding the nascent RNA emerging after transcription initiation . In this report , we performed a series of gel-shift analyses , which show that MTERF3 displays weak binding to single- or double-stranded RNA or DNA of random sequence . However , recombinant MTERF3 has a marked preference for binding mitochondrial rRNA fragments , containing both single and double stranded regions . RNA-IP studies further demonstrated a strong and specific interaction of MTERF3 with the 16S rRNA in vivo , suggesting that MTERF3 contributes to 16S rRNA stabilization and/or modification and thereby explaining the critical role for MTERF3 in the biogenesis of the 39S ribosomal subunit . We propose that without this putative 16S rRNA modification , the 39S ribosomal subunit cannot be properly assembled , which , in turn , leads to a severe translational defect . There is strong precedence that abolished modification of mitochondrial rRNAs can affect the assembly of the ribosome . The best understood example is TFB1M , which is an adenine methyltransferase that dimethylates two highly conserved adenines at a stem loop structure close to the 3′ end of 12S rRNA in mammalian mitochondria [23] . Another well characterized example is MTERF4 , which interacts with NSUN4 and brings this cytosine methyltransferase to the large ribosomal subunit , where it is thought to modify 16S rRNA [33] , [34] . In bacteria , transcription and translation are coordinated , and the rate of transcription is tightly coupled to the processivity of the translating ribosome [41] . Mitochondria may coordinate gene expression in a similar way , where transcription and translation are oppositely coordinated , because loss of assembled ribosomes leads to a massive increase in de novo transcription . Interestingly , knockout of Mterf3 [5] , Tfb1m [23] and Mterf4 [33] all cause a severely defective translation and a dramatic increase in de novo transcription , with increased steady-state levels of most or all mitochondrial transcripts . These findings suggest that one of the early responses to a ribosomal assembly defect is a massive transcriptional activation . It is interesting to note that the MTERF3 protein levels are down-regulated in both Tfb1m [23] and Mterf4 [33] knockouts , suggesting further that MTERF3 may have a key role in mediating the effects on transcription , and that up-regulation of mitochondrial transcription in the absence of MTERF3 cannot simply be attributed to a passive compensatory mechanism . We propose that MTERF3 promotes translation by regulation of ribosomal biogenesis and that this process is linked to repression of mtDNA transcription activation ( Figure 7C ) . In the absence of MTERF3 , the ribosomal biogenesis is impaired and there is an increased uncontrolled activation of mtDNA transcription leading to imbalanced steady-state levels of mitochondrial transcripts ( Figure 7C ) . Our present data in combination with previous reports [5] suggest that MTERF3 could have a dual function and be a part of a molecular checkpoint , acting to coordinate transcriptional and translational rates and thereby optimizing mtDNA expression . Unraveling the function of specific proteins is not always easy in mammalian systems and many of the methods used to study protein functions and interactions are plagued by experimental ambiguities . Here , we describe a strategy that takes advantage of genetic manipulation of the orthologous gene in two distantly related metazoans , accompanied by a comprehensive molecular characterization . By using this strategy , we present compelling evidence that MTERF3 has a conserved role in ribosomal biogenesis in metazoans and that it also coordinates mitochondrial transcription and translation . At least two members of the mammalian MTERF family , i . e . MTERF3 and MTERF4 , have now been found to have critical roles in mitochondrial ribosomal biogenesis . This makes it tempting to speculate that MTERF1 and MTERF2 could have similar , yet undiscovered , roles in ribosomal biogenesis . Future studies will have to clarify whether MTERF3 has protein interaction partners that are involved in modifying rRNA or if MTERF3 is essential for ribosomal biogenesis by some other mechanism .
This study was performed in strict accordance with the recommendations and guidelines of the Federation of European Laboratory Animal Science Associations ( FELASA ) . The protocol was approved by the “Landesamt für Natur , Umwelt und Verbraucherschutz Nordrhein-Westfalen” . In order to rescue phenotypes caused by DmMterf3 RNAi expression , we co-expressed the Mterf3 gene from Drosophila pseudoobscura ( Dp ) , which is not a target of the DmMterf3 RNAi line [42] . The fosmid clone FlyFos047383 that contains the DpMterf3 gene was kindly provided by Dr . Pavel Tomancak ( MPI for Cell Biology , Dresden , Germany ) . A 10 kb Fosmid fragment containing the DpMterf3 gene was cloned into the pBluescript II Sk+ vector ( Stratagene ) by ET recombination . Subsequently , the 10 kb fragment was released by NotI and BglII restriction enzyme cleavage and subcloned into the transfection vector pattB [43] . Oligonucleotide primers used for cloning are listed in Table S1 . Embryo injections to achieve site-specific integration into attP40 flies were performed by Best Gene Drosophila Embryo Injection Services ( Chino Hills , California , USA ) . DmMterf3 null mutants were generated by ends-out homologous recombination as described [37] . Approximately 4 kb of 5′ and 3′ flanking sequences of the DmMterf3 gene were cloned into the pBluescript II Sk+ vector ( Stratagene ) by ET recombination , using a DmMterf3 BAC clone as template ( BACPAC Resource Center , Oakland , California , USA ) . Both 5′ and 3′ homologous arms were sequenced to ensure the absence of base substitutions and subsequently subcloned into the pGX attP vector [44] to generate the DmMterf3 targeting plasmid . Primer sequences and restriction sites used for subcloning into the pGX attP vector are listed in Table S1 . The targeting plasmid was injected into D . melanogaster embryos via P-element-mediated germ line transformation using the Best Gene Drosophila Embryo Injection Services ( Chino Hills , California , USA ) . Crosses for ends-out homologous recombination were carried out as described [37] . Homologous recombination events were identified by PCR . Subsequently , the white ( hs ) marker was removed using cre-recombinase and the absence of the DmMterf3 gene was confirmed by PCR and sequencing ( primers are listed in Table S1 ) . The maintenance of the fly lines is described in Text S1 . The DmMterf3 cDNA clone LD27042 was purchased form DGRC and subsequently cloned into the transfection vector pUASTattB . A Flag tag was linked to the C-terminus of DmMTERF3 via a linker sequence ( GAAAAGAAAAG ) , generating the DmMTERF3-linker-Flag construct . Oligonucleotide primers used for cloning are listed in Table S1 . The construct was embryo injected into attP40 flies for generation of the transgenic flies . Fly larvae ( n = 3–7 ) were dissected in PBS and resuspended in 2 ml of respiratory buffer ( 120 mM sucrose , 50 mM KCl , 20 mM Tris-HCl , 4 mM KH2PO4 , 2 mM MgCl2 , 1 mM EGTA , 0 . 01% digitonin , pH 7 . 2 ) . Oxygen consumption was measured at 25°C using an oxygraph chamber ( OROBOROS ) . Complex I-dependent respiration was assessed by adding the substrates proline ( 10 mM ) , pyruvate ( 10 mM ) , malate ( 5 mM ) and glutamate ( 5 mM ) . Succinate and glycerol-3-phosphate dehydrogenase activities were measured using 20 mM succinate ( SUCC ) and 15 mM glycerol-3-phosphate ( G3P ) , respectively . The mitochondrial quality of each sample was assessed by measuring the respiratory control rate ( RCR ) using 1 mM ADP ( state 3 ) or 1 mM ADP and 2 . 5 µg/ml oligomycin ( pseudo state 4 ) . Permeabilized control mitochondria consistently had RCR values between 4 and 7 with complex I substrates . The respiration was uncoupled by the addition of 400 µM CCCP and the rotenone-sensitive flux was measured in the presence of 200 µM rotenone . Finally , the protein content was determined by the Bradford method ( BioRad ) in order to normalize the oxygen consumption flux to mitochondrial protein content . Mitochondria were isolated from fly larvae or mouse hearts and in organello transcription assays were performed as described [45] by incubating 200 µg mitochondria in a modified transcription buffer ( 30 µCi [α-32P]-UTP , 25 mM sucrose , 75 mM sorbitol , 100 mM KCl , 10 mM K2HPO4 , 50 µM EDTA , 5 mM MgCl2 , 1 mM ADP , 10 mM glutamate , 2 . 5 mM malate , 10 mM Tris-HCl ( pH 7 . 4 ) and 5% ( w/v ) BSA ) for 45 min . Labeled mitochondrial RNA was isolated using Totally RNA kit ( Ambion ) , separated on a 1 . 2% agarose gel and blotted to Hybond-N+ membranes ( GE Healthcare ) . In vitro assays to study mitochondrial de novo translation with [35S]-methionine were performed as described [20] and equal amounts of total mitochondrial protein were separated on 15% SDS-PAGE gels . Gels were fixed in isopropanol-acetic solution , stained with Coomassie , destained in ethanol-acetic acid solution and treated with Amplify Solution ( GE Healthcare ) . Afterwards gels were dried and [35S]–methionine-labeled proteins were visualized by autoradiography . For in organello transcription and translation fly mitochondria were incubated at 30°C and mouse heart mitochondria at 37°C . Mitochondrial ribosomes from fly larvae or mouse hearts were prepared as previously described [23] , [46] . Mitochondrial ribosomes were loaded onto 10–30% sucrose gradients and separated by centrifugation overnight . From sucrose gradients , fractions ( ∼500 µl ) were collected with continuous monitoring of absorbance at 260 nm . RNA was extracted from each fraction using TRIzol LS Reagent ( Invitrogen ) according to manufacturer's recommendations , subsequently treated with DNase and used for cDNA synthesis . Absolute qRT-PCR analysis using a standard curve composed of an equal amount of RNA from each fraction from the control and the KD group , was performed using SYBR green master mix and primers specific for 12S and 16S rRNA , as described in Text S1 . Fractions ( 750 µl ) were collected from the mouse heart sucrose gradients and proteins in each fraction were precipitated with trichloracetic acid and subjected to SDS-PAGE followed by immunoblotting . Sub-ribosomal particles were detected using antisera specific for individual proteins from the 28S and 39S ribosomal subunits , as described in Text S1 . RNA was extracted from each fraction using TRIzol LS Reagent ( Invitrogen ) according to manufacturer's recommendations , subsequently treated with DNase and used for cDNA synthesis and qRT-PCR with TaqMan probes specific for 12S and 16S rRNA [19] . Mitochondria were isolated by differential centrifugation from control ( w;;da-GAL4/+ ) and MTERF3-linker-Flag expressing ( w;;UAS-DmMterf3:linker:Flag/+;da-GAL4/+ ) larvae and from wild-type mouse heart mitochondria . RNA-IP was performed essentially as previously described [33] . The final mitochondrial pellet was suspended in a low-salt NET-2 lysis buffer buffer ( 50 mM Tris-HCl [pH 7 . 4] , 150 mM NaCl , 0 . 05% Nonidet P-40 , 1× complete EDTA-free protease inhibitor cocktail [Roche] ) supplemented with 100 U of RNasin Plus ( Promega ) . After 20 min incubation on ice , the mitochondrial lysates were centrifuged at 10 , 000 g for 10 min at 4°C in order to pellet the debris . Supernatants were collected and protein concentrations determined by Bradford-based assay ( Sigma ) . IPs were performed using ∼200 µg mitochondrial protein , and lysates were pre-cleared with agarose beads ( Sigma ) by rotation for 1 h at 4°C , followed by a 2 h incubation at 4°C with Anti-Flag M2 Affinity Gel ( Sigma ) or a mix of protein A agarose/protein G agarose ( Roche ) coupled to a polyclonal antibody directed against mouse MTERF3 ( peptide Specialty Laboratory ) , for fly or mouse samples , respectively . Both , anti-Flag M2 Affinity gel and protein A/protein G agarose bead mix coupled to MTERF3 antibody , were incubated for 1 h with 100 µg yeast tRNA prior to usage . After incubation with fly or mouse mitochondria , beads were washed by rotation for 2×10 min at 4°C in low-salt NET-2 buffer , followed by 2×5 min washes in high-salt NET-2 buffer ( 50 mM Tris-HCl [pH 7 . 4] , 300 mM NaCl , 0 . 05% Nonidet P-40 , 1× complete EDTA-free protease inhibitor cocktail [Roche] ) , and a final wash for 4×10 min in low-salt NET-2 buffer . The washed beads were resuspended in 120 µl reversion buffer ( 50 mM Tris-HCl [pH 6 . 8] , 1% SDS , 5 mM EDTA , 10 mM DTT ) supplemented with RNasin Plus ( Promega ) and incubated for 45 min at 65°C . RNA was recovered by TRIzol extraction ( Invitrogen ) following manufacturer's recommendations , using 10 µg yeast tRNA ( Ambion ) as a carrier . RNA was subjected to DNase treatment ( Turbo DNA-free kit , Ambion ) and reverse transcribed to cDNA by using the High-Capacity cDNA Archive kit ( ABI ) . Mitochondrial transcripts from the RNA-IP experiments were identified and quantified by qRT-PCR , with non-primed beads used as background controls . | One of the main functions of the mitochondrial network is to provide the energy currency ATP to drive a large array of cellular metabolic processes . The formation of the mitochondrial respiratory chain , which allows this energy supply , is under the control of two separate genetic systems , the nuclear and the mitochondrial genomes , whose expressions have to be tightly coordinated to ensure efficient mitochondrial function . The regulation of mitochondrial genome expression is still poorly understood despite the profound importance of this process in human physiology , disease , and aging . Here , we make one step forward by unraveling a new role for the mitochondrial transcription termination factor 3 ( MTERF3 ) , which was initially characterized as a factor able to decrease mitochondrial transcription . Using gene invalidation approaches , we show in two distinct model organisms , the fruit fly and the mouse , that MTERF3 is not only involved in mitochondrial transcription but also favors the assembly of the mitochondrial ribosome and thereby reinforces the coordination between transcription and translation events , two key steps in mitochondrial genome expression . | [
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| 2013 | MTERF3 Regulates Mitochondrial Ribosome Biogenesis in Invertebrates and Mammals |
Antibody ( Ab ) to the Wuchereria bancrofti ( Wb ) infective larval ( L3 ) antigen Wb123 , using a Luciferase Immunoprecipitation System ( LIPS ) assay , has been shown to be a species-specific , early marker of infection developed for potential use as a surveillance tool following transmission interruption post mass drug administration . To examine its usefulness in a single filarial-endemic island assessed at two time points with markedly different levels of transmission , Ab to Wb123 was measured in sera collected from subjects from Mauke , Cook Islands in 1975 ( no previous treatment ) and 1992 ( 5 years after a one time island-wide treatment with diethylcarbamazine [DEC] ) . Between 1975 and 1992 , Wb transmission decreased dramatically as evidenced by reduced prevalences of microfilariae ( 31% vs . 5% ) and circulating Ag ( CAg , 49% vs . 16% ) . Age specific prevalence analysis showed a dramatic reduction in Wb123 Ab positivity from 54% ( 25/46 ) in 1975 to 8% ( 3/38 ) in 1992 in children 1–5 years ( p<0 . 0001 ) , reflecting the effects of single-dose treatment five years earlier . By 1992 , Wb123 Ab prevalence in children 6–10 years had fallen from 75% ( 42/56 ) in 1975 to 42% ( 33/79 ) consistent with a lower cumulative transmission potential . In the whole population , Wb123 seropositivity decreased from 86% to 60% between 1975 and 1992 . In CAg+ subjects the levels of Wb123 Ab were indistinguishable between the 2 time points but differed in those who were CAg− ( p<0 . 0001 ) . In paired sample analysis , individuals who were CAg+ in 1975 but became CAg− in 1992 had significantly lower Ab levels in 1992 ( p<0 . 0001 ) , with 9/40 ( 23% ) becoming seronegative for Wb123 . The relationship between reduction in Wb123 Ab prevalence and the reduction of transmission , seen most clearly in young children , strongly advocates for the continuing assessment and rapid development of Wb123 as a surveillance tool to detect potential transmission of bancroftian filariasis in treated endemic areas .
The Global Programme to Eliminate Lymphatic Filariasis ( GPELF ) was begun in 2000 [1] in response to a World Health Assembly Resolution to eliminate filarial disease caused by Wuchereria bancrofti , Brugia malayi and Brugia timori . The success of this mass drug administration ( MDA ) -based program that utilizes the drugs diethylcarbamazine [DEC] , ivermectin , and albendazole given as 2-drug combinations once-yearly , is evidenced by the more than 3 . 4 billion treatments given to nearly 897 million individuals in 53 of the 72 endemic countries during the first 10 years of the program [1] . The health [2] and economic [3] benefits of the GPELF have been well documented . This dramatic progress of the GPELF has been made possible by two key factors - first , the discovery of effective single-dose drug combinations that are able to markedly diminish microfilaremia ( plus the subsequent donation of these drugs ) ; and second , the development of a rapid diagnostic tool that detects active infection ( the ICT circulating antigen [CAg] card test [4] ) and enables rapid mapping of regions endemic for bancroftian filariasis . As many of the first countries to implement MDA have already completed or are approaching completion of their elimination programs , the need for a sensitive and specific post-MDA surveillance tool has become a research priority [5] , [6] . While the ICT card test provides the ability to measure the decrease in active infection rates following MDA , an antibody ( Ab ) based surveillance tool that detects early exposure in individuals would be preferable for determining whether or not filarial transmission has been successfully interrupted; the most effective sentinel group for such surveillance has been posited to be children born during or after the MDA [7] , [8] . A number of serological Ab assays based on filarial Ags have been proposed for use as surveillance tools , including Bm14 [9] , BmR1 and BmSXP [10] , WbSXP-1 [11] , and Bm33 [12] . The most widely field tested assays have used Bm14 Ag in ELISA [5] , [8] , [9] , [13] , [14] or other formats [7] . Results of an extensive multicenter evaluation of many of the available diagnostic assays ( Ab , Ag , DNA ) was recently reviewed ( Gass , et al [15] ) . While sensitivity of the Ab assays has generally been high and specificity against non-filarial species has been relatively good , there are unresolved issues of specificity when tested against other filarial species , in particular , Onchocerca volvulus and Loa loa [5] , [9] which are often sympatric with W . bancrofti ( Wb ) in Africa . In such co-endemic countries , the inability to distinguish individuals who are truly exposed to bancroftian filariasis from those who may be infected with or exposed to other filarial species could potentially confound the interpretation of surveillance findings post-MDA . In Wb endemic regions that lie within or in close proximity to Loa- , Onchocerca- , or Mansonsella-endemic areas the need for a highly specific Ab assay to detect Wb infection might well be essential to program success . To address this need for a specific W . bancrofti diagnostic , a serological assay based on the W . bancrofti filarial antigen Wb123 was recently developed [Kubofcik et al . ; in press , PLoS NTDs] . This antibody assay , utilizing the Luciferase Immunoprecipitation System ( LIPS ) technology [16] , has been demonstrated to be not only highly sensitive in detecting Wb infections , but also highly specific , showing little to no cross-reactivity with sera from patients infected with filarial species other than Wb , including O . volvulus , L . loa , Mansonella perstans , and B . malayi [Kubofcik et al . ; in press , PLoS NTDs] . However , since the relationship between this Wb123 Ab response and different population prevalence levels of infection or changes in these prevalence levels has not been defined , the current study was designed to take advantage of earlier , longitudinal serologic studies in a Pacific island population ( Mauke , Cook Islands ) to assess the effect of filarial transmission on serologic reactivity to Wb123 prior to and 5 years after a one-time , island-wide treatment with DEC [17]–[20] .
Protocols for both population studies ( 1975 and 1992 ) on Mauke were approved by the government of the Cook Islands and the NIAID Institutional Review Board; informed written consent was obtained from all adult subjects . Consent from non-adult subjects was obtained through both verbal assent and written consent from each subject's legal guardian . The study population comprised the permanent residents of the island of Mauke in the Southern Cook Islands , a region endemic for the filarial parasite W . bancrofti; assessment occurred at two time points ( 1975 [n = 369; ∼58% of the population] and 1992 [n = 553; ∼88% of the population]; [17]–[20] ) . All subjects were evaluated for clinical ( history , physical examination , and complete blood count ) , parasitologic ( filtration of 1 ml of blood through a Nuclepore 3 mm filter to quantify microfilariae ) and immunologic parameters during the time of both studies . Serum samples from both 1975 and 1992 were frozen in liquid nitrogen within hours of collection and then subsequently stored at −80°C . Sera were tested for the presence of both Ab and CAg ( TropBioPty Ltd . , Townsville , Australia ) . A summary of the assessments made on the individuals evaluated for the current Wb123 study is shown in Table 1 . The LIPS assay for detection of IgG Abs to the Wb123 Ag is described in detail by Kubofcik et al . [in press , PLoS NTDs] . Briefly , 1 µl of sera was diluted 1∶10 in a Tris buffer in 96-well microtiter plates ( Nunc , Roskilde , Denmark ) and then added to 50 µl of a Ruc-Wb123 enzyme reporter . Plates were incubated for 5 minutes at room temperature after which 7 µl of a 30% suspension of protein A/G beads was added for another 5 minutes . Plates were washed and subsequently processed on a Berthold LB 960 Centro microplate luminometer using a colenterazine substrate mix ( Promega , Madison , WI ) . Data were interpreted as luminometer units from averaged duplicate samples . IgG4 Ab was measured by ELISA to a saline extract of adult B . malayi Ag ( BmA ) as described previously [21] . Statistical analyses were conducted using Graph Pad Prism ( version 5 . 0 ) . Geometric means ( GM ) were used to reflect central tendency . Comparisons of population parasitological parameters ( i . e . microfilaremia [Mf] and CAg prevalence ) and Wb123 positivity at the two study time points were carried out with the Fisher's Exact test . A comparison of Wb123 Ab levels between patients from the two time points was accomplished with the Mann-Whitney U test , while analysis of Ab levels in paired patients from 1975 and 1992 was carried out using the Wilcoxon Signed Rank test . Correlation analysis between IgG4 and Wb123 was accomplished with Spearman's Rank test .
Transmission of W . bancrofti , although still ongoing in 1992 , was reduced significantly between 1975 and 1992 , most likely because of a one time island-wide treatment ( MDA ) of everyone ≥5 years old with diethylcarbamazine ( DEC ) in 1987 , 5 years prior to the 1992 study [19] . Parasitological factors including Mf ( number [%] positive = 111/360 [31%] in 1975 vs 26/560 [5%] in 1992 ) and CAg ( number [%] positive = 178/360 [49%] in 1975 vs 88/558 [16%] in 1992 ) as well as Wb123 Ab positivity ( 319/369 [86%] in 1975 vs 334/553 [60%] in 1992 ) were all reduced in prevalence between 1975 and 1992 ( p<0 . 0001 for each ) . The prevalence of Wb123 Ab by age groups , compared with that of Mf and CAg positivity , is illustrated in Figure 1 . Wb123 positivity in 1975 and 1992 differed significantly ( p≤0 . 003 ) at all but two age groups ( 31–40 years and >60 years ) . Indeed , the difference in Ab prevalence between the two observation points was greatest for children 1–5 years ( positivity = 54% [25/46] in 1975 vs 8% [3/38] in 1992 ) . In addition , age-specience curves were steeper and peaked earlier and higher in 1975 than in 1992 , with 97% of those over 20 years being positive in the 1975 cohort but only 73% in 1992 . In total , there was a decrease in Ab prevalence of 25% . CAg and Mf prevalences for all age groups were similarly much reduced in 1992 compared to 1975 , again likely reflecting a major – but not complete – decrease in transmission in 1992 . Not surprisingly , Ab prevalences decreased , but to a lesser degree than did the levels of Mf or CAg . With but 2 exceptions , all CAg+ subjects in both 1975 and 1992 were also Wb123+ ( data not shown ) . Therefore , to focus particularly on the Wb123 Ab levels in individuals who were exposed to Wb infected mosquitoes but who themselves were not actively infected , we evaluated the Ab prevalence in those individuals who were CAg− ( Figure 2 ) . Again , Wb123 prevalence was seen to be significantly higher in the 1975 population ( exposed to greater Wb transmission ) than in the 1992 population for nearly every age group . In comparing Wb123 Ab prevalences in CAg− individuals compared to the Wb123 Ab prevalence of the entire population ( i . e . CAg+ and CAg− ) , the greatest difference was seen in children ≤15 years of age . By 16 years , the prevalence of Wb123 seropositivity was nearly identical in CAg− individuals compared to the entire population regardless of Ag status during both time periods ( Figures 1 and 2 ) . Figure 3 compares the level of Wb123 Ab in individuals as a function of CAg status . At both time points , Wb123 Ab levels were higher in CAg+ people than in those who were CAg− ( geometric mean ( GM ) = 232 , 067 vs . 33 , 432 [1975] and 210 , 115 vs . 11 , 095 [1992]; p<0 . 0001 for both periods ) . Interestingly , the levels of Wb123 Ab in CAg+ patients did not differ between 1975 and 1992 despite the decrease in transmission; however , in CAg− subjects there was a significant decrease in Ab levels ( p<0 . 0001 ) between 1975 and 1992 . For both time points , the levels of Wb123 Ab and those of IgG4 Ab to BmA were strongly correlated ( r = 0 . 671 [1975] and 0 . 729 [1992]; p<0 . 0001 for both time points ) but there was no correlation between the levels of CAg and Wb123 ( data not shown ) . A group of individual patients evaluated in both 1975 and 1992 [19] was studied to determine whether a change in CAg status affected Wb123 Ab production ( Figure 4 ) . Individuals who were CAg+ in 1975 and remained positive in 1992 ( n = 28 ) showed no change in the level of Ab production to Wb123 with all patients remaining seropositive . However , those subjects who were either CAg+ in 1975 and became CAg− by 1992 ( n = 40 ) or who were CAg− at both time points ( n = 46 ) did show significant decreases in Wb123 Ab production in 1992 ( p<0 . 0001 and p = 0 . 012 respectively ) with 9/40 ( 23% ) and 12/46 ( 26% ) becoming or remaining seronegative respectively . There were no differences in the initial 1975 levels of Wb123 Ab in those Ag+ individuals who remained Ag+ and in those who became Ag− by 1992 . Only one person seen at both time points became CAg+ in 1992 after being CAg− in 1975; this individual showed a 10-fold increase in Wb123 Ab units from 37 , 622 to 320 , 000 LU . To assess the effect of reduced transmission following MDA on Ab to Wb123 in young children ( the target group for monitoring and evaluation currently recommended in WHO guidelines [22] ) , Ab levels were examined in more detail in this age group . Figure 5 shows the level of Wb123 Ab in young children ≤5 years in 1975 prior to any treatment . Interestingly , the occurrence of maternal Ab was quite evident in infants <1 year as 7/7 were positive for Ab . This prevalence dropped significantly ( p = 0 . 003 ) in 1 year olds ( 3/12 [25%] Ab+ ) and then increased to levels of 50–75% in children 2–5 years of age . Since children 6–7 years old will be the primary age group for monitoring post-MDA [22] , Figure 6 illustrates the prevalence and levels of Wb123 Ab in children 2–11 years of age divided into 2-year age groups . Prevalence of Wb123 Ab in all children from 1975 , prior to any treatment , remained high with a Wb123 Ab seropositivity of 60–81% . However , a steep and significant decrease in Ab prevalence was seen in children assessed in 1992 in the 2–3 ( p = 0 . 0045 ) , 4–5 ( p = 0 . 0001 ) , and 6–7 ( p = 0 . 0035 ) year old groups compared with the same groups in 1975 . Furthermore , it is these children ( 2–7 years ) who were born either after or shortly before the single island-wide treatment with DEC . The prevalence of Wb123 Ab subsequently increases in older children and was not significantly different from that seen in 1975 . Comparison of Wb123 Ab levels in these children during the two time periods showed a similar pattern as prevalence ( Figure 7 ) . The level of Ab was significantly different in the age groups 2–3 , 4–5 , and 6–7 but not in 8–9 year olds . Interestingly , a significant decrease ( p = 0 . 017 ) in Ab levels was seen in children 10–11 years old , possibly reflecting the beginning of reduced exposure to infected mosquitos seen in the population as a whole , particularly in those who were CAg− ( Figure 3 ) .
One of the greatest needs of the GPELF is for a highly sensitive and specific surveillance tool to monitor exposure to filarial infection in sentinel populations of young children born during or after MDA [7] , [8] in regions completing the yearly MDA phase of their LF elimination programs [6] , [7] . With MDAs nearing or having reached an end in several countries , the availability of such a surveillance tool has become even more urgent . Though highly specific circulating filarial antigen tests for detecting active infection [4] , [8] , [13] , [14] , [23]–[25] are well established ( at least for Wb infection ) , a persistent challenge has been to devise an ‘exposure Ab’ test that is both sensitive and specific enough to be used in W . bancrofti-endemic countries that are also endemic for other filarial infections , particularly sympatric Loa loa , Onchocerca volvulus and Mansonella species . Indeed , many prior studies have identified highly sensitive Ab assays [5] , [7]–[14] , [25] , [26] , but none has had sufficient specificity to meet the current needs of the GPELF particularly in Africa and the Americas . The Wb123 LIPs assay was developed to resolve this issue . This assay has proved both highly sensitive and specific – detecting exposure to W . bancrofti infection but not to other non-filarial helminth infections or to other filarial species [Kubofcik et al . ; in press , PLoS NTDs] . The purpose of the current study was to test the performance of this new assay in a South Pacific island population assessed at two time points 18 years apart during which time the prevalence of W . bancrofti infection decreased dramatically . Included in this study were children born both before and after the single MDA . The decrease in infection prevalence on the island of Mauke , defined by markedly lower CAg and Mf levels ( Figure 1 ) in 1992 , was accompanied by a significant decrease in the Wb123 Ab levels in essentially all population age groups , but the decrease was most marked in young children . A similar decline in Ab levels between 1975 and 1992 in this Cook Island population was seen for IgG , IgG4 and IgE antibodies to crude parasite Ag but only for the uninfected ( ‘endemic normals’ ) and those previously infected individuals who became CAg negative [19] . However , while CAg and Ab levels to Wb123 were not directly correlated in this study , previous findings did demonstrate that CAg and IgG4 levels to adult worm Ag were strongly correlated [27] . Some of the decreased Wb123 Ab response clearly reflects diminished levels of infection in the population , as one can infer from the longitudinal observations in individual patients ( Figure 4 ) where those with persistent infection retained high Wb123 Ab responses but those who cleared their infections showed a dramatic fall in Ab levels . While this finding is interesting in itself , still more significant is the fact that when comparison of the Wb123 Ab rates at the two time points was restricted to those individuals who were CAg− ( i . e . , presumably uninfected ) , the rates were uniformly lower across all ages groups in 1992 when the prevalence of infection in the population ( and correspondingly the level of transmission ) was much less than in 1975 . Even in this CAg− population , Ab levels among the older population groups likely reflect not only current exposure to infection but also past exposure . However , in young children , Wb123 seropositivity appears to more closely reflect the actual exposure to infection ( Figure 2 ) , with the most pronounced differences seen in the youngest group of children ( ≤5 years ) born post-treatment , a finding similar to that seen in other studies [14] , [25] . Interestingly , the levels of Ab to Wb123 in the children 8–9 years of age did not differ between the two time periods whereas these levels differed significantly for all other age groups studied ( Figure 7 ) . The difference between those 8–9 year olds and the other groups might reflect their having been too young to receive the island-wide treatment but old enough to have been exposed to W . bancrofti , whereas the older group ( 10–11 ) would have received treatment , thereby lowering their Ab levels . An additional finding in children was the presence of Wb123 Ab in all 7 children <1 year of age in 1975 . Since Ab prevalence subsequently dropped to ∼20% in 1 year olds , this clearly indicates the presence of maternal Ab in this youngest group of children . There were not enough children <1 year old in 1992 to examine the effect of lower transmission , but presumably with fewer mothers infected the likelihood of a reduction in maternal Ab present in infants seems high . The Wb123 LIPS assay gives every indication of being at least as effective as previously available assays for detecting anti-filarial Abs and , in fact , a comparison of Wb123 has been recently made with these other assays , including those assays for the detection of Abs to Bm33 , Bm14 and WSP [Hamlin , et al . ; in press , PLoS NTDs] . As evidenced in the present study and the report by Kubofcik et al . [in press , PLoS NTDs] , the Wb123 assay combines three specific attributes that make it particularly valuable for use in the global LF elimination program: 1 ) it detects exposure to Wb infection; 2 ) its prevalence , especially in young children , reflects the Wb infection ( and presumably transmission ) levels in the population; and importantly 3 ) it is highly specific for detecting W . bancrofti infections , showing little or no cross-reactivity in sera from patients with onchocerciasis , loiasis , or mansonellosis . Finally , the ability to detect exposure to infection in young children suggests that Wb123 will work effectively as a surveillance tool in this sentinel population . Indeed , as the GPELF increases the number of countries beginning and completing their MDAs , the development of the Wb123 assay into a rapid diagnostic would seem to be of enormous value both for the Global Programme's endgame and its post-MDA surveillance needs . | Lymphatic filariasis ( LF ) causes an enormous disease burden throughout the tropics and subtropics . The Global Programme to Eliminate Lymphatic Filariasis was begun in 2000 following the advent of large donations from drug companies for treating LF and the development of a rapid antigen assay for detection of infection . As more countries undergo mass drug administration ( MDA ) , the driving need is for development of a highly sensitive and specific antibody assay for detecting ongoing exposure to vector-borne filaria following MDA . The target group for such surveillance is children born during or following MDA . Current assays , while sensitive , are not specific enough where non-LF filaria species are co-endemic . Recently , we developed an antibody assay based upon the highly specific larval antigen Wb123 using the Luciferase Immunoprecipitation System ( LIPS ) . In the current study , we determined that the Wb123 LIPS assay detects a reduction in LF transmission on an endemic island following a one-time island wide MDA with diethylcarbamazine , with the most pronounced reduction in prevalence of antibody to Wb123 occurring in young children born just prior to and following this MDA . We propose that Wb123 can be an extremely useful surveillance tool following MDA and should be developed into a rapid test format . | [
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| 2012 | Antibody to the Filarial Antigen Wb123 Reflects Reduced Transmission and Decreased Exposure in Children Born following Single Mass Drug Administration (MDA) |
The protein O-glucosyltransferase Rumi/POGLUT1 regulates Drosophila Notch signaling by adding O-glucose residues to the Notch extracellular domain . Rumi has other predicted targets including Crumbs ( Crb ) and Eyes shut ( Eys ) , both of which are involved in photoreceptor development . However , whether Rumi is required for the function of Crb and Eys remains unknown . Here we report that in the absence of Rumi or its enzymatic activity , several rhabdomeres in each ommatidium fail to separate from one another in a Notch-independent manner . Mass spectral analysis indicates the presence of O-glucose on Crb and Eys . However , mutating all O-glucosylation sites in a crb knock-in allele does not cause rhabdomere attachment , ruling out Crb as a biologically-relevant Rumi target in this process . In contrast , eys and rumi exhibit a dosage-sensitive genetic interaction . In addition , although in wild-type ommatidia most of the Eys protein is found in the inter-rhabdomeral space ( IRS ) , in rumi mutants a significant fraction of Eys remains in the photoreceptor cells . The intracellular accumulation of Eys and the IRS defect worsen in rumi mutants raised at a higher temperature , and are accompanied by a ∼50% decrease in the total level of Eys . Moreover , removing one copy of an endoplasmic reticulum chaperone enhances the rhabdomere attachment in rumi mutant animals . Altogether , our data suggest that O-glucosylation of Eys by Rumi ensures rhabdomere separation by promoting proper Eys folding and stability in a critical time window during the mid-pupal stage . Human EYS , which is mutated in patients with autosomal recessive retinitis pigmentosa , also harbors multiple Rumi target sites . Therefore , the role of O-glucose in regulating Eys may be conserved .
Diurnal insects possess “apposition eyes” in which ommatidia are optically isolated from each other [1] , [2] . In most diurnal insects like honeybee and butterflies , the apical rhodopsin-housing structures of each ommatidium—the rhabdomeres—are fused at the center . This allows the group of photoreceptors in each ommatidium to act as a single optical device [1] . A modification of the apposition eye arose during insect evolution in dipteran flies , where an extracellular lumen called the inter-rhabdomeral space ( IRS ) forms to separate and optically isolate the rhabdomeres in each ommatidium from one another . Due to this structural modification and the accompanying regrouping of photoreceptor axons among neighboring ommatidia , information from photoreceptor cells that receive light from the same point in the space merge on the same postsynaptic targets in the lamina [3] . This type of eye is referred to as a neural superposition eye , and these improvements allow for increased light sensitivity without sacrificing resolution [1] , [4] . Separation of the rhabdomeres in flies requires an evolutionarily conserved secreted glycoprotein called Eyes shut ( Eys; also called Spacemaker ) . eys mutant flies lack the IRS and exhibit an altered photoreceptor organization that resembles the closed rhabdom of other insects like honeybees and mosquitos [5] , [6] . Eys is secreted from the stalk membrane of the photoreceptor cells in an Ire1-dependent but Sec6-independent manner to separate the rhabdomeres and open the IRS [5] , [7] . Drosophila eys functions together with three other genes , crumbs ( crb ) , prominin ( prom ) and chaoptin ( chp ) , to regulate rhabdomere separation and IRS size [5] , [6] , [8] , [9] . Genetic experiments have established that prom and eys promote rhabdomere separation but chp and crb promote rhabdomere adhesion , and that the balance between their activities results in proper IRS formation [6] , [8] , [9] . The Crb extracellular domain and the Eys protein are primarily composed of epidermal growth factor-like ( EGF ) repeats and Laminin G domains [5] , [6] , [10] , [11] . However , the role of these protein domains and their posttranslational modifications in the function Eys and Crb is unknown . Five of the Eys EGF repeats and seven of the Crb EGF repeats contain the C1XSX ( P/A ) C2 consensus sequence , which predicts the addition of an O-linked glucose by the protein O-glucosyltransferase Rumi ( POGLUT1 in mammals ) [12] , [13] . Mutations in rumi were first isolated in a genetic screen for regulators of sensory organ development in Drosophila [12] . When raised at 18°C , rumi mutants are viable and only show a mild loss of Notch signaling in certain contexts including bristle lateral inhibition and leg joint formation [12] , [14] . However , when raised at higher temperatures the mutant animals show a broad and severe loss of Notch signaling , until 28–30°C , at which loss of rumi becomes larval lethal [12] , [14] , [15] . Mice lacking the Rumi homolog POGLUT1 die at early embryonic stages ( at or before E9 . 5 ) and some of the defects observed in mutant embryos are characteristic of loss of Notch signaling [16] . Moreover , transgenic expression of human POGLUT1 in flies rescues the rumi null phenotypes , indicating that the function of Rumi is conserved [17] . Drosophila Notch has 18 Rumi target sites in its extracellular domain , most of which have been confirmed to harbor O-glucose residues [12] , [18] . Moreover , serine-to-alanine mutations in the Rumi target sites of Notch result in a temperature-sensitive loss of Notch signaling [14] , establishing Notch as a biologically-relevant target of Rumi in flies . However , whether Rumi and its glucosyltransferase activity are required for the function of its other potential targets like Eys and Crb and for rhabdomere separation remained unknown . Here , we present evidence indicating that the enzymatic activity of Rumi is required for the separation of rhabdomeres in the Drosophila eye . When raised at 18°C , animals homozygous for a null allele of rumi or for a missense mutation that abolishes its protein O-glucosyltransferase activity show a highly penetrant rhabdomere attachment phenotype that cannot be explained by loss of O-glucose from Notch . Mass spectral analysis indicates that both Crb and Eys harbor O-glucose when expressed in a fly cell line . However , genetic experiments rule out Crb as a target of Rumi during rhabdomere separation . Our data indicate that O-glucosylation of Eys by Rumi promotes Eys folding and stability and thereby ensures that enough Eys is secreted into the IRS in a critical time window during the mid-pupal stage to fully separate the rhabdomeres .
When raised at 18°C , rumi mutant animals are viable and show only a mild loss of Notch signaling in some contexts [12] , [14] . To explore whether Rumi plays a role in rhabdomere morphogenesis and IRS formation , we raised animals homozygous for the protein-null allele rumiΔ26 ( rumi− ) in ambient light at 18°C and performed transmission electron microscopy ( TEM ) on adult fly eyes . In cross sections of wild-type retinas , the rhabdomeres of the seven visible photoreceptor cells are separated from neighboring rhabdomeres by the IRS [19] ( Figure 1A and 1F ) . However , 1-day old rumi−/− animals exhibited a moderate , yet 100% penetrant , rhabdomere attachment phenotype , i . e . attachment of two or more rhabdomeres per ommatidium ( Figure 1B and 1F ) . This phenotype can be fully rescued by P{rumigt-FLAG} ( Figure 1C and 1F ) , a genomic transgene expressing a FLAG-tagged version of Rumi [12] , indicating that attachment of rhabdomeres observed in rumi−/− flies is due to the loss of rumi . Sections of rumi−/− animals at 15 and 40 days of age show a similar degree of rhabdomere attachment , suggesting that the phenotype is not age-dependent ( Figure 1D–G ) . Together , these observations indicate that Rumi is required for optical isolation of individual photoreceptors in the Drosophila eye . We have previously shown that Rumi primarily regulates Notch signaling through its protein O-glucosyltransferase activity [12] , [14] . We wondered whether the enzymatic activity of Rumi is also required for rhabdomere separation . To test this , we performed TEM on adult Drosophila homozygous for rumi79 , a severe hypomorphic allele harboring a missense mutation which abolishes the enzymatic activity of Rumi but does not affect its expression level or stability [12] , [17] . rumi79/79 animals raised at 18°C also exhibit rhabdomere attachment in all ommatidia examined ( Figure 1H; n>50 ) . Surprisingly , the average number of separate rhabdomeres per ommatidium was somewhat lower in rumi79/79 animals ( 3 . 41±0 . 15 ) compared to rumi−/− animals ( 4 . 11±0 . 08 ) , indicating that the rhabdomere attachment phenotype is slightly more severe in rumi79/79 animals compared to rumi−/− animals . Statistical analysis indicated that the difference between rumi79/79 and rumi−/− average rhabdomere number per ommatidia is significant ( P<0 . 0001 ) . Given that rumiΔ26 is a protein-null allele [12] , these data suggest that rumi79 might have a dominant negative effect in the context of rhabdomere separation . However , one copy of the P{rumigt-FLAG} genomic transgene was able to fully rescue the rhabdomere attachment phenotype of rumi79/79 animals ( Figure 1I , n>50 ) . Moreover , overexpression of Rumi-G189E , which is the protein product of rumi79 [12] , did not result in any rhabdomere separation defects , similar to overexpression of wild-type Rumi ( Figure S1 ) . Together , these observations suggest that rumi79 is not likely to be a dominant negative allele . Since rumi79 was generated in an EMS screen but rumiΔ26 is the product of P-element excision , the modest worsening of the rhabdomere attachment in rumi79 might be due to a genetic background effect . Taken together , these observations indicate that enzymatic activity of Rumi is required for the separation of rhabdomeres in the fly eye . Rhabdomere morphogenesis and IRS formation occur during the second half of pupal development [19] . Until 37% pupal development ( PD ) , the apical surfaces of photoreceptors are attached to one another and do not exhibit any microvillar structures [19] . Around 55% PD , short microvilli and neighboring stalk membranes can be seen at the apical surfaces of the developing photoreceptors , and a thin IRS has formed [19] . By 65% PD , the rhabdomeres are clearly separated from one another by the IRS ( Figure 1J ) . Because one-day old adult rumi retinas have a well-formed IRS but exhibit rhabdomere attachment ( Figure 1B ) , we asked whether the absence of Rumi prevents rhabdomere separation during pupal development , or whether they initially separate but subsequently attach as the pupal eye assumes its adult structure . To address this question , we performed TEM on 65% PD rumi−/− retinas grown at 18°C , and found that by 65% PD , each rumi photoreceptor harbors distinct stalk membrane and rhabdomere structures ( Figure 1K ) . The IRS has formed but the average IRS size in mutant ommatidia ( 3 . 52±0 . 20 µm2 ) is 59% of the average IRS size in wild-type ommatidia ( 5 . 95±0 . 19 µm2 ) at a similar stage raised at the same temperature ( Figure 1L , P<0 . 0001 ) . Although the apical surfaces of photoreceptors adjacent to the IRS appear separated from one another , multiple local adhesions persist between the microvillar membranes of neighboring rhabdomeres ( and occasionally opposing rhabdomeres ) in each ommatidium ( Figure 1K , arrowheads ) . These observations indicate that the rumi rhabdomere attachment phenotypes are evident early during rhabdomere morphogenesis and strongly suggest that proper rhabdomere separation never occurs in rumi−/− animals . If Rumi regulates rhabdomere spacing via its protein O-glucosyltransferase ( Poglut ) activity , lack of glucose on Rumi target proteins is likely to be responsible for the observed phenotype . To identify all fly proteins with a potential Rumi target site , we used the MOTIF search engine ( http://motif . genome . jp/MOTIF2 . html ) to search the Swiss-Prot and KEGG-GENES databases for Drosophila proteins harboring one or more EGF repeats with the C1XSX ( P/A ) C2 consensus sequence [13] . Based on this search , 14 Drosophila proteins have at least one EGF repeat with a predicted Rumi target site ( Figure 2A ) , with Notch harboring the largest number of O-glucosylation sites , most of which have been confirmed to be efficiently O-glucosylated by Rumi [12] , [13] , [18] . rumi null animals raised at 18°C do not show photoreceptor specification defects characteristic of loss of Notch signaling ( Figure 2B–D′ ) , suggesting that Notch signaling is not significantly affected in rumi−/− developing photoreceptors at this temperature . Moreover , to our knowledge , Notch signaling has not been implicated in rhabdomere spacing . Nevertheless , given the broad roles that Notch plays in multiple developmental contexts , we sought to examine whether the rhabdomere spacing defects observed in rumi mutants can be explained by loss of O-glucose from Notch EGF repeats . To this end , we used a Notch genomic transgene ( PBac{Ngt-4-35} ) in which serine-to-alanine mutations are introduced in all 18 Rumi target sites and therefore expresses a Notch protein which cannot be O-glucosylated by Rumi [14] ( Figure 2E ) . When reared at 18°C , the PBac{Ngt-4-35} transgene rescues the lethality of Notch null mutations , and the N−; PBac{Ngt-4-35}/+ animals only show a mild loss of Notch signaling similar to rumi mutants [14] . TEM revealed that adult N−; PBac{Ngt-4-35}/+ eyes raised at 18°C do not show any rhabdomere attachment phenotypes ( Figure 2F ) , strongly supporting the notion that addition of O-glucose to Notch is not essential for proper rhabdomere spacing . The fly protein with the second largest number of Rumi target sites is Crb ( Figure 2A ) , an evolutionarily conserved transmembrane protein involved in the regulation of epithelial polarity , organ size , and photoreceptor development and maintenance [10] , [11] , [20]–[24] . Of note , crb mutant retinas exhibit attachment of neighboring rhabdomeres despite the presence of IRS [20] , [21] . Seven of the Drosophila Crb EGF repeats , 13 of the human CRB1 EGF repeats and eight of the human CRB2 EGF repeats harbor Rumi target sites , suggesting that O-glucosylation might play an important role in the function of Crb ( Figure 3A ) . We performed mass spectral analysis on peptides derived from a fragment of the Crb extracellular domain expressed in Drosophila S2 cells ( Figure 3A , the red line ) to examine whether Crb can be O-glucosylated in Drosophila . Indeed , peptides containing the predicted sites in this region are O-glucosylated ( Figure 3B–F , Figure S2 and Figure S3 ) . We next asked whether loss of O-glucose from Rumi target sites in Crb recapitulates the rhabdomere attachment phenotype observed in rumi mutants . Using a previously established platform [23] , [25] , [26] , we generated a knock-in allele of crb ( crb1-7-HA ) with serine-to-alanine mutations in all seven Rumi target sites ( Figure 3A ) . Animals homozygous for this allele or trans-heterozygous for this allele and the null allele crb11A22 are viable and do not exhibit any gross abnormalities when raised between 18°C and 25°C . Moreover , TEM indicates normal rhabdomere morphology and IRS formation with no defects in rhabdomere spacing in crb1-7-HA/1-7-HA animals raised at either 18°C or 25°C ( Figure 3G and 3H ) . These observations indicate that absence of Crb O-glucosylation does not explain the rhabdomere spacing defects of rumi mutants . In agreement with these data , Crb appears to be properly localized to the stalk membrane in 65% PD rumi−/− retinas , although an increase in the number of Crb+ puncta is seen in rumi mutants raised at 25°C compared to control animals ( Figure 3I–J′ , arrowheads ) . Together , these data indicate that although O-glucose modifications might affect the trafficking of Crb , they are not essential for the function of Crb during fly embryonic development and photoreceptor morphogenesis . As mentioned above , another Drosophila protein with multiple predicted Rumi target sites and an IRS phenotype is Eys ( Figure 2A and Figure 4A ) [5] , [6] . To examine whether Eys is the biologically-relevant target of Rumi in the context of rhabdomere spacing , we first performed mass spectral analysis on peptides derived from an Eys fragment harboring four Rumi target sites expressed in S2 cells ( Figure 4A , the red line ) . So far we have been able to identify peptides corresponding to three of these sites by mass spectrometric analysis and have identified O-glucose on all three sites ( Figure 4B–D and Figure S4 ) . The Rumi target site in EGF1 appears to be less efficiently O-glucosylated compared to those in other Eys EGF repeats . Nevertheless , these data indicate that Drosophila Eys contains several bona fide Rumi targets . We next performed genetic interaction studies between rumi and eys by using the protein-null allele eys734 [5] . As reported previously , loss of one copy of eys does not cause any rhabdomere defects ( Figure 4E ) [5] , [6] . However , removing one copy of eys in a rumi−/− background results in a strong enhancement of the rumi−/− rhabdomere attachment phenotype at 18°C ( Compare Figure 4F and 4G to Figure 1B , 1D and 1E ) . In eys+/−; rumi−/− animals , multiple rhabdomeres collapse into one another in each ommatidium , and there is a dramatic decrease in the IRS size ( Figure 4F , 4G , 4I and 4J ) . Of note , pockets of IRS can still be recognized in all eys+/−; rumi−/− ommatidia ( Figure 4F and 4G , asterisks ) , in contrast to eys−/− ommatidia , which completely lack IRS ( Figure 4H and 4J ) [5] . This dosage-sensitive genetic interaction strongly suggests that Rumi is critical for the function of Eys , especially when Eys levels are limiting . Secretion of Eys from the apical surface of the photoreceptor cells at the mid-pupal stage separates the rhabdomeres from one another and generates the IRS [5] , [6] . Based on the modENCODE Temporal Expression Data accessed on FlyBase ( http://flybase . org/reports/FBgn0031414 . html ) , expression of eys sharply increases at mid-pupal stage and gradually decreases in later pupal stages . Following the initial burst of Eys expression between 50–70% PD [5] and rhabdomere separation , Eys continues to be secreted into the IRS , which gradually enlarges and assumes its adult size late in the pupal stage [5] , [19] . Given the increased degree of rhabdomere attachment and the severe decrease in the IRS size in adult animals simultaneously lacking rumi and one copy of eys , we examined whether loss of Rumi affects Eys levels in the IRS . We first compared Eys expression in the early stages of IRS development between rumi−/− and control pupae raised at 18°C . At 55% PD , the rhabdomeres of control animals are separated from one another by a thin but continuous IRS filled with Eys , and only low levels of Eys can be detected in photoreceptor cell bodies ( Figure 5A and 5A′ ) [5] . In contrast , rumi−/− ommatidia almost invariably show some degree of rhabdomere attachment and a decreased and interrupted pattern of Eys expression in the IRS ( Figure 5B and 5B′ ) . In the majority of rumi−/− ommatidia examined , decreased levels of Eys in the IRS are accompanied by increased Eys levels in the photoreceptor cell bodies ( Figure 5B and 5B′ ) . Quantification of the total pixel intensity of Eys at 55% PD in animals raised at 18°C shows that in wild-type ommatidia , 87 . 1±2 . 0% of total Eys is found in the IRS and the rest is in photoreceptor cell bodies . However , there is a statistically significant decrease in the percentage of Eys found in the IRS in rumi−/− ommatidia ( 63 . 6±6 . 5% , P = 0 . 01 ) . These data indicate that during early stages of IRS formation , a significant amount of Eys remains inside the photoreceptor cells in rumi mutants , unlike wild-type animals , in which most of the Eys is efficiently secreted into the IRS . As shown above , rumi mutants raised at 18°C show rhabdomere attachment and a significantly decreased IRS size in the mid-pupal stage ( Figure 1K ) . However , in rumi−/− adults , even though the rhabdomere attachments persist , the IRS in the center of the ommatidia looks similar to that in control ommatidia ( Figure 1A–E ) , suggesting that enough Eys is secreted in later pupal stages to expand the IRS . To test this notion , we examined Eys expression in wild-type and rumi null animals at 95% PD . In wild-type animals , Eys fills the IRS in an uninterrupted manner and cannot be seen in the cell body ( Figure 5C–C′ ) . In rumi mutants , Eys is properly localized to the IRS at levels similar to that found in wild-type IRS and is not visible in the cell body ( Figure 5D and 5D′ ) . However , multiple gaps in the Eys expression domain are seen in the IRS , coinciding with rhabdomere attachments ( Figure 5D and 5D′ , white arrowheads ) . These data suggest that the rhabdomere attachments in rumi mutants result from decreased levels of Eys in the IRS in a critical period during the mid-pupal stage and that these attachments are not resolved later in pupal development despite continued secretion of Eys . Since the loss of Notch signaling in rumi mutants is temperature-sensitive [12] , [14] , [15] , we next examined whether the IRS defect observed in rumi animals becomes worse at higher temperatures . To bypass the larval lethality and photoreceptor specification defects of rumi mutants at 30°C , we kept rumi mutant and control animals at 18°C until the end of the third instar stage and shifted them to 30°C at zero hours after puparium formation ( APF ) so that they were kept at high temperature at mid-pupal stage , when eys expression starts [5] . However , these animals died by mid-pupal stage , precluding the study of Eys secretion and IRS formation . Therefore , we modified our temperature shift regimen by transferring rumi−/− and control animals to 25°C at zero h APF , shifted them to 30°C at 24 h APF and kept them at this temperature until 55%–75% pupal development , when we dissected them for staining or TEM . The patterns of Phalloidin and Eys staining in control animals looked similar to those raised at 18°C ( Figure 5E and 5E′ ) . In contrast , rumi mutants either lacked Eys in the IRS ( Figure 5F and 5F′ ) or had small Eys-containing regions ( Figure 5G and 5G′ ) . Most rumi mutant ommatidia showed high levels of Eys in the photoreceptor cells ( Figure 5F–G′ ) . TEM on rumi−/− animals reared under the above mentioned conditions showed multiple sites of rhabdomere attachment and a small IRS at 75% PD compared to control animals raised under the same conditions ( Figure 5H and 5I ) . These observations indicate that in rumi mutants grown at higher temperatures , a higher fraction of Eys remains inside the cell and the level of Eys in the IRS is further reduced . To examine whether Eys accumulates in a specific subcellular compartment in rumi mutant photoreceptor cells , we performed colocalization studies between Eys and markers of ER ( Figure 6A–A″ ) , Golgi ( Figure 6B–B″ ) , recycling endosome ( Figure 6C–C″ ) , and the late endosome ( Figure 6D–D″ ) in rumi null animals shifted to 25°C at zero h APF and later to 30°C at 24 h APF as explained above . Eys was transported to all cellular compartments examined , as shown by occasional colocalization with each marker ( Figure 6A–D″ , white arrowheads ) , indicating that Eys trafficking is not blocked at a single step in the secretion pathway but is likely slowed down through the secretory pathway , causing it to accumulate in the cell body as it travels to the membrane . Worsening of the IRS defect and further decrease in the extracellular levels of Eys in rumi mutant animals raised at higher temperature suggest that in the absence of Rumi , Eys is misfolded . To assess the effects of loss of Rumi on Eys protein levels at low and high temperatures , we performed Western blots on head extracts from 80% PD wild-type and rumi−/− animals . When raised at 18°C throughout development , wild-type and rumi−/− pupae did not show a significant difference in the level of Eys ( Figure 7A , left panel , P = 0 . 57 ) . However , when the animals were raised at 18°C until mid-pupal stage and shifted to 30°C during IRS formation , there was a significant decrease in the level of Eys in rumi−/− pupal heads ( Figure 7A , right panel , P<0 . 05 ) . These data support the notion that loss of Rumi decreases the ability of Eys to fold properly and to be secreted at a normal rate . The data also suggest that at higher temperatures , misfolding results in degradation of Eys and worsening of IRS defects in rumi mutant animals . If rhabdomere attachments observed in rumi mutants result from Eys misfolding , decreasing the level of chaperone proteins might enhance this phenotype . To test this hypothesis , we examined whether removing one copy of the ER chaperone Hsc70-3 ( BiP ) affects rhabdomere attachment in rumi−/− animals . As shown in Figure 7B , animals double heterozygous for a lethal P-element inserted in the coding region of Hsc70-3 ( Hsc70-3G0102 ) and rumi do not exhibit any rhabdomere attachment . However , Hsc70-3G0102/+; rumi−/− animals raised at 18°C show an enhancement of the rhabdomere attachment phenotype observed in rumi−/− animals raised at the same temperature ( Figure 7C; compare to Figure 1 ) . The average number of separate rhabdomeres in Hsc70-3G0102/+; rumi−/− animals is significantly different from rumi−/− animals ( Figure 7D , 2 . 85±0 . 11 vs . 4 . 11±0 . 08 , P<0 . 0001 ) . This observation further supports the conclusion that Eys is misfolded in rumi mutants . We next asked whether loss of Rumi triggers the unfolded protein response ( UPR ) in the pupal eye . One of the hallmarks of the UPR is the induction of chaperones , including Hsc70-3 [27] . Western blotting using anti-Hsc70-3 antibody did not show an increase in the level of Hsc70-3 expression in rumi mutants raised at 18°C or 30°C compared to control animals ( Figure 7E ) . This indicates that UPR is not induced in the pupal eyes upon loss of Rumi , in agreement with a previous report on lack of UPR induction in rumi−/− clones in wing imaginal discs raised at 28°C despite accumulation of the Notch protein [12] . If loss of the Poglut activity results in intracellular accumulation of Eys in the mid-pupal stage , mutating the Rumi target sites on Eys should affect its trafficking as well . To test this , we generated UAS-attB transgenes capable of overexpressing wild-type Eys ( Eyswt ) or Eys with serine-to-alanine mutations in four ( Eys1-4 ) or in all five Rumi target sites ( Eys1-5 ) ( Figure 8A ) . To minimize the expression variability associated with random insertion of transgenes , we used ΦC31 transgenesis and integrated all three constructs in the same docking site ( VK31 ) in the fly genome [28] , [29] . We used GMR-GAL4 to overexpress wild-type and mutant Eys in the developing photoreceptors and kept the animals at 18°C to avoid the very high levels of GAL4-driven transgene expression at high temperatures . In animals overexpressing wild-type Eys , the IRS is expanded and the majority of the Eys is within the IRS , although low levels of Eys are seen in photoreceptor cells ( Figure 8B–C′ ) . Overexpression of Eys1-4 and Eys1-5 also expands the IRS ( Figure 8D–E′ , compare to Figure 5 ) . However , unlike the wild-type protein , O-glucose mutant versions of Eys protein accumulate in the photoreceptor cells ( Figure 8D–E′ , white arrowheads ) . These data support a role for O-glucose residues in the proper folding and trafficking of Eys .
We have previously shown that the extracellular domains of Drosophila and mammalian Notch proteins are efficiently O-glucosylated , and have provided strong evidence that Rumi/POGLUT1 is the only protein O-glucosyltransferase capable of adding O-glucose to EGF repeats in animals [12] , [13] , [16] , [18] , [30] . The data presented here indicate that Drosophila Crb and Eys also harbor O-glucose residues , yet the impact of loss of Rumi and loss of O-glucose from these three target proteins , which harbor the highest number of Rumi target sites among all Drosophila proteins , is not equivalent . Loss of Rumi and mutations in Rumi target sites in a Notch genomic transgene both result in a temperature-dependent loss of Notch signaling [12] , [14] , indicating that the Notch protein becomes sensitive to temperature alterations in the absence of O-glucose . Although the Notch loss-of-function phenotypes in rumi mutants raised at 18°C are mild and limited to certain contexts , raising animals homozygous for rumi or harboring rumi mitotic clones at 28–30°C phenocopies Notch-null phenotypes [12] , [15] , [17] , indicating that O-glucose is indispensable for the function of Drosophila Notch at the restrictive temperature . At a functional level , loss of Rumi affects Eys similarly , with a moderate rhabdomere attachment phenotype at 18°C which becomes more severe when rumi animals are raised at 30°C during the IRS formation . However , even when raised at 30°C , rumi does not phenocopy an eys-null phenotype in the eye , as rhabdomeres show some degree of separation in the mid-pupal stage . The function of Crb , in contrast , does not seem to be significantly affected by loss of O-glucose , as flies homozygous for a mutant allele of crb which contains no intact Rumi consensus sequences are viable and fertile , and do not exhibit any obvious phenotypes in rhabdomere morphogenesis . The divergent effects of O-glucose on the function of these proteins does not seem to be correlated with the number of Rumi target sites or the overall structure of these proteins , as Notch and Crb are transmembrane proteins but Eys is secreted , Crb and Eys both have a combination of EGF repeats and Laminin G domains but Notch does not have Laminin G domains , and Crb has a higher number of Rumi target sites ( seven ) compared to Eys ( five ) . In summary , our data indicate that although the C1XSX ( P/A ) C2 motif is highly predictive for the addition of O-glucose to EGF repeats of Drosophila proteins , the functional importance of O-glucose depends on additional parameters beyond the number of O-glucose sites and the overall domain structure of a given target protein . In rumi mutant ommatidia , a significant amount of Eys remains inside the photoreceptor cells , while the extracellular levels of Eys in the IRS decrease . At the restrictive temperature , these phenotypes are enhanced and the total level of Eys in rumi mutant heads is significantly decreased . Moreover , removing one copy of an important ER chaperone enhances the rhabdomere attachment phenotype in rumi mutants . Finally , animals homozygous for the catalytically-inactive allele rumi79 also show rhabdomere attachment , and mutating the O-glucose sites of Eys results in its intracellular accumulation . Together , these observations strongly suggest that loss of O-glucosylation results in Eys misfolding and a decrease in its extracellular levels . In contrast , despite the almost complete loss of Notch signaling in rumi clones raised at 28–30°C , surface expression of Notch is not decreased upon loss of Rumi; indeed , Notch accumulates inside and at the surface of rumi mutant epithelial cells raised at the restrictive temperature [12] . Moreover , cell-based and genetic experiments suggest that in the absence of Rumi , Notch is able to bind ligands at the cell surface but fails to be cleaved properly by the ADAM10 metalloproteinase Kuzbanian [12] , [14] . Therefore , although these reports cannot rule out a redundant role for O-glucose in promoting the cell surface expression of Notch , they indicate that O-glucose is required for Notch signaling independently of its exocytic trafficking . Nevertheless , the temperature-dependent enhancement of loss of Notch signaling and Notch accumulation in rumi mutants [12] , [14] suggests that folding of Notch might also be affected by the loss of O-glucose . Similarly , the increase in the number of Crb+ puncta observed in rumi−/− photoreceptors raised at 25°C suggests that although the function of Drosophila Crb does not depend on O-glucosylation , loss of O-glucose affects Crb trafficking . Therefore , while we cannot rule out that O-glucosylation affects each of these targets differently at molecular and cell biological levels , we favor a scenario in which the folding of all three targets is affected by loss of O-glucose . In this scenario , the degree of functional defects observed for each target and the cellular compartment where the defect is observed varies depending on the extent of misfolding , the sensitivity of the target protein to lack of O-glucose and the cellular context where the target operates . It is intriguing to note that Rumi/POGLUT1 only glucosylates properly folded EGF repeats in vitro [31] , suggesting that Rumi/POGLUT1 may exert its effects on folding at the level of individual EGF repeats . Analysis of rhabdomere separation and IRS size in mid-pupal and late pupal/adult rumi−/− animals raised at 18°C suggests two temporally distinct steps for the function of Eys during IRS formation . In the early stages of IRS formation , some of the rhabdomeres in each ommatidium fail to separate from each other , and the mutant IRS is significantly smaller than control IRS . In late pupal stages , the level of Eys in the IRS of rumi−/− ommatidia is comparable to that in control ommatidia , in agreement with the more or less normal IRS size observed in adult rumi ommatidia . Nevertheless , rhabdomere attachments are not resolved . These observations suggest that Eys generates the IRS in two steps . At ∼45–55% PD , Eys secretion is required to sever the attachments among the rhabdomeres in each ommatidium ( step 1 ) , likely by opposing the adhesive forces mediated by Chaoptin [6] . Rhabdomere separation in turn generates conduits between stalk membranes—where Eys is secreted [5] —and the central IRS , and thereby allows Eys to increase the IRS size after rhabdomeres are separated ( step 2 ) . We propose that in rumi mutants , the Eys protein fails to fold properly and as a result , a significant fraction of Eys remains inside the cell instead of being secreted into the extracellular space . Therefore , at the mid-pupal stage Eys fails to fully separate the rhabdomeres from one another . Once the critical time window between 45–55% PD ( step 1 ) passes , continued Eys secretion in step 2 ( IRS expansion ) cannot separate rhabdomeres anymore . However , since in each rumi ommatidium some rhabdomeres separate from one another , Eys can reach the central IRS and can gradually increase the IRS size . This two-step model of rhabdomere separation and IRS expansion is further supported by the observation that overexpression of Eys in an eys null background after 65% PD fails to separate the rhabdomeres [6] . Lack of photoreceptor abnormalities in crb mutants with no intact Rumi target sites was somewhat surprising , given that Crb has the second highest number of O-glucosylation motifs in all fly proteins and that multiple EGF repeats in human CRB1 and CRB2 contain the Rumi consensus sequence . Our data indicate that O-glucosylation of Crb is not required for viability , fertility and photoreceptor morphogenesis in flies , at least in a laboratory setting . The Crb extracellular domain is dispensable for proper apical-basal polarity in embryos [32] , but is required in other contexts , such as stalk membrane formation [11] , regulation of the head size [24] , prevention of light-induced photoreceptor degeneration [21] and invagination of the salivary gland placode in embryos [33] . While the stalk membrane formation is not impaired upon mutating all Crb O-glucose sites , it remains to be determined whether O-glucosylation of Crb is required for the regulation of other processes regulated by the Crb extracellular domain , and whether O-glucosylation of mammalian CRB proteins is required for their function . Although a number of mammalian species including mice , rats , guinea pigs and sheep have lost Eys during evolution [34] , humans have an Eys homolog ( EYS ) , which shows an overall protein domain organization similar to the fly Eys ( Figure 4 ) [35] . Transgenic expression of human EYS in a Drosophila eys null background produces pockets of IRS , presumably at the location of secretion , but fails to rescue the rhabdomere attachment phenotype [9] . However , when human EYS is coexpressed in eys−/− animals with a human homolog of the Drosophila Prom called PROM1 , some rhabdomeres separate from their neighbors [9] . Since binding between Drosophila Eys and Prom is important for IRS formation [6] , these rescue experiments highlight the evolutionary conservation of the Eys-Prom interaction in the visual system . Of note , mutations in human EYS and PROM1 cause several forms of retinal degeneration including autosomal recessive retinitis pigmentosa , rod-cone dystrophies and cone-rod dystrophy [34]–[43] . Human EYS contains seven target sites for O-glucosylation , 4–5 of which are clustered similar to the Rumi target sites in EGF1-5 of Drosophila Eys . Therefore , O-glucosylation might play an important role in the function of the human EYS .
The following strains were used in this study: 1 ) Canton-S , 2 ) y w , 3 ) w; nocSco/CyO; TM3 , Sb1/TM6 , Tb1 , 4 ) y w; D/TM6 , Tb1 , 5 ) N55e11/FM7c , 6 ) y1 w67c23 P{Crey}1b; D*/TM3 , Sb1 , 7 ) y1 M{vas-int . Dm}ZH-2A w*; VK31 , 8 ) y1 M{vas-int . Dm}ZH-2A w*; VK22 , 9 ) w67 c23 P{lacW}Hsc70-3G0102/FM7c , 10 ) GMR-GAL4 ( on 2 ) ( Bloomington Drosophila Stock Center ) , 11 ) GMR-GAL4 ( on 3 ) [44] , 12 ) eys734 [5] , 13 ) P{rumigt-FLAG} ( rumi rescue transgene ) , 14 ) y w; FRT82B rumi79/TM6 , Tb1 , 15 ) y w; FRT82B rumiΔ26/TM6 , Tb1 [12] , 16 ) y w; PBac{Ngt-4-35}attVK22 [14] , 17 ) eys734/CyO; FRT82B rumiΔ26/TM6 , Tb1 , 18 ) y w; crb1-7::HA-A ( crb1-7-HA ) , 19 ) UAS-attB-eyswt-VK31 , 20 ) UAS-attB-eys1-4-VK31 , 21 ) UAS-attB-eys1-5-VK31 , 22 ) UAS-attB-rumiwt-FLAG-VK22 , 23 ) UAS-attB-rumi79-FLAG-VK22 ( this study ) , 24 ) y w; crbwt::HA-A ( crbwt-HA ) [25] , 25 ) crb11A22/TM6 , Tb1 [10] . All rumi mutant crosses were raised at 18°C to minimize the temperature-dependent defects in Notch signaling unless otherwise specified . To obtain N55e11/Y; PBac{Ngt-4-35}attVK22/+ animals , N55e11/FM7c females were crossed to y w/Y; PBac{Ngt-4-35}attVK22 males and the male progeny were selected based on the absence of the FM7 bar eye phenotype . To remove one copy of Hsc70-3 in rumi−/− animals , w67 c23 P{lacW}Hsc70-3G0102/FM7c females were first crossed to y w; FRT82B rumiΔ26/TM6 , Tb1 males . The w67 c23 P{lacW}Hsc70-3G0102/y w; FRT82B rumiΔ26/+ female progeny from this cross were backcrossed to y w; FRT82B rumiΔ26/TM6 , Tb1 males . w67 c23 P{lacW}Hsc70-3G0102/y w; FRT82B rumiΔ26/FRT82B rumiΔ26 progeny were selected based on the eye color from the Hsc70-3 allele and the rumi mutant bristle phenotype [14] and used for TEM analysis . A construct encoding EGF1-5 from Drosophila Eys ( harboring four out of the five Rumi target sites of Eys ) was synthesized ( Genewiz , Inc . ) . EGF12-17 from Drosophila Crb ( harboring five out of the seven Rumi targets sites of Crb ) was amplified using region-specific primers from genomic DNA extracted from flies carrying a UAS-crb-full-length transgene [20] . The genomic DNA was obtained using a DNA purification kit from Promega . The Eys fragment was cloned in frame to an N-terminal signal peptide from Drosophila Acetylcholine esterase and C-terminal V5 and 6x-Histidine tags in the pMT/V5-HisB-ACE vector [12] . The Crb fragment was cloned into a pMT/BiP/3xFLAG vector using EcoRI and XbaI [45] . Eys-EGF1-5-V5-His and Crb-EGF12-17-3xFLAG were expressed in Drosophila S2 cells , purified from medium by Nickel column or anti-FLAG resin , respectively , reduced and alkylated , and subjected to in-gel protease digests as described [46] , [47] with minor modifications . O-Glucose modified glycopeptides were identified by neutral loss of the glycans during collision-induced dissociation ( CID ) using nano-LC-MS/MS as described [13] . For dissection at 55% and 65% pupal development , animals were selected at the white prepupal stage and aged for 4 . 5 days ( 55% ) and 5 . 5 days ( 65% ) at 18°C . For animals raised at higher temperatures , the white prepupae were placed at 25°C at zero hours APF for 1 day and subsequently placed at 30°C until 55% or 75% PD for dissection . The pupal case was removed and heads were pierced to allow proper fixation . Corneas were removed from the eyes in PBS . Tissues were fixed using 4% formaldehyde for 30–40 minutes , and then washed in 0 . 3–0 . 5% Triton X-100 in PBS . Blocking and antibody incubations were performed in PBS containing 0 . 5% Triton X-100 and 5% Serum ( Donkey or Goat ) . The following antibodies were used: mouse anti-Eys ( 21A6 ) 1∶250 and mouse anti-ELAV ( 9F8A9 ) 1∶200 ( Developmental Studies Hybridoma Bank ) , guinea pig anti-Eys 1∶1000 [5] , guinea pig anti-Boca 1∶1000 [48] , rat anti-Crb 1∶500 [11] , rabbit anti-Lava lamp 1∶2000 [49] , rabbit anti-Rab11 1∶1000 [50] , Rabbit anti-Rab7 1∶100 [51] , mouse anti-Rab11 1∶100 ( BD Biosciences ) , guinea pig anti-Senseless 1∶2000 [52] , goat anti-mouse-Cy3 1∶500 , goat anti-mouse-Cy5 1∶500 , donkey anti-mouse-Dylight649 1∶500 , donkey anti-mouse-Cy3 1∶500 , donkey anti-rabbit-Cy3 1∶500 , donkey anti-guinea pig-Dylight649 1∶500 ( Jackson ImmunoResearch Laboratories ) . Phalloidin Alexa488 conjugated 1∶500 ( Life Technologies ) was used to visualize rhabdomeres . Confocal images were taken with either a Leica TCS-SP5 microscope with an HCX-PL-APO oil 63x , NA 1 . 25 objective and an HCX-PL-APO 20x , 0 . 7 NA objective with a PMT SP confocal detector , or a TCS-SP8 microscope with an HC-PL-APO glycerol 63x , NA 1 . 3 objective and HyD SP GaAsp detector . All images were acquired using Leica LAS-SP software . Amira 5 . 2 . 2 and Adobe Photoshop CS4 were used for processing and figures were assembled in Adobe Illustrator CS5 . 1 . To quantify IRS size , the electron micrographs were opened using “Fiji is just ImageJ” open source image processing software . The scale was set by tracing the scale bar in the image using the line tool and using the “set scale” function . The IRS was traced using the freehand selection tool and the area was measured using the “measure” function . To quantify total pixel intensity , the Amira 5 . 2 . 2 image processing software was used . A single ommatidium was cropped , which was done twice for each image to obtain data from 2 different ommatidia per animal . The desired channel for quantification was labeled with the “label field” function , and the “segmentation editor” was opened . The IRS was traced using the lasso freehand tool , placed in a separate “material” , and the rest of the pixels in the channel were selected using the threshold tool and placed in a separate “material” . The same threshold was used for all ommatidia . In the “object pool” module , the total pixel intensities for IRS and the rest of the ommatidium were obtained using the “material statistics” option . Proteins were extracted by lysing the heads in RIPA buffer ( Boston BioProducts ) containing a dissolved protease inhibitor cocktail tablet ( Roche Diagnostics ) . Approximately 10 µL RIPA buffer was used per fly head . The following antibodies were used: guinea pig anti-Hsc70-3 1∶5000 [27] , guinea pig anti-Eys 1∶10000 [5] , mouse anti-FLAG 1∶1000 ( M2 , Sigma-Aldrich ) , mouse anti-Tubulin 1∶1000 ( Santa Cruz Biotech ) , goat anti-guinea pig-HRP 1∶5000 and goat anti-mouse-HRP 1∶5000 ( Jackson ImmunoResearch Laboratories ) . Western blots were developed using Pierce ECL Western Blotting Substrates ( Thermo Scientific ) . The bands were detected and quantified using an ImageQuant LAS 4000 system and ImageQuant TL software , respectively , from GE Healthcare . At least two independent immunoblots were performed for each experiment . To process flies using transmission electron microscopy , heads were dissected and fixed overnight at 4°C in paraformaldehyde , glutaraldehyde and cacodylic acid and processed as previously described [53] . Briefly , after fixation , heads were post fixed with 1–2% OsO4 , dehydrated with ethanol and propylene oxide , and then embedded in Embed-812 resin . Thin sections ( ∼50 nm thick ) were stained with 1–2% uranyl acetate as the negative stain and then stained with Reynold's lead citrate . Images were obtained using three different transmission electron microscopes: 1 ) Hitachi H-7500 with a Gatan US100 camera: images were captured using Digital Micrograph , v1 . 82 . 366 software; 2 ) JEOL 1230 with a Gatan Ultrascan 1000 camera: images were captured with Gatan Digital Micrograph software; 3 ) JEOL JEM 1010 with an AMT XR-16 camera: images were captured using AMT Image Capture Engine V602 . All images were processed using Adobe Photoshop CS4 and figures were assembled in Adobe Illustrator CS5 . 1 . To generate the crb1-7::HA-A knock-in allele ( crb1-7-HA ) , a crb mutant founder line was used in which 10 kb of the crb locus harboring most of the coding region is replaced with an attP and a loxP site [25] . Multiple rounds of end overlap PCR were used to introduce serine-to-alanine mutations in all seven Rumi target sites of Crb in the pGE-attBGMR-crbwt::HA-A targeting vector [25] to generate the pGE-attBGMR-crb1-7::HA-A targeting construct . ΦC31-mediated integration was used to introduce the crb1-7::HA-A fragment into the crb locus of the crb mutant founder line . A Cre-expressing transgene [54] was used to remove the GMR-hsp::white and the remaining vector sequences from the knock-in allele and to obtain the white− allele crb1-7::HA-A used in our study . Genomic PCR with multiple primer pairs in the region was performed to confirm correct integration and Cre-mediated recombination , as described previously [25] . Primer sequences are available upon request . To generate the wild-type and mutant eys transgenes , the full length eys cDNA was retrieved from the pUAST-eys construct [6] using restriction digestion and cloned into the pUASTattB vector [28] , resulting in pUASTattB-eyswt . To generate the mutant eys transgenes , a 603-bp cDNA fragment containing EGF1-5 of Eys with serine-to-alanine mutations in the four target sites in this region ( EGF1-3 and EGF5 ) was synthesized ( Genewiz , Inc . ) . The wild-type EGF1-5 region in pUASTattB-eyswt construct was replaced with the synthesized mutant version using two rounds of end-overlap PCR [55] with three overlapping fragments . The resulting 1 . 2-kb fragment containing the first four mutant Rumi target sites was placed in pUASTattB-eyswt by using BglII and SacII restriction enzymes to generate pUASTattB-eys1-4 . To mutate the fifth ( last ) Rumi target site in eys , a 4-kb fragment of the eys cDNA containing the target site ( EGF9 ) and flanked by NdeI and KpnI restriction sites was PCR amplified using Phusion DNA polymerase ( New England Biolabs ) . The PCR product was cloned into pSC-B using the Strataclone blunt PCR cloning kit ( Agilent Technologies ) to generate pSC-B-Eys-EGF9 . Site-directed mutagenesis was performed using complementary primers and Phusion DNA polymerase to introduce the serine-to-alanine mutation . The wild-type 4-kb fragment from pUASTattB-eys1-4 was replaced with the mutant version by using NdeI and KpnI to generate pUASTattB-eys1-5 . All three constructs were integrated into the VK31 docking site using standard methods [28] , [29] . Correct integration was confirmed by PCR . To generate wild-type and mutant rumi transgenes , FLAG-tagged versions of rumiwt and rumi79 ( G189E ) ORF were excised from pUAST-rumiwt-FLAG and pUAST-rumi79-FLAG [12] by using EcoRI-KpnI double digestion and were cloned into the pUAST-attB vector [28] . After verification by sequencing , the constructs were integrated into the VK22 docking site using standard methods and verified by PCR [28] , [29] . Data are presented as mean ± SEM . To compare the number of rhabdomere clusters per ommatidium , ANOVA with Scheffé or Tukey multiple comparisons or t-test was performed . To compare the IRS size between wild-type and rumi ommatidia at 65% PD , unpaired t-test was used . | Glycosylation ( addition of sugars to proteins and other organic molecules ) is important for protein function and animal development . Each form of glycosylation is usually present on multiple proteins . Therefore , a major challenge in understanding the role of sugars in animal development is to identify which protein ( s ) modified by a specific sugar require the sugar modification for proper functionality . We have previously shown that an enzyme called Rumi adds glucose molecules to an important cell surface receptor called Notch , and that glucose plays a key role in the function of Notch both in fruit flies and in mammals . Using fruit flies , we have now identified a new Rumi target called “Eyes shut” , a secreted protein with a critical role in the optical isolation of neighboring photoreceptors in the fly eye . Our data suggest that glucose molecules on Eyes shut promote its folding and stability in a critical time window during eye development . Mutations in human Eyes shut result in a devastating form of retinal degeneration and loss of vision . Since human Eyes shut is also predicted to harbor glucose molecules , our work provides a framework to explore the role of sugar modifications in the biology of a human disease protein . | [
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| 2014 | The Protein O-glucosyltransferase Rumi Modifies Eyes Shut to Promote Rhabdomere Separation in Drosophila |
Angelman syndrome ( AS ) is a severe neurodevelopmental disorder caused by maternal deficiency of the imprinted gene UBE3A . Individuals with AS suffer from intellectual disability , speech impairment , and motor dysfunction . Currently there is no cure for the disease . Here , we evaluated the phenotypic effect of activating the silenced paternal allele of Ube3a by depleting its antisense RNA Ube3a-ATS in mice . Premature termination of Ube3a-ATS by poly ( A ) cassette insertion activates expression of Ube3a from the paternal chromosome , and ameliorates many disease-related symptoms in the AS mouse model , including motor coordination defects , cognitive deficit , and impaired long-term potentiation . Studies on the imprinting mechanism of Ube3a revealed a pattern of biallelic transcription initiation with suppressed elongation of paternal Ube3a , implicating transcriptional collision between sense and antisense polymerases . These studies demonstrate the feasibility and utility of unsilencing the paternal copy of Ube3a via targeting Ube3a-ATS as a treatment for Angelman syndrome .
Angelman syndrome ( AS ) is clinically manifested by features of intellectual and developmental disability , absence of speech , ataxic movement , epilepsy , and unique behaviors such as frequent laughter and fascination with water [1] , [2] . Despite absence of effective treatment currently , therapeutic development for Angelman syndrome could be potentially optimistic , since patients with AS have overall normal development and brain architecture early in life . Genetically , the disease is caused by deficiency of an E3 ubiquitin ligase termed UBE3A , which participates in many important neuronal functions such as synaptic development , signal transduction , and plasticity [3] . The gene encoding UBE3A is among a handful of human genes that are subject to genomic imprinting . In neuronal cells , it is highly expressed from the maternal allele , but silenced on the paternal allele . Disruption of the maternal allele , through genomic deletion , paternal uniparental disomy , imprinting defects , or point mutations , leads to the absence of UBE3A expression in neuronal tissues and hence Angelman syndrome . Indeed , in all cases of the disorder , at least one copy of paternal UBE3A is intact . One could speculate that by correcting the expression level of UBE3A via activating the silenced paternal allele , the disease might be treated . Imprinted genes usually form clusters in the genome and are controlled by the imprinting center ( IC ) . On human chromosome 15q11–q13 , paternally expressed genes , including MAGEL2 , NDN , SNRPN , SNORD115 and SNORD116 , are critical genes for Prader-Wiili syndrome ( PWS ) and form an 2-Mb imprinting cluster together with the AS gene UBE3A . Although not fully understood , it is generally believed that the PWS/AS region is regulated by a bipartite imprinting center composed of PWS-IC , which activates genes located in its proximity via looping and direct interacting with them , and AS-IC , which suppresses PWS-IC by transcription-mediated DNA methylation [4] , [5] . As a result of combined action of both PWS-IC and AS-IC , the paternal and maternal alleles of NDN and SNRPN show very distinct epigenetic patterns of DNA methylation and histone modifications [6] , [7] , [8] , which define the paternal alleles as transcriptionally active and maternal alleles as transcriptionally silent . Imprinting of UBE3A , however , is not associated with differential DNA methylation at the promoter region [9] , [10] . Instead , it is regulated by its antisense RNA , UBE3A-ATS , which is expressed from the paternally inherited chromosome in the brain [11] , [12] . As part of the large non-coding transcript ( Shng14 ) initiated from the Snrpn promoter in mice [13] , Ube3a-ATS expression is always negatively associated with Ube3a sense transcript . For example , when the Snrpn promoter was deleted , with or without the Prader-Willi syndrome imprinting center ( PWS-IC ) , the Ube3a-ATS level was found to be reduced , coupling with significant up-regulation of paternal Ube3a [12] , [14] . On the other hand , when maternal Ube3a-ATS was activated through replacement of the mouse imprinting center ( IC ) with the human one , or deletion of the putative AS-IC , maternal Ube3a was found to be repressed to some extent [15] , [16] . Recently , by terminating Ube3a-ATS transcription in neuronally differentiated ES cells , we have showed that paternal Ube3a can be activated to a comparable level as maternal Ube3a [12] , suggesting a direct role of Ube3a-ATS in suppressing paternal Ube3a . In the present study , we continue evaluating Ube3a-ATS as a potential therapeutic target for treating Angelman syndrome . By characterizing a novel mouse model expressing the truncated form of Ube3a-ATS , we provide the first in vivo evidence that eliminating Ube3a-ATS is sufficient to restore Ube3a expression and improve the abnormal behaviors in the AS mouse model . Mechanisms underlying paternal Ube3a silencing are also studied , and a hypothesis of transcriptional collision between Ube3a and Ube3a-ATS is proposed .
In order to test if suppression of Ube3a-ATS alone is sufficient to unsilence the paternal allele of Ube3a , mice with the Ube3aATS-stop allele were generated by inserting the triple SV40 poly ( A ) cassette [12] in between Snord115 and Ube3a ( chr7:66573289 NCBI37/mm9 ) ( Figure 1 ) . This design aims to prevent overlap between Ube3a and Ube3a-ATS and to minimize its effect on expression of the snoRNA clusters . The inserted cassette also contains a neomycin selection marker in the opposite transcriptional orientation to Ube3a-ATS to facilitate and enhance transcriptional termination . The mice were backcrossed to C57/BL6 background for six generations before subsequent expression and behavioral analysis . We first determined the effect of the termination cassette on the expression level of Ube3a-ATS and other genes located in the imprinting cluster . The Ube3a-ATS level downstream of the insertion site ( Ube3a-ATS 3′ , green arrows in Figure 1B ) was found to be significantly down-regulated by qPCR analysis when the stop allele was inherited paternally , while maternal inheritance of the allele has no effect ( Figure 2A ) . To exclude the possibility that the PCR amplification site is spliced out instead of terminated , a custom designed strand-specific microarray was further performed as previously reported [12] . A significantly lower level of Ube3a-ATS was detected beyond the stop cassette insertion site ( Figure S1 ) . Expression of most other imprinted genes located nearby , including Mkrn3 , Magel2 , Snrpn , Snord116 , and Ipw remained unchanged in both Ube3aATS-stop/+ and Ube3a+/ATS-stop mice ( maternal genotype precedes the paternal genotype ) , indicating that the imprinting status of the PWS/AS region is not disrupted by the insertion . The level of Ndn was found to be approximately doubled in Ube3a+/ATS-stop mice compared to the other two genotypes . It is interesting that similar observation has been found in delS-U/0 . 9 mice previously [12] , which expresses Ube3a-ATS at a lower level due to Snrpn promoter deletion . The reason for the observed up-regulation is unclear . Ube3a mRNA is doubled in the Ube3a+/ATS-stop mice , suggesting that paternal Ube3a may be unsilenced . To confirm this , male mice heterozygous for Ube3a-ATSstop were crossed with female mice heterozygous for Ube3aKO [17] ( C57/BL6 background ) , which is a constitutive Ube3a knock-out allele ( Figure 1 and 2B ) . In the progeny , littermates of wild-type ( WT ) , Ube3aKO/+ ( AS ) , Ube3a+/ATS-stop ( stop ) , Ube3aKO/ATS-stop ( AS/stop ) were compared . In the AS/stop mice , Ube3a protein was found to be activated to ∼70% of the WT level in neocortex , ∼60% in hippocampus , and ∼50% in cerebellum . The incomplete activation may be due to leaky termination of Ube3a-ATS , as about 20% of Ube3a-ATS can still be detected in Ube3a+/ATS-stop mice ( Figure 2A ) . Immunostaining with anti-Ube3a showed that in AS/stop mice , paternal Ube3a is expressed in most brain regions , including all layers of neocortex , CA1-3 and dentate gyrus of hippocampus , and Purkinje neurons of cerebellum ( Figure 2C and S2 ) . Its expression pattern is very similar to that of maternal Ube3a in the WT mice . The incomplete unsilencing of paternal Ube3a may be due to a smaller number of Ube3a positive neurons , or a lower expression level in each single neuron , or more likely a combination of both . Male mice heterozygous for Ube3aATS-stop were also crossed with female mice heterozygous for Ube3aYFP [18] , which carries the C-terminal YFP tag ( Figure S3 ) . Since Ube3a-YFP is expressed as a fusion protein with a higher molecular weight , it can be easily distinguished from wild-type Ube3a protein by western blot . Inheritance of Ube3aATS-stop from the paternal side leads to biallelic expression of Ube3a , while in contrast , maternal inheritance of the allele had no effect . Finally , the effect of Ube3aATS-stop on paternal Ube3a was compared with the other two alleles of del4 . 8 and del0 . 9 . The allele of del4 . 8 removes 4 . 8 kb of Snrpn promoter and functions as a PWS-IC deletion , while the allele of del0 . 9 removes 0 . 9 kb of Snrpn promoter and is equivalent to a Snrpn promoter deletion ( Figure 1 ) [19] . After crossing with female mice carrying genomic deletion over the Snrpn-Ube3a region ( delS-U/+ , Figure 1 ) [20] , the mRNA and protein levels of paternal Ube3a were found to be the highest in delS-U/4 . 8 mice , intermediate in delS-U/Ube3aATS-stop mice and the lowest in delS-U/0 . 9 mice ( Figure S4A , B ) . Interestingly , such order is in accordance with the suppression level of Ube3a-ATS ( Figure S4C ) . Plotting of paternal Ube3a against Ube3a-ATS fits into the curve of exponential decay ( R2 = 0 . 997 , Figure S4D ) , suggesting that suppression of paternal Ube3a by Ube3a-ATS is “dose-dependent” . We next tested whether inheritance of Ube3aATS-stop paternally can correct the phenotypic defects in the Angelman syndrome ( AS ) mouse model . To address this , male Ube3aATS-stop heterozygous mice were crossed with female Ube3aKO heterozygous mice [17] ( C57/BL6 background ) and the littermates of WT , Ube3aKO/+ ( AS ) , Ube3a+/ATS-stop ( stop ) , Ube3aKO/ATS-stop ( AS/stop ) were studied for various AS-related phenotypes . Obesity is associated with a small portion of AS patients [2] , [9] and constantly observed in many Angelman syndrome mouse models [15] , [21] , [22] . Ube3aKO/+ mice become overweight starting from three month of age , in both males and females ( Figure 3A , p ( WT vs . AS ) <0 . 01 for 4 , 5 , 6 months of age , two-way ANOVA of repeated measures ) . Activation of paternal Ube3a in the AS/stop mice completely reversed the obese phenotype ( p ( AS vs . AS/stop ) <0 . 05 for 4 , 5 , 6 months of age ) . The marble burying test measures repetitive behavior as potentially analogous to an autistic phenotype . Interestingly , AS mice were found to be dramatically impaired in performing this task ( Figure 3B , WT: 12 . 36±0 . 75 , AS: 0 . 50±0 . 27 , p ( WT vs . AS ) <0 . 001 , one-way ANOVA with Newman-Keuls post-hoc test ) . AS/stop mice showed a slight but significant improvement over AS mice ( AS/stop: 3 . 50±0 . 84 , p ( AS vs . AS/stop ) <0 . 05 ) . Hyperactivity with short attention span is a pronounced problem in young children with AS . Different from humans , AS mice have been reported to display hypoactivity [22] , [23] . When placed in an open field and allowed for exploration , AS mice showed significantly lower activity level as measured by total distance and central distance traveled , movement time , and vertical activity ( Figure 3C ) . A slight trend of improvement was consistently observed in the AS/stop mice for these parameters . However , the difference between AS mice and AS/stop mice does not reach statistical significance ( one-way ANOVA with Newman-Keuls post-hoc test ) . Ataxia and movement difficulty is one of the most severe defects in human AS patients and AS mouse models [17] . AS mice display severe motor coordination defects during the accelerating rotarod test ( Figure 3D , p ( WT vs . AS ) <0 . 05 for all eight trials , two-way ANOVA of repeated measures ) . AS/stop mice show restoration in the first few trials of accelerating rotarod , although they fail to improve in later trials ( p ( AS vs . AS/stop ) <0 . 05 for trial 1–4 ) . They also show full restoration of other motor defects during wire hanging test and dowel test , indicating a significant improvement of their motor coordination skills ( Figure 3E , F , and Figure S5 , p ( AS vs . AS/stop ) <0 . 01 for wire-hanging test and <0 . 001 for dowel test , one-way ANOVA with Newman-Keuls post-hoc test ) . It is noted that maternal inheritance of the Ube3aATS-stop allele does not affect the performance of the mice in all three motor tests ( Figure S6 ) , suggesting that the presence of neomycin cassette has minimal or no effect on motor coordination in mice . Individuals with AS are frequently affected with specific cognitive deficits [1] , [2] and Ube3aKO/+ mice are known to have learning and memory problems [17] . During a fear conditioning test , AS mice exhibited significantly less freezing behavior than did WT littermates ( Figure 3G , WT: 38 . 66±5 . 78% , AS: 21 . 68±5 . 35% , p ( WT vs . AS ) <0 . 05 , one-way ANOVA with Newman-Keuls post-hoc test ) . Remarkably , the freezing behavior displayed in the AS/stop mice is comparable to the WT mice , suggesting that long-term memory is fully restored ( AS/stop: 44 . 82±3 . 85% , p ( AS vs . AS/stop ) <0 . 01 ) . Lastly , we studied long-term potentiation ( LTP ) at Schaffer collateral–CA1 synapses , using high-frequency stimulation as the LTP-inducing protocol [17] , [24] . As expected , this protocol induced a stable LTP in WT slices but caused a decaying LTP in AS slices ( Figure 3H , LTP at 120 min , WT: 78±6 . 8% , AS: 33±8 . 6% , p ( WT vs . AS ) <0 . 01 , one-way ANOVA ) . Notably , the expression of paternal Ube3a reverses the LTP deficits ( AS/stop: 63±9 . 1% , p ( AS vs . AS/stop ) <0 . 05 ) . The LTP rescue in AS/stop slices cannot be attributed to abnormal basal synaptic transmission , since the relation of fiber volley versus stimulation intensity , initial slope of field EPSPs versus afferent volley size , and paired pulse facilitation were unaltered in these slices ( Figure S7 ) . In developing therapies for treating AS via activating paternal UBE3A , it is important to understand the molecular mechanism underlying genomic imprinting of Ube3a . Promoters of both the paternal and maternal UBE3A remain unmethylated in human brains [9] , [10] , [25] , therefore DNA methylation at the promoter cannot account for silencing of paternal UBE3A . In order to look for parent-of-origin epigenetic markers that may account for UBE3A imprinting , we first analyzed histone modifications of H3K4 trimethylation ( H3K4me3 ) in human cerebellum tissues by ChIP-on-chip experiment ( Figure 4A ) . In contrast to healthy controls , a PWS patient with a paternal class II deletion ( common 4 Mb deletion from break point 2 to 3 ) lacked H3K4me3 at the SNRPN promoter , suggesting that this modification is paternal specific , as previously reported [8] . However , in AS patients with maternal class II deletion , the peak of H3K4me3 was still present at the UBE3A promoter , and was indistinguishable from control and PWS samples . Therefore H3K4me3 is equally distributed between the paternal and maternal promoters of UBE3A in human cerebellum , regardless of the mono-allelic expression pattern . This conclusion from human was later supported by a recent ChIP-seq study in mice [26] , in which equal enrichment of H3K4me3 and H3K27 trimethylation at both parental promoters of Ube3a was observed . We next measured binding of the transcription preinitiation complex ( PIC ) at the Ube3a promoter by chromatin immunoprecipitation ( ChIP ) . The PIC is a large protein complex composed of RNA polymerase II , TATA binding protein ( TBP ) , TFIIB , and many other proteins assembled at the promoter of active genes . ChIP with antibody against RNA polymerase II was performed in brain samples of F1 hybrid of C57 ( female ) crossed with B6 . Cast . Chr7 ( male ) , which carries Mus . musculus castaneus chromosome 7 on the Mus . musculus domesticus C57BL/6 background . Single nucleotide polymorphisms ( SNPs ) between the two lines allow detection of parental specific alleles . In contrast to the Snrpn promoter , from which only the transcriptionally active paternal allele was precipitated , both parental alleles of the Ube3a promoter can be detected in the same IP fraction ( Figure 4B ) . ChIP in F1 hybrids of the reciprocal cross and ChIP with anti-TFIIB and anti-TBP revealed the same result ( Figure S8 ) . Altogether , the results suggest that the PIC is able to be properly assembled at the promoter of both paternal and maternal Ube3a alleles , regardless of theirs different expression status . Since paternal Ube3a shows multiple features of an active gene as we demonstrated above , we considered the hypothesis that it is actually transcriptionally active despite the absence of mature mRNA . To test this , a mouse model carrying a deletion from Ube3a to Gabrb3 ( delU-G , Figure 1A and 4C ) was used [27] . Since the deletion covers the promoter of Ube3a , only maternal Ube3a RNA is present in the paternal deletion +/delU-G mice and only paternal Ube3a RNA is present in the maternal deletion delU-G/+ mice . We set the RNA copy number of maternal Ube3a in +/delU-G mice equal to 1 across different portions of the gene and used it as the reference to calculate the relative RNA copy number of paternal Ube3a in delU-G/+ mice or total Ube3a in WT mice . Consistent with the known mono-allelic expression pattern , the mature mRNA of paternal Ube3a ( quantified by qPCR using primers spanning exon-exon junction ) is about 0 . 2 copy at both the 5′- and 3′-portions in delU-G/+ mice ( Figure 4C , ex1-4 , ex4-6 , and ex12-13 ) . However , when pre-mRNA of paternal Ube3a was quantified ( by strand-specific qRT-PCR [28] using tagged primers directed to introns ) , it is around one copy at the 5′ portion of Ube3a ( black bars of int1 , int3 , and int4 . 2 in Fig . 4C ) and drops to about 0 . 2 copy as the primers are moved to the 3′ portion of Ube3a ( black bars of int4 . 4 , int6 , and int12 in Fig . 4C ) . Altogether , our data supports a model that paternal Ube3a is transcribed at a comparable level as maternal Ube3a from the promoter , but later becomes suppressed during the process of transcription elongation . Airn and Kcnq1ot1 , two antisense RNAs playing a regulatory role in their respective imprinting cluster , are known based on FISH analysis to be localized around the transcribed regions [29] , [30] , consistent with their functional roles . Ube3a-ATS has been shown to be localized exclusively to the nucleus [12] , [31] , but the subnuclear detail was unknown . To address this question , a combined RNA/DNA FISH was performed in mouse brain sections . Signals of Ube3a-ATS form a single bright dot inside the nucleus ( Figure 5A ) and can be observed in multiple regions throughout the brain including olfactory bulb , neocortex , hippocampus , cerebellum , and hindbrain . Interestingly , the signal co-localizes with only one of the two foci formed by Ube3a DNA signal ( Figure 5B ) . Such co-localization is not random overlapping between the DNA and RNA probes because Ube3a-ATS does not overlap with the control DNA probe ( targeting an irrelevant gene on mouse chromosome 4 ) . Therefore , similar to Airn and Kcnq1ot1 , Ube3a-ATS remains located proximate to its transcription site after it being synthesized .
Patients with Angelman syndrome suffer from developmental delay , speech impairment , and epilepsy . Therapies for AS are limited and focus mainly on symptomatic management [2] . Recently , topoisomerase inhibitors have been identified as the first compounds to successfully unsilence paternal Ube3a in mice [32] , [33] . In the current research , we investigated a potential therapeutic strategy by activation of the silenced paternal allele of UBE3A via suppressing its antisense RNA . Previous studies have defined Ube3a-ATS as the negative regulator of Ube3a imprinting [12] , [14] . However , it was unknown if depletion of Ube3a-ATS without modulating other epigenetic factors is sufficient to activate paternal Ube3a . This question is crucial in determining whether knock-down of Ube3a-ATS is a suitable strategy for treating AS . By generating a mouse model with Ube3a-ATS being prematurely terminated , we observed unsilencing of Ube3a in multiple brain regions , implying that the antisense RNA plays a regulatory role in modulating Ube3a imprinting . We then compared mice which express paternal Ube3a on the maternal Ube3a knock-out background ( AS/stop ) with AS and WT mice . The AS/stop mice exhibit complete reversal of obesity , motor tests of wire-hanging and dowel walking , fear conditioning defect , and plasticity-related electrophysiology . They also display slight but significant improvement in the tests of accelerating rotarod and marble burying . Therefore , our research confirmed the clinical benefit of activating paternal Ube3a in treating Angelman syndrome and provided a mouse model as the positive control for future drug testing . Given the conservation of the PWS/AS region between mouse and human , activation of paternal UBE3A through inhibiting UBE3A-ATS expression/transcription should be a promising strategy for developing AS therapy . One important question in activating paternal UBE3A is how much UBE3A protein is needed to achieve phenotypic improvement in AS patients . In the mouse model of AS/stop , we observed some phenotypic reversal , such as obesity , and cognitive deficits . However , their performance during accelerating rotarod and marble bury test is only partially or moderately improved and their decreased locomotive activity is not restored . This may be due to the incomplete activation of paternal Ube3a , which is quantified to be 50–70% of the WT level in different parts of the brain by western blot . Some of the behavioral phenotypes might be more sensitive to the protein level of Ube3a and therefore are more difficult to reverse . Another possibility is the interference from the remaining neomycin cassette . However , paternal inheritance of the cassette on the WT background does not affect mouse behaviors , and maternal inheritance of the cassette does not change Ube3a expression and rotarod performance in mice , suggesting that the presence of the selection marker has no or minimal effect on Ube3a function . Among the human UBE3A mutations that have been reported so far , there is a striking preponderance of frameshift and nonsense mutations [34] . It is possible that individuals with less pathogenic missense mutation in UBE3A display some , but not all , clinical features associated with AS and thus are excluded from AS diagnosis and research . A patient with C21Y missense mutation located outside the HECT domain of UBE3A has been reported to have a less classical phenotype [35] , suggesting that partial activity of UBE3A may be beneficial . Another relevant issue is to understand the molecular mechanism underlying UBE3A imprinting . Interestingly , several pieces of evidence have suggested that the paternal allele of UBE3A/Ube3a is transcriptionally active . For example , the promoter of paternal UBE3A is unmethylated [9] , [10] , [36] , modified with active histone markers ( Figure 4A ) , and bound with transcription pre-initiation factors ( Figure 4B ) . Indeed , Ube3a pre-mRNA can be detected equally from the 5′-portion of both paternal and maternal alleles in mice ( Figure 4C ) . Therefore , paternal Ube3a is transcriptionally active and its suppression may occur during the process of transcription elongation . The previous observation of “biallelic” expression pattern at the 5′-portion of mouse Ube3a by SNP analysis is consistent with this conclusion [37] . As demonstrated in this and many other studies , Ube3a-ATS has a direct role in silencing paternal Ube3a . However the detailed mechanism is unclear . Research on the other two imprinted ncRNA Airn and Kcnq1ot1 has raised two different working models , promoter occlusion and RNA-directed targeting . When silencing the overlapping gene in embryonic tissues , Airn transcribes through the Igf2r promoter and precludes binding of RNA polymerase II to the Igf2r promoter [38] . In contrast , when silencing the respective non-overlapping genes in extraembryonic tissues , the RNA product of Kcnq1ot1 or Airn will bind to trans-acting protein factors and induce repressive higher-order chromatin changes [29] , [39] , [40] , [41] . Can either of the two models be applied to Ube3a-ATS ? Promoter occlusion is unlikely to be the cause of Ube3a imprinting since paternal Ube3a promoter is transcriptionally active . Components of PIC such as RNA polymerase II , TBP , and TFIIB are found to bind paternal and maternal Ube3a equally . Currently , it is unknown whether the RNA product of Ube3a-ATS is essential in mediating Ube3a imprinting . However , Ube3a-ATS has very low homology between mouse and human , and is quickly degraded [12] , implying a low functional importance of the RNA product . Here we proposed an alternative hypothesis of transcriptional collision as the mechanism for Ube3a-ATS mediated Ube3a imprinting ( Figure 6 ) . Our previous strand-specific microarray data revealed a significant decrease of Ube3a-ATS RNA signal around intron 4 of Ube3a , although the transcript remains detectable until ∼40 kb upstream of Ube3a promoter [12] . Interestingly , this is around the same region where the pre-mRNA level of paternal Ube3a becomes suppressed . Therefore , on the paternal chromosome , Ube3a sense and antisense RNAs are transcribed head-to-head at a relatively high level until the polymerases reach intron 4 , where both drop to a lower level . These findings are similar to what has been described for transcriptional collision occurring during convergent transcription [42] . Such collision will result in stalling , dissociation of both polymerases , and abortive transcription of both . Research in budding yeast has demonstrated both in vitro and in vivo that convergent transcription will result in collision of the two opposing polymerases [42] , [43] . The collision event can also be detected by atomic force microscopy in vitro when two promoters are aligned convergently on a linear DNA template [44] . Currently , we still lack direct evidence to support the Ube3a transcriptional collision hypothesis . It will be necessary to test it in the future by mapping RNA polymerase II stalling sites along Ube3a using GRO-seq or NET-seq technology [45] , [46] .
All animal procedures were performed in accordance with NIH guidelines and approved by the Baylor College of Medicine Institutional Animal Care and Use Committee ( IACUC ) . All human studies were performed in accordance with NIH guidelines and approved by the Baylor College of Medicine Institutional Review Board ( IRB ) . The insertion cassette composed of SV40 triple poly ( A ) signal and neomycin selection marker was inserted downstream of Ube3a by gene targeting in wild-type AB2 . 2 ES cells . After microinjecting into blastocysts of C57/BL6 mice , high percentage male agouti chimeras were obtained and germline transmission was established . The lines were then backcrossed to C57/BL6 mice for more than six generations . PCR genotyping was developed with TS-F ( TTCCCAGTGCTGAGACTAAAG ) , TS-R ( CCACAATCTGAA-CCCTAAAAC ) and SV40-R ( AAAAGGGACAGGATAAGTATG ) . Total RNA was prepared with miRNeasy Mini Kit ( Qiagen , Valencia , CA ) . On-column DNase treatment was performed for all the samples . The cDNA was generated using 0 . 2–1 µg of total RNA with SuperScript III First-Strand Synthesis System ( Invitrogen , Carlsbad , CA ) , and qRT-PCR was performed using Applied Biosystems StepOnePlus Real-Time PCR System and SYBR Green Master Mix ( Applied Biosystems , Carlsbad , CA ) . Primers used are listed in Table S1 . Western blot against Ube3a and β-tubulin was performed as previously described [12] . Quantification was performed based on densitometry with ImageJ . Tissue preparation and immunohistochemistry were performed by Neuropathology Core of Baylor College of Medicine , as previously described [47] . Immunostaining was carried out with Rabbit polyclonal anti-Ube3a ( 1∶500 , A300-352A , Bethyl Laboratories , Montgomery , TX ) and horseradish peroxidase conjugated goat anti-rabbit ( 1∶200 , Dako Inc . , Carpinteria , CA ) . The localization of the antibody was visualized using diaminobenzidine ( DAB , 0 . 5 mg/ml , Vector Laboratories Inc . , Burlingame , CA ) as a chromogen . A battery of behavioral tests was performed using a protocol previously described and used in Behavioral Core facilities at Baylor College of Medicine [27] , [48] . A detailed protocol for each test is described in Text S1 . Tests start when the mice are 2 month-old in both males and females and the order of tests are kept the same as listed in the supplementary material . The interval between two tests is one week , except wire hanging , dowel tests and rotarod were performed in two consecutive days . Horizontal hippocampal slices ( 350 µm ) were cut with a Leica ( VT 1000S ) vibratome ( Buffalo Grove , IL ) from brains of WT , AS and AS/stop mice in 4°C artificial cerebrospinal fluid ( ACSF ) and kept in ACSF at room temperature for at least one hour before recording , as previously described [49] , [50] . Slices were maintained in an interface-type chamber perfused ( 2–3 ml/min ) with oxygenated ACSF ( 95% O2 and 5% CO2 ) containing in mM: 124 NaCl , 2 . 0 KCl , 1 . 3 MgSO4 , 2 . 5 CaCl2 , 1 . 2 KH2PO4 , 25 NaHCO3 , and 10 glucose . Bipolar stimulating electrodes were placed in the CA1 stratum radiatum to excite Schaffer collateral and commissural fibers . Field EPSPs were recorded at 30–31°C , with ACSF-filled micropipettes . The recording electrodes were placed in the stratum radiatum and the intensity of the 0 . 1 ms pulses was adjusted to evoke 40–50% of maximal response . A stable baseline of responses at 0 . 033 Hz was established for at least 20 min . Tetanic LTP was induced by using two 1 s , 100 Hz tetani , 20 s apart at baseline stimulus intensity , as previously described [17] . Postmortem brain tissues from control , PWS , and AS individuals were obtained from NICHD Brain and Tissue Bank for Developmental Disorders from University of Maryland School of Medicine . ChIP-on-chip analysis was performed as previously described [51] . Immunoprecipitation was performed with Protein A Dynabeads ( Invitrogen ) coated with normal rabbit IgG or anti-H3K4me3 antibodies ( 17-614 , Millipore ) according to manufacturer's instructions . Precipitated and input DNA was amplified with GenomePlex Complete Whole Genome Amplification ( WGA ) kit ( Sigma , St . Louis , MO ) and labeled with Cy3 ( input DNA ) or Cy5 ( ChIP DNA ) using BioPrime Array CGH Genomic Labeling System ( Invitrogen ) . The DNA was then applied to a custom designed human chromosome 15q11 . 2–q12 focused array ( Agilent , Santa Clara , CA ) , with genomic tilling probes covering regions from MAGEL2 to GABRB3 ( chr15:21 , 361 , 151–25 , 487 , 147 , genome build NCBI36/hg18 ) in the 4X44k format . Hybridization , wash and scanning were performed according to manufacturer's instructions . The image files were processed with Agilent Feature Extraction software using protocol CGH-v4_95_Feb07 and further analyzed with Agilent G4477AA ChIP Analytics 1 . 3 software . Brain tissues of 50 mg from newborn mice was chopped into fine pieces , crosslinked with 1% formaldehyde in DMEM and lysed in SDS lysis buffer ( 50 mM Tris , 10 mM EDTA , 1% SDS ) . The lysate was then sonicated ( Fisher Scientific 500 Sonic Dismembrator ) and centrifuged . The supernatant was collected and combined with IP buffer ( 2 mM Tris , 15 mM NaCl , 0 . 2 mM EDTA , 0 . 1% Triton X-100 , 1× proteinase inhibitor ) . Immunoprecipitation was then performed with Protein G Dynabeads ( Invitrogen ) coated with anti-pol II ( 05-623 , Millipore ) , anti-TFIIB ( sc-225 , Santa Cruz Biotechnology ) , or anti-TBP ( MAB3658 , Millipore ) overnight . Immunoprecipitated DNA was PCR amplified with Snrpn or Ube3a promoter primers , purified with MinElute PCR purification kit ( Qiagen ) and analyzed by Sanger sequencing to identify allelic SNPs . Alternatively , unpurified PCR products were digested with BsaI for the Snrpn promoter or BbsI for the Ube3a promoter , and analyzed by eletrophoresis on a 1 . 5% agarose gel . Total RNA of 200 ng from cortices of newborn mice was used as the input in the analysis . The cDNA synthesis was performed using tagged gene-specific primers in the RT reaction to detect Ube3a pre-mRNA in a strand-specific manner [28] , and then amplified in the SYBR Green q-PCR system using the tag as the reverse primer and locus specific forward primer . All primers used are listed in Table S2 . Tissue preparation and RNA FISH were carried out by RNA In-Situ Hybridization Core at Baylor College of Medicine as previously described [52] . Briefly , brains of adult mice were embedded in O . C . T . , fresh frozen , and sectioned sagittally at 25 µm thickness . After paraformaldehyde fixation , acetylation , and dehydration , the slides were assembled into flow-through hybridization chambers and placed into a Tecan ( Mannedorf , Switzerland ) Genesis 200 liquid-handling robot . The DIG labeled RNA probes were prepared with in-vitro transcription and corresponded to chr7:66 , 530 , 657–66 , 531 , 391 ( NCBI37/mm9 ) . Primers for DNA template synthesis are SP6-Ube3a-int5 . 1F ATTTAGGTGACACTATAGAAGCGAAGATGAGTCAG-TTTGGTTTT and T7-Ube3a-ex6 . 1R TAATACGACTCACTATAGGGAGATTCTGAGTCTTCTTCCATA-GC ) . The T7 promoter was used to generate Ube3a-ATS probe . Hybridized probes were detected by a dual amplification strategy and visualized by Alexa488 conjugated streptavidin [52] . After RNA FISH , the slides were washed in 2XSSC at 37°C for 15 min , dehydrated in 70% , 85% , 95% ethanol at −20C for 2 min each and denatured in 70% formamide/2XSSC at 70°C for 2 min . After washing with 70% , 85% and 100% ethanol , the slides were air-dried before hybridization with the DNA FISH probe . The probe was prepared from Ube3a BAC clone bMQ311i10 ( Source BioScience , UK ) or Lepre1 ( chr 4 ) with FISH Tag DNA Red Kit ( Invitrogen ) and hybridized to the sections at 37°C overnight . The slides were washed in 50% formamide/2X SSC solution twice for 8 min at 42°C , once in 2XSSC for 8 min at 37°C and mounted with SlowFade Gold antifade reagent ( Invitrogen ) . Statistical analysis was performed with GraphPad Prism 5 ( GraphPad Software , Inc . La Jolla , CA ) . One-way ANOVA with Newman–Keuls post-hoc test and two-way ANOVA with repeated measures were used . | Angelman syndrome ( AS ) is a devastating neurodevelopmental disorder diagnosed in young children , currently with no effective treatments . It is characterized by absence of speech , ataxia , intellectual disability , epilepsy , and a characteristic behavior of frequent laughter and smiling . The disease is caused by loss of the maternal allele of UBE3A , which is preferentially silenced on the paternal chromosome and expressed on the maternal chromosome in neurons due to genomic imprinting . It has been long proposed that by activating the originally silenced paternal allele of UBE3A , the disease may be cured . Here in our research , we demonstrated the feasibility of activating paternal Ube3a in mice by terminating the transcription of its antisense RNA Ube3a-ATS genetically . In the AS mouse model who additionally receives the terminated Ube3a-ATS allele from the paternal side , we observed restoration of Ube3a expression , amelioration of behavioral defects and reversal of the impaired long-term potentiation . We further studied the imprinting mechanisms of Ube3a and proposed a novel transcriptional collision model . These results provide solid in vivo evidence for a key regulatory role of Ube3a-ATS in the disease and open up an exciting possibility of a gene-specific treatment for Angelman syndrome . | [
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| 2013 | Truncation of Ube3a-ATS Unsilences Paternal Ube3a and Ameliorates Behavioral Defects in the Angelman Syndrome Mouse Model |
Dendritic spines are the main postsynaptic site of excitatory contacts between neurons in the central nervous system . On cortical neurons , spines undergo a continuous turnover regulated by development and sensory activity . However , the functional implications of this synaptic remodeling for network properties remain currently unknown . Using repetitive confocal imaging on hippocampal organotypic cultures , we find that learning-related patterns of activity that induce long-term potentiation act as a selection mechanism for the stabilization and localization of spines . Through a lasting N-methyl-D-aspartate receptor and protein synthesis–dependent increase in protrusion growth and turnover , induction of plasticity promotes a pruning and replacement of nonactivated spines by new ones together with a selective stabilization of activated synapses . Furthermore , most newly formed spines preferentially grow in close proximity to activated synapses and become functional within 24 h , leading to a clustering of functional synapses . Our results indicate that synaptic remodeling associated with induction of long-term potentiation favors the selection of inputs showing spatiotemporal interactions on a given neuron .
Integration of synaptic signals during learning processes is critical to the function of cortical networks . This processing is achieved through various mechanisms that involve generation of coincident rhythmic activity , induction of properties of plasticity such as long-term potentiation ( LTP ) , but also growth of new protrusions and remodeling of synaptic networks [1–5] . The precise functional contribution of this structural remodeling to network properties remains unclear . In vitro experiments have demonstrated that LTP induction results during the next few hours in the growth of new filopodia and spines [6–9] which then rapidly become functional [10] and show all characteristics of morphologically mature synapses over the course of 24 h [11] . Also , work by several laboratories has shown that under in vivo conditions , spines and varicosities undergo a continuous turnover and replacement that vary in intensity as a function of development [12–16] . This process is further regulated by sensory activity , because under conditions of deprivation such as whisker trimming [17] or unbalanced activity such as chessboard whisker trimming [12 , 18] , spine turnover increases , new spines form synapses and become stabilized , and others are eliminated . These experiments therefore clearly demonstrated that stable synaptic contacts can be removed or created de novo through experience , raising the possibility that synapse remodeling , together with Hebbian forms of plasticity , could contribute to information processing and learning [3 , 19] . It remains unclear , however , whether and how sensory activity regulates this synaptic remodeling and whether it could actually affect signal integration by the neuron and/or the network . Also , the rules and mechanisms determining which synapse should be removed or restructured and where new synapses should be created are unknown . These are important issues because both the number and localization of spines may greatly affect the properties of integration of synaptic responses by a neuron . Recent studies have shown that spatiotemporal clustering of synaptic currents on small or remote dendrites represents a critical aspect for the expression of plasticity and the contribution to neuronal firing [20–22] . Identification of the mechanisms that underlie spine and synapse remodeling is therefore critical to a better understanding of the processing properties of synaptic networks . We investigated these issues , using a repetitive imaging approach applied to hippocampal slice cultures , and analyzed how precisely learning-related activity patterns affected the long-term behavior of identified spines .
Hippocampal slice cultures were transfected to express enhanced green fluorescent protein ( EGFP ) using a biolistic approach; we then monitored the behavior of identified protrusions ( spines and filopodia ) over several days following induction of learning-related activity patterns ( Figure 1 ) . For this , we used two different conditions that trigger LTP , a property believed to underlie learning mechanisms: first , we applied theta burst stimulation ( TBS ) to Schaffer collaterals , which triggers robust LTP , and second , we treated slice cultures for 20–60 min with carbachol ( Cch , 10 μM ) , a cholinergic agonist , which , in the hippocampus and in slice cultures , triggers rhythmic activity in the theta and gamma range and induces a lasting synaptic enhancement ( Figure 2C , inserts ) [1 , 23] . In humans , these theta activities have been directly implicated in memory processes [24] . Fluorescent cells and dendritic segments were then imaged repetitively and the changes in protrusion number and long-term spine stability monitored ( Figure 1A–1C ) through analysis of single z-stack images ( Figure 1D and 1E; see criteria in Materials and Methods ) . Control experiments with propidium iodide staining showed that transfection and repetitive confocal imaging of slice cultures did not alter cell viability over periods of weeks . Analysis of protrusion turnover over periods of 3–8 d showed that the dynamics of synaptic networks is high at this developmental stage ( 11 d in vitro ) with an average of 20 . 3% ± 1 . 1% new protrusions formed per 24 h and 20 . 8 ± 0 . 9% disappearing within the same period of time ( Figure 2A and 2B ) . The other protrusions either remained stable without changes or underwent some sort of morphological transformations ( 16 . 2% ± 0 . 3% [25] ) . These values are in the range of those reported in vivo in the cortex of very young mice [12 , 14 , 15] . Following theta burst activity we found that this basal turnover rate markedly increased . The effect was not short-lived [7 , 9] , but the increase lasted for several days following a brief stimulation episode . This lasting increase in turnover rate was observed both following LTP induction by TBS ( Figure 2C ) and by Cch-induced rhythmic activity ( 10 μM; Figure 2D ) . The insert in Figure 2C shows the potentiation of the slope of evoked excitatory postsynaptic potentials ( EPSPs ) recorded in slice cultures following TBS . In Figure 2D , we illustrate the spontaneous baseline activity of 1–3 Hz observed under control conditions , the increased 5–10 Hz field activity recorded in the stratum pyramidale of CA3 during application of 10 μM Cch and the synaptic enhancement observed in the cornus ammonis 1 ( CA1 ) region . The proportion of new and lost protrusions , which includes spines and filopodia , increased markedly under both conditions to values of 34% ± 6% and 43% ± 6% of new protrusions and 33% ± 2% and 44% ± 3% of lost protrusions over the first 24 h for TBS and Cch , respectively ( see also Figure 3A ) . These changes reflected a similar increase in the formation of thin spines and filopodia , filopodia representing only a very small fraction of the new protrusions both under control conditions and after stimulation ( 4 . 3% ± 0 . 9% , n = 30 cells , control; 4 . 7% ± 1 . 4% , n = 17 , LTP and 3 . 4% ± 1 . 6% , n = 17 , Cch ) . Together , these experiments indicate a 70% and 115% increase in protrusion turnover rate following TBS or Cch treatment , respectively . To allow comparisons , the data obtained at the different observation times are expressed in Figure 2C and 2D as percentage of the basal rate of protrusion formation or loss observed under control condition . To test for the specificity of the effect , we then carried out the same experiments , but applied the N-methyl-D-aspartate ( NMDA ) receptor antagonist D ( − ) -2-amino-5-phosphonopentanoic acid ( D-AP5; 100 μM ) during the stimulation protocol or during the application of Cch . As shown in Figure 2C and 2D , D-AP5 specifically prevented the lasting increase in protrusion turnover under both conditions . As an additional control , we also analyzed hippocampal slice cultures stimulated in the same way at low frequency ( 0 . 3 Hz ) , but without induction of rhythmic activity . These controls showed no significant changes in turnover rate over time . Finally , we also tested whether this increase in protrusion turnover was dependent upon protein synthesis . For this , slice cultures were incubated in the presence of 25 μM anisomycin ( Ani ) and stimulated with either TBS or Cch . Under these conditions , both forms of potentiation were prevented ( ratio of potentiation at 60 min: 1 . 13 ± 0 . 2 , n = 6 and 1 . 08 ± 0 . 11 , n = 3 for TBS and Cch , respectively ) and , as shown in Figure 3A , no significant increase in the rate of protrusion formation or loss could be observed over the next 24 h . Note also that Ani treatment of cultures for 5 h without TBS or Cch stimulation did not affect the rate of formation and loss of protrusions over 24 h . These results thus indicated that the changes in protrusion turnover associated with induction of LTP lasted several days and included formation and elimination of spines and filopodia . To assess these results further and test for possible changes in spine stability and/or occurrence of populations of transient spines or filopodia , we next analyzed protrusion growth each day over a period of 5 h , a period during which most new events can be detected [25] . Following LTP induction by TBS , the rate of protrusion formation expressed per 5 h and per 100 μm of dendritic segment increased by a factor of 2 , and this for several days , an effect fully prevented by D-AP5 applied during the stimulation protocol ( Figure 3B and 3C ) . We then also assessed spine stability , restricting the analysis to spines , since filopodia are essentially transient [25] and mostly disappeared within 24 h . The stability of pre-existing spines , calculated as the proportion of spines still present on consecutive days , significantly decreased following LTP induction ( Figure 4A ) , a change also dependent upon NMDA receptor activation . The stability of the new spines formed within the first 5 h following LTP induction was however not affected ( Figure 4B ) and remained particularly low as under control conditions . Thus , LTP induction promoted protrusion growth , but also destabilization of pre-existing spines . Altogether , these different effects approximately cancelled each other , so that the protrusion density did not greatly vary; actually , a significant increase was only observed transiently 2 d following LTP induction ( Figure 4C ) . A similar situation was observed following Cch treatment . Protrusion growth increased in association with a decrease in stability of pre-existing spines and no effect on the process of new spine stabilization or on protrusion density ( Figure 4D–4F ) . With both types of experiments , therefore , the net effect on several days of this increased turnover was to promote the replacement of existing spines by new ones . We then wondered how this increased spine remodeling could contribute to the specificity of the synaptic network and thus investigated whether it affected similarly activated and naive synapses . For this , we transfected pyramidal neurons with the red fluorescent dye monomeric red fluorescent protein ( mRFP ) [26] , to visualize the structural changes in spine morphology , and costained them 3 d later with Fluo-4 AM , a calcium indicator , to identify spines activated by single pulse and TBS stimulation protocols ( Figure 5A–5C , see also Material and Methods ) . Figure 5 illustrates the example of a dendritic segment with one spine that showed a clear increase in calcium fluorescence upon stimulation , while another one on the same segment remained silent . In all experiments carried out , we verified that spines activated by stimulation were always surrounded by other silent , nonactivated spines in order to exclude global activation effects . Also , we checked that analyses were done on spines of similar size ( see Figure 6 ) and that the maximum calcium signal perfectly coincided with the center of the spine head . We then assessed the stability of activated and nonactivated spines for the next 3 d . Overall , with the stimulation pulses used under these conditions , on average , 36% of all spines tested on analyzed dendritic segments were found to be activated ( n = 349 spines , 18 cells or segments ) . TBS was then applied to the same synapses using the same stimulation pulses in ten cells ( 62 activated and 130 nonactivated spines analyzed ) , which resulted in a differential effect on spine stability: activated spines showed a striking increase in stability in comparison to nonactivated spines present on the same dendritic portions ( Figure 5D; p < 0 . 001 ) . Nonactivated spines actually underwent pruning with regard to spines in nonstimulated slice cultures ( Figure 5E; p < 0 . 05 ) . Interestingly , this differential stabilization was prevented by D-AP5 applied during TBS ( Figure 5E ) . We also verified that simple activation of spines without TBS did not affect the long-term stability of spines ( Figure 5E , squares ) . Although for technical reasons we could not directly assess LTP in these stimulated spines , we found that most of them exhibited an enlargement of their head over the next 5 h . Several previous studies have indeed reported an enlargement of the spine head as a consequence of LTP induction [27–29] or used this criteria for identifying potentiated synapses [30] . In the group of 272 activated and nonactivated spines analyzed before TBS , there was no difference in mean head width ( Figure 6A ) . However , when analyzed 5 h after TBS , most activated spines now exhibited an enlargement of their head , an effect not observed with nonactivated spines ( Figure 6B ) . Interestingly , we also found that this differential enlargement was transient , as most activated spines reversed their size after 24 h and the differences with nonactivated spines then became nonsignificant ( Figure 6C ) . Note , in addition , that the head width of nonactivated spines tended to become smaller after TBS and that the size of spine heads , when analyzed individually , showed regular fluctuations over consecutive days for both activated and nonactivated spines . A robust effect , however , was the close correlation observed between activated spines , spines that showed an enlargement 5 h after stimulation , and spines that became stabilized by activity . When using spine enlargement as a criteria to analyze spine stability , we found , as for activity , that enlarging spines exhibited the same differential stabilization ( Figure 6D ) . Thus LTP induction is very likely to promote a long-term stabilization of potentiated synapses . To verify whether Cch-induced rhythmic activity also produced the same selective stabilization process , we then analyzed how Cch treatment affected spine size . Analysis of 218 spines taken from nine dendritic segments showed that 34% of them exhibited enlargement of their head 5 h after Cch treatment . We then tested the stability of these spines over the next 2 d . As shown in Figure 6E , spines that enlarged as a result of Cch-induced rhythmic activity also became significantly more stable , while nonenlarging spines tended to be eliminated , showing the same differential behavior as after TBS-induced potentiation . We then asked how these mechanisms could affect spine organization and distribution and analyzed whether newly formed spines could appear at specific hot spots . As shown in Figure 7A and 7B , we found that , indeed , newly formed spines tended to appear in close proximity to activated spines . In Figure 7C , we analyzed the proportion of activated versus nonactivated spines that had a new protrusion formed within a distance of 1 . 5 μm in the next 48 h ( defined as hot spot ) . As indicated , almost half of activated spines had a new spine growing close by , something that did not occur with nonactivated ones . As shown by Figure 7D , we then examined all newly formed spines and asked how many actually grew close to an activated or a nonactivated spine . The results show that , again , about half of newly formed spines grew less than 1 . 5 μm from an activated spine , while only a small number of them grew close to a nonactivated spine , the others growing close to spines that could not be determined . The overall stability of newly formed spines was , however , not dependent on their localization ( Figure 7E ) , because new spines generated close to or far from an activated spines showed the same probability of being present on subsequent days . We then tested whether these newly formed spines became functional . For this , TBS was applied to an mRFP-transfected neuron , and the new spines formed within the next 24 h monitored by repetitive imaging and their functionality tested through loading with Fluo-4 AM and stimulation trials of Schaffer collaterals . Figure 8A shows an example of such a newly formed spine . Line scan analysis performed 24 h after TBS shows that this newly formed spine did indeed respond to stimulation through a calcium signal ( Figure 8B and 8C ) , indicating that it was functional . Similar results were obtained in 30 spines out of 47 analyzed ( n = 5 cells ) , indicating that a majority of them were functional . The mean ΔF/F0 ratio ( i . e . , [fluorescence − basal fluorescence]/basal fluorescence ) at the peak of the calcium signal recorded in these experiments was 4 . 3 ± 0 . 8 ( n = 30 ) . For the other spines , it remains unclear whether they were silent or whether we simply could not activate them . We then asked whether the new functional synapses were also likely to be more stable than those that did not exhibit any calcium signal in response to stimulation . Of the 47 newly formed spines analyzed here , we found that the probability to persist for 48 h was 82% ± 12% for the 30 functional spines ( n = 5 ) , but only 30% ± 10% for the 17 nonactivated spines ( Figure 8D ) , indicating that activity is a major criteria for long-term stability . Together these results indicate that LTP induction favored a clustering of new functional spines around activated spines , promoting in this way possibilities of spatiotemporal interactions between them .
Together , these experiments provide evidence for an important new functional role of LTP-inducing activity in promoting a refinement of synaptic networks . Previous work in hippocampal slice cultures has shown that LTP induction is associated with two major types of structural remodeling . First , within minutes , potentiated synapses become larger and express larger and more complex postsynaptic densities [27–30] , a change possibly associated with receptor expression and/or spine stabilization [31] . Second , within minutes to hours , LTP induction also results in the growth of new filopodia and spines [7 , 9 , 32] , which then eventually become functional synapses [8 , 10 , 11 , 25] . These in vitro data are consistent with other in vivo experiments indicating that sensory deprivation or unbalanced activity does indeed affect cortical spine turnover and promote formation of new synapses [17 , 18 , 33] . Here we add three new pieces of information providing a novel , important function for structural plasticity: namely , to operate as a selection process for the long-term stability of synaptic contacts and the promotion of spatiotemporal interactions between spines . First , we provide the first ( to our knowledge ) direct evidence that spines stimulated with LTP-inducing protocols are selectively stabilized over periods of several days . Although LTP could not be directly assessed together with repetitive imaging , we find that stabilization occurred specifically at spines stimulated with TBS and not at nonstimulated spines . Also , stabilized spines did exhibit an enlargement of the head at 5 h , a characteristic now demonstrated to be directly associated to LTP by several recent studies [27–30] . Finally , spine enlargement and spine stabilization were both D-AP5 sensitive and protein synthesis dependent . It seems therefore likely that the stabilization of stimulated synapses revealed here represents a central mechanism for the persistence of potentiated synapses . The second new feature uncovered by these experiments is that LTP is not only associated with a short-term increase in protrusion growth , but a lasting , enhanced turnover that affects pre-existing spine stability , probably through competition mechanisms . Consistent with previous data [7 , 9] , protrusion growth initially tended to predominate over spine loss , leading to a transient increase in spine or protrusion density . However , all together , LTP mainly affected turnover , resulting not only in protrusion growth , but also in an increased loss and destabilization of spines , which , importantly , specifically affected nonstimulated spines . The net effect of LTP over several days was therefore to promote the replacement of nonactivated spines by new ones . This selective destabilization of nonactivated spines was quantitatively significant , because in these experiments more than 10% of the spines of the neurons were actually replaced . Accordingly , regular occurrence of activity susceptible to induce LTP works as a selection mechanism leading to a progressive stabilization of inputs showing coincident activity , increasing in this way the coherence of the synaptic information provided to the neuron and reducing background noise . The last important finding of these experiments is that newly formed protrusions do not appear just anywhere , but tend to cluster around activated spines . These new spines also become functional , and when functional , tend to remain stable . Together with the evidence that LTP induction is facilitated between spines located close to each other [30] , this result indicates that LTP will actually promote the creation of hot spots of functional synapses . This provides therefore a means to promote spatiotemporal clustering of synaptic signals , a property recently shown to be critical for determining the characteristics of plasticity and processing at synapses on small or remote dendrites [20–22] . At the molecular level , an interesting implication of these results is that LTP mechanisms are likely to involve specific changes that could directly affect spine stability . Spine enlargement has been previously proposed to reflect this process [31] and , consistent with this idea , we indeed found that activated spines did enlarge 5 h after stimulation . Curiously , however , this effect did not seem to remain stable over 24 h , and analyses of spine head width suggest that most spines regularly exhibit significant variations of their size [30] . It could be , therefore , that stability is not only reflected in the size of the spine , but is linked to the expression of specific molecules . The current evidence indicating a contribution of protein synthesis to the long-term changes in synaptic strength and to the regulation of spine turnover as reported here could actually suggest such a mechanism [34] . In order to become stable , activated spines would need to accumulate the machinery required for protein synthesis [35] and/or express specific molecules conferring stability to the synaptic contact . Taken together , the mechanisms reported here provide a new framework for understanding how the specificity of cortical networks may progressively develop . These results might be particularly important during critical periods when refinement of connections represents a major process shaped by rhythmic activity and dynamic regulations between excitatory and inhibitory transmission [36] . This network plasticity might , however , also contribute in the adult and provide the functional rules underlying the spine dynamics described in association with sensory activity [18] or following brain damage [37] . Together the synaptic mechanisms described here certainly point to the important role played by structural plasticity in association to Hebbian changes in synaptic strength for the refinement and specificity of cortical networks .
Transverse hippocampal organotypic slice cultures ( 400 μm thick ) from 6- to 7-d-old rats were prepared as described [38] using a protocol approved by the Geneva Veterinarian Office ( authorization 31 . 1 . 1007/3129/0 ) and maintained for 11–18 d in a CO2 incubator at 33 °C . Transfection was done either with a pc-DNA3 . 1-EGFP or a pCX-mRFP1 [39] plasmid using a biolistic method ( Helios Gene Gun , Bio-Rad ) 2–3 d before the first observation . Fluorescence usually started to be expressed after 24–48 h and then remained stable for at least 15 d . For electrophysiological recordings , slice cultures were maintained at 32 °C in an interface chamber under continuous perfusion as described [40] . EPSPs were evoked by stimulation of a group of Schaffer collaterals and recorded in the stratum radiatum of the CA1 region with pipettes filled with medium . Potentiation was analyzed by measuring EPSP slopes expressed as percent of baseline values using an acquisition program written with Labview . LTP was induced by TBS ( five trains at 5 Hz composed each of four pulses at 100 Hz , repeated twice at 10-s intervals ) . As controls , we used slice cultures stimulated at low frequency ( 0 . 3 Hz ) and recorded in the same manner as well as slice cultures stimulated with TBS but in the presence of 100 μM D-AP5 . In these experiments , D-AP5 was only applied for 30 min during application of TBS . Cch treatment was applied for 20–60 min at a concentration of 10 μM with or without concomitant application of D-AP5 . The protein synthesis inhibitor was Ani applied 1 h before TBS or Cch treatment at a concentration of 25 μM and then maintained for 3 h . Short imaging sessions ( 10–15 min ) of transfected slices were carried out with an Olympus Fluoview 300 system coupled to a single ( Olympus ) and a two-photon laser ( Chameleon; Coherent ) as described [25] . Laser intensity in all these experiments was kept at the minimum and acquisition conditions maintained mostly unchanged over the different days of observation . Control experiments showed that transfection and repetitive confocal imaging of slice cultures did not alter cell viability over periods of weeks . We focused on dendritic segments of about 35 μm in length and located between 100 and 300 μm from the soma on secondary or tertiary dendrites using a 40× objective and a 10× additional zoom ( final resolution: 25 pixels per micron; steps between scans: 0 . 4 μm; Figure 1 ) . We did not find differences in protrusion turnover within the limits of these dendritic locations . For calcium imaging of spine activity , transfected cells were additionally loaded with the cell-permeable calcium indicator Fluo-4 AM ( F-14201 , Invitrogen ) . For this , 50 μg of Fluo-4 AM was dissolved in 10 μl Pluronic ( F-127 , Invitrogen ) and then diluted in 90 μl of standard pipette solution ( 150 mM NaCl , 2 . 5 mM KCl , 10 mM Hepes ) for a final dye concentration of 500 μM . A standard patch pipette was then filled with 10 μl of dye solution and placed at a distance of about 10 μm from the soma of a mRFP1-expressing CA1 pyramidal cell . Dye was ejected by short pulses of pressured air at a frequency of three per minute during one-half hour . Calcium transients in 10–26 identified spines per dendritic segment were then recorded using line scans through the spine heads obtained during application of stimulation pulses to Schaffer collaterals . These pulses were of identical intensity and duration to those used for subsequent induction of LTP . Confocal aperture was set to the minimum during line scans , and matching with the mRFP fluorescence in the red channel was systematically checked . For each spine tested , calcium transients evoked by two or three consecutive stimulation pulses were recorded , and spines were determined as activated whenever the fluorescence signal increased by more than 20% over background in any of the recordings . In average , 36% of all spines tested corresponded to these criteria with the stimulation pulses used . To avoid biases , we then also verified that the size distribution of the spine heads did not differ between spines classified as activated and nonactivated ( 0 . 56 ± 0 . 02 μm versus 0 . 58 ± 0 . 02 μm , respectively; Figure 6A ) . In this study we refer to protrusions , whenever analyses were carried out by considering filopodia and spines . Filopodia were defined as protrusions devoid of enlargement at the tip , while we classified as spines all protrusions exhibiting an enlargement at the tip . All turnover and stability analyses were carried out on single z-stacks of raw images ( Figures 1E and S1 ) using a plug-in specifically developed for OsiriX software ( http://www . osirix-viewer . com ) . The measures of turnover were carried out by analyzing all protrusions , i . e . , filopodia and spines . We counted as new protrusions all new structures ( spines or filopodia ) appearing between two observations ( 5 or 24 h ) and characterized by a length of >0 . 4 μm . All filopodia were counted as separate protrusions . We also counted spines located behind each other on z-stacks whenever distinction was possible ( Figures 1E and S1 ) . For disappearances , we counted all protrusions ( spines and filopodia ) that could no longer be identified on the next observation . Dubious situations due to possible changes in protrusion shape , size , or orientiation were discarded , but overall accounted for only a small number of cases ( less than 1% ) . To further ensure reliability of analyses , all measurements of spine turnover and stability were carried out blind by two experimenters . Comparisons of the analyses made in this way showed variations in the results that were less than 3% . Furthermore , we used high numbers of n for both cells and spines , and labeled all new or lost protrusions directly on the raw data ( Figure 1E ) to allow multiple checks . Due to the lack of survival of filopodia on several days , stability analyses carried on 48 or 72 h periods only included pre-existing spines , i . e . , spines present at the beginning of the experiment . For analyses of spine width , we measured the maximum diameter of the spine head on individual z-images , setting the fluorescence level on the levels obtained in the dendrite . Situations that did not allow a precise spine head width measurement ( two spine heads overlapping each other on the same z sections ) were excluded . Calcium fluorescence intensities were acquired and analyzed with Fluoview software ( FV300 , Olympus ) . Note that for illustration purposes , images presented in the figures are maximum intensity projections of z stacks , further treated with a Gaussian blur filter . All statistics are given with the standard error of the mean . Normality was tested for each distribution ( D'Agostino and Pearson test ) , and α was set to 5% for all tests . | In the central nervous system , excitatory contacts between neurons occur mainly on postsynaptic protrusions called dendritic spines . For decades , these structures have been considered static , and the adaptive properties of neuronal networks were thought to be only due to changes in the strength of neuronal connections . But recently , new imaging techniques used on living neurons revealed that spines and synapses are dynamic structures that undergo continuous turnover and can be formed or eliminated as a function of activity . The functional consequences of this structural remodeling , however , were still unknown . This work shows that application of learning related paradigms ( such as induction of long-term potentiation or rhythmic activity ) to hippocampal neurons allows them to operate a selection of synaptic inputs that show coincident activity . This is done through a competitive mechanism that promotes a selective stabilization of synapses activated by the learning paradigm and a replacement of non-activated inputs by new spines . Furthermore these new dendritic spines preferentially grow in close proximity to activated synapses and become functional . These findings provide evidence that learning related paradigms play a major role in shaping the structural organization of synaptic networks by promoting their specificity . | [
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| 2008 | LTP Promotes a Selective Long-Term Stabilization and Clustering of Dendritic Spines |
Previous studies have shown that exponentially growing Escherichia coli can detect mild acidity ( ~pH 5 . 5 ) and , in response , synthesize enzymes that protect against severe acid shock . This adaptation is controlled by the EvgS/EvgA phosphorelay , a signal transduction system present in virtually every E . coli isolate whose genome has been sequenced . Here we show that , despite this high level of conservation , the EvgS/EvgA system displays a surprising natural variation in pH-sensing capacity , with some strains entirely non-responsive to low pH stimulus . In most cases that we have tested , however , activation of the EvgA regulon still confers acid resistance . From analyzing selected E . coli isolates , we find that the natural variation results from polymorphisms in the sensor kinase EvgS . We further show that this variation affects the pH response of a second kinase , PhoQ , which senses pH differently from the closely related PhoQ in Salmonella enterica . The within-species diversification described here suggests EvgS likely responds to additional input signals that may be correlated with acid stress . In addition , this work highlights the fact that even for highly conserved sensor kinases , the activities identified from a subset of isolates may not necessarily generalize to other members of the same bacterial species .
The species Escherichia coli comprises a remarkably diverse collection of bacteria , reflecting their capacity to colonize and manipulate disparate in vivo and ex vivo niches . Most of the well-documented phenotypic differences between E . coli strains are associated with genes found in only a subset of isolates [1] . Genes that are ubiquitous or nearly ubiquitous across the species , on the other hand , are generally assumed to have the same function in each cell type . However , polymorphisms in these core genes could have significant effects on the activities of the proteins that they encode and contribute to natural variation across the species . Furthermore , highly conserved networks of interacting proteins can be perturbed by components that are not encoded in all strains . Thus , the properties of a conserved regulatory circuit may depend on the E . coli isolate and be quite different from the properties established in the well-studied laboratory strain , E . coli K-12 . Here we report an unexpected example of such natural variation in the EvgS/EvgA phosphorelay , a two-component system that has been identified in virtually all E . coli isolates . The E . coli EvgS/EvgA phosphorelay is at the top of a pathway associated with acid and drug resistance [2–10] . Studies of this system in E . coli K-12 indicate that the sensor kinase EvgS is stimulated by mild acidity ( pH 5 . 5–5 . 7 ) , possibly via the protein’s periplasmic domain [9–11] , resulting in EvgS autophosphorylation and subsequent phosphoryl transfer to the response regulator EvgA [12] . Phosphorylated EvgA regulates transcription of a number of genes , including the safAydeO operon , which is a node for two branches of the Evg network ( Fig 1A ) [6] . The transcription factor YdeO is a key component of the glutamate-dependent acid resistance network AR2 [2 , 7 , 8 , 13] ( for comprehensive reviews on E . coli acid resistance ( AR ) systems see [14–16] ) that upregulates the activator GadE , leading to increased expression of AR2 effector genes . SafA encodes a small membrane protein that activates the sensor kinase PhoQ , thereby connecting the EvgS/EvgA and PhoQ/PhoP two-component signaling systems ( Fig 1A ) [17 , 18] . PhoQ is stimulated by conditions of low divalent cations ( Mg++ and Ca++ ) and antimicrobial peptides [19 , 20] . In Salmonella , PhoQ is also stimulated directly by low pH [21] , but in E . coli pH stimulation of PhoQ is indirect via SafA [8] . PhoQ controls the phosphorylation state of the response regulator PhoP , which in turn regulates transcription of a large regulon that includes genes associated with acid resistance . In exponential phase cells , PhoP contributes to AR2 by indirectly elevating RpoS levels [22] , which contributes to the expression of the central regulator GadE as well as downstream effectors ( Fig 1A ) [22] . Thus , EvgS is believed to contribute to acid resistance via both the SafA and YdeO branches of the pathway . The evgAevgS operon is found in almost all of the fully-sequenced E . coli genomes currently in the NCBI database . In contrast with this almost universal conservation among E . coli , close orthologs of evgA and evgS have not been identified in other bacterial species , including other species within the Escherichia genus . In addition , the safAydeO operon has a similarly high level of conservation in E . coli and , like evgAevgS , has not been identified in other species . Recently , we noticed that safAydeO is missing in E . coli MP1 , a mouse commensal isolate [23] , suggesting key links in the Evg network may be severed in this strain . This observation was the starting point for the work presented here . While establishing the effects of these missing genes , we determined that EvgS is unresponsive to pH in MP1 , as well as in many other E . coli isolates , despite the high conservation of the Evg network . We also show that the divergence is due to natural variation in the EvgS sequence . In addition , we find that low pH activation of the PhoQ/PhoP system is similarly variable across E . coli .
E . coli strain MP1 lacks a 13 kb segment of DNA containing the genes safA and ydeO that mediate two branches of the EvgS/EvgA AR2 pathway ( Fig 1A and 1B ) . To explore the physiological effects of this disruption on the Evg network , we monitored the transcription of a PhoQ/PhoP-regulated promoter ( PmgrB ) [24] , [25] and a GadE-regulated promoter ( PhdeA ) [6 , 26] using fluorescent protein fusions . The reporter constructs were integrated in the chromosome at ectopic sites , leaving the native loci undisturbed . The EvgS/EvgA two-component system is activated by mild acidity ( pH 5 . 5–5 . 7 ) in glucose minimal medium [7 , 8 , 10] . In addition , the signaling cascade can be initiated with a constitutively active EvgS variant , EvgS1 , that has the amino acid substitution F577S [4 , 6 , 22] . Both mild acidity and the presence of the EvgS1 allele activated mgrB and hdeA transcription in the standard laboratory strain E . coli K-12 ( MG1655 ) . However , neither condition had an effect on transcription of these genes in E . coli MP1 ( Fig 2A ) . Transcription from the mgrB promoter in MP1 was not stimulated over a range of acid pH values ( 5 . 1–7 ) and was similar to the behavior of MG1655 ΔsafA ( S1A Fig ) . To rule out the possibility that the PhoQ/PhoP system itself was compromised in MP1 , we verified that mgrB transcription is activated by low Mg2+ in a similar fashion in both MG1655 and MP1 ( S1B Fig ) . These results are consistent with the observation that safA and ydeO are absent in the MP1 genome and that no other proteins in MP1 perform equivalent functions . The absence of two branches of the Evg network suggests that MP1 may be less acid resistant than other E . coli isolates . We therefore grew MP1 to exponential phase in minimal medium at pH 5 . 7 , to induce the Evg system , and then shocked the cells at pH 2 . 5 in rich medium , as described previously [13] . After one hour , MP1 survival was over 3 orders of magnitude lower than the survival of MG1655 ( Fig 2B ) . This difference emerged quite rapidly , with MP1 showing a 40-fold reduction compared to MG1655 by 5 minutes following the transition to pH 2 . 5 . The acid shock sensitivity of MP1 in these growth conditions was comparable to that of MG1655 cultures in exponential phase at neutral pH , a condition for which the EvgS/EvgA two-component system is inactive ( Fig 2B ) [2 , 7 , 8 , 13 , 27] . In contrast , MP1 and MG1655 in stationary phase withstand acid challenge equally well , regardless of pre-exposure to mildly acidic pH ( S2A Fig ) . These observations confirm that inducible exponential phase acid resistance is impaired in MP1 , consistent with the absence of safAydeO . To determine whether the safAydeO operon from MG1655 ( Fig 1B ) would restore the interrupted EvgS-PhoQ and EvgS-GadE pathways in MP1 , we introduced this segment of DNA into MP1 on a single copy plasmid ( psafAydeOMG1655 ) , see Materials and methods . Comparison of the acid resistance of MP1 carrying either the empty vector or psafAydeOMG1655 revealed that restoration of safAydeO did not increase survival of MP1 ( Fig 3A ) . However , psafAydeO does complement a ydeO deletion in MG1655 and fully restores acid resistance in this strain . In addition , transduction of a segment of DNA that includes the full 13 kb region missing in MP1 ( Fig 1B ) failed to rescue acid resistance ( S2B Fig ) . We also found that MP1/ psafAydeOMG1655 did not activate transcription of the PhoQ/PhoP and GadE reporters PmgrB-yfp and PhdeA-yfp , respectively , in response to low pH ( Fig 3B ) . In contrast , the constitutively active EvgSMG1655 variant EvgS1 activated expression of both reporters when psafAydeOMG1655 was present . These results indicate that the SafA-PhoQ and YdeO-GadE interactions were successfully restored by psafAydeOMG1655 in MP1 and that components upstream of safA and ydeO involved in pH sensing are divergent between MP1 and MG1655 . It is also noteworthy that in MG1655 , neither safA nor phoQP deletions affected acid resistance , even after prolonged exposure to low pH , in contrast with the behavior of a ΔevgAS strain ( S2C Fig ) . These results indicate that for exponential phase cultures , the SafA-PhoQ-PhoP branch of the Evg network does not provide increased protection to acid shock following induction at pH 5 . 7 . Based on the above results , we hypothesized that low pH might not function as an input signal for the EvgS/EvgA phosphorelay in MP1 . We therefore measured pH induction of the emrK promoter , which is directly regulated by EvgA [4] ( Fig 1A ) . We found that transcription was induced in MG1655 , as expected , but not in MP1 ( Fig 3C , S3 Fig ) . In contrast , the constitutively active EvgS1 mutant was able to induce the emrK reporter in MP1 . The failure of low pH to activate EvgS in MP1 could be due to differences between the evgAS operon in MG1655 and in MP1 , or due to an upstream factor required for acid-sensing that is missing or non-functional in MP1 . To explore these possibilities , we compared the pH induction of PemrK-yfp in MP1 ΔevgAS and MG1655 ΔevgAS transformed with single-copy plasmids expressing the evgAS operon from one or the other strain ( pevgASMG1655 or pevgASMP1 ) . We found that pevgASMG1655 restores pH induction of PemrK-yfp in both MG1655 ΔevgAS and MP1 ΔevgAS ( Fig 4A ) . In contrast , pevgASMP1 shows minimal pH induction in either strain , although pevgASMP1 in MG1655 does show a small amount of induction , suggesting that there may be some factors outside of the evgAevgS operon that contribute to pH sensing . Overall , however , the above results indicate that the primary differences in pH response for the EvgS/EvgA systems in MG1655 and MP1 are due to differences in the sensor kinase and/or response regulator proteins themselves . Furthermore , since the EvgA amino acid sequences from MG1655 and MP1 are identical whereas the EvgS sequences differ at 39 residues , the different pH response in the two strains is likely due to polymorphisms in EvgS . This conclusion is further supported by the fact that a plasmid expressing a hybrid operon consisting of evgAMP1evgSMG1655 restores pH induction of PemrK-yfp in MG1655 ΔevgAS ( S4 Fig ) . The above results identify two properties of MP1 that potentially affect acid resistance: the chromosomal deletion containing safAydeO ( Fig 1B ) and differences in EvgS between MP1 and MG1655 . To determine whether these factors account for the absence of inducible exponential phase acid resistance in MP1 , we tested the survival of an MP1 derivative containing one or both of these loci from MG1655 following acid shock . Incorporation of both loci from MG1655 into MP1 ( MP1 ΔevgAS / pevgASMG1655 -safAydeOMG1655 ) rescued the inducible acid resistance phenotype by two orders of magnitude compared to MP1 with either plasmid psafAydeOMG1655 or plasmid pevgASMG1655 ( Fig 4B ) . These results indicate that in addition to the absence of the chromosomal segment containing safAydeO , sensitivity to acid shock in MP1 results from the inability of EvgSMP1 to respond to stimulation by mild acidity . We also note that the amino acid substitution F577S , which renders the EvgSMG1655 allele constitutively active ( EvgS1 ) , causes the same effect in EvgSMP1 ( S5 Fig ) . This finding supports the hypothesis that although EvgSMP1 is expressed and functional , the protein cannot sense pH change . The 39 residues in EvgS that differ between MG1655 and MP1 are distributed throughout the protein ( S6 Fig ) . In an attempt to determine if a subset of these residues that are localized to a particular domain account for the pH insensitivity of EvgSMP1 , we tested the activity of several EvgS hybrids containing swapped regions of EvgSMP1 and EvgSMG1655 ( S4A Fig ) . These constructs were expressed from a single copy plasmid in a MG1655 ΔevgS strain , and activation of the EvgA-dependent reporter PemrK-yfp in response to acid stimulation was assessed . We found that all of the hybrids showed a strong pH-response ( S4B Fig ) , indicating that pH insensitivity of EvgSMP1 cannot be ascribed to a single domain of EvgSMP1 . Strains MG1655 and MP1 belong to different phylogenetic groups: A and B2 , respectively [23] . We therefore wondered whether the properties noted above are unique to MP1 or are shared by other E . coli isolates . We considered eight representative strains ( Table 1 ) , which include commensals of group A and B2 ( HS and Nissle respectively ) , intestinal pathogens ( H10407 , EDL933 , and E2348/69 ) , extra-intestinal pathogens ( CFT073 and UTI89 ) , and an “atypical” E . coli isolate classified in Clade I and of enterotoxigenic pathotype ( TW10509 ) . EvgS amino acid sequences from these strains have varying degrees of divergence from the MG1655 sequence ( Table 1 , S6 Fig ) , and a tree based on these sequences clusters according to each strain’s phylogenetic group ( Fig 5A ) . A similar analysis that includes 285 EvgS sequences from fully sequenced E . coli genomes indicates that clustering according to the phylogenetic group is a general characteristic ( S7 Fig ) . Among the ten isolates , pairwise EvgS divergences are as large as 5 . 43% , and no two strains share 100% EvgS amino acid sequence identity ( S8 Fig ) . This natural variation in EvgS is not a property of proteins encoded in neighboring genes: the EvgA amino acid sequence is identical across all ten strains , and YfdE , which is encoded by a gene just downstream of evgS , shows only a moderate level of divergence ( S8 Fig ) . Additionally , the variation is not a general property of hybrid sensor kinases: two other hybrid kinases in E . coli , ArcB and BarA , are highly conserved among the ten isolates considered in this study ( S8 Fig ) . Based on the substantial sequence variation in EvgS , we hypothesized that the Evg systems in different E . coli strains would show varying responsiveness to acid pH that would be correlated with the degree of divergence from EvgSMG1655 . To test this hypothesis , we assessed the pH induction of EvgS/EvgA in each strain using a single-copy plasmid containing a transcriptional fusion of yfp to the yfdX promoter ( PyfdX-yfp ) , which is directly activated by phosphorylated EvgA [6] . We found that for group B2 strains , EvgS is not responsive to acid pH , with the exception of the EPEC strain E2348/69 ( Fig 5B ) . The EvgS sequence from this isolate has the fewest substitutions ( relative to MG1655 ) within the group B2 strains that we tested ( Table 1 ) . For strains outside the B2 clade , reporter expression varied from a 60-fold induction for HS , whose EvgS sequence is the closest to that of MG1655 , to 8-fold induction in isolate TW10509 . We also compared the survival from acid shock for the various isolates following growth at pH 5 . 7 ( Fig 5C ) . The pattern across the E . coli strains shows some correlation with EvgS sequence relatedness . Isolates in group A that were tested have comparably high resistance to acid challenge whereas the group B2 isolates Nissle , UTI89 , and MP1 are quite sensitive . The association is imperfect however , as the two B2 isolates E2348/69 and CFT073 had significantly higher survival , albeit both were still more susceptible to acid shock than the group A strains ( Fig 5C ) . Interestingly , CFT073 is very sensitive to slightly harsher acid shock ( pH 2 . 25 ) , as shown in S9A Fig . There is also a correlation between the extent of activation of the EvgS/EvgA system from growth at pH 5 . 7 ( as assessed with a yfdX transcriptional reporter ) and resistance to acid shock across the strains ( Fig 5D ) . CFT073 is an exception to this trend for acid shock at pH 2 . 5 , suggesting that this isolate has an exponential phase acid resistance pathway that does not require activation of the Evg system for this stress . However this strain clusters with the other closely related Group B2 isolates when shocked at pH 2 . 25 or lower pH ( S9B Fig ) . Above we showed that low pH fails to directly activate PhoQ/PhoP in MP1 ( Fig 2A ) . Since we found that EvgS is not stimulated by mild acidity for three other group B2 isolates that we tested ( Nissle 1917 , CFT073 , UTI89 ) , we wished to determine if the PhoQ/PhoP system in these strains could be activated by low pH . To test this , we used a GFP reporter plasmid containing the PhoP-activated phoPphoQ promoter [25] and measured fluorescence of exponential phase cultures at pH 5 . 7 relative to pH 7 . We found that like MP1 , the three additional B2 strains that we tested showed no induction of the PhoP reporter ( Fig 6 ) , consistent with the conclusion that EvgS in these isolates is unresponsive to pH and that E . coli PhoQ is not directly stimulated by mild acidity ( S1A Fig ) [8] . The strains Nissle 1917 and UTI89 are similar to MP1 in their sensitivity to acid shock and inability to activate the Evg system in response to low pH ( Fig 5 ) . However , unlike MP1 , these strains ( as well as all of the other E . coli isolates that we tested ) have an intact safAydeO locus . We therefore tested whether EvgS from MG1655 is sufficient to restore acid resistance in these strains by transforming each with the plasmid pevgASMG1655 . For both Nissle 1917/ pevgASMG1655 and UTI89/ pevgASMG1655 , growth in mild acidity induced expression of the PyfdX-yfp reporter ( Fig 7A ) . Likewise , in these strains EvgSMG1655 can activate the GadE-dependent reporter PhdeA-yfp ( YdeO-dependent pathway ) and the PhoQ/PhoP-dependent reporter PmgrB-yfp ( SafA-dependent pathway ) ( S10 Fig ) , although the extent of activation is lower than that of MG1655 . In addition , with the pevgASMG1655 plasmid , both Nissle 1917 and UTI89 were as resistant to acid shock as MG1655 ( Fig 7B ) . Thus , the pathway leading from mild pH induction to exponential phase acid resistance for strains UTI89 and Nissle 1917can be rescued with the evgAevgS operon from MG1655 . Since EvgA is identical across all of the E . coli strains used in this study , the above results suggest that the strain variability in pH response is due to differences in EvgS . We therefore analyzed the nucleotide sequence evolution of evgS . To test functional conservation across evgS orthologs , we calculated the dN/dS ratio , which is a measure of selection on protein-coding sequences [28 , 29] . For any given gene , dN/dS is defined as the ratio of the average number of nucleotide substitutions per non-synonymous site ( dN ) to the average number of substitutions per synonymous site ( dS ) . A dN/dS ratio that is less than one indicates purifying or stabilizing selection , dN/dS equal to one indicates neutral selection , and dN/dS greater than one indicates positive or adaptive selection . To estimate dN/dS , evgS sequences were aligned using TranslatorX [30] and analyzed with the Synonymous Non-synonymous Analysis Program ( SNAP v2 . 1 . 1 ) [31] . The dN/dS ratio calculated for the ten E . coli isolates is less than one ( 0 . 1 ) , indicating that evgS is under purifying selection and , overall , is functionally conserved ( Table 2 , S4 Table ) . To assess whether different clades experience varying selective pressures , we also calculated dN/dS for evgS within phylogenetic groups A and B2 separately . The group A isolates have a dN/dS ratio of 0 . 32 , which is significantly higher than that of the group B2 isolates as well as the overall ratio for all ten organisms ( ~0 . 1 ) . In addition , the Z-test for neutral selection ( MEGA 4 . 0 , [32] ) gives a p-value = 0 . 2 for group A evgS . We therefore cannot reject the null hypothesis that dN = dS , suggesting that evgS from group A is under weaker purifying selection compared to that of B2 . We observed a similar trend when we analyzed only the periplasmic region of evgS ( 1–537 , 87–234 ) ( Table 2 , S4 Table ) . We extended our dN/dS analysis to the two genes that flank evgS in the E . coli genome ( evgA and yfdE ) and also two other hybrid sensor kinases ( barA and arcB ) ( Table 2 , S4 Table ) . In contrast with evgS , these four genes appear to be under strict purifying selection , with similar values for dN/dS for all ten isolates together and for groups A and B2 separately . To check if the dN/dS statistics estimated here are consistent across other E . coli , we calculated the dN/dS values for evgS as well as control genes for all complete E . coli genomes in the NCBI database . The resulting values closely match those obtained for the ten isolates ( Tables 2 , S4 and S5 Tables ) . The EvgS/EvgA system in E . coli is homologous to the virulence-associated BvgS/BvgA system in Bordetella pertussis [33] and KvgS/KvgA system in Klebsiella pneumoniae [34] . We therefore tested whether these orthologs showed similar levels of variation as that of EvgS/EvgA . We repeated the dN/dS analyses for the bvgAbvgS and kvgAkvgS genes using the available complete sequences from B . pertussis and K . pneumoniae , respectively ( S5 Table ) . For bvgAbvgS , the sequences from different isolates are highly invariant and showed very few non-synonymous or synonymous changes , if at all . This result is consistent with previous observations [35] , and suggests that bvgAbvgS may either be recently acquired by B . pertussis and/or that the selective pressures to maintain the sequences of bvgAbvgS are strong , which is not surprising given the significant role of BvgS/BvgA system in virulence regulation . In contrast with BvgS , the variation for K . pneumoniae kvgS is similar to that of E . coli evgS ( S5 Table ) . However , whereas E . coli evgA shows very little variation relative to evgS , the variation of K . pneumoniae kvgA is comparable to that of kvgS , suggesting weaker selection for kvgA relative to evgA . In addition , the flanking gene yfdX in K . pneumoniae has a high level of variation as well , and the variations of all three genes kvgA , kvgS , yfdX are substantially higher than those of K . pneumoniae arcB and barA .
It is well established that the EvgS/EvgA system in the laboratory strain E . coli K-12 is stimulated by moderately acidic pH and that this response enables exponentially growing cells to resist severe acid shock [7 , 8] . As shown here , this behavior extends to additional E . coli isolates—namely those in the same phylogenetic group as the K-12 strain , group A , as well as isolates from several additional groups ( Fig 5 ) . However , for other E . coli isolates , the Evg system is much less responsive , or not responsive at all , to acid pH . Furthermore , this behavior correlates with a decreased ability to survive extreme acid shock following growth in mild acidity ( Fig 5 ) . For the three strains with the largest difference in acid pH response relative to that of E . coli K-12—MP1 , Nissle 1917 , UTI89—we found that the primary differences in pH-sensing capacity are intrinsic to the EvgS/EvgA system itself , rather than arising from accessory proteins or other factors that vary between cells . For two of these strains ( Nissle 1917 and UTI89 ) , transformation with a K-12 EvgS/EvgA system led to acid resistance that was comparable to K-12 . Thus , the suite of genes required to confer protection from acid stress is intact in these strains and is under control of the Evg system , despite the fact that the native system cannot be activated by mild acidity . In contrast , for the mouse commensal strain MP1 , activation of EvgS/EvgA was not sufficient to provide protection from acid shock due to the absence of a chromosomal segment encoding the EvgA-regulated transcription factor YdeO and the connector protein SafA . Restoration of this chromosomal region from K-12 , in combination with the K-12 EvgS/EvgA proteins , led to exponential phase acid stress resistance comparable to that of K-12 . The absence of the safAydeO region in MP1 indicates that the Evg network has been significantly reduced , since YdeO is a key component of this network due to its control of GadE , which in turn regulates many downstream genes involved in acid resistance [6–8 , 13 , 26 , 36] . In addition , since SafA functions as a connector between the Evg and PhoQ/PhoP systems , its absence disconnects the two pathways , abrogating activation of the PhoQ/PhoP system by signals that stimulate EvgS . However , we find that this branch of the Evg pathway does not play an important role in exponential phase acid resistance , at least for the acid stress assays that we employed . Our results indicate that the diversity in pH response of the Evg system stems from natural variation in the EvgS sensor kinase . Furthermore , a dN/dS analysis indicates that evgS is under purifying selection , but that the selection is significantly weaker than that of the hybrid sensor kinase genes arcB and barA . This observation is consistent with the fact that the Evg system is broadly conserved in E . coli but that the ability of pH to function as an input signal is highly variable across isolates . The pH-sensing mechanism of EvgS ( for those EvgS variants that have this capacity ) is not known . An analysis of EvgS mutants in MG1655 or related K-12 strains has implicated the periplasmic and cytoplasmic domains as playing a role in the pH response [9–11] , although it remains to be established whether the sensing is direct or requires additional cellular components . Our own analysis of hybrid molecules in which domains of EvgS were substituted by analogous portions of the pH insensitive EvgSMP1 suggests that differences across multiple domains account for the absence of pH sensing in MP1 . These observations are consistent with a recently proposed model of EvgS pH sensing in which pH modulates the strength of EvgS dimerization mediated by interactions in the periplasmic , transmembrane , and cytoplasmic domains [11] . In addition , from the EvgS amino acid sequence alignments ( S6 Fig ) , we were unable to identify specific residues that likely account for the differences in pH-responsiveness of EvgS natural variants . For example , EvgSE2348/69 , which is stimulated by low pH and is in group B2 , differs from the four group B2 EvgS proteins that are not stimulated by pH at residues S382 ( in the periplasmic domain ) , N859 ( in the histidine kinase domain ) , and A1191 ( at the C-terminus ) . However , strain E2348/69 is an exception since these residues are conserved across all of the nine other strains in this study . These observations indicate that multiple independent polymorphisms account for variability in pH sensing by EvgS . The diversification of the Evg two-component system within E . coli suggests that pH is not the only input signal for the EvgS sensor kinase , and that the primary selective pressure for maintaining this signaling system may be associated with some other ( unknown ) stimulus . The fact that for some strains with a pH-nonresponsive EvgS the EvgA regulon still confers acid resistance further suggests the unknown input signal is strongly correlated with conditions of acid stress . The emergence of these EvgS variants may reflect selection for different dose-response behaviors with respect to other signals . However , since some isolates such as MP1 have evolved a network that disconnects acid resistance effectors from the Evg regulon ( through loss of safAydeO ) , it seems likely that Evg input signals are not always correlated with acid stress . The presence of the EvgS/EvgA system in the vast majority of E . coli isolates indicates there is a significant selective pressure to maintain this system , which makes the natural variation and genetic flexibility of EvgS all the more striking . This behavior stands in stark contrast with the common assumption that sensor kinases and response regulators of conserved two-component systems behave uniformly across a species ( or even across closely related genera ) . Similar diversification may be present in other core signaling systems and poses a challenge for extrapolating from well-studied members of a bacterial species .
Liquid cultures were grown at 37°C in minimal A medium [37] supplemented with 0 . 2% glucose and 0 . 1% casamino acids , or in minimal A medium buffered at pH 5 . 7 with 100 mM 2- ( N-morpholino ) ethanesulfonic acid ( MES , Sigma-Aldrich ) and HCl . Minimal medium cultures of strain UTI89 , which is auxotrophic for nicotinamide , and its derivatives were supplemented with 5 μg/ml nicotinamide . Bacterial cultures that were used to prepare electro-competent cells were grown in SOB with the appropriate antibiotic , when necessary . Cultures for preparing P1vir lysates and for transductions were grown in LB ( Miller ) broth . LB-agar plates were used to grow cultures for CFU ( colony forming unit ) counts . To select for antibiotic resistance and to maintain plasmids , antibiotics were added to culture media to the following concentrations: ampicillin 50 μg/ml; for MG1655 and its derivatives—kanamycin 25 μg/ml , chloramphenicol 25 μg/ml or 12 . 5 μg/ml for single copy plasmids ( pSMART derivatives ) ; for all other strains—chloramphenicol 12 μg/ml or 6 μg/ml for single copy plasmids ( pSMART derivatives ) ; kanamycin 50 μg/ml for Nissle 1917 and its derivatives , 35 μg/ml for all other strains . Strains and Plasmids used in this study are described in S1 and S2 Tables , respectively . Primers used in this study are listed in S3 Table . Transformations of plasmids and linear DNA for chromosomal integration were performed by electroporation . Antibiotic cassettes flanked by FRT sites were removed , when necessary , with the plasmid pCP20 as described in [38] . Transductions were conducted with phage P1vir [37] . For details on strain and plasmid constructions , see S1 Methods . Fluorescence was quantified by microscopy . Cultures were inoculated from single colonies on LB Agar plates and grown in minimal medium aerobically at 37°C overnight to saturation , then diluted 1:1000 into fresh medium that was at pH 7 or pH 5 . 7 ( as indicated ) . To test PhoQ/PhoP activation via stimulation of EvgA and the connector SafA and to avoid stimulation by low magnesium , overnight cultures were diluted in minimal medium with 10 mM MgSO4 . Conversely , to activate PhoQ/PhoP with low magnesium , overnight cultures were diluted in minimal medium at pH 7 and containing 1 μM MgSO4 . Cultures were grown at 37°C to an optical density at 600 nm ( OD600 ) of 0 . 2–0 . 3 , then rapidly cooled in an ice-water slurry and kept on ice for at least 1 hour . Fluorescence microscopy and data analysis were performed as previously described [39] . For each data set , the fluorescence of at least one hundred cells was recorded . The mean fluorescence from each data set was background subtracted , and the average between replicas was calculated . Cultures were grown as described above for fluorescence microscopy to OD600 of 0 . 2 . Cultures were concentrated to one sixth of the volume by centrifugation . Because of the low density of the cultures , this step was necessary to lower the limit of detection of viable cells following acid challenge . Fifty microliters of the concentrated cultures ( approximately 1-5x107 cells ) were transferred to 1 ml sterile phosphate-buffered saline ( PBS 137mM NaCl , 2 . 7mM KCl , 10mM Na2HPO4 , 2mM KH2PO4 ) , pH 7 . 4 , and 50 μl were transferred to 1 ml of LB broth , pH 2 . 5 ( acidified with HCl ) , that was pre-warmed to 37°C . The bacterial suspensions in acidified LB broth were incubated at 37°C for one hour , or the indicated times . Both LB and PBS cell suspensions were serially diluted in PBS , and aliquots were immediately plated in triplicate . After incubation overnight , CFUs were counted . The percentage survival was calculated as the number of CFUs/ml of acid shocked cultures divided by the number of CFUs/ml of the cultures diluted in PBS . For S9A Fig , the experiments were conducted in the same way , except the cultures were exposed to LB at pH 2 . 25 , 2 . 0 , or 1 . 75 as indicated . Full-length EvgS , EvgA , YfdE , BarA , and ArcA sequences were extracted from complete E . coli genomes in NCBI using tblastn ( http://blast . ncbi . nlm . nih . gov ) using the corresponding sequences from E . coli K-12 . Sequence alignments of S6 and S8 Figs were obtained with Clustal Omega , [40 , 41] on the EMBL-EBI server . EvgS sequences in Fig 5A and S7 Fig were aligned and neighbor-joining trees were constructed using Muscle [42] on the EMBL-EBI server with the default parameters and displayed using Figtree ( http://tree . bio . ed . ac . uk/software/figtree/ ) with midpoint rooting . For S7 Fig , the phylogenetic groups for the corresponding E . coli isolates from which these EvgS sequences were derived were determined as described in [43] . Nucleotide sequences for the genes of interest were obtained from the NCBI database . Multiple sequence alignments were generated in a codon-delimited format on the TranslatorX server [30] using MUSCLE alignment software [42] . To obtain gene-specific dN/dS estimates , we utilized the Synonymous Non-synonymous Analysis Program ( SNAP v2 . 1 . 1 ) implementing Nei and Gojobori’s method [28] and its statistic output tool [31] available on the HIV sequence database website ( www . hiv . lanl . gov , Korber , 2000 ) . The codon-aligned nucleotide sequence alignments ( . aln or fasta file ) were provided as input into the SNAP tool to compute dN/dS ratios . Alignments were imported into MEGA software v4 . 0 [32] to perform a Z-test of neutral selection , for a null hypothesis ( dN = dS ) using modified Nei-Gojobori ( Jukes-Cantor ) method . | Bacteria employ a class of proteins , sensor kinases , to sense environmental cues and initiate cellular responses through phosphorylation of partner response regulator proteins . Individual kinases are generally assumed to have the same sensory activity across members of a bacterial species . In this work , we report an unexpected counterexample in which the well-established capacity of the kinase EvgS to sense mild acidity is limited to a subset of Escherichia coli isolates . Despite this natural variation , EvgS activation still confers resistance to acid stress in strains that have lost EvgS pH-sensing activity . Thus , most E . coli share a conserved output of the Evg system but do not require identical sensory functions . This work highlights the potential for significant functional divergence of a sensor kinase within a species and also indicates that there are additional input signals for the highly conserved EvgS protein . | [
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| 2017 | Natural variation of a sensor kinase controlling a conserved stress response pathway in Escherichia coli |
Cumulative evidence supports a role for neutralizing antibodies contributing to spontaneous viral clearance during acute hepatitis C virus ( HCV ) infection . Information on the timing and specificity of the B cell response associated with clearance is crucial to inform vaccine design . From an individual who cleared three sequential HCV infections with genotypes 1b , 1a and 3a strains , respectively , we employed peripheral B cells to isolate and characterize neutralizing human monoclonal antibodies ( HMAbs ) to HCV after the genotype 1 infections . The majority of isolated antibodies , designated as HMAbs 212 , target conformational epitopes on the envelope glycoprotein E2 and bound broadly to genotype 1–6 E1E2 proteins . Further , some of these antibodies showed neutralization potential against cultured genotype 1–6 viruses . Competition studies with defined broadly neutralizing HCV HMAbs to epitopes in distinct clusters , designated antigenic domains B , C , D and E , revealed that the selected HMAbs compete with B , C and D HMAbs , previously isolated from subjects with chronic HCV infections . Epitope mapping studies revealed domain B and C specificity of these HMAbs 212 . Sequential serum samples from the studied subject inhibited the binding of HMAbs 212 to autologous E2 and blocked a representative domain D HMAb . The specificity of this antibody response appears similar to that observed during chronic infection , suggesting that the timing and affinity maturation of the antibody response are the critical determinants in successful and repeated viral clearance . While additional studies should be performed for individuals with clearance or persistence of HCV , our results define epitope determinants for antibody E2 targeting with important implications for the development of a B cell vaccine .
Over 70 million people worldwide are infected with hepatitis C virus ( HCV ) , with an annual mortality of approximately 400 , 000 associated with liver failure and hepatocellular carcinoma [1 , 2] . Encouragingly for patients with chronic infections with HCV , advances in understanding of HCV virology have led to the development of virus-specific direct acting antivirals ( DAAs ) [3 , 4] . However , the large majority of infected patients remain undiagnosed and/or live in countries with limited resources and with minimal or no access to DAA-based therapies . Indeed , global access to DAAs has been estimated to be less than 10% of HCV infected individuals [5] . Thus , the prevention of global spread and eradication of HCV infection will require a protective vaccine . A necessary step in the design of an effective vaccine is to identify relevant mechanisms of immune protection . Vigorous and sustained CD4+ and CD8+ T cell responses are associated with successful clearance , which occurs in 25% of acute infection episodes [6] . While humoral immunity has been traditionally thought to have a minor role in controlling acute HCV infection , emerging evidence supports the importance of neutralizing antibodies in spontaneous viral clearance . The induction of a neutralizing antibody response in the acute phase of infection has been associated with clearance of infection in single source outbreaks of acute HCV infection [7] . Further , control of acute infection has been associated with the early appearance of neutralizing antibody responses [8] . Broad reactivity of these neutralizing antibody responses appears to be associated with viral clearance . However , further information is needed on the timing and specificity of the neutralizing antibody responses associated with viral clearance during acute infection and reinfection , and better understanding on how these responses differ from those found during chronic infection will inform vaccine design . We have addressed these issues by characterizing a panel of human monoclonal antibodies ( HMAbs ) isolated from peripheral B cells of an individual that sequentially cleared genotypes 1b and 1a HCV infections , as well as subsequently clearing a genotype 3a HCV infection . Neutralizing HMAbs from B cells obtained after resolved infections with genotype 1b and 1a HCV strains demonstrated broad reactivity and targeted overlapping conformational epitopes in highly immunogenic clusters that are similar to those that have been characterized from HCV infected subjects during chronic infections . Interestingly , the earliest neutralizing antibodies induced in this individual were directed against a region on the E2 glycoprotein ( including residues 434–446 ) that is likely to be of low immunogenicity but is highly conserved . The early appearance of these antibodies suggests an important role in viral clearance . Overall , these findings support that natural clearance of acute HCV infection is associated with broadly neutralizing antibodies that are similar to those observed during chronic infection , and that key determinants in spontaneous clearance are the timing and affinity maturation of this response .
We studied an individual , designated as 300212 , who had three documented episodes of acute HCV infection acquired through injection drug use over a course of 320 weeks . Each episode was associated with spontaneous clearance . Sequential samples of blood were obtained to isolate peripheral blood mononuclear cells ( PBMCs ) and serum . The subject was a 21-year-old , immunocompetent male who screened negative for HIV , HBV and HCV antibodies before enrollment in an ongoing prospective cohort study of high risk , uninfected injection drug users—the Hepatitis C Incidence and Transmission Study in prisons ( HITS-p ) . He was HCV seropositive and RNA positive with a genotype 1b infection at 41 weeks after his initial evaluation , and 21 weeks after his first estimated infection ( midpoint between his last known HCV antibody negative test and first antibody positive test ) ( S1 Table ) [9] . The collection dates described in this report are designated as the number of weeks after this first estimated date of infection . He was also found to have the IL28B rs12979860 polymorphism associated with spontaneous viral clearance [10] . On enrollment into HITS-p , he reported that he started injecting drugs four years earlier . During regular follow-up in HITS-p , he reported periods of daily injecting drug use and sharing of needles with other inmates . At week 76 post-infection testing , he was HCV RNA negative , but remained HCV seropositive . At week 122 , he became transiently HCV RNA positive with a second infection , with a genotype 1a isolate . After only one week of viremia , he became HCV RNA negative . This aviremic status persisted until week 277 , when he was diagnosed with a third infection with a genotype 3a isolate . Viremia continued until week 303 , but was not detected at week 320 . To assess whether neutralizing antibodies contributed to viral clearance of the first two episodes of acute HCV infection in the individual 300212 , serum serial dilutions , 1:100 , 1:500 , 1:1000 , 1:5000 and 1:10 , 000 , at each timepoint from week 21 to 182 ( except for week 161 ) were tested for binding and neutralizing activities against the autologous 1b isolate and a heterologous 2a isolate ( Table 1 ) . Thus , an autologous genotype 1b HCVpp was constructed from the first infection at week 21 , termed 212 1b HCVpp . For the second and third infections , recovery of autologous E1E2 was not successful . Serum antibody binding was determined against cell lysates expressing 212 1b recombinant E1E2 by ELISA . To begin to assess breadth of neutralization , one other isolate was tested , JFH1 2a HCVcc . In the viremic phase of the first infection at week 21 , significant binding for serum antibodies to autologous E1E2 was only detected at 1:100 dilution ( as defined by >0 . 5 optical density ( O . D . ) ) . However , no significant neutralizing activities were present ( defined by ≥50% neutralization , IC50 ) at this timepoint even at a reciprocal serum dilution of 100 ( Table 1 ) . By contrast at week 76 , when the subject was not viremic , peak antibody binding titer of 5000 was observed against 212 1b E1E2 . Neutralizing serum antibody titers of 500 and 1000 were observed , respectively , against autologous 212 1b HCVpp and heterologous 2a HCVcc . The presence of neutralizing activity against a heterologous isolate is consistent with the induction of broadly reactive neutralizing antibodies . While it is not known when in the weeks 21 and 76 interval that clearance occurred , a pattern of increasing binding and neutralizing antibody titers was observed . To assess whether this response at week 76 was directed against conformational epitopes on E1E2 , serum antibody binding studies were performed employing native and denatured antigens . As shown by significant reduction in antibody binding titers to denatured autologous 212 1b and heterologous H77C 1a E1E2 antigens ( Fig 1 ) , the antibody responses were directed mainly at conformational epitopes . The subject did not have further follow-up until week 122 , when a second infection with a genotype 1a isolate was detected at a lower viral load than the first infection ( Table 1 ) . Serum antibody binding titer at week 122 was reduced to 1000 against 212 1b , but neutralization was maintained at 500 against this isolate . Seven days later at week 123 , spontaneous viral clearance had occurred . An increase in neutralizing titer to 1000 was observed at week 135 against 212 1b HCVpp . At weeks 150 and 182 , neutralizing titers were 100 and 500 against 212 1b HCVpp and 2a HCVcc , respectively . To further assess the breadth of neutralizing antibody responses , serum neutralization at the same dilutions ( with the addition of 1:50 ) from week 21 to 182 were tested against a panel of 11 heterologous HCVpp ( Fig 2 and S1–S4 Figs ) . Consistent with the findings against autologous 212 1b HCVpp and a heterologous 2a HCVcc ( Table 1 ) , no significant neutralizing activity ( defined by ≥50% neutralization ) against any heterologous HCVpp was observed for week 21 , even at a reciprocal serum dilution of 50 ( S1–S4 Figs ) . By week 76 , all heterologous genotype 1 HCVpp ( S1 Fig ) were neutralized with a minimum neutralizing titer of 50 and similarly , one of two genotype 2 HCVpp ( S2 Fig ) , one of three genotype 3 HCVpp ( S3 Fig ) , a single genotype 5 and a single genotype 6 HCVpp ( S4 Fig ) . The variants , e . g . , UKN3A1 . 9 and UKN4 . 11 . 1 ( S3 and S4 Figs ) , that are poorly neutralized are likely escape/resistant isolates . Taken together , these findings at week 76 provide further evidence for the induction of broadly neutralizing antibodies ( Fig 2 ) . During the second infection ( week 122 ) , while cross-genotype neutralizing titers had dropped , neutralizing titers against other genotype 1 isolates remained at 50 or higher ( Fig 2 ) . Serum neutralizing titers after this timepoint decreased , except for the two isolates H77 . 20 ( 1a ) and UKN5 . 14 . 4 ( 5a ) , for which they remained unchanged even after spontaneous viral clearance . Collectively , a pattern of no or low neutralizing activity at week 21 ( initial viremia ) that increased to higher neutralizing activity levels at week 76 against a genotype-diverse panel of HCVpp is indicative of broadly neutralizing antibodies being induced as part of the antibody response to the first infection . The second infection also was associated with development of higher neutralizing titers against an autologous isolate ( from the first infection ) temporally associated with viral clearance , observed at week 135 ( Table 1 ) . To characterize the specificity of the antibody response , week 123 was selected for investigation . This timepoint was one week after the detection of the second HCV infection with a genotype 1a isolate at week 122 , when viral RNA was no longer detected ( Table 1 ) . B cells isolated from PBMCs were used to construct a yeast scFv displayed library [11] . Of 600 monoclonal scFv cells that bound to E2 , 27 having unique combinations of heavy and light chain CDR1 , 2 and 3 regions were identified and converted to full IgG1 molecules . Of note are that two scFv clones , 212 . 1 . 1 and 212 . 9 , accounted for more than 50% of the total clones analyzed . HMAbs 212 . 1 . 1 to 212 . 1 . 4 have the same VH but different VL . Similarly , 212 . 2 . 1 and 212 . 2 . 2 , as well as 212 . 3 . 1 and 212 . 3 . 2 , respectively have the same VH but different VL . Full-length IgG1 converted HMAbs were initially analyzed for their neutralization against heterologous H77C 1a HCVpp and JFH1 2a HCVcc , and autologous 212 1b HCVpp ( Table 2 ) . Fourteen of 27 E2 binding HMAbs neutralized at least one of two heterologous isolates , 1a H77C HCVpp and 2a JFH1 HCVcc , or autologous 212 HCVpp by >40% . The remaining 13 E2 binding HMAbs did not show significant neutralization activity . Among the 14 neutralizing antibodies , five HMAbs neutralized all three isolates , four HMAbs neutralized two isolates and five HMAbs neutralized one isolate . The last group of five antibodies neutralized only 2a JFH1 HCVcc , but not the autologous 212 HCVpp or heterologous 1a H77C HCVpp . Five HMAbs were selected for additional studies , 212 . 1 . 1 , 212 . 9 , 212 . 10 , 212 . 15 and 212 . 25 that represent the range in neutralization patterns ( i . e . against all three isolates , or two with or without the autologous isolate ) . Cross-reactivity of the HMAbs was examined against ten E1E2 proteins derived from six different HCV genotypes and subtypes ( S2 Table ) . Broad binding patterns were observed for all antibodies , except for 212 . 1 . 1 that bound to only three of ten isolates . Two HMAbs , 212 . 10 and 212 . 25 bound to all ten isolates . Lower reactivity against genotype 3a was generally observed . Denaturation of 1b 212 E1E2 completely abrogated the binding reactivity for all five HMAbs by ELISA , demonstrating that these antibodies are directed against conformational epitopes on HCV E1E2 ( Fig 3 ) . As controls , HC33 . 1 , an antibody directed to a predominantly linear epitope on the E2 glycoprotein , retained 80% binding [12] and HC-11 , an antibody to a conformational epitope on the E2 glycoprotein , lost more than 95% binding to denatured E2 [13] . To further examine the cross-genotype neutralization potential of HMAbs 212 , 212 . 1 . 1 , 212 . 10 and 212 . 25 were tested in dose-response focus forming unit ( FFU ) reduction neutralization assays [14] against an HCVcc genotype panel consisting of genotypes 1a ( strains H77 and TN ) , 1b ( J4 and DH1 ) , 2a ( J6 ) , 2b ( J8 ) , 3a ( S52 and DBN ) , 4a ( ED43 ) , 5a ( SA13 ) and 6a ( HK6a ) ( Table 3 ) [4 , 15–20] . No neutralization activity was observed for the negative control HMAb R04 ( targeting HCMV ) against any of these HCVcc , whereas the positive control antibody HC84 . 27 neutralized all tested HCVcc ( S5–S8 Figs ) [11] . Among the HMAbs 212 , only 212 . 10 was cross-genotype reactive and neutralized all HCVcc strains tested except S52 . Compared to HC84 . 27 , 212 . 10 was more effective against DBN and SA13 , and less effective against H77 , S52 and ED43 . 212 . 1 . 1 neutralized HCVcc of genotypes 1a , 4a and 6a , whereas 212 . 25 did not neutralize any of the tested HCVcc ( S5–S8 Figs ) . We previously reported that lack of neutralization against HCVcc could involve E2 hypervariable region 1 ( HVR1 ) related antibody protection [21–24] . We thus tested the HMAbs 212 against HCVcc H77 lacking HVR1 [25] , and found potent neutralization for 212 . 1 . 1 and 212 . 10 , and neutralization potential for 212 . 25 ( Table 3; S5 Fig ) ; 212 . 25 had 670–7700 fold lower efficiency against H77ΔHVR1 than 212 . 1 . 1 , 212 . 10 and HC84 . 27 . Broadly neutralizing HMAbs against HCV are predominantly directed against epitopes in E2 ( reviewed in [26] ) and epitope mapping and competition analysis has revealed that many of these neutralizing antibodies are directed at overlapping epitopes , which can be grouped into four distinct clusters , designated as antigenic domains B , C , D and E . Three of these clusters , B , C and D , contain conformational epitopes , and E contains mainly linear epitopes on E2 [11–13 , 27] . It should be noted that domains B and D antibodies are distinguished by their respectively shared contact residues on E2 . But some epitopes within domains B and D do overlap with shared contact residues in the 441–443 region forming a domain B-D supersite of conformational epitopes on the exposed surface of E2 [28] . The five 212 HMAbs initially selected for further analysis were therefore examined for their reactivity to these antigenic domains by competition studies with HMAbs HC-11 ( domain B ) , CBH-7 ( domain C ) , HC84 . 27 ( domain D ) and HC33 . 1 ( domain E ) against autologous 212 1b recombinant E1E2 ( S3 Table ) . HMAbs 212 . 1 . 1 and 212 . 10 blocked domains B , C and D HMAbs by >60%; HMAb 212 . 9 blocked mainly domains B and D; and HMAbs 212 . 15 and 212 . 25 blocked antigenic domain C . There was no significant competition ( defined as >40% ) between these 212 HMAbs and HMAb HC33 . 1 ( domain E ) . The findings place 212 . 15 and 212 . 25 epitopes within domain C , and the remaining three , 212 . 1 . 1 , 212 . 9 and 212 . 10 , in domains B and/or D . To address the question regarding whether the HMAbs isolated from this individual are part of a successful neutralizing antibody response , dilutions of sequential serum samples obtained throughout the course of the first two infections were tested for the presence of these HMAbs . Two antibodies , 212 . 1 . 1 , and 212 . 15 , were selected representing the antigenic domains B and C-like antibodies that have been isolated . Serum samples from week 21 to 182 ( S1 Table ) were diluted 1:100 to 1:10 , 000 and tested for their ability to block the binding of labeled 212 . 1 . 1 or 212 . 15 to recombinant 212 1b E1E2 ( Fig 4A and 4B ) . No detectable inhibition was observed in association with the first genotype 1b infection at week 21 . Dose-dependent inhibition was observed similarly with all subsequent serum samples , with some decrease against 212 . 15 HMAb at week 182 . The patterns of domain B- and C-like antibodies being induced in response to the first two infections are consistent with their role in viral clearance . It is also possible that non-domain B- and C-like antibodies are induced that competed with 212 . 1 . 1 or 212 . 15 . To definitively map the full set of E2 binding determinants of the HMAbs 212 . 1 . 1 , 212 . 10 , 212 . 15 and 212 . 25 , we performed global alanine scanning of E2 with these four antibodies , in addition to CBH-5 , which is a previously described antigenic domain B HMAb ( Fig 5A ) [27] . Epitope mapping of HMAb 212 . 9 was not performed because this antibody did not bind to or neutralize 1a H77C ( S2 Table ) . After combining these measurements with results from our previously mapped panel of 16 HMAbs , which target five antigenic domains on E2 [29] , we performed unsupervised clustering of this full set of antibodies ( Fig 5B ) . This recapitulates initial assignment of HMAbs 212 . 15 and 212 . 25 as targeting antigenic domain C , while 212 . 1 . 1 and 212 . 10 , in addition to CBH-5 , are clustered with antibodies targeting the domain B-D supersite . HMAb 212 . 1 . 1 diverges from 212 . 10 based on global binding data and is clustered with antibodies targeting antigenic domain D , albeit with lower significance than the parent B-D supersite cluster ( bootstrap probability 91% , versus 97% probability for the B-D supersite ) . To highlight shared and differential residue-level effects on antibody recognition , the four 212 HMAbs are compared with CBH-5 and other HMAbs for four segments on E2 ( respectively designated as regions 1 , 2 , 3 , and 4 in Fig 6 ) , including several key regions of E2 neutralizing antibody recognition . The antibody concentration used in epitope mapping was optimized by a dose-dependent study employing 0 . 005–2 μg/ml against wt 1a H77C E1E2 and a dose was chosen at 50% of maximum binding and in the linear portion of the binding curve , 212 . 1 at 2 μg/ml , 212 . 10 at 1 μg/ml , 212 . 15 at 0 . 5 μg/ml and 212 . 25 at 2 μg/ml ( S9 Fig ) . Control antibodies , HC-1 , HC-11 , CBH-5 , HC84 . 26 and CBH-7 were at 1 μg/ml . 212 . 1 . 1 and 212 . 10 showed binding reduction patterns similar to HC-1 and HC-11 , including two key domain B contact residues at G530A and D535A [13] . The involvement of binding to residues within aa 529–540 is central to domain B , and these residues are also key for antibodies targeting E2 antigenic region 3 ( AR3; HMAbs AR3A , AR3B , AR3C , AR3D ) , as shown by recent global epitope mapping study [30] . The involvement of binding to 440–445 and without binding to aa 529–540 is central to domain D [11] . Thus , 212 . 1 . 1 and 212 . 10 HMAbs are within the domain B cluster , though their mapping shows differential binding determinants , including residue F442 where alanine substitution does not disrupt 212 . 1 . 1 binding but results in over 80% loss of 212 . 10 binding . Notably , AR3 antibodies also lose binding when F442 is mutated to alanine [30 , 31] , suggesting greater similarity to HC-11 and 212 . 10 than to HC-1 and 212 . 1 . 1 . As expected , these patient 212 antibodies blocked E2 interaction with CD81 ( S10 Fig ) . Pre-incubation of 1a H77C E2 glycoproteins with either 212 . 1 . 1 or 212 . 10 reduced E2 binding to CD81 , as observed with a control domain B antibody , HC-11 . However , 212 . 9 is likely a domain B antibody as evidenced by its ability to block HC-11 and 212 . 10 binding to autologous 1b E2 by > 60% ( S11 Fig ) . HMAbs 212 . 15 and 212 . 25 blocked mainly CBH-7 , an antigenic domain C antibody ( S3 Table ) . Epitope mapping confirmed that their epitopes have contact residues within aa 544–549 , as shown by greater than 60% reduction in binding to P544A , P547A and W549A ( Fig 6 ) . These patterns are similar to CBH-7 . In addition , both antibodies blocked E2 binding to CD81 ( S10 Fig ) . However , 212 . 15 and 212 . 25 HMAbs have a critical binding determinant at residue R639 , which is not shared with CBH-7 , but is shared with previously described antibody AR5A that targets the E1E2 heterodimer [32] . Taken together , the neutralizing antibody response in the individual 300212 during the acute phase of spontaneous clearance of a second virus is directed mainly at two antigenic clusters , B and C . Using an established experimental assay [33] , we assessed whether HMAbs 212 impaired viral cell-to-cell transmission in HCV Jc1-infected Huh7 . 5 . 1 cells . As shown in S12 Fig , the HMAbs did not have a major inhibitory effect on cell-to-cell transmission of HCV Jc1 strain . It should be noted that HMAb 212 . 10 neutralized the cell-free J6 HCVcc that is derived from the same isolate as Jc1 , while 212 . 1 . 1 could not neutralize cell-free J6 HCVcc ( Table 3 ) [15 , 34] . To assess sequence features underlying the HMAbs 212 and antibodies targeting domains B and C , germline genes , CDR3 amino acid sequence , and levels of somatic hypermutations ( SHMs ) were compared with previously described domain B and C HMAbs ( Table 4 ) [13 , 35–38] . HMAb 212 germline percent identities are at or above 89% for heavy chains variable regions ( Vh ) , and over 95% for light chain variable regions ( Vl ) , in concordance with a previous study where broadly neutralizing antibodies from donors who spontaneously cleared HCV were observed to have few SHMs [38] ( one of those HMAbs , HEPC3 , is included in Table 2 ) . One feature of the 212 and other domain B and C HMAbs in Table 4 is that all of their heavy chains share the same germline gene , IGHV1-69 , despite variability in CDR H3 sequence and length , and light chain germline genes . This preferential usage of IGHV1-69 gene in E2 HMAbs was noted previously [38–40] and may suggest a shared set of solutions for targeting these conformational epitopes on E2 , possibly facilitated by hydrophobic germline residues in the CDR H2 loop which underlie IGHV1-69 gene usage in influenza hemagglutinin stem antibodies [41] . Several of the domain B HMAbs also share a distinctive double-cysteine and double-glycine motif ( CxGGxC ) in the context of CDR H3 loops ranging from 19 and 27 residues long . Additionally , in four out of five cases , this motif is preceded by a proline residue ( i . e . , PxxCxGGxC ) . The precise cysteine-glycine organization of these HMAbs may be critical for antibody affinity and neutralization; for the AR3C HMAb , which has been structurally characterized in complex with E2 , the cysteine residues form a disulfide bond , likely stabilizing the CDR H3 loop in a β-hairpin conformation which makes numerous key hydrophobic contacts and hydrogen bonds with E2 [42] . A search of the set of experimentally determined antibody structures in the PyIgClassify database [43] ( June 2018 release ) for other CDR H3 loops containing the CxGGxC motif identified two motif-containing CDRs out of 2172 unique CDR H3 sequences . These correspond to the broadly neutralizing antibody F10 which targets the influenza hemagglutinin stem and also includes the IGHV1-69 germline gene ( PDB code 3FKU ) [44] , and the 8f9 antibody which binds a peptide antigen from human cytomegalovirus ( PDB codes 3EYF , 3EYO ) [45] . In both cases , intra-loop disulfide bonds are present , as with AR3C , suggesting that this is a shared mechanism to stabilize CDR H3 loops , and that other domain B HMAbs with this motif are likewise disulfide-stabilized , possibly in an AR3C-like β-hairpin conformation . A conserved region on E2 , encompassing aa 412–423 ( designated as antigenic domain E ) , mediates broadly neutralizing antibodies to linear epitopes , but is known to be of lower immunogenicity than other regions [12 , 46] . An adjacent region , encompassing aa 434–446 , is also likely to be less immunogenic , participates in forming conformational epitopes ( designated as antigenic domain D ) and mediates broad virus neutralization [11] . Although domain D antibodies are directed against conformational epitopes , a significant number of these antibodies also bind to a linear peptide encompassing aa 434–446 on E2 . Thus , to assess whether the antibody response in individual 300212 included antibodies to domains D and E , sequential serum samples from weeks 21 to 182 were tested in serial dilutions for binding to synthetic peptides encompassing aa 410–425 and aa 434–446 . Minimal reactivity was observed against aa 410–425 with a significant dropoff in binding ( >0 . 4 O . D . ) after 1:1000 serum dilutions for the tested timepoints ( Table 5 ) . Significantly stronger reactivities were observed against aa 434–446 at multiple timepoints , weeks 76 , 122 and 123 , where >0 . 4 O . D . at 1:5000 or greater in dilution ( Table 6 ) . Interestingly , binding by serum antibodies at week 21 to aa 434–446 was observed . This finding was surprising in that antigenic domain B and C antibodies ( e . g . 212 . 1 . 1 and 212 . 15 , Fig 4A and 4B ) were not present at this timepoint . To characterize more definitively the antibody response to antigenic domain D and because these antibodies are to conformational epitopes , the ability of sequential serum samples to block HMAbs to D and E epitopes was tested ( Fig 4C and 4D ) . As expected , no inhibition was observed against HC33 . 1 ( a domain E HMAb [12] ) but dose-dependent inhibition was observed against HC84 . 27 ( a domain D HMAb [11] ) . Maximum inhibition at 1:100 serum dilution against HC84 . 27 was highest at week 76 , which then persisted throughout the remaining of the course of clinical observation ( Fig 4C ) . In contrast to serum binding at 1:100 dilution to peptide aa 434–446 at week 21 ( Table 6 ) , the serum at this timepoint did not inhibit HC84 . 27 ( Fig 4C ) . It is possible that this discrepancy is due to different assay formats having differential sensitivity to detect the presence of domain D antibodies , with a direct binding assay being more sensitive , as shown in Table 6 . It is also possible that peptide aa 434–446 is recognized by other antibodies to mainly linear epitopes [47] . To determine the presence of any particular sequence features underlying elicitation of broadly neutralizing antibodies and viral clearance , 212 1b and 212 3a E1E2 sequences were aligned along with H77C and genotype 1a consensus reference sequences , with the latter sequence obtained from the Los Alamos National Laboratory HCV database [48] ( Fig 7 ) . Inspection of three regions that contain binding determinants of HMAbs 212 and domain B , C and D antibodies did not show major sequence changes for the 212 sequences , in particular for the 212 1b sequence , which was present at a timepoint that would potentially influence selection and maturation of 212 HMAbs . It is unclear whether specific variations ( e . g . G440A ) or combinations thereof in the 212 1b sequence possess any features leading to antibody elicitation , though further investigation of these and other viral sequences may provide avenues to optimize E2 and E1E2-based vaccine immunogens .
The majority of neutralizing antibodies against HCV are directed against the E2 envelope glycoprotein , because E2 directly interacts with the HCV co-receptors , scavenger receptor class B type 1 ( SR-B1 ) [49] and the tetraspanin CD81 [50] during virus entry . There is recent evidence that E1E2 heterodimers , and not E2 alone , interacts with a third co-receptor , the tight junction protein Claudin-1 [51] . While effective neutralizing antibodies are directed at the HVR1 on E2 , this region is associated with mutations leading to rapid viral escape without compromising viral fitness [24 , 28] . Based on the isolation and characterization of HCV HMAbs from B cells of patients with chronic HCV infections , the majority of broadly neutralizing antibodies recognize conformational epitopes on E2 and inhibit E2 binding to CD81 [26] . Cross-competition analyses delineate at least four immunogenic clusters of overlapping conformational epitopes with distinct properties [11 , 27] . Non-neutralizing HMAbs fall within one cluster , which is designated as antigenic domain A . It is probable that this cluster of conformational epitopes and other non-neutralizing determinants are highly immunogenic and account for a substantial portion of the antibody response to E2 [52] . Neutralizing HMAbs segregate into three clusters of conformational epitopes , antigenic domain B , C and D . A fifth cluster of linear epitopes mediating neutralization , domain E , located at aa 412–423 on E2 , has been identified by both murine and human monoclonal antibodies ( reviewed in [26] ) . As the human antibodies isolated from chronically infected subjects co-existed with viremia , a minimal role for neutralizing antibodies in viral clearance has been the conventional view . Nonetheless , accumulated findings have now provided strong support for neutralizing antibodies facilitating spontaneous viral clearance during acute infection [7 , 8] . The question that has been addressed in the current detailed study of the antibody response in an individual who repeatedly cleared HCV infections of different genotypes is whether the specificity of the response is similar or dissimilar to the response observed in individuals that developed chronic infections . Our findings are in agreement with the recent report of other antibodies isolated from individuals during acute HCV infections that spontaneously resolved infections . Their isolated HMAbs were similar to those isolated during chronic infection [38] . Overall , the studied individual showed a pattern of cross-genotype reactive neutralizing antibodies increasing in titers from the viremic phase to spontaneous clearance of the two first HCV infections . This pattern is consistent with earlier studies on the timing of appearance of broadly neutralizing antibodies response correlating with spontaneous viral clearance [7 , 8] . However , the individual did get re-infected in spite of having a robust antibody response at week 76 after the first documented infection ( Table 1 ) . Contributing to viral clearance are most likely robust CD4+ and CD8+ T cell responses that were temporally observed in the studied individual during the primary and repeated infections ( personal communication and manuscript in preparation , A . Lloyd ) . We believe that both cellular and humoral immunity contributed to the repeated episodes of spontaneous clearance . As for the specificities of the antibody responses , the first indication that these antibodies are directed to conformational epitopes is based on the decrease in serum antibody binding against denatured autologous and heterologous E1E2 recombinant proteins . Autologous and heterologous E2 proteins were employed to isolate a panel of HMAbs . Of 14 antibodies that neutralized at least one of the tested isolates , eight neutralized autologous 212 1b HCVpp and these eight also neutralized at least one of the heterologous isolates . No neutralizing antibodies directed at HVR1 were isolated . This is somewhat surprising since HVR1 is immunodominant . A likely contributing factor is that these antibodies were isolated from a B cell response associated with the second infection with a different isolate , and we screened with autologous E2 from the first infection . A second possible factor is that the subject reported long periods of at least daily injection drug use and sharing of these injections with other users . It is possible that during the 24 months from the initial infection ( week 21 ) to the second infection ( week 122 ) , the subject had other undocumented episodes of HCV infection . Thus , the profile of the antibody response at week 123 may reflect multiple exposures to HCV that are associated with repeated viral clearance . This may also pertain to the interval from weeks 123 to 182 , underpinning the high serum antibody binding and neutralizing titers maintained during this period . In the panel of HMAbs that were isolated , two antibodies , 212 . 1 . 1 and 212 . 9 , accounted for more than 50% of the total clones analyzed . These two antibodies and a third , 212 . 10 , are within a highly immunogenic cluster , designated as domain B . Antibodies to overlapping epitopes within this antigenic domain account for the majority of described broadly neutralizing antibodies [26] , and here we found that 212 . 10 indeed neutralized strains representing 8 subtypes of genotypes 1–6 . While many of these antibodies exhibit broad neutralization , they can be associated with viral escape , with and without compromised viral fitness [53] . Both 212 . 1 . 1 and 212 . 10 appear to bind to overlapping epitopes involving aa 523–527 , a region on E2 with a possible role in cell-to-cell transmission [54] . However , no inhibition was observed with either antibody . HMAb 212 . 15 and 212 . 25 represent antibodies to another cluster of overlapping epitopes , antigenic domain C [27] . Epitope mapping of these antibodies was similar to CBH-7 ( a domain C antibody ) that was previously isolated from a chronically infected patient . Antibodies to both of B and C domains remained at relatively stable titers from weeks 76–182 , although somewhat higher at week 76 ( Fig 4A and 4B ) . The implication being that by week 76 , optimal in vivo affinity maturation of these antibodies had occurred , and there is no significant bias in the induction of antibodies to these domains . Taken together , these results suggest that antigenic domain B and C antibodies contributed to the protective immunity in this individual . While this immunity did not prevent reinfection , it did potentially prevent progression to chronic infection . Additional studies are required to determine whether these antibodies elicited during acute infection are associated with viral escape , as demonstrated for other HMAbs [11 , 25 , 53 , 55] . The studied individual with acute HCV infections also developed antibodies against antigenic domain D . Although domain D antibodies are directed to conformational epitopes , some of these antibodies also bind to synthetic peptides encompassing aa 434–446 [11] . Sequential serum reactivity to this peptide and inhibition of the binding of a labelled domain D HMAb to E2 indicate the presence of domain D-specific antibodies in these sera . Finding antigenic domain D antibodies is unusual in that this region is of lower immunogenicity , as indicated by the identification of HMAbs to this domain only after eliminating the detection of antigenic domains A and B antibodies [11] . This domain is known to have highly conserved overlapping epitopes and the associated antibodies have the broadest reactivity among diverse HCV genotypes and subtypes , as compared to domains B and C antibodies . Not only are these part of the antibody response in the individual reported here , but the antibodies may appear at the earliest time point at week 21 , as detected by direct serum antibody binding to aa 434–446 , when antibodies to antigenic domain B and C were not present . Although serum neutralizing activity titers were less than 100 at this timepoint , the binding studies clearly indicated the presence of domain D antibodies , albeit at low levels . One possible interpretation of these findings is that protective B cell immunity is associated with the early induction of neutralizing antibodies to antigenic domain D . To support this observation , other studies will be needed to remove the possibility that aa 434–446 is recognized by non-domain D antibodies in this individual . Additional studies will be required in other individuals that naturally cleared HCV infections to confirm this observation on early induction of these protective antibodies . Interestingly , the study subject carries the IL28B rs12979860 polymorphism that has been associated with spontaneous viral clearance [10 , 56] raising the suggestion that this innate immune response genotype may be linked to development of effective neutralizing antibody responses . The monoclonal antibodies described in this study underscore the importance of features of the human antibody repertoire , including the IGHV1-69 germline gene , in targeting HCV E2 . Others recently isolated three IGHV1-69 HMAbs that target antigenic domain B ( two of these , HEPC3 and HEPC74 , are noted in Table 4 ) from two individuals that spontaneously cleared HCV [38] , while intriguingly , a fourth HMAb described in the same study with the IGHV1-69 germline gene had its binding mapped to residues in HVR1 [38] . Likewise , we found that antigenic domain C , which is distinct from antigenic domain B [27] , is targeted by antibodies with the IGHV1-69 germline gene , including two HMAbs described here . Future studies can address the mechanistic and structural basis underlying the usage of this gene by antibodies targeting these distinct sites . Interestingly , HMAbs HEPC3 and HEPC74 had their structures determined in complex with E2 , and the HMAb AR3A-E2 complex structure was described in a separate recent study [40]; all feature disulfide-stabilized HCDR3 loops forming critical interactions with antigenic domain B [57] , in a similar manner as AR3C [36] . This supports the HCDR3 putative disulfide bond and common mode of E2 targeting for HC-11 and other antigenic domain B HMAbs with the cysteine pair motif noted in Table 4 . Collectively , our results indicate that the development of a successful B cell vaccine will require an understanding of the timing and affinity maturation of the humoral immune response . In addition , there appears to be no advantages to study individuals with spontaneous clearance as sources for B cells for the isolation of broadly neutralizing antibodies compared to patients with chronic infection .
Human research ethics approvals were obtained from Human Research Ethics Committees of Justice Health ( reference number GEN 31/05 ) , New South Wales Department of Corrective Services ( 05/0884 ) , and the University of New South Wales ( 05094 , 08081 ) , all located in Sydney , Australia . Written informed consent was obtained from the adult participants . | Studies of hepatitis C virus ( HCV ) infected individuals spontaneously clearing acute infections provide an opportunity to characterize the specificities of associated protective antibody responses . In an individual who resolved three separate HCV infections with different HCV genotypes , the antibodies induced during these acute infection episodes were similar to those induced during chronic infection . Surprisingly , the earliest detected antibodies were directed against conformational HCV epitopes on the envelope glycoprotein E2 ( including polyprotein residues 434–446 ) known to be targeted by broadly neutralizing antibodies . Taken together , the key B-cell determinants in spontaneous clearance are the timing and affinity maturation of broadly neutralizing antibody responses . | [
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| 2019 | Broadly neutralizing antibodies from an individual that naturally cleared multiple hepatitis C virus infections uncover molecular determinants for E2 targeting and vaccine design |
GC-biased gene conversion ( gBGC ) is a recombination-associated process that favors the fixation of G/C alleles over A/T alleles . In mammals , gBGC is hypothesized to contribute to variation in GC content , rapidly evolving sequences , and the fixation of deleterious mutations , but its prevalence and general functional consequences remain poorly understood . gBGC is difficult to incorporate into models of molecular evolution and so far has primarily been studied using summary statistics from genomic comparisons . Here , we introduce a new probabilistic model that captures the joint effects of natural selection and gBGC on nucleotide substitution patterns , while allowing for correlations along the genome in these effects . We implemented our model in a computer program , called phastBias , that can accurately detect gBGC tracts about 1 kilobase or longer in simulated sequence alignments . When applied to real primate genome sequences , phastBias predicts gBGC tracts that cover roughly 0 . 3% of the human and chimpanzee genomes and account for 1 . 2% of human-chimpanzee nucleotide differences . These tracts fall in clusters , particularly in subtelomeric regions; they are enriched for recombination hotspots and fast-evolving sequences; and they display an ongoing fixation preference for G and C alleles . They are also significantly enriched for disease-associated polymorphisms , suggesting that they contribute to the fixation of deleterious alleles . The gBGC tracts provide a unique window into historical recombination processes along the human and chimpanzee lineages . They supply additional evidence of long-term conservation of megabase-scale recombination rates accompanied by rapid turnover of hotspots . Together , these findings shed new light on the evolutionary , functional , and disease implications of gBGC . The phastBias program and our predicted tracts are freely available .
Gene conversion is the nonreciprocal exchange of genetic information from a ‘donor’ to an ‘acceptor’ sequence , primarily resulting from the repair of mismatched bases in heteroduplex recombination intermediates during meiosis [1] . In many cases , the process of resolving mismatches between G/C ( guanine or cytosine; denoted ‘strong’ or ‘S’ ) and A/T ( adenine and thymine; ‘weak’ or ‘W’ ) alleles appears to be biased in favor of S alleles [1]–[3] . Such GC-biased gene conversion ( gBGC ) elevates the fixation probabilities for S alleles relative to W alleles at positions of W/S polymorphism , and , if it acts in a recurrent manner over a sufficiently long time , can result in a significant excess of W→S over S→W substitutions and a consequent increase in equilibrium GC content . It has been known since the 1980s both that gene conversion occurs in various eukaryotes [4] and that mismatch repair can be significantly biased [5] . As complete genome sequences have become widely available , evidence has accumulated that gBGC may have played an important role in genomic evolution across many branches of the tree of life . In particular , it has been argued that gBGC has significantly influenced the genomic distribution of GC content , the fixation of deleterious mutations , and rapidly evolving sequences in many species [6]–[13] . Aside from limited experimental evidence of a GC-bias in meiosis , mostly from yeast [14] , much of what is known about gBGC comes from two indirect sources of information: global patterns of variation within or between species suggesting a fixation bias favoring S alleles [11] , [12] , [15]–[17] and the existence of numerous loci exhibiting dense clusters of substitutions with a pronounced W→S bias [7]–[9] , [13] . Both types of evidence correlate strongly with recombination rates , consistent with the hypothesis that they are caused by gBGC , although other recombination-associated factors might also contribute [16] . However , these observations provide limited information about the general prevalence , strength , and functional consequences of gBGC in humans and other mammals . Genome-wide patterns of variation are influenced by diverse forces that act in a highly heterogeneous manner across the genome , and it is difficult to measure the specific contribution of gBGC to these patterns . Clusters of biased substitutions perhaps provide more direct evidence of a local influence from gBGC . However , such clusters have so far been identified by considering either genomic windows of fixed size or pre-identified genomic segments ( such as protein-coding exons or fast-evolving noncoding regions ) , which has limited the regions that can be detected . In addition , many studies have considered only fairly small numbers of clusters showing extreme substitution rates and W→S biases . For modelers of molecular evolution , gBGC is an anomaly—a process separate and distinct from the fundamental processes of mutation , recombination , drift , and selection that underlie most models , yet one with the potential to profoundly influence patterns of variation within and between species . Like selection , gBGC acts in the window between the emergence of genetic polymorphism due to mutation and its elimination due to the fixation or loss of derived alleles . Unlike selection , however , gBGC is neutral with respect to fitness . The influence of gBGC at individual nucleotides can be modeled approximately by treating it as a selection-like force that depends only on whether a new mutation is W→S , S→W , or neither [13] , [16] , [18] . However , this approach ignores the close association of gBGC with the notoriously difficult-to-model process of recombination , which leads to a complex correlation structure along the genome ( i . e . , gBGC “tracts” separated by regions of no gBGC ) . Owing to these difficulties , with a few exceptions [9] , [13] , [19] , gBGC has generally been ignored in phylogenetic or population genetic models , and considered at most in post hoc analyses ( e . g . , by examining identified genomic regions for an excess of W→S substitutions ) . These approaches are clearly limited in efficiency and effectiveness , and there is a need for improved models of gBGC that can be applied on a genome-wide scale . There is also a need for high quality annotations of gBGC-affected regions that can be used by investigators in other comparative and population genomic analyses . Another reason to develop improved models of gBGC is that gBGC-induced nucleotide substitutions provide a unique window into historical recombination processes , by serving as a proxy for average recombination rates along a lineage of interest . By contrast , the other main sources of information about recombination—sperm typing [20] , genotypes for known pedigrees [21] , and patterns of linkage disequilibrium in present-day populations [22]—provide information about recombination that goes back no farther than the coalescence time between individuals . Pronounced differences between the human and chimpanzee recombination maps suggest that recombination rates in hominoids have changed rapidly [23]–[25] . gBGC may provide useful information about the recombination processes during the critical period between the divergence of humans and chimpanzees ( 4–6 million years ago [Mya] ) and the coalescence time for human individuals ( roughly 1 Mya , on average ) . Notably , archaic hominin genome sequences are of limited use for this purpose , because they are still few in number and result in only a modest increase in coalescence times . In this article , we address these issues by introducing a novel model-based approach for the identification of gBGC tracts . Our approach makes use of statistical phylogenetic models that jointly consider gBGC and natural selection [13] . In addition , it approximates the recombination-associated correlation structure of gBGC along the genome using a hidden Markov model . We have implemented this approach in a computer program called phastBias , which is available as part of the open-source PHylogenetic Analysis with Space/Time models ( PHAST ) software package ( http://compgen . bscb . cornell . edu/phast ) [26] . Using simulations , we show that phastBias can identify tracts of various lengths from unannotated multiple alignments with good power . We then analyze genome-wide predictions of gBGC tracts in the human and chimpanzee genomes , comparing them with recombination rates , patterns of polymorphism , functional elements , fast-evolving sequences , and other genomic features . This analysis sheds light on the prevalence and fitness consequences of gBGC , and on recombination processes during the time since the human/chimpanzee divergence . Our predictions of gBGC tracts are freely available as browser tracks ( http://genome-mirror . bscb . cornell . edu ) . We anticipate that these tracks will be useful for avoiding false positives in scans for positive selection , understanding the evolution of specific loci , and investigating the broader evolutionary forces shaping the human genome .
We model gBGC tracts using a phylogenetic hidden Markov model ( phylo-HMM ) with four states , representing all combinations of gBGC or no gBGC in a specified “target” genome ( e . g . , human or chimpanzee ) , and of evolutionary conservation or no evolutionary conservation across the phylogeny ( Figure 1; Methods ) . The phylo-HMM framework [27] allows the distinct rates and patterns of nucleotide substitution for each state to be described using a full statistical phylogenetic model , and it captures the pronounced correlations along the genomes in these patterns using a first-order Markov model . Our phylo-HMM can be thought of as a straightforward generalization of the two-state model used by the phastCons program for prediction of evolutionarily conserved elements [28] that additionally predicts gBGC tracts in the target genome . We directly consider evolutionary conservation together with gBGC because the dramatic reduction in substitution rates in functional elements would otherwise be a confounding factor in the identification of gBGC tracts . The model allows conserved elements and gBGC tracts to overlap or occur separately . The joint effects of gBGC and selection are modeled by treating gBGC as a selection-like force that specifically favors the fixation of G and C alleles , as in other recent work . In particular , the influence of selection is described using a population-scaled selection coefficient , , and the influence of gBGC is described using an analogous population-scaled GC-disparity parameter , ( where is the effective population size ) [13] ( see also [16] , [18] ) . The parameter measures the strength of gBGC , and values cause W→S substitution rates to increase and S→W substitution rates to decrease . A key feature of our approach is that it permits identification of gBGC tracts of any length based on characteristic substitution patterns , independent of predefined windows or genomic annotations . Because the signal for gBGC in the data is typically quite weak , we make several assumptions to reduce the complexity of the model . Briefly , we model negative selection as uniformly decreasing evolutionary rates on all lineages , we ignore positive selection , and we assume that the disparity parameter is the same for all gBGC tracts . In addition , we pre-estimate the parameters describing the neutral phylogeny and evolutionary conserved elements using restricted models , we fix the tract-length parameter based on our prior expectation for tract lengths , and we treat the parameter as a “tuning” parameter to be set by trial and error ( see summary of model parameters in Table 1 ) . Our simulation study indicates that fairly high accuracy in tract prediction is possible despite these simplifying assumptions and approximations ( see below and Methods for details ) . We have implemented our model in a program called phastBias in the PHAST package [26] . PhastBias makes use of existing features in PHAST for alignment processing , phylogenetic modeling , efficient HMM-based inference , and browser track generation . While the absence of high-quality annotations of gBGC tracts makes it difficult to assess prediction accuracy , we are able to gain some insight into the performance of phastBias using simulated data . To make our simulated data as realistic as possible , we started with real genome-wide alignments , and simulated new human sequences only , using our phylogenetic model to define neutral and conserved sequences , and interspersed gBGC tracts of fixed lengths ( see Methods ) . This strategy ensures that features such as variation in mutation rates , changes in equilibrium GC content , conserved elements , indels , alignment errors , and missing data are all retained in the nonhuman sequences . We used phastBias to predict human-specific tracts based on these partially simulated alignments and compared our predictions with the “true” tracts assumed during simulation . We found that the nucleotide-level false positive rate was always very low in these experiments ( /bp , Figure S1 ) , so we measured the specificity of our predictions using the nucleotide-level positive predictive value ( PPV ) , defined as the fraction of all bases predicted to be in gBGC tracts that truly belong in gBGC tracts . As a measure of power , we used the nucleotide-level true positive rate ( TPR ) , the fraction of bases in true gBGC tracts that were correctly predicted as being in tracts . First , we explored the performance of phastBias on simulated gBGC tracts of various lengths , generated with several different values of the GC-disparity parameter ( denoted ) . Under our model , increasing produces tracts with more substitutions and greater GC bias in their substitution patterns . As expected , both our power to detect gBGC and the specificity of our predictions increases with the lengths of the true tracts and with ( Figure 2 ) . We found that power and specificity were both quite good for tracts of 1 , 000–1 , 500 bases or longer , provided gBGC is reasonably strong ( ) . Current estimates of the lengths and GC-disparity of real gBGC tracts [8] , [29] suggest that phastBias should have good power for many tracts ( see Discussion ) . Next , we examined how our choice of the tuning parameters for expected tract-length ( ) and gBGC strength ( ) influence prediction performance . We found that the performance of the method was not highly sensitive to the value of , so we decided to fix the expected tract length at 1 kilobase ( kb ) ( by setting ) based on empirical evidence indicating that mammalian gene conversion tracts are approximately this size [1] , [29] . By contrast , the choice of had a much stronger influence on the observed prediction performance . Power was highest for small values of , regardless of the value used to simulate the tracts ( ) ( Figure S2 ) . However , this increase in power comes at only a modest cost in PPV , which remains fairly high ( >90% ) except when the elements are both short and under weak gBGC ( e . g . , mean length bases , ) . These results suggest that phastBias is inherently somewhat conservative with its predictions , and that setting to a relatively low value helps to improve sensitivity for tracts having a range of true gBGC intensities , at minimal cost in specificity . We applied phastBias to genome-wide alignments of the human , chimpanzee , orangutan , and rhesus macaque genomes , and used it to predict tracts in the human and chimpanzee genomes likely to have experienced gBGC since the divergence of these two species 4–6 Mya ( see Methods ) . In separate runs , we selected either the human or the chimpanzee genome as the “target , ” and we set the tuning parameter to values of 2 , 3 , 4 , 5 , and 10 ( in increasing strength of gBGC ) . As expected from our simulation study , the number , lengths , and genomic coverage of the predicted tracts depend fairly strongly on the choice of . In particular , coverage decreases from more than 1% to 0 . 07% as is increased from 2 to 10 ( Table 2 ) . Because the tracts predicted with high are largely found within those predicted with lower ( Table S1 ) , and because a value of appears to result in good power while controlling false positives ( see above ) , we will focus on the tracts predicted with for the remainder of the article . The absolute sensitivity of these predictions of course depends on unknown properties of true gBGC tracts , but our simulation experiments indicate that power is fairly good , at least for the subset of tracts 1 kb or longer with a reasonably pronounced GC-disparity ( Figure 2 ) . With , the predictions for the human genome include 9 , 439 gBGC tracts covering 0 . 33% of the genome ( Table 2 ) . These predicted tracts average 1 , 018 bp in length ( median 788 bp ) , consistent with our choice of , but they display a fairly broad length distribution ( Figure 3 ) , indicating that our choice of tuning parameters is not overly restrictive . Most predicted tracts contain exclusively or predominantly W→S substitutions ( Figure S3 ) . The statistics for the chimpanzee genome are similar , but in this case there are somewhat fewer tracts ( 8 , 677 ) , their lengths are reduced ( mean = 842 bp , median = 663 bp ) , and genomic coverage is about 25% lower ( at 0 . 25% ) . The reduced coverage of the chimpanzee genome holds even if we consider only tracts that completely fall within regions of high-quality , syntenic alignment between the two genome assemblies . These differences between the human and chimpanzee predictions could reflect differences between species in the degree to which recombination events are concentrated in recombination hotspots [25] ( see Discussion ) . The human and chimpanzee predictions are broadly distributed across the two genomes , but show a clear tendency to cluster near the ends of chromosomes ( Figure 4; Text S1 , Figures S4 and S5 ) , consistent with previous findings [12] , [15] , [30] . In human , the median distance from the nearest telomere is only about one third that observed for a set of GC-content-matched control regions ( 9 . 6 megabases ( Mb ) vs . an average of 30 . 4 Mb over 1000 replicates , ) . Similarly , the median distance between tracts is less than one third that for the controls , even after merging tracts less than 1 kb apart to account for possible biases from the HMM-based prediction method ( 24 . 3 kb vs . an average of 86 . 0 kb , ) . The chimpanzee predictions are similarly distributed . In human , there is an obvious cluster of predicted tracts near the centromere of chromosome 2 , reflecting the telomeres of two ancestral chromosomes that fused at this site along the human lineage after the human/chimpanzee divergence [15] , [31] . However , the tract density in this region is somewhat lower in human than in the orthologous telomeric regions in chimpanzee ( Figure S6 ) , consistent with a reduction in the human recombination rate following the fusion event [12] , [15] ( see Discussion ) . Together , the human and chimpanzee tracts account for about 1 . 2% of all human/chimpanzee nucleotide differences apparent in our genome-wide alignments ( 435 , 729 differences ) . About half ( 214 , 195 ) of the nucleotide differences within the tracts can be confidently explained by W→S substitutions on either the human or chimpanzee lineage , of which slightly more than half ( 115 , 699 ) fall on the human lineage . Thus , even with our limitations in power , our predictions suggest a non-negligible influence of gBGC on overall levels of human/chimpanzee nucleotide divergence . The predicted human gBGC tracts are substantially enriched for recombination hotspots from the HapMap project [32]: 1 , 228 ( 13% ) overlap a hotspot , compared with an average of 796 for the GC-matched control regions ( ) . In addition , the average recombination rate [33] within these tracts is more than twice the rate in the control regions ( 3 . 85 centimorgans per megabase ( cM/Mb ) vs . 1 . 61 cM/Mb , ; Table 3 ) . A parallel analysis of the chimpanzee gBGC tracts based on the genome-wide recombination rate map from the PanMap Project [25] showed , similarly , that recombination rates in predicted gBGC tracts were more than twice as high as in control regions ( Table 3 ) . Pedigree-based human recombination maps [21] produced similar results ( data not shown ) . At fine scales , the human and chimpanzee tracts show a modest , but significant , degree of overlap ( Figure 4 ) : 605 ( 6 . 4% ) of the human tracts directly overlap a chimpanzee tract , compared with an average of 86 for the control regions ( ) . Shared recombination hotspots account for only a small minority ( <1% ) of the overlapping tracts . However , the correlation in tract locations between species is much stronger at broader scales . For example , if the fractions of nucleotides in gBGC tracts ( “gBGC density” ) are compared in orthologous genomic blocks of various sizes , the human/chimpanzee Pearson's correlation increases from for 10 kb blocks to for 100 kb blocks , and to for 1 Mb blocks ( Figure S7 ) . These observations mirror those for human and chimpanzee recombination rates , which correlate well at scales of 1 Mb or larger but much more poorly at finer scales [23]–[25] . To gain further insight into the conservation of the gBGC tracts , we mapped the human gBGC tracts to orthologous locations in the chimpanzee genome , and the chimpanzee tracts to orthologous locations in the human genome . We then compared the recombination rates in these “ortho-tracts” with those in control regions , as with the tracts directly predicted for each species . Unlike recombination hotspots [25] , the predicted gBGC tracts do show significantly elevated recombination rates at orthologous positions in the other species ( Table 3 ) . However , these recombination rates are not nearly as elevated as those for the directly predicted tracts . An analysis of the correlation between gBGC tract densities and recombination rates within and between species yielded similar results . Human gBGC tract densities are significantly correlated with human recombination rates , and this correlation increases with block size . A similar pattern is present in chimpanzee . When these correlations are examined across species ( e . g . , human gBGC densities vs . chimpanzee recombination rates ) , they are weaker but still significant ( Figure S8 ) . Differences in recombination rates between species are modestly predictive of differences in gBGC densities ( at 1 Mb; Figure S9 ) . In general , we find much stronger correlations of gBGC- and recombination-associated features within species than between species , but these features nevertheless exhibit residual correlations between species , probably because they reflect average recombination rates over millions of years ( see Discussion ) . In both human and chimpanzee , the predicted tracts show a weak positive correlation with GC-content on a megabase scale . This correlation is somewhat stronger for human ( Pearson's correlation for 1 Mb blocks: ) than for chimpanzee ( ) , mirroring observations of a stronger correlation of recombination rate with GC-content in human than in chimpanzee [25] . To shed light on the functional implications of gBGC , we examined the degree of overlap of the predicted human gBGC tracts with various sets of genomic annotations ( listed in Methods ) . In comparison with the control regions , we found that the human gBGC tracts were significantly depleted for overlap with known protein-coding exons , core promoters ( 1 kb upstream of annotated transcription start sites ) , miscellaneous RNAs , LINEs and SINEs , while they were significantly enriched for overlap with introns , lincRNAs , and a collection of ChIP-seq-supported transcription factor binding sites ( Figure S10 ) . However , all of these enrichments and depletions were modest in magnitude , with fold-changes of about 0 . 8–1 . 3 . Overall , the gBGC tracts appear to be fairly representative of sequences of the same GC content . It is possible that the depletion for gBGC tracts in protein-coding exons and promoters could result in part from strong purifying selection counteracting GC-biased fixation . To distinguish between fixation- and mutation-related biases , we compared the derived allele frequencies at polymorphic W→S and S→W sites in the predicted tracts and control regions . To control for the possibility of an ascertainment bias from polymorphic sites at which the derived allele is present in the human reference genome , we performed this analysis twice: once with the original gBGC tracts , and once with predictions based on alignments in which polymorphic sites in the human genome had been masked with ‘N’s . Based on pilot data from the 1000 Genomes Project [33] ( YRI population ) , the predicted gBGC tracts displayed significantly elevated derived allele frequencies at sites of inferred W→S mutations compared with sites of inferred S→W mutations ( W→S DAF skew of ; Figure 5A ) . This skew in DAFs was significantly greater than that observed genome-wide ( ) or in recombination hotspots ( ; Figure 5B ) , and it was larger than observed in any of the 1000 control region replicates ( ) . The tracts are also far more biased than any of the regions considered by Katzman et al . [17] , which were identified using sliding windows of fixed size and likely contained a mixture of gBGC tracts and non-tracts . Results were similar for the CEU ( W→S DAF skew of ) and CHB-JPT populations ( ) . These results held for the tracts based on the polymorphism-masked alignments , although the magnitude of the skew was somewhat reduced in this case ( for YRI; Figure S11 ) . Together , these results strongly indicate an on-going preference for the fixation of G and C alleles in the predicted gBGC tracts . There is much less polymorphism data available for chimpanzees than for humans , but data for 10 individual chimpanzees from the PanMap project [25] indicates a similar ongoing fixation bias within the predicted chimpanzee tracts ( Figure S12 ) . As in human , the W→S DAF skew in the predicted chimpanzee tracts is significantly stronger than that observed in recombination hotspots . We also compared the W→S DAF skews of the tracts predicted for each genome and the “ortho-tracts” mapped from the other genome . As with recombination rates , we found that , in both species , the predicted tracts have significantly greater W→S DAF skews than the ortho-tracts ( Figure 5B and Figure S12B ) . These findings are consistent with gBGC currently acting on a subset of our predicted tracts in association with transient , species-specific recombination hotspots . Theoretical modeling has shown that gBGC , in principle , can overcome negative selection and result in the fixation of weakly deleterious alleles [3] , [8] , [10] . However , there is currently little direct empirical evidence of a contribution of gBGC to fixed or segregating deleterious alleles [11] . Our genome-wide tract predictions enabled us to investigate the link between gBGC and deleterious alleles by testing for enrichments for disease-associated genomic regions in gBGC tracts . We examined the relationship between the gBGC tracts and four sets of putatively disease-associated genomic regions: 10 , 711 polymorphic sites from dbSNP annotated as “pathogenic” or “probable pathogenic” [34]; 43 , 952 polymorphic sites from the Human Gene Mutation Database ( HGMD ) [35] ( see also [11] ) ; 11 , 444 genomic regions from the Genetic Association Database ( GAD ) [36]; and 6 , 435 , 165 polymorphic sites with evidence of functional importance ( classes 1–5 ) in RegulomeDB [37] . For the dbSNP pathogenic and HGMD comparisons , we considered sets of control regions that overlapped the same number of exonic SNPs as the gBGC tracts . This control is designed to avoid misleading findings of significance that simply reflect the GC content , exon coverage , and/or rates of polymorphism in the gBGC tracts , since these disease-associated region sets are mostly found in coding regions . Similarly , we used control regions matched to SNPs considered by RegulomeDB , since it only includes non-coding SNPs ( Methods ) . We found that the gBGC tracts overlapped significantly more putatively disease-related SNPs from the dbSNP , HGMD , and RegulomeDB collections , and significantly more of the GAD regions , than did the matched control regions ( Table 4; for each ) . In the cases of the two collections of disease-associated SNPs ( dbSNP and HGMD ) , the enrichment within the predicted gBGC tracts was particularly striking ( fold-enrichments of 2 . 4 and 1 . 9 , respectively ) , while in the other cases it was more modest but still significant . These results suggest that gBGC may contribute in important ways to elevated allele frequencies , and perhaps , to the eventual fixation of deleterious mutations . Many fast-evolving regions of the human genome display an excess of W→S substitutions , leading to the suggestion that gBGC may play a role in their evolution [6] , [7] , [9] , [13] , [38] , [39] . Supporting this hypothesis , our predicted gBGC tracts overlap 13 of the 202 ( 6 . 4% ) HARs identified by Pollard et al . [38] , more than observed for any of the 1000 GC-control region replicates ( ) . Notably , the HARs overlapped by gBGC tracts included HAR1 , HAR2 , and HAR3 , the three fastest evolving sequences in this set . We also examined an expanded set of 721 HARs [40] and found that gBGC tracts overlapped 75 of them ( 10%; ; see example in Figure 6 ) . Next , we compared the gBGC tracts with 10 protein-coding genes identified as showing signatures of positive selection on the human branch based on a likelihood ratio test [41] . One of these genes is overlapped by a gBGC tract , significantly more than expected based on exon-aware controls ( ) . The overlapped gene , ADCYAP1 , was also highlighted by another group [9] as showing strong evidence of an influence from gBGC . We repeated our analysis with 157 genes identified in another recent study as showing signatures of human-specific positive selection [42] , and found that the gBGC tracts overlapped 11 ( 7% ) of these genes , somewhat more than average for the exon-aware control replicates ( 7 . 4 , ) . Considering our limitations in power ( see Discussion ) , these results indicate the gBGC has contributed to a substantial fraction of fast-evolving sequences in the human genome . Our predicted tracts for human and chimpanzee are available as a UCSC Genome Browser track at http://genome-mirror . bscb . cornell . edu ( Figure 6 ) . This track displays both our discrete predictions of gBGC tracts and a continuous-valued plot indicating the posterior probability that each position is influenced by gBGC . Using this track it is possible to browse the predicted tracts in their full genomic context , perform queries intersecting them with other browser tracks , and download them for further analysis . We expect this track to be particularly useful for other investigators who wish to exclude gBGC-influenced regions of the genome from other molecular evolutionary analyses , such as the identification of genes under positive selection . The tracts themselves will also be directly useful for studying the evolution of recombination rates and their relationship to substitution rates and patterns .
This paper describes an analysis of predicted gBGC tracts in the human and chimpanzee genomes , based on a new computational method called phastBias . PhastBias makes use of a hidden Markov model and statistical phylogenetic models that consider the influence of both natural selection and gBGC on substitution rates and patterns . Unlike previous methods for identifying signatures of gBGC , it does not depend on a sliding window or predefined annotations of protein-coding genes or conserved noncoding elements [9] , [13] , [15] , [19] , but instead can flexibly identify tracts of various sizes directly from genome-scale multiple alignments . The method appears to have good power for tracts of about 1 kilobase or longer , provided gBGC has acted with a reasonably high average intensity along the lineage of interest . Our predictions in the human and chimpanzee genomes cover about 0 . 3% of each genome and explain 1 . 2% of human/chimpanzee single nucleotide differences . Consistent with the hypothesis that they are caused by gBGC , the predicted tracts are correlated with recombination rates , tend to fall in subtelomeric regions , and exhibit an ongoing fixation bias for G and C alleles . In addition , they are enriched for disease-associated human polymorphisms , and they tend to overlap previously identified fast-evolving coding and non-coding regions , suggesting that gBGC has contributed significantly to both deleterious mutations and rapid sequence evolution . Overall , our analyses indicate that gBGC has been an important force in the evolution of human and chimpanzees since their divergence 4–6 million years ago . Many attributes of the predicted gBGC tracts are consistent with the hypothesis that recombination is the driving force behind the observed patterns of biased substitution . Nevertheless , the tract locations are only partially correlated with recombination rates in human and chimpanzee . Moreover , while the tracts are enriched for recombination hotspots in both species , there are thousands of hotspots that do not overlap a gBGC tract , and the majority of tracts do not overlap a hotspot . These differences can be explained by several factors . First , the hotspots we have analyzed reflect recombination patterns in modern human populations , while the gBGC tracts reflect average patterns since the divergence of humans and chimpanzees . Many current hotspots presumably have not had sufficient time to produce a detectable signature of biased substitution , while many extinct hotspots contributed to gBGC for long periods of time in the past . Second , models of gBGC suggest that it can occur in conjunction with both crossover and noncrossover recombination events , but current recombination maps reflect crossover events only [3] . An imperfect correlation of these types of events , together with statistical noise in current estimates of crossover rates , likely accounts for some of the absence of correlation between recombination rates and gBGC tracts . Third , biased substitution rates are influenced by many factors other than recombination , such as mutation rates , natural selection , and GC content [43] . For example , strong purifying selection at or near a hotspot could eliminate the signature of gBGC . Finally , limitations in power for both recombination events and gBGC tracts undoubtedly reduce the apparent correlation between these features . The locations of the human and chimpanzee tracts are strongly correlated on megabase scales , but , like recombination rates , they differ significantly on fine scales , and few human and chimpanzee tracts directly overlap one another ( Figure 4; Figure S7 ) . Nevertheless , even at fine scales , the human and chimpanzee gBGC tracts agree better than recombination hotspots , which are essentially uncorrelated between the two species [25] . This observation probably stems from the fact that gBGC tracts reflect time-averaged recombination rates , and historical recombination rates were presumably better correlated than those in present-day humans and chimpanzees . In general , the predicted gBGC tracts provide a valuable window into historical recombination processes , but this window is “blurred” by time-averaging over millions of years . Nevertheless , together with other sources of information about historical recombination processes—such as new methods based on patterns of incomplete lineage sorting ( K . Munch , T . Mailund , J . Y . Dutheil , and M . H . Schierup , submitted ) —predictions of gBGC tracts may help to provide a more detailed picture of the evolution of recombination rates in hominoids . The different time scales associated with crossover-based recombination maps and our predicted gBGC tracts are particularly well illustrated by the region of the chromosome 2 fusion in human ( Figure S6 ) . Consistent with its location near a centromere in the human genome , this region displays no elevation of crossover rates in human populations , while the orthologous regions of the chimpanzee genome show elevated crossover rates typical of telomeres . Accordingly , this region exhibits little W→S DAF skew in human , but a clear skew in chimpanzee . However , the density of predicted gBGC tracts in this region is elevated in both species , only slightly more so in chimpanzee than human , suggesting that this region was telomeric for most of the approximately 6 million years during which human-specific recombination-associated substitutions could have occurred . Thus , our observations indicate that the fusion event is fairly old relative to intraspecies coalescence times but young relative to the human/chimpanzee divergence time . They are qualitatively consistent with Dreszer et al . 's [15] estimate of 0 . 74 Mya ( 95% confidence interval: 0–2 . 81 Mya ) for the date of the fusion event and inconsistent with the argument that this event contributed to the initial speciation of humans and chimpanzees [44] . Despite the overall similarity of the human and chimpanzee predictions , the coverage of the predicted tracts is about 25% lower in the chimpanzee genome . A possible cause of this difference is the greater concentration of recombination events in hotspots in humans [25] . This difference could lead to a stronger population-level signal for gBGC in humans , allowing for more predictions and longer predicted tract lengths . It has been proposed that the difference in the concentration of recombination events may derive from differences in the activity of the hotspot-specifying protein PRDM9 , which shows substantially greater allelic diversity in chimpanzees than in humans [25] . Consistent with this hypothesis , Auton et al . [25] found a much weaker signal for sequence motifs potentially involved in PRDM9 binding at chimpanzee hotspots than at human hotspots . In an attempt to shed light on the ancestral binding preferences of PRDM9 , we applied motif discovery methods to the predicted gBGC tracts in the human and chimpanzee genomes . However , in both species this analysis turned up only a few motifs , none of which resembled the well-defined motifs reported for the human genome [25] , [45] . This absence of strong motifs may occur because the ancestral recombination hotspots in both species are more like those in present-day chimpanzees than humans . Alternatively , it may simply reflect the difficulty of motif discovery given rapidly evolving PRDM9 binding preferences and the time-averaged nature of the gBGC tracts . Given what is currently known about gBGC , it is impossible to obtain direct measurements of the completeness and accuracy of our predicted tracts . Our simulation experiments suggest that both sensitivity and specificity are reasonably good for tracts at least 1 kb in length with , but we often miss shorter or less biased gBGC tracts ( Figure 2 ) , and the true distributions of tract lengths and values are unknown ( although average estimates of [46] and [8] have been reported for highly recombining regions ) . It is important to bear in mind that represents an average along an entire branch of the phylogeny . Many regions may have experienced quite strong gBGC but for short evolutionary intervals , resulting in small average values of and poor detection power . Thus , while our genome-wide predictions improve on what is currently available , it seems plausible that they still represent the “tip of the iceberg”—a relatively small subset of all genomic regions significantly influenced by gBGC , perhaps unusual for their length or GC-disparity . It is worthwhile to consider two other indirect sources of information about our power for gBGC tract prediction . First , Katzman et al . [17] found that about 20% of the 40 kb genomic intervals they examined show significant W→S DAF skew . If we conservatively assume one 1–2 kb tract per gBGC-influenced window , this observation would imply that at least 0 . 5–1 . 0% of the human genome has been influenced by gBGC on population genomic time scales , compared with the phastBias estimate ( for ) of 0 . 3% . Second , using a method optimized for the analysis of individual HARs , Kostka et al . [13] estimated that 24% of HARs experienced significant gBGC ( 19% exclusively and 5% in combination with positive selection ) , or 3 . 7 times as many as overlap our phastBias predictions ( 6 . 4% ) . Thus , these two imperfect indicators of power suggest that , with , phastBias underpredicts gBGC tracts by a factor of at least about 2–4 . The genomic coverage of our predictions may be closer to the truth ( 1 . 1%; Table 2 ) , but these predictions appeared to be of poorer quality on inspection , apparently because the phylo-HMM states with and without gBGC were insufficiently distinct to control false positive rates . While the likelihood ratio tests of Kostka et al . [13] appeared to have greater power for gBGC in HARs overall , phastBias sometimes achieves improved sensitivity by considering the entire genome ( including flanking sequences ) rather than just a designated collection of elements . Indeed , of the thirteen HARs that overlap one of our gBGC tracts , three were not identified by Kostka et al . , apparently for this reason . These instances of improved sensitivity are especially noteworthy given that phastBias must address the more difficult problem of unconstrained genome-wide prediction , with the attendant potential for large numbers of false positives predictions . In principle , gBGC can overcome purifying selection and help to drive deleterious alleles to high frequencies [3] , [8] , [10] , but it has been difficult to find direct empirical evidence for a reduction in fitness ( genetic load ) caused by gBGC . Our predicted gBGC tracts are significantly enriched for disease-associated polymorphisms in current human populations , suggesting that gBGC has helped to drive at least some of these alleles to appreciable frequencies , and , indeed , may still be active in maintaining them . We attempted to establish an orthogonal link between gBGC and deleterious alleles by looking for evidence of purifying selection in chimpanzees and other species at the locations of W→S substitutions within the predicted human tracts ( Text S1 ) . The idea behind this analysis was that , if a substantial number of these mutations were driven to fixation by gBGC despite negative selection against them , one would expect an excess of evolutionary conservation , a deficiency of polymorphisms , and/or a skew toward low-frequency derived alleles at orthologous locations in other species , relative to an appropriate control . However , this analysis yielded inconclusive results: the human tracts are significantly enriched for overlap with evolutionarily conserved elements at locations of W→S substitutions ( Figure S13 ) , but evolutionary conservation scores and chimpanzee polymorphisms do not display the expected patterns ( Figures S14 , S15 , and S16 ) . It seems likely that the signal for excess conservation in the gBGC tracts is simply too weak to detect by these methods , owing to the sparseness of functional sites within the tracts and the difficulty of establishing appropriate control regions . Nevertheless , it may be possible in future work to develop refined comparative genomic methods for measuring the genetic load associated with gBGC .
Our phylogenetic hidden Markov model has four states: one that assumes both evolutionary conservation and gBGC ( ) , a second with gBGC but no conservation ( ) , a third with conservation but no gBGC ( ) , and a fourth with neither conservation nor gBGC ( ) ( Figure 1 ) . To avoid over-parameterization , we make the following simplifying assumptions . First , we model gBGC only on the lineage leading to a pre-defined “target” genome ( human or chimpanzee ) , because gBGC is expected to be a transient phenomenon , typically affecting a single lineage in any genomic position of interest . gBGC tracts are allowed to occur on other lineages , but these tracts are expected to have a negligible influence on inferences in the target genome and are not directly modeled . Second , negative selection , in contrast to gBGC , is assumed to apply uniformly across all branches of the phylogeny . Third , positive selection is ignored . We omit positive selection and lineage-specific negative selection from the model because they are expected to be fairly rare , to leave a relatively weak signal in the data at human-chimpanzee evolutionary distances [47] , and to primarily operate at a somewhat different genomic scale from gBGC ( e . g . , at the level of individual binding sites or clusters of amino acids , rather than genomic tracts of hundreds or thousands of bases ) . We expect our modeling framework to be robust to occasional sequences under positive or lineage-specific selection , because the primary signal for tract prediction is a W→S substitution bias , and selection generally will not produce such a bias consistently across many bases . Finally , we assume that the strength of gBGC and the strength of negative selection in the target genome are constant across the genome . A similar homogeneity assumption is employed in phastCons and appears to have a minimal impact on power and accuracy for element identification [28] . With these assumptions , the phylogenetic models for the four states are defined as follows ( with further mathematical details given in Text S1 ) . The state-transition probabilities are defined by four parameters , denoted , , , and ( Figure 1 , Table 1 ) . The parameters and are inherited from phastCons [28] and describe the conditional probabilities of transitioning from a conserved state to a neutral state , and from a neutral state to a conserved state , respectively . The parameters and are analogous , defining the conditional probabilities of transitioning out of , and into , a gBGC tract , respectively . The sixteen possible state transition probabilities are obtained by multiplying the appropriate pairs of conditional probabilities and enforcing the standard normalization constraints ( Figure 1 ) . This “cross-product” construction corresponds to a prior assumption of independence for the two types of transitions ( conservation no conservation and gBGC no gBGC ) . Given a multiple sequence alignment , standard algorithms for statistical phylogenetics and hidden Markov models can be used to calculate the likelihood of the data under this model , to predict the most likely state path ( Viterbi ) , or to calculate the marginal posterior probability of each state at each alignment column ( reviewed in [27] ) . In principle , the nine free parameters in our model ( Table 1 ) could all be estimated directly from the data by maximum likelihood , using an expectation maximization or numerical optimization algorithm . In practice , however , parameter estimation is difficult because there are no validated gBGC tracts to use for supervised training of the model , and the signal in the data is not sufficiently strong to support a fully unsupervised estimation procedure . Instead , we partition the parameters into three groups: those for the neutral substitution process , those for the model of conserved elements , and those specific to the gBGC tracts . The first two groups of parameters are pre-estimated from the data without consideration of gBGC , by what can be considered an empirical Bayes approach . The parameters in the third group are then estimated by a combination of methods . Specifically , the free parameters for the neutral substitution process ( , , and ) are estimated per alignment block ( see below ) using phyloFit [26] , after conditioning on the tree topology and branch-length proportions ( as described above ) . This strategy assumes that conserved elements and gBGC tracts are sparse and have at most a minor effect on average substitution rates for large genomic blocks . The three additional parameters that describe conserved elements ( , , and ) are inherited directly from phastCons and therefore were simply set to the values used for the Conservation tracks in the UCSC Genome Browser . The remaining parameters include the GC-disparity and the gBGC transition probabilities and . As discussed in the Results section , we found that —which can be interpreted as an inverse prior expected length for gBGC tracts—has only a weak influence on our predictions ( within a reasonable range ) and decided to simply fix it at 1/1000 , corresponding to a prior expectation of 1 kb tracts . We treated as a “tuning” parameter and considered various possible values in a plausible range . The final parameter , , was estimated from the data ( separately for each alignment block ) by expectation maximization , conditional on fixed values of all other parameters . To predict gBGC tracts based on our model , we computed marginal posterior probabilities for the four model states at each genomic position using the forward/backward algorithm . We then computed the marginal posterior probability of gBGC by summing the probabilities for states and , and we predicted tracts by applying a threshold of 0 . 5 to this probability ( i . e . , the predicted tracts are maximal segments in which every position has a posterior probability of at least 50% of gBGC ) . We settled on this strategy after discovering that the more conventional Viterbi algorithm performed poorly in this setting , evidently due to uncertainty about the endpoints of tracts . This uncertainty causes the probability mass for a putative gBGC tract to be distributed across many possible HMM state paths , and as a result , the Viterbi algorithm often fails to predict a tract even when the posterior probability of gBGC is close to one . A potential drawback of our thresholding strategy is that fluctuating posterior probabilities could lead to highly fragmented tract predictions . However , we found that the posterior probability function was quite smooth in practice ( probably owing to small values of the state transition probabilities ) and fragmentation was not a problem . For example , at , only about 2% of the predicted human tracts fall within 50 base pairs of another tract . Nonetheless , when analyzing the genomic distribution of gBGC tracts relative to one another and to telomeres , we merged adjacent tracts ( within 1 kb ) in order to reduce any bias introduced by over fragmentation ( Text S1 ) . Our analyses of both simulated and real data were based on genome-wide alignments obtained from the UCSC Genome Browser ( http://genome . ucsc . edu ) [49] . We began with the 44-way vertebrate alignments produced with multiz [50] ( hg18 assembly ) and extracted the rows corresponding to the human , chimpanzee , orangutan , and rhesus macaque genomes , discarding alignment columns containing only gaps in these sequences . We also discarded columns in which the human genome contained a gap . Human-referenced alignments were used for both the human and chimpanzee gBGC tract predictions , as chimpanzee-based multiple alignments are not available . For convenience in processing , the resulting four-way alignments were partitioned into blocks of approximately 10 megabases ( Mb ) in length . The boundaries between blocks were required to occur in regions uninformative about gBGC ( due to greater than 1 kb with lack of alignment with the other species ) . We experimented with several alternative block sizes , ranging from 1–30 Mb , and found that the predictions were fairly robust to the choice of block size ( Table S2 ) . We simulated human sequences with gBGC tracts for each 10 Mb block in the real genome-wide alignments as follows . First , we identified positions at which any sequence contained a CpG dinucleotide , because substitution rates are likely to be substantially elevated at such sites . Next , we used phastCons to identify conserved elements in the four species . We then fitted a phylogenetic model to the alignment columns in each of four categories ( neutral/non-CpG , conserved/non-CpG , neutral/CpG , conserved/CpG ) by estimating , , and for the most data-rich category ( neutral/non-CpG ) , then estimating a separate for the CpG category ( using phyloFit ) and applying a branch-length scale-factor of 0 . 31 to the conserved categories . Next , we defined an alternative “gBGC” instance of each of the four estimated models by modifying the substitution rate matrix for the human branch according to our model of gBGC [13] and a given choice of ( here denoted ) . In this way , we obtained eight phylogenetic models , representing all combinations of conservation/no conservation , CpG/no CpG , and gBGC/no gBGC . We generated synthetic human sequences by assigning one of these eight models to each alignment column , as follows . The conservation and CpG status of each column was maintained as originally annotated , so that the synthetic alignments would resemble the original ones as much as possible . The gBGC status was set to “no gBGC” for most columns , but set to “gBGC” for tracts of fixed size at randomly selected locations , at an average gBGC coverage of 0 . 1% . We then simulated a new human base for each alignment column conditional on the assigned phylogenetic model and the observed chimpanzee , orangutan , and rhesus macaque bases . This was accomplished using the ‘postprob . msa’ function in RPHAST , which computes the marginal distribution over bases at any node in the phylogeny conditional on a given phylogenetic model and collection of observed bases , using the sum-product algorithm . This function computes the desired distribution for the human base if the human sequence is masked and treated as missing data in the input . A particular base was selected by sampling from this marginal distribution . We performed this simulation procedure for combinations of and fixed tract lengths of 200 , 400 , 800 , 1600 , 3200 , and 6400 . For each set of simulated alignments , we predicted gBGC tracts as described in the previous section , assuming several different values for the tuning parameter . For each data set and value of , we calculated the true positive rate ( number of correctly predicted gBGC bases/total number of gBGC bases ) , false positive rate ( number of incorrectly predicted gBGC bases/total number of non-gBGC bases ) , and positive predictive value ( number of correctly predicted gBGC bases/number of predicted gBGC bases ) . We compared the predicted gBGC tracts with exon and intron definitions from Gencode version 3c and Ensembl genes [51] , and with annotations of lincRNAs , miRNAs , miscRNAs , small non-coding RNAs , NMD transcripts , and pseudogenes from Gencode version 14 [52] . We also compared them with LINE and SINE elements from the rmskRM327 table in the UCSC Table Browser [53] , and with a set of high-confidence predictions of transcription factor binding sites based on ChIP-seq data from ENCODE [54] . In addition , we compared the tracts with genome-wide recombination rate estimates from the 1000 Genomes Project [33] , recombination hotspots from the October 2006 release of HapMap [32] , and chimpanzee recombination rate estimates from the PanMap project [25] . Disease-associated SNPs were obtained from several sources . SNPs annotated with “pathogenic” or “probable pathogenic” clinical significance were downloaded on October , 25 , 2011 from dbSNP [34] . The HGMD dSNPs were obtained from the Supplementary Material of reference [11] . Regions of the human genome with positive genetic associations with disease were taken from the Genetic Association Database [36] on February 2 , 2012 . The level of evidence for the function of non-coding SNPs was downloaded from the RegulomeDB [37] web site on December 12 , 2012 . All data not in reference to the GRCh36/hg18 assembly were mapped to hg18 using the ‘liftOver’ tool from the UCSC Genome Browser . To evaluate the statistical significance of various properties of interest , we compared the predicted gBGC tracts with sets of control regions matched to them in number , length distribution , and chromosome assignment . We also ensured that the control regions were matched to the gBGC tracts by GC content ( by stratifying predictions and controls into 100 bins ) , which is known to correlate strongly with several relevant genomic features . We obtained a null distribution for each statistic of interest ( such as the number of tracts overlapping exons , or the number human tracts overlapping orthologous chimpanzee tracts ) , by computing a value of the statistic for each of 1000 randomly sampled replicates of the control regions . One-sided empirical p-values were computed as the fraction of sampled control sets for which the statistic was at least as extreme as observed in the predicted tracts . As noted in the text , we occasionally considered alternative sets of control regions designed to accommodate known biases in genomic regions of interest . For example , when evaluating the significance of overlap with disease-associated SNPs from HGMD and dbSNP , we used control regions matched to the predicted tracts in terms of their degree of exon overlap , since these sets consist mostly of coding SNPs . Similarly , for RegulomeDB , which is focused on non-coding SNPs , we used control regions that matched the overlap of the gBGC tracts with the set of SNPs considered by RegulomeDB . Our analysis of human derived allele frequencies was based on genotype data and ancestral allele predictions from the low-coverage pilot data set from the 1000 Genomes Project released in July 2010 [33] . These comprise SNP calls for the 22 autosomes in three HapMap population panels: YRI ( 59 individuals ) , CEU ( 60 individuals ) , and CHB-JPT ( 60 individuals ) . The chimpanzee derived allele frequency analysis was based on genotype data for 10 individuals downloaded from the PanMap project [25] . SNP locations were mapped to the human genome , and the 1000 Genomes predicted human chimpanzee ancestral allele was used to identify the derived allele . Sites with a low quality genotype call ( GQ quality score less than 5 ) , more than two alleles , or no predicted ancestral allele were not considered . We computed the W→S DAF skew of all human and chimp gBGC tract SNPs as normalized values from a Mann-Whitney test on the derived allele frequencies of W→S and S→W SNPs , as previously described [17] . A W→S DAF skew of 0 . 5 indicates no bias , and values greater than 0 . 5 indicate that W→S mutations are favored . | Interpreting patterns of DNA sequence variation in the genomes of closely related species is critically important for understanding the causes and functional effects of nucleotide substitutions . Classical models describe patterns of substitution in terms of the fundamental forces of mutation , recombination , neutral drift , and natural selection . However , an entirely separate force , called GC-biased gene conversion ( gBGC ) , also appears to have an important influence on substitution patterns in many species . gBGC is a recombination-associated evolutionary process that favors the fixation of strong ( G/C ) over weak ( A/T ) alleles . In mammals , gBGC is thought to promote variation in GC content , rapidly evolving sequences , and the fixation of deleterious mutations . However , its genome-wide influence remains poorly understood , in part because , it is difficult to incorporate gBGC into statistical models of evolution . In this paper , we describe a new evolutionary model that jointly describes the effects of selection and gBGC and apply it to the human and chimpanzee genomes . Our genome-wide predictions of gBGC tracts indicate that gBGC has been an important force in recent human evolution . Our publicly available computer program , called phastBias , and our genome-wide predictions will enable other researchers to consider gBGC in their analyses . | [
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| 2013 | A Model-Based Analysis of GC-Biased Gene Conversion in the Human and Chimpanzee Genomes |
Biological network figures are ubiquitous in the biology and medical literature . On the one hand , a good network figure can quickly provide information about the nature and degree of interactions between items and enable inferences about the reason for those interactions . On the other hand , good network figures are difficult to create . In this paper , we outline 10 simple rules for creating biological network figures for communication , from choosing layouts , to applying color or other channels to show attributes , to the use of layering and separation . These rules are accompanied by illustrative examples . We also provide a concise set of references and additional resources for each rule .
Biological networks are present in many areas of biology , including studies of cancer and other diseases , metagenomics , pathway analysis , proteomics , molecular interactions , cell–cell interactions , epidemiology , network rewiring due to perturbations or evolution , etc . Increasingly , published studies in these areas and many others include figures meant to convey the results of one or more experiments or of the network analysis carried out . As a result , biological network figures are ubiquitous in the biology and medical literature . On the one hand , a good network figure is able to quickly provide information about interactions between items and can often convey the nature and degree of interactions , as well as enable inferences about the reason for those interactions . On the other hand , good network figures are difficult to create . The scale of data can often obscure the relationships that the figure is trying to convey , the spatial layout and distribution of the network can be difficult to interpret , and the many ways in which data can be mapped onto network representations provide an easy pathway to violating best practices of data visualization . Some relatively simple rules , when followed , can significantly improve the likelihood that a network visualization will "tell the story" the author intends . The following set of rules was a result of a week-long seminar that brought together leading biology , bioinformatics , and visualization researchers from different countries [1] . Note that the rules we give are meant for static figures as used for publications , not for dynamic figures or for interactive or exploratory tools that allow users to manipulate the data view . The rules are tightly interconnected and , in general , follow the typical visualization design decision process ( without forming a decision tree , due to their interconnectedness ) , from determining first the intended message of the illustration we seek to create [2 , 3] to selecting appropriate encodings for that message and network . In order to provide a useful interpretation of these rules , we use real data for our illustrations below , and in many cases , we utilize network figures from the bioinformatics literature . In no way do we mean to detract from the science or experimental results that these published figures are trying to represent . As already noted , good network figures are difficult to create , and even some of the figures we use to illustrate specific rules below may come up short with respect to another rule . Last but not least , for each rule we also provide a concise set of references and resources where the interested reader may find additional information on the topic .
The first rule is also arguably the most important: Before creating an illustration , we need to establish its purpose [4] and then the network characteristics . When establishing the purpose , it helps to first write down the explanation ( caption ) we wish to convey through the figure and note whether the explanation relates to the whole network; to a node subset in the network; to a temporal , causal , or functional aspect of the network; to the topology of the network; or to some other aspect . This analysis needs to happen before we draw the network because the data included in the view , the focus of the figure , and the sequence we use to visually encode the network should support the explanation that we wish to convey . For example , salient aspects of the figure may need to be displayed centrally , in larger size , or marked by annotations . Second , we need to assess the network in terms of scale , data type , structure , etc . These network characteristics will further constrain salient aspects of the visualization , such as the color , the shape , the marks used , and the layout of the network [5] . Fig 1 delivers two messages about proteins known to be involved in glioblastoma multiforme ( GBM ) . The first figure is a RAS signaling cascade in a curated GBM network . Because the message of the figure relates to protein interaction functions , the figure uses a data flow encoding , with nodes connected by arrows . The nodes are colored by the expression variance across samples . The second figure is a STRING protein–protein interaction ( PPI ) network representing proteins that show significant expression changes in subtype 3 of GBM , in addition to 20 additional proteins to improve connectivity; the colors represent the fold change , and the size represents the number of mutations . Because the message of this figure relates to the structure of the network , not its functionality , the nodes are connected by undirected edges , and the nodes are placed to reinforce the structure . Furthermore , note how the quantitative color scheme ( yellow to green gradations ) in the first network shows expression variance , whereas the divergent color scheme ( red to blue ) in the second network emphasizes the extreme values of differential expression for one GBM subtype . Similarly , the edges in the first network are arrows indicating function , whereas in the second network , they are edges to indicate structure . Each image tells a different story: The first message is about network functionality , the second about the network structure .
Node-link diagrams are the most common way to display network data . Node-link diagrams are familiar to readers , and they can show relationships between nodes that are not immediate neighbors . However , node-link diagrams also have drawbacks: For dense and large networks , they tend to produce significant clutter , edge attributes are difficult to visualize , and node labels often cause even more clutter . An alternative network representation is adjacency matrices ( see Fig 2 ) . An adjacency matrix lists all nodes of a network horizontally and vertically . An edge is represented by a filled cell at the intersection of the connected nodes . Adjacency matrices have several advantages: First , they are well suited for dense networks with many edges , as every possible edge is represented by a cell [7] . Second , they can encode edge attributes , for example , with color or color saturation of a cell . Third , adjacency matrices excel at showing neighborhoods of nodes and clusters , provided the node order is optimized [8] . Fourth , the layout of the matrix makes it easy to display readable node labels , whereas labels in a comparable node-link layout would cause significant clutter . Matrix layouts are easy to implement , e . g . , in R , Python , or JavaScript , even without dedicated graph visualization libraries . In practice , using an appropriate column/row reordering algorithm is crucial [8] . Another alternative to traditional node-link layouts is fixed layouts: Here , the nodes are positioned such that the position of the nodes themselves encodes data . A common example is networks shown on top of maps , or links on top of linear or circular layouts , such as is commonly used for genomic data visualization in Circos [9] . Finally , when the graph to be shown is a tree , we can also make use of implicit layouts , such as icicle plots [10] , sunburst plots [11 , 12] , or treemaps [13 , 14] . Implicit layouts encode the relationships between parents and children by adjacency , and the size of the leaves is commonly scaled according to an attribute . S . Ribecca's Data Visualisation Catalogue ( datavizcatalogue . com ) provides a wide although nonexhaustive array of possible representations .
Node-link diagrams map nodes to locations in space . In turn , Gestalt theory ( in particular , the principles of grouping ) teaches us that the spatial arrangement of nodes and edges influences the reader’s perception of the network information—even if there is no meaning [4] . Thus , the right layout can effectively enhance features and relations of interest , but the wrong layout might easily lead to misinterpretation . An example of such a misinterpretation can be found in the Atlas of Science [16] . Although aesthetically pleasing , the node-link diagram shows a defective spatial encoding that suggests a black hole of knowledge . Proximity , centrality , and direction of node arrangement are the most prominent principles to be considered when integrating spatiality into meaningful network representations: Nodes drawn in proximity will be interpreted as conceptually related; nodes grouped together are also perceived as more similar to each other than nodes outside the group . We may use as a similarity measure the connectivity strength between two nodes ( an edge-based measure ) , similarity of the content carried by the nodes , e . g . , nodes being part of the same brain region or conceptual group ( a node-based measure ) , or a mixture of both . This measure is then used as an optimization criterion for the layout algorithm ( Fig 3 ) . Most prominent layouts are force directed and interpret the given similarity measure as an attracting force for nodes , whereas graph layouts based on multidimensional scaling perform better for cluster detection [15] . Centrality is a design principle in which the center and periphery may represent metaphorically high relevance and secondary relevance , respectively . A layout may be spatially constrained to display the focus of the illustration in the center of the figure . The third design principle is direction: The vertical dimension represents power , from light/good ( up ) to heavy/bad ( down ) and also flow of information or development ( up to down ) or in the horizontal direction ( left to right in Western cultures ) . Most open-source network drawing tools like Cytoscape ( Cytoscape Consortium; https://cytoscape . org/ ) and yEd ( yWorks GmbH; https://www . yworks . com ) provide a rich selection of different layout algorithms . Beside these resources , drawing networks and developing appropriate layout methods is a whole scientific discipline by itself . An excellent source for diving deeper into the world of graph drawing algorithms is http://graphdrawing . org/ .
The proper use of labels and captions can help explain and clarify the icons , colors , and visual representations present in a network figure . First , network labels and , in general , text in a network figure have to be legible . To be legible , labels in the figure should use the same ( or larger ) font size as the caption font , not smaller . Fig 4A shows PPI data from Andrei and colleagues [34] , in which the node labels are too small to be legible . In Fig 4B , the layout has been modified to make better use of the available space , resulting in larger labels . Although this type of manipulation may not always be possible ( for example , Fig 10 in Wenskovitch and colleagues [19] shows the similarity among 4 large-scale network models with no room for larger labels ) , in such cases , one should at least provide an online high-resolution version of the network that can be zoomed in . Furthermore , whereas it is tempting to rotate text affiliated with specific network elements in order to optimize space , all network text should use a horizontal orientation: Vertical or tilted text is hard to read . To be legible , all text should also have good contrast with the background , preferably black on white or white on black . The figure and its caption ( the brief explanation appended to an image ) should each be able to stand on their own and provide both context and interpretation . The caption , in particular , should tell the reader what to notice in the network figure , without the reader needing to chase the figure reference in the manuscript text . The network figure text should further clarify the meaning of all unusual visual markers and channels used in the network representation , including all colormaps . Last but not least , labels should be properly placed within the network figure . For example , inset and subfigure labels should be placed in clear proximity to that element . Whenever possible ( i . e . , when the figure is not too cluttered ) , use direct labeling instead of numerical pointers to a legend; numerical pointers place a higher cognitive load on the reader .
Depending on the intended meaning of a figure , it may be beneficial to show fewer details , even if they are relevant , in order to bring into better focus the item ( s ) or relationship ( s ) of interest ( reference [5] , Chapter 13 ) . The level of detail shown can also change locally across the figure . If , for example , one is interested in showing centrally the details of a network , there is no need to display the data at the periphery with the same ( high ) level of detail . To keep the context of the visualization clear , the entire structure can be shown in an aggregated form , around the item of interest . Aggregation can be performed at the level of items , based on dimensionality reduction over the item attributes ( e . g . , principal component analysis ) , or based , for example , on a spatial aggregation of geo-collocated items into groups . Aggregation may also be performed at the level of relationships , via , e . g . , edge bundling algorithms . The wise use of aggregation in combination with a variety of visual marks and channels can significantly reduce visual clutter . Fig 5 shows images made with Cytoscape of protein interaction data with 5 complexes ( computationally determined ) colored using data from Kuhner and colleagues [21] . This figure replicates the sequence of steps described in Gehlenborg and colleagues [20] . Network a is the original protein interaction network ( > 400 proteins ) . According to Gehlenborg and colleagues , this first network is hardly readable , and nothing really interesting is visible . Network b is a recomputed network after removing nodes not of interest . Clusters based on the complexes’ color start to emerge . Network c is a manual refinement to emphasize the structure of protein complexes and the interactions between them . Finally , network d proposes to collapse nodes in each complex core ( e . g . , nodes inside each colored circle are replaced by only one triangle of the same color ) to simplify the network and emphasize global properties , which is the aim of the figure .
Color is a complex topic [23] , and here , we touch only on the aspects most relevant to bionetwork visualization . Color is a perception and not visible electromagnetic radiation ( light waves are not colored ) : Most , but not all , people experience the sensation "blue" with wavelengths near 400 nm . The color humans perceive depends on the eye–brain mechanism , and therefore color perception is influenced by context , training , or abnormalities such as color blindness , which affects 8% of the male population and often results in an inability to distinguish red from green . For this reason , red–green color encodings of network data should be avoided . Human vision is also much more sensitive to slight changes in the luminance of a color ( its intensity or value ) than slight changes in the quality of a color ( its hue and saturation ) [24] . Therefore , it is a good idea to convert the network figure to grayscale and make sure that the information encoded in the diagram is still legible . In a nutshell , get the figure right in grayscale first . In terms of saturation , areas of saturated color draw attention and are best used on small areas such as nodes; use saturated colors sparingly and to draw attention . The hue component ( the color quality that distinguishes red , green , blue , etc . ) is also powerful: Hue families can code related items . Qualitative maps ( i . e . , multihued maps ) should be used only for categorical coding , to indicate different qualities or identities of data . Because humans have no sense of whether blue is more or less than orange , to encode ordinal data , figures should use a progression of luminance values , similar to topographic maps . All else being equal , blue-family hues tend to recede , whereas warmer red-family hues tend to come forward , and so the use of these two families together in a network may result in an unwanted 3D effect [25] . Transparency can be further used to modulate a color: Transparent markers tend to be perceived as being in the background . In Fig 6A , the colormap encodes the node degree using a two-tailed gradient ( saturated yellow to saturated green ) and saturated red for 1 . The color scheme is not color-blind safe and employs saturation incorrectly . Some edges use , confusingly , the same hue as some unconnected nodes . The gray figure text has also poor contrast with the background ( i . e . , the text and background have similar luminance ) , making it hard to read . The revised image in Fig 6B uses a ColorBrewer ( http://www . colorbrewer2 . org ) sequential colormap for the node degree , a separate sequential colormap for edges , and black figure text . The result is a significantly clearer figure , although the text contrast with colored backgrounds could be further improved .
Whereas color is incredibly powerful , other visual marks and channels are also important . Marks are basic geometric elements that depict items or links , whereas channels control the appearance of marks . Marks can be , with increasing dimensionality , dots , lines , arrows , blobs or polygons ( marks with area ) or volumetric glyphs ( marks with volume ) . Some channels are position ( see Rule 4 ) , color ( see Rule 6 ) , shape , size , tilt , area , and volume . Using a variety of marks wisely can create more powerful displays , through increased flexibility , and further allows layering and separation of information for more effective displays ( Rule 8 ) . With respect to marks , in general , dots and glyphs represent items , whereas lines and arrows represent relationships between items . Blobs represent regions or containers of items . Arrows are asymmetric lines that represent asymmetric relations and can change drastically the meaning of a figure: diagrams with arrows tend to be interpreted as functional , presenting a sequence of actions and outcomes . In contrast , diagrams without arrows tend to be interpreted as structural , specifying the location of parts relative to one another [4] . With respect to channels , position , color , and shape are identity channels , which means that a set of shapes can be used to distinguish different categories and so can a set of colors or a set of predefined positions [5] . The remaining channels are magnitude or quantitative channels , which means that a set of sizes ( small , medium , large , etc . , or weak , medium , strong , etc . ) can be used to distinguish different quantities or attribute strength of a specific category , and so on . The example in Fig 7 shows network data from Morris and colleagues [26] and makes effective use of multiple visual marks and channels .
The goal of any figure is to communicate information . Communication can be difficult if the key information is obscured by too much “clutter . ” We can raise the prominence of key information by imagining that different classes of information belong in different layers and that the key information is sitting on a higher layer in the figure and by providing visual separation between the layers . Once we decide on how we would like the information organized , layering and separation [27] are traditionally accomplished by means of assigning a specific weight , color , opacity , or size to each layer of information although we can also use spatial cues such as grouping to highlight relationships . For example , we can decrease the weight , luminance , saturation , opacity , or size of less important information , and increase the weight , luminance , saturation , opacity , or size of the key information to make it more visually salient . As an example , consider the images in Fig 8 . The left image is a reconstruction of Fig 5A from Preston and colleagues [28] , showing the largest subnetwork resulting from a pathway and enrichment analysis . Based on the callouts , the key data the authors want to convey are the neighborhoods around SRSF2 and NTRK1 . The image on the right is an improved version in which we decreased the weight of those edges that do not connect to the key nodes and increased the size of key nodes ( Rule 7 ) . Nonkey nodes and self-edges were also rendered transparent , which effectively leads to a perception of these nodes and edges being in the background ( Rule 6 ) . Typically , if self-edges are not germane to the point being made by the image , they would be removed . Last but not least , subtle shading behind the two key nodes was applied to provide additional separation .
Another kind of clutter in a network figure happens when there is too much information vying for the attention of the viewer . Under these circumstances , it is often better to split that information into multiple figures , each emphasizing a different point . Multiple figures can also effectively illustrate a sequence in the illustration . Thus , as a rule of thumb , count the number of visual properties an image uses to map data . If it is greater than 3 , and they are not redundant ( i . e . , not intentionally mapping the same value for emphasis ) and their interaction is not the point being made ( i . e . , overexpressed genes are also hubs ) , think about separating the image into multiple separate figures , each one emphasizing a different point and potentially focusing on relevant subnetworks . Another interesting aspect is the use of one image ( e . g . , A in Fig 9B ) to provide overall context for the visualization of subnetworks . This overview + detail approach can be very useful . However , an extremely dense network with many overlapping nodes will not provide effective overview or context . Alternative models to the "overview-first" paradigm [30] include a "search-first" paradigm [31] and a "details-first" paradigm [32] , depending on the interests and background of the target audience . As an example , Fig 9A shows an image constructed from the data provided by Zhu and colleagues [29] . The "overview" network ( A ) is itself a 51-node subnetwork of the full 195-node network that the authors initially queried . This image includes several different pieces of information: The node colors indicate whether the node is a hub , square nodes represent a cluster found by the molecular complex detection algorithm ( MCODE ) , and the purple borders indicate the first neighbors of that cluster . The result is a confusing image , in which it is hard to determine what is important—the information does not rise above the clutter . Now , consider Fig 9B , which was the image the authors used . They split the network into three views . The first figure uses color to show degree , and it also provides an overall context for the subnetworks . The second network shows the results of the MCODE algorithm , and the third network shows those nodes plus their first neighbors . In each case , it is much easier to determine the point of the image .
Many people think that if two dimensions ( 2D ) are good , three dimensions ( 3D ) must be better . As the printed medium evolves , video recordings and interactive displays , including virtual reality technologies , also become of interest . However , in the context of biological network displays , it is important to be aware that depth has important differences from the other two planar dimensions . 3D is seldom appropriate for such displays , due to documented issues related to depth perception inaccuracies , occlusion , perspective distortion , and so on ( reference [5] , Chapter 3 ) . 3D is easy to justify when the users’ tasks involve 3D shape understanding , for example , in molecular structures , which inherently have spatial structures . In such cases , the benefits of 3D absolutely outweigh the perception costs , and designers are justified in investing in interaction idioms designed to mitigate such costs . For example , occlusion hides information—some objects cannot be visible because they are hidden behind other objects . Even though the occluded nodes can be discovered via interactive navigation , the navigation has a time and cognitive cost . Occlusion can be also mitigated through the use of motion parallax ( motion cues ) [33] , which also has an associated cost . In all other contexts , using 3D needs to be carefully justified in the context of the higher cognitive costs . As shown in the previous rules , there are other , more convenient techniques available for handling large scales , for example , avoiding showing an overview of the entire network altogether or choosing an alternative representation ( e . g . , an adjacency matrix ) instead of node-link diagrams . The example in Fig 10 shows a network illustration in which the height of each 3D cylinder is mapped to the size of specific network attributes . Note how the different cylinder heights can be mistakenly perceived as perspective foreshortening instead of different attribute sizes . A clearer illustration would use 2D instead and map the attribute size to a visual channel like 2D marker size .
Several of the examples shown in this paper illustrate the many inherent difficulties in creating biological network figures that are appropriate for communication . The 10 simple rules we outlined in this paper show ways to improve such figures and in several cases , also illustrate the variety of means to visually encode information that circumvent data constraints . We believe these rules will benefit researchers who handle biological networks , be they bioinformaticians , neuroscientists , clinicians , and so on . We strongly believe that creation of a biological network figure should start with an analysis of the intended figure message ( Rule 1 ) . Ideally , this analysis should be performed in conjunction with the domain scientists who generated the network data and its interpretation . Choosing an appropriate basic representation ( node-link , matrix , etc . ) and layout of the data comes next ( Rule 2 and Rule 3 ) , along with the appropriate labels and clarifying text ( Rule 4 ) . Gradual data preprocessing through aggregation ( Rule 5 ) , appropriate color mappings ( Rule 6 ) , the use of an appropriate variety of marks and channels ( Rule 7 ) , layering and separation ( Rule 8 ) , and sequencing information along several figures ( Rule 9 ) can then help reduce visual clutter and effectively emphasize the message of the figure . With advancements in media technology , we believe 3D figures should be used extremely cautiously , due to documented issues in depth perception ( Rule 10 ) . An important aspect of network visualization that we have shown implicitly , although not discussed directly , is the power of network images to support the integration of a wide variety of data and to encode that data in a number of ways ( for example , mapping expression fold change onto node fill color ) . This is an important and powerful feature of network visualization , particularly for exploring the results of multiple experiments in a single visualization in order to find new hypotheses or to confirm hypotheses , as often done in environments such as Cytoscape . On the other hand , too much information mapped onto a single figure can obscure the key aspects of that figure ( see Rule 9 ) , so it is important to balance how much of the network image is about the topology of the network and how much is about the integration of other -omics results in the context of gene or protein relationships . Fittingly , this observation rounds back the discussion to Rule 1—we first need to determine the purpose of the figure . Another important aspect of network visualization that we have implicitly discussed is the issue of subnetworks . Whereas our rules suggest providing less detail at the periphery of a network , a periphery subnetwork may still be of major interest . Such situations may be addressed through the careful application of Rule 1 ( determine first the message of the figure ) , Rule 8 ( use layering and separation ) to emphasize the subnetwork , and if necessary , Rule 9 ( use multiple figures ) to allocate a separate figure to that subnetwork . Many of the illustrations in this manuscript have been generated using the open-source software platform Cytoscape . Wherever possible , we provided references to the software packages , as well as specific instructions . In an effort to make the application of these rules more accessible , we also provide , wherever possible , the session files for generating these images in a public repository ( http://github . com/uic-evl/10RulesBionets ) . Whereas obviously there are many other software tools for network visualizations , we hope that knowing how to implement these rules in one tool might help the reader more easily transfer that knowledge to another tool . Beyond the basic "how-to" mechanics of the rules , we further that recommend biology researchers contact the biological data visualization community ( e . g . , http://biovis . net , http://bivi . co , http://visguides . org ) for expert advice and help . We trust that this minimal set of rules helps demystify the process of creating quality static biological network illustrations for communication . Although the landscape of visualization design is far more complex than briefly discussed in this paper , we hope this discussion clarifies some of the most common issues that arise in the creation of network figures , along with basic guidelines to help address those issues . We hope the interested reader will pursue the additional resources and references we include under each rule . | Biological network figures are ubiquitous in the biology and medical literature . In this paper , we outline 10 simple rules for creating biological network figures for communication , from choosing layouts , to applying color or other channels to show attributes , to the use of layering and separation . | [
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| 2019 | Ten simple rules to create biological network figures for communication |
Great strides have been made in understanding the evolutionary history of simian immunodeficiency virus ( SIV ) and the zoonoses that gave rise to HIV-1 and HIV-2 . What remains unknown is how long these SIVs had been circulating in non-human primates before the transmissions to humans . Here , we use relaxed molecular clock dating techniques to estimate the time of most recent common ancestor for the SIVs infecting chimpanzees and sooty mangabeys , the reservoirs of HIV-1 and HIV-2 , respectively . The date of the most recent common ancestor of SIV in chimpanzees is estimated to be 1492 ( 1266–1685 ) , and the date in sooty mangabeys is estimated to be 1809 ( 1729–1875 ) . Notably , we demonstrate that SIV sequences sampled from sooty mangabeys possess sufficient clock-like signal to calibrate a molecular clock; despite the differences in host biology and viral dynamics , the rate of evolution of SIV in sooty mangabeys is indistinguishable from that of its human counterpart , HIV-2 . We also estimate the ages of the HIV-2 human-to-human transmissible lineages and provide the first age estimate for HIV-1 group N at 1963 ( 1948–1977 ) . Comparisons between the SIV most recent common ancestor dates and those of the HIV lineages suggest a difference on the order of only hundreds of years . Our results suggest either that SIV is a surprisingly young lentiviral lineage or that SIV and , perhaps , HIV dating estimates are seriously compromised by unaccounted-for biases .
HIV/AIDS is the result of at least eleven cross-species transmission events of simian immunodeficiency virus ( SIV ) from non-human African primates to humans . Three transmissions of SIVcpz from the central African chimpanzee subspecies ( Pan troglodytes troglodytes ) gave rise to HIV-1 groups M , N and O [1] , and the other eight SIVsm transmissions from sooty mangabeys ( Cercocebus torquatus atys ) gave rise to HIV-2 groups A through H [2] , [3] . All three HIV-1 groups , plus HIV-2 groups A and B , have established human-to-human transmission chains , with HIV-1 group M causing pandemic HIV/AIDS . The six other HIV-2 lineages do not appear to be transmissible among humans [2] . Determining when the virus jumped into humans has been a priority for HIV researchers . By analyzing viral sequences obtained over several decades and calibrating a molecular clock based on observed nucleotide changes , a reliable rate of sequence evolution can be inferred . Korber et al . used this method to estimate the time of most recent common ancestor ( tMRCA ) for HIV-1 group M at 1931 ( 1915–1941 ) [4]; this estimate has recently been pushed back slightly to 1908 ( 1884–1924 ) [5] . The tMRCA of HIV-1 group O was estimated to be 1920 ( 1890–1940 ) [6] . Both HIV-1 group M and O dates were inferred using a relaxed molecular clock , which allows the rate of evolution to vary along different branches of the tree . HIV-2 group A and B tMRCAs were estimated to be 1940 ( 1924–1956 ) and 1945 ( 1931–1959 ) , respectively [7] . These dates were estimated using a strict molecular clock , ( i . e . , a single , constant evolutionary rate along all branches ) . No estimate currently exists for the tMRCA of HIV-1 group N . There has also been success in locating the populations of chimpanzees and sooty mangabeys whose SIVs are the direct ancestors of the transmissible HIV lineages ( i . e . , the SIVs that lie basal to HIV-1 and HIV-2 on the SIV/HIV phylogeny ) . Extensive non-invasive fecal sampling of wild chimpanzees pointed to the origin of HIV-1 group M in southeastern Cameroon and HIV-1 group N in south central Cameroon [8] . Although a reciprocally monophyletic clade of SIVcpz has been found in the eastern chimpanzee subspecies ( Pan troglodytes schweinfurthii ) , virus from this group does not appear to have jumped successfully into humans [9] . Surprisingly , the SIV lineage that falls immediately basal to HIV-1 group O was found in gorillas , suggesting that they might have been an intermediate host between chimpanzees and humans [10] . Similar fecal analysis in sooty mangabeys indicated that HIV-2 groups A and B were likely transmitted to humans in Côte d'Ivoire [11] . Despite these findings , an important question about the origins of SIV/HIV remains unanswered: How long have these primate hosts been infected with SIV ? Answering this question would help determine the length of time SIV was in sooty mangabeys and chimpanzees before giving rise to the transmissible HIV lineages . It might also shed light on the tMRCA of the dozens of other SIV lineages . Determining the age of SIV would provide perspective on the spread of the virus among African primate species and the subsequent zoonoses . Knowing the age may also have implications for the evolution of pathogenicity and virulence in HIV . AIDS-like symptoms have rarely been observed in non-human African primates infected with SIV [12] , [13] . Historically , this lack of disease was attributed to the codivergence and coevolution of SIV and their primate hosts over millions of years [14] ( we use the term codivergence instead of cospeciation , because codivergence considers phylogenetic congruence irrespective of species classification , whereas cospeciation implies that SIVs infecting different primates can be classified as species complexes ) . Although there is significant correspondence between the SIV and host phylogenies , detailed analysis of this relationship suggested that a preferential host switching model , in which cross-species transmissions of SIV are more likely to occur between closely related primates , could account for this correspondence [15] . Furthermore , subsequent analysis of SIV infecting various African green monkey species , thought to be exemplary of codivergence , demonstrated a lack of evidence for host-virus codivergence [16] . In addition , the codivergence hypothesis does not account for the observation that SIV is geographically confined and naturally infects only African primates . Finally , even with biologically unrealistic assumptions about a molecular clock , Sharp et al . were unable to push the tMRCA of all SIV beyond 2500 years [17] . If it were demonstrated that SIV has evolved in a clock-like manner , then we might be able to accurately determine the age of SIV . Here , we use relaxed molecular clock phylogenetic inference to determine the tMRCA of SIVsm/HIV-2 and SIVcpz/HIV-1 . We also provide , to our knowledge , the first estimate of the age of HIV-1 group N . Taken together , these dates suggest that SIV may indeed be a relatively young viral clade and that its transmission into humans is a natural process .
We inferred phylogenies for SIVsm/HIV-2 gag , pol , and env loci under a relaxed molecular clock in a Bayesian Markov chain Monte Carlo ( BMCMC ) framework ( Figure 1A–C ) . In each tree there was very high posterior support for monophyly in HIV-2 group A , HIV-2 group B , and the major SIVsm clades identified by Apetrei et al . [18] . The position of the root , determined by the BMCMC analysis , was also highly supported in each of the three trees . Phylogenetic inference using the three loci produces different topologies , which was expected given the observation of recombination by Apetrei et al . in their initial analysis of these loci in SIVsm [18] . The tMRCA estimates for the root of the SIVsm/HIV-2 trees differed as well ( Table 1 ) . The pol locus had the oldest root , putting the tMRCA of SIVsm/HIV-2 at 1686 ( 1525–1811 ) . Estimates from gag and env were significantly younger , placing the SIVsm/HIV-2 tMRCA at 1809 ( 1798–1875 ) and 1861 ( 1788–1915 ) , respectively . Although gag was older than env , this difference was not significant . The pol results also indicated older dates than gag and env for the tMRCA of HIV-2 groups A and B , although these differences were not significant . With the exception of the env tMRCA estimate for HIV-2 group A , all three genes suggested a slightly older origin of both HIV-2 groups A and B than previously reported by Lemey et al . [7] . There were no discernable differences in the tMRCA estimates of these three genes for the major SIVsm clades ( Table 2 ) , although there were significant differences in the age of deeper SIVsm coalescent events among SIVsm groups 1 , 2 , 3 , 4 , and 7 [P ( gag<pol ) = 0 . 003; P ( env<pol ) <0 . 001 ) ] . We inferred phylogenies for SIVcpz/HIV-1 gag , pol , and env loci under a relaxed molecular clock in a BMCMC framework ( Figure 1D–F ) . There was very high posterior support for monophyly in each of the three HIV-1 lineages as well as for the position of the root . The three loci produced different topologies , which is not surprising given the recombinant history of HIV-1 group N [1] , [19] . Like in the previous SIVsm/HIV-2 analyses , the three loci produced variable tMRCA estimates for the root and the major HIV-1 lineages ( Table 1 ) . Again , pol had the oldest dates , with the SIVcpz/HIV-1 tMRCA at 1265 ( 658–1679 ) . In contrast , gag had the youngest date at 1618 ( 1471–1746 ) , and env produced an intermediate date at 1492 ( 1266–1685 ) . tMRCA estimates from gag and env for both HIV-1 group M and HIV-1 group O agreed with previous estimates from Worobey et al . [5] and Lemey et al . [6] , although the pol tMRCA dates were nearly twice as old . There was good agreement between gag and env when dating HIV-1 group N , placing the tMRCAs at 1966 ( 1953–1977 ) and 1963 ( 1948–1977 ) , respectively . We also performed additional phylogenetic inference to ensure that we captured the deepest available HIV-1 group N lineages in our analyses ( Figure S1 ) . There were significant discrepancies among the tMRCA estimates from gag , pol , and env in both the SIVsm/HIV-2 and SIVcpz/HIV-1 analyses . We initially thought that this discordance was due to recombination among the loci . If recombination were responsible , the different tMRCA estimates would actually represent different times of coalescence . When examining the phylogenies , however , we found very little evidence for this scenario . There were highly similar patterns of diversity in the SIVsm clades and in the HIV-1 group M sub-types among the three loci . An explanation of recombination would necessitate selective sweeps in gag and env , which would then go on to recreate the ancestral diversity seen in the pol phylogeny . For example , HIV-1 group M would have a mean tMRCA around 1795 , and , approximately 100 years later , part of the genome would have experienced a selective sweep that gave rise to the same pattern of sub-type diversity ( Table 1 ) . We then explored the possibility that some of these analyses were biased . That this discrepancy among tMRCA estimates was most pronounced in the older nodes indicated a loss of signal due to this bias deeper in the phylogeny . We examined the demographic parameters ( e . g . population size or growth rate ) from the three loci in the SIVsm/HIV-2 and SIVcpz/HIV-1 analyses . There were significant differences in these parameter estimates from gag to pol and from env to pol ( P<0 . 05 ) . Even though these genes evolved along different topologies , their demographic history , and therefore the demographic parameters inferred from them , should be the same . We hypothesized that some genes lack sufficient demographic signal to draw accurate inference about tMRCAs and that allowing the three loci to combine their demographic signal might homogenize their tMRCA estimates . To test this hypothesis , we compared a partition analysis where the concatenated genes shared a single demographic scenario to analyses where that scenario was inferred for each gene independently . This analysis was performed separately for SIVsm/HIV-2 and SIVcpz/HIV-1 under two different coalescent scenarios: constant population size and exponential growth . In all cases , allowing the three loci to share demographic information homogenized the tMRCA estimates such that there were no longer significant differences in the age of the root among the phylogenies . Among the SIVsm/HIV-2 loci , gag tMRCA estimates change the least between the partition analysis and the analyses where demographic parameters were inferred for each gene independently ( Table 3 ) . Since tMRCA estimates from gag are the most robust to combining demographic parameters , these dates should be taken as the best estimates . Among the SIVcpz/HIV-1 loci , env produced the most stable tMRCA estimates , changing as little as 0 . 01% under the exponential growth model ( Table 3 ) . This finding suggests that env provided the best tMRCA estimates for SIVcpz/HIV-1 . Given the different selective regimes that these loci experienced , it is unlikely that the differences in the tMRCA estimates among the three loci were due entirely to variable demographic signal . Nevertheless , accounting for this variation in demographic signal appears to have resolved the majority of the discrepancy among the tMRCA estimates . In addition , even if the differences among the tMRCA estimates were real , and due to recombination , all three loci suggest root ages that are of the same order of magnitude . Therefore , although we discuss these results with reference to what appear to be the most robust loci ( gag for SIVsm/HIV-2 and env for SIVcpz/HIV-1 ) , we would be able to draw the same general conclusions from any of the three loci . Finally , we emphasize that although the tMRCA estimates presented include the mean of the posterior distribution , this mean estimate is meaningful only in context of the 95% highest probability density ( HPD ) . We next sought to determine if the dates we obtained were the result of clock-like signal within SIVsm or whether SIVsm had no clock-signal and we were inadvertently extrapolating HIV-2 rates across the entire tree . We compared the date estimates from gag , pol , and env to analyses where all non-SIVsm sequences were excluded . For all three genes , there were no significant differences in the tMRCAs between the full and SIVsm-only datasets in any of the clades measured , including all SIVsm ( Table 2 ) . Furthermore , there was no significant difference between the SIVsm gag substitution rate we estimated of 1 . 38×10−3 ( 1 . 03×10−3–1 . 73×10−3 ) substitutions/site/year and the HIV-2 group A substitution rate of 1 . 22×10−3 substitutions/site/year estimated by Lemey et al . [7] . This similarity indicates that SIVsm does indeed have sufficient clock-like signal to date tMRCAs , and it does not appear to evolve at a different rate than HIV-2 group A , despite differences in host biology and pathogenicity . BMCMC analysis of an alignment containing only SIVcpz did not provide meaningful date estimates , as the tMRCA estimates from these runs were indistinguishable from the prior distribution of tMRCA estimates . Therefore , the tMRCA date we inferred for SIVcpz may have incorporated HIV-1 rates that could be biasing this estimate . However , our previous analysis of SIVsm/HIV-2 suggested that HIV-2 rates did not appreciably affect SIVsm tMRCA estimates . In a population of constant size , the most basal lineages are consistently lost due to normal coalescent processes; the age of the root is expected to be approximately two times the effective population size [20] . However , if the population is expanding exponentially , the basal lineages will be maintained until a carrying capacity is reached . The BMCMC method used here provides a convenient framework in which to test whether a constant size or exponential growth model better describes the dynamics of a population: If the 95% HPD of the exponential growth rate excludes zero , then a constant population size can be strongly rejected . To determine if exponential growth explains the SIVsm population dynamics better than a constant population size , we looked at the exponential growth rate in alignments containing only SIVsm sequences . Exponential growth rate 95% HPDs from gag and env in the SIVsm analysis excluded zero; however , the growth rate 95% HPD from pol did not exclude zero . Nevertheless , the exponential growth rate 95% HPD estimated in partition analysis ( combining demographic signal from all three loci ) rejected a constant population size . Thus , it seems probable that pol failed to reject a constant population size because it simply lacked sufficient demographic signal . Therefore , it is likely that SIVsm has not been evolving at a constant population size for the past 200 years . As a result of the discovery of SIVcpz lineages that are very closely related to HIV-1 groups M and N [8] , we were able to investigate when HIV-1 groups M and N shared a most recent common ancestor ( MRCA ) with an SIVcpz lineage . Prior to our study , there existed one estimate of this date for HIV-1 group M and SIVcpz at 1675 ( 1590–1761 ) [21]; however , this date was obtained using only two SIVcpz sequences , neither of which lies directly basal to HIV-1 group M . Our env analysis suggested that HIV-1 group M and the SIVcpz sequence that lies immediately basal to it shared an MRCA in 1853 ( 1799–1904 ) , and HIV-1 group N and its sister SIVcpz shared a MRCA in 1921 ( 1885–1955 ) . These dates represent the maximum age for the introduction of HIV-1 groups M and N into humans . We determined the number of years between the SIVsm and SIVcpz tMRCAs and those of the five transmissible HIV lineages ( Table 4 ) . If the SIVsm and SIVcpz tMRCAs represent the time SIV has been infecting each host , then this estimate would tell us the number of years that SIV was present in sooty mangabeys and chimpanzees before jumping into humans and giving rise to the transmissible lineages of HIV . We note , however , that a tMRCA estimate will tend to post-date the actual introduction of viral lineages into a new host if genetic diversity has since been lost or is not fully sampled . We believe such comparisons still provide useful information as long as this caveat is recognized . The times between the root of the SIVsm/HIV-2 tree and the base of the HIV-2 group A clade and the group B clade were 122 . 8 ( 57 . 2–199 . 9 ) and 126 . 2 ( 59 . 2–203 . 7 ) years , respectively . The time between the SIVcpz root and the HIV-1 lineages was 402 . 8 ( 231 . 0–601 . 4 ) years for HIV-1 group M , 471 . 6 ( 291 . 6–693 . 2 ) years for HIV-1 group N , and 413 . 5 ( 247 . 1–621 . 3 ) years for HIV-1 group O . These estimates are from the gag locus for SIVsm/HIV-2 and from the env locus for SIVcpz/HIV-1; partition analyses indicated that these genes were the most reliable for each clade . Ninety-five percent HPD intervals are larger for these estimates than for other single clades because the age estimates for any two clades are not perfectly correlated .
The findings presented in this study indicate that the tMRCA of SIV in sooty mangabeys and chimpanzees is 1809 ( 1729–1875 ) and 1492 ( 1266–1685 ) , respectively , assuming the relaxed molecular clock is unbiased . In addition , our results suggest that the time between the MRCA of SIVsm and SIVcpz and the MRCA of the human-to-human transmissible HIV lineages may be only hundreds of years . We present the tMRCA for all five of these HIV lineages , though we note that previous age estimates for HIV-1 groups M and O were based on larger datasets [5] , [6] . We estimate the tMRCA for HIV-2 group A to be 1932 ( 1906–1955 ) and HIV-2 group B to be 1935 ( 1907–1961 ) ; these estimates were generated by incorporating a more biologically plausible model of rate variation among lineages , compared with the strict molecular clock used to obtain the previous HIV-2 tMRCA estimates [7] . In addition , we present the first date , to our knowledge , for the tMRCA of HIV-1 group N at 1963 ( 1948–1977 ) . This date suggests that HIV-1 group N is the youngest transmissible HIV lineage and the only lineage to have originated in the second half of the twentieth century ( though the possibility of a deeper history cannot be excluded given the sparse sampling ) . Taken together with the previous tMRCA estimates for HIV-1 groups M and O ( circa 1900s and 1920s , respectively ) and our updated HIV-2 group A and B dates ( circa 1930s ) , it appears that SIV has given rise to transmissible HIV lineages throughout the twentieth century . The dispersed timing of these transmissions to humans implies that no single external factor is needed to explain the cross-species transmission of HIV . This observation is consistent with both of the two prevailing views of the origin of the HIV epidemics . The first is the bushmeat hypothesis [22] , whereby SIV is transmitted to humans during the slaughter or butchering of infected primates . The second is that the growth of sub-Saharan African cities allowed for these nascent lineages to gain a foothold [5] , [7] . According to the second hypothesis , SIV may have been jumping into humans since it first infected chimpanzees and sooty mangabeys . A change in human ecology then may have altered the evolutionary dynamics , whereby a virus that historically may have only infected a few individuals and then died out now has the potential to become an epidemic lineage . It does not seem farfetched to venture that SIV will continue to be transmitted to humans well into the twenty-first century . There are several arguments suggesting that SIV has been present in sooty mangabeys and chimpanzees longer than our results indicate . First , coalescent processes or selective sweeps might have removed the deeper lineages from the phylogeny . While we cannot discount the latter , our finding that the SIVsm population has not evolved under a constant size suggests that deep SIVsm lineages may still be present . It remains unclear whether coalescent processes may have removed deep SIVcpz branches . Nevertheless , the full SIV/HIV tree suggests that there is a relatively short period of time between the MRCAs of SIVsm and SIVcpz and the branches that lead to SIVs that infect other primates ( Figure 2 ) . A second argument is that our sampling was not thorough enough , and deep SIV branches were not included . While possible , other studies that included additional non-dated SIVsm and SIVcpz sequences did not uncover additional deeper branches [8] , [18] , [23] . Thirdly , it has been suggested previously that SIV may lack the clock-like signal necessary to draw inference about tMRCAs . As a part of this study , we demonstrated that one major SIV lineage evolves in a clock-like fashion and at a rate indistinguishable from HIV . SIVsm sequences sampled over 30 years contain enough information to calibrate the molecular clock and date the tMRCA of an SIV clade . While we were able to use the SIVsm rate to date the tMRCA of SIVsm/HIV-2 , this dating was not possible for SIVcpz . This difference is likely because we had far fewer SIVcpz sequences that were sampled over a relatively small window of time . Lastly , it is possible that our relaxed-clock models are biased and therefore unable to accurately date SIV coalescent events . We cannot dismiss this possibility , but the accuracy of these methods has been previously confirmed by other studies predicting the year of sampling of older HIV isolates from 1959 and 1960 [4] , [5] . Furthermore , our analyses recovered HIV tMRCA estimates that are in line with those previously inferred for the age of the HIV clades . Conversely , if one were to accept the HIV dates , one would need to provide a compelling reason not to accept the tMRCA estimates for SIVsm and SIVcpz as well . If the SIV tMRCAs are not correct , then we would need to determine what would be biasing their estimates , because it might also be affecting the HIV tMRCAs and those of other RNA viruses . The young ages of SIVsm and SIVcpz suggest that the entire SIV phylogeny may be relatively young ( Figure 2 ) . Even if SIV was present in sooty mangabeys and chimpanzees prior to the coalescence of their current diversity , we have identified divergence events deep in the SIV phylogeny that are on the order of hundreds of years old . The case of SIVsm is particularly compelling in this context since SIVsm sequences alone returned such a young date . It is difficult to reconcile these ages with an SIV phylogeny that is millions of years old . It seems more reasonable that the SIV phylogeny is on the order of thousands or tens of thousands of years old . While it had previously been suggested that a young-looking phylogeny could actually be the result of codivergence over millions of years , this argument was partly predicated on the assumption that SIV did not have a reliable clock-like signal [17] . In light of our findings , this argument is no longer tenable . What is still needed , however , is a reliable estimate of the age of the entire SIV phylogeny . SIV is not the only virus once thought to be ancient whose phylogeny may be better explained by the preferential host switching model . Hantaviruses infect a wide array of rodent and insectivore species . Their phylogeny was thought to be the result of an ancient infection followed by codivergence , but recent evidence suggests that the virus and host phylogenies are too dissimilar to suggest codivergence [24] . Furthermore , the molecular clock in hantaviruses suggests a tMRCA orders of magnitude younger than that of their hosts [25] . In addition , the similarity of the Arenavirus phylogeny to that of its host may also be the result of preferential host switching [26] . Furthermore , it has been proposed that feline immunodeficiency virus , a lentivirus whose lack of associated disease in natural feline hosts was thought to be the result of an ancient infection , codiverged and coevolved with its feline hosts [27] , [28]; however , in light of the possible young age of SIV , it may be worth taking a more detailed look at the relationship between the feline immunodeficiency virus and feline phylogenies . Given the ages of the SIV clades presented here , it seems unlikely that SIV evolved apathogenicity over millions of years of coevolution and codivergence with its primate host species . It is still possible that SIV evolved avirulence in its natural hosts . If SIV were highly pathogenic when it first infected sooty mangabeys and chimpanzees , then it might have decreased in virulence over a remarkably short period of time , possibly on the order of hundreds of years . There remains the distinct possibility , however , that SIV was rarely pathogenic in its natural hosts and the low level of disease associated with SIV infection is actually the ancestral phenotype . The theory of ancient coevolution towards apathogenicity appears less plausible , given the recent discovery that SIVcpz is pathogenic in wild populations of the eastern chimpanzee subspecies ( Rudicell RS , Jones JH , Pusey AE , Terio KA , Estes JD , Raphael J , Lonsdorf EV , Wilson ML , Keele BF , and Hahn BH . ( 2009 ) SIVcpz is pathogenic in its natural host . Oral Abstract . 16th Conference on Retroviruses and Opportunistic Infections ) . Future work distinguishing between these two alternative theories on SIV apathogencity is needed . A young age for SIV contrasts with other ancient retroviruses . The simian foamy virus appears to be at least 30 million years old , based on congruence between the viral and primate host phylogenies [29] . Furthermore , lentiviruses , the viral group to which SIV belongs , are also millions of years old , based on the presence of defective endongenous lentiviruses in rabbits and lemurs [30]–[32] . None of these findings , however , preclude the possibility that SIV is a much younger lentiviral clade . Finally , it is possible that SIV itself is much older than the tMRCA of the extant lineages . Dating the tMRCA of influenza A viruses infecting avian hosts suggested that deep viral lineages were constantly lost , which resulted in younger than expected tMRCA estimates for subtypes [33] . A similar process of lineage birth and death may have occurred among SIV , in which SIVs infecting particular primate species would occasionally go extinct and later be replaced by a new species-specific SIV . This process would involve the loss of deep SIV lineages with replacement by younger ones . This extinction and reinfection would be analogous to the loss of deep branches due to the coalescent . If such a phenomenon operated across the entire SIV tree , it could mask the ancient age of the virus . Combined with a preferential host switching mechanism , a macro-evolutionary process such as this could account for a young tMRCA for an ancient virus whose phylogeny is similar to that of its host .
SIVsm/HIV-2 ( gag , pol , and env sequences ) and SIVcpz/HIV-1 ( non-recombinant full-length genome sequences ) with sampling dates were obtained from the Los Alamos National Laboratory HIV sequence database ( http://hiv . lanl . gov/content/index ) ( Table 5 ) . The majority of the SIVsm sequences ( >85% ) were sampled from infected sooty mangabeys in US primate centers between 1975 and 2005 [18] , [34] . Dated sequences from macaques infected with SIV from sooty mangabeys were also included . We excluded HIV-1 group M subtype G , as this lineage is likely of recombinant origin [35] . To prevent sampling bias from HIV-1 group M lineages , only two sequences , selected randomly , of each subtype from each year were included in the alignment . Sections of the SIVcpz/HIV-1 genomes that correspond to the gag , pol , and env regions used for the SIVsm/HIV-2 analyses were designated ( Table 5 ) . To improve the accuracy of phylogenetic inference , we excluded ( i ) recombinant regions , determined using BootScanning in the RDP package [36] , [37] , ( ii ) multiple sequences from single individuals , ( iii ) sequences containing frame-shift mutations , and ( iv ) ambiguously aligned regions . Sequences containing frame-shifts were removed to accommodate codon-partitioning models in our phylogenetic analyses . Alignments were performed using Clustal X [38] and manually cleaned in Se-al ( http://tree . bio . ed . ac . uk/software/seal/ ) . SIVsm/HIV-2 and SIVcpz/HIV-1 alignments are provided as supporting information ( Datasets S1 , S2 , S3 , S4 , S5 , S6 ) . To infer the tMRCA for the major SIVsm/HIV-2 and SIVcpz/HIV-1 lineages , we employed a BMCMC approach implemented in BEAST v1 . 4 . 7 [39] , [40] . Initially , each of the three loci for both SIVsm/HIV-2 and SIVcpz/HIV-1 datasets was analyzed independently . Uninformative priors ( i . e . , tree priors ) were placed on all internal nodes whose tMRCAs were estimated . We tested the appropriateness of GTR+Γ4 and SRD06 models; the latter allows for different Κ and Γ values for the third codon position [41] . Three different coalescent tree priors were investigated: constant population size , exponential growth , and Bayesian skyline plot . We compared the six different model combinations for each locus using Bayes factor in Tracer v1 . 4 ( http://beast . bio . ed . ac . uk/Tracer ) . The Bayes factor provided strong support for SRD06 over GTR+Γ4 ( Bayes factor>20 ) , but there was not support for one coalescent scenario over any of the others . For SIVcpz , an exponential coalescent model produced substantially younger ages; this observation is not surprising given that a single exponential growth rate for SIVcpz and the HIV-1 group M pandemic lineage would likely underestimate the age of SIVcpz . The date estimates from the Bayesian skyline plot runs were used for both SIVsm and SIVcpz analyses because this model places the fewest constraints on the data [42] . XML input files for the SIVsm/HIV-2 gag and SIVcpz/HIV-1 env Bayesian skyline plot BMCMC runs are provided as supporting information ( Datasets S7 and S8 ) . Additional XML input files are available from the authors upon request . Two BMCMC runs of 50 million generations were performed for each analysis to ensure convergence of parameter estimates . Tracer was used to check for convergence and mixing ( estimated sample size>200 ) . Trees were annotated using the maximum clade credibility tree . All analyses were performed using an uncorrelated lognormal relaxed molecular clock [39] . Each analysis was also run without data to better appreciate how the prior may be affecting the tMRCA estimates . The complete SIV/HIV phylogeny was constructed using a heuristic search in a maximum likelihood framework using a GTR+Γ4 model in PAUP* v4 . 1 [43] . Topological support was assessed using non-parametric bootstrapping ( 100 replicates using a heuristic search in a maximum likelihood framework ) . We used env instead of the entire SIV genome because many SIV lineages are of recombinant origin [44] . The env alignment used to construct this phylogeny was obtained from the curated Los Alamos National Laboratory sequence database . To determine which locus's tMRCA estimates may be affected by a lack of demographic signal , we performed partition analyses . First , we pruned the SIVsm/HIV-2 dataset to contain only those sequences that were found in all three genes from the same individual and sampling year . In BEAST , we analyzed these reduced datasets assuming constant population size and exponential growth . We then concatenated all gag , pol , and env alignments , and each locus was partitioned to allow it to have its own tree topology , substitution model , relaxed clock model , and tMRCA estimates; they shared only the coalescent demographic parameter ( s ) . This analysis was performed assuming constant population size or exponential growth . Partitioning was not possible for Bayesian skyline plot , as this model's demographic estimates are topology-dependant . The same protocol was used with the SIVcpz/HIV-1 dataset . All date estimates provided are mean values with 95% HPD . Comparisons of tMRCA estimates among BMCMC runs ( e . g . , among loci and SIV/HIV versus SIV-only ) were performed by asking how many times the estimate from one run was greater than the estimate from another run . This value was taken as the probability ( P ) that the two runs were different . | HIV/AIDS continues to be a major health problem worldwide . An understanding of the evolution of HIV in humans may be greatly improved by detailed knowledge of its predecessor , simian immunodeficiency virus ( SIV ) , in non-human primates . While HIV causes AIDS in humans , SIV generally produces a benign infection in its natural hosts . This avirulence is often attributed to coevolution between the virus and its host , possibly due to codivergence over millions of years . Here , we provide a temporal reference for evolution of SIV in its natural primate hosts . Using state-of-the-art molecular clock dating techniques , we estimate the time of most recent common ancestor for SIV in sooty mangabeys and chimpanzees at 1809 ( 1729–1875 ) and 1492 ( 1266–1685 ) , respectively . These ages indicate that SIV may have infected these natural hosts for only hundreds of years before giving rise to HIV . This short duration suggests that viral–host coevolution over millions of years is not a likely explanation for the widespread avirulence of SIV . Finally , despite differences between SIV and HIV in host biology and viral pathogenicity , we have found clear and direct evidence that SIV evolves at a rapid rate in its natural hosts , an evolutionary rate that is indistinguishable from that of HIV in humans . | [
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| 2009 | Dating the Age of the SIV Lineages That Gave Rise to HIV-1 and HIV-2 |
MicroRNAs ( miRNAs ) posttranscriptionally regulate targeted messenger RNAs ( mRNAs ) by inducing cleavage or otherwise repressing their translation . We address the problem of detecting m/miRNA targeting relationships in homo sapiens from microarray data by developing statistical models that are motivated by the biological mechanisms used by miRNAs . The focus of our modeling is the construction , activity , and mediation of RNA-induced silencing complexes ( RISCs ) competent for targeted mRNA cleavage . We demonstrate that regression models accommodating RISC abundance and controlling for other mediating factors fit the expression profiles of known target pairs substantially better than models based on m/miRNA expressions alone , and lead to verifications of computational target pair predictions that are more sensitive than those based on marginal expression levels . Because our models are fully independent of exogenous results from sequence-based computational methods , they are appropriate for use as either a primary or secondary source of information regarding m/miRNA target pair relationships , especially in conjunction with high-throughput expression studies .
Micro RNAs ( miRNAs ) are small ( 20–22 bp ) RNAs transcribed by a wide variety of organisms , from viruses [1] , to plants [2] , [3] , to animals such as C . elegans , Drosophila and humans [4]–[6] . While most RNAs function in ribosomes or splicesomes , or are translated into proteins necessary for cellular function , miRNAs instead serve as negative regulators of gene expression by preventing the translation of messenger RNAs ( mRNAs ) . Through their regulatory activities , miRNAs have been shown to affect organismal development , physiological function and stress responses . Abnormal miRNA production has also been associated with the development of several types of cancer [7]–[10] . Posttranscriptional gene silencing through miRNA activity occurs through a multistep process ( Figure 1 ) [11]–[17] with an overall structure that has been remarkably conserved across organisms . This process begins with primary miRNA transcripts ( pri-miRNAs ) being either transcribed from “miRNA genes” or spliced from the intronic regions of mRNAs . In the nucleus , pri-miRNAs fold into hairpin structures from which trailing 3′ and 5′ ends are cleaved away by the RNase Drosha . The resulting precursors to mature miRNAs ( pre-miRNAs ) are then exported from the nucleus to the cytoplasm , where a second RNase enzyme ( Dicer ) removes the hairpin loop . This produces a segment of double stranded RNA that is separated into two single strands by helicase enzymes . After separation , one of the single stranded RNAs is combined with an Argonaute ( Ago ) protein to form an RNA-induced silencing complex ( RISC ) . ( Although other proteins may be incorporated into the structure , an Ago protein and miRNA compose a minimal functional RISC [18] , [19] . ) Once assembled , RISCs composed of a given miRNA interfere with the translation of select mRNAs by hybridizing to them at target sites complementary to the miRNA sequence and either cleaving the mRNA or blocking its translation while leaving the molecule intact . Any mRNA translationally regulated by a particular miRNA can be anticipated to have a limited number of target sites usable by that miRNA . Each miRNA can target multiple mRNAs , and an mRNA may contain target sites for multiple miRNAs . While both mRNA cleavage and blocking ribosomal activity disrupt translation , the latter does not directly alter mRNA abundance . Whether a particular RISC cleaves or blocks translation is determined by both the qualities of the hybridization and properties of the Ago protein contained in the RISC . The number and function of distinct Ago proteins shows substantial variability across organisms . For example , in Arabidopsis there are 10 different variants of Ago and miRNAs preferentially associate with only one in forming RISCs [20] , [21] , while in humans there are 4 commonly coexpressed Ago proteins , and miRNAs can be effectively regarded to have equal propensity to combine with each [22] , [23] . The variant of Ago primarily utilized by miRNAs in Arabidopsis is competent for target cleavage [21] , which is consistent with previous observations that the dominant means of miRNA-based regulation of mRNA translation is cleavage rather than translational repression . In humans , only RISCs composed of Ago 2 have been demonstrated to have the ability to cleave and degrade targeted mRNAs [22] , [24] . Since miRNAs have equal propensity to combine with each of these , it is reasonable to conclude that targeted mRNAs are repressed through a combination of both cleavage and ribosomal blockage . This is consistent with results described by Nakamoto et al [25] which demonstrate simultaneous increases in both target mRNA and polyribosomal fraction in human miRNA knockdown studies , and recent experiments reported by Bartel et al [26] that suggest in mice ( which share many of the complexities found in human Ago properties and RISC formation ) , most mRNA targets of miRNA-mediated repression are cleaved . To determine whether a miRNA targets a particular mRNA , sequence-based computational target prediction methods may be used to identify potential miRNA hybridization sites within that mRNA [27]–[34] . Algorithms such as miRanda [35] use m/miRNA alignments and hybridization energies as metrics to score mRNA subsequences , and report high-scoring subsequences as putative target sites . More recently proposed methods additionally utilize evolutionary conservation of a predicted site across multiple organisms ( PicTar [36] ) , information regarding target site position and base content ( TargetScan [37]–[39] ) , or mRNA secondary structure [40] to improve prediction performance . Although existing computational target prediction algorithms provide important information regarding potential m/miRNA target pairings , they are acknowledged to have issues with specificity and sensitivity [29] , [30] as well as inter-algorithm consistency [30] , [34] . ( These issues are discussed in relation to this study in the Methods and Discussion sections . ) The problem of how to reliably predict target pair relationships from sequence data alone is currently unresolved . With the limitations of purely sequence-based methods of miRNA target prediction , it has been suggested that the statistical analysis of expression data may play an important role not only in verifying computationally predicted m/miRNA targeting relationships , but also for generating de novo target pair predictions [27] , [29] . Such analysis would require that both mRNA and miRNA abundance be measured on the same tissue samples , and naturally would consider the marginal correlation between a miRNA and its putative target . Marginal approaches are attractive because they are simple and they aim to capture the fundamental negative relationship between miRNAs and their targets . However , determining reliable and replicable targeting relationships through marginal expression comparisons either on their own or in combination with computational prediction has proven to be difficult both previously [41] and in our own analysis ( see following results ) . We hypothesize that statistical models guided by knowledge of the miRNA pathway can be used to reduce error in both validating and predicting targeting relationships . The premise of our approach is that although a negative abundance relationship may exist in an m/miRNA pair , this relationship may only be detectable within the context of the abundance of other molecules that participate in mRNA silencing . In a marginal comparison of m/miRNA expression levels for the purpose of verifying a predicted targeting relationship , miRNA expressions are compared directly to those of a putatively targeted mRNA . When expression data from homo sapiens are under study , such a comparison uses miRNA expressions as a direct substitute for those of RISC composed of Ago 2 protein and a targeting miRNA . Additionally , marginal comparisons do not compensate for indirect effects on mRNA abundance caused by the blocking RISCs composed of Ago 1 , 3 or 4 proteins and the targeting miRNA . Although ceteris parabis increases of the levels of these RISCs cannot observably reduce the concentration of the targeted mRNA , because they utilize the same target sites as RISCs containing Ago 2 such increases can be anticipated to affect the ability of Ago 2 RISCs to cleave targeted mRNAs . Finally , marginal comparisons do not compensate for either the targeting of the mRNA in a putative target pair by RISCs constructed from miRNAs other than that under consideration , or targeting of mRNAs other than the one under analysis by the miRNA . In this paper , we develop a linear regression model that accounts for a variety of elements and interactions in the human miRNA pathway and that compensates for idiosyncratic aspects of two data collections on which it is applied . Central to this model is the comparison of the expression levels of a putatively targeted mRNA to a proxy for RISC expression composed of an interaction between Ago 2 and a targeting miRNA , rather than to miRNA expression alone . To demonstrate that our approach offers superior performance to marginal m/miRNA comparisons , we compare the two methods on sets of m/miRNA pairs both previously shown and predicted to have targeting relationships using expression data from two different studies as well as a combination of the data . We find that: 1 ) the system biological regression approach explains a higher proportion of the observed variation in known mRNA target levels , even after compensating for increases in model complexity . 2 ) The estimated effects of proxies to targeting Ago 2 RISC expressions on the expressions of known mRNA targets are more consistently and appropriately negative than those of marginal miRNA expressions . 3 ) A larger number of known m/miRNA target pairs are identified as such using the regression approach compared to marginal m/miRNA methods . 4 ) The system biological regression approach provides evidence supporting substantially more computationally predicted m/miRNA pairs as bona fide than do marginal m/miRNA comparisons . Because we obtain these improvements in performance without directly utilizing exogenous information from sequence-based computational target prediction methods , our approach provides a basis for statistical methods to putative m/miRNA target pair analysis that can play useful roles in both verifying computational target predictions as well as generating de novo information regarding m/miRNA target relationships .
There are two categories of covariates that ought to be compensated for when comparing the expression levels from a putative m/miRNA target pair in homo sapiens for the purpose of inferring a targeting relationship: those corresponding to elements of the miRNA system biology , and those corresponding to idiosyncratic data effects ( if any ) . Of these two categories , covariates related to the miRNA system biology can be further subdivided into those pertaining to the effect of the particular miRNA under analysis on the putatively targeted mRNA rather than that of other miRNAs potentially targeting the mRNA , those related to observable target cleavage rather than those resulting in translational repression without cleavage through a maintained hybridization at a target site , and those related to the affinity of both the miRNA under analysis as well as other miRNAs to mRNAs not under direct consideration . It can be presumed that the covariates in these categories are related to one another and to target mRNA expression in a complicated and nonlinear manner , and any statistical or computational procedure for inferring m/miRNA targeting relationships ought to have some degree of fidelity to the system biology represented by the model it is explicitly or implicitly based upon . However , the fidelity of the model also should be balanced against the need for a computationally efficient procedure that works well given the limitations of sample size and the levels of variation in the system . A well-formulated regression model is computationally tractable ( especially if large numbers of putative m/miRNA pairs are to be evaluated ) and is a standard approach to decomposing variation in a response . Further , although a linear formulation may not emerge from first principles , it may capture the dominant relationships sufficiently well to identify bona fide targeting relationships . Thus we relate the categories of system biologic covariates to the expression of a putatively targeted mRNA as in ( 1 ) : ( 1 ) where [message] refers to expression of the putative targeted mRNA; [putative cleaving RISC] represents the effect of RISCs composed of the putative targeting miRNA and Ago 2 on the targeted mRNA; [putative blocking RISC] is the effect of RISCs composed of the putative targeting miRNA and Ago 1 , 3 or 4; [non-specific cleaving RISC] is the effect of RISCs composed of Ago 2 and miRNAs not under particular consideration; [non-specific blocking RISC] is the effect of RISC composed of Ago 1 , 3 or 4 and the unconsidered miRNAs; [other targets] refers to the effect that the expression of other mRNAs have on the putative targeted mRNA , especially through their affinity for interactions with the putative targeting miRNA; [idiosyncratic effects] are dataset-specific effects; noise represents natural variation in [message] as well as that due to systemic effects not adequately captured in our model . Although the levels of RISCs of various types used in ( 1 ) are unobserved in RNA microarray expression level measurements , proxies to them can be obtained using available microarray expression data by constructing interaction terms from observable targeting miRNA Ago RNA levels . This preserves a representation of the relevant miRNA biology leading to target cleavage while avoiding complications leading to model nonlinearities , such as seen in equilibrium points of typical chemical kinetics systems . We note that Ago RNA levels are proxies to ( unobserved ) protein levels . As discussed in Protocol S1 , the microarray data was processed to approximate mRNA concentration levels . We assume that these levels are positively related to protein concentration , and so the interaction between Ago mRNA and targeting miRNA levels ought to be positively related to RISC concentration . Model ( 2 ) refines the system biological elements in ( 1 ) and provides the beginnings of a formal statistical model . Let i index tissue sample , j index an m/miRNA pair , and consider that expression levels are measured on the logarithmic scale . Further , let mRNAij represent the level of the putative target mRNA in the ith tissue sample of the jth pair; Ago2i and Ago134i be levels of Ago 2 and Ago 1 , 3 and 4 ( combined ) ; miRNAij and miRNAi−j be levels of the targeting miRNA in the jth pair and the combined levels from other miRNAs; and εij be a random error term assumed to be normally distributed . As suggested , proxies for the concentration of targeting RISCs composed of Ago 2 and Ago 1 , 3 or 4 are obtained as products of miRNAij and Ago2i or Ago134i respectively , and analogously for such RISCs composed of miRNAs not under explicit study . ( 2 ) Under model ( 2 ) , if the jth m/miRNA pair have a targeting relationship then β1j<0 ( indicating a negative relationship between expression levels of the mRNA and putatively targeting Ago 2 RISC proxy ) would be anticipated . Therefore , a targeting relationship between the jth m/miRNA pair under consideration can be inferred by evaluating the no-targeting relationship hypothesis H0: β1j = 0 vs . HA: β1j<0 . To contrast this approach with marginal expression level comparisons of mRNAs to miRNAs , note that an alternative to correlating m- and miRNA levels and evaluating the analogous no-targeting hypothesis H0: ρj = 0 vs . HA: ρj<0 ( where ρ represents the true correlation level between m- and miRNA expression levels ) would be to estimate the simple linear regression: ( 3 ) and evaluate the hypothesis H0: β1j = 0 vs . HA: β1j<0 . Of the other effect terms in ( 2 ) , β5j has arguably the most compelling physical interpretation - if the m/miRNA possess a targeting relationship ( as evidenced by rejection of the no-targeting hypothesis ) , β5j is anticipated to be positive and scaling in magnitude with β1j due to the aforementioned competition for targeting sites between RISCs composed of Ago 2 and Ago 1 , 3 or 4 . The remainder of covariates and effects used in ( 2 ) are included to conform to statistical modeling standards that require inclusion of individual covariates in models that analyze interaction terms ( e . g . miRNAij and Ago2i terms ) , and to have a full representation of the variety of possible effects justified by the system biology ( e . g . Ago2imiRNAi−j ) . Regression models ( 2 ) and ( 3 ) were developed on and fit to data from two studies in which both human m- and miRNA expression levels were measured on a reasonably large set of tissue samples . A study of nasopharyngeal cancer ( NPC ) by researchers in Madison , WI and elsewhere [10] , [42] derived whole genome Affymetrix hgu133plus2 microarrays for mRNA profiling , a custom cDNA array for miRNA profiling and RT-PCR for the expression of Epstein-Barr ( EBV ) genes . Data are available on 31 NPC and 10 normal tissue samples . The second data source was derived from that produced from a study of miRNA expression patterns over a wide variety of tumor and normal tissue types conducted by the Broad Institute [43] . This data collection measures m- and miRNA expression across 67 tissue samples from 10 different normal and tumor tissue types , each tissue type is represented by at least 5 sample observations . Additionally , we merged the Madison and Broad data to create a third dataset in order to fit ( 2 ) and ( 3 ) to data from the largest number of tissue samples possible . The merged dataset measured m- and miRNA expression across 108 tissue samples from 12 different normal and tissue types ( the tissue states from the Madison dataset were not represented in the Broad study ) . Details of the Madison , Broad and combined data collections is provided in Protocol S1 . In order to validate the system biological regression model , the TarBase miRNA target database [44] was used to derive a set of m/miRNA target pairs that both had been previously validated through the use of gene mRNA and protein-specific techniques ( such as PCR , luciferase reporters and immunoblotting ) and were represented in the Madison and Broad datasets . ( We did not include relationships that were supported by microarray data alone . ) In total , there were 76 such m/miRNA target pairs that were commonly measured in both the Madison and Broad datasets and that fit the above criteria ( these target pairs were used in the combined data analysis ) , and 23 additional pairs measured in the Madison data alone . See Table S1 for information pertaining to each of these m/miRNA target pairs . We note that TarBase classifies target pairs into those reported to result in cleavage or translational repression . To assure that the known target pairs used in this study are competent for observable cleavage , we examined the original studies supporting their inclusion in TarBase . We found no reason to reject any of the pairs labeled in TarBase as resulting in mRNA cleavage as being so competent . However , simultaneous translational repression and cleavage of was demonstrated by a number of target pairs classified in TarBase as translationally repressive [25] , and in other studies the use of only protein to miRNA comparisons could not justify such a distinction . Based on our examination of the supporting studies and underlying system biology ( as previously described ) , we did not reject any of the known target pairs based on their TarBase cleavage/translational repression classification and instead regarded all target pairs as competent for Ago 2 RISC-mediated cleavage . To evaluate the performance of the system biological regression model on computationally predicted but unverified m/miRNA target pairs we used the results of sequence-based comparisons summarized in the miRBase [45]–[47] and TargetScan databases and expression data from the Madison dataset to derive a set of putative target pairs that met three criteria: 1 ) They were predicted by both miRBase and TargetScan simultaneously , rather than either database singularly; 2 ) The putative targeting miRNAs in the pairs under consideration were previously identified as differentially expressed between NPC and normal tissue samples [10]; 3 ) The putative targeted mRNAs in the pairs were those that had above median expression variability . These criteria were used to assure confidence in both the computational target predictions as well as the data used to verify them . The use of putative target pairs simultaneously predicted by both miRBase and TargetScan was motivated by the relatively low overlap between predicted target pairs from these databases – conditional on the miRNA under consideration , TargetScan averaged 301 predicted targets meeting criteria ( 2 ) and ( 3 ) and miRBase averaged 379 , with 48 in common . Constraining the analysis to those pairs with differentially expressed miRNAs and targeted mRNAs with above average expression variability assured that there was sufficient variability in expression levels to permit a statistical analysis to be conducted . ( Using mRNAs with above median mean expression rather than variability yielded no substantial differences in the results of our study . ) In total , there were 874 putative m/miRNA target pairs that were evaluated using the Madison dataset . See Table S2 for the specific predicted target pairs studied . The Madison , Broad and combined data collections each exhibit a number of idiosyncratic data effects that might affect the ability to detect m/miRNA target pair relationships . Both Madison and Broad datasets consist of expression measurements from multiple tissue types with highly differentiated expression profiles not directly related to m/miRNA targeting . In the Madison data the tumor samples exhibit varying levels of EBV activity , which has been related to the up- and downregulation of a wide variety of genes both previously [48] and in the Madison data set [42] . In the Broad data , no measurements for Ago 3 expression are available . Finally , in addition to the idiosyncratic effects from the Madison and Broad datasets individually , the composition of the merged dataset from two data studies can be anticipated to introduce complications to even a marginal analysis of m/miRNA expressions . To compensate for these issues , when analyzing target pair expressions isolated from the Madison data we added two covariates to model ( 2 ) : a dichotomous variable representing tumor/normal tissue sample state and the expression of the EBV gene EBNA 1 . When analyzing target pairs from the Broad data , we added a vector of dichotomous covariates representing tissue type to compensate for tissue type effects and substituted terms aggregating only Ago 1 and 4 for those using Ago 1 , 3 and 4 . In marginal analyses of the Madison and Broad datasets , no compensation for tissue state was made – as described below , introduction of similar dichotomous variables to model ( 3 ) had no effect on the substantive results of the marginal analyses . When analyzing target pairs from the combined dataset , we added to model ( 3 ) a dichotomous covariate that represented the dataset origin ( Madison/Broad ) of the observation under analysis , and added to model ( 2 ) the expression of the EBV gene EBNA 1 , a vector of dichotomous covariates representing tissue state , and a dichotomous covariate that represented dataset origin . As for ( 2 ) and ( 3 ) , models that include idiosyncratic data covariates can be used to infer a targeting relationship for the jth m/miRNA pair by evaluating the suggested no targeting relationship hypothesis . Evaluation of such a hypothesis is typically performed via a t-test , and for marginal m/miRNA comparisons using any of the Madison , Broad or combined datasets this procedure is appropriate as the number of parameters are relatively low compared to the number of tissue samples available . However , the high parameterization of the system biological models motivated an alternative analysis based on AIC score minimization [49] . From a fully specified model containing both system biology and idiosyncratic data effects , minimum AIC submodels were computed and examined to determine whether the proxy variable to RISCs composed of Ago 2 and the putatively targeting miRNA was retained as a covariate , and if so , whether the effect of that variable was negative . For observational studies , estimated parameters in models selected for parsimony are sufficient to infer an effect of the associated covariate and so such cases were taken as rejections of the no targeting hypothesis . Additionally , we note that models selected by the AIC criterion can be regarded as implicitly passing a cross-validation test [50] . Therefore , there is a strong relationship between our technique and those that would be predicated upon dividing the data into training and validation sets ( e . g . for developing a predictive model for mRNA expression , in which putatively targeting miRNAs might be evaluated as a potential predictor ) . To evaluate the significance of the numbers of positive identifications we repeatedly applied the marginal and system biology-based regression model approaches to randomized control data , recording the number of m/miRNA pairs identified as targeting for each repetition . Two complementary randomization schemes were used . In the “no-targeting null” scheme miRNA expressions from each pair under study were permuted across tissue samples , holding the m/miRNA pairing constant – i . e . we condition on the set of m/miRNA expression levels in a given pair , but we randomize their association by resampling the observed miRNA levels . ( This randomization was done separately in the two sources to preserve dataset-specific effects in the combined dataset analysis . ) By contrast , in our “random pairs” scheme sets of non-targeting m/miRNA pairs were constructed by independently sampling unrelated m- and miRNAs from those under study ( i . e . from the set of known target pairs ) , thus randomizing the pairings while holding the expression levels unchanged across tissue samples . For the analysis of the known target pair data , multiple ( 1000 ) iterations of both randomization procedures were used to construct no-targeting null and random pairs distributions of numbers of positive identification . These distributions provided the basis for calculating p-values for the numbers of positively identified target pairs actually obtained by the marginal m/miRNA comparisons and system biology-based regression models . Under the no-targeting null randomization , dependency between m- and miRNA expressions is explicitly removed . Therefore the distributions of numbers of identifications across repetitions obtained from this procedure can be regarded to be what might be expected if none of the m/miRNA pairs under consideration had true targeting relationships , and the p-values correspond to tests of the hypothesis that the statistical procedure detects more targeting relationships that what would be anticipated if none of the m/miRNA pairs under consideration were bona fide target pairs . Further , the median numbers of identifications from the distributions can be used to infer measures of test specificity . We note that the random pairs procedure does not guarantee that the pairs under analysis do not have a targeting relationship ( although known target pairs are rejected from those used in the method , it is possible that the m/miRNA pair is targeting but not yet verified as such ) , and so inflated numbers of identifications relative to what might be observed under the no-targeting null are expected . In other respects , the distribution and p-values of observed numbers of identifications against the random pairs distribution can be used in the same manner as those from the no-targeting null . We performed two different analyses that verified the intuitions and results from our randomization tests on the known target pair data . To assure that our no-targeting null distributions were composed of a sufficient number of samples , we reconstructed no-targeting null distributions for the Madison data using 10000 iterations of the procedure described above ( rather than the 1000 originally used ) , and recomputed p-values for the numbers of positive identifications obtained by the marginal and model-based procedures . These p-values were substantially identical to those obtained using 1000 iterations , and considering the heavy computational resources these procedures require we therefore constrained our analysis to the 1000 iteration case . Next , to verify our expectations regarding inflated numbers of identifications in the random pairs distribution we constructed a version of this distribution for the Madison data that was composed of randomly paired m/miRNA expressions taken from the full set of measurements ( rather than the subset of m- and miRNAs involved in known target pairs ) , recomputed p-values as previously and compared these p-values to those originally obtained . In this analysis , no effort was made to remove known target pairs from those randomly sampled . The results of this version of the random pairs scheme are described below , however the test strongly verified our original intuitions . In the analysis of the computationally predicted target pairs , we conditioned on miRNA and used no-targeting null distributions to obtain 95% upper bounds for the numbers of verifications that might observed from either the marginal comparisons or system biology-based models if none of the predicted m/miRNA target pairs were bona fide . The numbers obtained from the marginal and system biology-based methods on the actual data were then compared to these bounds to provide an indicator of the relative commonality of the results and an informal assessment of the specificity of the methods , analogous to those obtained on the known target pairs . The 95% upper bounds of the no-targeting null distributions were generated using 100 iterations – again , considering the computational resources required we regarded this number as sufficient to obtain a reasonable estimate of the 95% level . We began by analyzing the expression data from known target pairs . Marginal m/miRNA comparisons were made initially in order to provide a performance baseline for the regression models that incorporated system biological covariates . For the Madison and Broad data analyses , we calculated Pearson correlation statistics on m/miRNA expression levels to measure negative marginal associations , and R2 statistics from the simple linear regression described in model ( 3 ) to determine the amount of variation in targeted mRNA expressions attributable to that of targeting miRNA . For the combined data analysis partial correlations of m/miRNA expression controlling for data source were calculated , and adjusted R2 statistics were computed to compensate for the increase in model complexity due to the introduction of the dichotomous data origin covariate . ( A partial correlation is a measure of the amount of common variation between two variables after accounting for the effects of a set of related covariates on both . It is analogous to a standard marginal correlation between two variables , which does not account for covariate effects . An adjusted R2 statistic is a measure of model fit analogous to the standard R2 statistic that compensates for the number of covariates in the model . See [51] , Chapter 7 . 10 and 7 . 7 for technical descriptions of the partial correlation and adjusted R2 statistic respectively . ) In each data analysis using marginal methods , the total numbers of positive identifications of m/miRNA target pairing obtained from evaluation of the no-targeting hypothesis through a t-test at the 5% level were obtained . Next , the performance of the system biological regression model on the data from known target pairs was evaluated . Partial correlation statistics for pairs of targeted mRNAs and proxies to RISCs constructed of targeting miRNA and Ago 2 , adjusted R2 statistics , and numbers of positive identifications of m/miRNA target pairing obtained from use of the minimum AIC submodel procedures on the versions of model ( 2 ) that included data idiosyncratic covariates were computed and compared to the analogous baselines from the marginal m/miRNA comparisons . The number of positive identifications were additionally evaluated using the randomization controls to assure that we obtained greater numbers of identifications than what would be expected under the null hypothesis of none of the m/miRNA pairs under analysis being a legitimate target pair . We continued by analyzing the computationally predicted target pairs using the Madison dataset . For each putative target pair , the simple linear regression described in model ( 3 ) and the version of the system biology-based regression model ( 2 ) that incorporated idiosyncratic data effects was used to evaluate the no-targeting hypothesis through a t-test at the 5% level and the minimum AIC submodel procedure respectively . The results from the marginal procedure provided a baseline for evaluating the performance of the system biology-based regression model . The total numbers of verifications both the marginal and system biology-based procedures were conditioned on miRNA and compared directly to one another . Our analyses were implemented as scripts in the R programming language [52] , which were executed on Macintosh OS X computers with installations of R 2 . 8 . 0 ( earlier versions of R were used at earlier stages in our analysis ) . Dataset S1 contains the scripts and associated data used to study the known target pairs in the Madison , Broad and combined datasets . Alternatively , the first author may be contacted to provide the archive directly . The archive is commented and can be used to provide further information regarding our procedures , or to rerun our analyses on any system with an R installation ( available through the Comprehensive R Archive Network , http://cran . r-project . org ) . Please direct any questions regarding the archive to the first author .
Because the known target pairs under examination were previously observed to have targeting relationships , it was anticipated that the marginal correlations between m/miRNA expression levels using any of the Madison , Broad and combined datasets would typically be significantly negative . Contrary to these expectations , the sensitivity of marginal m/miRNA expression level comparisons was demonstrated to be quite low . Only 5 of the 99 target pairs in the Madison dataset , 6 of the 76 pairs in the Broad dataset and 7 of the 76 pairs in the combined dataset have significantly negative marginal relationships between m/miRNA expressions ( Table 1 ) , and the majority of observed correlations are positive ( Figure 2 ) . An example of the relationship between marginal m- and miRNA expression levels in the Madison data is provided in Figure 3 ( top row , left column ) . The example provided compares miR-17-5p to E2F1 , a known oncogene . Although miR-17-5p is known to target E2F1 , the relationship between m- and miRNA levels is positive . As suggested in Methods , adding idiosyncratic data effects to our marginal m/miRNA comparison in ( 3 ) resulted in nearly no differences in the number of known m/miRNA target pairs successfully identified as such . Using t-testing procedures to evaluate the no-targeting hypothesis after doing so yields 3 of 99 , 7 of 76 and 6 of 76 known m/miRNA target pairs identified as such in the Madison , Broad and combined datasets respectively . Similarly , a variety of data transformations were used to attempt to generate an improvement in the overall results without success , and the model fits were checked to assure that the results were not due to systemic outlier effects , model misspecifications or non-normal error terms . Finally , it was notable that the number of detections obtained by marginal comparisons was well within what might be observed under either the no-targeting null or random pairs distributions ( Figure 5 , second row ) . For the analysis of the Madison data , the p-values of the number of positive identifications under the no-targeting null and random pairs distributions were 0 . 491 and 0 . 279 respectively , for the Broad data p = 0 . 193 and 0 . 800 respectively , and for the combined analysis p = 0 . 947 and 0 . 419 ( Table 2 ) . In the context of the previously discussed identification performance , these values suggest that the specificity of the marginal procedure approximates the false positive rate under the null hypothesis of no targeting , and therefore that marginal m/miRNA expression level comparisons are as likely to detect evidence of a targeting relationship for unrelated m- and miRNAs as they are for bona fide target pairs . The observed R2 values from marginal m/miRNA expression level comparisons using data from known target pairs range from less than 0 . 001 to 0 . 365 with an average score of 0 . 061 for the pairs in the Madison data , less than 0 . 001 to 0 . 196 with an average of 0 . 035 for the pairs in the Broad data , and 0 . 008 to 0 . 880 with an average score of 0 . 411 for the pairs in the combined data ( Figure 2 ) . In the case of the Madison and Broad analyses , these values indicate that variation in the expression levels of targeting miRNAs explains only a small proportion of that in targeted mRNA levels . As a consequence of this , it would be anticipated that marginal comparisons of m/miRNA expression levels would not be useful for determining whether or not a targeting relationship exists , and therefore the low R2 values rationalize the previously observed performance of the marginal m/miRNA comparisons in identifying the known target pairs as such . In the case of the combined data analysis the observed R2 scores are substantially larger . However , the true relationships of the known m- and miRNA target pairs were not captured when analyzing the combined dataset with the marginal model . Therefore , these high R2 scores simply suggest that the majority of the observed variance in targeted mRNA expression is explained by the origin of the data observation , rather than the appropriateness of the model . The mean and range of adjusted R2 values for fits of the system biological regression model were ( 0 . 524 , −0 . 102–0 . 922 ) for the Madison data , ( 0 . 310 , −0 . 077–0 . 602 ) for the Broad data and ( 0 . 712 , 0 . 055–0 . 974 ) for the combined data ( Figure 4 ) . The increases in observed R2 scores from the baselines obtained from fits of the marginal model indicate that the regression model captures a greater percentage of the variation in targeted mRNA levels , even after compensating for its increased complexity . Because inclusion of the system biological covariates yielded a model that explained greater amounts of variation in target mRNA levels explained than the marginal model , it was anticipated that it would also better represent the true negative relationship between m/miRNA expression levels from the known target pairs . In fact , partial correlations between targeted mRNAs and proxies to targeting Ago 2 RISCs under model ( 2 ) were appropriately negative at substantially higher rates than marginal m/miRNA correlations ( 53% vs . 38% , 59% vs . 42% and 61% vs . 32% for the target pairs in the Madison , Broad and combined datasets respectively ) . Additionally , there was a reduction in observed correlation scores taken across the sample of m/miRNA target pairs ( mean marginal and partial scores were ( 0 . 0934 , −0 . 0247 ) , ( 0 . 057 , −0 . 012 ) and ( 0 . 232 , −0 . 083 ) for the Madison , Broad and combined data ) . To formalize this comparison , a null hypothesis of equality of marginal and partial correlation scores was tested and rejected for all three datasets using a paired Wilcoxon rank-sum test ( p = 0 . 014 , 0 . 018 and <0 . 001 for the Madison , Broad and combined data ) . Validation of these results consisted of checking the model fits for evidence of systemic outlier effects , model misspecifications or non-normal error terms , as was done for the marginal model fits . A comparative example of the model fits achieved in the Madison data is provided in the top row , right column of Figure 3 . After controlling for system biological and idiosyncratic covariates , the relationship between miR-17-5p ( which was positive under the marginal model ) is appropriately negative . In a further examination , the effects of the covariates used in the AIC-optimal submodels of the fits of ( 2 ) on the Madison data were studied to assure that the model was not overspecified . Of the variety of covariates used in the version of ( 2 ) compensating for the idiosyncratic data effects , only the dichotomous variable indicating tissue type found low levels of use in the AIC-optimal submodel – in fact , it was never included in the AIC-optimal submodels , indicating that tissue type never had a substantive effect on a targeted mRNA level after compensating for other effects . Because few if any of the known m/miRNA target pairs under consideration have been previously observed to be differentially expressed in NPC , this might be reasonable . Alternatively , this result can be explained by noting that EBV expression in the Madison data is highly associated with NPC , and therefore statistical control of EBV expressions rather than tissue type may be sufficient for both . Related to this analysis , the estimated effects of proxies for targeting RISCs composed of Ago 1 , 3 and 4 from the AIC-optimal submodels were compared to those composed of Ago 2 in order to assure that the model was performing in a reasonable manner . Figure 6 displays the relationships of estimated effects of targeting Ago 2 RISC proxies to targeting Ago 1 , 3 and 4 RISC proxies , for AIC-optimal submodels estimated on the Madison data in which both covariates were included and the estimated effect of the targeting Ago 2 RISC covariate was appropriately negative ( there were 13 such cases out of the 33 in which the effect of the targeting covariate was so ) . It can be observed that , as anticipated , the estimated effects of targeting Ago 1 , 3 and 4 RISCs on targeted mRNA levels are indeed generally positive with effect sizes scaling with those of targeting Ago 2 RISCs . Overall , these results demonstrate that the relationships between targeted mRNAs and proxies to targeting Ago 2 RISC , compensating for other relevant biological covariates , better represent the actual relationship of the known target pairs than marginal m/miRNA expression level correlations . Based on the improvements in model fit , it was further anticipated that evaluating the no-targeting hypothesis using the system biological model and the minimum AIC submodel procedure would indicate a greater number of positive identifications of targeting relationships than obtained by marginal m/miRNA comparisons . In fact , model ( 2 ) identified 33 of 99 , 20 of 76 and 36 of 76 known m/miRNA target pairs as having expression profiles consistent with targeting relationships in the Madison , Broad and combined datasets respectively . This represents up to a sevenfold increase from the baseline obtained by marginal m/miRNA expression level comparisons ( Table 1 ) , and demonstrates the improved sensitivity of model ( 2 ) in detecting m/miRNA target pair relationships . We note that although under 50% of known target pairs were recovered by model ( 2 ) , this level of identification performance is similar to the individual performances obtained by a number of sequence-based computational methods [30] . In particular , using the Madison and combined datasets we were able to successfully identify 33 and 47% of the known targets pairs we evaluated , whereas TargetScan and miRBase are reported to have 21 and 48% consistency with experimentally supported target pairs . The numbers of detections obtained by model ( 2 ) relative to what might be expected under either of the randomization techniques show similar improvements from the baseline obtained by marginal m/miRNA expression level comparisons ( Figure 5 , first row ) . For the analysis of the Madison , Broad and combined datasets , the p-values of the number of positive identifications under the no-targeting null were 0 . 008 , 0 . 096 and 0 . 001 respectively . Likewise , under the random pairs distributions the p-values were 0 . 053 , 0 . 241 and 0 . 072 . In the case of the Madison , Broad and combined data analyses , the numbers of identifications obtained are at least marginally significantly greater than what is typically observed under the no-targeting null . Under the random pairs distribution the Madison and combined data analysis show similar results , while the number of identifications made using the Broad data is not significantly greater than what might be expected under the null . As suggested above we anticipated an overall inflation in p-values under the random pairs technique due to inadvertent sampling of as-of-yet unverified target pairs from the sets of known target pairs used as a basis for the technique . As discussed in Methods , to verify this intuition we performed a secondary analysis of the number of detections obtained for the Madison data against a random pairs distribution constructed from the full set of m- and miRNAs for which expression measurements were available . The p-value from this study was 0 . 007; based on this result we regarded the inflated p-values under the random pairs distributions as a statistical artifact and focused our attention on the results from the no-targeting null . In total , the results from our analysis imply that the improvements in sensitivity for detecting target pairs obtained through model ( 2 ) are greater than any loss in specificity that might be incurred relative to that of the marginal procedure . The overall specificity of ( 2 ) for rejecting non-targeting pairs in the Madison and combined datasets are approximately 80% , as can be observed from median numbers of acceptances under the non-targeting null distribution . The analysis of the Broad data did not yield significantly larger numbers of correct identifications under either the no-targeting null or random pairs distributions , however it is useful to note that the high number of tissue types and missing Ago 3 measurements in the Broad dataset can be anticipated to negatively affect our ability to detect m/miRNA target pair relationships from expression levels . As well , the Madison dataset was processed to provide measurements in terms of concentration estimates that can more naturally be aggregated than the RMA measurements provided in the Broad data . The overall results of evaluating the computationally predicted m/miRNA target pairs on the Madison data with the system biological regression model and marginal m/miRNA comparison are described in Figure 7 . ( Table S2 provides further detail on results obtained for particular m/miRNA pairs analyzed by the system biologic regression model . ) For each miRNA under consideration , the first , second and third columns of Fig . 7 provide the numbers of putative target pairs evaluated and positive validations obtained by the system biological model and marginal m/miRNA comparisons respectively . In the second and third columns , 95% upper bounds on number of positive validations expected under the no-targeting null are provided . Visual inspection of Figure 7 suggests that model ( 2 ) yields substantially more verifications than the marginal method in nearly every case . In fact , the marginal method most often yields no verifications of computationally predicted targets of any of the miRNAs considered . The average percentage of predicted targets validated by the system biologic regression model is 25 . 68% , taken across all miRNAs . For 6 of the 18 miRNAs conditioned upon , the number of verifications obtained was significantly ( p<0 . 05 ) greater than what might be expected under the no-targeting null ( miR-130b , -15a , -16 , -181a , -181c , -30d ) , and analyses conducted on targets predicted for an additional three miRNAs ( miR-192 , -224 and -212 ) yielded numbers of identifications that were substantially greater ( p = 0 . 08 , 0 . 13 and 0 . 28 respectively ) . In comparison , marginal comparisons validate an average of 7 . 83% of predicted targets , and yielded three miRNAs ( miR-212 , -29a and –29c ) associated with significantly greater numbers of verifications than what might be expected under the no-targeting null with one additional miRNA ( miR-133a ) having a substantially greater number ( p = 0 . 21 ) . Although some inflation in the number of verifications that might be observed under the no-targeting null was incurred through when using the system biologic regression model rather than marginal m/miRNA comparisons , the results obtained here are roughly consistent with the performance of the marginal and system biologic regression methods on the set of known target pairs . Based on these results , a further comparative inspection was made of the distributions of the estimated marginal and Ago 2 mediated effects from fits of the miRNAs under analysis against all mRNAs in the Madison dataset . Sample distributions for estimated and normalized marginal effects of miR-29c and estimated miR-30d Ago 2 RISC effects are provided in Figure 8 . ( These were selected due to their high numbers of predicted target pair verifications , as seen in Figure 7 . ) The estimated marginal effects of miR-29c are clearly negatively biased , explaining the high numbers of validations . The estimated miR-30d Ago 2 RISC effects do not have such a bias . Instead , they demonstrate a bimodality with a main mass centered at 0 effect and a smaller mass centered at −1 . 5 . Such a distribution is consistent with a categorization of genes into two classes: those regulated by miR-30d , and those not . Although analyses of such large-scale screen results are ongoing , the results in Figures 7 and 8 provide further evidence that that use of statistical models which compensate for the system biology related to miRNA-based gene silencing are more appropriate for validating and predicting m/miRNA targeting relationships than marginal expression level comparisons .
The effects of miRNAs on mRNA stability and translation are presently understood to have effects on organism development and physiological function , and have been linked to diseases such as cancer . It is of acknowledged importance to develop greater insight into the targeting relationships between m- and miRNAs . In this paper , we considered the role that biology-based statistical modeling and methods might play in the m/miRNA target prediction problem . Currently , the statistical techniques used for these purposes are typically based on marginal comparisons of individual m- and miRNA expressions across tissue samples . In some respects this is a natural comparison to consider – many early studies verifying predicted targeting relationships were based on transfection experiments with small numbers of samples , for which marginal m/miRNA comparisons might be the only procedure available . However , it has been observed previously ( and was demonstrated here ) that in practice these methods typically yield relatively disappointing results . We hypothesized that improvements in the performance of statistical methods for detecting m/miRNA target pair relationships might be achieved through development of a statistical model and associated hypothesis testing procedure better tied to the underlying system biology . In an investigation of this biology in homo sapiens we identified a number of factors that we expected to affect the ability of marginal m/miRNA expression level comparisons to detect targeting relationships , many related to the dependence of the gene silencing mechanism on the construction and varied actions of RISCs . Based on this as well as additional information pertaining to the data under analysis , we developed regression methodology for testing hypotheses of no targeting relationship between m- and miRNA . Our rationale for choosing regression methods ( as opposed to other possible statistical or computational methods ) was motivated by the balance it offered between the competing goals of fidelity to the system biology , having a methodology with understood theoretical underpinnings and computational tractability for analyzing large number of putative m/miRNA target pairs , while being appropriate to the data quality and sample size . In comparison to procedures based on marginal m/miRNA expressions , our models and procedures were shown to provide substantial improvements in overall model fit and detection performance for sets of known m/miRNA target pairs , although the degree of such improvement was somewhat dependent on the study design . As would be hoped , we further demonstrated that such improvements were carried over into the problem of validating predicted m/miRNA target pairs . Our study suggests that use of the regression models and associated hypothesis testing procedures developed here ( or equivalent techniques based on the system biology ) represent a reasonable alternative to methods based on marginal m/miRNA comparisons for analyzing expression data in m/miRNA targeting studies , and in conjunction with high throughput data can be used to either verify computationally predicted relationships or generate de novo information regarding m/miRNA target pairs . In fact , our model demonstrates consistency with known target pairs on par with many computational target prediction algorithms [30] . Because there have been few systematic studies of statistical methods for detecting m/miRNA targeting , there is little context that can be used to help evaluate our results . The most relevant external work is that recently conducted by Huang et al [53]–[55] , however there are a number of differences between our studies . Huang et al focus on Bayesian methods to update a set of prior probabilities of targeting relationships between m- and miRNAs using marginal expression comparisons . These prior probabilities are , in their reported work , highly tied to the results of computational target prediction algorithms ( in particular , TargetScan ) . The posterior probabilities obtained through their technique are compared to a threshold based on those obtained from a high-confidence set of m/miRNA target pair expression values; m/miRNA pairs with posterior targeting probabilities meeting the threshold are accepted as valid target pairs . In contrast , our study is framed in terms of evaluating a single m/miRNA pair for evidence of a targeting relationship , compensating for the underlying system biology ( which includes the effects of other targeted and targeting m- and miRNAs on the m/miRNA pair under consideration ) . Our use of a hypothesis testing framework allows us to avoid the need to set a thresholding value based on a separate set of m/miRNA expression data for evaluating whether potential m/miRNA pairs evidence a targeting relationship . We do not tie our work to any particular computational target prediction algorithm , a position we view as appropriate given the issues with their specificity , sensitivity and inter-algorithm consistency . Further , the emphasis of our presentation of algorithm development and results is substantially different from Huang et al . We choose to focus development of a statistical method on known m/miRNA pairs and then use the resulting procedure to validate a set of computational target predictions . Huang et al are primarily concerned with using their algorithm to validate computational predictions , with verification of their method on known target pairs taking place only on those that are represented in their set of computational target predictions [53] . It is unclear whether these differences in presentation have a substantial difference in performance . The methods proposed here and by Huang et al verify approximately the same proportion of computational target predictions evaluated , and Huang et al [53] demonstrate that of 19 known target pairs contained in the set of computationally predicted targets that they attempt to evaluate , 9 are identified as such . Overall , comparing the two methods and constructing new statistical procedures that incorporate elements of each may be one direction for achieving further improvements in the ability to detect m/miRNA target relationships from high-throughput expression data . A similar issue that this study only indirectly addresses is the topic of how to best combine results across multiple sequence-based computational or expression-based methods , in order to obtain an aggregate estimate of the full set of m/miRNA target pairs occurring in humans . Such techniques can be classified into two categories: Those that would use sequence-based and expression-based methods sequentially ( e . g . using expression-based methods to validate sequence-based predictions or using sequence-based methods to rationalize de novo expression-based predictions with a target site ) , and those that would use them simultaneously ( i . e . without using one type of method conditional on the results of the other ) . Here , after establishing the utility of our data on known target pairs , we demonstrate how it might be used in a sequential study conditional on the results of sequence-based methods . To perform either a sequential study in which sequence-based methods are used conditional on de novo expression-based predictions or a simultaneous study using both sequence-based and expression-based predictions , the development of statistical methods which can distinguish between a bona fide m/miRNA target pair and m/miRNA pairs related through an intermediate , targeted , translationally activating mRNA must be developed . We are currently working on the development of such a technique . Additional complications that ought to be addressed in such studies is how best to handle the multiple comparisons problems that occur due to the large number of m/miRNA pairs that might be evaluated ( which are orders of magnitude larger than those encountered in typical differential expression studies , for example ) , and how to best align results from multiple algorithms and datasets . We feel that , much as this study utilized known m/miRNA target pairs to validate our regression model , it is reasonable for future proposed methods for handling these technical problems to use them as a basis for evaluation and validation . Aside from our current work towards the development of a statistical technique capable of de novo m/miRNA target pair prediction , we are extending our work in large-scale screening of putative m/miRNA target pairs ( such as described in Figure 8 ) . Our work consists of both investigating and improving our statistical procedures for inferring such relationships as well as aligning predictions from sequence- and expression-based methods , and by further supplementing the data used in this study with new samples as they become available . In a study of a recent dataset originally analyzed by Ambs et al [56] , many of the results obtained here are reiterated . Figure 3 provides an example . Consistent with our result using the Madison data , miR-17-5p shows no substantial relationship with E2F1 in a marginal analysis ( bottom left panel ) , but after controlling for the biological and idiosyncratic covariates the true negative relationship between them can be observed ( bottom right ) . Based on this study , those of Huang et al , and the continued release of high-throughput data studies comparing m- and miRNA expression , we look forward to the further development of statistical methods for detecting m- and miRNA targeting relationships from expression data . | MicroRNAs are a family of small RNAs that play important roles in the development , physiological function and stress responses of a wide variety of organisms , and if abnormally expressed are associated with multiple types of cancer in humans . Rather than being translated into proteins , members of the family of microRNAs operate by preventing the translation of messenger RNAs to which they have some degree of sequence complementarity . Although sequence-based bioinformatics techniques have yielded large numbers of predicted messenger- and microRNA targeting relationships , verifying these as bona fide has proven practically difficult . We have developed a novel statistical approach based on the system biology of microRNAs in humans to detect such targeting relationships using high-throughput RNA expression data . Because our approach is not based on information from external target pair predictions , it can play a fully independent role in verifying such predictions as well as be used to obtain de novo target pair predictions . Using two separate data studies , we show that our approach is capable of both reproducing previously observed target pairs and verifying putative target pairs predicted from sequence data , at rates substantially better than marginal comparisons of messenger- and microRNA expression levels . | [
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| 2009 | Statistical Use of Argonaute Expression and RISC Assembly in microRNA Target Identification |
TRAM is a conserved domain among RNA modification proteins that are widely distributed in various organisms . In Archaea , TRAM occurs frequently as a standalone protein with in vitro RNA chaperone activity; however , its biological significance and functional mechanism remain unknown . This work demonstrated that TRAM0076 is an abundant standalone TRAM protein in the genetically tractable methanoarcheaon Methanococcus maripaludis . Deletion of MMP0076 , the gene encoding TRAM0076 , markedly reduced the growth and altered transcription of 55% of the genome . Substitution mutations of Phe39 , Phe42 , Phe63 , Phe65 and Arg35 in the recombinant TRAM0076 decreased the in vitro duplex RNA unfolding activity . These mutations also prevented complementation of the growth defect of the MMP0076 deletion mutant , indicating that the duplex RNA unfolding activity was essential for its physiological function . A genome-wide mapping of transcription start sites identified many 5′ untranslated regions ( 5′UTRs ) of 20–60 nt which could be potential targets of a RNA chaperone . TRAM0076 unfolded three representative 5′UTR structures in vitro and facilitated the in vivo expression of a mCherry reporter system fused to the 5′UTRs , thus behaving like a transcription anti-terminator . Flag-tagged-TRAM0076 co-immunoprecipitated a large number of cellular RNAs , suggesting that TRAM0076 plays multiple roles in addition to unfolding incorrect RNA structures . This work demonstrates that the conserved archaeal RNA chaperone TRAM globally affects gene expression and may represent a transcriptional element in ancient life of the RNA world .
RNA chaperones impact mRNA metabolism including transcript synthesis , processing and degradation as well as translation . They play an important role in controlling gene expression , especially in response to environmental perturbations such as cold shock [1] . The distinctive features of RNA chaperones include the absence of an energy requirement for activity , transient interactions with their RNA targets without obvious sequence specificity , and the ability to melt kinetically trapped RNA hairpin structures [2 , 3] . These characteristics enable RNA chaperones to interact with many classes of RNAs and through lowering the energetic barriers to assist RNA folding into thermodynamically favorable conformations [3] . Extensively studied bacterial RNA chaperones include cold shock proteins ( Csps ) , translation initiation factors ( IFs ) , ribosomal proteins and the Sm-like protein Hfq . Csps are small proteins that affect cellular processes by remodeling RNAs for recycling or translation and preventing premature transcriptional termination via melting RNA hairpin structures , particularly at low temperatures [4–7] . Hfq is a global post-transcriptional regulator that impacts gene expression in a wide range of bacteria by facilitating annealing of small noncoding RNAs ( sRNAs ) and target mRNAs at two distinct binding faces so as to repress translation and/or accelerate mRNA decay [8 , 9] . In contrast , only a few archaeal RNA chaperones have been reported , and little is known about their physiological functions . In methanogens as well as other Archaea , transcription of messenger as well as stable RNAs employs a homolog of the eukaryotic RNA polymerase II [10] . Transcription initiation in Archaea requires two basal transcriptional factors , TATA-box binding protein ( TBP ) and transcription factor B ( TFB ) [11 , 12] , and a third conserved factor , transcription factor E ( TFE ) [13 , 14] . However , transcriptional regulation in archaea is not well understood . Sigma factors , which are common in bacteria , are absent [15] . Some Archaea , such as haloarchaea , encode multiple basic transcription factors , different combinations of which could be used to achieve a certain level of differential transcription [16–18] . However , many methanogenic Archaea only possess single copies of the tbp and tfb genes , so this general mechanism of transcriptional regulation appears to be restricted to just a few groups . While homologs of some of the bacterial transcription regulatory proteins have been identified in methanogens , these genes are not abundant in many methanogens and archaea in general [19] . For instance , the genome of Methanococcus maripaludis S2 possesses only 29 homologs of bacterial transcription factors , and the function of most of these genes is unknown . This suggests that transcription regulation may not be prevalent in methanoarchaea . Noticeably , genome-wide transcription start site mapping indicates that long 5′ un-translation regions ( 5′UTR ) are common in methanogenic archaea [20–22] . In bacteria , the 5′UTRs of mRNAs are often targeted by non-coding small RNAs , ribonucleases and RNA chaperones and are potential elements for RNA-based regulation of gene expression [23 , 24] . The long 5′UTRs found in methanogens could act similarly [20 , 21] , and they might also misfold into stable but biologically inactive structures that need to be unfolded during active growth . However , RNA chaperones that interact with 5′UTRs to regulate gene expression in methanogens and Archaea have not been demonstrated . TRAM is an archetype domain found in many proteins involved in RNA modifications , such as TRM2 methylases and methylthiotransferase RimO , as well as the translation initiation factor IF-2β subunit and ribosomal protein S2 [25] . Proteins containing TRAM domain are found in all the living organisms [26] . Distinctively , most archaea possess a small protein that contains a standalone TRAM domain with an OB ( Oligonucleotide-Binding ) fold [26] , which is widely distributed in most archaeal phyla ( S1 Fig ) . The in vitro studies indicated that these TRAM proteins exhibit typical properties of RNA chaperones . A cold-responsive standalone TRAM protein from the cold-tolerant methanogenic archaeon Methanococcoides burtonii called Ctr3 was reported to preferentially bind tRNAs and 5S rRNA [27] . The psychrophilic methanogen Methanolobus psychrophilus R15 possesses four TRAM proteins that can replace the cold shock proteins in E . coli and exhibit nonspecific RNA binding and duplex RNA melting activity in vitro [28] . However , the biological significance of these standalone archaeal TRAM proteins remains to be clarified . In this study , M . maripaludis was selected to interrogate the biological significance of archaeal standalone TRAM proteins and their regulatory role in gene expression . M . maripaludis has only a single TRAM-encoding gene ( MMP0076 ) and is genetic tractable . Through genetic , physiological , biochemical and transcriptomic studies , we demonstrate that the standalone TRAM protein MMP0076 ( named TRAM0076 hereafter ) from M . maripaludis acts as an RNA chaperone and is required for the normal expression of more than half of the genome and normal growth . TRAM0076 controls the expression of some mRNAs through unfolding the kinetically trapped RNA structures in the 5′UTR elements . Thus , the archaeal standalone TRAM protein appears to play a fundamental role in transcription .
To examine the biological significance of TRAM proteins in Archaea , MMP0076 , which encodes a standalone TRAM protein ( TRAM0076 ) , was replaced with the pac cassette for puromycin resistance in M . maripaludis S0001 as described by Tumbula et al . [29] , resulting in the construction of the ΔMMP0076::pac deletion mutant ( Δ0076 ) . The growth rate of the mutant ( 0 . 065 ± 0 . 003 h-1 vs 0 . 15 ± 0 . 001 h-1 of strain S0001 ) was markedly reduced at the optimal growth temperature of 37°C . To confirm that the growth reduction phenotype was not due to a mutation at a second site , the Δ0076 mutant was complemented by expressing TRAM0076 from the replicative plasmid pMEV2-MMP0076 carrying the MMP0076 gene ( MMP0076-com strain ) . Growth of strain MMP0076-com was completely restored to wild-type levels ( Fig 1A ) . These results indicate that TRAM0076 is necessary for optimal growth of M . maripaludis . Moreover , Western blotting indicated that the levels of TRAM0076 increased by about 50% in older cultures ( Fig 1B ) . By reference to the recombinant purified protein ( Fig 1B , lane recombinant ) , the cellular TRAM0076 level was estimated to account for about 0 . 05–0 . 1% of the total protein . Growth of the Δ0076 mutant at low temperatures was also examined . Compared to the parental strain S0001 , the Δ0076 mutant showed a similar reduction of the growth rate at 22°C as at 37°C ( Fig 1C ) . Moreover , cold shock at 4°C led to an increase of the cellular TRAM0076 content by 50–80% ( Fig 1D ) . These results suggest that while TRAM0076 is differentially expressed depending on the growth conditions , its function is not limited to withstanding cold stress . To learn more about the physiological function of TRAM0076 , the transcriptome of the MMP0076 deletion mutant was compared to that of the parental strain by RNA-seq , which was implemented with biological triplicates ( S1 Dataset ) . As expected , the read count for the transcript of MMP0076 ranked in top 20% of the highly expressed genes in the parental strain but was hardly detectable in the mutant , indicating a complete deletion of MMP0076 . Importantly , transcription of the adjacent genes , MMP0075 and MMP0077 , were not significantly affected in the mutant , indicating that the mutation did not have polar effects on these neighboring genes . Using Padj <0 . 05 by the DESeq algorithm as a threshold of significant differences [30 , 31] , the expression of more than nine hundred genes ( 55% of total genes ) was changed by the MMP0076 deletion ( Fig 2A and 2B , S1 Dataset ) , suggesting that TRAM0076 was a factor globally affecting the transcriptome . It is noteworthy that the most markedly upregulated genes in Δ0076 included several RNA unwinding or binding proteins , such as DEAD RNA helicase ( MMP0457 ) , an ATP-dependent RNA-helicase ( MMP1141 ) , an RNA binding S1 domain protein ( MMP1127 ) , and translation initiation factor IF-2α ( MMP1707 ) ( Fig 2A ) , implying a possible RNA metabolism stress upon the loss of TRAM0076 . Functional category analysis indicated that most of the down regulated genes in Δ0076 were those involved in energy production and conservation , such as those for CO2 reduction to methane , cofactor biosynthesis , and V-type ATP synthase complex ( Fig 2C , S1 Dataset ) . This could explain the growth reduction of the Δ0076 mutant . Collectively , differential transcriptomic analysis indicated that TRAM0076 globally affects the transcriptome and thus suggests that it could be an important factor involved in cellular RNA metabolism . To probe whether TRAM0076 has RNA chaperone activity similar to that of the bacterial cold shock proteins , its complementation of the deletion phenotype of cold shock proteins in E . coli was tested . MMP0076 was cloned into the pINIII expression vector and transformed into the E . coli mutant BX04 , which has a quadruple deletion of cspA , B , G and E and does not grow at low temperatures [32] . TRAM0076 complemented the cold-sensitive phenotype by recovering growth of the E . coli mutant BX04 at low temperatures ( S2A Fig ) . PAGE of cell extracts readily demonstrated the IPTG-dependent expression of the Csp proteins and TRAM0076 , whose expression was confirmed by mass spectroscopy ( S2B Fig ) . This demonstrates that TRAM0076 shares some of the biochemical activities of Csps . To determine the RNA chaperone activity more directly , we first determined if TRAM0076 has RNA binding ability and if the binding is sequence specific . Twelve RNA Pentaprobes ( PP ) were tested as substrates in RNA electrophoretic mobility shift assay ( rEMSA ) . These RNAs are about 100 nt in length and possess all possible 5-nt combinations ( 1024 ) [33] . Each of the 3′-end biotinylated RNA Pentaprobes was incubated with purified TRAM0076 protein , and TRAM0076-RNA complexes were found with all 12 Pentaprobes at approximate ratios of 5000 to 10000:1 ( Fig 3A ) . The ability to bind every RNA Pentaprobe suggested that TRAM0076 binds RNA without obvious sequence specificity . To determine the RNA binding affinities of TRAM0076 more quantitatively , a surface plasmon resonance ( SPR ) assay was employed . The apparent KD values of TRAM0076 were 4 . 6 and 6 . 3 μM for the Pentaprobes PP1 and PP10 , respectively . For comparison , the KD values for CspA were both about 1 μM ( S3 Fig ) . Together , these experiments indicated that TRAM0076 binds RNA without significant sequence specificity , one of the key properties of RNA chaperones . Next , the transcription antitermination ability of TRAM0076 in E . coli was measured . Strain RL211 of E . coli carries a cat ( chloramphenicol resistance ) gene immediately downstream of a strong trpL transcriptional terminator ( Fig 3B upper panel ) [34] , which serves as an effective system in testing the transcription antitermination activity of Csps [7 , 35] . In the presence of an RNA chaperone , the hairpin structure of the trpL RNA terminator is unwound , so the downstream cat is expressed , and RL211 becomes resistant to chloramphenicol . Similar to the positive control CspE from E . coli , expression of TRAM0076 led to chloramphenicol resistance ( Fig 3A lower panel ) . In contrast , the empty expression vector pINIII and expression of TRAM3066 , which binds but does not unfold RNA structure [28] , did not . Thus , TRAM0076 possesses transcription antitermination activity similar to E . coli CspE , a well-established RNA chaperone . A molecular beacon ( MB ) assay [35] was then performed to quantitatively test the in vitro duplex nucleic-acid unfolding activity of TRAM0076 protein . Two partially complementing oligodeoxyribonucleotides , one of which was FITC labeled at the 5′ terminus and the other labeled with a quencher ( BHQ1 ) at the 3′ terminus , were used as substrates ( Fig 3C ) . Oligodeoxyribonucleotides are commonly used in the MB assay [7 , 35] as they are more resistant to the trace RNase contamination in buffer or purified recombinant proteins than oligoribonucleotides . The annealed MB yielded only 13% of the fluorescence of the denatured form due to the quenching of the FITC fluorescence by BHQ1 . When the annealed MB substrates were unfolded by the E . coli CspA or CspE , the fluorescence was dramatically increased to 88% and 97% of the value of the denatured form . For comparison , addition of purified TRAM0076 ( 20 μM ) increased the fluorescence intensity to about 54% ( Fig 3C ) of the denatured values . Thus , both in the E . coli cells and in vitro , TRAM0076 displayed the duplex nucleic-acid unfolding capability , another characteristic of RNA chaperones . In the E . coli Csps , aromatic and positively charged amino acid side chains play important roles in unfolding RNA structures by base stacking and electrostatic compensating interactions [3 , 7] . To obtain insights into the key residues of TRAM0076 in unfolding duplex RNAs , the structure of TRAM0076 ( PDB:1YVC ) was first compared with that of CspA ( PDB:1MJC ) ( Fig 4A ) . Although there is no sequence similarity , apparently similar OB fold structures formed , mainly by a five-stranded antiparallel β-barrel , in both TRAM0076 and CspA . The CspA RNA binding surface comprises β1 to β3 . In TRAM0076 , β3 to β6 was predicted to associate with RNA . Notably , structurally equivalent aromatic ( Phe39 , Phe42 , Phe63 and Phe65 ) and positively charged ( Lys28 , Arg35 and Lys62 ) amino acids that are essential to the RNA chaperone activity of CspA were also found in TRAM0076 . Sequence alignment of archaeal TRAM homologs indicated that these aromatic and positively charged residues were highly conserved ( Fig 4B ) , further suggesting their importance . Thus , these seven and other four conserved residues ( Asp25 , Gly30 , Gly32 and Ile33 ) within the putative RNA binding surface of TRAM0076 were selected for site-directed mutagenesis . Replacement of the four aromatic residues ( Phe39 and Phe42 in β4 sheet and Phe63 and Phe65 in β6 sheet ) and two positively charged residues ( Arg35 in β3 sheet and Lys62 in β6 sheet ) with alanine greatly reduced the structured RNA unfolding activity of TRAM0076 in the transcription antitermination assay in E . coli RL211 ( Fig 4C ) . Although the Ile33 mutation also failed to suppress transcription termination in this assay , Western blotting of cell extracts detected an obvious reduction of TRAM0076 protein for this mutation , indicating that the mutant protein was labile . If structured RNA unfolding activity was necessary for the physiological activity of TRAM0076 , mutations that inactivate the activity should be unable to complement the growth defect of the Δ0076 mutant . To test this hypothesis , the eleven single-site mutations were each transformed into the Δ0076 strain of M . maripaludis . Five TRAM0076 mutants that failed to suppress transcription termination in E . coli , R35A , F39A , F42A , F63A and F65A , also did not complement the MMP0076 deletion ( Fig 4D ) . The mutant K62A partially complemented . In contrast , the mutants with transcription antitermination activity , K28A , G30A , and G32A , as well as I33A fully restored growth to the deletion mutant ( Fig 4D and S4 Fig ) . Together , these results indicate that amino acid residues key for structured RNA unfolding and transcription antitermination activity in RL211 were also important for TRAM0076’s physiological role in M . maripaludis . Structural signals such as hairpins of the mRNA 5′UTRs are common elements interacting with RNA binding proteins [5 , 20 , 36 , 37] . To identify potential interactions between TRAM0076 and methanococcal 5′UTRs , the genome-wide transcription start sites ( TSSs ) and 5′UTRs were mapped in M . maripaludis by dRNA-seq as previously described [20] . In summary , 461 gTSSs were found for 325 monocistronic and 136 polycistronic operons comprising a total of 720 genes . Of these , 249 operons comprising 54% of genes possess 5′UTRs in length of 20–60 nt , or long enough to form secondary structures ( S2 Dataset ) . Notably , 155 of the 466 down-regulated transcripts in the Δ0076 mutant possessed 5′UTRs , and 65% ( 101 mRNAs ) of which were 20–60 nt in length ( S5 Fig ) . As predicted by Mfold software ( unafold . rna . albany . edu/ ? q=mfold ) , transcription terminator-like hairpin structures were common in the 5′UTRs of this length ( S6 Fig ) . These structures in the 5′UTRs of the down-regulated transcripts are candidate targets of TRAM0076 antiterminator activity ( see below ) . In addition , 38 of the 104 up-regulated transcripts possessed 20–60 nt 5′UTRs . Moreover , a few of the genes upregulated in the Δ0076 mutant possessed much longer 5′UTRs , such as 223 nt in the DEAD RNA helicase ( MMP0457 ) transcript , 116 nt in the F420-non-reducing hydrogenase subunit ( vhcD , MMP0821 ) transcript and 104 nt in a peptide methionine sulfoxide reductase ( MMP0848 ) transcript ( S2 Dataset ) . These secondary structures are additional targets for TRAM0076 . Because expression of these genes is upregulated in the Δ0076 mutant , antitermination is not a likely mode of action . To test if TRAM0076 facilitates transcription via unfolding 5′UTR hairpin structures in Methanococcus , the 5′UTR leaders of three transcripts ( MMP0127 , MMP1515 and MMP1697 ) whose abundances were reduced in the Δ0076 mutant and possessed potential hairpin structures were examined in the molecular beacon system ( Fig 5A ) . Mfold predicted hairpin structures of 9–13 Watson-Crick base pairs in the three 5′UTRs ( Fig 5B ) . The three 5′UTR leaders labeled with FITC fluorophore at the 5′ terminus and quencher ( BHQ1 ) at the 3′ terminus were used as MB substrates . Addition of TRAM0076 protein ( 20 μM ) to the three MB substrates increased the relative FITC fluorescence intensity to 55–88% , confirming the ability of TRAM0076 to unfold the predicted hairpins in these 5′UTRs , which could be the basis for regulation through transcription antitermination . To determine if TRAM0076 actually facilitates transcription of its targeted mRNA through unfolding the 5′UTRs in vivo , a mCherry reporter was fused to each of the three 5′UTRs ( MMP0127 , MMP1515 and MMP1697 ) in the replicative vector and transformed into the Δ0076 and parental strains of M . maripaludis . Expression of the mCherry reporter gene was assayed by measuring the fluorescence intensity of whole cells during growth . Compared to the parental strain , the expression of mCherry in the Δ0076 mutant was reduced by approximately 50% for MMP1515 and MMP1697 ( Fig 5C ) and was slightly reduced for MMP0127 . Furthermore , when the 5′UTR hairpin structure in MMP1515 was abolished by base substitutions ( Fig 5B , UTR1515MT ) , the differential expression of mCherry reporter in the Δ0076 and parental strains was lost ( Fig 5D ) . These results provided direct evidence that TRAM0076 can facilitate transcription of at least some of its targets via its RNA chaperone activity acting on 5′UTR structures . Given that cold shock induced TRAM0076 expression by 50–80% ( Fig 1D ) , expression of mCherry reporter was also tested in cold shocked cells . As anticipated , the fluorescence of cells with mCherry fused to each of the three 5′UTRs increased about 1 . 4-fold in the cold shocked parental strain but not the cold shocked Δ0076 mutant . Cold shock induction was also not observed in the mCherry reporter of UTR1515MT , the hairpin structure of which was abolished by nucleotide substitutions ( S7 Fig ) . This experiment indicated that cold induced TRAM0076 increased the transcription of its targeted transcripts , further verifying the ability of TRAM0076 in facilitating transcription for some of its targets with 5′UTR hairpin structures . To explore more possible action mechanisms of TRAM0076 , the targets of TRAM0076 in the M . maripaludis cells were determined by means of RNA co-immunoprecipitation ( RIP ) ( Fig 6A ) . The Flag-tagged MMP0076 gene was expressed in the Δ0076 mutant using the replicative vector pMEV4 , and the untagged MMP0076 complementation strain served as a mock control . TRAM0076 bound RNAs collected from Flag-tagged and untagged TRAM0076 complemented strains were co-immunoprecipitated using the anti-Flag magnetic beads . The corresponding RNA pools prior to RIP ( the Input RNAs ) were also examined . Sensitivity to ribonucleases confirmed that the RIP collected nucleic acid was RNA . The RNA abundance in the Flag-tagged TRAM0076 precipitant was much higher than that of the mock control ( S8A Fig ) . Compared to the Input RNA pool , more mRNAs were precipitated than rRNAs ( S8B Fig ) . Flag-tagged TRAM0076 precipitated RNAs were subsequently sequenced and both the readcount and relative abundance calculated as Fragments Per Kilobase of transcript per Million fragments mapped ( FPKM ) were listed in S3 Dataset . The amount of RNA precipitated in the untagged TRAM0076 mock control was too low to be sequenced . By taking the FPKM median value of 320 ( S9 Fig ) as a threshold of RIP collected RNAs , Flag-tagged TRAM0076 precipitated at least 900 transcripts . This result suggests that TRAM0076 binds abundant cellular RNAs in vivo , a trait consistent with the low sequence specificity measured in vitro and the characteristic of other RNA chaperones . Among the RNAs precipitated by Flag-tagged TRAM0076 , some were highly enriched . These included the messages encoding RNase P protein ( MMP1407 ) and its RNA component , two histones ( MMP0386 and MMP1347 ) , translation initiation factors ( MMP1406 ) , elongation factor 1α and β subunits ( MMP1370 and MMP1401 ) , RNA polymerase subunits RpoN and RpoF ( MMP1326 and MMP0092 ) , ribosomal proteins ( rpl40e , MMP0151; rps27e , MMP1708; and rplX , MMP0060 ) , some methanogenesis genes ( mcrG , MMP1558; mtrG , MMP1566 ) , and an ArsR family transcriptional regulator ( MMP1442 ) . Of note , the TRAM0076 transcript was also highly enriched , suggesting a potential self-regulation of its own expression . In contrast , some RNAs were conspicuously unenriched , including rRNAs and tRNAs . These stable RNAs are either highly structured or tightly associated with proteins . Precipitation of some of the highly enriched RNAs and downregulated methanogenic genes in the Δ0076 mutant were selected for validation by quantitative RT-PCR ( qPCR; Fig 6B ) . While the 16S rRNA was not enriched in the Flag-tagged TRAM precipitant , the transcripts for TRAM0076 , RNase P RNA component , two ribosomal subunits ( MMP1289 and MMP1579 ) , a RNAP subunit ( MMP1360 ) , and three downregulated transcripts ( MMP0127 , MMP1515 and MMP1697 ) were enriched in the Flag-tagged TRAM0076 precipitant by 6 . 8 to 100-fold higher than in the mock precipitant . These experiments confirmed that TRAM0076 bound a large number of transcripts in vivo .
RNA chaperones , through RNA binding and structured RNA unfolding activities , ensure the correct folding of targeted RNAs and enable them to adopt their functionally active states , thereby playing important roles in control of gene expression [3 , 38 , 39] . However , compared to bacterial and eukaryotic RNA chaperones , little is known about archaeal RNA chaperones , particularly of their biological significance and functional mechanisms . This work , through physiological , genetic and transcriptomic , and molecular and biochemical studies , reports for the first time that an archaeal RNA chaperone TRAM shapes the transcriptome and ensures the optimal growth of an archaeon . The methanococcal TRAM0076 displays the defining properties of an RNA chaperone . It binds RNAs without obvious sequence specificity both in vivo and in vitro , and unfolds RNA hairpins and other secondary structures . Importantly , mutations of the amino acid residues essential for RNA unfolding also eliminate the biological effects of TRAM0076 in Methanococcus , demonstrating that its biological role depends on its structured RNA unfolding activity . Further , the methanococcal TRAM0076 facilitated transcription of some genes by unfolding the 5′UTR structures , presumably to prevent premature transcriptional termination or pausing . Although TRAM0076 shares the RNA chaperone activities of the bacterial cold shock proteins , unlike the E . coli Csps , which exert roles mainly when the organism encounters cold shock or lower temperatures [4] , TRAM0076 appears to play a more fundamental role in Methanococcus as deletion of the gene impaired the growth at both optimal and lower temperatures . Accordingly , absence of TRAM0076 causes changes in the expression of 55% of the total genes , much more than the gene numbers that are affected by Csps [40] . Thus , this archaeal RNA chaperone affects transcription globally . Meanwhile , the prevalence of 5′UTRs in methanococcal transcripts could provide the wildly distributed targets for TRAM to implement a regulatory role on transcription . For instance , 65% of the down-regulated transcripts in the Δ0076 mutant possess a 5′UTR of 20–60 nt , and both in vivo and in vitro experiments demonstrate that TRAM0076 unfolds 5′UTR structures . Therefore , one of the mechanisms that TRAM0076 facilitates archaeal transcription would be prevention of 5′UTR structure formation by unfolding the kinetically trapped stem-loop structures and transcription antitermination activity , similar to that of the bacterial Csp proteins [5 , 37] . In addition to unfolding 5′UTR structures , TRAM0076 may exert roles in mediating gene expressions via other RNA chaperone activities , such as binding the RNAs so to prevent the formation of the kinetically trapped non-active conformations . Deletion of MMP0076 also causes increased expression of about one third of the total genes , including a DEAD/DEAH RNA helicase ( MMP0457 ) and a SAM-dependent methyltransferase ( MMP0874 ) . Transcripts of the two genes were precipitated by the Flag-tagged TRAM0076 , evidence that they were bound by TRAM0076 in vivo . Moreover , the transcripts of MMP0457 and MMP0874 possess long 5′UTRs of 223 nt and 84 nt , respectively . As the two genes are upregulated in the Δ0076 mutant , TRAM0076 must act on them by some mechanisms other than transcriptional antitermination . TRAM0076 also bound the transcript of an AsrR family transcription regulator . By controlling the expression of other regulatory proteins , TRAM0076 could exert an indirect role in regulation of gene expression . It is worthy to note that , unlike the in vitro experiment results of the cold-responsive TRAM protein Crt3 from M . burtonii [27] , TRAM0076 , at the physiological level , did not show a preferential binding to tRNA and rRNAs in the in vivo assay , though the two proteins have highly similar sequences ( Fig 4B ) . While the apparent difference in RNA targets determined for the two homologous TRAM proteins could have resulted from the different experimental approaches , it is also possible that small differences in the protein sequences significantly changed the substrate specificities . TRAM0076 is a very small protein with only a single TRAM domain . Because it lacks a regulatory domain , its physiological activity is probably controlled via its expression . Given that TRAM0076 binds its own transcript , an auto-regulation mode for its expression is also predicted . Expression of TRAM0076 increases following cold shock . For the bacterial CspA , temperature-dependent regulation results from different structures adopted by its 5′UTR at different temperatures [41] . However , the mechanism of cold shock induction of TRAM0076 must differ because the structure of its 5′UTR at 37°C and 4°C are predicted to be the same . In addition to cold shock , the abundance of TRAM0076 also increases during the stationary growth phase . However , little is known about growth phase regulation in the archaea . TRAM0076 is estimated to be 0 . 05–0 . 1% of the cellular proteins at the exponential growth phase . Given a protein content of 4 . 3 × 10−13 g per cell [42] and a molecular mass of 7 . 4 KDa for TRAM0076 , each M . maripaludis cell is estimated to contain ~17 , 500 to 35 , 000 molecules of TRAM0076 . Given the aqueous volume of 10 × 10−16 L per cell [43] , the calculated molar concentration of TRAM0076 is 29 to 58 μM . Given its relative high affinity of ~5 μM for Pentaprobe RNAs ( S3 Fig ) and low in vivo binding of the highly structured tRNAs and rRNA , it is reasonable to expect that most of the cellular mRNAs will be bound to TRAM0076 under physiological conditions . By binding renascent mRNAs and remodeling the conformations to facilitate the transcription and / or translation via its RNA chaperone activity , TRAM0076 could also associate with transcription and / or translation machineries that are involved in the fundamental processes of life . Thus , abolishment of these associations in the Δ0076 deletion mutant could impair translation and transcription and hence growth on multiple levels . The TRAM domain occurs in the N- or C-terminal regions of proteins with diverse functions , such as RNA methylases , translation initiator factor eIF2β , ribosomal protein S2 and methylthiolase , which are widely distributed in all organisms including Archaea . Therefore , Anantharaman et al . proposed that TRAM domains are mobile and fused to diverse proteins during evolution [26] . The observation that the stand-alone TRAM domain proteins are widely distributed in Archaea and plays a fundamental role in mRNA expression and the growth of modern Archaea further suggests that RNA chaperones may also have been important elements in the ancestor to the archaeal lineage . Moreover , RNA chaperones are also involved in biological processes in addition to cold protection of organisms . When expressed in Arabidopsis , rice and maize , the two bacterial RNA chaperones , the E . coli CspA and the Bacillus CspB , improve growth of the plants under water-limited conditions and other abiotic stresses [44] . Similarly , ectopic expression of two TRAM proteins from Methanolobus psychrophilus R15 significantly improves the tolerance of the transgenic rice to drought and high salinity stresses [45] . Given that the archaeal TRAM can replace the function of the E . coli cold shock proteins , this ancient RNA chaperone may function in a number of basic cellular processes regardless of the types of organism .
The microbial strains used in this study and their characteristics are listed in S1 Table . Escherichia coli strains were routinely grown at 37°C in Luria-Bertani ( LB ) medium with shaking . M . maripaludis strains were cultured in pre-reduced DSMZ medium 141 containing 40 mM formate at 37°C under a gas phase of N2/CO2 ( 80: 20 , v/v; 0 . 1 MPa ) as previously described [46 , 47] . Growth was determined by measurement of the optical density of at 600 nm ( OD600 ) . Plasmids used for genetic manipulations are listed in S1 Table . The MMP0076 gene of M . maripaludis S0001 was deleted by replacement with the pac cassette , which encodes puromycin resistance , through homologous recombination using the integration vector pIJA03 and polyethylene glycol ( PEG ) -mediated transformation [47 , 48] . For complementation analysis , the coding region of MMP0076 was PCR amplified and inserted into the shuttle vector pMEV2 at the NsiI/XbaI site to generate pMEV2-0076 . After verifying the sequence , pMEV2-0076 was transformed into the Δ0076 mutant to construct MMP0076-com strain , and the transformants were screened in the presence of 1 mg/ml neomycin . As a control , the empty vector pMEV2 was transformed into both the wild-type strain and the Δ0076 mutant , respectively . For expressing TRAM0076 in E . coli BX04 and RL211 strains , the open reading frame of MMP0076 was PCR amplified from the genomic DNA using primer pairs P3/P4 ( S2 Table ) and then cloned into the NdeI/BamHI sites of pINIII ( pIN-III-lppP-5 ) , resulting in the expression plasmid pIN-0076 . In parallel , the E . coli cspA and cspE and Mpsy_3066 ( encoding TRAM3066 ) of M . psychrophilus R15 [28] were cloned as controls . For overexpression of the His6-tagged recombinant TRAM0076 , MMP0076 sequence was amplified by primer pairs P11/P12 ( S2 Table ) and cloned into NcoI/XhoI sites of pET28a ( Novagen , Madison , USA ) , resulting in p28a-0076 . Similarly , the E . coli cspA , cspE and M . psychrophilus Mpsy_3066 were also cloned into pET28a , resulting in the expression plasmids as p28a-cspA , p28a-cspE and p28a-3066 using the primer pairs listed in S2 Table . To introduce the site-directed mutations in TRAM0076 , the replicative plasmids pIN-0076 and pMEV2-0076 served as templates for expression in E . coli RL211 [34] and M . maripaludis [48] , respectively . To produce recombinant TRAMs , plasmids p28a-3066 , p28a-0076 and its mutants , p28a-cspA and p28a-cspE were each transformed to E . coli BL21 ( DE3 ) plysS . Expression of the 6×His tagged proteins were induced by IPTG ( isopropyl-D-thiogalactopyranoside ) and purified as described previously [28] by Ni2+-nitrilotriacetatic acid-agarose columns ( Novagen ) , HiTrap Q HP anion exchange columns and gel filtration chromatography according to the manufacturer’s protocols ( GE Healthcare ) . Using the approach of Zhang et al . [28] , rEMSA was performed for purified TRAM0076 protein . The 3′ end biotinylated Pentaprobes ( PP ) library RNAs [33] were used as substrates , and the RNA binding assay was carried out as described previously [28] with some modifications . Briefly , the 20 μl binding reaction including purified TRAM0076 protein and 3′ labeled PP RNAs was incubated at 25°C for 20 min and then electrophoresed under 100 V for 1 h in 0 . 5× TBE running buffer ( 1 mM EDTA , 45 mM Trisboric acid , pH 8 . 0 ) . Free RNA and RNA-protein complexes were transferred to a nylon membrane , and after cross-linking by UV they were detected using a Chemiluminescent Nucleic Acid Detection Module kit ( Thermo Scientific ) according to the manufacturer’s instruction . To determine the nucleic-acid unfolding activity of TRAM0076 and its mutants , plasmids pIN-0076 and its derivatives were each transformed into E . coli RL211 . The transformants were cultured overnight in LB complemented with 100 μg/ml ampicillin and then diluted a 100-fold into the fresh medium . When the culture grew to an OD600 nm of 0 . 4–0 . 6 , 1 mM IPTG was added , and cultivation continued for another 2 h . Next , six μl of 10-fold diluted cultures were spotted on LB plates containing 100 μg/ml ampicillin , 1 mM IPTG , with or without 30 μg/ml chloramphenicol , and grown for 2–3 days . To detect the role of TRAM0076 to facilitate transcription in M . maripaludis , another replicative vector pMEV4-mCherry-neo carrying a neo cassette ( neomycin resistant ) and a mCherry reporter gene was used . Each of the three 5′UTRs ( MMP0127 , MMP1515 and MMP1697 ) was inserted upstream the mCherry gene in the vector , resulting plasmids pMEV4-0127PUO , pMEV4-1515PUO , and pMEV4-1697PUO , and then transformed into the Δ0076 mutant and the wild-type strain , respectively . The pMEV4-1515PUOMT was constructed by the site-directed mutation of the pMEV4-1515PUO described above . Expression of the mCherry reporter gene was assayed by detecting the fluorescence at 575 nm ( excitation ) and 610 nm ( emission ) for the cultures during growth . A fluorescent molecular beacon system developed by Nakaminami et al . [35] was used to determine the nucleic-acid unfolding activity of TRAM0076 . Briefly , two partially complementary oligodeoxynucleotides were labeled with FITC and BHQ1 , respectively ( Sangon , Shanghai , China ) . Similarly , FITC and BHQ1 were used to label three oligodeoxynucleotides of the 5′UTRs of MMP0127 , MMP1515 and MMP1697 ( Fig 4A ) at the 5′ and 3′ terminus , respectively . FITC and BHQ1-labelled oligodeoxynucleotides were mixed at a ratio of 1:1 , denatured at 95°C for 5 min and cooled gradually to 4°C . 20 picomole annealed oligonucleotides was incubated with 20 μM ( final concentration ) purified recombinant TRAM0076 at room temperature in 200 μl of 200 mM Tris-Cl ( pH 7 . 5 ) and 10 mM MgCl2 . 20 μM ( final concentration ) CspA and CspE were included as positive controls . Fluorescence was measured in 96-well plates using a Synergy H4 hybrid Reader ( BioTek , USA ) at 460 nm for excitation and 515 nm for emission . Cultures at the mid-exponential phase of M . maripaludis S0001 and the Δ0076 mutant were harvested from three independent cultures . The total RNA was extracted using TRIzol ( Ambion ) , and the whole-transcript cDNA libraries , high-throughput sequencing and quality control ( QC ) were performed as previously described by Li et al . [20] . Uniquely mapped reads were aligned to the M . maripaludis S2 reference genome ( Methanococcus_maripaludis/GCF_000011585 ) . The differential expression analysis of transcripts between wild-type and Δ0076 mutant was performed by using the DESeq algorithm with the normalized mapped read counts , and a threshold of the adjusted P value Padj <0 . 05 was considered as a significant differential expression [30 , 31] . The Pearson correlation ( R2 ) among the three biological repeats ranged 0 . 889 to 0 . 989 . Genome-wide transcription start sites ( TSS ) were mapped using the differential RNA-sequencing ( dRNA-seq ) approach as described previously [20] . After TSS calling , the operon composition and 5′ untranslated regions ( UTR ) were analyzed for M . maripaludis S0001 as described in [20] . MMP0076 gene tagged with a C-terminal his6 or a his6 plus 3×Flag-tag was inserted into pMEV4 that carries a neo cassette by using the Gibson assembly method [49] and transformed into the Δ0076 mutant to construct MMP0076-his6 and MMP0076-his6-3×Flag strains . Cells at the late exponential phase were collected , and the pellet was washed with 1 mL of buffer A ( 50 mM Tris-HCl ( pH7 . 5 ) , 500 mM NaCl , 10% ( w/v ) glycerol ) , snap-frozen in liquid nitrogen , and stored at -80°C . RNA co-immunoprecipitation ( RIP ) was then performed as described previously [39 , 50 , 51] with some modifications . Briefly , cells were broken in the lysis buffer B ( 50 mM Tris-HCl ( pH7 . 5 ) , 1mM MgCl2 , 150 mM NaCl , 0 . 5% Triton X-100 supplemented with cOmplete mini protease inhibitor ( Roche ) and 10 U/ml RNasin ( Promega ) by repeated pipetting . Prior to immunoprecipitation , 1% of the supernatant was used to prepare total RNA as input . The remainder was pre-cleared by mouse IgG-agarose ( Sigma ) , and then incubated with 40 μl of Anti-Flag M2 Magnetic Beads ( Sigma ) at 4°C and pre-blocked by incubation in 1% BSA . The Co-IP anti-Flag beads were washed three times with 1 ml buffer B , and two times with 1 ml buffer B plus 150mM NaCl . RNA-bound to the Flag-tagged TRAM0076 was eluted using the FLAG Peptide ( Sigma ) , and RNA was extracted and precipitated with ethanol with NaOAc and glycogen as carrier . Extracted RNAs were quantified by Quant-iT RiboGreen RNA kit ( invitrogen ) . The immunoprecipitated RNAs were treated with DNase I , and cDNA libraries were constructed using NEBNextH UltraTM Directional RNA Library Prep Kit for IlluminaH ( New England Biolabs ) as described previously [20] . Sequencing was performed on an Illumina HiSeq 2000 platform , and the filtrated sequencing reads were mapped to the M . maripaludis S2 genome ( Methanococcus_maripaludis/ GCF_000011585 ) . Read counts were normalized to the Fragment Per Kilobase of transcript per Million fragments mapped ( FPKM ) . Quantitative RT-PCR ( qPCR ) was performed to verify RIP-seq data . cDNAs were generated from 2 μg of input RNA and 10 μl of immunoprecipitated RNA by RT-PCR with random primers using Moloney murine leukemia virus reverse transcriptase ( Promega ) . qPCR amplification was performed using the corresponding primers ( S2 Table ) at Mastercycler EP realplex2 ( Eppendorf , Germany ) . To estimate copy numbers of tested mRNAs , standard curves of the corresponding genes were generated using 10-fold serially diluted PCR product as templates . RIP enriched percentage of each mRNA was calculated as the copies in RIP divided by those in the input sample times 100 . 16S rRNA gene was used as a control . Using the same approach as described previously [28] , TRAM0076 complementation of the cold sensitivity of E . coli BX04 [33] was performed . Briefly , plasmid pIN-0076 was transformed into E . coli BX04 and 10-fold serial dilutions of the transformant were spotted on LB plates containing 100 μg/ml ampicillin and incubated for 2–5 days at 37°C , 22°C and 18°C . | RNAs frequently misfold into stable but biologically inactive structures especially under stresses , while RNA chaperones interact with various RNAs to prevent the structures that may cause premature transcriptional termination or pausing , and affect mRNA decay and translation . This work for the first time reports that an archaeal RNA chaperone TRAM0076 globally affects the transcription of methanogenic Archaea through posttranscriptional actions . TRAM0076 also binds many cellular mRNAs , possibly at the 5′-untranslated regions . This work uncovers an important regulatory element of ancient life in the RNA world . | [
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| 2019 | The archaeal RNA chaperone TRAM0076 shapes the transcriptome and optimizes the growth of Methanococcus maripaludis |
Across all kingdoms of biological life , protein-coding genes exhibit unequal usage of synonymous codons . Although alternative theories abound , translational selection has been accepted as an important mechanism that shapes the patterns of codon usage in prokaryotes and simple eukaryotes . Here we analyze patterns of codon usage across 74 diverse bacteriophages that infect E . coli , P . aeruginosa , and L . lactis as their primary host . We use the concept of a “genome landscape , ” which helps reveal non-trivial , long-range patterns in codon usage across a genome . We develop a series of randomization tests that allow us to interrogate the significance of one aspect of codon usage , such as GC content , while controlling for another aspect , such as adaptation to host-preferred codons . We find that 33 phage genomes exhibit highly non-random patterns in their GC3-content , use of host-preferred codons , or both . We show that the head and tail proteins of these phages exhibit significant bias towards host-preferred codons , relative to the non-structural phage proteins . Our results support the hypothesis of translational selection on viral genes for host-preferred codons , over a broad range of bacteriophages .
The genomes of most organisms exhibit significant codon bias—that is , the unequal usage of synonymous codons . There are longstanding and contradictory theories to account for such biases . Variation in codon usage between taxa , particularly within mammals , is sometimes attributed to neutral processes—such as mutational biases during DNA replication , repair , and gene conversion [1]–[4] . There are also theories for codon bias driven by selection . Some researchers have discussed codon bias as the result of selection for regulatory function mediated by ribosome pausing [5] , or selection against pre-termination codons [6] , [7] . However , the dominant selective theory of codon bias in organisms ranging from E . coli to Drosophila posits that preferred codons correlate with the relative abundances of isoaccepting tRNAs , thereby increasing translational efficiency [8]–[13] and accuracy [14] . This theory helps to explain why codon bias is often more extreme in highly expressed genes [15] , or at highly conserved sites within a gene [14] . Translational selection may also explain variation in codon usage between genes selectively expressed in different tissues [16] , [17] . However , recent work suggests that synonymous variation , particularly with respect to GC content , affects transcriptional processes as well [18] . The codon usage of viruses has also received considerable attention [19] , [20] , particularly in the case of bacteriophages [21]–[26] . Most work along these lines has focused on individual phages , or on the patterns of genomic codon usage across a handful of phages of the same host . Here , we provide a systematic analysis of intragenomic variation in bacteriophage codon usage , using 74 fully sequenced viruses that infect a diverse range of bacterial hosts . Motivated by energy landscapes associated with DNA unzipping [27] , [28] , we develop a novel methodological tool , called a genome landscape , for studying the long-range properties of codon usage across a phage genome . We introduce a series of randomization tests that isolate different features of codon usage from each other , and from the amino acid sequence of encoded proteins . Thirty-three of the phages in our analysis are shown to exhibit non-random variation in synonymous GC content , as well as non-random variation in codons adapted for host translation , or both . Additionally , we demonstrate that phage genes encoding structural proteins are significantly more adapted to host-preferred codons compared to non-structural genes . We discuss our results in the context of translational selection and lateral gene transfer amongst phages .
We start by introducing the concept of a genome landscape , which provides a simple means for visualizing long-range correlations of sequence properties across a genome [29] . A genome landscape is simply a cumulative sum of a specified quantitative property of codons . The calculation of the cumulative sum is straightforward , and it consists of scanning over the genome sequence one codon at a time , gathering the property of each codon , and summing it with the properties of previous codons in the genome sequence . Similar cumulative sums are used in solid-state physics for , e . g . , the calculation of energy levels [30] . In the case of the GC3 landscape , we have ( 1 ) where ηGC3 ( m ) equals one or zero , depending upon whether the mth codon ends in a G/C or A/T , respectively . Note that we subtract the genome-wide average GC3 content , , so that FGC3 ( 0 ) = FGC3 ( N ) = 0 , where N is the length of the genome . In other words , we convert the genome codon sequence into a binary string of 1's and 0's according to whether each codon is of type GC3 or AT3 , and we cumulatively sum this sequence to compute FGC3 ( m ) . The interpretation of a GC3 landscape is straightforward . Regions of the genome whose landscape exhibits an uphill slope contain higher than average GC3 content , whereas regions of downhill slope contain lower than average GC3 content . The genome landscape provides an efficient visualization of long-range correlations in sequence properties across a genome , similar to the techniques introduced by Karlin [31] . Traditional visualizations of GC3 content involve moving window averages of %GC3 over the genome [32] . In order to compare these techniques with the landscape approach , we focus on the E . coli phage lambda as an illustrative example . Figure 1A shows the lambda phage GC3 landscape above its associated “GC3 histogram” . The histogram shows the GC3 content of each gene , and the width of each histogram bar reflects the length of the corresponding gene . Thus , the gene-by-gene histograms mimic a sliding window average view of nucleotide content across the genome , but focus on the contributions of individual genes to these sequence properties . Figure 1A reveals a striking pattern of lambda phage codon usage: the genome is apparently divided into two halves that contain significantly different GC3 contents [33] , [34] . The large region of uphill slope on the left half of the GC3 landscape reflects the fact that the majority of the genes in this region contain an excess of codons that end in G or C . This trend is also reflected in the GC3 histogram bars , which are higher than average in the left half of the genome ( Figure 1 ) . It is clear that genome landscapes contain the same information as gene-by-gene histograms . However , as has been noted before [29] , genome landscapes also represent a powerful visualization tool that emphasizes genome-wide trends in sequence properties . As we demonstrate below , gene-by-gene histograms offer a mechanism by which to quantify these trends , while the landscapes offer striking views of these trends that can aid in their interpretation . In addition , GC-landscapes are directly useful for modeling physical properties of DNA unzipping [28] . Genome landscapes also provide a natural means of evaluating whether or not features of codon usage are due to random chance . Under a null model in which the η ( i ) 's above are chosen as independent random variables with var ( η ( i ) ) = 〈η ( i ) 2〉−〈η ( i ) 2〉 = Δ , one can show ( see Methods ) that the standard deviation of F ( GC3 , m ) is ( 2 ) This quantity is shown as a purple band in Figure 1 . For η ( i ) 's chosen to be 0 or 1 at random , ΔGC3 = 1/4 and the maximum width is obtained at m = N/2 . Since the scale of variation across the lambda phage GC3 landscape is much greater than its expectation under the null , we can conclude that the distribution of G/C versus A/T ending codons is highly non-random in the lambda phage genome . We can also gain intuition about the degree of non-randomness in the GC3 landscape by considering what would happen if the lambda phage genome were to accumulate random synonymous mutations . Figure 2A shows snapshots of the lambda GC3 landscape as we simulate synonymous mutations to the genome . Between each snapshot , N synonymous mutations were introduced by picking a codon at random along the genome , and then choosing a new synonymous codon at random according to the global lambda phage codon distribution . By preserving the global codon distribution in each synonymous variation of the genome , this procedure inherently controls for any mutational bias or other source of global codon usage bias that may be present in the phage genome nucleotide content . The same is true for all randomization tests discussed in this paper . As more mutations are introduced , the GC3 landscape of the synonymously mutated lambda genome approaches the purple band , indicating that the GC3 pattern in the real lambda phage genome is highly non-random . The procedure of producing a genome landscape can be applied to other properties of codon usage . In addition to GC3 , we will study patterns in the Codon Adaptation Index ( CAI ) . CAI measures the similarity of a gene's codon usage to the ‘preferred’ codons of an organism [35]—in this case , the host bacterium of the phage under study . Every bacterium has a preferred set of codons defined as the codons , one for each amino acid , that occur most frequently in genes that are translated at high abundance . These genes are often taken to be the ribosomal proteins and translational elongation factors [35] ( see Methods ) . In order to calculate CAI , the preferred codons are each assigned a weight w = 1 . The remaining codons are assigned weights according to their frequency in the highly-translated genes , relative to the frequency of the w = 1 codon . The CAI of a gene is defined as the geometric mean of the w-values for its codons ( 3 ) where wi is the w-value of the ith codon , and M is the length of the gene . This quantity can be re-written as ( 4 ) The latter formulation is more useful for calculating genome landscapes , because the argument of the exponential function is now a sum of the logs of the w-values . Therefore , we define the CAI landscape as ( 5 ) where ηCAI ( m ) = ln ( wm ) . The CAI landscape for lambda phage is shown in Figure 1B , along with the CAI histogram of lambda phage . For the CAI histograms , the height of each bar represents the CAI value of that gene ( Equation 3 ) . As in the case with the GC3 landscape , we find that the lambda phage CAI landscape corresponds closely to the CAI histogram , but it offers a more striking global view of the long-range CAI structure in the lambda phage genome . One contiguous half of the lambda phage genome exhibits elevated CAI , whereas the other half exhibits depressed CAI . The observed CAI landscape lies far outside the purple band in Figure 1 , calculated according to Equation 2 , indicating that the pattern of CAI across the lambda phage genome is non-random . However , the purple band is wider for the CAI landscape than for the GC3 landscape , because the variance in the ln ( wi ) 's , ΔCAI , is greater than ΔGC3 . The GC3 and CAI landscapes for lambda phage are highly correlated with each other ( Figure 1 ) . In particular they both have large uphill regions on the left-hand side of the genome , indicating a region containing codons with elevated GC3-content and CAI values , compared to the genome average . It is possible that the observed correlation between the GC3 and CAI landscapes could be caused by the conflation between high CAI and GC3 in the preferred E . coli codons , as we discuss below . We note that the genes in the region of elevated CAI primarily encode the highly translated structural proteins that form the capsid and tail of the lambda phage virions . This pattern suggests the hypothesis that , because of the need to produce structural genes in high copy number during the viral life cycle , structural genes preferentially use codons that match the host's preferred set of codons . We will explore this translational-selection hypothesis in greater detail below . The previous section illustrated that the codon usage across the lambda phage genome is highly non-random with respect to both GC3 and CAI . In this section we quantify this statement , and we focus on aspects of lambda's codon usage patterns that are independent of the amino acid sequences of the encoded proteins . Since we are interested in studying the patterns of synonymous codon usage , it is important that we control for the amino acid sequence of encoded proteins . Phages utilize a diverse spectrum of proteins , ranging from those that form the protective capsid for nascent progeny , to those encoding for the tail and tail fibers , to those that regulate the switch between lytic or lysogenic infection pathways . As with other organisms , phage proteins have been selected at the amino acid level for function and folding . Some portion of a phage's codon usage is surely influenced by selection for amino acid content . We can construct a simple randomization test to interrogate the potential influence of the amino acid sequence on the GC3 and CAI landscapes of lambda phage . In this test , we generate random genomes that have the exact same amino acid sequence as lambda phage , but shuffled codons , such that the genome-wide , or global , codon distribution is preserved in each random genome ( see Methods ) . As summarized in Table 1 , we refer to this test as the ‘aqua’ randomization test . For each of the randomized genomes , we calculate GC3 and CAI landscape . Similar to a recent randomization method [36] , we then compare the observed landscape of the actual genome to the distribution of landscapes generated from the randomized genomes . Figure 3 shows the results of this comparison , with the observed landscapes plotted as black lines , and the mean±one and two standard deviations of random trials shown in dark and light aqua , respectively . As the figures show , the observed landscapes lie in the far extremes of the randomized distributions – indicating that the amino acid sequence of the lambda phage genome does not determine the extraordinary features of the observed landscapes . It is also instructive to query the influence of amino acid content on codon usage in each gene individually . The histogram view of these randomization tests allows us to ask this question precisely . Because the amino acid sequence is preserved exactly across the genome , each histogram bar in Figure 3 can be considered as its own randomization test , one for each gene . The position of the horizontal black bar reflects the actual codon usage of each gene , and it can be compared to the distribution of random trials in order to compute a quantile for each gene: ( 6 ) Note that we have defined two quantiles , q> and q< , that describe the proportion of random trials strictly less or strictly greater than the observed data . These two quantities sum to a values less than one ( and equal to one if there are no ties ) . A value of q>>0 . 5 signifies that the observed statistic ( e . g . GC3 or CAI ) is greater than most of the random trials . Associated with each of these quantiles is a p-value quantifying whether the observed gene sequence has significantly different codon usage than the random trials: p< = 1−q< and p> = 1−q> . If either one of these p-values is low , it signifies that the GC3 ( or CAI ) content of the gene is significantly different than the genomic average , controlling for the amino acid sequence of the gene . p< tests for significantly depressed GC3 ( or CAI ) in a gene; and p> tests for significantly elevated GC3 ( or CAI ) in a gene . We will use these p-values , which arise from the ‘aqua’ randomization test , in two ways . Since we are interested in studying the effects of synonymous codon usage alone , we first wish to filter out any genes whose codon usage does not significantly deviate from random , given the amino acid sequence . Therefore , in the subsequent gene-by-gene analyses reported in this paper , we retain only those genes whose quantiles fall in the extreme 5% of random trials . That is , we only keep those genes for which or . These genes are said to ‘pass’ the aqua test , and they are unshaded in Figure 3 . We also use the gene-by-gene p-values to quantify the degree to which codon usage is independent of amino acid sequence across the genome as a whole . To do so , we combine all the gene-by-gene p-values into an aggregate p-value for the entire genome , paqua , using the method of Fisher [37] . We calculate the combined p-value by summing the logs of twice the minimum of each gene-specific p-value ( 7 ) where represents the aqua p<-value for gene i , and k is the number of genes in the genome . It is well known that faqua is chi-squared distributed with 2k degrees of freedom [37] . Thus , the combined p-value for the entire genome , , where is the cumulative chi-squared distribution with 2k degrees of freedom . In the case of lambda phage , we find for GC3 and for CAI . Thus , we conclude that the neither the GC3 nor the CAI patterns across the lambda phage genome are determined by the genome's amino acid sequence . In the following sections we will use the aqua test ( see Table 1 ) and its associated gene-by-gene and combined p-values as a control to verify that features of codon usage are not driven by the amino acid sequence . Depending upon the preferred codons of the host species , the effect of selection for high CAI in a viral gene is not necessarily independent from the effect of selection for other features of viral codon usage , such as high GC3 . For example , codons with high CAI values associated with a given host may be biased towards high GC3 values as well ( see Figure 4 ) . It is important , therefore , to disentangle the effects of selection for CAI versus selection for GC3 , in order to determine which one of these forces is responsible for the non-random patterns of codon usage observed in the lambda genome . The weights used to compute CAI for E . coli are shown in Figure 4 . The 61 codons are placed into one of four groups according to whether they are GC3 or not ( red or blue , respectively ) , and whether they have high CAI or not ( dark or light , respectively ) . High CAI is determined by an arbitrary cutoff of w≥0 . 9 . As this table demonstrates , the set of preferred codons in E . coli is slightly biased towards GC-ending codons ( 58% ) . The GC bias of preferred codons , although slight , could conflate the results of selection for CAI versus GC3 in phages that infect E . coli , such as lambda . We therefore introduce another randomization test that allows us to disentangle patterns of CAI content from patterns of GC3 content . Similar to the aqua randomization test described above , we draw random phage genomes such that the amino acid sequence is conserved , but we add the additional constraint of conserving the exact GC3 sequence as well ( see Methods ) . For example , at a site containing a GC3 codon for leucine , in our random trials we only allow those leucine codons terminating in G or C . By comparing the observed landscapes of the genome with the distribution of randomly drawn landscapes , we can isolate the features of codon usage driven by CAI , independent of GC3 and amino acid content . We refer to this randomization procedure at the ‘orange’ randomization test ( Table 1 ) . Conversely , we also wish to assess the strength of patterns in GC3 content , independent of CAI and amino acid content . The appropriate randomization procedure in this case requires that we constrain the amino acid sequence and the sequence of codon CAI values while allowing GC3 to vary . However , because CAI values are not binary , CAI cannot be constrained exactly while still allowing for enough variability to produce a meaningful randomization test . Thus , we introduce a binary version of the CAI measure , called BCAI , that is qualitatively the same as and , for our purposes , interchangeable with CAI . The BCAI w-value for a codon is defined to be 0 . 7 if the codon is high CAI , and 0 . 3 if the codon has low CAI . High CAI is defined by the threshold of w≥0 . 9 ( see Figure 4 ) . The threshold value w≥0 . 9 is arbitrary , and our results are robust to changing this threshold ( see Figures S1 and S2 ) . Our use of the term ‘binary’ here refers to the binary classification scheme and not the particular values of BCAI . The actual values assigned for BCAI are arbitrary , for the most part , and have no effect on our results . Nevertheless , we cannot assign low BCAI a value of zero , because this value would be problematic when included in the geometric averaging procedure , or when computing the logarithm of w-values for BCAI landscapes . BCAI provides a useful surrogate for CAI because its values are binary , thereby allowing us to constrain a gene's amino acid sequence and BCAI sequence exactly , while varying GC3 content in random trials . The BCAI landscapes and histograms are calculated in the same way as CAI landscapes and histograms , except using BCAI w-values . As expected , the BCAI landscape of a genome is qualitatively similar to its CAI landscape ( compare Figures 5B and 3B ) , and the two landscapes are highly correlated ( e . g . r = 0 . 72 for lambda phage ) . Thus BCAI is interchangeable with CAI for the purposes of our randomization tests . Figure 5 shows the results of the two randomization tests outlined above: the ‘green’ test that compares the observed GC3 landscape to a distribution of random trials constraining the amino acid sequence and the BCAI sequence; and the ‘orange’ test that compares the observed BCAI landscape to a distribution of random trials constraining the amino acid sequence and the GC3 sequence . Our convention for naming these two tests is summarized in Table 1 . As seen in Figure 5A , the observed GC3 landscape lies significantly outside of the random trials that preserve amino acid sequence and BCAI sequence . Combining the gene-by-gene p-values for this test , we find – indicating that the lambda phage genome as a whole has non-random GC3 variation independent of amino acid and CAI ( actually , BCAI ) sequence . Conversely , Figure 5B shows that the BCAI landscape contains non-random features when controlling for both GC3 and amino acid sequence ( ) . In other words , the lambda phage genome exhibits highly non-random patterns of both GC3 and CAI codon variation , independent of one another and independent of the amino acid sequence . In the sections above we have demonstrated and quantified highly non-random patterns of GC3 and CAI codon usage variation across the lambda phage genome . We have also demonstrated that these trends are independent of one another . In this section , we will extend our analysis to a large range of diverse phages . In this section we consider all sequenced phages that infect E . coli , Pseudomonas aeruginosa or Lactococcus lactis as their primary host . The latter two hosts were chosen because of they contain unusually extreme GC3 content: 88 %GC3 for P . aeurginosa and 25 %GC3 for L . lactis , genome-wide . The extreme GC3 content of these hosts give rise to opposing relationships between high CAI and GC3 – as indicated schematically in Figure 6 . In particular , P . aeruginosa strongly favors GC3 in high-CAI codons ( 94% ) , and L . lactis strongly favors AT3 in high-CAI codons ( 72% ) . Thus , these three hosts span a large spectrum of relationships between CAI and GC3 . Since our randomization tests constrain amino acid and BCAI exactly ( the ‘green’ test ) , and amino acids and GC3 exactly ( the ‘orange’ test ) , we can control for any possible conflation between GC3 and CAI trends . Thus , the randomization tests are equally applicable to all of the phage genomes , regardless of their host . We performed the aqua , green , and orange randomization tests on the 45 phages of E . coli , 12 phages of P . aeruginosa , and 17 phages of L . lactis whose genomes have been sequenced ( see Methods ) . In the first step of our analysis , we removed any phages which failed either the aqua GC3 or aqua CAI tests , because the codon usage of such genomes are influenced by their amino acid sequence . A phage was said to pass these two control tests if its Fisher combined p-values for both aqua GC3 and aqua CAI were significant . The significance criterion for each test is pcombined<5%/74 , which incorporates a Bonferroni correction for multiple tests . With this cutoff , 50 of the initial 74 phages passed the aqua control tests . Figure 7 shows results of these tests for several example genomes . P2 , a temperate phage , and T3 , a non-temperate phage both infect E . coli and both pass the control tests and exhibit significant ‘orange’ and ‘green’ results , as does D3112 , a temperate phage that infects P . aeruginosa . However , not all phages that pass the control test exhibit significant ‘orange’ and ‘green’ results – as evidenced by bIL286 , a temperate phage infecting L . lactis . Figure 8 plots the distribution of combined Fisher p-values of the orange and green tests , for the 50 phages that pass the control tests . The majority of these p-values are highly significant . Using a Bonferoni-corrected threshold of 5%/50 , a total of 22 genomes show significance in the orange test , 29 in the green test , and 17 in both orange and green . These results indicate that non-random patterns in codon usage are not unique to lambda phage . Indeed , over a range of bacterial hosts and a range of phage viruses , there is apparent pressure for non-random patterns of both GC3 content and CAI content , independent of one another and independent of the amino acid sequence . In this section , we investigate a natural hypothesis concerning the patterns of non-random CAI usage we have observed in phage genomes – namely , that these patterns may be driven by selection for translational accuracy and efficiency , which is stronger in more highly expressed proteins [9] , [21] . Among all phage proteins , the structural proteins are the most highly expressed [38] . The structural proteins form the protective capsid that encloses the viral genome , as well as the tail , which is often used for transmission of the phage genome to the inside of the host [39] . These proteins must be produced in high copy number – many tens of copies of each type of structural protein needed to form each of hundreds of viral progeny [38] . For each gene in a phage genome , we assigned a structural annotation of 1 if the gene was known to encode a structural protein and 0 otherwise ( see Methods ) . According to the standard hypothesis of translational selection , the structural genes of phages should exhibit elevated CAI levels compared to other phage genes , since they are translated ( by the host ) in high copy numbers . To test this hypothesis , we performed regressions between the structural annotation of phage genes and their aqua CAI and orange BCAI p-values . In other words , we compared the structural properties of genes against their CAI content , controlling for amino acid sequence , and against their BCAI content , controlling for both amino acid sequence and GC3 sequence . In the case of lambda phage , Figure 9 shows the results of the aqua CAI and orange BCAI randomization tests , with the structural genes highlighted . The plot reveals a striking pattern: the vast majority of the structural proteins lie on the left half of the genome , exactly in the region where genes have elevated CAI values . In order to quantify this association we performed ANOVAs . Before regressing structural annotations against codon usage , we first removed the non-informative genes – i . e . genes whose codon usage are influenced by their amino acid content , as indicated by a failure to pass the aqua CAI test . Table 3 shows the results of the regression between aqua CAI and orange BCAI p>-values versus structural annotations in lambda phage . The results are highly significant: structural annotations explain half of the variation in CAI , even when controlling for genes' amino acid sequences ( aqua , r2 = 56% ) as well as GC3 sequences ( orange test , r2 = 46% ) . The median p>-value among structural genes is close to zero , whereas the median p>-value among non-structural genes is close to one – indicating that structural genes exhibit significantly elevated CAI values . These highly significant results are consistent with the hypothesis of translational selection on structural proteins . In order to examine the relationship between structural annotation and CAI across all 74 phages in our study , we performed the same ANOVA on the 1 , 309 informative genes ( i . e . genes that pass the aqua CAI randomization test ) . Once again , Table 3 shows a highly significant relationship between structural annotation and CAI values , controlling for amino acid content and GC3 . Thus , the tendency toward elevated CAI values in structural genes holds across all the phages in this study , despite the fact that they infect a diverse range of hosts with a wide variety of GC contents . Similar to reports for other organisms [40] , we find a relationship between gene length and codon adaptation . In our case , however , longer viral genes are associated with more significant p>-values in the aqua and orange tests . However , the strength of this relationship is weak , and controlling for gene length does not affect our results on elevated CAI in structural proteins ( ANOVA p-values analogous to Table 3 are less than 10−9 after controlling for gene length ) .
In this paper , we have developed genome landscapes as a tool for visualizing and analyzing long-range patterns of codon usage across a genome . In combination with a series of randomization tests , we have applied this tool to study synonymous codon usage in 74 fully sequenced phages that infect a diverse range of bacterial hosts . Genome landscapes provide a convenient means to identify long-range trends that are not apparent through conventional , gene-by-gene or moving-window analyses . Using a statistical test that compares codon usage to random trials , controlling for the amino acid sequence , we found that we found that many of the phages studied exhibit non-random variation in codon usage . However , not all of the phages exhibit non-random variation as exemplified by phage bIL286 ( Figure 7D ) . In light of long-standing [9] and recent [18] literature from other organisms , we have focused on two aspects of phage codon usage: variation in third-position GC/AT content ( GC3 ) and variation in the degree of adaptation to the ‘preferred’ codons of the host ( CAI ) . Almost three-quarters of the phages in our study exhibit non-random intragenomic patterns of codon usage , even when controlling for the amino acid sequence encoded by the genome . Almost half of such genomes also show non-random patterns of CAI when additionally controlling for the GC3 sequence . In other words , there is substantial variation in CAI above and beyond what would be expected by random chance , given the amino acid and GC3 sequences of these genomes . We have also compared the CAI values of phage genes to their annotations as structural or non-structural proteins . We have conclusively demonstrated that phage genes encoding structural proteins exhibit significantly elevated CAI values compared to the non-structural proteins from the same genome . These results hold even when controlling for the amino acid sequence and GC3 sequence of genes . Our conclusions across a diverse range of phages are consistent with early observations on lambda's codon usage [34] , early results for T7 [21] , and with the general hypothesis of translational selection , which predicts elevated CAI in genes expressed at high levels [9] , [15] , [35] . The pattern of elevated CAI in structural proteins is particularly striking the case of lambda phage . It is also worth noting that we find no significant relationship between a phage's life-history ( i . e . temperate versus non-temperate ) and the degree to which its structural proteins exhibit elevated CAI ( see Table 6 ) . This observation likely reflects the fact that at some point every phage , regardless of its life history , must generate certain structural proteins in high abundance – and so it is beneficial to encode such protein using the host's translationally preferred codons . Some of the phages examined are known to encode their own tRNA genes . Table 5 lists the number of tRNA genes for the ten phages in this study that encode tRNA genes . We have inspected these examples for signs that structural genes might be preferentially encoded by endogenous tRNAs , or the converse , but have concluded that the data are equivocal . There are too few informative examples to make a strong conclusion in either direction . Our results on translational selection in phages shed light on the nature of selection on viruses . The standard interpretation of elevated CAI in highly expressed bacterial proteins assumes a fitness cost ( per molecule ) associated with inefficient or inaccurate translation . We have observed a similar relationship between expression level and CAI across a diverse range of bacteriophages , which presumably do not incur a direct energetic cost from inefficient translation by their hosts . Thus , our results suggest that either there is an adaptive benefit ( to the virus ) of elevated CAI in phage structural proteins , or that costs incurred by the host bacterium also reduce the fitness of the virus . In addition to our results on CAI , we have also observed non-random patterns of GC3 variation across the genomes of many phages . These patterns are highly significant even after controlling for potential conflating factors , such as the amino acid sequences and CAI sequences of genes . Unlike our results on CAI , there is no clear mechanistic hypothesis underlying the non-random patterns of GC3 in phages . It is possible that these patterns reflect selection for efficient transcription [18] or for mRNA secondary structure . But in the absence of independent information on such constraints , we cannot assess the merits of these selective hypotheses , nor rule out the possibility of variation in mutational biases across the phage genomes . It is interesting to note that we find these significant non-random patterns of GC3 predominantly in temperate phages ( see Table 6 ) . Our study benefits from the number and breadth of phages we have analyzed . Unlike previous studies , here we analyze phages whose suspected hosts span a diverse range of bacteria , which themselves differ in their genomic GC3 content and preferred codon choice . We have calibrated CAI for each phage according to its primary host , and nevertheless we find consistent relationships between CAI and viral protein function . These results therefore conclusively extend the classical theory of translational selection to the relationship between viruses and their hosts . The present study also benefits from the development of randomization tests that isolate the patterns of variation in CAI from variation in GC content . Due to intrinsic biases in the GC content of the preferred codons of hosts , previously studies on codon usage in phage have conflated these two types of synonymous variation [23]–[26] . The mechanisms underlying GC3 variation and CAI variation likely differ , and so it is critically important that we have analyzed each of these features controlling for the other one . There is a large literature on the structure and evolution of phage genomes which is pertinent to our analyses of phage codon usage . The genomes of phages that infect E . coli , L . lactis , and Mycobacteria are known to be highly mosaic in structure [41]–[46] . In other words , these genomes exhibit many similar local features that suggest each genome was assembled from a common pool of bacteriophage genomic regions [47] . Recently , mosaicism was discussed in the lambdoid phages focusing specifically on the E . coli phages lambda , HK97 and N15 [38] . We note that both HK97 and N15 have peaked landscape structures like lambda , although not as pronounced , indicating that some degree of mosaicism can be observed in genome landscapes among closely related phages . The postulated mechanism for mosaicism is homologous and non-homologus recombination between co-infecting phages or between a phage and a prophage embedded in the host genome [42] , [47] , [48] . Some have argued that the latter mechanism occurs more frequently , due to the large number of lysogenized prophages in bacterial genomes [48] . Lateral gene transfers could affect the codon usage patterns of phages , especially if recombination occurs between phages whose preferred hosts differ . In this case , the codon usage patterns of each phage may be expected to reflect the preferred codons of their preferred hosts; a recent recombination may result in regions of dramatically different codon usage from the average phage codon usage . In particular , regions of unusual GC3 content in a phage genome could reflect gene transfers between phages that typically infect hosts of different GC3 content , in analogy with lateral gene transfer amongst bacteria [49] . Morons are genes in phage genomes that are under different transcriptional control than the rest of the phage genes , and are often expressed when the phage is in the lysogenic state [50] . These morons have been observed to have very different nucleotide compositions compared to the rest of the phage genome suggesting that they are the result of such gene transfers [50] . Thus one interpretation for our observations of the 29 phages exhibiting non-random GC3 patterns is that these genomes arose through recent recombination events , and have not subsequently experienced enough time to equilibrate their GC3 content to that of their current host . Given the lack of reliable estimates for time scales between putative phage recombination events , or for codon usage equilibration , this study neither supports nor refutes this interpretation . However , the predominance of significant non-random patterns of GC3 in the genomes of temperate phages ( see Table 6 ) suggest that such recombination may occur more frequently among temperate phage populations . We have demonstrated that phage genes encoding structural proteins exhibit significantly elevated CAI values compared the non-structural phage genes . These results support the classical translation selection hypothesis , now extended to the relationship between viral and host codon usage . We do not find much variation in codon usage among the structural genes themselves . This observation has two plausible interpretations within the literature of lateral gene transfers: either phages of different preferred hosts rarely co-infect , or there is substantially less recombination among the structural proteins of phages . The latter hypothesis has been independently suggested for the capsid proteins of phages , based on the idea that capsid proteins form a complex with multiple physical interactions whose function would be disrupted by individual gene transfer events [43] . Unlike capsid genes , phage tail genes often exhibit mosaicism , and they can include elements from diverse viruses with variable host ranges [43] , [51] . To investigate this phenomenon in the context of codon usage , we refined the structural annotation to separate head from tail genes ( see Methods ) . We performed three separate ANOVAs to compare the CAI usage in these genes: comparing head versus non-structural , tail versus non-structural , and head versus tail ( Table 4 ) . These regressions indicate that the head genes are primarily responsible for that pattern of elevated CAI in structural proteins . In addition , we detect a difference in codon usage between head and tail genes . These results have at least two possible explanations: either the head proteins are produced in higher copy number than the tail proteins , or lateral gene transfers between diverse phages occur frequently enough in the tail genes to impair their ability to optimize codon usage to their current host . The first hypothesis is very plausible , in light of evidence on the copy number of head and tail proteins [38]; nevertheless , we cannot rule out the second possibility . Finally we note that our methodologies could offer a mechanism to analyze the recently growing amount of phage DNA sequences gathered through metagenomic studies [52] , [53] . We have shown that , especially for genes encoding structural proteins , there is a strong host-specific signature in the viral genome – namely the enrichment of host-preferred codons . Raw metagenomic data seldom identify the relationship between the viral DNA segments sequenced and the hosts they infect . We may be able to help glean such information using a form of the randomization tests developed here to search over all possible host master tables , identifying potential hosts as those that maximize the statistical significance of the randomization tests .
Bacteriophage genomes were downloaded from NCBI's GenBank ( http://www . ncbi . nlm . nih . gov/Genbank/index . html ) release 156 ( October , 2006 ) using Biopython's [54] NCBI interface . We only used reference sequence ( refseq ) phage genome records with accessions of the form NC_00dddd in order to have the most complete records available . Of the 396 phage refseq's available , we focused on the 74 genomes of phages whose primary host , as listed in the specific_host tag in the Genbank file , were E . coli , P . aeruginosa or L . lactis . All phage genomes were downloaded from GenBank . Before being used for the rest of this study , every gene within a genome was scanned for overlaps within other genes in the same genome , and all overlapping sequences were removed . A codon was only retained if all three of its nucleotides occurred in a single open reading frame . Thus the final genome sequence used was a concatenation of all non-overlapping coding sequences , omitting any control elements and other non-coding sequences . The definition of the Codon Adaptation Index requires the construction of a ‘master’ w-table for the host organism . Each of the 61 sense codons is assigned a w-value based on the codon's frequency among the most highly expressed genes in the host organism . In defining this set of genes , we follow Sharp [35] , who specified highly expressed genes for E . coli . In order to calculate the CAI master w-tables for P . aeruginosa and L . lactis , we identified the homologs of the highly expressed E . coli genes within the other host genomes , using BLAST [55] . In particular , we used qblast to find homologs to these E . coli genes by inputting the gene protein sequences , and blasting ( blastp ) against the nr database , restricting the database to include proteins of the target organism . In all cases , we used the most significant blast result as the ortholog , provided its e-value was less than 1×10−10 . Given the set of highly expressed genes , the CAI master w-table was calculated as follows . For each host , the GenBank file ( GenBank release 156 ) was downloaded locally and transformed into a local data structure using Biopython's [54] GenBank parser . The data structure was then scanned for each of the genes in the highly translated gene set , and the collective CDS codon sequences of these genes were concatenated together into one long sequence . Stop codons and codons encoding for amino acids methionine ( M ) , and tryptophan ( W ) ( each encoded by only one codon ) were removed from the concatenated sequence . The frequencies of codons encoding all other amino acids were then tabulated , and divided into groups according to which amino acid they encode . The w-values are then calculated , according to the procedure of Sharp [35] , as these frequencies , normalized by the maximum frequency within each group . Thus each amino acid has a codon with a w-value of 1 , representing the most commonly used codon for that amino acid . The w-values for the stop codons and codons for methionine and tryptophan were set to the average w-value of the remaining codons . Our randomization tests require drawing randomized phage genomes that are constrained to have specific properties . In all of the randomization tests discussed , the random sequences were drawn as a sequence of synonymous codons from the global codon distribution at each position , thereby exactly preserving the amino acid sequences of proteins . Furthermore , each test preserves the global codon distribution in each synonymous variation of the genome , and thus inherently controls for any mutational bias or other source of global codon usage bias that may be present in the phage genome nucleotide content . The tests thus isolate the feature that we wish to interrogate which is local patterns in synonymous codon usage . The three randomization tests used in this work can all be considered variants of a canonical randomization test that preserves both the amino acid sequence and a bit mask sequence exactly , while drawing codons from the global , genome-wide distribution . A bit mask sequence is string of zeros and ones corresponding to all codons in the genome . For example , GC3 is 1 if the third position of a codon is G or C , and 0 otherwise . Using the GC3 bit mask as an example , the randomization test procedure is initialized by calculating the global codon frequencies that fit into categories specified by the amino acid and the bit-mask value . Each amino acid has associated with it two distributions: one for a bit-mask value of 1 and one for a bit-mask value of 0 . For example , alanine ( A ) , is encoded by four codons , GCC ( 1 ) , GCG ( 1 ) , GCT ( 0 ) , GCA ( 0 ) , where the GC3 bit-mask is shown in parenthesis . Thus to calculate the codon distribution of alanine GC3 codons ( A1 ) , we compute the frequency of GCC and GCG codons across the whole phage genome . Similarly , the distribution of A0 codons is determined from the frequency of GCT and GCA codons across the genome . In order to produce a random genome , random codons are drawn at each position according to the distribution associated with the position's amino acid and bit-mask value . Thus the three null tests can be specified by the definition of the bit mask along the sequence , which determines the constraints on the randomize trials . The aqua randomization test constrains the amino acid sequence and nothing else , and so its bit mask consists of all 1's . The orange randomization test preserves the amino acid and the GC3 , and so its bit mask is the GC3 sequence mentioned above . The green randomization test preserves the amino acid and BCAI exactly , thus its bit mask is the thresholded BCAI ( 1 if BCAI = 0 . 7 , 0 otherwise ) . In considering the power of the green and orange randomization tests , we must ask how many synonymous families permit one to constrain BCAI and change the last codon position from G/C to A/T . The answer to this question depends upon the CAI master table of the host species . For E . coli ( see Figure 4 ) , all nine the 3- , 4- , and 6-fold degenerate codon families permit one to constrain BCAI ( at 0 . 3 ) while varying G/C to A/T . However , constraining BCAI typically determines GC3 for the 2-fold degenerate families . As a result , roughly 60% of the codons in a phage genome are informative for the green randomization test . Similar results hold for P . aeriginosa and L . lactis , and for the orange test . For both of these tests , even if few synonmous families were informative , this feature would serve to weaken the power of statistics , making our conclusions conservative . All phage genes were annotated as structural or non-structural by inspecting the annotations of high-scoring BLAST hits among viral proteins . This procedure is described in detail below . Each gene was considered separately within each genome object , although overlaps were removed in the process of creating the genome objects . The amino acid sequence of each gene was blasted against all known viral protein sequences using Biopython's interface [54] to the NCBI blast utility [55] . Specifically , we used the blastp utility specifying the nr database , with entrez query ‘Viruses [ORGN]’ . We retained only those BLAST hits with e-values below the cutoff 1×10−4 . All words in the title of these BLAST hits were collected , using white space as a word-delimiter . The unique words from the blast hits were then compared against a set of structural keywords: “capsid” , “structural” , “head” , “tail” , “fiber” , “scaffold” , “portal” , “coat” , and “tape” . The words associated with the BLAST hits were scanned for matches to the keywords , where each keyword was treated as a regular expression . As a result , partial matching was counted as a match . For example , a BLAST title containing the word ‘head-tail’ would match both keywords ‘head’ and ‘tail’ . If a gene had at least one structural keyword match in its BLAST hit title , it was annotated as structural . Otherwise , it was annotated as non-structural . We further subdivided the structural annotation into two classes: head and tail genes . Tail genes were identified with the keywords “tail” , “fiber” , and “tape” . These remaining structural genes that did not contain any of these keywords were annotated as head genes . Two false positives for tail identification in the lambda phage genome were manually corrected . In the sections above we have compared the genome landscapes calculated from real genome sequences to a null model in which the sequences are randomly drawn from a defined distribution . In this section , we compute several properties of genome landscapes calculated from these random genomes . We write the general genome landscape of length N as ( 8 ) where η ( i ) are independent , and chosen from a random distribution with var ( η ( i ) ) = 〈η ( i ) 2〉−〈η ( i ) 〉2 = Δ , and ( 9 ) which ensures F ( 0 ) = F ( N ) = 0 . The purple regions in Figure 1 represent the variance in the genome landscapes of this null model at each m , . Using the definitions above , we have ( 10 ) and ( 11 ) When we use 〈η ( i ) η ( j ) 〉 = 〈η2〉δi , j+ ( 1−δi , j ) 〈η〉2 , with δi , j = 1 if i = j and 0 otherwise , we find ( 12 ) leading to . In the case of GC3 landscapes , η ( i ) is either 1 or 0 with equal probability , giving ΔGC3 = 1/4 . We can also calculate the full probability distribution , P ( f;m , N , Δ ) that the genome landscape of length N has an intermediate value F ( m ) = f , at point m , by considering an N-step random walk that is constrained to start and stop at 0 . This probability distribution can be written as a product of two conditional probabilities for a walk that starts at 0 and ends at f in m steps , and a walk that starts at f and ends at 0 in N−m steps ( 13 ) where A is a normalization constant , and the last step used the inversion symmetry of the random walks . Thus we seek the form of the conditional probability G ( 0 , f;m , Δ ) . In the same way as in Equation 13 , we decompose this conditional probability into a multiplication of the conditional probabilities for two walks , one that starts at 0 and ends at y in x steps , and one that starts at y and ends at f in m−x steps , and integrate over all possible intermediate values y ( 14 ) We can continue this decomposition for each intermediate step to give ( 15 ) Keeping the order of integration the same , and noting that G ( y1 , y2;1 , Δ ) = G ( y2−y1;1 , Δ ) for these random walks , we can write yi+1−yi = si+1 to give ( 16 ) where the delta function is added to force the constraint that the sum of all the intermediate steps must be equal to f . All of the intermediate conditional probabilities now represent one step walks , and so are equal to the underlying probability distribution of drawing a step size sm , p ( sm;Δ ) ( 17 ) Making use of the integral representation of the delta function [56] ( 18 ) we have ( 19 ) where is the Fourier transform of p ( s , Δ ) ( 20 ) For the purpose of this discussion , we assume p ( s , Δ ) has a Gaussian form , and note that the results are general . In this case , , and we have ( 21 ) To determine A , we enforce the normalization condition ( 22 ) which gives ( 23 ) ( 24 ) Note that from the full distribution , we can immediately identify , confirming the explicit calculation above . | Any protein can be encoded by multiple , synonymous spellings . But organisms typically prefer one spelling over another—a phenomenon known as codon bias . Codon bias is generally understood to result from selection for synonymous spellings that increase the rate and accuracy of protein translation . In this work , we have examined the complete genomes of all sequenced viruses that infect the bacteria E . coli , P . aeruginosa , and L . lactis , and have found that many of these viral genomes also exhibit codon bias . Moreover , the degree of codon bias varies across the viral genome , as visualized using a technique called a “genome landscape . ” By comparing the observed genomes to randomly drawn genomes , we demonstrate that the regions of high codon bias in these viral genomes often coincide with regions encoding structural proteins . Thus , the proteins that a virus needs to produce in high copy number utilize the same encoding as its host organism does for highly expressed proteins . Our results extend the translational theory of codon bias to the viral kingdom: parts of the viral genome are selected to obey the preferences of its host . | [
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| 2008 | Genome Landscapes and Bacteriophage Codon Usage |
The dominant phenotype of greying with age in horses , caused by a 4 . 6-kb duplication in intron 6 of STX17 , is associated with a high incidence of melanoma and vitiligo-like skin depigmentation . However , the progressive greying and the incidence of melanoma , vitiligo-like depigmentation , and amount of speckling in these horses do not follow a simple inheritance pattern . To understand their inheritance , we analysed the melanoma grade , grey level , vitiligo grade , and speckling grade of 1 , 119 Grey horses ( 7 , 146 measurements ) measured in six countries over a 9-year period . We estimated narrow sense heritability ( h2 ) , and we decomposed this parameter into polygenic heritability ( h2POLY ) , heritability due to the Grey ( STX17 ) mutation ( h2STX17 ) , and heritability due to agouti ( ASIP ) locus ( h2ASIP ) . A high heritability was found for greying ( h2 = 0 . 79 ) , vitiligo ( h2 = 0 . 63 ) , and speckling ( h2 = 0 . 66 ) , while a moderate heritability was estimated for melanoma ( h2 = 0 . 37 ) . The additive component of ASIP was significantly different from zero only for melanoma ( h2ASIP = 0 . 02 ) . STX17 controlled large proportions of phenotypic variance ( h2STX17 = 0 . 18–0 . 55 ) and overall heritability ( h2STX17/h2 = 0 . 28–0 . 83 ) for all traits . Genetic correlations among traits were estimated as moderate to high , primarily due to the effects of the STX17 locus . Nevertheless , the correlation between progressive greying and vitiligo-like depigmentation remained large even after taking into account the effects of STX17 . We presented a model where four traits with complex inheritance patterns are strongly influenced by a single mutation . This is in line with evidence of recent studies in domestic animals indicating that some complex traits are , in addition to the large number of genes with small additive effects , influenced by genes of moderate-to-large effect . Furthermore , we demonstrated that the STX17 mutation explains to a large extent the moderate to high genetic correlations among traits , providing an example of strong pleiotropic effects caused by a single gene .
Recent developments in molecular genetics have enabled molecular dissection of quantitative traits in humans [1] , model organisms [2] and domestic animals [3] . Genome-wide association ( GWA ) studies suggest that variability of complex traits is caused by many loci , most exerting tiny effects , whereas loci exerting moderate-to-large effects or loci that explain more than 5–10% of phenotypic variation are rare [3] , [4] , [5] . In human populations , those genes with moderate-to-large effects do appear in low frequency as rare or “private” mutations [6] . In contrast , appearance of moderate-to-large effect mutations at intermediate or high frequencies is documented in domestic animal populations , perhaps , as consequence to change in selection pressure caused by domestication [7] . The genetic variation of several complex traits in Grey horses is considerably affected by at least one gene of moderate-to-large effect . Grey horses are born with their base colour ( e . g . black , bay , chestnut ) , which then greys early in life due to loss of melanocytes , a process similar to the greying of hair in humans , which typically occurs much later in life . The mode of inheritance of the greying phenotype is autosomal dominant . A grey horse will be either GG or Gg , non-grey horses carry the gg genotype . Previously we reported that greying with age is caused by a 4 . 6-kb duplication in intron 6 of STX17 , which encodes syntaxin 17 [8] . In addition to grey level , the Grey mutation was found to strongly influence melanoma , vitiligo and speckling grade as well as to capacitate the effects of the loss-of-function mutation at the agouti ( ASIP ) locus on melanoma grade . Recently , we have shown that the duplicated region contains a melanocyte-specific enhancer that becomes much stronger when duplicated [9] . Examination of melanoma tissue revealed copy number variation of the 4 . 6 kb intron in STX17 , with a difference between blood and tumor DNA . The highest copy numbers occurred in tumors classified as aggressive [10] . Grey horses are the only animals to present progressive coat greying , melanoma , vitiligo-like skin depigmentation and coat speckling , making them an excellent model for studying the inheritance of traits with complex genetic background . Studies of coat colour in horses led to the definition of discrete phenotypes ( e . g . bay , black , brown , grey ) controlled by a few genes showing epistatic interaction [11] . This typology , although practical for breeders to some extent , could not account for variation in greying level . Curik et al . [12] provided a quantitative description of progressive greying , while Toth et al . [13] found relationship between that quantitative greying measure and total melanin content of horse hair . Melanoma occurs frequently in grey horses , in three histopathologically defined clinical patterns . In the first pattern , which describes the majority of cases , the melanoma grows slowly over many years without evidence of regional or distant metastases . In the second pattern , the melanoma results from malignant transformation of a benign melanoma ( melanocytoma; [14] ) . In the third pattern , which is rare , the melanoma is malignant from onset [15] . Although most melanomas in Grey horses present benign features at diagnosis , nearly 66% become malignant later [16] . Melanoma in Grey horses occurs most frequently underneath the tail , in the peri-anal region , and around the lips and eyelids [17] , [18] . The frequency of melanoma occurrence is around 80% in horses older than 15 years [19] . The hereditary component of melanoma in Grey horses was first studied by Rieder et al . [20] . Using segregation analysis , they were unable to establish whether the mode of inheritance was monogenic , polygenic or mixed , because of the relatively low number of horses examined ( n = 71 ) . Nevertheless , models including a polygenic component fitted the data significantly better than did a non-genetic model . In a more recent study involving 296 grey Lipizzan horses , Seltenhammer et al . [21] estimated a heritability of 0 . 36 for melanoma grade . Grey horses also show vitiligo-like depigmentation . In fact , Seltenhammer et al . [21] found equine vitiligo in 50% of older grey Lipizzaner horses . In humans , vitiligo is a chronic disorder characterized by sharply delimited , progressive , patchy loss of pigmentation resulting from death or dysfunction of the cells responsible for skin and hair pigmentation [22] . While the pathogenesis of human vitiligo is unknown , it is considered a complex hereditary disease , and several theories have been proposed to explain it [23] , [24] . Similarly , although linked to progressive greying , the pathogenesis of equine vitiligo remains largely unknown . Like melanoma , vitiligo occurs far more frequently in Grey horses than for those with solid colours , indicating that it has a hereditary component that may be associated with progressive greying . Although rare , studies in humans seem to indicate that melanoma and vitiligo might be genetically linked [25] , [26] . When this study was conceived , we hypothesized that while the effects of STX17 are large for all four traits and ASIP has a considerable effect of melanoma , a large part of the genetic variation remains unexplained . We further hypothesized that the genetic correlations among those traits are strongly influenced by STX17 . Here we have extended our previous studies by increasing the number of genotyped horses and parameters examined , including genotype at the STX17 and ASIP loci . Firstly , for all four traits ( melanoma , grey level , vitiligo , speckling ) , we decomposed estimated repeatability into a component due to permanent environmental effects and a component describing narrow sense heritability . We further decomposed estimated narrow sense heritability into a component describing background polygenic heritability and a components describing heritability caused by STX17 and ASIP . We also estimated genetic correlations among all four traits using various models designed to evaluate the contribution from STX17 and ASIP genes and the contribution from pleiotropy due to additional polygenic background versus that of STX17 and ASIP genes . Finally , we graphically illustrated the genetic relationship among melanoma grade , grey level , vitiligo grade and speckling grade with respect to STX17 genotype .
Estimated fixed effects and variance components for melanoma grade , grey level , vitiligo grade and speckling grade are shown in Table 1 , together with means and standard errors . Linear effects of age were significant ( P<0 . 001 ) for all four traits . Thus , for example , a 6-year-old grey horse will present a grey colour that is 5 . 88 L* units darker than that of a 7-year-old . Linear regression coefficients relating each trait to STX17 were highly significant ( P<0 . 001 ) for all traits studied , while the corresponding coefficients involving ASIP were significant only for melanoma grade ( P<0 . 001 ) . In quantitative genetics , estimated linear regression coefficients can be interpreted as gene substitution effects of STX17 ( αSTX17 ) and ASIP ( αASIP ) . Thus , the regression coefficient of −13 . 78 for the Grey mutation in relation to the trait Greying indicates that heterozygotes Gg on average will be 13 . 78 L* units darker than homozygous GG horses . ASIP has three genotypes , so the melanoma grade is expected to change by 0 . 38 units ( 2αASIP ) when moving from an AA to an aa genotype . The frequency of the Grey allele was between 0 . 85 and 0 . 89 for the various data sets . Univariate estimates were obtained for heritability ( h2 ) and for repeatability ( R ) , which here corresponds to the upper bound of broad sense heritability , together with estimates of phenotypic variance ( VP ) and variance due to permanent environmental effects ( c2 ) , here variance introduced by repeated measurement ( Table 2 ) . Proportions of variance in grey level and vitiligo grade due to permanent environmental effects were 0 . 03 and 0 . 00 , respectively , whereas these proportions were much larger for melanoma grade ( 0 . 26 ) and speckling grade ( 0 . 12 ) . Very high h2 values , that explained 63–79% of the phenotypic variance , were estimated for grey level , vitiligo grade and speckling grade . In contrast , moderate h2 was estimated for melanoma grade . Across all models standard errors of hertiability estimates ranged from 0 . 08 to 0 . 12 . For each trait we decomposed h2 into h2POLY , h2STX17 and h2ASIP ( melanoma grade ) ( Table 1 ) . The polygenic component was highest for grey level ( h2POLY = 0 . 57 ) , followed by the estimated components for vitiligo grade ( h2POLY = 0 . 41 ) , melanoma grade ( h2POLY = 0 . 18 ) and speckling grade ( h2POLY = 0 . 11 ) . The heritability caused by the STX17 mutation was highest for speckling grade ( h2STX17 = 0 . 55 ) and moderate for vitiligo grade ( h2STX17 = 0 . 23 ) , grey level ( h2STX17 = 0 . 22 ) and melanoma grade ( h2STX17 = 0 . 18 ) . The additive component of ASIP was significantly different from zero only for melanoma grade ( h2ASIP = 0 . 02 ) . When polygenic heritability was assessed only for homozygous GG horses , obtained values were still high for all four traits: grey level , 0 . 76; melanoma , 0 . 30; vitiligo , 0 . 57; and speckling , 0 . 36 . An analysis of this data set for melanoma including ASIP revealed no substantial increase of heritability ( h2ASIP = 0 . 03 ) while the effect of ASIP on other traits remained non-significant . Multivariate models were used to estimate phenotypic ( rP ) and genetic correlations ( rPOLY ) according to three scenarios ( Table 3 ) . In the first scenario , we estimated genetic correlations with a standard polygenic model , ignoring the effects of STX17 and ASIP . In the second scenario , we used a model that includes the fixed effects of these two mutations . For the third scenario , we applied a polygenic model only to the data for GG horses . In the first scenario , the genetic correlations among the traits followed the same pattern as the phenotypic ones , with the genetic values occasionally somewhat higher ( e . g . 0 . 67 vs . 0 . 52 in the case of grey level and vitiligo grade ) . When the genetic effects were included ( second scenario ) or when only homozygous grey horses were considered ( third scenario ) , correlations dropped substantially . This is a strong indication of the pleiotropic effects of the Grey mutation . Only the genetic relationship between grey level and vitiligo grade remained significant and considerable across all three scenarios ( 0 . 67±0 . 06 in the first , 0 . 48±0 . 09 in the second , and 0 . 50±0 . 10 in the third ) . Visualization of the genetic correlations due to polygenic effects is presented by scatter plots of estimated breeding values ( Figure 1 ) . Distances from a homozygous ( GG ) genotype of STX17 to a heterozygous one ( Gg ) have a diagonal shift in the contour plot centres . In addition to these pleiotropic effects of the STX17 mutation , the fact that the contour plots for breeding values were elliptical specifically for the GG and Gg genotypes indicates that genetic correlations due to polygenic background additive effects remained even after accounting for the STX17 mutation . This was particularly obvious for the genetic correlation between grey level and vitiligo grade , for which the estimated genetic correlation after accounting for major genes remained significant ( rPOLY = 0 . 48±0 . 09 ) .
We present a case in which the genetic components of four complex traits could be decomposed into the effects of polygenic additive effects and the monogenic effects of STX17 and ASIP mutations . The data available was still insufficient to allow accurate estimation of polygenic dominance variance or of higher-order , non-additive genetic variances . Although melanoma and vitiligo have been intensively studied in humans , few estimates of their quantitative inheritance are available . The present study provides some of the first quantitative insights into their inheritance in grey horses . While STX17 explains large proportions of the phenotypic variance , we were surprised to see that , when only the data from homozygous GG horses were analysed , the residual polygene component still explained a large part of the variation in melanoma and vitiligo grade ( Table 3 ) . These results argue for the need to search for more genes involved in the expression of these traits . The STX17 mutation accounts for considerable amount of the estimated genetic correlations among all traits analysed; in other words , it exhibits strong pleiotropic effects . While precise estimates of genetic correlation require extremely large data sets , our findings are supported by scatter plots of estimated breeding values ( Figure 1 ) . Graphical illustrations also indicated that polygenic additive effects were causing negative genetic correlations between melanoma grade and speckling grade as well as between grey level and speckling grade . However , we were not able to confirm those evidences numerically since related estimates of genetic correlations had very wide confidence intervals ( Table 3 ) . On the other side , we were able to show that estimated genetic correlation between melanoma and vitiligo was negligible after accounting for the pleiotropic effect of STX17 mutation ( Table 3 and Figure 1 ) . However , it is highly speculative to conclude the same pattern , just one gene linking melanoma and vitiligo , is applying to humans . The results obtained here , further , support the evidence of recent studies in domestic animals that some complex traits are , in addition to the large number of genes with small additive effects , influenced by genes of moderate-to-large effect at intermediate to high frequencies . For example , a single nucleotide substitution in intron 3 of IGF2 in pigs explain about 30% of the residual phenotypic variance for lean meat in ham in a wild boar/Large White intercross [27] , [28] . Grisart et al . [29] have also shown that the mutation in DGAT1 , appearing in different breeds at varying gene frequencies , considerably decreases milk fat percentage ( h2DGAT1 = 0 . 51; h2POLY = 0 . 29 ) and yield ( h2DGAT1 = 0 . 15; h2POLY = 0 . 55 ) while increases milk protein yield ( h2DGAT1 = 0 . 08; h2POLY = 0 . 65 ) and milk volume h2DGAT1 = 0 . 18; h2POLY = 0 . 49 ) . A strong effect of PLAG1 mutation ( h2PLAG1 = 0 . 07 ) on the postpartum calf weight was recently estimated in the outbreed dairy cattle population [30] . Moderate-to large effects have been also demonstrated for the myostatin mutation on the meat and carcass quality in beef cattle [31] . The genetic architecture of complex traits presented here , in which quantitative variation is explained by a strong contribution from a single gene and/or mutation together with a substantial polygenic additive component , may be frequent in traits related to colouration and pigmentation of coat and skin as successful adaptation requires fast and slow responses . A similar genetic architecture was presented by Hayes et al . [32] in a study where KIT , MITF , and a locus on chromosome 8 together explained 24% of the variation in the proportion of black coat in cattle . Recently , Liu et al . [33] also indicated that a few genes played a major role in human eye colour . Coat and skin colouration , the consequences of melanin-dependent pigmentation , are evolutionarily important traits because they are involved in several aspects of survival , such as hiding from predators and thermoregulation [34] . Fang et al . [35] concluded that coat colour variation in domestic animals has been shaped by artificial selection . The increase in the frequency of mutated allele G , here up to 89% , is strongly associated with human appreciation of grey colour in horses . The high incidence of melanoma and vitiligo in Grey horses is most likely the result of negative pleiotropic effects of the mutations contributing to the grey phenotype , in particular the Grey mutation itself . The presence of this prime example provides , thus , an additional insight into genetic architecture of complex traits and their biology .
Data were recorded from grey Lipizzan horses situated at six state-owned studs from Austria ( Piber ) , Bosnia and Herzegovina ( Vucjak ) , Croatia ( Djakovo ) , Hungary ( Szilvasvarad ) , Slovakia ( Topol'cianky ) and Slovenia ( Lipica ) . Studs were visited repeatedly over nine years ( 1999–2007 ) and a total of 7146 measurements were collected from 1119 horses . All horses analysed were connected by a complete pedigree that includes 4961 members and extends back to the 18th century . Most data came from the offspring of 189 sires ( mean , 5 . 97; maximum , 33 ) and 496 dams ( mean , 2 . 26; maximum , 11 ) . Melanoma was detected by adspection and palpation . Melanoma grade was defined according to a modified classification system on a scale from 0 . 0 to 5 . 0 that allows for intermediate grades ( e . g . 0 . 0 , 0 . 5 , 4 . 5 ) , described in [17] ( Table 4 ) . Adspection was conducted at sites melanomas typically occur , such as the peri-anal and anal region , the perineal region , udder , and praeputium . The tail was bent upwards in order to detect even the smallest , plaque-like lesions . Lips and eyelids , the parotis , the peri-ocular region and ears were also examined . Finally , the whole integument was checked for potential tumours . Variation of melanoma grade in four grey Lipizzan horses is illustrated in Figure 2 . Progressive greying was quantified with a Minolta Chromameter CR210 using the CIE L*a*b* colour space . In this system , colour is quantified by its reflection along three axes: white-black ( L* ) , red-green ( a* ) and yellow-blue ( b* ) . We referred only to the L* parameter defined on a scale from 0 ( black - total absorption ) to 100 ( white - total reflection ) . Various grey levels in seven Lipizzan horses are illustrated in Figure 3 . Each horse was measured at four places ( neck , shoulder , belly and croup ) , and the average of the four measurements was used to represent the grey level . Vitiligo was graded on a scale from 0 ( no vitiligo ) to 3 ( severe vitiligo ) based on adspection of typical sites , such as the peri-anal and anal region , the perineal region , udder , praeputium and the face , especially around the nostrils and eyes . Vitiligo grade was evaluated simultaneously with melanoma . The overall vitiligo grade was the average of included vitiligo grades from patches in the perianal and the facial regions . Variation of vitiligo grade across four grey Lipizzan horses is illustrated in Figure 4 . Adult grey horses may present coloured specks or spots on a grey background . The amount of speckling was graded on a scale from 0 ( not speckled ) to 3 ( heavily speckled ) . Variation in speckling grade across four grey Lipizzan horses is illustrated in Figure 5 . Genotyping for the STX17 mutation was carried out on 760 horses and for the ASIP gene on 667 horses; the agouti locus controls the inheritance of black and bay base colour [36] . Genotyping procedures were performed as described in Pielberg et al . [8] . The genotypes of additional horses were deduced by combining genotyping and pedigree information . For instance , a single coloured offspring qualifies a grey parent as heterozygous , grey offspring with one coloured parent must be heterozygous . In this way we extended the number of genotyped horses to 966 for the STX17 mutation and 873 for the ASIP . However , deducing genotypes may introduce bias in the estimation of major gene effects . To avoid potential bias , we performed analyses on data sets with and without deduced genotypes . The results were very similar , those presented here are therefore based on data sets containing deduced genotypes . All traits analysed ( grey level and grades of melanoma , vitiligo and speckling ) strongly depend on the age of the horse . Grey level can vary widely in animals during the first 6–10 years; from 10 years onwards , this variation drops considerably and all horses reach their final coat colour . Both melanoma and vitiligo show an age of onset of 6–8 years , though they do appear earlier in rare cases . Speckling has been defined only for adult horses , because the density of spots or speckles is difficult to assess while horses are still dark . Thus , we analysed grey level data only for horses seven years old and younger . Conversely , data on melanoma , vitiligo and speckling grade were used only for horses seven years old and older . The dynamics of melanoma , grey level , vitiligo and speckling with respect to age has been shown in Pielberg et al . [8] . Statistical analyses were performed with univariate and multivariate general mixed linear models using the ASReml package ( version 3; [37] ) . These models are known as ( Individual ) Animal models; see [38] for more detailed explanation . For all of the variables analysed , the statistical models showed no serious statistical violations; residuals from the models followed a normal distribution and variances were roughly homogeneous . The following modeling strategy was applied . First , we fitted a model that included the effects of the stud by sex by year of measurement ( fixed ) , age at measurement in months ( covariate ) , STX17 mutation effects ( indicator variables , with GG = 0 and Gg = 1 treated as covariates ) , ASIP additive effects ( indicator variables , with AA = 0 , Aa = 1 and aa = 2 treated as covariates ) and ASIP dominance effects ( indicator variables , with AA = 0 , Aa = 1 and aa = 0 treated as covariates ) and all two-factor interactions . Two factor interactions and the ASIP dominance effects were non-significant for any trait and were dropped from further analyses . Initial and all subsequent models also included additive genetic animal effects ( random ) and permanent environmental effects ( random ) , since measurements were taken repeatedly . The following final model was used to analyse the data:where the vector y represents the phenotypic values; b is the vector of fixed effects; a , pe and e are vectors of , respectively , polygenic additive effects ( breeding values ) , permanent environmental ( repeated measurement ) effects and residual error values; X is the design matrix for fixed effects; and Z and W are design matrices for random effects . For all four traits analysed , the final models accounted for the stud by sex by year of measurement effect , for the effect of age at measurement and for the effect of STX17 , while the effect of ASIP was also accounted for only in the case of melanoma grade . In addition , additive polygenic ( VPOLY ) , permanent environmental ( Vpe ) and residual variances ( Ve ) , respectively , were defined aswhere A is an additive genetic relationship matrix and I is an identity matrix . In multivariate models we respected the modeling results obtained in univariate models . Based on variance components and effects estimated from statistical analyses , together with allele frequencies obtained by counting , we derived quantitative genetic parameters to explain the complex inheritance of the traits analysed . Additive genetic variances of STX17 mutation ( VSTX17 ) and ASIP ( VASIP ) were calculated as VSTX17 or VASIP = 2pqα2 , where estimated linear regression coefficients are equal to gene substitution effects α ( α = a for STX17 and α = a+ ( p−q ) d for ASIP ) , p and q are allele frequencies of STX17 or/and ASIP , a is an additive value and d is a dominance value . Calculation of single gene ( mutation ) variances enabled us to calculate additive genetic variance as a sum of VPOLY , VSTX17 and VASIP , and phenotypic variance as a sum of VPOLY , VSTX17 , VASIP , Vpe , and Ve . In further analysis , we calculated proportions of genetically explained variations from the phenotypic variance as follows: repeatability , defined as correlation among observations within individuals that provides an upper limit to broad sense heritability , R = ( VPOLY+VSTX17+VASIP+Vpe , ) /VP; permanent environmental effects , c2 = Vpe/VP; narrow sense heritability , h2 = ( VPOLY+VA-STX17+VA-ASIP ) /VP; background polygenic heritability , h2POLY = VPOLY/VP; STX17 mutation heritability , h2STX17 = VSTX17/VP; and ASIP heritability , h2ASIP = VASIP/VP . For grey level , vitiligo grade and speckling grade , αASIP was not significant , so we assumed VASIP was zero . The analyses were performed for three scenarios: the first and third ignored STX17 and ASIP effects , using the full data set and a data set containing GG animals only; the second applied the full model as described above for melanoma and excluded ASIP ( because not significant ) for the other traits . Bivariate models were run for all pairs of traits applying the single trait model specifications from above . Genetic ( rPOLY ) and environmental ( re ) correlations between traits x1 and x2 were calculated as rPOLY = CovPOLY_ ( x1 , x2 ) /[ ( VPOLY_x1 ) ( VPOLY_x2 ) ]0 . 5 and re = Cove ( x1 , x2 ) /[ ( Vex1 ) ( Vex2 ) ]0 . 5 , while phenotypic correlations ( rP ) were calculated as rP = rPOLY ( h2x1 ) 0 . 5 ( h2x2 ) 0 . 5+re[ ( 1−h2x1 ) ]0 . 5[ ( 1−h2x2 ) ]0 . 5 . Contour plots with 95% confidence intervals , which present breeding values standardized to a mean of zero and a standard deviation of one , were created for two traits obtained from bivariate animal models with respect to STX17 genotype . These plots illustrate the decomposition of genetic correlations into additive polygenic background effects and effects of a single mutation . More detailed derivations of quantitative genetic parameters are provided in [39] . | Clarifying the genetic architecture of complex traits is a problem with profound implications for agriculture , biology , and medicine . Using data from Lipizzan horses with the grey coat phenotype , we present an example of a single mutation ( intronic duplication in STX17 ) that explains 18%–55% of phenotypic variation in four complex traits , while polygenic background additive effects also explain 11%–57% of phenotypic variation . This study provides a prime example of complex traits being influenced by genes of moderate-to-large effect and supports further the evidence of recent studies in domestic animals that some complex traits are , in addition to the large number of genes with small additive effects , influenced by genes of moderate-to-large effect . We further show that the STX17 mutation accounts for a large proportion of the estimated genetic correlations between the traits . This case of strong pleiotropic effects of a single mutation on complex traits makes this work of significant general interest for biology and medicine . | [
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| 2013 | Complex Inheritance of Melanoma and Pigmentation of Coat and Skin in Grey Horses |
Next-generation sequencing technology provides novel opportunities for gathering genome-scale sequence data in natural populations , laying the empirical foundation for the evolving field of population genomics . Here we conducted a genome scan of nucleotide diversity and differentiation in natural populations of threespine stickleback ( Gasterosteus aculeatus ) . We used Illumina-sequenced RAD tags to identify and type over 45 , 000 single nucleotide polymorphisms ( SNPs ) in each of 100 individuals from two oceanic and three freshwater populations . Overall estimates of genetic diversity and differentiation among populations confirm the biogeographic hypothesis that large panmictic oceanic populations have repeatedly given rise to phenotypically divergent freshwater populations . Genomic regions exhibiting signatures of both balancing and divergent selection were remarkably consistent across multiple , independently derived populations , indicating that replicate parallel phenotypic evolution in stickleback may be occurring through extensive , parallel genetic evolution at a genome-wide scale . Some of these genomic regions co-localize with previously identified QTL for stickleback phenotypic variation identified using laboratory mapping crosses . In addition , we have identified several novel regions showing parallel differentiation across independent populations . Annotation of these regions revealed numerous genes that are candidates for stickleback phenotypic evolution and will form the basis of future genetic analyses in this and other organisms . This study represents the first high-density SNP–based genome scan of genetic diversity and differentiation for populations of threespine stickleback in the wild . These data illustrate the complementary nature of laboratory crosses and population genomic scans by confirming the adaptive significance of previously identified genomic regions , elucidating the particular evolutionary and demographic history of such regions in natural populations , and identifying new genomic regions and candidate genes of evolutionary significance .
Population genetics provides a rich and mathematically rigorous framework for understanding evolutionary processes in natural populations . This theory was built over the last hundred years by modeling the processes of selection , genetic drift , mutation and migration in spatially distributed populations [1]–[6] . The field has concentrated primarily on the dynamics of one or a small number of genetic loci , largely because of methodological limitations . However , genes are not islands , but rather form part of a genomic community , integrated both by physical proximity on chromosomes and by various evolutionary processes [7]–[10] . With technological advances , such as Next Generation Sequencing ( NGS ) [11]–[13] , the emerging field of population genomics now allows us to address evolutionary processes at a genomic scale in natural populations [14]–[20] . Population genetic measures like Wright's F statistics [2] , [21] , [22] , traditionally viewed as point estimates , can now be examined as continuous distributions across a genome [23]–[29] . As a result , in addition to estimating genome-wide averages for such statistics , we can identify specific genomic regions that exhibit significantly increased or decreased differentiation among populations , indicating regions that have likely been under strong diversifying or stabilizing natural selection [9] , [30]–[41] . These signatures of selection can then be used to identify candidate pathways , genes and alleles for targeted functional analyses [42]–[47] . An excellent opportunity for this type of population genomics approach exists in the threespine stickleback , Gasterosteus aculeatus [48]–[50] . This small fish is distributed holarctically and inhabits a large number of marine , estuarine and freshwater habitats in Asia , Europe and North America . In many regions replicate extant freshwater stickleback populations have been independently derived from oceanic ancestors when stickleback became isolated postglacially in newly created freshwater habitats [49] , [51] . Population genetic data support this inference , and also indicate that present day oceanic populations can be used as surrogates for stock that gave rise to nearby derived freshwater populations [52]–[64] . Because of the varied selection regimes in novel habitats , derived stickleback populations have quickly evolved along numerous phenotypic axes , leading to significant variation in behavior , life history , and morphology [65]–[75] . Importantly , despite little or no gene flow between them , populations in similar freshwater habitats often evolve in parallel along the same phenotypic trajectories at a variety of local , regional and global scales [59] , [76]–[80] . Because of their extreme diversification some stickleback populations are actually incipient [81]–[83] or completely differentiated species [84]–[88] . Diversification has happened very rapidly , on the order of just a few thousand years [50] , [58] , [60] , [84] , or in a few rare instances in just a few decades [82] , [89] . Thus , the biogeography of stickleback offers an excellent opportunity to examine the developmental genetic and genomic basis of rapid adaptation by comparing ancestral oceanic and derived freshwater populations . Importantly , these population genomic analyses are greatly advanced by a first draft of the stickleback genome , generated from a line derived from one of the populations used in this study ( Bear Paw Lake; Ensembl: http://www . ensembl . org/Gasterosteus_aculeatus/Info/Index ) . Stickleback can be crossed in the laboratory to produce viable offspring and genetic mapping crosses [79] , [90] , [91] which have been used to successfully identify nearly two dozen quantitative trait loci ( QTL; [78] , [79] , [91]–[97] ) . A surprising result of this work is that , at least in some cases , parallel phenotypic evolution is due to different types of parallel genetic changes . The parallel evolution appears to occur mostly through the fixation of alleles of the same genes from the standing genetic variation in oceanic populations [78]–[80] , [93] , [95] , but these alleles may be the product of single [93] or multiple [96] mutational events . Despite these advances in our understanding of evolutionary genetics in natural populations , a fundamental question remains: Are these instances of parallel evolution at individual loci representative of genome-wide patterns of parallel evolution in independently derived freshwater populations ? To address this question we have performed the first analysis of genome-wide patterns of polymorphism and differentiation using densely spaced single-nucleotide polymorphism ( SNP ) markers in replicate derived freshwater and ancestral oceanic stickleback populations . We used a novel and efficient genotyping approach based on Illumina sequencing of libraries of Restriction-site Associated DNA ( RAD ) tags [98] , [99] . Using short sequence reads , this technique provides genotype information on a large number of SNP markers , although it does not provide gametic phase across SNPs in different tags or haplotype sequence information . We use a kernel-smoothing analysis of these SNP genotype data aligned to the reference genome sequence to assess genome-scale patterns . Here we present a population genomic analysis based on several thousand SNPs across the genomes of 100 individuals from five populations . We focus on three freshwater populations which previous evidence suggests are quite young ( less than 10 , 000 years old ) and are independently derived from oceanic ancestral populations , with little or no gene flow directly among them [53] , [55] , [79] . Because of this history , we expect most of the adaptive evolution in the freshwater habitats to be the result of selection on standing genetic variation present in the founding populations . Accordingly , we focus primarily on measures of nucleotide diversity and differentiation in allele frequencies between the derived freshwater populations and two replicate oceanic populations , quantified with the statistic FST [7] , [21] , [22] , [32] , [100] , [101] . We further support our inferences with genomic distributions of private allele density and Tajima's D [102] . We have identified numerous genomic regions that are likely under diversifying selection , and a smaller number of regions that appear subject to balancing selection . We find that many of these regions are shared across the independently derived populations , confirming past results on the genetic basis of morphological evolution from laboratory crosses , and also implicating many other previously unidentified genomic regions as adaptively significant .
RAD tag sequencing provided a genome-wide distribution of over 45 , 000 single nucleotide polymorphisms ( SNPs ) that were simultaneously identified , scored , and used in a genome-wide scan of 100 individuals , 20 each from two oceanic and three freshwater stickleback populations ( Figure 1 and Figure 2; Table 1 ) . The published stickleback genome contains 22 , 830 identifiable SbfI restriction sites across the 21 linkage groups and unassembled scaffolds ( Ensembl , assembly Broad S1 ) . Each site is expected to produce at most two RAD tags ( sequence reads in each direction from the restriction site ) , and our sequencing effort recovered a large proportion of the expected RAD tags ( Table S1 ) . The sites were spread evenly throughout the genome ( Figure 3A ) , and on average each tag was sequenced approximately five to ten times in every individual ( Figure 3B ) . This depth of coverage allowed the identification of SNPs and statistical estimation of the diploid genotype for each individual at most nucleotide sites; sites at which coverage was insufficient were not assigned a genotype ( see Methods ) . The overall frequency of SNPs ( Table 1 ) agrees well with previous estimates of nucleotide polymorphism in stickleback populations . From these SNP genotype data we identified significant genetic variation within and across populations , with average genetic diversity ( π ) equal to 0 . 00336 across all populations and 0 . 0020–0 . 0027 within each population ( Table 2 ) . These findings are in rough agreement with previous studies of genetic variation within and among stickleback populations [55] , [57] , [59] , [60] , although they are somewhat reduced . This may be a consequence of the conservative ( and unbiased ) nature with which SNPs are called using our methodology ( see Methods ) , and additional sequencing of these samples may increase the number of SNPs identified . Furthermore , in agreement with the hypothesis that freshwater populations in this region have been derived post-glacially from oceanic populations [49] , [55] , [65] , [79] , global genetic diversity measures are increased only slightly when combining pairs of populations whether they are both oceanic , both freshwater , or one of each ( Table 2 ) . Our data support the hypothesis that oceanic stickleback populations have few barriers to dispersal , relatively large amounts of gene flow , and little population genetic subdivision [55] , [57] , [59] , [60] , [103] , [104] . Rabbit Slough and Resurrection Bay , the two oceanic populations in our study , are the most geographically distant from one another ( >1000 km as the fish swims ) . Despite this distance , the oceanic populations show the least amount of differentiation between them ( FST = 0 . 0076; Table 2 ) . In contrast , higher values of FST were observed in pairwise comparisons among freshwater populations and between freshwater and oceanic populations ( 0 . 05–0 . 15 ) , which is generally interpreted as low to moderate amounts of population structuring ( Table 2 ) . The freshwater populations , despite their younger age , are more divergent both from the oceanic ancestral populations and from each other , consistent with our supposition that they represent independent colonizations from the ancestral oceanic population . These results are remarkably similar to results obtained previously from some of these same populations using a small number of microsatellite and mtDNA markers [55] . This combination of large amounts of genetic variation and overall low-to-moderate differentiation between populations , coupled with recent and rapid phenotypic evolution in the freshwater populations , presents an ideal situation for identifying genomic regions that have responded to various kinds of natural selection . To assess genome-wide patterns we examined mean nucleotide diversity ( π ) and heterozygosity ( H ) using a Gaussian kernel smoothing function across each linkage group ( Figure 4 and Figure S1 ) . Although the overall mean diversity and heterozygosity values are 0 . 00336 and 0 . 00187 , respectively , values vary widely across the genome . Nucleotide diversity within genomic regions ranges from 0 . 0003 to over 0 . 01 , whereas heterozygosity values range from 0 . 0001 to 0 . 0083 . This variation in diversity across the genome provides important clues to the evolutionary processes that are maintaining genetic diversity . For example , while expected ( π ) and observed ( H ) heterozygosity largely correspond , they differ at a few genomic regions ( e . g . , on Linkage Group XI ) . Genomic regions that exhibit significantly ( p<10−5 ) low levels of diversity and heterozygosity ( e . g . on LG II and V , Figure 4 and Figure S1 ) may be the result of low mutation rate , low recombination rate , purifying or positive selection that is consistent across populations , or some combination of factors [9] , [36] , [105]–[107] . In contrast , other genomic regions , such as those on LG III and XIII ( Figure 4 ) , show very high levels of both diversity and heterozygosity . The most striking such region , found near the end of LG III , corresponds precisely with a region of reduced differentiation among populations ( Figure 5 ) . This suggests the presence of balancing selection maintaining a common pool of genetic variation at this genomic region within and among populations . To further investigate the pattern of increased genetic variation on LG III , we delineated a region from 14 . 8 to 16 . 1 Mb ( Figure 5; see Methods ) . Within the corresponding 1 . 3-Mb interval in the published stickleback genome are several candidate targets of balancing selection , namely genes implicated in the first line of defense against pathogens [108]: ZEB1 ( ENSGACG00000017648 ) , and two adjacent APOL genes ( ENSGACG00000017778 , ENSGACG00000017779 ) . Supporting the importance of this region in immune response , there are also orthologs of several inflammation pathway genes: LTB4R ( ENSGACG00000017812 ) , SHARPIN ( ENSGACG00000017834 ) , and CEBPD ( ENSGACG00000017927 ) [109]–[111] . The region of significantly elevated nucleotide diversity on LG XIII ( 18 . 1–19 . 1 Mb ) also contains candidate targets of balancing selection including a TRIM14 ( ENSGACG00000014283 ) and three TRIM35 genes ( ENSGACG00000014250 , ENSGACG00000014251 , ENSGACG00000014403 ) . Many members of this large gene family have been implicated in innate immune response ( reviewed in [112] ) , and one gene , TRIM5alpha , bears the signature of balancing selection in primates [113] . The stickleback TRIM cluster on LG XIII provides a second example of balancing selection acting at a TRIM locus . Evidence for balancing selection on Major HistoCompatibility ( MHC ) loci is somewhat weaker . An MHC Class II gene ( ENSGACG00000017967 ) falls nearly 580 kb outside the interval of maximum nucleotide diversity on LG III , although both π and H are moderately elevated at this region as well ( π = 0 . 0046 , p<0 . 02; H = 0 . 0030 , pH 0 . 01 ) . In addition , a 250 kb unassembled genomic contig ( scaffold 131 ) contains a block of six MHC class II genes ( ENSGACG00000000330 , ENSGACG00000000336 , ENSGACG00000000344 , ENSGACG00000000346 , ENSGACG00000000348 , ENSGACG00000000350 ) . Nucleotide diversity ( π = 0 . 0046 , p<0 . 02 ) , heterozygosity ( H = 0 . 0030 , pH 0 . 01 ) , and freshwater-oceanic differentiation ( FST = 0 . 0218 , pH 0 . 05 ) averaged over this scaffold are somewhat consistent with a hypothesis of balancing selection . Profiles of population differentiation across each linkage group are generally consistent with the genome-wide average FST values described above . In agreement with the genome-wide results of little genetic structuring among the oceanic populations , we found no genomic regions that exhibit either significantly elevated or reduced ( p<10−5 ) differentiation between the two oceanic populations ( Figure 6A ) . In contrast , comparisons between the ancestral oceanic and individual derived freshwater populations ( Figure 6B–6D ) exhibit several genomic regions of significant differentiation , with FST>0 . 35 , as do the overall freshwater-oceanic comparison ( Figure 6E ) and the comparison among freshwater populations ( Figure 6F ) . Examining more closely the height and location of peaks in FST across these comparisons , we can discern a set of general patterns to generate hypotheses about the modes of genetic variation and selective forces operating in the adaptation to freshwater , and to identify putative candidate genes . Single linkage groups illustrating examples of these distinctive patterns are shown in Figure 7 and Figure 8 . First , the large majority of genomic regions of elevated FST are shared across the three freshwater populations . This pattern suggests independent , parallel evolution in the form of similar genomic regions responding to directional selection across freshwater populations . Second , some , but not all , of these peaks also appear in the overall oceanic-freshwater comparison ( Figure 6E ) . A striking example of this situation is seen on LG XXI ( Figure 8D ) , where a remarkable consistency in both the levels of FST and the location of peak margins across the three freshwater populations is matched by a large peak in the overall oceanic-freshwater comparison . Nucleotide diversity and heterozygosity are reduced in the freshwater populations in this region as well ( at 5 . 7 Mb , π<0 . 001 , p = 0 . 0003; H = 0 . 0006 , p = 0 . 0003 ) . We delineated the nine most consistent and significant of these peaks ( see specific criteria in Methods ) . These regions occur on six linkage groups ( I , IV , VII , VIII , XI , XXI ) and are highlighted in Figure 7 and Figure 8 . Also plotted in Figure 7 and Figure 8 are all FST values at individual SNPs where population differentiation in the overall oceanic-freshwater comparison is significant at the α = 10−20 level ( equivalent to p<6 . 85×10−23 ) following false discovery rate correction of individual G-tests ( see Methods ) . These highly significant SNPs largely correspond with the genomic regions of elevated differentiation , indicating that the averaged results from the kernel smoothing analysis are not anomalous . Of the 44 , 841 SNPs in this comparison at which FST and a G-statistic could be calculated , 307 were significant at this level . Of these 307 , 227 occur on these six linkage groups , and 119 of these are within the boundaries of the nine peaks , despite the fact that these nine regions collectively account for just ∼2 . 5 percent of the entire genome . In contrast , some of the genomic regions that show consistent differentiation in all of the individual freshwater populations do not exhibit a peak in the overall oceanic-freshwater comparison . An example of this situation is observed on LG II ( Figure 7B ) , where substantial peaks in each of the individual freshwater comparisons cover the same genomic region but differ slightly in their precise location . Accordingly , we do not observe significant differentiation in the overall comparison , and the freshwater populations are substantially differentiated from each other in this region; in fact , the largest peak in the among-freshwater FST ( FST = 0 . 5147 , p<10−7; Figure 6F ) occurs at this region . Both of these patterns are observed together on LG IV . Of the three LG IV peaks highlighted in Figure 7C , the third is most consistent in its height , width , and location across the freshwater populations . It corresponds to the most substantial peak of the three in the overall oceanic-freshwater comparison ( FST = 0 . 4262 , p<10−7 ) and shows virtually no differentiation among the freshwater populations . In contrast , the second peak and neighboring region to 22 . 5 Mb shows more variation among the freshwater populations and is substantially lower in the overall oceanic-freshwater comparison ( FST = 0 . 3269 , p<10−7 ) . Finally , there are peaks of differentiation observed in one or two , but not all three , freshwater populations . One example of this is seen at 11 . 5–12 Mb on LG VIII ( Figure 8B ) , where the Mud Lake population exhibits a peak in differentiation ( FST = 0 . 3092 , p<0 . 02 vs . RS; FST = 0 . 2737 , p<0 . 01 vs . RB ) that is not observed to the same extent in the other two populations . Correspondingly , there is a peak in differentiation among the freshwater populations at this location . This contrasts with the peak at ∼8 . 3 Mb on the same linkage group , which is consistent across the three populations and also observed in the overall oceanic-freshwater comparison ( FST = 0 . 3844 , p<10−7 ) , but not present in the comparison among freshwater populations . The interpretation of these peaks of population differentiation as foci of selection is further supported by the genome-wide distributions of other statistics ( Figure 9 ) . First , we estimated Tajima's D [102] across the genome in the oceanic populations ( Figure 9A ) . ( Because of their young age and expected non-equilibrium allele frequency distributions , we did not consider this statistic to be informative in the freshwater populations ) . D is negative overall in the oceanic populations , perhaps as a result of demographic processes affecting the entire genome equally . However , regions of significantly negative D correspond with peaks of freshwater-oceanic differentiation . In addition , we examined the genomic distribution of the density of private alleles–alleles that are found in only a single population or group of populations in a comparison . Overall , the private allele density ( ρ ) is higher in oceanic populations compared to freshwater than vice versa ( Figure S2 ) . This is consistent with the view that the genetic variation in the freshwater populations is largely a sample from the oceanic stock . However , peaks in private allele density in freshwater populations relative to the ocean ( Figure 9B–9D ) correspond well with FST peaks in the freshwater-oceanic comparisons ( with the exception of the peaks on LG I and XI ) . Thus the peaks in FST are largely the result of alleles that we did not detect in the oceanic populations . The hypothesis that these are new mutations in the freshwater populations is rejected by the absence of corresponding peaks in private allele density among the freshwater populations ( Figure 9E–9G ) . Instead , while selection in freshwater has acted on haplotypes that were rare ( and not detected in our samples ) in the oceanic stock , these haplotypes are nonetheless shared among the independently derived freshwater populations . Previous work has shown that freshwater-adapted alleles may persist at a very low frequency in the ocean , low enough that we would not expect to detect many of them in our sample of 40 individuals [74] . However , the maintenance of such low-frequency alleles in the ocean by gene flow from freshwater populations , combined with selection against them in the oceanic habitats , could also account for the significantly negative Tajima's D in the ocean at these genomic regions . Exceptions to the pattern described above are found at the FST peaks on LG I and XI . Here , the private allele density in freshwater does not differ significantly from the genome-wide average ( Figure 9B–9D ) , but private allele density in the ocean relative to freshwater is significantly higher ( Figure S2B ) . In addition , π is elevated in oceanic populations at the LG I region ( Figure S1A , S1B , S1C ) . These data suggest the hypothesis that the oceanic environment may be permissive for multiple haplotypes at these genomic regions , of which only a subset have relatively high fitness in freshwater . In contrast , in the region centered at 13 . 3 Mb on LG II , the freshwater populations exhibit high densities of private alleles , both with respect to the oceanic populations and with respect to each other ( Figure 9B–9G ) . These correspond with peaks in FST both between oceanic and freshwater populations and among freshwater populations ( Figure 7B ) . Here different haplotypes have evolved to high frequency among the different freshwater populations . To set our results in the context of previous QTL mapping studies , and to explore a set of putative candidate genes associated with adaptation to freshwater , we focused on the nine peaks highlighted in Figure 6 . Our results are complementary to previous QTL mapping of traits relevant to freshwater adaptation , although direct comparison with QTL results is complicated because many of those previous studies used microsatellite markers placed on a genetic linkage map . The order of those markers on the genetic map does not always correspond with the marker order on the physical map of the stickleback genome ( Ensembl , database version 56 . 1j , assembly Broad S1 ) , leading in some cases to quite large physical distances between QTL-associated markers . Also , some of the previously used microsatellite markers do not appear at all in the genome sequence . Nonetheless , of the nine peaks we identified , the three on LG IV co-occur with previously identified QTL and specific genes [78] , [79] , [93] , [97] , [99] . This includes the gene Ectodysplasin A ( Eda ) , implicated in the loss of the lateral plate phenotype [93] , which occurs within the first peak of population differentiation that we identified on LG IV . An additional three peaks show the possibility of an association with previous QTL: Shapiro et al . [95] identified very broad QTL that overlap large portions of LG IV and VII , including all five peaks we identified on those linkage groups , and Albert and colleagues [97] identified a QTL adjacent to our peak on LG XXI . In addition , evidence for directional selection based on microsatellite markers has been found just adjacent to two of our delineated peaks . One of these occurs at ∼22 . 3 Mb on LG I [103] ( but see reanalysis by [28] ) . The other lies at ∼9 . 5–9 . 8 Mb on LG VIII [104] , just outside the strict delineation of the peak in Figure 8C , but within the broader region in which we detected substantially elevated FST values and highly significant SNPs . Other regions outside the nine most significant peaks also exhibit a correspondence with QTL studies . For example , the peak on LG XII ( Figure 6E ) contains many osteogenesis genes and overlaps a QTL peak for many skeletal characters [97] . In contrast , the region at the distal end of LG VII previously associated with the pelvic structure phenotype , specifically containing the Pitx1 gene [79] , [95] , [99] , did not correspond to elevated levels of divergence in any of our comparisons . To evaluate potential candidate genes , we identified all loci overlapping the boundaries of the nine most consistent peaks ( Table S2 provides the complete list ) . Many genes in these defined intervals are already annotated by name and orthology in the Gasterosteus genome database ( Ensembl , database version 56 . 1j , assembly Broad S1 ) ; the orthology relationships of the remaining genes , those for which no gene name is yet listed , were further analyzed by a BLAST comparison of the predicted protein sequence for each of them against the NCBI protein database . We then assessed the ontological relationships of all protein coding genes in each interval with respect to skeletal biology and to osmoregulation , two axes of the phenotype known to change drastically as stickleback evolve in response to freshwater environments with very different ecological and chemical conditions than the ocean . Table 3 identifies genes for which a strong association with either of these two broad ontological classes is supported in the literature . From the nine annotated peaks , covering a total of 12 . 2 Mb , we list 31 candidate genes: 23 candidates for patterning and homeostasis of skeletal traits , 8 candidates for response to osmotic stress and development of osmoregulatory organs , and three candidates with pleiotropic roles in both skeletogensis and osmoregulation . The total numbers of all protein-coding genes within each peak are also listed in Table 3 . The abundance of annotated genes within the nine consistent peaks of differentiation does not appear to be an artifact of the distribution of genes across the genome ( Figure S3 ) . Rather , gene density shows no apparent correlation with the regions of population differentiation that we identified here . Although we focused on the nine significant peaks of differentiation that appear most consistent across freshwater populations , several other regions show strong evidence of selection in derived freshwater populations and contain candidate genes worthy of further study . In particular , large regions of LG IV and LG VII outside the delineated peaks appear to be important in differentiation of freshwater stickleback , and these two linkage groups have been the focus of much previous attention . Intriguingly , duplicate synteny groups containing six genes ( CLINT1 , EBF1 , IL12B , ADRB2 , ABLIM3 and AFAP1L1 ) lie just adjacent to Peak 1 of LG IV and partially overlapping Peak 2 of LG VII . Of these , EBF1 , IL12B and ADRB2 are skeletal trait candidates [114]–[116] . As mentioned above , a region of LG XII previously implicated by QTL analysis also shows a signature of selection here . We provide a list of candidate genes in these additional genomic regions in Table S3 .
Population genomic studies depend on having a very high density of markers that can be scored across many individuals . Depending upon demographic factors such as population size and structure , and the strength and nature of selection [117] , [118] , blocks of linkage disequilibrium ( LD ) can be as small as a few hundred base pairs ( as in flies [105] ) to several dozens of kilobases ( kb ) ( as in dogs [119] ) . For most natural populations , the likely size is on the order of 1 to 100 kb , meaning that tens or hundreds of thousands of markers are required to adequately cover an average-sized genome . Furthermore , population genetic sampling variances occur for single point estimates at each marker , requiring numerous individuals to be analyzed from each group or subpopulation of a study . Illumina-sequenced RAD tags provide a powerful new tool to meet these needs , generating a dense battery of SNP markers that are likely to cover a large proportion of the LD blocks produced by stickleback adaptation , and which can be simultaneously identified and scored across entire genomes . The density of markers that can be scored across individuals using RAD-seq holds promise for association mapping of phenotypic traits in natural populations of other organisms . Although we used the stickleback reference genome sequence for the alignment of RAD tags , this tool can be used for population genomic studies in organisms that do not yet have a sequenced genome . Instead of aligning against a genome , the sequence reads can instead be aligned to one another , with SNPs identified and zygosities scored for individuals in the same manner as we describe here ( Hohenlohe and Cresko unpublished data ) . Although these identified RAD sites are initially unanchored with respect to one another , if scored in an F2 or backcross mapping family , they could be ordered to produce a high-density linkage map . This genetic map could then be used to perform genome scans , as well as to help order a physical map from subsequent genome sequencing projects . Such data may be useful even when a preliminary genome assembly already exists . For instance , our approach revealed that nearly 60 Mb - equivalent to two of the largest chromosomes - of the stickleback genome are segregating alleles and show significant signatures of selection , but have not been incorporated into the existing assembly of 21 linkage groups ( Ensembl , Broad S1 assembly ) . A forthcoming RAD genetic map will help incorporate this nearly 10% of the genome into its proper locations . In sum , RAD sequencing has the potential to combine population genetic and genomic studies with genetic and association mapping in populations of both model and non-model organisms , and in addition can help quickly produce or enhance essential genomic resources for organisms that presently have few . We produced genome-wide estimates of population diversity and differentiation for five stickleback populations that have been the focus of intense previous research . These data are largely in agreement with previous estimates of genetic diversity for stickleback , and support the view that oceanic stickleback populations have differentiated little from each other due to extensive gene flow over long distances . Each freshwater population exhibits a greater amount of divergence from the oceanic populations and from the other freshwater populations , but the overall amount is generally moderate and in line with previous estimates of population genetic divergence derived from microsatellite markers [55] . Taken together our data support the biogeographic hypothesis that large populations of oceanic stickleback have given rise repeatedly to freshwater populations , which have become phenotypically differentiated on a background of minor neutral population divergence [55] , [79] . Furthermore , we were able to determine the distribution across the genome of genetic diversity and differentiation among the replicate populations . Identifying genomic regions of significantly increased or decreased diversity and differentiation allows us to make inferences about evolutionary processes , and to generate hypotheses about the evolutionary role of specific loci . Overall , the genome-wide patterns showed remarkable consistency across replicate populations and across pairwise comparisons . For example , the region with the most substantially elevated nucleotide diversity , observed on LG III , was consistent across populations and also exhibited increased heterozygosity and greatly reduced differentiation among populations . This pattern indicates balancing selection . This situation is best known for the vertebrate Major HistoCompatability ( MHC ) loci , which encode proteins responsible for tagging and presenting antigens to the immune system [120] . Greater levels of heterozygosity increase the range of antigens that can be identified by the immune system . Other genes that mediate a host's ability to repel or mitigate infection by parasites and other pathogens may also be the object of balancing selection [108] . Such loci can show strong signatures of balancing selection such as the persistence of old and highly polymorphic alleles ( e . g . , [121] ) . The region on stickleback LG III contains several candidates that fit this description . In mammals , ZEB1 helps maintain viral latency by binding the promoter of a virally encoded latency-to-lysogeny switch gene [122] . The direct interaction of ZEB1 with the viral genome makes it an attractive candidate target for host-pathogen co-evolution and balancing selection . The LG III peak contains a stickleback ZEB1 and two members of the APOL gene family , which encode proteins that may also directly interact with pathogens . APOL1 is a secreted protein that causes the lysis and death of trypanosome parasites in the blood , and variation at this locus affects resistance to trypanosome infection in humans [123] . Among primates , APOL genes show signs of rapid evolution and selective sweeps , possibly linked to their role in immunity [124] . Interestingly , the signature of balancing selection in the region of these host-pathogen-related loci was stronger than that in two regions with MHC orthologs: one MHC class IIB ortholog adjacent to the peak identified on LG III , and a cluster of six MHC class II loci on scaffold 131 . Members of this latter group were found in a previous microsatellite analysis to show evidence of balancing selection in stickleback [125] . Similarly , the interval of increased nucleotide diversity on LG XIII overlaps a region rich in TRIM family genes , and includes a TRIM14 and three TRIM35 genes . Antiviral gene TRIM5alpha provides a rare example of balancing selection in primates [113] . It is possible that the increase in polymorphism on stickleback LG XIII has likewise been driven by selection on innate immunity genes , as has been suggested for clusters of other TRIM genes in teleost fish [126] . The patterns of nucleotide diversity and FST across this LG XIII interval in stickleback provides a second example of balancing selection acting at a TRIM cluster locus and bolsters the hypothesis that the largely unstudied mammalian TRIM14 and TRIM35 genes may be involved in immune response [127] . The inference of balancing selection on these identified regions is clearly not conclusive , but can be used as the starting point for more focused , sequence-based or functional analyses . We can draw further evolutionary inferences by focusing on the patterns of differentiation among replicate oceanic and freshwater stickleback populations , taking advantage of the rapid and often parallel phenotypic evolution coupled with little background population genetic structuring . In comparisons between freshwater and oceanic populations , we found numerous regions of the stickleback genome that exhibit significantly greater differentiation than observed in the rest of the genome , providing clear signatures of divergent selection distributed across numerous linkage groups . Although there were several instances in which a private signature could be observed in just one population , the strikingly common pattern is one of very similar regions being selected in all three independently derived populations . We can thus answer the question posed in the Introduction: the previously identified parallel genetic basis for the loss of armor traits in stickleback appears to be a general rule across the genome , in that much of the adaptation of stickleback populations to freshwater conditions likely involves the repeated use of the same repertoire of developmental and physiological systems , genes , and perhaps even alleles . However , the details of this parallel evolution – for example , whether it involves independent fixation of alleles that are identical by descent in multiple derived populations , or fixation of different alleles at the same locus – appear to differ in different parts of the genome . Population genomic scans of replicate derived populations in combination with laboratory mapping and sequence-based studies provide a powerful repertoire of tools for distinguishing among these hypotheses . Other researchers [32] , [34] , [35] , [128] , [129] have distinguished between two types of selective sweeps . A hard sweep occurs when one or a small number of haplotypes present in standing genetic variation ( in this case , in the ancestral oceanic pool ) is selected to high frequency ( in this case , in the newly established freshwater populations ) . Following such a hard sweep , a large proportion of the haplotypes at a given genomic region will be identical by descent . This is contrasted with a soft sweep , in which multiple alleles at a locus or genomic region are selected to high frequency . Hard sweeps are expected to produce regions with reduced nucleotide diversity within populations , more significant differentiation between populations , and more extensive linkage disequilibrium ( LD ) [14] , [16] , [36] , [117] , [130] , [131] . Soft sweeps are expected to be more easily detected by changes in patterns of LD than by alterations of diversity or differentiation [24] , [32] , [34] , [35] . In the case of replicate freshwater stickleback populations , we can identify instances of parallel hard sweeps , in which the same one or a few haplotypes present in the ancestral oceanic population were selected to high frequency independently in multiple freshwater populations . Alternatively , non-parallel sweeps are observed when different alleles from the oceanic standing variation are swept to high frequency in different derived freshwater populations , producing a hard sweep pattern within each freshwater population . The distinctions between these cases are apparent in the overall oceanic-freshwater comparison and in the comparison among freshwater populations . In fact , the ability to differentiate between parallel and non-parallel hard sweeps is a particular strength of natural systems with multiple replicate populations like stickleback . For example , the examination of parallel hard sweeps in several populations may help identify causative mutations if each sweep is only partially overlapping , narrowing the search to the region common in all populations . The strongest example of a parallel hard sweep was observed here on LG XXI . Each of the three freshwater populations was strongly diverged from the oceanic ancestors , the overall oceanic-freshwater differentiation was similarly elevated , and there was no substantial differentiation among the freshwater populations ( Figure 8D ) . In addition , nucleotide diversity within each population was substantially reduced in this region ( Figure S1 ) . Matching the FST results , private allele density was significantly elevated in freshwater relative to oceanic populations ( Figure 9B–9D ) , but not in reciprocal comparisons among freshwater populations ( Figure 9E–9G ) . These data suggest that the same haplotype , likely present at low frequency in the standing genetic variation in the ancestral oceanic stock , was selected to high frequency independently in all three freshwater populations . Despite their likely independent derivation from ancestral oceanic stocks , these three freshwater populations have evolved in a remarkably consistent manner at this genomic region . Alternative alleles at this region are favored in oceanic populations , leaving a signature of selection against the low-frequency freshwater alleles that are maintained by gene flow from freshwater back to the ocean . In contrast , the region of LG II centered at 13 . 3 Mb provides an example of a non-parallel sweep , in which all three freshwater populations underwent substantial differentiation from the ancestor at the same region , but without exhibiting such consistency in the overall oceanic-freshwater comparison . Such a situation leads to several alternative hypotheses: the same allele at a particular locus was selected to high frequency in each population , but LD with surrounding variation was reduced in the oceanic pool . Alternatively , the same gene was under selection but different alleles were fixed in each freshwater population . Lastly , different genes in a genomic cluster may have responded to selection in each population . In this case , further data support the latter two hypotheses; private allele density is elevated in the freshwater populations , with respect to both the oceanic populations and the other freshwater populations . Additional peaks of population differentiation and private allele density in the broader genomic region , somewhat coincident across freshwater populations , also suggest that multiple loci in this section of LG II may have responded to selection in freshwater . The examples highlighted above are the most striking of the general patterns observed , and many genomic regions are intermediate in their structure of population differentiation . In fact there is roughly continuous variation in the degree to which selective sweeps show a parallel genetic basis across replicate freshwater populations . Nonetheless , the large majority of genomic regions exhibiting substantial differentiation are shared across the freshwater populations . While the particular nature of allelic variation responding to selection appears to differ among these genomic regions , the adaptive significance of the regions themselves remains consistent . In this respect , genomic patterns of evolution are remarkably parallel among these populations . Genome scans are inherently comparative , and as with all correlative studies conclusions about adaptive evolution drawn from observed population genetic patterns should be accepted provisionally . These patterns provide support for signatures of selection , but are also the source of testable hypotheses for future studies . For example , although the clear expectation in genomic comparisons between ancestral and derived populations is that extreme values of the population genetic parameters we examined will be due to selection , combinations of non-selective processes may in some instances generate similar patterns . Variation across populations in mutation and recombination rates of homologous genomic regions may lead to a pattern similar to those that occur under selection . Although we do not expect this sort of variation in mutation or recombination to occur among these closely related stickleback populations , this hypothesis deserves exploration through subsequent comparative and manipulative studies . For example , the nature of the data we present here - SNP genotypes spread throughout the genome - does not allow the use of the full battery of molecular evolution tools developed recently for the analysis of sequence data [132] . However , regions that have been identified in our frequency-based genome scan can be the focus of subsequent re-sequencing research , or studies to test the association between the identified genomic region and fitness ( e . g . [74] ) . Nonetheless , the particular stickleback system examined here–replicate , independently and recently derived freshwater populations that exhibit little neutral divergence from their extant ancestral stock–allows for uniquely strong inferences from comparative genomic data about the adaptive basis of parallel phenotypic evolution . Previous studies [103] , [104] , [133] used a set of microsatellite markers across the genome to identify selective sweeps in replicate stickleback populations in Finland , identifying a region of significant differentiation between oceanic and freshwater populations on LG VIII . That analysis focused on the region from ∼9 . 3 to 9 . 9 Mb on LG VIII [103] , [104] , just adjacent to the peak delineated in Figure 8B . In fact , in this region of LG VIII we observed signatures of both a parallel hard sweep ( from ∼8 . 0 to 9 . 0 Mb ) , in which differentiation among freshwater populations is reduced but the overall oceanic-freshwater comparison is elevated , and a non-parallel sweep ( from ∼9 . 3 to 10 . 0 Mb ) , in which differentiation among the freshwater populations is elevated . Taken together , these results suggest the intriguing hypothesis that this region includes two adjacent genomic regions of importance for freshwater adaptation , at least one of which has undergone rapid evolution in both Alaskan and Fennoscandian populations , and which demonstrate two different modes of adaptive evolution in Alaskan populations . Comparisons between QTL mapping and population genomic studies can help discern the pattern of adaptation ( see [42] , [43] , [45] for a fine example of this approach ) . Laboratory mapping of phenotypic variation in stickleback has been quite successful , leading to the identification of numerous QTL for a variety of different morphological and behavioral traits [50] . An open question is whether these QTL-containing regions also exhibit patterns of selective sweeps in natural populations . Our data clearly show this to be the case for some QTL , but also provide novel insights into the precise evolutionary trajectories . For example , major loci for the loss of the bony lateral plates and pelvic structures have been mapped previously to LG IV and LG VII respectively , including in two of the three freshwater populations used in this study [79] , [99] . On LG IV , the three regions of differentiation between oceanic and freshwater populations that we observed ( Figure 7C ) were previously associated with the lateral plate phenotype in QTL studies of laboratory crosses . The first peak contains the gene Ectodysplasin A ( Eda , found at ∼12 . 8 Mb ) , which has specifically been implicated in the parallel loss of bony lateral plates in freshwater populations [78] . Furthermore , previous mapping studies using RAD genotyping in our laboratory have shown that two additional regions of LG IV , corresponding to the second and third peaks recovered here , also co-segregate with the lateral plate phenotype [99] . Thus all three of these regions previously identified in laboratory mapping studies show evidence of a hard selective sweep within each of the freshwater populations and varying degrees of parallel evolution across the populations . The presence of three regions spread across nearly 20 Mb of a chromosome associated with a single phenotype was difficult to explain in the previous mapping cross . However , if loci in all three regions interact epistatically then the entire region may be subject to selection . If true , then although alleles along LG IV may be recombined in the oceanic environment , selection acting in isolated populations to favor haplotypes that contain the high fitness multilocus genotype could manifest as a hard sweep across the freshwater populations . In contrast to the lateral plate QTL on LG IV , the major pelvic structure reduction QTL exhibits a very different pattern with respect to signatures of selection . The major locus for pelvic loss was mapped to the very distal end of LG VII in two of these three populations [79] , [95] , [134] . Additional work on other populations pointed to Pitx1 as a likely candidate responsible for loss of the pelvic structure [95] . Although we found significant signatures of selection on LG VII ( Figure 8A ) , none of them corresponds to the region of the pelvic structure QTL mapped in laboratory crosses . In fact , the distal 7 . 5 Mb of LG VII exhibits levels of differentiation in all populations that is indistinguishable from background levels . Furthermore , one of these populations , Mud Lake , retains a full pelvic structure , whereas fish from both Bear Paw and Boot Lakes exhibit pelvic reduction . Despite these phenotypic differences , the three populations show very similar levels of differentiation from each other and the oceanic populations . This may be because selection has not occurred on the locus despite the loss of pelvic structure in two of the three populations . Alternatively , multiple different pelvic-loss alleles that are not identical by descent may have been selected in each of the pelvic reduced populations , leading to a soft sweep pattern . This hypothesis is supported by results from previous laboratory complementation results [79] . Although crosses between the derived populations did not show evidence for complete complementation , there was a statistically significant increase in the size of the pelvic structure . We interpreted this quantitative complementation result as likely due to different alleles at the same major pelvic locus having the ability to partially complement one another [79] . These new population genomic data fit this scenario . In addition to these two major armor QTL , others have been identified in stickleback crosses for a variety of traits . Previous QTL mapping analyses , using crosses between oceanic and freshwater stickleback populations or among freshwater ecotypes , uncovered genomic regions co-segregating with various morphological traits , including the aforementioned presence or absence of lateral plate or pelvic armor elements and aspects of head and body geometry [91] , [135] . A few of these QTL overlap peaks uncovered in our SNP marker genome scan . For example , Albert and colleagues [97] found that changes in jaw and head morphology are associated with regions on LG IV and XII; in our analysis , peaks overlapping these regions contain orthologs of SCUBE1 , NFYB , and WNT5A , all known or suspected to impact craniofacial development ( Table 3 , Table S3 ) [136]–[138] . Complementary to the fruits of QTL mapping , our study highlights new genomic regions that had not yet been recognized as important in the evolution of freshwater phenotypes from oceanic , namely significant peaks on Linkage Groups I , VII , VIII , XI , and XXI . These examples demonstrate the ways in which QTL mapping and population genomic studies complement each other . While QTL studies can implicate genomic regions and specific genes in the evolution of particular phenotypes , population genomic results such as those presented here can provide evidence for the adaptive significance of these genomic regions in natural populations . A population genomics approach covering multiple replicate populations provides further insight into the standing genetic variation , types of selective sweeps , and extent of parallel evolution across natural populations for genes previously linked to particular phenotypes . A population genomics approach may also narrow a region of interest previously identified in mapping studies , especially when blocks of linkage disequilibrium in natural populations are smaller than in laboratory crosses . Even situations in which a population genomic approach does not implicate a genomic region previously identified as a QTL , as here on LG VII , are informative . The type of soft sweep postulated for the pelvic structure locus may lead to a bias against detecting selection on some previously identified loci with a genome scan . In addition , the converse situation is also informative: population genomic studies can identify putative regions of adaptive significance and candidate genes that no previous mapping approach has identified . We identified a list of candidate genes within peaks of parallel divergence among stickleback populations that may be important for adaptation to freshwater . Most work on adaptation to freshwater in stickleback has focused on genes and pathways associated with bone development and skeletal morphology . Changes in teeth , jaw and gill elements correlate with feeding mode in some lacustrine threespine stickleback populations [91] , [135] . An assumption that differently shaped fish might be adapted , for example , to capturing suspended zooplankton or to foraging on benthic prey is reflected in the label “ecotypes” [83] . Likewise , derived states of loss or reduction in the number and robustness of bony elements in freshwater stickleback populations might be driven by predator regime or by the reduced mineral availability of fresh water [73] . Differences between oceanic and freshwater stickleback predict that selection acts on developmental processes that shape the skeleton and on pathways that regulate bone density and ion physiology . Orthologs of many genes known to affect bone development by modulating specification , differentiation , proliferation , migration and patterning of skeletogenic tissues fall within genomic regions associated with differentiation between oceanic and freshwater stickleback . In other vertebrates , profound effects on the developmental patterning of the teeth , jaw , and other branchial arches result from changes in expression of EDA , EYA1 , FBLN1 , NFYB , RDH10 , and Wnt5a genes [136] , [137] , [139]–[142] . Orthologs of these six genes fall within genomic intervals associated with differentiation between oceanic and freshwater sticklebacks ( Table 3 and Table S3 ) . Skeletal structure is continuously maintained and shaped throughout life by a balance between bone deposition and removal , carried out by osteoblasts and osteoclasts . Several osteogenic candidates in genomic regions differing between oceanic and lake stickleback are orthologs of genes that are also associated with human bone density variation , including imbalanced , disease states such as osteoporosis and osteopoikilosis . These genes include LEMD3 , LEPR , ARHGEF3 and RHOA ( Table 3 and Table S3 ) [143]–[145] . Anadromous fish such as salmon undergo smoltification , a set of morphological and physiological changes that prepare the juvenile fish for the demanding transition from freshwater to marine . Stickleback entrained in freshwater lakes have lost this portion of their life history , and are probably no longer under strong selection pressure to maintain tolerance and physiological adaptability to saline conditions . On the other hand , fish adapted to freshwater must contend with limited access to minerals ( e . g . , calcium ) and with a steep gradient of internal to external ion concentration . Peaks of oceanic-freshwater differentiation on LG IV , VII and XXI in stickleback contain genes associated with acute physiological adaptation to hypo- or hyperosmotic conditions in other species of fish , namely PRL2 , a hormone controlling osmoregulation , and CA4 and ATP6V1A , important for ion transport across the gill epithelium and skin ( Table 3 ) [146]–[148] . Two genes , CA4 and FLT1 , of which we found stickleback orthologs within peaks of differentiation on LG VII and XXI , have pleiotropic roles in both bone biology and osmoregulation [146] , [149]–[152] , suggesting a possible pleiotropic basis for coordinated evolutionary responses to freshwater conditions in skeletal characters and ion physiology . Evolved responses to the host of physical and biological constraints that differ between freshwater and oceanic life histories are expected to be genetically complex . It is not surprising , therefore , that we find many genomic regions displaying strong patterns of differentiation between populations . What is surprising is the consistency of the regions of differentiation and the number of compelling candidate targets for selection they contain , suggesting the possible co-selection of functionally related , multilocus genotypes . This work represents the first whole-genome analysis of threespine stickleback in which high-density SNP markers reveal signatures of selection in natural populations . The patterns we detected confirm findings from earlier studies that used QTL analysis in controlled crosses or research that used microsatellite markers in natural populations to scan the genome . However , because of the dense coverage of SNPs across the genome , and our ability to sample numerous individuals in multiple populations , our findings are a significant extension of previous work . The present investigation complements these prior efforts by exposing new genomic regions that had not yet been recognized as important in the transition from oceanic to freshwater life histories . In particular , we find remarkably similar patterns of conservation and differentiation between three independently derived freshwater populations as compared to a common oceanic ancestor . Our data support the view that these patterns are driven in part by alleles that are repeatedly selected for in freshwater populations , and maintained at low frequency in oceanic populations by a balance between gene flow from freshwater and selection against them in the ocean . Previous work supported the role of parallel genetic evolution associated with parallel phenotypic evolution in a small number of traits . Our data indicate that this pattern is not limited to these traits , and that parallel phenotypic evolution in stickleback may be underlain by extensive , genome-wide , parallel genetic evolution .
Threespine stickleback were collected from five populations in Alaska: Rabbit Slough ( oceanic ) , Resurrection Bay ( oceanic ) , Bear Paw Lake ( freshwater ) , Boot Lake ( freshwater ) , and Mud Lake ( freshwater ) ( Figure 1 ) . Fish were collected by beach seine ( Resurrection Bay ) or by minnow trap ( lakes and Rabbit Slough ) from wild populations in the summers of 1997 and 1998 . Bear Paw Lake ( 61°36′ N , 149°45″ W , elev . 88 m ) , Boot Lake ( 61°43′ N , 150°07′ W , elev . 55 ) , and Mud Lake ( 61°56′N , 150°58′W , elev . 38 m ) are all in different drainage systems , separated by geographic barriers of distance and elevation . Rabbit Slough ( 61°32′ N , 149°15′ W , elev . 5 m ) and Resurrection Bay ( 60°07′ N , 149°23′ W , elev . 14 m ) empty to opposite sides of the Kenai Peninsula . Fish were anaesthetized with a tricaine methane sulphonate solution ( MS222 ) , frozen on dry ice in the field , and later transferred to 100% ethanol . Genomic DNA was purified from fin tissue using the DNeasy Blood & Tissue Kit ( Qiagen ) . Genomic DNA was purified from 20 individuals from each of the five populations . DNA from each fish was digested with high fidelity SbfI ( New England Biolabs ) . RAD tag libraries were created as in Baird et al . [99] with the following modifications: only barcodes that differed by at least three nucleotides were used , a longer P2 adapter ( with the following sequences: P2-2 top oligo 5′/5Phos/GATCGGAAGAGCGGTTCAGCAGGAATGCCGAGACCGATCAGAACAA3′; P2-2 bottom oligo 5′ CAAGCAGAAGACGGCATACGAGATCGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT 3′ ) was used in the production of all libraries , libraries produced for the May 2009 run and thereafter used P1 and P2 adapters modified with a phosphorothioate bond between the last two 3′ nucleotides on both oligos of the P1 adapter and the bottom oligo of the P2 , adaptor ligated DNA was subjected to fewer rounds ( 14 or 16 ) of PCR amplification and PCR products were gel purified by excising a DNA fraction of 400–600 bp . Each Illumina sequencing lane contained a library representing approximately equal amounts of DNA from 16 individual fish ( refer to Table S1 ) . Sequences are available at the NCBI Short Read Archive ( http://www . ncbi . nlm . nih . gov/Traces/sra; accession number SRA010788 . 9 ) . Sequence reads from the Illumina runs were filtered as follows: reads with a barcode that did not match one of the expected barcodes ( i . e . a sequencing error in the barcode ) , and sequence reads of poor overall quality , were removed from the analysis . Sequence reads were then sorted by barcode and aligned to the stickleback genome using Bowtie [153] with a maximum of 2 mismatches within the first 28 bases and a sum of base quality for all mismatches in the read no greater than 70 . Following alignment , the read counts of the four possible nucleotides at each nucleotide site were tallied for each individual ( see Figure 2 ) . Reads were further trimmed by removing the portion of the sequence within the restriction enzyme recognition site , since any nucleotide polymorphism in this area would result in the absence of RAD tags , and including these data would underestimate total nucleotide diversity . Diploid genotypes at each nucleotide site for each individual were determined in a maximum likelihood statistical framework as follows . For a given site in an individual , let n be the total number of reads at that site . Let n = n1 + n2 + n3 + n4 , where ni is the read count for each possible nucleotide at the site ( disregarding ambiguous reads ) . For a diploid individual , there are ten possible genotypes ( four homozygous and six heterozygous genotypes ) . We calculate the likelihood of each possible genotype by using a multinomial sampling distribution , which gives the probability of observing a set of read counts ( n1 , n2 , n3 , n4 ) given a particular genotype . For example , the likelihoods of a homozygote ( genotype 1/1 ) or a heterozygote ( 1/2 ) are , respectively: ( 1a ) ( 1b ) where ε is the sequencing error rate . If we let n1 be the count of the most observed nucleotide , and n2 be the count of the second-most observed nucleotide , then the two equations in ( 1 ) give the likelihood of the two most likely hypotheses out of the ten possible genotypes . For all the analyses that follow , we assigned a diploid genotype to each site based on a likelihood ratio test between these two most likely hypotheses with one degree of freedom . If this test was significant at the α = 0 . 05 level , we assigned the most likely genotype at the site . If this test was not significant , we did not assign a genotype at the site for that individual . This effectively removes data for which there are too few sequence reads to determine a genotype , instead of establishing a constant threshold for sequencing coverage . We account for the resulting variance in sample size among sites in the analyses below . This basic multinomial-based statistical framework has been proposed elsewhere [154] . Our approach differs from that of Lynch [154] , however , in that we estimate the sequencing error rate ε separately by maximum likelihood for each nucleotide site , rather than assuming or estimating a single global error rate . We have found empirical evidence that sequencing error varies among sites , and that this approach is more robust to other assumptions than using a single global error rate ( Hohenlohe and Cresko , unpublished data ) . Note that equations ( 1 ) allow for a random sequencing error rate but do not account for any systematic biases in , for instance , the frequency of sequence reads for alternative alleles at a heterozygous site . The generation of likelihoods for each of the ten possible genotypes at each site also allows for more sophisticated methods than were used here of carrying error and uncertainty through the analysis to the final population genetic measures . We will address these and other aspects of this statistical genotyping method in a forthcoming paper ( Hohenlohe and Cresko , in preparation ) . We first calculated four population genetic measures at each nucleotide site for the population ( s ) under examination . To estimate nucleotide diversity , we calculated π ( equivalent to expected heterozygosity ) as ( 2 ) where ni is the count of allele i in the sample , and . Observed heterozygosity H was calculated as the proportion of diploid genotypes in the sample that are heterozygotes . To estimate differentiation among populations , we adapted a formula for FST from [155] that accounts for unequal sample sizes among populations by weighting: ( 3 ) where nj is the number of alleles sampled in population j , πj is the nucleotide diversity within population j from equation ( 2 ) , and is the total nucleotide diversity across the pooled populations . We compared this measure of FST to others , including the analysis of variance approach of [21] , and found that it gave similar results but performed well with small sample sizes . In particular , the consistency and location of the peaks examined in detail here did not change with different methods of estimating FST ( not shown ) . Finally , for each population in a comparison we assessed whether each single nucleotide polymorphism ( SNP ) was the result of a private allele . Here ρj = 1 if an allele at the SNP is found only in population j and at least one individual was genotyped at that nucleotide site in each population , and ρj = 0 otherwise . To generate smooth genome-wide distributions of these four population genetic measures , we used a kernel-smoothing moving average . For each genomic region centered on a nucleotide position c , the contribution of the population genetic statistic at position p to the region average was weighted by the Gaussian function , where σ = 150 kb . For computational efficiency , we truncated this weighted average at 3σ in each direction ( beyond which nucleotide sites have a relative weight less than ∼0 . 01 ) . We evaluated multiple choices for the width σ and found 150 kb to be large enough to overcome sampling variance but still small enough to detect relatively narrow genomic regions of differentiation , with a precision greater than many QTL studies ( data not shown ) . For example , in the overall freshwater-oceanic comparison each 6σ window contained a mean of 81 . 6 SNPs . We shifted the moving average by a step size of 100 kb . Because of the variance in sample size across sites ( due to sampling variance in Illumina sequencing and sites where a genotype could not be assigned using the maximum likelihood technique above ) , we further weighted each statistic at each nucleotide position by , where nk is the number of alleles sampled at site k [156] . As above , we explored different weighting formulas , as well as unweighted averages , and these did not appreciably change the consistency or location of major peaks in population differentiation ( not shown ) . Nucleotide diversity π and heterozygosity H were weighted and averaged across all nucleotide sites; FST and private allele density ρ were weighted and averaged across all SNPs . We also estimated the allele frequency spectrum within populations or groups of populations using Tajima's D [102] , applied to the nucleotide diversity π and number of SNPs within σ bp of the center of each window ( i . e . 2σ = 300 bp windows ) . Sample size n was taken to be the mean of nk across all sites within the window . We assessed statistical significance at two levels . At individual SNPs , we estimated the significance of FST values with a goodness-of-fit G test statistic [157] . We corrected for false discovery rate in multiple tests using the Benjamini-Hochberg correction [158] . We assume that population differentiation at linked SNPs may be positively correlated , so this method of correction is still valid [159] . To assign significance values to moving average values of π , H , FST , and ρ , as well as window values of Tajima's D , we used bootstrap resampling within each population comparison . For each nucleotide position ( for π , or H ) or SNP position ( for FST or ρ ) within each truncated Gaussian window described above , we randomly sampled with replacement from across the entire genome a value for the statistic ( π , H , FST , or ρ ) and the corresponding sample size ( nk ) . We calculated the weighted average as above for each replicate . For Tajima's D , for each nucleotide position within the 2σ window we randomly sampled with replacement from across the genome and calculated the overall D for the re-sampled dataset . For computational efficiency , at each region we began with 100 ( for π or H ) , 1 , 000 ( for D ) , or 10 , 000 ( for FST or ρ ) replicates and stepped up to 1 million ( π , H , or D ) or 10 million ( FST or ρ ) replicates as necessary to provide accuracy in the tails of the distribution . Essentially this bootstrapping technique gives a null distribution of expected genomic region averages , accounting for the observed genome-wide average of each statistic in a given population or population comparison , but assuming no correlation among neighboring positions . It thus indicates genomic regions that differ significantly from the genome-wide average as a result of the combination of linkage disequilibrium and evolutionary or demographic processes . Significance values ( p ) given in the text and tables represent proportions of these bootstrap distributions exceeding the particular statistic . We used these significance values to delineate regions of interest for identification of candidate genes . For nucleotide diversity , two regions on LG III and XIII were delineated to include all regions with p<10−5 for π in the combined 5-population dataset , including positions within 2σ ( = 300 kb ) of the outer positions . For FST , we identified all genomic regions for which p<10−5 in the overall freshwater-oceanic comparison as well as in all six of the pairwise freshwater-oceanic comparisons . We then delineated the region of interest using the overall freshwater-oceanic comparison , +/− 2σ as above . Note that this 2σ margin includes locations that may contribute to a highly significant average value of a statistic , even if the value for the genomic region directly over the gene is not as significant ( examples in Table 3 ) . We took this approach in order to cast a wide net for selection on potential candidate genes , including their associated cis-regulatory regions . For several reasons , we believe that our method may provide an underestimate of nucleotide diversity within populations . First , we expect polymorphism in RAD sites , such that the restriction enzyme recognition site is missing in some haplotypes and a RAD tag sequence will not be obtained for this allele . Individuals homozygous for absence of a RAD site will lack any sequence information for those two RAD tags; individuals heterozygous for the presence of a RAD site will be represented by one of only two possible sequences for each tag , so they will likely be scored as homozygous for all nucleotide positions in those tags . ( It is intuitive to use the total number of reads to identify such RAD-site heterozygotes , although the sampling process and other sources of variation in read counts may make such inferences tenuous ) . We removed sequence data within the restriction enzyme recognition site prior to analysis . However , to the extent that presence/absence of a RAD site is in linkage disequilibrium with SNPs in the adjacent RAD tag sequence , this polymorphism will be underestimated . Second , RAD tags with low coverage are not assigned a genotype by the method above if the likelihood ratio test is not significant . Because of the multinomial sampling process , true heterozygotes may be more likely to go unscored than true homozygotes at the same , low level of sequencing depth . Third , we have some evidence that there is bias in number of reads and read quality between alternative alleles at heterozygous sites during library construction and/or Illumina sequencing ( unpublished data ) . As described above , our method does not account for these unknown sources of bias , but they could also lead to the analysis assigning homozygous genotypes to heterozygous sites . We are currently exploring ways to account for all of these issues in the analysis ( Hohenlohe and Cresko , in preparation ) . In any case , we believe that while our method may lead to an underestimate of nucleotide diversity measures within groups ( i . e . , π and H ) , these issues are not likely to bias the distribution of these measures along the genome . Also , they should not bias measures of population differentiation ( FST ) , assuming that these sources of error affect different population samples equally . | Oceanic threespine stickleback have invaded and adapted to freshwater habitats countless times across the northern hemisphere . These freshwater populations have often evolved in similar ways from the ancestral marine stock from which they independently derived . With the exception of a few identified genes , the genetic basis of this remarkable parallel adaptation is unclear . Here we show that the parallel phenotypic evolution is matched by parallel patterns of nucleotide diversity and population differentiation across the genome . We used a novel high-throughput sequence-based genotyping approach to produce the first high density genome-wide scans of threespine stickleback populations and identified several genomic regions indicative of both divergent and balancing selection . Some of these regions have been associated previously with traits important for freshwater adaptation , but others were previously unidentified . Within these genomic regions we identified candidate genes , laying the foundation for further genetic and functional study of key pathways . This research illustrates the complementary nature of laboratory mapping , functional genetics , and population genomics . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
]
| [
"evolutionary",
"biology/genomics",
"ecology/marine",
"and",
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| 2010 | Population Genomics of Parallel Adaptation in Threespine Stickleback using Sequenced RAD Tags |
Reduced antimicrobial susceptibility threatens treatment efficacy in sub-Saharan Africa , where data on the burden and correlates of antibiotic resistance among enteric pathogens are limited . Fecal samples from children aged 6 mos—15 yrs presenting with acute diarrhea in western Kenya were cultured for bacterial pathogens . HIV-uninfected children with identified Shigella or Salmonella species or pathogenic Escherichia coli ( EPEC , ETEC , EAEC or EIEC ) were included in this cross-sectional sub-study . Non-susceptibility to ampicillin , ceftriaxone , ciprofloxacin , cotrimoxazole , and tetracycline was determined using MicroScan Walkaway40 Plus . Multivariable log-binomial regression was used to identify correlates of multi-drug non-susceptibility ( MDNS , non-susceptibility to ≥ 3 of these antibiotics ) . Of 292 included children , median age was 22 . 5 mos . MDNS was identified in 62 . 5% of 318 isolates . Non-susceptibility to cotrimoxazole ( 92 . 8% ) , ampicillin ( 81 . 3% ) , and tetracycline ( 75 . 0% ) was common . Young age ( 6–24 mos vs . 24–59 mos adjusted prevalence ratio [aPR] = 1 . 519 [95% confidence interval: 1 . 19 , 1 . 91] ) , maternal HIV ( aPR = 1 . 29 [1 . 01 , 1 . 66] ) ; and acute malnutrition ( aPR = 1 . 28 [1 . 06 , 1 . 55] ) were associated with higher prevalence of MDNS , as were open defecation ( aPR = 2 . 25 [1 . 13 , 4 . 50] ) , household crowding ( aPR = 1 . 29 [1 . 08 , 1 . 53] ) and infrequent caregiver hand-washing ( aPR = 1 . 50 [1 . 15 , 1 . 95] ) . Young age , HIV exposure , acute malnutrition and poor sanitation may increase risk of antibiotic non-susceptible enteric pathogen infections among children in Kenya .
Diarrheal diseases account for an estimated 10% of childhood deaths in Sub-Saharan Africa[1] . Bacterial enteric infections such as Shigella species ( spp . ) , Salmonella spp . , and pathogenic Escherichia coli ( E . coli ) are leading causes of diarrhea[2 , 3] and the attribution of diarrhea cases to these bacteria appears to increase as more sensitive diagnostic tools are used[4] . These bacterial pathogens are also associated with mortality[2] and linear growth faltering[5] , which has cognitive , economic , and health consequences that extend into adulthood[6–8] . Current World Health Organization ( WHO ) guidelines recommend antibiotic treatment in cases of suspected shigellosis or suspected cholera[9] . While some evidence suggests a benefit with antibiotic treatment of other diarrheal pathogens[10] , widespread antibiotic resistance is a concern , particularly in low-resource settings where alternative treatments are limited . In addition , antibiotic resistance is associated with increased costs to the individual[11] and to health systems[12] . There are multiple mechanisms driving the emergence of antibiotic resistant diarrheal pathogens . Antibiotic use directly alters the gut flora by eliminating susceptible bacteria , favoring the propagation of non-susceptible species . This resistance may emerge as a result of spontaneous gene mutations that confer resistance in the presence of selective pressure or as a result of gene transfer from an organism carrying resistance genes[13 , 14] . When shed into the environment , these non-susceptible organisms can serve as a reservoir of infection or colonization of other individuals . Recent data suggest that resistance to commonly used antibiotics among enteropathogens is high in sub-Saharan Africa . For example , resistance to ampicillin has been found to range between 50%–90% among pathogenic E . coli[15–17] , 28–50% in Salmonella spp . [17–19] , and 50–93% in Shigella spp . [16–20] . Resistance to cotrimoxazole in these pathogens has been found to be similarly high[16–20] . Further , resistance to newer antibiotics has been emerging in the region . Resistance to ciprofloxacin has been reported in up to 25% of Shigella isolates [15 , 19–23] , 6% of Salmonella [17 , 19 , 23] , and 50% of E . coli isolates [15 , 17 , 21 , 23–26] . Similarly , prevalence of resistance to ceftriaxone has been estimated to range up to 55% in Shigella spp . [22 , 27] , 75% in Salmonella spp . [22 , 27–29] , and 43% in E . coli [24 , 26] isolates . Despite the high prevalence and associated morbidity and mortality of bacterial diarrhea in sub-Saharan Africa , data on risk factors for antibiotic non-susceptibility in enteric pathogens from this region are limited . We conducted a cross-sectional study using previously-collected data from HIV-uninfected children with acute diarrhea in whom Shigella spp , Salmonella spp , or enteropathogenic ( EPEC ) , enterotoxigenic ( ETEC ) , enteroaggregative ( EAEC ) , or enteroinvasive ( EIEC ) E . coli were isolated . We determined the proportion of these isolates that were non-susceptible to antibiotics from various classes ( ampicillin , ceftriaxone , ciprofloxacin , cotrimoxazole , and tetracycline ) as well as the prevalence and correlates of multi-drug non-susceptibility ( MDNS , phenotypic non-susceptibility to 3 or more antibiotics from different classes ) . Understanding correlates of MDNS in pathogenic enteric bacteria can help identify groups of children who may not respond to commonly used antibiotics and who may benefit from alternative interventions , as well as identify opportunities for effective interventions for antibiotic resistance control .
The parent study was approved by the Institution Review Boards ( IRB ) of the University of Washington and Kenya Medical Research Institute . The University of Washington IRB determined that the current study did not meet the criteria for human subjects research due to the use of de-identified data and was therefore determined to be exempt from IRB review . We conducted a cross-sectional study nested within a hospital-based surveillance study of acute diarrhea in Kisii Referral , Homa Bay District , and Migori District hospitals in the Nyanza province of western Kenya . We used previously collected data from approximately 2000 children aged 6 months to 15 years , presenting to the health facility with acute diarrhea ( ≥3 loose or watery stools per day , for < 14 consecutive days ) between 2011 and 2014[30] . Children were excluded from the parent study if they had a diagnosis of chronic non-infectious diarrhea , were not accompanied by a primary caregiver , were previously enrolled in the study , or were unable to provide a stool sample or rectal swab . All data were coded to protect patient privacy and confidentiality . After collection , stool samples and rectal swabs were transferred into Cary-Blair transport media ( Medical Chemical Corporation , Torrance , CA , USA ) and shipped at 2–10°C within 24–72 hours of collection to the Kenya Medical Research Institute / United States Army Medical Research Unit Microbiology Hub Laboratory in Kericho , Kenya . The specimens were aseptically innoculated using sterile polyester tipped swabs , streaked on primary plates ( BD Difco Sorbitol-MacConkey agar to select for non-sorbitol fermenting E . coli , BD BBL MacConkey agar to select lactose fermenting E . coli colonies , and BD Difco Hektoen agar to select for Salmonella and Shigella spp . ) and incubated for characteristic morphological identification of the organisms of interest and biochemical tests . The following characteristics were used to identify colonies for further processing: on Hektoen agar , green colonies with black centers ( appearance typical of Salmonella spp . ) and green colonies without black centers ( appearance typical of Shigella spp . ) ; on MacConkey agar , pink or brick-red colonies ( appearance typical of lactose fermenting organisms ) or colorless or clear colonies ( appearance typical of non-lactose fermenting organisms ) ; on MacConkey Sorbitol Agar , colorless colonies ( appearance typical of non-sorbitol fermenting organisms ) . Morphologically distinct colonies of E . coli were sub-cultured and processed individually by PCR to identify pathogenic E . coli . Bacterial DNA was extracted from at least 3 colonies with distinct morphology and subjected to PCR to identify the following E . coli virulent genes: heat labile enterotoxin ( elt ) and/or heat- stable enterotoxin ( est ) ; enteroaggregative E . coli ( EAEC ) , aatA; enteroinvasive E . coli ( EIEC ) , invasion plasmid antigen H ( ipaH ) ; enterohemmorhagic E . coli ( EHEC ) , Shiga toxin 1 , 2 and variants ( stx ) ; enteropathogenic E . coli ( EPEC ) , bundle forming pilus ( bfpA ) prior to MicroScan testing . Starting in March 2013 , additional gene targets for EPEC , intimin ( eae ) , and for EAEC , aaiC , were incorporated into the PCR . The primer sequences have been described elsewhere for ETEC and EAEC[31 , 32] , EIEC[33] , and EPEC[34] . Single colonies exhibiting these characteristics on the various media above were sub-cultured on non-selective plates ( MacConkey ) to obtain a pure culture that was then used to prepare an inoculum equivalent to 0 . 5 McFarland standard for biochemical identification and antibiotic susceptibility testing using MicroScan WalkAway 40 Plus ( Siemens , Erlangen , Germany ) . Minimum Inhibitory Concentrations ( MIC ) for selected antibiotics were determined using the automated Microscan Walkaway 40 Plus System . This system uses conventional gram-negative panels that include extended spectrum beta-lactamases ( ESBL ) testing . Interpretations of antibiotic susceptibility testing were based on standard Clinical and Laboratory Standards Institute ( CLSI ) guidelines M100-S19 . Isolates were classified as non-susceptible if the MIC was greater than the cut-off for intermediate susceptibility: ampicillin MIC >16 μg/ml , ceftriaxone MIC >8 μg/ml , ciprofloxacin MIC >2 μg/ml , cotrimoxazole MIC >2/38 μg/ml , and tetracycline MIC >8 μg/ml . MDNS was defined as intermediate or resistant susceptibility to 3 or more antibiotics from different classes[35] . Children were included in this secondary analysis if they were HIV-uninfected , and had any of the following pathogenic enteric bacteria isolated from their stool or rectal swab samples: Shigella spp , Salmonella spp , EPEC , ETEC , EAEC , or EIEC . We calculated the proportion of isolated strains that were non-susceptible to each antibiotic of interest ( ampicillin , ceftriaxone , ciprofloxacin , cotrimoxazole , and tetracycline ) and that were multi-drug non-susceptible , stratified by pathogen and overall . We used χ2 tests to compare MDNS proportions between pathogens , MDNS proportions between age groups stratified by identified pathogen ( Fishers-exact test ) , prevalences of non-susceptibility to each antibiotic between pathogens , and the likelihood of antibiotic treatment between groups categorized by age , malnutrition , and HIV-exposure status . To determine correlates of MDNS in enteric infections , we compared MDNS prevalence in groups of children defined by the following characteristics: age group , HIV-exposure ( having a HIV-infected biological mother ) , acute malnutrition ( weight for height/length Z-score < -2 [WHZ < -2] ) , chronic malnutrition ( height/length for age Z-score < -2 [HAZ < -2] [36 , 37] ) , antibiotic use in the previous 7 days , hospitalization in the last year , toilet type , protected water source or water treatment , household crowding , and frequency of hand-washing . Anthropometric Z-scores were calculated using WHO reference standards [36 , 37] . Univariate and multivariate prevalence ratios ( PRs ) and 95% confidence intervals ( 95% CIs ) were estimated using log-binomial regression with robust standard errors . If a model failed to converge due to small sample size per cell , log-poisson regression models with robust standard errors were used . For the multivariable models , child’s age and indicators of socioeconomic status ( SES ) were considered to be a priori confounders based on prior literature[38 , 39] . The SES factors evaluated were caregiver educational attainment and estimated monthly income . Children with more than 1 pathogen of interest isolated were classified as multi-drug non-susceptible if at least 1 of the pathogens was multi-drug non-susceptible . All analyses were done in Stata 13 . 0 .
Of 1758 children enrolled in the parent study , 1444 did not have any of the bacteria of interest isolated from their stool and 22 were HIV-infected , leaving 292 children included in the current analysis ( Fig 1 ) , from whom 318 pathogens of interest were isolated . Median age of included subjects was 22 . 5 months ( interquartile range [IQR]: 10 . 5–41 . 5 months ) , and 56 . 8% were male ( Table 1 ) . Twenty-two children ( 7 . 8% ) were HIV-exposed , uninfected ( HEU ) , defined as an HIV-uninfected child with a HIV-infected biological mother . All caregivers reported that the child had been exclusively breastfed , with a reported median duration of 6 . 0 months ( IQR: 4 . 0–6 . 0 months ) . Thirty-three ( 11 . 3% ) caregivers reported their child having received at least 1 antibiotic in the last 7 days . The most commonly reported antibiotics were cotrimoxazole ( n = 13 ) , metronidazole ( n = 11 ) and amoxicillin ( n = 9 ) . Antibiotics were prescribed to 154 ( 52 . 7% ) children enrolled after a stool sample was received . Erythromycin ( 22 . 7% of prescriptions ) , cotrimoxazole ( 14 . 9% ) , and amoxicillin ( 14 . 3% ) were those most frequently prescribed . Antibiotics were more likely to be prescribed to children who were less than 24 months old ( Pearson χ2 p-value = 0 . 08 ) compared to children older than 24 months , who were acutely malnourished ( WHZ < -2 ) ( Pearson χ2 p-value = 0 . 01 ) , and those who were HEU ( Pearson χ2 p-value <0 . 0001 ) . EAEC was the most commonly identified enteric infection ( 39 . 4% ) among included children ( Table 1 ) . Twenty-six children ( 8 . 2% ) had more than one bacteria of interest identified , 17 of which had more than 1 E . coli pathotype ( Table 2 ) . Among the 26 children who had more than one bacteria of interest identified , none had more than 2 unique pathogens and all pairs of isolates from the same child had perfect agreement in non-susceptibility to the tested antibiotics . Children with E . coli pathotypes tended to be younger ( mean: 27 . 9 months , standard deviation [SD] = 32 . 4 months ) than children with Shigella spp . infections ( mean age: 39 . 9 months , p = 0 . 0006 ) , and this difference was driven by the age distribution of children with EPEC ( 22 . 0 months ) or EAEC ( 22 . 5 months ) ( Table 3 ) . Children with Salmonella spp . ( mean age = 46 . 4 months ) were of similar age to those with Shigella spp . infections . Almost all children ( 97 . 3% ) had an enteric infection that was non-susceptible to at least 1 of the antibiotics of interest , and 60 . 6% had MDNS . Children who had more than one pathogen of interest identified were 38% more likely to have MDNS in their isolated pathogens than children who had only 1 of the pathogens of interest ( prevalence ratio: 1 . 38; 95% confidence interval [95% CI]: 1 . 11 , 1 . 70 ) . Non-susceptibility to ampicillin , cotrimoxazole , and tetracycline was frequently observed among E . coli pathotypes and Salmonella spp , and non-susceptibility to cotrimoxazole and tetracycline was frequently observed among all pathogens ( Fig 2 ) – 80 . 5% of all bacteria were non-susceptible to ampicillin , 92 . 1% to cotrimoxazole , and 76 . 2% to tetracycline . Non-susceptibility to ciprofloxacin and ceftriaxone was rare ( in 3 . 8% and 1 . 4% , respectively ) , as was presence of ESBL ( 3 . 6% ) . There were notable differences in ampicillin non-susceptibility and MDNS prevalence between bacterial groups; Shigella spp . and Salmonella spp . were less likely to have MDNS compared to any E . coli pathotype ( p<0 . 0001 and p = 0 . 003 , respectively ) . Shigella spp were also less likely to be non-susceptible to ampicillin than any E . coli pathotype ( p<0 . 0001 ) , whereas prevalence of resistance to ampicillin was not statistically significantly different between Shigella spp . and E . coli pathotypes ( p = 0 . 884 ) . E . coli pathotypes were also more likely to be non-susceptible to ciprofloxacin than either Salmonella or Shigella spp ( p = 0 . 018 ) There was no significant difference in tetracycline , cotrimoxazole , or ceftriaxone non-susceptibility between pathogen categories . Young age , HIV-exposure , and WHZ < -2 were associated with higher MDNS prevalence ( Table 4 ) . Compared to children aged 24–59 months , children 6–24 months were 51% more likely to have a MDNS in enteric bacteria isolated ( adjusted PR [aPR]: 1 . 51 [95% CI: 1 . 19 , 1 . 91] ) . This association was less apparent when stratifying by pathogen category ( Table 3 ) , though these results were inconclusive due to small sample size per stratum . Further , children whose WHZ was under 2 SD below the reference ( WHZ < -2 ) were more likely to have a multi-drug non-susceptible pathogen ( aPR = 1 . 28 [95% CI: 1 . 06 , 1 . 55] ) than children with a WHZ ≥ -2 , as were HEU children compared to HIV-unexposed ( HU ) children ( aPR: 1 . 29 [95% CI: 1 . 01 , 1 . 66] ) . All 22 HEU children had pathogenic enteric infections that were non-susceptible to cotrimoxazole , compared to 92 . 3% of HU children ( Fisher’s exact test p-value = 0 . 37 ) . In contrast , the two other measures of malnutrition ( middle upper arm circumference [MUAC] < 12 . 5 and HAZ < 2 SD below the reference ) were found to have no association with MDNS . Duration of exclusive breastfeeding of 6 months or more was not associated with prevalence of MDNS compared to a duration of less than 6 months . Children who had taken antibiotics in the last 7 days per caregiver report or who were hospitalized in the last year also had no statistically significant difference in prevalence of multi-drug non-susceptible enteric infections . Several factors pertaining to exposure to environmental contamination were identified as correlates of MDNS . Children in households with a pit latrine were 76% more likely to have MDNS in bacteria isolated than those with a flush toilet ( aPR: 1 . 76 [95% CI: 1 . 01 , 3 . 10] ) , and children whose caregivers reported open defecation were more than twice as likely ( aPR: 2 . 25 [95% CI: 1 . 13 , 4 . 50] ) . Children whose caregivers reported “sometimes” or “never” washing their hands after defecating were 50% more likely to have MDNS in identified bacteria than those whose caregivers “always” or “usually” washed their hands ( aPR: 1 . 50 [95% CI: 1 . 15 , 1 . 95] ) . Results were similar for children whose caregivers reported “sometimes” or “never” washing their hands before eating ( aPR: 1 . 53 [95% CI: 1 . 09 , 2 . 14] ) . Living with 3 or more persons per room in the household was associated with higher prevalence of MDNS ( aPR = 1 . 29 [95% CI: 1 . 08 , 1 . 53] ) , with a 10% higher prevalence of MDNS for each additional person per room ( aPR = 1 . 10 [95% CI: 1 . 03 , 1 . 17]; p = 0 . 005 ) . Conversely , use of treated or protected water was not associated with MDNS in enteric pathogens compared to use of untreated or unprotected water .
Among children under age 15 presenting to clinic with acute diarrhea in Western Kenya , non-susceptibility to ampicillin , cotrimoxazole , and tetracycline was highly prevalent among pathogenic enteric bacteria . These three antibiotics are widely used due to their availability and affordability in Kenya and we found cotrimoxazole and amoxicillin to be commonly prescribed to children with acute diarrhea at study facilities . Cotrimoxazole is also used prophylactically in HIV-infected individuals and in HEU children prior to 2 years of age[40] , and tetracycline is widely used in livestock husbandry in Kenya[41] . The pattern of lower prevalence of non-susceptibility to ciprofloxacin and ceftriaxone , and higher non-susceptibility to ampicillin , cotrimoxazole , and tetracycline , presented here are consistent with trends reported by recent studies in Sub-Saharan Africa [16–18 , 42 , 43] . Age under 24 months was associated with a higher prevalence of MDNS in enteric pathogens . Young age has previously been described as a risk factor for resistance in commensal enteric bacteria among children in resource-limited countries [26 , 44–46] and among livestock [47 , 48] . This analysis suggests this may be true of pathogenic bacteria as well . This association may be due to high incidence of infections in this population , resulting in selective pressure exerted by frequent antibiotic use that may contribute to the selection of MDNS . Younger children were more likely to be infected with EPEC or EAEC in this study , the two pathogens with the highest prevalence of MDNS . Given that the association of young age with MDNS was less apparent when stratifying by pathogen , the distribution of EPEC and EAEC may , at least in part , explain the association between which young age and higher risk of MDNS . Younger children may also be exposed to a wider diversity of pathogens in their environment through oral investigation ( the process of mouthing nearby objects ) , resulting in frequent ingestion of pathogens from their environment . By the sheer volume of pathogen exposure , younger children may have a higher burden of non-susceptible infections that either remain in the gut or transfer genetic material to commensal bacteria which in turn transfer genetic material to future infections . Acutely malnourished children ( those with WHZ < -2 ) and those who were HEU had a higher prevalence of non-susceptible enteric pathogens in our study . Children with chronic malnutrition ( HAZ < -2 ) or MUAC under 12 . 5 cm tended to have higher prevalence of MDNS , though these associations were not statistically significant . Children with acute malnutrition and HIV-exposure are at higher risk of frequent and more severe infections than their healthy counterparts which may lead to more frequent antibiotic use including prophylactic antibiotic use[40 , 49 , 50] . HEU children also typically live with HIV-infected household members , which may increase exposure to non-susceptible enteric pathogens as a result of the increased use of antibiotics among HIV-infected individuals . Indeed , children who were acutely malnourished and those with HEU were more likely to be prescribed an antibiotic at presentation with diarrhea in the parent study . We did not , however , find an association between reported recent antibiotic use and MDNS , suggesting there may be other explanations for the higher MDNS prevalence in acutely malnourished and HEU populations . Unimproved sanitation and infrequent hand-washing were also associated with higher prevalence of MDNS in this analysis , suggesting that exposure to environmental fecal pathogens may selectively place children at risk for multi-drug non-susceptible enteric infections . Antibiotic use in the community could lead to selection pressure in the environment by eliminating susceptible bacteria in the intestines prior to excretion[51 , 52] and the excretion of the antibiotics themselves which can drive non-susceptibility among environmental bacteria even at low-levels[53] . Animal waste contamination , naturally occurring minerals , and agricultural pollutants can also co-select for non-susceptible bacteria in the environment[13 , 51] . Much like the gut , where bacteria are in close proximity to one-another and thus easily able to share resistance genes between species , settings of poor sanitation may similarly offer ample opportunity for the acquisition , persistence , and dissemination of non-susceptibility[13 , 51] . Household crowding was associated with MDNS in pathogenic enteric infections in children , with a trend toward higher prevalence of MDNS with greater numbers of persons per room in the household . Person-to-person transmission of non-susceptible bacteria may occur in the household . Attendance at a large primary school[44] and sharing a bed with another child[43] have been found to be risk factors for carriage of resistant commensal E . coli . However , other studies report no association between sharing a home with 2 or more children and resistance[43 , 45] . These studies did incorporate a measure of household size , suggesting that the degree of actual contact with other household members may be a more important predictor of acquiring a non-susceptible infection than the absolute number of household members . Antibiotic use in the previous 7 days and hospitalizations in the previous year were not associated with multi-drug non-susceptible infections . This could suggest that individual-level consumption of antibiotic use may be less of a driver of antibiotic resistance than community-use of antibiotics and population density , as has been shown elsewhere[54] . Such findings suggest that sanitation interventions , as well as efforts to control community-wide availability of antibiotics may have more impact on reducing antibiotic resistance in resource-limited settings than interventions targeting health workers and hospitals . Antibiotic use may also be inaccurately recalled by caregivers , a limitation which may also explain the lack of association between MDNS and duration of exclusive breastfeeding . The lack of association between MDNS and duration of exclusive breastfeeding could also be explained by the fact that the study population was older than the age at exclusive breastfeeding occurs , given that children up to 15 years of age were included . Shigella and Salmonella spp . isolates were less likely to have MDNS than E . coli pathotypes . E . coli often acquire genes through horizontal gene transfer[55 , 56] and this is known to be an important mechanism for acquisition of antimicrobial resistance[14 , 56–58] . Horizontal gene transfer may occur more commonly among E . coli isolates than the other pathogen genuses , leading to higher prevalences of non-susceptibility to commonly used antibiotics . Ampicillin resistance was less common in Shigella isolates than E . coli isolates , whereas there was a similar prevalence of non-susceptibility to the other commonly used antibiotics , cotrimoxazole and tetracycline . This difference has been observed in some[16 , 17] but not all studies[15 , 19] evaluating resistance in these bacteria . The strengths of this study include the use of data from a large surveillance study in Sub-Saharan Africa , where very little data on risk factors for carriage of antibiotic non-susceptibility in enteric pathogens are available . However , there are several limitations to our study as well . The small sample size may have resulted in low statistical power . Multiple statistical tests were conducted which could have inflated the false discovery rate of statistically significant associations . Due to the cross-sectional design of this study , we can only draw conclusions about associations between these factors and MDNS at the single time point of data collection . Future studies should evaluate the clinical outcomes in children with and without multi-drug non-susceptible infections . While combining all pathogens in the regression analysis created more stable estimates , this may have created heterogeneous categories since these bacterial genuses may have different mechanisms of non-susceptibility acquisition . In addition , children categorized as wasted ( WHZ < -2 ) may have been misclassified , as accurate measures of weight are difficult to ascertain in children with diarrhea due to dehydration , though this is expected to be non-differential between the multi-drug non-susceptible and non- multi-drug non-susceptible children . Finally , antibiotic susceptibility testing for erythromycin resistance was not done , despite erythromycin being commonly used to treat diarrhea in this setting .
Overall , there appears to be widespread non-susceptibility to commonly used antibiotics ( ampicillin , cotrimoxazole , and tetracycline ) in western Kenya , which may present challenges to management of severe childhood infections , including bacterial diarrhea . However , resistance to ciprofloxacin , the antibiotic currently recommended for dysentery and cholera by the WHO , was low . That MDNS prevalence was higher in younger , HEU , and acutely malnourished children is concerning , particularly given that these groups are at highest risk of morbidity and mortality from diarrheal disease[2 , 7 , 59–61] . The higher prevalence of MDNS associated with unimproved sanitation , household crowding , and infrequent hand-washing suggests that interventions to reduce fecal-oral transmission of enteric pathogens may also be effective in control of antibiotic non-susceptibility in enteric pathogens . | Children in Sub-Saharan Africa experience frequent enteric infections and antibiotics are often used to treat diarrheal disease . Some bacterial causes of diarrhea have developed resistance to commonly used antibiotics yet this information is rarely available to managing clinicians . We sought to identify which children have antibiotic resistance in hopes that such information could guide clinical decision-making and possible intervention points for reducing the spread of antibiotic resistance . Among children with a bacterial infection identified in stool , nearly all had resistance to at least one antibiotic , and most had bacteria that were resistant to at least three . Children who were younger , those who had an HIV-infected biological mother , and those who were acutely malnourished were more likely to have resistance to at least three antibiotics . This is concerning since these groups of children suffer frequent infections for which antibiotics may be necessary . We also found that children with limited access to flushing toilets and those who lived in crowded homes were more likely to have resistance to at least three antibiotics . Reducing contamination in a child’s home environment may help in controlling antibiotic resistance . | [
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| 2017 | Correlates of multi-drug non-susceptibility in enteric bacteria isolated from Kenyan children with acute diarrhea |
Understanding the factors underlying the spatio-temporal distribution of infectious diseases provides useful information regarding their prevention and control . Dengue fever spatio-temporal patterns result from complex interactions between the virus , the host , and the vector . These interactions can be influenced by environmental conditions . Our objectives were to analyse dengue fever spatial distribution over New Caledonia during epidemic years , to identify some of the main underlying factors , and to predict the spatial evolution of dengue fever under changing climatic conditions , at the 2100 horizon . We used principal component analysis and support vector machines to analyse and model the influence of climate and socio-economic variables on the mean spatial distribution of 24 , 272 dengue cases reported from 1995 to 2012 in thirty-three communes of New Caledonia . We then modelled and estimated the future evolution of dengue incidence rates using a regional downscaling of future climate projections . The spatial distribution of dengue fever cases is highly heterogeneous . The variables most associated with this observed heterogeneity are the mean temperature , the mean number of people per premise , and the mean percentage of unemployed people , a variable highly correlated with people's way of life . Rainfall does not seem to play an important role in the spatial distribution of dengue cases during epidemics . By the end of the 21st century , if temperature increases by approximately 3°C , mean incidence rates during epidemics could double . In New Caledonia , a subtropical insular environment , both temperature and socio-economic conditions are influencing the spatial spread of dengue fever . Extension of this study to other countries worldwide should improve the knowledge about climate influence on dengue burden and about the complex interplay between different factors . This study presents a methodology that can be used as a step by step guide to model dengue spatial heterogeneity in other countries .
Dengue fever is the most important mosquito-borne viral disease , with an estimated 50 million people being infected each year and 2 . 5 billion people living in areas at risk of dengue worldwide [1] . The true burden of clinically apparent dengue could be twice as high , and the total burden of dengue fever infections could reach 390 million people when including asymptomatic cases [2] . Whereas only nine countries were affected by dengue epidemics in the 1970's , more than a hundred countries are now reporting dengue outbreaks on a regular basis , making dengue fever the most rapidly spreading mosquito-borne viral disease in the world [1 , 2] . This rapid global spatial spread over the past 40 years probably results from recent socio-economic changes such as global population growth and uncontrolled urbanisation . Lack of effective mosquito control in endemic areas , increased international air traffic or decay in public health infra-structure in developing countries are also important factors that could explain the rapid regional spread of the disease [3–6] . However , in a given country where there are sufficient numbers of susceptible hosts , these factors need to be associated with suitable climate conditions before dengue fever can establish , since it is transmitted by mosquito species whose life cycle is influenced by temperature , humidity and rainfall [7–9] . Indeed , several studies have pointed out that the current geographic distribution of dengue fever or its vector worldwide could be predicted accurately based on climate variables using either statistical models [10] or deterministic models [11–13] . Other studies have pointed out that climate change could have profound consequences on the epidemiology of dengue fever , because increased temperature and rainfall could facilitate viral transmission and could lead to the geographic expansion of the mosquito species responsible for its transmission [11 , 14–17] . The complex interplay and relative importance of each factor in the occurrence and spread of dengue fever epidemics might differ from one country to another , depending on the specific climate conditions , cultural and socio-economic environment the virus circulates in [18] . Identifying the factors limiting dengue fever spatial spread at a national level could help understanding the worldwide pattern of dengue disease , could help predicting its future spatial distribution , and could provide national decisions-makers with useful information regarding the appropriate control measures to be implemented . Most studies trying to identify dengue risk factors spatially were performed at a city scale or a local scale ( < 80 km ) [19–34] . Among these studies , some have identified risk factors for the presence of Aedes mosquito species , such as socio-economic factors [29 , 30] , proximity to specific plantations [28 , 32] , proximity of potential breeding sites [22 , 28 , 29 , 32] , or human behaviour [30 , 32] . Some studies highlighted the importance of human movement [23 , 26 , 31 , 33] or population immunity [34] in shaping the spatial transmission of dengue fever at small spatial scales . High dengue incidence rates have also been found in neighbourhoods with low social income [20 , 26] , difficult access to piped water [21 , 26] , or no implementation of mosquito protection measures [21 , 27] . Spatial analyses at a country or territorial scale ( > 200 km ) are scarce . Some of these studies focused on the spatio-temporal dynamics of the disease only [35–37] and proposed hypotheses about the underlying processes , but did not include analyses of potential explicative factors . To our knowledge , there are only five studies to date identifying and quantifying spatial risk factors for dengue at a “national” scale > ~200 km and < ~1000 km . Four studies identified temperature as having a major influence on the spatial distribution of locally acquired dengue cases [38–41] , the last one did not assess the role of climate factors [42] . The role of other factors in the spatial distribution of dengue cases varied from one place to another . For example , in Australia ( Queensland ) [38] and Taiwan [40] , rainfall seemed to play a minor role whereas in Brazil , rainfall played a major role [39] . In Taiwan [40] and Argentina [41] , urbanization level was a key factor in dengue fever spatial distribution , and in one province of Thailand [42] , the main factor identified was the proximity to major urban centres . No association was found with socio-economic covariates in Argentina [41] or Australia [38] . New Caledonia , where the present study takes place , has a unique situation: it is a developed insular territory located in the inter-tropical area of the South Pacific where the access to high quality data and the lack of terrestrial borders with other countries make it a natural laboratory to study dengue dynamics . A gross average of ten imported cases is detected each year by the Public Health authorities . However , large dengue epidemics develop only every three to five years , sometimes causing the circulation of the same serotype during two consecutive years [43] . A recent study analysing the temporal relationship between dengue epidemic occurrence and climate variables at an inter-annual scale showed that the development of an epidemic in New Caledonia needs precise climate conditions relying on both temperature and relative humidity [43] . The objectives of the present study were i ) to characterise the spatial distribution of dengue cases in New Caledonia once an epidemic spreads over the territory; ii ) to determine which of possible covariates are shaping the observed distribution; iii ) to explore the potential spatial distribution of dengue cases under future climate projections . We present a complete methodology , from data collection , data transformation , variable selection , and application to future climate projections . We address a number of methodological issues such as spatial autocorrelation , correlation between explanatory variables , or potential non-linearity between epidemiological data and explanatory covariates .
New Caledonia is a French territory , located in the Pacific Ocean 1 , 500 km East of Australia . It is divided into 33 communes covering 18 , 576 km2 . Out of the 245 , 344 inhabitants ( 2009 ) , around 58% ( 147 , 365 people ) live in Noumea , the main city , and its surroundings . The rest of the population is scattered in small towns of about 2 , 000 people , or live in rural areas , including traditional Melanesian settlements locally called “tribes” ( Fig 1 ) . Although the average population density outside Noumea is very low ( 5 . 3 inhabitants per km2 ) , local densities can be high as people gather in small settlements . New Caledonia is located at the limit of the tropical zone between latitudes 19° and 23° South . The East coast and the West coast are separated by a mountain range culminating at 1629 m . Climate is heterogeneous: the East coast and the southern tip of the main island get more rain than the West coast , as mountains provide a vertical lift to the warm and humid air brought by trade winds . Average rainfall range from 800 mm/year in some western weather stations to 3200 mm/year in the East . Temperature can drop below 10°C during the cool season on clear nights and sometimes rise above 35°C due to the influence of tropical air masses [45] . From an oversimplified point of view , there are three population groups , having different cultural and social habits: Melanesian people , people of French descent who migrated two hundred years ago , and people from various origins who migrated recently . Although the three groups are spatially partially mixed , Melanesian people live mostly on the East coast , whereas the second group live mostly on the West coast and the third group live mainly in Noumea . In New Caledonia , dengue represents a major public health problem with large epidemics affecting the territory every three to five years and involving a succession of all four serotypes [43 , 46–48] . Co-circulation of different dengue virus serotypes ( DENV1-4 ) during major epidemics is rare , and has been observed only once ( 2009 ) . Before 2003 , vector control measures consisted in systematic chemical control of adult mosquitoes covering large areas during the warm season , independently from the occurrence of dengue cases . Since 2003 , systematic spreading of adulticide has been stopped , and vector control measures include continuous large communication and prevention campaigns fostering source reduction aimed at all citizens , as well as focal chemical control of adult mosquitoes 100 m around declared cases within 24 h of notification . Public Health infrastructure is reliable , and the surveillance system for dengue fever has been efficient for many years . All people have access to medical care , even though people living in remote areas might have more difficulties to reach local health centres . As described below in the results section , temperature is a key factor determining dengue spatial variability over New Caledonia . We thus decided to explore the evolution of dengue average annual incidence rates during epidemics under changing climate conditions ( considering all others variables as remaining constant ) , by applying the best explicative multivariable model with inputs from maps of temperature for the future ( see methods/data/climate covariates: assessing the trends of future mean temperature in New Caledonia ) . Because the use of kernels in non–linear SVM models impairs predictions outside the observed range of explanatory variables , we built a linear approximation of the best SVM model on present observed data . The linear approximation consists of a simple linear model linking the two best explanatory variables to observed dengue age-standardised average ( across epidemic years ) annual incidence rates as the response variable . Normality and homoscedasticity of residuals were confirmed by the Shapiro-Wilks' test and the Bartlett's test respectively [67] . To evaluate the error in incidence rates predictions due to the inter-GCM variability of mean temperature increase projections , we calculated , for each time-period and each scenario , the average annual incidence rates during epidemics as predicted by each GCM , and then calculated a standard deviation of predicted annual incidence rates across the different models .
Fig 3 shows that once an epidemic spreads over the territory , dengue cases are distributed heterogeneously . Mean annual age-standardised incidence rates across epidemic years range from 22 to 375 cases per 10 , 000 people per year , with a mean across communes of 168 cases per 10 , 000 people per year and a standard deviation across communes of 83 cases per 10 , 000 people per year . On average the East coast is more affected than the West coast . We can also see that the North-eastern corner of New Caledonia is heavily affected , with dengue incidence rates two to three times higher than in the rest of the territory . By definition , the average across epidemic years of age-standardised annual incidence rates reflects mainly the spatial pattern of severe epidemics , i . e . epidemics of years 1995 , 1998 , 2003 and 2009 . During years 1995 , 1998 and 2003 , the North-eastern corner was the most affected . During the 2009 epidemic , the most affected communes were Voh and Koné , on the West coast ( see Fig 3 for the location of these communes ) , but the North Eastern corner was still severely affected [72] . The semi-variograms of dengue incidence rates did not reveal any significant spatial autocorrelation , whether they were calculated for each epidemic year separately or on the average incidence rates across years . This suggests that the local spread of dengue viruses around a case imported in a commune do not exceed the mean radius of a commune in New Caledonia , e . g . approximately 13 kilometres . Hence , we did not incorporate any spatial structure into the subsequent models . Table 1 shows Pearson's correlation coefficients ( rho ) between each explanatory variable and dengue age-standardised annual incidence rates averaged across epidemic years . Dengue is spatially positively correlated with variables related to temperature and precipitation , but is negatively correlated with variables reflecting mean thermal range or extreme thermal conditions ( see "Isothermality" , "Temp range" or the number of days when maximum temperature exceeds 32°C in January , February and March in Table 1 ) . This suggests that , in a given commune , marked temporal variations of temperature is a factor limiting viral circulation . Based on the linear dependence measure of correlation , dengue is also more strongly associated with temperature than with precipitation . Socio-economic variables are highly spatially correlated to dengue average ( across epidemic years ) annual incidence rates . Variables reflecting people's way of life ( e . g . place of birth ) , local human density ( e . g . mean number of people per household , percentage of premises under 40 m2 ) , or human movement are more correlated with dengue average ( across epidemic years ) annual incidence rates than variables related to the housing type ( e . g . premises with inside toilets ) ( absolute value of rho up to 0 . 75 for the former and 0 . 58 for the latter ) . In particular , the fact that the place where people were born is spatially significantly associated with dengue fever incidence rates ( correlation coefficient around 0 . 5 for people born in New Caledonia and– 0 . 5 for people born elsewhere ) whereas the type of premise is not ( absolute correlation coefficient lower than 0 . 3 for variables describing access to water or electricity ) suggests that individual behaviours have a stronger influence on incidence rates than local housing conditions . Fig 4 shows the PCA results . For clarity reasons , we only show the results of PCA performed on the variables most spatially correlated with dengue average ( across epidemic years ) annual incidence rates , with an absolute Pearson correlation coefficient over 0 . 6 for socio-economic variables , and over 0 . 4 for climate variables ( these thresholds were selected after verifying that they did not modify the variable pre-selection results ) . PCA of climate variables ( Fig 4A ) shows that in New Caledonia , temperature is the factor accounting for most of the spatial climatic variability among communes . Temperature is highly correlated with the first PCA axis which represents 68% of the total climatic variance . Temperature and rainfall are not spatially correlated at the commune level . In each group of temperature or rainfall variables , the variables most spatially correlated with dengue average ( across epidemic years ) annual incidence rates were the average mean temperature ( Mean temp ) and the mean daily rainfall during the wettest quarter of the year ( Wettest quarter ) ( see Table 1 ) . In addition to these two variables , we decided to keep a third variable , the average daily rainfall , for further statistical modelling , as this variable is more easily available in other countries or climate model simulations . Fig 4B shows that the spatial variability of socio-economic factors mainly reflects the spatial distribution of people with different cultural habits . Communes where a high proportion of inhabitants live in a tribal way , in small premises , with few means of transportation and a high percentage of unemployment are opposed to communes where many people live a western way of life , in permanent buildings , using air conditioning and getting around using cars . Even though the number of people per premise seems to be correlated with the proportion of people living in tribes , we kept this variable as it stands out of the cluster of variables representing the way of life . We thus decided to keep the percentage of unemployed people and the mean number of inhabitants per housing as representative of socio-economic factors for further statistical modelling . Table 2 shows the RMSE of the optimised models built on all possible combinations of one , two or three of the five selected explanatory variables ( Mean temperature , daily rainfall averaged over the wettest quarter , average daily rainfall , number of people per household and fraction of unemployed people ) . When looking at univariable non-linear SVM models , the best variable explaining the spatial heterogeneity of dengue average ( across epidemic years ) annual incidence rates is the percentage of unemployed people per commune . The second most important explanatory variable is the mean temperature . Rainfall is the least explanatory variable of those selected for multivariable regression modelling . Moreover , the RMSE of models based on observed rainfall almost equal the initial standard deviation ( across the territory ) of dengue average annual incidence rates , which means that rainfall are poor predictors of dengue average annual incidence rates during epidemic years . The relationship between dengue average annual incidence rates and each of the explanatory variables is linear , except for the fraction of unemployed people ( S4 Fig ) . When looking at the spatial structure of dengue average annual incidence rates predicted by SVM models based only on one of the selected variables ( S5 Fig ) , we see that temperature captures mainly the South to North gradient of increasing incidence rates ( S5B Fig ) whereas socio-economic variables captures the spatial heterogeneity between the West coast and the East coast ( S5E and S5F Fig ) . Temperature seems to have no influence in communes located below 21°S ( S5B Fig ) . All models based on two explanatory variables and including at least one variable related to rainfall ( best RMSE of ~58 cases per 10 , 000 people per year ) performed worse than the best univariable model ( RMSE of ~53 cases per 10 , 000 people per year ) . This suggests that in New Caledonia , rainfall has little influence on the spatial variability of dengue viral circulation at the commune level . Models combining two explanatory variables ( excluding rainfall ) performed better than models based on only one variable . The addition of a third explanatory variable did not improve significantly model performances . Hence we focused our attention on models combining two explanatory variables . The best explicative model is a model predicting increasing average annual incidence rates during epidemics in communes where the mean temperature and the mean number of people per premise increase ( see Fig 5 ) . The influence of these two variables on the spatial structure of dengue incidence rates is close to linear as shown by almost parallel contour lines on Fig 5A . This model accurately predicts the sharp mean increase in incidence in the three communes of the North East of New Caledonia ( Hienghène , Ouégoa and Pouébo ) . The maximal error of the model is observed for Farino ( West coast ) , which is the only commune where all inhabitants live at an altitude higher than 200 m above sea level . Fig 6 shows the spatial structure of observed average ( across epidemic years ) annual incidence rates ( Fig 6A ) and of average annual incidence rates as predicted by the best SVM model based on two explanatory variables ( Fig 6B ) . This model captures the observed spatial heterogeneity in average annual incidence rates between the East coast and the West coast , as well as the sharp increase in the three communes of the North East . Table 3 shows , for both climate change scenarios , the average increase of mean temperature for the two selected 20-year periods compared to the 1980–1999 historical simulations . All models predict that the mean temperature will increase over time , with projections being more pessimistic for RCP 8 . 5 simulations . The CMIP5-AR4 inter-model variability in temperature increase is presented in Table 3 and S3 Fig . According to the RCP 8 . 5 scenario , temperature could increase by more than 3°C by the end of the next century , with a standard deviation across models of only 0 . 6°C , showing the strong coherency in different model projections . Fig 6 shows a comparison of the average ( across epidemic years ) annual dengue incidence rates predicted by the SVM model ( panel 6B ) or the linear model ( panel 6C ) . The SVM model performs slightly better than the linear one: the correlation coefficient between observed and predicted incidence rates are 0 . 89 ( SVM ) and 0 . 85 ( linear ) , and the RMSE are 42 and 43 cases/10 , 000 people/year respectively for the SVM and the linear model . The low RMSE of the linear model ( ~43 cases per 10 , 000 people per year ) shows that the linear model based on the two best explanatory variables is suitable . The Shapiro-Wilks and the Bartlett's test confirmed the normality and homoscedasticity of residuals . Fig 6D and 6E show the potential future spatial distribution of dengue incidence rates during epidemics according to the RCP 4 . 5 and RCP 8 . 5 emission scenarios . By the end of the century , dengue incidence rates during epidemic years could reach a maximum of 378 cases per 10 , 000 people per year in the most affected commune under the RCP 4 . 5 scenario ( Fig 6D ) , and 454 cases per 10 , 000 people per year in the most affected commune under the RCP 8 . 5 scenario ( Fig 6E ) . Under the RCP 8 . 5 scenario , communes at low risk now might experience a sharp increase in dengue incidence rates during epidemic years from 64 to more than 200 cases per 10 , 000 people per year . According to RCP 8 . 5 climate projections , the average ( across communes ) dengue mean annual incidence rates during epidemic years could raise by 29 cases per 10 , 000 people per year for the 2010–2029 period , and by 149 cases per 10 , 000 people per year for the 2080–2099 period , almost doubling dengue burden in New Caledonia by the end of the century ( Table 3 ) .
The spatial association found between temperature and dengue incidence rates during epidemics in New Caledonia can be explained by the influence of temperature on the life cycle of the mosquito transmitting the virus in New Caledonia , Aedes aegypti . High temperatures increase the productivity of the breeding sites through an acceleration of the metabolism of the mosquito , and a faster development of the micro-organisms the larvae feed on , resulting in a higher vector density even with the same number of breeding sites [7 , 73–75] . High temperatures also speed up the extrinsic incubation period [7 , 76 , 77] , with the effect that an increased proportion of females Ae . aegypti can reach the infectious stage before dying . Finally , warmer temperatures accelerate the mosquito gonotrophic cycle , and make females Ae . aegypti more aggressive [7 , 74 , 78–80] , increasing the biting rate and the frequency of potential transmission of viral particles to susceptible hosts . Regarding the effect of increasing temperatures on the mortality of Ae . aegypti adult mosquitoes , a review of 50 field mark-release-recapture studies has shown that in the field , unless temperatures become extreme ( over 35°C or less than 5°C ) , temperature has little effect on daily mortality rate [81] , highlighting the central importance of the length of the extrinsic incubation period in the ability of adult mosquitoes to transmit dengue viruses . In Noumea , the main city , precise climate variables and important thresholds values have been identified as necessary conditions to trigger an epidemic ( e . g . number of days when maximal temperature exceeds 32°C in January/February/March , and number of days when maximal relative humidity exceeds 95% during January [43] ) . At the scale of the entire territory , we found that the spatial distribution of dengue cases during epidemic years is strongly influenced by the average mean temperature . These results suggest that temperature has a major role in dengue dynamics in an insular territory characterised by climate seasonality . However , we did not find a strong association between the spatial distribution of dengue cases during epidemics and average rainfall or with the number of days when maximal temperature exceeds 32°C . The variables influencing either the triggering of an epidemic [43] or its spatial distribution are not the same . Our findings highlight the complexity of studying and understanding dengue dynamics , the importance of well separating the two epidemiological processes of epidemic triggering in a susceptible population , and its intensity once it has started by clearly defining the modelling target ( incidence rates for epidemic intensity , or dummy variables for epidemic triggering ) , and the importance of well defining the scale of study ( temporal evolution , or spatial distribution ) . The positive association found between the mean temperature and dengue incidence rates is consistent with the one found in previous studies having analysed the spatial distribution of dengue cases at spatial scale > 200 km [38–42] . In these studies as well as in ours , all regions were located between 10° and 25° of latitude , at the fringe of the tropical area , except Argentina , where the region studied extends to 35° South . In the 10° ˗ 25° latitudinal band , annual mean temperature lies in a range of temperature where the life cycle of the mosquito is very sensitive to temperature changes [7] . Some Aedes species , including Ae . aegypti , are able to breed in very small amounts of water , e . g . snails’ shells . Rainfall can play a role in dengue transmission cycle by filling up potential breeding sites [7] , thus influencing the vector density . Rainfall also increases the relative humidity , which extends the mosquitoes’ lifespan and therefore the likelihood of those who had an infectious blood meal to reach the infectious stage . However , our study suggests that in New Caledonia , there is no strong association between rainfall and the spatial distribution of cases during epidemics . A plausible explanation can be the multi-factorial nature of dengue fever , and the relative influence each factor plays on dengue dynamics: despite suitable rainfall conditions , dengue might not circulate well if other factors are limiting dengue viral circulation , such as some human behaviour influencing the contact between vector and host . This aspect has been highlighted very clearly in the United States [27] . Some studies have found that the effect of rainfall on vector density can be modulated by human activities such as water storage practices [82] . However , in New Caledonia , we are not aware of specific practices to store water that could explain the lack of association between rainfall and virus circulation intensity . Another potential explanation could be that in dry areas , breeding sites are filled up by other non-climatic mechanisms , such as automatic irrigation or plant watering . Worldwide , the spatial association between rainfall and the spatial distribution of dengue cases at a “national” scale ( > 200 km ) is not as clear as the one for temperature: one spatial study did not find any association between dengue incidence rates and rainfall [40] , whereas two others did [38 , 39] . Other factors influencing dengue transmission ( e . g . anthropogenic factors influencing the availability of filled breeding sites ) and not included in the different studies might blur the rainfall signal . It would be interesting to perform the same kind of multi-factorial spatial analysis in areas of epidemic or endemic transmission located closer to the equator , where the mean temperature is higher , to see what climatic factors impact the spatial distribution of cases . This kind of study could help understand better the complex interplay between the different factors ( climate , socio-economic , immunologic , viral , entomologic… ) associated with dengue fever transmission . Regarding the link between socio-economic variables and dengue incidence rates during epidemics , a limitation of this study is the absence of historical time series of socio-economic variables . We then had to assume that the data retrieved from the 2009 census is representative of the mean socio-economic spatial pattern over the epidemic years of the 1995–2012 period . As there has been no major historical event leading to population migration in New Caledonia during this time period and as socio-economic variables represent mainly people’s way of life , we think this assumption is realistic . Our results are consistent with previous studies that have pointed out the importance of socio-economic factors on the spatial distribution of dengue cases , whatever the spatial scale studied: national ( >200 km ) [39–42] or local ( <10 km ) [20 , 26 , 29 , 83] . The spatial association between the percentage of unemployed people and dengue in New Caledonia cannot be interpreted in terms of lack of economic activity only , as shown by the PCA on socio-economic factors . This variable we selected as input for the models is highly correlated with other variables reflecting the way of life , socio-economic and cultural differences existing in New Caledonia , which are in turn highly correlated to housing type . Therefore , at this spatial scale in New Caledonia , it is difficult to statistically differentiate the role played by human behaviour , human activity or housing type in dengue fever transmission . However , those three factors influence the contact rate between viraemic patients or susceptible hosts on one hand , and mosquitoes on the other hand . This highlights the importance of limiting the contact between humans and vectors and should lead local authorities to strengthen communication campaigns about personal protection measures towards populations at risk . Regarding the spatial association found between the fraction of unemployed people ( i . e . people’s way of life ) and dengue incidence rates during epidemics in New Caledonia , it is interesting to point out that on the East coast , a larger fraction of inhabitants are Melanesian people living in tribes , whereas on the West coast , the majority of people are people from French descent having a western way of life . It would be interesting to perform sociologic studies to precisely identify which human behaviour leads to an increased risk of catching dengue fever . Such information would be useful to define communication messages towards at risk populations . The spatial association between the number of people per household and dengue incidence rates can be explained by the short flight range of Ae . aegypti mosquitoes . These mosquitoes are often captured in the very house where they emerged or in the neighbouring houses , flying an average of 40 to 80 m during their life [84–87] . Hence , dengue outbreaks involving Ae . aegypti as the main vector are known to be highly spatially focal , with dengue cases usually clustering within 200 m to 800 m of each other [23 , 33 , 34 , 88–94] . Our results suggest that , in New Caledonia , dengue cases probably cluster within houses . Sick people should protect themselves until they are no longer vireamic to avoid human to mosquito transmission , and people living around a case should protect themselves to avoid getting infected while infectious mosquitoes are still active in the neighbourhood . Taking such individual actions could reduce the intensity of dengue transmission and reduce dengue burden over the territory . This message could be strengthened in the recommendations given by the authorities . The results about climate change must be interpreted keeping in mind that they represent a climate risk only , and that the spatial association between dengue incidence rates during epidemics and temperature might change over time depending on socio-demographic changes , or changes in dengue control strategy . Assuming all other factors remain constant in time , our results suggest that Public Health authorities can expect the dengue burden to raise significantly during the next century over the territory , and can expect the dengue spatial range to increase . As the GCM projections are spatially homogeneous over the territory , and as the model used to predict dengue incidence rates in the future is linear and is based on only one climate variable , the predicted absolute increase in dengue incidence rates is currently the same for all communes . This highlights the need for spatially downscaling GCM projections to gain a better understanding of the impact of climate change in the future . Communes that are already severely affected by dengue epidemics will have to prepare to face higher burden of dengue fever . For communes that are at low risk now , we can see that in the future they might be affected as severely as communes at high risk now . These communes might not be prepared now to face severe epidemics of dengue fever , and they will probably need support for adaptation . As said earlier , the positive association found between temperature and dengue incidence rates during epidemics for mean temperatures ranging from 22°C to 25°C can be explained by the effect of temperature on the mosquito life cycle and duration of extrinsic incubation period . Here , by applying a statistical model built using current observed temperature to future projections , we make the assumption that the biological effect of temperature on the mosquito life cycle and on the extrinsic incubation will remain the same under the range of temperature that might be observed in the future . For most parameters influencing transmission , this assumption is reasonable . For example , we know that in Thaïland , for DENV-2 , the extrinsic incubation period is reduced from 15 days at 30°C to 7 days at 32–35°C [77] , which is in support of increasing temperatures inducing an increase in dengue incidence rates under future climate . However , because we used a statistical model , we were not able to incorporate the known negative effect that an increase in temperature might have on dengue transmission when temperature reaches extremes . For example , a review of fifty mark-release-recapture studies has shown that the survival and longevity of Ae . Aegypti mosquitoes is highly reduced when temperatures exceed a threshold , which might be around 35°C [81] . It would be interesting to develop models that are able to integrate these negative effects in the future in order to gain a better understanding of the effects of climate change on dengue transmission . Here we used data collected routinely by the Direction of Sanitary and Social Affairs . As any surveillance system , it is highly probable that not all dengue cases have been recorded . However , in New Caledonia , the data is of high quality , and the spatial standardisation of the surveillance system ( i . e . all the actions taken to be able to compare data collected by different people , at different places [95] ) is good , which means that the proportion of cases that are not recorded by the surveillance system are probably comparable from one commune to another . Hence , maps of incidence rates calculated from routinely collected data can be used to study the spatial variability of true incidence rates . To calculate mean incidence rates , we have used the consultation date of cases . Consultation can occur 1 to 5 days after the onset of symptoms , and the incubation period lasts 4 to7 days on average [3] , which means that the consultation date can differ from one to two weeks from the date of infection . This loss of temporal precision is not important here to calculate maps of incidence rates , as for each commune , we have averaged incidence rates across many years . As in any epidemiological spatial study using routinely collected surveillance data , it is possible that some spatial bias has been introduced due to the fact that the spatial data recorded is the commune where people live , which can sometimes differ from the commune where they got infected . Another limitation of using routinely collected data is that only clinically apparent cases are recorded , dismissing clinically inapparent cases , whose proportion can vary in time and space [96 , 97] . A seroprevalence survey is currently undertaken by the Public health authorities . It will be interesting to compare the spatial distribution of seroprevalence to the spatial distribution of average incidence . The analysis of the spatial pattern of infectious diseases , in relation with environmental or socio-economic factors raises a number of methodological issues , such as the presence of spatial auto-correlation , the spatial scale of aggregation of the data , the existence of possible non-linear links between the response and the explanatory variables , or the presence of multi-collinearity between the response variables . Most issues have already been addressed in the past , and solutions already exist to handle them . For example , the reviews by Dormann et al . deal with multi-collinearity [66] or spatial-autocorrelation [98] . The main issue we have been confronted with in our study was the spatial upscaling of meteorological data observed at precise locations to the same spatial level of aggregation as the epidemiological data . In existing spatial studies of dengue fever at a national scale , authors have geo-spatially interpolated climate variables on regular grids using kriging methods , and have averaged gridded values over a given administrative division [38–41] . This approach has two drawbacks in New Caledonia . Simple kriging models do not take into account the potential elevation between two given points , leading to biased estimates of temperature in mountainous regions . Moreover , the traditional approach used in climatology , which consist in aggregating temperature over grid points taken uniformly over the whole aggregative area makes the implicit assumption that people at risk are distributed homogeneously over the aggregative area . This is particularly problematic in New Caledonia where large areas are not inhabited . In our approach , as epidemiological data are collected at the individual level , we tried to estimate the climate conditions for each individual ( and therefore for the mosquitoes surrounding each individual ) . However , the algorithm used introduces some noise , due to the fact that the weather stations are sometimes kilometres away from some towns or tribes . High spatial resolution climate data obtained from high resolution modelling of atmospheric conditions could be used , but some noise will be introduced by the modelling error compared to the observed data . This issue needs further attention in the future to increase the quality of spatial epidemiological and environmental studies . Some factors that could influence the spatial distribution of dengue cases during epidemics have not been taken into account in this study: the location of the first cases introduced each year , the spatial variability in population immunity , viral factors such as the serotype circulating , or factors associated to the mosquito such as the spatial variability in vector competence , or dengue vector control measures . We decided not to include the serotype , as we performed the analysis on averages over several years , and as , except in 2009 , there was no co-circulation of different serotypes over the territory . Therefore , spatial differences in the level of viral circulation cannot be associated with genetic differences between serotypes . A territorial seroprevalence survey to assess population immunity has been implemented recently in New Caledonia , but data are not available yet . It could be interesting to include environmental variables derived from GIS data or remote-sensing in this kind of study . For example , GIS data about built areas could be used to create indicators of the proximity of houses to reflect the fragmentation of the Ae . aegypti habitat per commune , given that fragmentation of this habitat could potentially slow down viral circulation . The amount of vector control effort implemented in each commune is heterogeneous on the territory , as this activity falls within the commune’s authority , and each commune is free to implement or not the territorial guidelines . The vector control effort in each commune is thus difficult to quantify , and data were not available yet at the time of analysis . As soon as these data will be collected by local authorities , they could be incorporated in the modelling process to assess the efficiency of vector control measures . The study we present here is about the spatial heterogeneity of dengue incidence rates across epidemic years , independently of the inter-annual variability of dengue incidence rates from one epidemic year to another . The mean spatial pattern studied is very robust to changes in the definition of an epidemic year , as severe epidemics will always be considered in the calculation of the mean , whatever the threshold used to distinguish epidemic from non-epidemic years . It would be interesting to know whether the spatial association found here between the severity of dengue epidemics , temperature , local people’s density and people’s way of life is consistent through time or not , and to identify the factors associated to the temporal variability of spatial patterns . Two other viruses transmitted by Ae . aegypti like dengue virus caused outbreaks recently in New Caledonia . Chikungunya virus has been introduced on four occasions since 2011 but in each case , the outbreaks were limited to a few cases in Noumea and surroundings . Conversely , Zika virus caused large epidemics over the territory in 2014 and 2015 , with more than 1 , 500 confirmed cases and more than 11 , 000 estimated cases . Although these viruses are transmitted by the same mosquito as dengue fever , no sufficient data are available to know if the socio-economic and climatic factors driving epidemics are the same . It is likely that local vector competence and population immunity represent major limiting factors . Although dengue has caused major outbreaks in NC in 2013 , chikungunya viruses have only caused a limited number of cases for reasons that remain unexplained today and despite the competence of local Ae . aegypti for chikungunya virus transmission [99] . It is likely that climatic factors and interactions between viruses circulating together between human-hosts and mosquito-vectors influence the epidemiology of arboviruses in New Caledonia . A comparative analysis of the spatio-temporal distribution of these three arboviruses in an insular territory accommodating only Ae . aegypti represents an important issue to understand and predict outbreaks . | Dengue fever is the most important viral arthropod-borne disease worldwide and its geographical expansion during the past decades has been of growing concern for scientists and public health authorities because of its heavy sanitary burden and economic impacts . In the absence of an effective vaccine , control is currently limited to vector-control measures . In this context , understanding the sociologic , entomologic and environmental factors underlying dengue dynamics is essential and can provide public health authorities with sound information about control measures to be implemented . In this study , we analyse socio-economic , climatic and epidemiological data to understand the impact of the studied factors on the spatial distribution of dengue cases during epidemic years in New Caledonia , a French island located in the South Pacific . We identify at risk areas , and find that temperature and people’s way of life are key factors determining the level of viral circulation in New Caledonia . Hence , communication campaigns fostering individual protection measures against mosquito bites could help reduce dengue burden in New Caledonia . Using projections of temperature under different scenarios of climate change , we find that dengue incidence rates during epidemics could double by the end of the century , with areas at low risk of dengue fever being highly affected in the future . | [
"Abstract",
"Introduction",
"Material",
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"Methods",
"Results",
"Discussion"
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| 2015 | Socio-economic and Climate Factors Associated with Dengue Fever Spatial Heterogeneity: A Worked Example in New Caledonia |
Understanding the functional relationship between intracellular factors and extracellular signals is required for reconstructing gene regulatory networks ( GRN ) involved in complex biological processes . One of the best-studied bilaterian GRNs describes endomesoderm specification and predicts that both mesoderm and endoderm arose from a common GRN early in animal evolution . Compelling molecular , genomic , developmental , and evolutionary evidence supports the hypothesis that the bifunctional gastrodermis of the cnidarian-bilaterian ancestor is derived from the same evolutionary precursor of both endodermal and mesodermal germ layers in all other triploblastic bilaterian animals . We have begun to establish the framework of a provisional cnidarian “endomesodermal” gene regulatory network in the sea anemone , Nematostella vectensis , by using a genome-wide microarray analysis on embryos in which the canonical Wnt/ß-catenin pathway was ectopically targeted for activation by two distinct pharmaceutical agents ( lithium chloride and 1-azakenpaullone ) to identify potential targets of endomesoderm specification . We characterized 51 endomesodermally expressed transcription factors and signaling molecule genes ( including 18 newly identified ) with fine-scale temporal ( qPCR ) and spatial ( in situ ) analysis to define distinct co-expression domains within the animal plate of the embryo and clustered genes based on their earliest zygotic expression . Finally , we determined the input of the canonical Wnt/ß-catenin pathway into the cnidarian endomesodermal GRN using morpholino and mRNA overexpression experiments to show that NvTcf/canonical Wnt signaling is required to pattern both the future endomesodermal and ectodermal domains prior to gastrulation , and that both BMP and FGF ( but not Notch ) pathways play important roles in germ layer specification in this animal . We show both evolutionary conserved as well as profound differences in endomesodermal GRN structure compared to bilaterians that may provide fundamental insight into how GRN subcircuits have been adopted , rewired , or co-opted in various animal lineages that give rise to specialized endomesodermal cell types .
During metazoan development one cell gives rise to thousands of daughter cells , each acquiring a particular fate depending on their temporal and spatial coordinates within the organism . The information required to assume a specific fate of a given cell is present in the genome of all cells , requiring a fine tuned mechanism for controlling and coordinating gene expression during development of the growing embryo . The fate of each cell is determined by its set of expressed genes and controlled by the action of transcriptional activators and/or repressors whose activity is governed by intracellular ( e . g . localized cytoplasmic factors , RNA binding proteins ) , or extracellular signals ( e . g . endocrine or exocrine signaling pathways ) . All together , these components form gene regulatory networks that underlie the formation of distinct cell types or germ layers . Understanding the relationship between intracellular factors and extracellular signals can provide key insight in how and when the molecular and morphological characters of each organism are built . Triploblastic organisms , also called “bilaterians” due to their bilaterally symmetrical body ( possessing an anterior-posterior axis and dorso-ventral polarity ) , constitute the vast majority of all metazoan animals . These animals are characterized by the formation of three distinct primary germ layers during embryogenesis called the endo- , meso- and the ectoderm , that subsequently differentiate into more specialized adult tissues . Ectoderm gives rise to skin and nervous system , endoderm gives rise to the derivatives of the digestive tract including the intestine and digestive glands , and mesodermal derivatives include muscle , connective tissue , blood , coelomic cavities , kidneys/nephridia , somatic portions of the gonad , and skeletal elements . Both classic descriptions as well as modern molecular analyses of germ layer formation in bilaterian organisms as diverse as nematodes , sea urchins , and vertebrates have indicated that these decisions are largely made in a two steps: ectodermal fates first separate from a bicompetent endomesodermal ( also called mesendodermal ) domain , and then endodermal fates become distinct from mesodermal tissues [1]–[3] . In 2002 , the extensive amount of experimental data collected during the past decades by the sea urchin community was assembled into a provisional endomesodermal ( EM ) gene regulatory network representing interactions between signals/transcription factors ( TF ) and their downstream targets , which in turn activate/repress other signals/TF's required for endomesoderm formation in the sea urchin embryo [4]–[11] . To date , a very limited number of model organisms have been used to establish GRN's for endomesoderm specification and/or differentiation ( for review see [12] ) . Endomesodermal GRNs have been established only for the nematode C . elegans [13] , the sea urchin ( S . purpuratus , P . lividus , L . variegatus ) [6] , [7] , [10] , [14]–[16] , a sea star ( A . miniata ) [17] , [18] and Xenopus [19] . Comparison of the sea star and sea urchin endomesoderm GRNs indicates that there is a set of highly conserved genes , thought to be part of the “kernel” of the endomesodermal circuit present in the echinoderm ancestor [18] , [20] . In Drosophila , a well-established genetic model system , mesoderm and endoderm are created by fundamentally different regions of the animal [21]–[23] , reviewed in [24] . Although some of the endomesodermal kernel genes appear to be involved in gut formation in insects , the differences in gut development in flies has so far made it difficult to compare with other endomesodermal GRNs from other bilaterian studied . The origin of the mesodermal germ layer and all of its unique cell types ( e . g . muscle , connective tissue , blood , kidney and somatic gonad ) during metazoan evolution is a matter of intense debate and investigation ( reviewed in [25]–[34] . The sister group to all triploblastic animals is a group of animals called cnidarians ( sea anemones , corals , sea fans , and ‘jellyfish’ ) . Cnidarians are diploblastic animals formed exclusively by an epidermis ( ectoderm ) and a gastrodermis ( also historically called entoderm ) . There are no classical bilaterian muscle cells [35] or a mesodermal tissue layer in cnidarians , however , the cnidarian gastrodermis is a bifunctional tissue capable of both absorption and contractile functions via myoepithelial cells [29] , [36]–[38] . The cnidarian gastrodermis also express a large number of both endodermal factors and genes historically associated with mesoderm formation such as otx , snail , twist [26] , [39] , [40] suggesting that the cnidarian gastrodermis has a bifunctional endomesodermal capacity that never segregates into two distinct tissues . It also suggests that it contains components of an ancestral triploblastic ( bilaterian ) endomesodermal gene regulatory network and that endodermal and mesodermal tissues in triploblastic organism may be derived from the bifunctional gastrodermis of the cnidarian/bilaterian ancestor . This provides us with the opportunity to gain insight in to the ancestral endomesodermal GRN in a living organism . Recent studies have shown the favorable features and utility of the cnidarian Nematostella vectensis as a developmental and evolutionary model system [39] , [41]–[46] . Importantly the whole genome has been recently sequenced by the Joint Genome Institute ( JGI ) and is publicly available [47] . As an anthozoan , it has a simple anatomy , an undetermined long life span , and a short life cycle of 10–14 weeks . The sexes are separate allowing in vitro fertilization and manipulating the light cycle can induce spawning of several hundreds of eggs/female . When raised at 17 degrees Celsius , a hollow blastula forms approximately 10–12 hours post fertilization ( hpf ) and the embryo begins to gastrulate around 24–28 hpf via invagination at the animal pole [48]–[50] , the side of the animal that gives rise to the single oral opening and the gastrodermis ( endomesoderm ) . The canonical Wnt ( cWnt ) signaling pathway plays crucial roles during various bilaterian developmental processes such as axis specification and germ layer formation [51]–[58] . Recent studies have suggested that the cWnt/β-catenin pathway has an ancient role in axis and endomesoderm formation in N . vectensis [50] , [59] . Treatments with lithium chloride ( LiCl ) , perturbs nuclear ß-catenin ( nß-catenin ) distribution ectopically stabilizing nß-catenin in all blastomeres along the A/V axis and induces hyper-proliferation of endomesoderm . In addition , inhibition of the cWnt pathway by overexpressing either cadherin , a cell adhesion molecule that titrates the cytoplasmic pool of ß-catenin , or a β-catenin∶engrailed fusion ( acting as transcriptional repressor ) blocks gastrulation and endomesoderm formation [59] . Recently , Lee and colleagues have shown that Dsh is required for nuclearization of β-catenin and endomesoderm development by over expression of a dominant negative form of Dsh ( NvDsh-DIX ) that specifically stabilizes the canonical Wnt pathway [50] . Taken together , those results show that the cWnt/β-catenin pathway is required for proper endomesoderm formation in N . vectensis . Although the authors of these studies suggest that endoderm specification may be affected by cWnt inhibition , they only characterize endomesodermal gene expression by the analysis of a single gene at the late gastrula stage , a time point long after endomesoderm specification . Therefore , additional information is required to better understand early endomesoderm specification in N . vectensis . Deciphering the cnidarian endomesodermal GRN is important for a number of reasons . It can become a useful resource to understand the basic developmental mechanisms of a “simple” animal , help understand germ layer formation in a diploblastic animal providing a framework for future developmental studies ( predicting relationships with new identified genes , cis-regulatory analysis etc . ) , and comparative work may provide important information to understand how components of the GRN have been adopted , re-wired or co-opted that lead to the evolution of biological novelties ( such as “true” mesoderm ) . Recent studies comparing echinoderm endomesodermal ( EM ) GRNs , revealed changes in GRN structure and offered the opportunity to present testable hypotheses for the molecular basis of body plan and cell type evolution across echinoderms [17] . In order to understand how and when the cnidarian endomesodermal GRN is deployed and to define the initial input of the cWnt pathway , we employed a set of complementary approaches ( Figure S1 ) . We re-analyzed previously published genes expressed in the pharynx or gastrodermis using a combination of fine scale qPCR for the first 48 hours of development coupled to whole mount in situ hybridization prior to the onset of gastrulation . In order to identify additional putative members of the cnidarian “endomesoderm” GRN , we performed genome wide microarrays on mRNA extracted from embryos in which the canonical Wnt pathway has been activated using two distinct reagents , Lithium chloride ( LiCl ) and 1-azakenpaullone ( AZ ) . These two pharmaceutical drugs both induce ectopic nuclearization of ß-catenin , but intriguingly , cause significant differences at the molecular and morphological levels . Fine scale temporal and spatial gene expression analysis of newly identified genes in combination with re-evaluated expression data allowed us to draw a first blueprint of putative transcriptional interaction in the presumptive cnidarian endomesoderm ( gastrodermis ) . Finally , using complementary knockdown experiments , we investigated the earliest input of the cWnt pathway into the first non-bilaterian endomesoderm GRN . While inhibition of cWnt blocks pharynx formation , affects endomesodermal gene transcription and is required for spatial restriction of gene expression domains within the animal hemisphere prior to gastrulation , our global analysis suggests that proper specification of endomesoderm in N . vectensis also requires activation of both FGF and BMP , but not Notch , signaling pathways .
Activation of the cWnt pathway can be induced by inhibition of Gsk3ß using pharmaceutical or chemical components . In order to compare the concentration dependent effects of two Gsk3ß inhibitors , lithium chloride ( LiCl ) and 1-azakenpaullone ( AZ ) we treated zygotes with increasing concentrations of LiCl and AZ and analyzed their effects on expression of NvfoxB ( an oral/pharyngeal marker [42] ) in the presumptive oral endomesoderm ) and NvfgfA1 ( an aboral pole marker [60] , [61] ) at 24 hpf , prior to the onset of gastrulation and the appearance of endomesoderm ( Figure 1 , Table 1 ) . With the exception of embryos treated with 100 mM LiCl that appeared developmentally delayed ( Figure 1F , 1L ) , the general external morphology of the AZ and LiCl treated embryos ( Figure 1B–1E , 1H–1K , 1N–1R , 1T–1X ) resembled blastula control embryos ( Figure 1A , 1G , 1M , 1S ) . Both treatments induced in a concentration dependent manner an extension of NvfoxB expression towards the vegetal hemisphere ( Figure 1B–1E , 1N–1R ) and a decrease in Nvfgfa1 expression ( Figure 1H–1K , 1T–1X ) , compared to control embryos ( Figure 1A , 1G , 1M , 1S ) . However , while Nv-fgfA1 expression was undetectable in AZ treated embryos at 10 µM and 30 µM ( Figure 1W , 1X ) its expression appeared only slightly reduced in LiCl treated embryos at the highest concentrations ( Figure 1J , 1K ) . Based on the strong expansion of Nv-foxB expression in 30 mM LiCl and 10 µM AZ treatments ( Figure 1D and 1Q , Table 1 ) we utilized these treatments for further developmental and molecular characterization . To compare the effects of LiCl and AZ on ß-catenin nuclearization in N . vectensis , we injected mRNA encoding a GFP tagged form of Nvß-catenin ( Nvßcat:GFP ) [59] ( Figure 2A ) , treated the injected uncleaved zygotes with either LiCl ( 30 mM , Figure 2E ) or 1-azakenpaullone ( 10 µM , Figure 2I ) and determined nuclear localization of ß-catenin at 24 hpf . As previously described [59] , Nvßcat:GFP was uniformly expressed during early cleavage stages ( data not shown ) , then progressively degraded in one hemisphere of the embryo and localized to the nuclei of cells in the presumptive endomesoderm ( animal pole ) prior to the onset of gastrulation ( Figure 2A , Figure S2 [59] ) . In both treatments ( Figure 2E , 2I ) , the domain of nuclear localization of Nvßcat:GFP was drastically expanded compared to control embryos . However , in LiCl treated embryos the nuclear localization of ß-catenin did not appear to extend all the way to the vegetal pole ( aboral pole , Figure 2E ) , while in AZ treated blastula stages all cells of the embryo showed nuclear staining ( Figure 2I ) . Treatment of embryos with either LiCl or AZ did not cause any visible developmental perturbation for the first 48 hours post fertilization and the embryos gastrulated normally ( Figure 2B , 2C , 2F , 2G , 2J , 2K ) . However after four days of development when control embryos reached the planula stage ( Figure 2D ) , we distinguished two clear phenotypes resulting from the treatments . LiCl treated embryos became elongated with an increased amount of disorganized endomesodermal tissue and were devoid of any definite pharyngeal structure ( Figure 2H , [59] ) . In contrast , AZ treated embryos displayed presumptive pharyngeal structures and endomesoderm everting from the oral pole , causing progressive exogastrulation after 10 days of development ( Figure 2L , Figure S3 ) . In AZ treated embryos the formation of endomesoderm increased at the expense of ectodermal tissue . The extension of Nv-foxB expression and nuclear ß-catenin localization towards the vegetal pole suggests a shift of the endomesoderm-ectoderm boundary and may involve changes in proliferation rates of endomesodermal cells ( Figure 2L ) . Both of these treatments reinforce the idea that interfering with cWnt signaling affects endomesoderm formation in N . vectensis development . However , the distinct phenotypes suggested differences in either the efficacy or specificity of drug interaction . Taken together these results support previous ideas of an ancestral role of Wnt/ß-catenin in endomesoderm specification and axial patterning in N . vectensis [50] , [59] and suggest that AZ might be more effective than lithium in affecting the cWnt pathway . In order to identify genes expressed in the presumptive endomesoderm of N . vectensis , and to analyze in more detail the similarities ( and differences ) in Gsk3ß inhibition using different reagents , we treated zygotes with either AZ or LiCl , extracted RNA prior to the onset of gastrulation ( 24 hpf ) and screened an expression array designed to represent all protein coding genes in the N . vectensis genome . Out of 24 , 021 represented genes in our Nimblegen ( Inc . ) expression microarray , we selected genes with a significant 2-fold or greater change compared to the wild-type controls in the average of two biological replicates . Although the Pearson's correlation factors between biological replicates were low ( 0 . 53 and 0 . 42 for the AZ and LiCl arrays respectively ) , a total of 399 or 411 genes were significantly ( P<0 . 05 ) upregulated in AZ or LiCl treated embryos , respectively , while 362 or 256 genes were significantly ( P<0 . 05 ) down regulated in AZ or LiCl treated embryos , respectively ( Table S1 ) . To gain insight into the percentage of genes that are affected by either one of the cWnt activating treatments , we compared the two datasets to determine the degree of overlap of significantly up- or downregulated genes ( Figure 3A , 3B ) . Surprisingly , from the total of 731 unique significantly upregulated genes , only 79 genes ( 10 . 8% ) were shared in both datasets . Of the remaining 652 genes , 303 genes ( 41 , 5% ) were upregulated by AZ but not by LiCl and 349 genes ( 47 . 7% ) were upregulated by LiCl but not by AZ ( Figure 3A ) . Similarly , from a total of 538 genes that were significantly downregulated in both treatments , 132 genes ( 25 . 7% ) were unique to LiCl , 282 genes ( 52 . 4% ) were unique to AZ and only 124 genes ( 23% ) were shared between the two treatments ( Figure 3B ) . Both compounds are supposed to target the ATP-binding pocket of Gsk3ß [62] and have been used in a wide range of organisms to study the role of cWnt signaling during early development [55] , [63]–[66] , regeneration [67] and cells in culture [68] , [69] . Previous biochemical studies have described the difference in Gsk3ß affinity of AZ and LiCl [62] and shown that lithium chloride has additional targets such as inositol-phosphate phosphatases [70] . In order to gain insight into which Gsk3ß-inhibiting treatment in N . vectensis may be more specific to cWnt activation we over-expressed a stabilized form of Xenopus ß-catenin-GFP ( Xßcat69:GFP , [50] , [59] in which the GSK-3ß/CK-1 phosphorylation sites had been mutated to alanines and is resistant to proteolytic destruction [71] . In contrast to LiCl , but similar to AZ treatments , over-expression of Xßcat69:GFP mRNA induced ectopic localization of its protein in the nuclei of all cells along the oral-aboral axis ( Figure 3C ) and caused a strong exogastrulation phenotype after 4 days of development ( Figure 3D–3F ) . In addition , expression of Nv-foxB in Xßcat69:GFP mRNA injected embryos was strongly expanded ( Figure 3G ) , and Nv-fgfA1 expression downregulated ( Figure 3H ) similar to that seen in AZ treatments ( Figure 1Q , 1W ) . These observations suggest that in N . vectensis the effects caused by AZ treatments may reflect a more specific activation of the cWnt pathway than LiCl , although a more thorough analysis perhaps including other commonly used Gsk3ß inhibitors such as alsterpaullone [72]–[75] is required to identify the best cWnt activator in this system . cWnt signaling has previously been shown to be involved in endomesoderm formation in N . vectensis [50] , [59] and ectopic activation of the pathway not only induces exogastrulation ( Figure 2L , Figure 3F ) but also the expansion of at least one endomesodermal transcription factor in the animal hemisphere prior to the onset of gastrulation ( Figure 1D and 1Q , Figure 3G ) . To determine additional transcriptional differences between nß-catenin stabilized and control embryos with the goal of identifying putative genes that are required for specification and formation of endomesoderm in N . vectensis , we used gene profiling with a N . vectensis specific oligonucleotide based genome-wide microarray ( Nimblegen , Inc ) . We chose to analyze differential expression in late blastula stages prior to the onset of gastrulation ( 24 hpf ) of AZ and LiCl treated embryos . Transcription factors and signaling molecules build the basis of complex gene regulatory network that are deployed during embryogenesis [14] , [76] . Therefore , we focused on the identification and characterization of genes that can be separated in the following classes: i ) transcription factors , ii ) signaling molecules ( ligands and receptors ) and iii ) signaling pathway modulators ( extracellular , membrane bound or cytoplasmic ) , that will constitute the main structure of the cnidarian endomesoderm GRN . Although the specificity of LiCl to activate the canonical Wnt pathway is questionable , at least one gene expressed in the presumptive endomesoderm , Nv-foxB , was visibly upregulated in embryos treated with that chemical ( Figure 1D ) . For the purpose of identifying the largest possible set of new genes putatively playing a role in the gene regulatory network underlying endomesoderm formation in N . vectensis , we included microarray data from LiCl as well as AZ treatments that displayed at least a 2-fold upregulation from two biological replicates ( Table S1 ) . Of the 731 genes identified as being upregulated by LiCl or AZ treatments , 104 unique genes belonging to distinct definitive/putative transcription factors or signaling molecules ( Table 2 ) met our selection criteria for detailed characterization . The majority of the selected genes ( ∼66% , 64/104 ) belonged to various families of transcription factors ( Table 2 ) , defined by their structure and DNA binding motifs , and involved in diverse developmental and biological processes . The largest group of transcription factors we selected belongs to the homeodomain containing molecules ( 28/64 , e . g Nvevx , Nvhd050 , NvhlxB9 ) that constitute an ancient class of regulatory genes with diverse roles in fungi , plants and animals [77] . Other transcription factors that were upregulated following Gsk3ß inhibitor treatment prior to gastrulation in N . vectensis belong to the Forkhead ( e . g NvfoxQ1 , NvfoxA , NvfoxB ) , T-box ( e . g Nvtbx20-like , Nvbra ) , Ets ( e . g NvelkA-like ) , Mad1 ( e . g Nvsmad4-like , Nvnfix-like ) , HMG ( e . g Nvtcf ) , zinc finger ( e . g NvsnailA ) , bHLH ( e . g Nvtwist , Nvhes3 ) or achaete-scute ( e . g NvashB ) . These data indicate that a diverse set of transcription factor families may be involved in endomesoderm formation during cnidarian development ( Table 2 ) . The Wnt , Hedgehog ( Hh ) , RTK ( Receptor Tyrosine Kinase , e . g . FGFR ) , Notch , Tgfß/Activin and Bmp signaling pathways are associated with diverse biological events during embryonic development in metazoan and have been previously described from N . vectensis [50] , [56] , [59] , [78] , [79] . With the exception of Notch signaling , putative ligands and/or receptors associated with all remaining pathways have been upregulated by ectopic canonical Wnt activation ( Table 2 ) . In particular , we identified 9 of the 13 described N . vectensis Wnt ligands [56] , [80] , Nvactivin [81] , three Activin/TGFß Receptor-like genes , Nvbmp2/4 [81] , Nvadmp-related , one Bmp Receptor-like gene , Nvfgf8A [61] , two FGF-like , three Tyrosine Kinase Receptor-like genes , Nvhint3 [82] and one Patched-like receptor gene ( Table 2 ) . Interestingly , we also identified Nvfollistatin [81] a putative modulator of Activin [83] , Nvsprouty3-like a putative modulator of FGF [84] , as well as three modulators of Wnt signaling , Nvaxin-like , Nvnkd1-like ( naked cuticle ) and Nvporcupine-like [85]–[87] , suggesting that these three signaling pathways ( Activin , BMP and FGF ) , in addition to cWnt signaling , are deployed to specify and pattern the early N . vectensis embryo . 53 of the 104 genes identified above have been previously isolated , however only 23 have had their expression pattern characterized ( e . g . Nvbrachyury , NvfoxA , Nvtcf/lef [26] , [39] , [50] , [88] ) . All but two ( Nvhint3 [82] and Nvhes3 [79] of the 23 previously characterized genes are expressed in endomesodermally related regions during development , demonstrating the effectiveness of the approach in N . vectensis . Previous work in N . vectensis has shown that there appears to be at least two distinct complementary expression domains within the animal plate that give rise to endomesdoerm prior to gastrulation: i ) the central domain , located at the animal pole of the embryo and characterized by NvsnailA expression and ii ) the central ring expressing NvfoxA that surrounds the central ring [26] , [39] , [48] . To gain a basic understanding of when and where the transcription factors and signaling molecules with potential roles in endomesoderm formation are expressed in the developing embryo , we performed whole mount in situ hybridization ( Figure 4 , Figure 5 ) . We combined genomic sequence information with available EST data to design primers for the longest possible probes and were able to subclone and synthesize Dig-labeled antisense probes for 73 of the 104 identified genes . 49 of the 73 genes had never been characterized before in N . vectensis . In order to analyze their expression pattern and determine their putative implication in the N . vectensis endomesoderm GRN , we performed in situ hybridization focusing on the late blastula stage ( 24 hpf ) ( Figure 4 ) . This embryonic stage is the same stage that was used to perform the initial microarray experiments that lead to the identification of the genes and corresponds to the timing in which the presumptive endomesoderm is specified . We identified 18 new genes expressed in defined domains within the presumptive endomesoderm ( Figure 4A–4R ) that were upregulated by treatments described to affect cWnt signaling . Two genes ( Nvhd043 and Nvngfr-like ) were expressed in the gastrodermis at the late gastrula stage ( http://www . kahikai . org/index . php ? content=genes ) but we were unable to detect differentially localized gene expression for the 29 remaining probes during the first 48 hours of development after fertilization . From the 20 genes that displayed localized expression , eleven were exclusively induced by AZ , five exclusively by LiCl and four by both treatments ( Table 2 ) . Although it was difficult to identify sharp boundaries of expression for a few genes ( e . g . Nv-smad4-like , Nv-unc4-like and Nv-foxQ1 ) at the blastula stage , detailed analysis of animal views of the expression patterns revealed that the newly identified genes could also be characterized as being expressed in one of these two domains ( Figure 4A–4R insets ) that may constitute distinct synexpression groups [89] . Fourteen genes ( Nvtbx20-like , Nvadmp-related , Nvvasa-like , NvduxABC , Nvnk2-like , Nvsmad4-like , NvelkA-like , Nvhd050 , Nvphtf1-like , Nvporcupine-like , Nvnk-like 13 , Nvnfix-like , Nvunc4-like and NvfoxQ1 ( Figure 4A–4N ) ) were expressed in the central domain , the transcripts of two genes ( Nvhd147 and NvbicaudalC-like1 ( Figure 4O , 4P ) were detected in the central ring surrounding the central region , while Nvaxin-like and Nvnkd1-like appeared to be expressed in cells spanning both territories ( Figure 4Q , 4R ) . In order to establish the ground work for analyzing the gene regulatory network underlying endomesoderm specification/formation that includes the largest possible number of candidate genes , we re-analyzed spatial gene expression with longer probes at 24 hpf ( blastula ) of 51 formerly published genes ( Table S2 , highlighted in green ) . From all re-analyzed genes , we obtained clear expression patterns prior to gastrulation ( Figure 5 ) for 33 genes: the transcription factors NvotxA , NvotxB , N-otxC , Nvsmad1/5 , NvsnailA , NvsnailB , Nvgli , Nvgsc , NvhlxB9 , NvashB , Nvevx , Nvbra , NvfoxA , NvfoxB , Nvtcf , Nvlmx , Nvlhx1 , the signaling molecules and receptors , Nvfgf8A , Nvfz10 , Nvbmp2/4 , Nvwnt3 , Nvwnt2 , Nvwnt4 , Nvwnt8 , NvwntA , Nvstrabismus the modulators of FGF and BMP signaling , Nvsprouty , Nvtolloid , Nvchordin and putative germ line specific markers Nvpl10 , Nvnanos2 , Nvvasa1 and Nvvasa2 . In addition , the genes Nvactivin , NvmoxD , Nvrepo , Nvwnt1 , Nvwnt11 , and NvWnt16 [80] , [81] , [90]–[92] show faint expression in the animal hemisphere but require additional analysis to confirm a localized expression at the blastula stage ( data not shown ) . Systematic analysis of animal views of the obtained expression patterns allowed us to extend the number of genes that belong to the above-mentioned co-expression groups within the animal hemisphere . Eighteen genes NvotxA , NvotxB , NvotxC , Nvpl10 , Nvsmad1/5 , Nvnanos2 , NvsnailA , NvsnailB , Nvsprouty , Nvvasa1 , Nvvasa2 , Nvgli , Nvgsc , Nvfgf8A , Nvfz10 , Nvtolloid , NvhlxB9 and Nvevx ( Figure 5A–5R ) are expressed in the central domain . The transcripts of nine genes Nvwnt3 , Nvbmp2/4 , Nvbra , NvfoxA , NvfoxB , Nvwnt8 , NvwntA , Nvtcf , and Nvlmx ( Figure 5S–5Za ) are detected in the central ring surrounding the central domain , while NvashB , Nvstrabismus appeared to be expressed in cells spanning both territories ( Figure 5Zb , 5Zc ) . The genes Nvwnt4 , Nvwnt2 , Nvlhx1 and Nvchordin are expressed in a third domain defining the animal hemisphere , the external ring ( Figure 5Zd–5Zg ) . While we confirmed localized expression at the blastula stage for NvotxB , Nvsmad1/5 , NvsnailA , NvsnailB , Nvsprouty , NvfoxA , NvfoxB , Nvtcf , NvashB and Nvlhx1 , ( Figure 5B , 5E , 5G , 5H , 5I , 5V , 5W , 5Z , 5Zf ) [26] , [39] , [40] , [42] , [48] , [50] , [61] , [81] , [93]–[95] we also detected an earlier onset of gene expression than previously reported for NvotxA , NvotxC , Nvpl10 , Nvnanos2 , Nvvasa1 , Nvvasa2 , Nvgli , Nvgsc , Nvfgf8A , Nvfz10 , Nvtolloid , NvhlxB9 , Nvevx , Nvwnt3 , Nvbmp2/4 , Nvbra , Nvwnt8 , NvwntA , Nvlmx , Nvwnt4 , Nvwnt2 and Nvchordin ( Figure 5A , 5C , 5D , 5F , 5J , 5K , 5L , 5M , 5N , 5O , 5S , 5T , 5U , 5X , 5Y , 5Zb , 5Zc , 5Zc ) [40] , [56] , [61] , [80]–[82] , [90]–[92] , [96]–[100] ( Table S2 ) . Taken together , our systematic gene expression analyses of 18 new and 33 previously identified genes ( Figure 4 , Figure 5 ) define at least four complementary expression domains ( central domain , central ring , central domain+ring , external ring ) within the animal hemisphere at the blastula stage ( Figure 6A , 6B ) . Because in situ hybridizations are not the most sensitive way to detect the onset of gene expression we used qPCR in order to gain a more precise idea about the temporal expression on cDNA made at embryonic stages sampled every two to four hours , up to 48 hpf . As a frame of reference , embryos at 8 hpf , 18 hpf and 24 hpf contain approximately 430 , 2160 or 3480 nuclei respectively ( Figure S4 ) . Collected data were analyzed for the presence of maternal transcripts ( Cp value>34 . 00 ) in unfertilized eggs and , if detectable , for their first zygotic expression inferred from positive changes in transcript levels ( Figure 6C , Figure S5 ) . Maternal transcripts were detected for 42 . 5% ( 31/73 ) of the analyzed genes , no significant zygotic upregulation observed for 8 . 2% ( 6/73 ) while only one maternally expressed gene , Nvtcf , appears to be zygotically expressed after the onset of gastrulation 32–40 hpf ( Figure 6C ) . The remaining genes ( 89% , 65/73 ) are zygotically upregulated between 8 and 24 hpf , with NvashB , Nvbra , NvfoxB , NvduxABC ( Figure 6C ) , Nvhd043 , Nvhd032 and NvmoxC ( Figure S6A ) being the first upregulated genes 8–10 hours post fertilization . Zygotic expression of 29 genes ( Nvbmp2/4 , Nvfgf8A , Nvnfix-like , NvfoxA , Nvfz10 , Nvhd050 , Nvhd147 , NvhlxB9 , Nvlhx1 , Nvlmx , Nvnkd1-like , NvsnailA , NvsnailB , Nvvasa-like , Nvvasa2 , Nvwnt2 , Nvwnt3 , Nvwnt8 , Nvsprouty ( Figure 6C ) , Nvactivin , NvfoxA/B-like , Nvhes3 , Nvtwist , Nvwnt1 , Nvwnt11 and Nvwnt16 ( Figure S6A ) are detected only a couple of hours later , 10–12 hpf ( Figure 6C , Figure S6A ) . An additional three waves of zygotic upregulation were observed at 14–16 hpf ( Nvevx , Nvfoxq1 , Nvgli , Nvnk2-like , NvotxB , Nvsmad1/5 , Nvstrabismus , Nvtbx20-like , NvwntA ( Figure 6C ) , Nvfollistatin-like , Nvhd017 , NvmoxD , NvmsxB , and Nvrepo ( Figure S6A ) , 16–18 hpf ( Nvwnt4 , NvbicaudalC-like1 , Nvporcupine ( Figure 6C ) and Nvgata ( Figure S6A ) ) , and just prior the onset of gastrulation at 20–24 hpf ( Nvchordin , NvelkA-like , Nvgsc , Nvnanos2 , Nvnk-like13 , NvotxA , NvotxC , Nvpl10 , Nvtolloid , Nvunc4-like ( Figure 6C ) , Nvfgf8/17-like and Nvtbx15-like ( Figure S6A ) . Transcripts of genes zygotically activated during the first 5 waves of expression ( 8–10 , 10–12 , 14–16 , 16–18 hpf ) are localized to one of the four animal hemisphere domains at 24 hpf ( Figure 6C , Figure S6 ) . With the exception of Nvchordin that is expressed in the external ring , 90% ( 9/10 ) of the genes zygotically upregulated at 20–24 hpf are expressed in the central domain , suggesting the beginning of segregation events that define distinct domains within the animal hemisphere at this time of embryonic development in N . vectensis . A spatial and temporal co-expression map ( Figure 7 ) summarizes our expression data analysis ( in situ hybridization and qPCR ) and provides a visual representation of the sequential deployment of the putative members of the cnidarian endomesoderm GRN . The distinction of three co-expression domains within the animal hemisphere has only been determined for the blastula stage at 24 hpf ( Figure 4 , Figure 5 ) . We assume that genes we analyzed that were detected ubiquitously may also have a defined ( not necessarily exclusive ) role in the presumptive endomesoderm/animal hemisphere prior to gastrulation . We have organized the genes thought to be involved in endomesoderm formation by their maternal presence and zygotic upregulation in presumptive endomesoderm during the first 48 hours of development and by the co-expression group they belong to at 24 hpf ( Figure 6A , 6B ) . We have shown that treatments designed to ectopically activate the cWnt pathway can be used to identify genes expressed spatially and temporally consistent with involvement in a putative cnidarian endomesodermal GRN . In order to specifically analyze the effect of disrupting canonical Wnt signaling at the phenotypic and transcriptional level in N . vectensis and to determine provisional inputs of that pathway into the cnidarian endomesoderm GRN prior to the onset of gastrulation , we injected morpholino antisense oligonucleotides targeting the translation initiation site of the canonical Wnt effector NvTcf ( MoTcf_trans ) ( Figure 8A ) . While control ( Figure 8C–8E ) and dextran injected embryos ( not shown ) gastrulate normally and form distinct pharyngeal structures ( arrows in Figure 8E ) , MoTcf_trans injected embryos ( Figure 8F–8H ) gastrulate but fail to form a pharynx ( Figure 8H ) . Previous reports using various approaches to inhibit cWnt signaling in N . vectensis have shown that the gastrodermis initially forms normally but later loses its epithelial organization [50] , [92] . In contrast , in Nv-Tcf morphants , the body wall endomesoderm went ahead and formed a monolayer of epithelial cells ( Figure 8H ) , suggesting only a partial effect of NvTcf knock down . In order to verify the efficiency of the translational MoTcf_trans that targets a region spanning the 5′ UTR and the translational initiation site of Nvtcf , we performed a series of experiments ( Figure 9 ) . We made two constructs of NvTcf fused to the fluorescent protein Venus: i ) NvTcf:Venus , lacking 15 nucleotides of the morpholino recognition site and ii ) Nv-Tcf5′:Venus that contains the entire 5′UTR+ORF region targeted by MoTcf_trans ( Figure 9A ) . When mRNA encoding Nvtcf:Venus ( 400 ng/µl ) was injected alone or in presence of MoTcf_trans ( 1 mM ) , we observed nuclear localization of NvTcf:Venus in all the cells at the blastula stage ( Figure 9B , 9C ) . In contrast , nuclear localized NvTcf5′:Venus ( Figure 9D ) was no longer detected when co-injected with MoTcf_trans ( Figure 9E ) . These results show that MoTCF_trans effectively inhibits translation of a synthetic mRNA encoding Nvtcf ( sequence based on genome prediction corroborated by EST data ) and that Nv-tcf:Venus mRNA is not recognized by MoTcf_trans making this construct suitable for the following rescue experiments ( Figure 9F ) . When we injected Nvtcf:Venus ( 400 ng/µl ) alone we observed no significant variation in expression of four genes putatively downstream of canonical Wnt signaling ( Nvlmx , Nvbra , NvfoxA and Nvnkd1-like ) by qPCR compared to dextran injected control embryos ( Figure 9F ) . The only exception was Nvbra , which was slightly downregulated , reflecting the repressive capacity of Tcf in the absence of nß-catenin [101] . Microinjection of MoTcf_trans ( 1 mM ) causes a downregulation of all four of these genes , while co-injection of Nvtcf:Venus together with MoTcf_trans restores similar expression levels compared to the injection of Nvtcf:Venus alone ( Figure 9F ) . While NvotxA ( a gene not affected by ectopic Wnt activation ) is slightly upregulated in Nvtcf_Venus injections , it remains unaffected following knock-down or rescue conditions ( Figure 9F ) . Taken together , these data support the idea that MoTcf_trans can effectively block translation of Nvtcf:Venus and that the observed effects on reduced gene expression in MoTcf_trans injected embryos are primarily caused by the inhibition of NvTcf function ( Figure 9F ) . Nvtcf transcripts are strongly detected in the egg and during early cleavage stages ( [56] , Figure 6C2 ) suggesting that the presence of maternally loaded Nv-Tcf protein may circumvent the translational morpholino approach we used to knock-down NvTcf function . In order to interfere with maternally presence of NvTcf , we injected mRNA encoding a dominant negative form of NvTcf fused to Venus ( Figure 8A , Nvdntcf:Venus ) lacking a 92 amino acid region of the N-terminus that contains the ß-catenin binding domain required for proper signal transduction of canonical Wnt signaling [102] . While injection of Nvdntcf:Venus into the egg clearly induced nuclear localization of Venus in all cells of the blastula stage ( 24 hpf , Figure 8B ) no effect was observed on early invagination and gastrulation movements ( Figure 8I , 8J ) . However , similar to MoTcf_trans injections , 4 day old Nvdntcf:Venus planula larvae ( 96 hpf ) lacked an identified pharynx in over 90% ( 30/32 ) of the cases , with no mouth opening observed in appoximately 50% ( 15/32 ) of injected embryos ( Figure 8K ) . Intriguingly , in 30% ( 11/32 ) of cases we observed various degrees of exogastrulation ( Figure S8B , S8C ) , in addition to the lack of pharynx . When injected at slightly higher concentrations ( 450 ng/µl ) the endomesoderm loses his epithelial organization ( Figure S7D ) , similar to earlier observations of inhibition of cWnt [50] , [92] that may eventually lead to apoptosis of the cells [103] . The morpholino ( MoTcf ) and dominant negative ( NvdnTcf:Venus ) based approaches we used to interfere with Nv-Tcf function did not perturb gastrulation movements but clearly affected pharynx formation . In Nvdntcf:Venus injected embryos we also observed the absence of a mouth opening in addition to a disorganized gastrodermis , supporting the idea that the dominant negative approach interferes with the maternal pool of NvTcf and is thus a more effective strategy to study the role of this gene during early N . vectensis development .
The Gsk3ß/APC/Axin protein complex plays a crucial role in regulating the cytoplasmic pool of ß-catenin and inhibition of that complex by its naturally interacting protein , Dsh ( disheveled ) . This complex is also the target of a variety of pharmaceutical drugs causing the activation of canonical Wnt signaling . Historically , lithium chloride ( LiCl ) was used to inhibit Gsk3 function , mimic Wnt signaling and interfere with sea urchin , zebrafish and Xenopus development [105] , [106] . While currently more than 30 different pharmalogical Gsk3 inhibitors have been described and characterized biochemically [62] only a handful of reagents ( lithium chloride ( LiCl ) , 1-azakenpaullone ( AZ ) , 1-alsterpaulllone ( AP ) and 6-Bromoindirubin-30-oxime ( BIO ) are commonly used in developmental and cellular [107] studies . The IC50 values ( the half maximal ( 50% ) inhibitory concentration ( IC ) of AZ , AP and BIO are comparable ( 0 . 004–0 . 0018 µM ) , while LiCl requires higher concentration for effective Gsk3 inhibition ( ∼2000 µM ) [62] . Nonetheless , all four components are broadly used in a variety of animals and generally considered universal canonical Wnt activators [59] , [72] , [74] , [105] , [108] , [109] . While direct comparisons of two or more Gsk3 inhibitors in a single organism are sparse , recent studies in Hydractinia primary polyps ( hydrozoan cnidarian ) [110] , or acoel flatworms [111] have shown that AZ and LiCl or AZ and AP respectively induce similar phenotypes . These results as well as the fact that different Gsk3ß inhibitors are interchangeably used to ectopically activate canonical Wnt signaling in various animals , predict that AZ and LiCl cause comparable developmental perturbations and should affect a largely overlapping pool of downstream targets . Surprisingly , at the molecular level , the genes affected by these treatments in N . vectensis are largely non-overlapping and closer analysis of the morphological phenotype revealed clear differences . While AZ causes an exogastrulation ( Figure 2L ) , LiCl treated embryos become elongated and the internal endomesodermal tissue disorganized ( Figure 2H ) . Both treatments enhance Nv-foxB expression at the blastula stage at the working concentrations ( Figure 1D , 1Q ) but only AZ has drastic effects on Nv-fgfa1 at the vegetal pole ( Figure 1J , 1W ) . A higher concentration of LiCl is needed to visibly reduce Nv-fgfa1 expression ( Figure 1K ) . Our array data show that only approximately 11% of significantly upregulated genes or 25% of significantly downregulated genes are simultaneously affected by AZ and LiCl treatments ( Figure 3A , 3B ) . One plausible explanation for this observation would be that the concentrations used for the treatments only cause a partial overlap of common targets . However , although only two biological replicates were performed , and the Pearson's correlation factors between biological replicates were low ( 0 . 53 and 0 . 42 for the AZ and LiCl arrays respectively ) , both our molecular and morphological observations of different phenotypes caused by LiCl or AZ treatment ( Figure 1 , Figure 2 ) , suggest that these drugs might have radically different modes of action during N , vectensis development . A greater understanding of targets of LiCl action might also lend insight into additional inputs of endomesoderm specification acting in parallel to other signaling systems . A recent study on N . vectensis suggests that continuous AP treatments for the first 48 hours after fertilization induces a phenotype that is similar to LiCl treated embryos [59] . While the duration of drug application by the authors was different from the continuous treatments of AZ or LiCl in our study , the described similarities between AP and LiCl add another level of confusion on what pharmaceutical drug to use to mimic ectopic canonical Wnt signaling . Interestingly , overexpression of a constitutively active form of ß-catenin , Xßcat69:GFP , causes exogastrulation ( Figure 3F ) similar to AZ treatments ( Figure 2L ) . These data suggest that AZ may better mimic ectopic activation of ß-catenin than LiCl ( and perhaps AP ) in N . vectensis . The differences in morphological phenotypes and molecular targets revealed by our array experiments also highlight that these drugs may have additional non-canonical Wnt specific targets in addition to the effect on Gsk3 . A broader comparative study that includes a wide range of different Gsk3 inhibitors would be beneficial to better understand which component actually mimics cWnt activation in vivo . Because AZ and LiCl treatment generate different phenotypes and molecular responses , it raises concerns about the interpretation of experiments made with pharmacological treatments , and underlines the importance of gene specific knock-down experiments for making concrete statements about gene function . The observation that some genes upregulated by AZ/LiCl treatments were also upregulated by NvTcf inhibition ( and not downregulated as expected , Figure 10 , Figure 11Z , 11Za ) further illustrates how misleading ectopic activation experiments that are not followed up by gene specific knock-down analysis can be . For the sake of identifying putative downstream targets of the canonical Wnt pathway that may be part of the cnidarian endomesoderm GRN , we focused this study on genes that are upregulated by treatment of inhibitors of Gsk3ß and therefore could positively respond to canonical Wnt signaling . However , a total of 538 genes were significantly ( 2-fold or more ) downregulated by ectopic activation of cWnt signaling ( Figure 3B , data not shown ) . One gene that was downregulated in the array data obtained from AZ but remains unaffected in LiCl treatments is a gene expressed in the presumptive apical domain ( vegetal pole ) , NvfgfA1 ( Figure 1W , [61] ) , supporting the different phenotypes and molecular effects observed by these two treatments ( Figure 1 , Figure 2 ) . A thorough analysis of genes negatively affected by AZ or LiCl treatments will be the focus of a subsequent paper . A precise understanding of the timing of gene expression and their spatial distribution in the embryo is crucial in order to gain insight into the architecture of developmental GRNs . As our goal was to determine a large framework for future endomesoderm GRN studies in N . vectensis , we carefully analyzed spatial and temporal expression of previously published as well as newly identified genes by in situ hybridization and high-density qPCR ( Figure 4 , Figure 5 , Figure 6 ) . In some bilaterian embryos , the initiation of the bulk of zygotic gene expression is called the MBT ( mid-blastula transition , [112] . While the timing of the MBT seems controlled by the ratio of nuclei to cytoplasm [113]–[115] , the pre-MBT embryo is defined by synchronous cell divisions [116] , heterochromatically repressed genes [117] and the translation of the maternal pool of mRNA [118] . Interestingly , our systematic gene expression profiling analysis shows that in N . vectensis more than 40% of the endomesodermal genes analyzed are expressed maternally ( Figure 6 ) . In addition , of the 66 genes for which we detected zygotic upregulation , none were activated earlier than 8–10 hours post fertilization . While we could have simply not identified earlier zygotically controlled genes , these observations suggest that N . vectensis undergoes an MBT-like event approximately 10 hours post fertilization . Interestingly , the timing correlates with the previously described end of blastula oscillations and the associated shift from synchronous to asynchronous cell divisions in N . vectensis [49] . Additional experiments including a careful analysis of the early cleavage pattern and analysis of the heterochromatic state are however required to better understand the initial zygotic transcriptional control of N . vectensis . To determine spatial expression patterns and potential clustering of putative endomesodermal co-expression groups we carried out whole mount in situ hybridization at the blastula stage . Figure 6 A , 6B summarizes the presence of at least five clear distinct co-expression groups present in the blastula in N . vectensis: Four in the animal hemisphere and one at the vegetal pole ( the apical domain ) . In the animal hemisphere 32 genes are expressed in the central domain , 11 genes in the central ring , 4 genes in a territory that covers both the central domain and the central ring vegetal to the central ring , and 4 in an external ring ( Figure 6A , 6B ) . The existence of co-expression groups in the animal hemisphere is not only of interest for establishing the endomesoderm GRN but also for our understanding of the putative “blastoporal organizer” in cnidarians . In fact , a recent work using ectopic grafting experiments has shown the potential of the N . vectensis blastoporal lip ( a derivate of the central and external rings ) to induce a secondary axis suggesting an expression of the same subset of signaling molecules in cnidarian and chordate blastoporal lips as axial “organizers” [119] . While our analysis allowed us to cluster gene expression patterns at the blastula stage to one of the co-expression groups , double in situ hybridization experiments are required to better understand the spatial relationship between genes on a cell-by-cell basis . A previous study from N . vectensis has shown by double in situ hybridization that the expression domains of the Nvsnail ( central domain ) genes and NvfoxA ( central ring ) at the blastula/early gastrula stages do not overlap and proposed that their boundary can be viewed as the boundary between the endomesoderm and ectoderm [48] . In later stages ( gastrula/early planula ) NvsnailA and NvsnailB are expressed in body wall endomesoderm [26] , [39] while NvfoxA is detected in ectodermal portions of the pharynx and the mesenteries [26] , [39] . In order to verify the generality of this observation , we compared genes expressed at blastula stages in either the central domain or the central ring , to their expression at the late gastrula/early planula stage ( if data available , Table S3 ) . Of the 32 genes expressed in the central domain ( including NvsnailA ) , 12 genes were detected in endomesodermal structures in later stages , 6 genes were expressed in ectoderm related tissue and two genes were associated with endo- as well as ectodermal territories . On the other hand , of the 11 genes expressed in the central ring ( including NvfoxA ) the majority ( 8/11 ) are detected in ectodermal structures and 3 in endomesodermal tissue . While clearly not all genes from this analysis follow a similar pattern to NvsnailA , NvsnailB and NvfoxA , it seems that the gastrodermis forms primarily from the central domain and pharyngeal/oral ectoderm from the central and external ring and support the idea that ectodermal versus endomesodermal structures are being specified prior to the onset of gastrulation . However , transcriptional control of gene expression is context dependent and can quickly change during embryonic development . In fact , NvashB is expressed in the central domain and central ring at 24 hpf ( Figure 5Zb ) , is not detectable during gastrula stages but is re-expressed in the blastoporal ectoderm in planula stages , suggesting differential transcriptional control during embryogenesis [94] . Therefore using gene expression domains at 24 hpf does not provide a clear answer to the cellular fate of the central domain or ring , or their relationship to an ectodermal-endomesodermal boundary . Labeling of the cells belonging to either of the co-expression groups and following them over time is required to definitively address this question . The comparison of gene expression domains in N . vectensis also reveals something subtler about regional patterning during early development relative to other systems studied . In echinoderms , the basic principle for the origin of the endomesoderm GRN follows four principal steps . Maternal factors activate ( 1 ) endomesoderm specific specification genes in the vegetal hemisphere , which after a signal that induces endo- and mesodermal segregation signal activate ( 2 ) two distinct sets of endo- or mesoderm specification genes that in turn inhibit ( 3 ) the reciprocal specification genes in a given tissue and activate ( 4 ) the germ layer specific differentiation genes [9] , [120] . This would suggest that in sea urchins once the mesodermal germ layer is differentiated , its specification genes are either downregulated or maintained at basic levels while differentiation genes are upregulated . At the same time endoderm specification genes have to be strongly downregulated in the mesodermal germ layer so as not to interfere with its own specification program . Therefore , no specification genes are expressed in either one or the other germ layer after the segregation signal . The current version of the echinoderm endomesoderm GRN is in agreement with this idea ( http://sugp . caltech . edu/endomes/ ) . Our observations in N . vectensis suggest significant differences in the GRN architecture . Not only are endodermal and mesodermal genes expressed in the same gastrodermal precursors ( e . g . not repressing each other ) ( Table S3 ) but genes of the presumptive endomesoderm ( central domain ) are later expressed in derivatives of the central ring ( ectoderm ) and vice versa . These data suggest that in N . vectensis the feedback loop mechanisms for segregation and subsequent specification of two distinct germ layers ( endo- and mesoderm ) are not operating as they are in triploblastic ( bilaterian ) animals . Comparisons of the endomesoderm GRNs from sea urchins and sea stars suggested the existence of a network “kernel”: a conserved GRN subcircuit of five regulatory genes ( blimp1 , otx , bra , foxA and gataE ) that are tightly linked by positive feedback loops . This kernel is required upstream of initial endomesoderm specification and if expression of any of the genes is perturbed , endomesoderm specification is disrupted [17] . In N . vectensis , no Nvblimp1 orthologue is expressed prior to the end of gastrulation ( Ormestad & Martindale , unpublished ) and Nvgata is not expressed in the animal plate at the blastula stage but only in individual cells of the presumptive ectoderm [26] . The temporal expression of Nvblimp-like after the initial specification of endomesoderm and the spatial expression of Nvgata suggests , that neither of these two transcriptional regulators are part of a putative ancestral kernel for endomesoderm formation . On the other hand , Nvotx ( A , B and C ) , Nvbra and NvfoxA are all expressed in time and space suggesting that they may play a crucial role in specifying this germ layer in this cnidarian . Knock-down experiments analyzing the individual roles of these transcription factors in connecting the network and germ layer specification will shed light on the question about the existence of an endomesderm “kernel” that precedes the bilaterian split . In order to functionally analyze the role of canonical Wnt signaling during early N . vectensis development , we specifically knocked down NvTcf function using an antisense oligonucleotide morpholino and a dominant negative approach . Overexpression of NvdnTcf:Venus shows that while canonical Wnt signaling has no effect on gastrulation movements ( Figure 8 ) , it is required for germ layer specification ( Figure 10 , Figure 11 ) , proper pharynx and mouth formation ( Figure 8H , 8K ) and maintenance of endomesoderm ( Figure 8 , Figure S7 [50] , [92] ) . The lack of oral structures ( pharynx and mouth ) is in agreement with the expression of Nvtcf in the pharyngeal and blastoporal endomesoderm in late gastrula/early planula stages [56] . One puzzling observation was the exogastrulation phenotype observed in 30% of NvdnTCF:Venus injected planula stages ( Figure S7 ) , suggesting that a normal pharynx is required for maintaining the developing endomesoderm inside the planula larvae . However , a properly patterned endomesoderm may also be a pre-requisite for the formation of a normal pharynx . Therefore , additional experiments are required to address the question about the relationships between pharyngeal structures and endomesoderm integrity . In past studies , the role of cWnt signaling in N . vectenis has been analyzed by interfering with the function of the cytoplasmic/membrane-bound members of that pathway Disheveled ( dsh ) and Axin , as well as the over-expression of constructs designed to inhibit ß-catenin function ( ß-catenin:engrailed fusion ( Xßcat-Eng ) or the cytoplasmic domain of Cadherin ) [50] , [59] , [92] . With the exception of Cadherin ( whose specificity to cWnt remains unclear , [92] ) that blocks gastrulation movements and gut formation , over-expression of the other constructs has no significant effects on early gastrulation movements but clearly prevents maintenance of the gut epithelium . The NvdnTcf:Venus injection phenotypes observed in our study are in line with these results . Currently , we cannot rule out that the knock-down experiment from our study , as well as from previous studies [50] , [59] , [92] are incomplete which may explain the lack of gastrulation phenotype . NvdnTcf:Venus injected embryos show a weak downregulation of Nvstrabismus ( Figure 10 ) , a gene that has been shown to be required for gastrulation movements in N . vectensis [92] . However , the current data in N . vectensis [92] and work in another cnidarian [121] , [122] suggests that the PCP/Wnt pathway is involved with the morphological aspects of epithelial folding/invagination in N . vectensis and that the cWnt pathway is required for activation of a partial subset of genes involved in endomesoderm specification . In this study we combined predicted genome-wide microarray approaches ( Figure S1 , Figure 3 , Table 2 , Table S1 ) , with precise temporal and spatial gene expression analysis ( Figure 4 , Figure 5 , Figure 6 , Figure 7 ) as well as NvTcf gene specific functional information ( Figure 8 , Figure 9 , Figure 10 , Figure 11 ) to propose the assembly of the framework for the first provisional cnidarian endomesoderm GRN ( Figure 12 ) . The current view of endomesoderm specification up to 24 hours post fertilization ( Figure 12 ) allows to clearly distinguish four co-expression domains characterized in this study ( Figure 4 , Figure 5 ) . No assumptions about direct or indirect interactions are made at this point , and detailed gene specific cis-regulatory analyses are needed to address this question in the future . NvTcf function is required for normal expression of genes belonging to all four co-expression domains of the animal hemisphere ( central domain , central ring , central domain+ring and external ring ( Figure 6 ) ) . An interesting finding is that most of the genes affected by NvTcf inhibition are expressed in the central ring ( e . g . Nvbra , NvfoxA , Nvbmp2/4 and Nvwnt8 ) . This observation is consistent with NvTcf expression in that domain at the blastula stage ( [56] , Figure 5Za ) and with the lack of pharynx formation in NvTcf depleted embryos ( Figure 8H , 8K ) . In addition , NvTcf is crucial for regionalizing the animal hemisphere prior to gastrulation . In fact , analysis of the spatial expression of NvduxABC and Nvfgf8A by in situ hybridization shows that central domain expression of both genes is extended to the central ring ( Figure 11T , 11U , 11Z , 11Za ) suggesting that NvTcf function is required to restrict NvduxABC and Nvfgf8A expression to the central domain in wild-type embryos . Endomesoderm GRNs have been proposed for only one protostome ( C . elegans , [13] , [123] ) and three deuterostomes ( sea urchin , sea star and Xenopus , [6] , [7] , [17] , [18] , [9] , [10] , [19] ) . However , for the sake of simplicity , and because early development between N . vectensis and echinoids is in certain aspects comparable [124] , we will begin our discussion with echinoderms . However , it is obvious that the GRNs of a broad range of organisms including Xenopus and C . elegans will need to be included in the future . In echinoderms , a maternal canonical Wnt pathway in the vegetal hemisphere plays a crucial role in patterning the animal - vegetal ( A/V ) axis and is required for endomesoderm specification and gastrulation [55] , [58] , [125] , [126] . In N . vectensis , genes from all four animal expression domains are downregulated in NvTcf depleted embryos prior to the onset of gastrulation ( Figure 10 , Figure 11 ) . However , gastrulation movements and invagination of the endomesodermal germ layer is initiated normally in NvTcf depleted embryos ( Figure 8 , [50] , [59] , [92] ) . One reason for normal endomesoderm formation may be that we did not efficiently block maternal NvTcf proteins or the existence of additional signals that specify the endomesoderm in cnidarians . However multiple functional approaches used to inhibit cWnt all failed to prevent gastrulation ( Figure 8 , [50] , [59] , [92] ) . Interestingly , putative molecules activating other signaling pathways are also expressed in the animal hemisphere prior to gastrulation . Nvfgf8A ( a putative ligand for Fgf/MAPK signaling ) and its putative modulator Nvsprouty are both expressed in the central domain ( [61] , Figure 5I , 5M ) . Nvbmp2/4 a putative ligand for Bmp signaling is also expressed in the central ring ( Figure 5T ) while the potential effector of this pathway Nvsmad1/5 is expressed in the central domain ( Figure 5E ) . In echinoderms MAPK and Fgf signaling are required to maintain initial cell-autonomous specification of the skeletogenic mesoderm ( primary mesenchyme cells , PMCs ) , specification of a subset of non-skeletogenic secondary mesenchyme cells ( SMCs ) , PMC ingression and differentiation of the larval skeleton [127]–[130] . In contrast , Bmp2/4 signaling is involved in dorso-ventral ( oral-aboral ) patterning of all three germ layers after the segregation of the mesoderm from endomesodermal precursors [131]–[134] . The role of Bmps has been recently analyzed in N . vectensis and shown a clear implication of NvBmp2/4 in patterning the directive axis ( which is perpendicular to the oral/aboral axis ) of the endomesoderm and oral ectoderm and patterning and differentiation of the endomesoderm at the late gastrula stages using morpholino approaches [135] . While no delay in gastrulation or morphological signs of a defective endomesoderm was reported from NvBmp2/4 morphants , all endomesodermal markers analyzed in this study were strongly downregulated [135] . This observation is similar to inhibition of cWnt signaling , in that morphogenetic movements of gastrulation and initial gastrodermis formation occurs normally , but endomesodermal markers are no longer detected at the end of gastrulation ( [50] , [59] , [92] , this study ) , suggesting that Bmp2/4 signaling may also be involved in endomesoderm specification prior to gastrulation in N . vectensis . As our experiments interfering with the cWnt pathway show , dominant negative approaches might revel additional roles for these other pathways in early endomesodermal patterning . Unfortunately , little is known about the early role of Fgf/MAPK signaling in the animal hemisphere in cnidarians , it would be important to analyze the role of NvFgf8A signaling on endomesoderm specification in N . vectensis . Re-analyzing the role of NvBmp2/4 signaling prior to gastrulation and formation of the directive axis may also reveal whether NvBmp2/4 is involved in endomesoderm specification prior to its role in patterning the directive axis . This would considerably improve our basic understanding of the ancestral relationship between three main signaling pathways ( Bmp2/4 , Fgf/MAPK , and Wnt/Tcf ) and underline their respective inputs into the endomesoderm GRN required to form a functional gut in N . vectensis . In the context of our study it appears likely that Bmp2/4 and FGF signaling are likely to be involved in specification of the central domain while Wnt/Tcf is more important for specifying the central ring and its derivatives ( e . g . pharynx ) . Another very important signaling pathway involved in endoderm and mesoderm segregation from an initial endomesodermal germ layer in echinoderms is the Notch signaling pathway . After initial endomesoderm specification by maternal cWnt , nß-catenin induces the expression of the Notch ligand , Delta , in the presumptive endoderm , which in turn activates the Notch signaling pathways in the neighboring cells ( presumptive mesoderm ) that actively inhibits cWnt signaling and induces the mesodermal specification program [136]–[138] , [8] [16] , [139]–[142] . Recently , gene expression of members of the Notch signaling pathway and its role during N . vectensis development have been reported [79] . Using pharmaceutical and gene specific approaches to knock-down Notch signaling this study has shown that this pathway is required for proper cnidocyte ( cnidarian-specific neural sensory cells ) development . While the endomesoderm in Notch inhibited embryos appeared disorganized during later development , expression of two markers ( NvsnailA and NvotxA ) was largely unaffected suggesting that initial endomesodermal patterning occurs normally in these animals . This study also suggests that , in contrast to echinoderms , the Notch signaling pathway does not seem to be involved in early germ layer segregation . However , a more detailed analysis of endomesodermal markers prior and during gastrulation after Notch inhibition might be required to fully exclude any important role of that pathway in specifying endomesodermal territories . To summarize , we have used ectopic activation of canonical Wnt signaling to carry out a genome wide survey of putative members of the cnidarian endomesoderm GRN . In combination with previously described endomesodermal genes we systematically analyzed over 70 genes by in situ hybridization and real time qPCR to establish a set of potential components of an extensive gene expression network . Finally we have used functional NvTcf knock-down experiments to assemble the framework for the first provisional inputs into a complex cnidarian gene regulatory network underlying germ layer formation and show that canonical Wnt function is required to regionalize the animal pole into a central domain , central ring and an external ring at the blastula stage and to allow normal pharynx formation of the early planula . The current view of the network suggests that additional signaling pathways ( Bmp2/4 and FGF ) are tightly interwoven to correctly specify and pattern the endomesoderm of N . vectensis prior to the onset of gastrulation .
N . vectensis embryos were cultivated at the Kewalo Marine Laboratory/PBRC of the University of Hawaii . Males and females were kept in separate glass bowls ( 250 ml ) in 1/3x seawater ( salinity: 12pp ) [41] . To keep the animals in a healthy reproductive state , they were kept at 17°C in dark and water was changed weekly . Animals were fed twice a week with oysters or brine shrimps . Manipulating the light cycle induced spawning and oocytes and sperm were collected separately [143] . The gelatinous mass around the eggs was removed with 2–4% L-Cystein in 1/3x seawater before fertilization and then washed 3 times with 1/3x seawater . For a simultaneous development of the embryos , all the oocytes were fertilized in glass dishes at the same time with 0 . 5 ml of sperm dilution . The fertilized eggs were kept in dark in filtered 1/3 seawater ( 12pp ) at 17°C until the desired stage . The canonical Wnt agonist 1-azakenpaullone ( AZ , Sigma , #A3734 ) was dissolved at a stock concentration of 10 mM in DMSO and added at final concentrations as indicted ( 1–30 µM ) in 1/3x-filtered seawater . Lithium chloride ( LiCl ) was dissolved in H2O and added at final concentrations as indicated ( 1–100 mM ) [81] . Embryos were treated with 1-azakenpaullone or lithium chloride directly after fertilization and kept at 17°C . At 12 hours the 1-azakenpaullone and lithium chloride solution were replaced with fresh solutions to maintain activity of the Gsk3ß agonists . The described phenotypes were observed in more than 80% of the analyzed embryos in at least three individual experiments . Treatments were compared to DMSO ( for AZ treatments ) treated or untreated control embryos . Embryos were fixed for in situ hybridization and morphological analysis at indicated stages . mRNA of embryos was extracted at 24 h after fertilization ( late blastula ) from two distinct biological replicates for microarray analysis . RNA for qPCR and microarray analysis was isolated with TriPure ( Roche , # 11667157001 ) or TRIzol ( Invitrogen , #15596-026 ) according to the manufacturer's instructions and genomic contamination removed using RNase-free DNase ( Quiagen , #79254 ) for 15 minutes at 37°C . The total amount of RNA was quantified with a NanoDrop 2000 spectrophotometer ( Thermo Scientific ) and the quality analyzed with a Bioanalyzer 2100 ( Agilent Technologies Inc . ) . 1 µg of total RNA was used to generate cDNA with the Advantage RT-PCR kit ( Clontech , #639506 ) for qPCR analysis . For the fine scale temporal analysis ( Figure 6 , Figure S5 , Figure S6 ) total RNA was extracted from the following stages ( in hours post fertilization , hpf ) : 0 , 2 , 4 , 6 , 8 , 10 , 12 , 14 , 16 , 18 , 20 , 24 , 28 , 32 , 40 , 48 . qPCR analysis using a LightCycler 480 ( Roche ) utilizing LightCycler 480 SYBR Green 1 Master mix ( Roche , #04887352001 ) was carried out as described previously [94] . Efficiencies for each gene specific primer pair was determined using a five-fold serial dilution series and only primers with an efficiency ranging from 80% to 115% were used for further analysis ( Table S4 ) . The houskeeping genes Nvactin and/or Nvgadph were used to normalize relative fold changes between control and manipulated embryos and each qPCR analysis was repeated on independent biological replicates . 20 µg of total RNA was sent to NimbleGen , Iceland for further cDNA synthesis , labelling and array hybridization . The 4-plex microarray ( 72 , 000 features ) is an oligonucleotide-based chip version , custom designed and produced by NimbleGen Systems ( Roche ) . Gene expression levels were normalized in the Nimblescan software according to [144] and [145] and fold-changes calculated by comparing expression values from control and treated embryos . Array results were screened based on the provided genome annotations assigned to each array spotID . If no clear blast hit or gene information was assigned to the prediction gene model from the Joint Genome Institute , we retrieved the genomic sequences ( http://genome . jgi-psf . org/Nemve1/Nemve1 . home . html ) for the given gene and performed manually Blast ( blastx ) searches [146] against the NCBI database to determine the nature of the predicted gene product . All sequences from genes of interest have been used for Blast analysis to confirm their nature and to determine previously published genes . To distinguish between previously published genes , and newly identified putative TFs and signaling molecules , we used the best Blast Hit identification , followed by “- like” to designate the newly identified gene sequences . In order to verify the potential accuracy of the “best blast hit” naming system , we used published phylogenetic reconstruction techniques to confirm the orthologies of Nv-admp-related , Nvfgf20-like , Nvfgf20-like as well as forkhead transcription factors ( see Table 2 for references ) . Thus , while “Blast hit” approaches can be used to provide a general idea of the protein family , a detailed phylogenetic analysis is required to better resolve these gene orthologies , especially when paralogy issues or when multiple gene predictions are present for one gene family . The constructs pC2+Nvßcat:GFP and pCs2+Xßcat69:GFP have been described previously [59] , [71] . cDNA constructs encoding the wild type ORF ( NvTcf ) , the wild type ORF including 16 nucleotides of the 5′UTR ( NvTCF5′ ) and a dominant negative form ( NvdnTcf ) lacking 276 nucleotides of the 5′ coding sequence of NvTcf , were generated by PCR . The forward primers used were: NvTcf_FWD ( 5′ CACCATGCCTCAGCTTCCTAGGAATTCC 3′ ) NvTcf5′_FWD ( 5′ CACCACATGAGACGGTAGTATGCCTCAG 3′ ) NvdnTcf_FWD ( 5′ CACCATGAACCAGCATGGTAGTGACAGTAAAC 3′ ) The reverse primer ( 5′ GTGTCTGATGTTACTGGATTACTTG 3′ ) used was lacking the stop codon for fusion with a C-terminal Venus fluorescent tag . NvTcf cDNA constructs were cloned into pENTR dTOPO vectors ( Invitrogen ) and subsequently recombined into a C-terminal Venus containing pDEST expression vector [147] . pDest expression vectors were linearized with the restriction enzyme ACC651 and transcribed using the Ambion mMessage mMachine T3 kit ( Ambion , AM1348 ) . pCs2+ expression vectors were linearized with the restriction enzyme Not1 and transcribed using the Ambion mMessage mMachine SP6 kit ( Ambion , AM1340M ) . Synthetic mRNA was purified using Megaclear columns ( Ambion , AM1908 ) followed by one phenol-chloroform extraction and isopropanol precipitation . Nvßcat:GFP , Xßcat69:GFP , NvTCF:Venus , NvTCF5′:Venus and NvdnTCF:Venus mRNAs were injected in zygotes at final concentrations of 0 . 3–0 . 5 µg/µl . A morpholino antisense oligonucleotide ( Gene Tools ) was designed to target a region spanning the 5′UTR and tranlsation inition site of Nv-Tcf ( MoTcf_trans: 5′ CTG AGG CAT ACT ACC GTC TCA TGT G 3′ , Figure S7 ) . The morpholino was used at 1 mM without noticeable toxicity . Absence of gene expression perturbation after injection of a control morpholino ( 5′ AGAGGAAGAATAACATACCCTGTCC 3′ ) at 1 mM has been reported previously [94] . All injections were compared to either rhodamine dextran injected or uninjected control embryos . Microinjections were performed using a PLI-90 Pico-Injector ( Harvard Appartus ) . All embryos developed in 1/3x filtered-seawater at 17°C . Previously described gene sequences were used to sub-clone into pGemT ( Promega , #A3600 ) from mixed stage cDNA . All other sequences used in this study were isolated in the course of a microarray analysis . Genome predictions as well as EST sequence information were combined to design primers ( Table S5 ) that allow the amplification and cloning of genes between 05 . kb and 2 kb as described above . Accession numbers for all analyzed genes in this study can be found in Table 2 . Embryo fixation , probe synthesis and in situ hybridization were performed as previously described [26] , [148] . 0 . 5 kb–2 kb digoxigenin-labelled ( Roche , #11573152910 ) riboprobes were synthesized using the MegaScript Transcription Kit ( Ambion ) . Hybridization of riboprobes ( 1 ng/µl ) was carried out at 62°C in 50% formamide hybe buffer and visualization of the labeled probe was performed using NBT/BCIP as substrate for the alkaline phosphatase-conjugated anti-DIG antibody ( Roche , #11093274910 ) . To analyze embryonic and larval morphology , we used Biodipy FL Phallacidin ( Molecular Probes/Invitrogen , #B607 ) and propidium iodide ( Sigma , #81845 ) to stain f-actin and the cell nuclei respectively as described previously [48] . in situ hybridization images were taken on a Zeiss AxioScop 2 mounted with with an Axiocam camera triggered by Axiovision software ( Carl Zeiss ) . All expression patterns described here have been submitted to Kahi Kai , a comparative invertebrate gene expression database [149] hosted at http://www . kahikai . org/index . php ? content = genes . Scoring of treatment , overexpression and morphant phenotypes was performed on a Zeiss Z-1 Axio imager microscope and confocal imaging was conducted on a Zeiss LSM710 microscope running the LSM ZEN software ( Carl Zeiss ) . Fluorescent images were false-colored , the fluorescent channels merged using ImageJ ( http://rsbweb . nih . gov/ij/ ) and cropped to final size in Photoshop Cs4 ( Adobe Inc . ) . | Cnidarians ( anemones , corals , and “jellyfish” ) are an animal group whose adults possess derivatives of only two germ layers: ectoderm and a bifunctional ( absorptive and contractile ) gastrodermal ( gut ) layer . Cnidarians are the closest living relatives to bilaterally symmetrical animals that possess all three germ layers ( ecto , meso , and endoderm ) ; and compelling molecular , genomic , developmental , and evolutionary evidence exists to demonstrate that the cnidarian gastrodermis is evolutionarily related to both endodermal and mesodermal germ layers in all other triploblastic bilaterian animals . Little is known about endomesoderm specification in cnidarians . In this study , we constructed the framework of a cnidarian endomesodermal gene regulatory network in the sea anemone , Nematostella vectensis , using a combination of experimental approaches . We identified and characterized by both qPCR and in situ hybridization 51 genes expressed in defined domains within the presumptive endomesoderm . In addition , we functionally demonstrate that Wnt/Tcf signaling is crucial for regionalized expression of a defined subset of these genes prior to gut formation and endomesoderm maintenance . Our results support the idea of an ancient gene regulatory network underlying endomesoderm specification that involves inputs from multiple signaling pathways ( Wnt , FGF , BMP , but not Notch ) early in development , that are temporarily uncoupled in bilaterian animals . | [
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| 2012 | A Framework for the Establishment of a Cnidarian Gene Regulatory Network for “Endomesoderm” Specification: The Inputs of ß-Catenin/TCF Signaling |
Phlebotomine sand flies transmit Leishmania , phlebo-viruses and Bartonella to humans . A prominent gap in our knowledge of sand fly biology remains the ecology of their immature stages . Sand flies , unlike mosquitoes do not breed in water and only small numbers of larvae have been recovered from diverse habitats that provide stable temperatures , high humidity and decaying organic matter . We describe studies designed to identify and characterize sand fly breeding habitats in a Judean Desert focus of cutaneous leishmaniasis . To detect breeding habitats we constructed emergence traps comprising sand fly-proof netting covering defined areas or cave openings . Large size horizontal sticky traps within the confined spaces were used to trap the sand flies . Newly eclosed male sand flies were identified based on their un-rotated genitalia . Cumulative results show that Phlebotomus sergenti the vector of Leishmania tropica rests and breeds inside caves that are also home to rock hyraxes ( the reservoir hosts of L . tropica ) and several rodent species . Emerging sand flies were also trapped outside covered caves , probably arriving from other caves or from smaller , concealed cracks in the rocky ledges close by . Man-made support walls constructed with large boulders were also identified as breeding habitats for Ph . sergenti albeit less important than caves . Soil samples obtained from caves and burrows were rich in organic matter and salt content . In this study we developed and put into practice a generalized experimental scheme for identifying sand fly breeding habitats and for assessing the quantities of flies that emerge from them . An improved understanding of sand fly larval ecology should facilitate the implementation of effective control strategies of sand fly vectors of Leishmania .
The leishmaniases are a group of diseases endangering some 350 million people in 88 countries , most of them in the poorer regions of the globe . The two major clinical forms are cutaneous leishmaniasis ( CL ) and visceral leishmaniasis ( VL ) . CL manifests as a sore at the bite site of the infected sand fly and is usually self healing . VL is a life-threatening systemic infection . There are an estimated 1–1 . 5 million cases of CL and half a million new cases of VL annually [1] , [2] . CL caused by Leishmania tropica and L . major are considered emerging diseases in Israel as well as other East Mediterranean countries [3] , [4] . The vectors of leishmaniasis are blood-sucking phlebotomine sand flies ( Diptera: Psychodidae ) belonging to two genera , Phlebotomus in the Old World , and Lutzomyia in the New World . There are some 700 known species of sand flies but only about 30 of those transmit leishmaniasis to humans [5] , [6] . Sand flies are small and fragile nocturnal insects that normally fly close to the ground and refrain from flight activity under windy conditions . Although experimentally marked flies have occasionally been demonstrated to travel over a kilometer , most sand flies remain within several hundred meters of their breeding place during their entire life [6] . Because of their limited flight range , transmission of Leishmania within CL endemic areas is often geographically discontinuous , with characteristically small and separate foci close to the reservoir host habitats [7] , [8] . The widest gap in our understanding of sand fly biology remains their larval ecology . Sand flies , unlike mosquitoes , do not breed in water and there is relatively little information on their breeding sites [9] . Small numbers of larvae have been recovered from diverse habitats including caves , crevices , animal burrows , termite mounds , cracks in the soil , domestic animal shelters , cracked walls , tree-holes , birds' nests and leaf litter [9] , [10] . However , there are only two documented examples of more productive sites: one from Sardinia , where several hundred Ph . ( Larrousius ) spp . larvae were recovered from top soil inside an abandoned shed [11] , [12] , [13] and another from Panama where over two thousand Lutzomyia spp larvae were found in soil samples obtained from forest floors [14] . In the insectary , optimal rearing conditions for different sand fly species are often remarkably uniform . For example , desert dwelling Ph . papatasi from the Middle East and Neo-tropical Lu . longipalpis from Latin America , are optimally reared under the same conditions ( 26±2°C , 85–95% RH , composted rabbit feces-based larval diet ) [15] , [16] , [17] . Such observations coupled with scanty field studies , indicate that in nature , immature sand flies develop in moist and dark microhabitats affording stable climatic conditions . Eggs are deposited separately and hatch within 7–10 days . Larvae feed upon composted organic matter of animal and plant origin and undergo four larval instars lasting around three weeks . Pupal development lasts 7–10 days [6] . Kfar Adumim is a small township in the Judean Desert where CL caused by L . tropica was initially documented in the early 1990s [18] . A more thorough ecological study performed some 10 years later , characterized the L . tropica strains from patients and Ph . sergenti sand flies [19] . Sporadic cases of CL have continuously been reported from the area since then and sand fly populations , have been monitored intensively [20] . Ph . sergenti were shown to be primarily exophilic but towards the end of summer their numbers indoors increased [21] . On the other hand the majority of sand flies captured inside houses were Ph . papatasi but only few were collected outside [19] , [21] . Significantly , despite the existence of its known vector , Ph . papatasi , CL caused by L . major is absent from the region probably because the reservoir hosts Psammomys obesus are not found in rocky terrain [4] , [19] . The current study was designed to characterize larval breeding habitats in an arid region that supports exceptionally dense sand fly populations comprising chiefly one species , Ph . sergenti [20] , [21] , [22] . We were specifically interested to determine whether Ph . sergenti breed only in natural habitats or if they may adapt to man-made habitats such as gaps between boulders forming artificial support walls of irrigated gardens . Characterization of breeding sites of Ph . sergenti may facilitate the application of larval source-reduction as a component within integrated sand fly control strategies [23] .
Kfar Adumim ( 31°49′N : 35°20′E ) is a rural community located 20 km east of Jerusalem ( altitude 316 m ) . Climate is semiarid with 260 mm mean annual rainfall , and 20°C mean annual temperature . Flora is predominated by perennial desert shrubs and annual grasses [24] . The study area was located on the lime-stone slopes to the south east of the village and in the gorge below . Parts of the slope were strewn with large rocks and debris left over from the construction of the houses and the road above . The slope itself comprises alternate strata of hard flint and soft chalk producing natural terraces , perforated with small caves and cervices . The crevices were occupied by rodents such as spiny mice ( Acomys cahirinus ) and the larger caves were frequently used by rock hyraxes ( Procavia capensis ) , the principal reservoir hosts of L . tropica in Israel [4] , [8] , [25] . Three cave systems were explored during the summers of 2010 and 2011: Other putative breeding and/or resting habitats studied included: natural rocky ledges ( some marked by red asterisks ) with abundant nooks and cervices , dry river beds , shady areas under trees close to river beds . Artificial habitats were also investigated . These included rock piles ( marked with yellow star in Figure 1A ) as well as support walls constructed down-slope from houses and gardens . These walls were made of layers of very large boulders placed one on top of the other leaving 2–5 cm gaps . Somewhat wider gaps of 15–20 cm were left between adjacent boulders in the same tier ( Figs 2E , 2F ) . The gardens and lawns above the support wall were irrigated regularly . Sand flies were removed from the sticky straps using fine watchmakers' forceps and placed in ethanol . Traps were wiped clean and smeared again with castor oil . Emergence studies were conducted over consecutive nights in order to distinguish between resting and emerging sand flies . Those exiting during the first night were considered either resting or emerging sand flies . On the other hand , those flies captured 24 hours and longer after the cave ( or other habitat ) had been covered , were considered more likely to be flies emerging from breeding sites [27] . In the laboratory , sand flies were placed in a strainer and washed with dilute detergent solution to remove oil and other debris . For identification , sand flies were mounted in Hoyer's medium with their heads separate from thoraces . Flies were identified to species based on cibarial and pharyngeal armature as well as spermathecae of females and external genitalia of males [28] , [29] , [30] . For all other purposes , flies were kept in 70% ethanol . The external genitalia of male sand flies rotate on the longitudinal body axis through 180° during the initial 16–24 hours of adult life to assume their mature ( = rotated ) position ( see experimental data below ) . Therefore , males with un-rotated or partially rotated external genitalia can be considered to have been captured during their first night of activity as adults . Like other dipterans , male phlebotomines eclose from the pupae with un-rotated genitalia ( Fig . 2C ) [31] , [32] . In order to make use of this easily discernable physical characteristic to identify young males , we needed to establish the timing of the rotation of male genitalia . Ph . sergenti adults were collected in the study area using CDC light traps and colonized using standard methods [16] . Emerging F1 male sand flies were removed from the breeding pots at intervals of 5 hours and placed in the freezer . Thereafter , these male flies were mounted in Hoyer's medium on microscope slides and the position of their genitalia was determined under a microscope at ×100–200 ( Figs . 2C , D ) . Ten soil samples were collected in and around caves 1–3 and several sites in the dry riverbed below ( Fig . 1A ) as well as from cracks in an artificial support wall ( Fig . 2E ) . Selection of sites to be sampled was conducted after the sand fly data had been analyzed in order to provide a well balanced representation of the ecosystems under study . There was no possibility of reaching the depths of caves and gaps between boulders in order to sample the actual breeding site of the larvae . Thus , samples comprising top soil , were weighed in the field , sifted over 2 mm sieve and sealed in heavy plastic sample bags for transport . In the laboratory a 2 . 0 g aliquots were removed from each sample , dried in an oven at 105°C for 24 hrs and weighed again . The hygroscopic water content was calculated as the ratio of weight loss to dry weight [33] . To determine the pH , electrical conductivity and salinity , equal weights of air-dried soil and deionized water ( 30 g ) were mixed and allowed to equilibrate for one hour . The mixture was shaken well using a rotary shaker ( 135 rpm for 5 min ) , and centrifuged ( 8 , 000 rpm for 10 min at 25°C ) . The supernatant was decanted; pH was measured using a pH meter model SA 520 ( Orion Research Inc . , Beverly , MA , USA , ) . Electrical conductivity was determined using a TH-2400 conductometer ( El-Hamma Instruments , Mevo-Hamma , Israel ) and the salinity was derived from the conductivity values . To determine values for organic matter , soil aliquots weighing 3 g each ( 3 aliquots per sample ) were subjected to dry combustion ( 450°C , 8 hr ) and reweighed . The weight of combustible organic matter was calculated after reducing the gravimetric water content . The soil texture was established based on particle sedimentation rates using the hydrometer method [34] . The numbers of sand flies captured on the first and second nights by traps placed inside and outside sealed caves were tested for normality by the 1-Sample Kolmogorov - Smirnov Z test ( K-S ) . Thereafter , mean ( ±SE ) trap yields on consecutive nights were compared using a 2- sample t test for data complying with normal distribution . Otherwise , the Mann Whitney rank sum test was applied . All statistical analyses were carried out on GraphPad PRISM® , version 5 , ( San Diego , CA ) .
A total of 36 laboratory-reared ( 26°C ) Ph . sergenti ( F1 ) males were collected and examined at different times after eclosion . Males with fully rotated genitalia ( Fig . 2D ) were first observed amongst those collected 25 hours post-eclosion ( Table 1 ) . In order to obtain baseline data on density and species composition of sand flies in different habitats , we sampled sand flies in and around four cave systems using CDC light traps with green light sticks ( Fig . 1B ) . A total of 1 , 372 sand flies comprising 1 , 049 males and 323 females was trapped during six nights . The male sand flies were identified and shown to comprise 79% Ph . sergenti and >1% Ph . papatasi . The rest were Sergentomyia spp . The three cave systems where most flies were captured were selected for further study ( marked 1 , 2 & 3 in Fig . 1A ) . In order to determine the presence of sand flies in and near artificial support walls , 50 , A4 sticky traps were inserted horizontally into gaps between tiers of boulders and vertically between adjacent boulders of a support wall ( Fig . 2F ) . Traps were collected the next day and 111 sand flies were removed from the sticky traps . Of these 75 were Ph . sergenti males all of which had fully rotated genitalia ( Fig . 2D ) . Rocky ledges near caves - Four tunnel emergence traps with sand fly-proof netting enclosing four large sticky traps ( 60×80 cm ) and covering approximately 10–14 m2 , were deployed for one night on lime-stone rock ledges above and below the caves ( Fig . 1A asterisks ) . One female Ph . sergenti was captured in one of these traps . Fifteen Ph . sergenti ( six males and nine females ) were captured on a single large exposed sticky trap deployed in the same area . Twelve sand flies , including 10 male Ph . sergenti were captured in emergence tunnel traps covering piles of rocks stacked upon rock ledges ( Fig . 2B ) . The trap was deployed for one night in one location trapping no flies . Thereafter , the trap was moved to an adjacent location where three flies were trapped during the first night and nine male Ph . sergenti were trapped the following night . Six of these males had un-rotated or partially rotated genitalia ( Fig . 2C ) , indicating they were emerging from a breeding habitat . Unfortunately , due to safety concerns , potential breeding sites in this loose-rock slope could not be investigated any further . No sand flies at all were capture by a “tunnel” trap placed under an Acacia tree for three nights . During the same three nights , 127 sand flies ( 68 males ) were trapped on four large sticky traps ( 12 trap/nights ) placed next to the tunnel ( Fig . 2A ) . Similarly , no flies were captured in an emergence “tunnel”-trap deployed over-night covering a small rock mound in the dry river bed . Thirteen emergence traps ( Fig . 1F ) were deployed for one night each over un-cracked soil in additional dry river beds and slopes around Kfar Adumim . No flies were captured in any of those . A total of 4 , 787 sand flies ( Phlebotomus and Sergentomyia ) were trapped inside and outside 3 covered caves over 18 nights ( = 185 trap/nights ) using large sticky traps ( Fig . 1D ) . Of these 3 , 468 ( 72% ) were males , and the predominant species was Ph . sergenti accounting for 84% of all male sand flies . A significant proportion ( 25% ) of the Ph . sergenti males had un-rotated or partially rotated genitalia suggesting proximity to breeding habitats ( Table 2 ) . Sand flies trapped inside caves covered with sand fly-proof nets comprised 1 , 247 males , 90% of which were Ph . sergenti . A relatively high percentage ( 18% ) of the Ph . sergenti males captured inside covered caves had un-rotated genitalia ( Fig . 3 ) . Of the sand flies trapped outside the caves , 2 , 221 were males and 80% of the male sand flies were Ph . sergenti . A significantly higher proportion of the Ph . sergenti males captured outside sealed caves had un-rotated genitalia ( 29% , χ 2 = 49 . 97 , P<0 . 0001 , Fig . 3 ) . Thus , breeding sites were not limited to the sealed caves and sand flies were also emerging from neighboring caves , cracks , small holes or burrows ( Table 2 ) . The number of sand flies captured during the first night inside covered caves was somewhat lower than those captured outside caves . Sand fly numbers dropped both inside and outside the caves on the second night of all experiments ( Fig . 4A ) . The drop in numbers inside the caves was statistically significant ( Two samplest test , P = 0 . 0056 ) while the decline in numbers of sand flies captured outside the caves was not statistically significant ( Mann – Whitney rank sum test , P = 0 . 4642 ) . After the second night , the numbers of sand flies remained more or less stable . An identical trend was observed amongst Ph . sergenti males which numbers inside sealed caves declined significantly after one night ( Two samplest test , P = 0 . 0019 ) . In baseline collections 20 A4 sticky traps were inserted horizontally in gaps between boulders of the support wall . Sand flies were removed the next day and males were identified . Of the 111 sand flies , 75 were Ph . sergenti males , all of them with fully rotated genitalia ( Fig . 2D ) . To determine whether sand flies were breeding in the support wall , 50 A4 sticky traps were inserted in gaps between boulders along three , 7 m long sections of the wall . These sections were covered with sand fly-proof mesh . Additional 15 sticky paper traps were placed on stones and vegetation outside the mesh . The experiment lasted four nights and the sticky traps were collected and replaced every day . In all , 203 Ph . sergenti males were identified out of a total of 213 sand flies trapped during the experiment . Of the Ph . sergenti males 13% of those trapped inside and 19% of those trapped outside the net had un-rotated genitalia ( Fig . 3 , Table 3 ) . The difference in the percentages of males less than 24 h old inside the netting and outside it were not significant ( χ 2 = 1 . 402 , P , ns ) . The soil texture was predominantly sandy in eight of the ten sites sampled . Air drying of the soil samples for 72 hours resulted in insignificant reduction in gravimetric water content . Hygroscopic water content determined by heating for 24 h at 105°C varied between 2 . 09% to 6 . 26% . The highest values were found in caves and borrows and lowest ones outside caves and in the support wall . The pH values were uniformly slightly alkaline . Salinity calculated from the electric conductivity values was high in all samples . The highest values were measured in caves and the support wall - presumably due to these habitats being protected from rain . The organic matter content also varied widely with the higher values recorded in some of the caves and under an acacia tree ( Table 3 ) .
Caves and crevices as well as rodent burrows and cracked rocks have all been postulated to afford suitable environments for sand fly breeding [9] . However , no attempts were made to conclusively demonstrate that sand flies were in fact breeding in such habitats . In the current study we monitored adult activity as an indicator for sand fly resting and breeding sites . By sealing off caves with sand fly-proof netting , we were able to ascertain that sand flies captured inside were emerging from within the enclosed space . To separate possible resting populations from those emerging from pupae , we continued trapping inside sealed caves 2–7 additional nights . Although there was a significant decline in numbers of sand flies captured inside the cave after the first night , sand flies continued to be collected inside sealed caves over several nights ( Fig . 4 ) . If we assume that sand flies captured during the first night were mostly resting adults leaving their diurnal shelters to forage , the majority of flies captured during subsequent nights ( 2–8 ) can be considered as emerging from breeding sites [27] . Interestingly , in all five repetitions of the experiment in three different caves , sand fly numbers outside sealed caves also dropped after the first night , albeit insignificantly . Perhaps sand fly activity is restricted to a small area , close to their emergence site , where they use the same resting habitat night after night . In such a case , those sand flies trying to exit during the evening hours were stopped by the mesh and many were caught on the sticky traps . Similarly , sand flies attempting to enter the covered cave towards the end of the night were either captured on the external traps or eventually moved on to other suitable habitats nearby . These displaced sand flies were “lost” to the monitored cave's potential population during subsequent trapping nights . This scenario would explain the sharp decline in numbers observed on the second night both inside and outside the caves ( Fig . 4 ) . The tendency of Ph . sergenti , the vector of zoonotic L . tropica , to congregate in and around their diurnal resting/breeding sites , which are frequently in rocky habitats with caves or boulder mounds inhabited by hyraxes , has been previously documented [21] , [35] . In preceding studies performed in Kfar Adumim and elsewhere in Israel , it was shown that Ph . sergenti were abundant in caves and rocky slopes but conspicuously absent from nearby homes [19] , [21] , [36] . In our initial experiments we demonstrated that sand fly males with un-rotated genitalia can be considered young males that are active during the first night of adulthood ( Table 1 ) . Since such males were abundant inside sealed caves , these caves must have contained sand fly breeding habitats . However , since even higher percentages of young males were captured outside the covered caves ( Figs . 3 ) , it is clear that sand flies were also breeding in other sites not covered by nets . Our efforts to identify such places were largely unsuccessful and no flies were captured in emergence traps placed in various locations including rocky ledges close to the caves . One notable exception were young male sand flies with un-rotated genitalia captured using a tunnel-type emergence trap covering a pile of stones next to cave 1 ( Fig . 1A marked with star ) . Hence , young males emerging from this pile ( on nights when it was not covered by mesh ) and neighboring caves and cracks , could have accounted for the ones captured on sticky traps outside covered caves . Although the topological conditions made it too dangerous to perform intensive studies in the rock pile , we do not believe sand flies were breeding in the pile itself since suitable larval habitats ( organic matter , cool temperatures and high humidity ) would not be expected in such a loose-rock pile . Thus , breeding probably took place in caves and caverns with openings under the rock pile . These may even have been contiguous with the large cave systems . Male Ph . sergenti with un-rotated genitalia were also caught in and near an artificial support wall but in much smaller numbers than around caves ( Table 3 , Fig . 5 ) . The presence of these young males indicates that sand fly breeding does take place within these walls . Although cracks are mostly too small for hyraxes , various rodents such as house mice ( Mus musculus ) and spiny mice ( Acomys cahirinus ) are plentiful in such walls ( Warburg , unpublished ) . Young male Ph . sergenti captured outside the net probably emerged from the wall in adjacent areas not covered by the net or they may have flown from caves and burrows some 20 m downhill . The suitability of support walls constructed using large boulders leaving wide gaps for sand fly breeding , should be taken into consideration in future planning of residential neighborhoods . No flies were captured in any of the emergence traps placed over bare soil , grass covered soil , dry river beds , valley slopes , rock-covered soil or dried sewage treatment basin . These negative findings indicate that sand flies emerge through visible cracks , burrows and cave openings and not from unbroken surfaces . We know that sand flies require habitats with stable temperatures and high humidity and such conditions would not be met at the upper horizons of desert soils . Moreover , the combustible organic matter in soils is not a suitable food source for sand fly larvae . Much like the rearing conditions used in insectaries , natural larval breeding habitats must contain composting animal feces and/or plant-derived matter as larval food [17] . The terrain where the current study was conducted was particularly difficult to study and there was no possibility of obtaining soil samples from the actual dwelling place of the larvae . Therefore , we extracted soil samples from productive caves , and compared them with samples taken from areas where sand flies do not breed . All the samples were rather desiccated and characterized by high salinity . The organic matter content was rather low but somewhat higher inside caves and under a particular tree . On the whole we cannot deduce too much from these results as differences between productive areas and barren ones were inconsistent ( Table 4 ) . Caves did contain ample quantities of rock hyrax feces . The fecal pellets found close to the opening of the cave were hard and dry . However , deeper inside caves pellets would be expected to be more humid and , therefore , softer making them suitable as sand fly larval food . Although the soil analyses do not pertain to the exact location where larvae dwell , they were included in this report as putatively important points of reference for future studies ( by us and others ) . Our results are in accord with previous studies that postulated the existence of larval breeding habitats in rocky slopes , caves and support walls in Kfar Adumim , based on the high proportion of male sand flies captured near such habitats [21] . Interestingly , other studies performed in the Judean Desert suggested sand fly breeding and resting occurs primarily in valley floors covered with vegetation [27] . Our efforts failed to capture any sand flies emerging from soil in valley floors or slopes with or without vegetation or stones . These differing findings may be due to the fact that Muller et al [27] were dealing primarily with Ph tobbi and Ph major while our study and that of Orshan et al [21] focused on Ph . sergenti . | Sand flies are small blood sucking flies that transmit Leishmania , the etiologic agent of leishmaniasis - a prevalent disease over large areas of the World . Unlike mosquitoes , sand flies do not breed in water . Their larvae develop in humid habitats containing decaying organic matter ( e . g . habitats such as burrows , tree holes and caves ) . However , in most cases , larval breeding habitats are unknown and larvae remain inaccessible to control efforts . In this paper we identified the breeding sites of an important sand fly vector of cutaneous leishmaniasis by using emergence traps to collect adult sand flies exiting caves and cracks . We identified young male sand flies ( less than 24 hours old ) by examining their external sex organs . The data collected enabled us to determine that sand flies were breeding primarily inside caves and in adjacent cracks but also in man-made support walls constructed with large boulders . These findings will be useful for applying more effective sand fly and leishmaniasis control measures . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
]
| [
"medicine",
"infectious",
"diseases",
"ecology",
"biology"
]
| 2012 | Breeding Sites of Phlebotomus sergenti, the Sand Fly Vector of Cutaneous Leishmaniasis in the Judean Desert |
Understanding buffering mechanisms for various perturbations is essential for understanding robustness in cellular systems . Protein-level dosage compensation , which arises when changes in gene copy number do not translate linearly into protein level , is one mechanism for buffering against genetic perturbations . Here , we present an approach to identify genes with dosage compensation by increasing the copy number of individual genes using the genetic tug-of-war technique . Our screen of chromosome I suggests that dosage-compensated genes constitute approximately 10% of the genome and consist predominantly of subunits of multi-protein complexes . Importantly , because subunit levels are regulated in a stoichiometry-dependent manner , dosage compensation plays a crucial role in maintaining subunit stoichiometries . Indeed , we observed changes in the levels of a complex when its subunit stoichiometries were perturbed . We further analyzed compensation mechanisms using a proteasome-defective mutant as well as ribosome profiling , which provided strong evidence for compensation by ubiquitin-dependent degradation but not reduced translational efficiency . Thus , our study provides a systematic understanding of dosage compensation and highlights that this post-translational regulation is a critical aspect of robustness in cellular systems .
Robustness in biological systems is a general trait of living cells and a fundamental feature involving the maintenance of stability during perturbation [1–4] . It is a universal challenge to cope with perturbations leading to fluctuations in biological processes because cells are exposed to changes in internal and external environments [5 , 6] . The robustness of cells to various perturbations can be understood as a consequence of fluctuations in gene expression and buffering of fluctuations [5–8] . Therefore , understanding buffering mechanisms is essential to understanding the optimization of gene expression and adaptation to changes in environmental conditions . The decoding of genetic information is achieved through irreversible processes from DNA to RNA to protein as stated in the central dogma of molecular biology [9] . The gene expression level at each step is generally in a linear relationship with gene copy number , namely an increase in gene copy number leads to a proportional increase in messenger RNA ( mRNA ) and corresponding protein levels . However , in the face of perturbations , this linear relationship should become nonlinear for maintaining cellular homeostasis . This prediction highlights the importance of studying the quantitative aspects of the central dogma in the context of robustness . For example , previous studies have investigated the robustness of gene expression level under genetic perturbations caused by an increase in gene copy number [10–12] . These efforts have demonstrated that the copy number of a subset of genes in the genome correlates with mRNA levels but not directly with protein levels . This phenomenon is known as protein-level dosage compensation , reported in yeast and mammalian cells [13–15] . Although dosage compensation is expected to contribute to cell robustness , we lack a systematic understanding of the underlying mechanisms that confer robustness to biological systems . Systematic investigations of the robustness in cellular systems have been performed by focusing on the effects of manipulating gene copy number on cell growth [12 , 16–18] . We previously measured cell robustness to gene overexpression using a genetic technique termed genetic tug-of-war ( gTOW ) , by which fragility to protein overproduction is indirectly and quantitatively assessed as an upper limit of gene copy number in Saccharomyces cerevisiae [17 , 19 , 20] . The genome-wide gTOW analysis has revealed fragile points as a set of 115 dosage-sensitive genes that cause impaired growth when the gene copy number is slightly increased [17] . In other words , only 2% of the yeast genome ( 115 out of 5806 genes ) is sensitive to gene dosage such that a copy number increase leads to breakdown of biological systems . Conversely , this result indicates that genetic perturbations to biological processes are generally buffered . However , the buffering mechanisms behind the robustness against gene overexpression remain to be investigated . In this study , we developed a screening system for genes with dosage compensation based on the gTOW technique . Here , our findings suggest that the proportion of the dosage-compensated genes in the genome is approximately 10% and that these genes may encode subunits of protein complexes . We investigated the compensation mechanism by focusing not only on protein degradation but also on translational efficiency by using a ribosome profiling technique [21] . Our data suggest that the robustness of gene expression reflects transient degradation , dynamic changes in protein lifetime , produced in response to environmental changes .
To identify genes with dosage compensation , we developed a screening method as shown in Fig 1A . The key idea of this method is to determine the protein level expressed from a single copy of a target gene when its copy number is increased . We monitored the level of each target protein labeled with the tandem affinity purification ( TAP ) tag expressed from the genomic locus when the copy number of the same target gene without the TAP tag is increased by a multicopy plasmid ( Fig 1A , middle and right panels ) . If the expression level of the TAP-tagged protein is reduced in this situation , we consider that the target gene is subjected to dosage compensation ( Fig 1A , right panel ) , since the compensation mechanism should not distinguish the TAP-tagged endogenous protein from the non-tagged exogenous protein . Here , we call the condition where the target protein is expressed from the single genomic copy “Single” ( Fig 1A , left panel ) and the condition where the target protein is expressed from the genomic copy and the multicopy plasmid “Multi” ( Fig 1A , middle and right panels ) . We used a series of strains in which the TAP tag is integrated into the 3´-region of each gene [22] , and a plasmid collection in which each target gene with native regulatory regions , including promoter and 5´ and 3´ untranslated regions , is cloned into a multicopy plasmid , pTOWug2-836 [17] . We screened 54 genes on chromosome I whose TAP-tagged strains were available as representatives of the yeast genome ( S1 Fig ) . By this screening , we identified five genes ( RBG1 , MTW1 , POP5 , SAW1 , and ERP2 ) whose protein expression was reduced when their copy numbers were increased ( Fig 1B ) . We did not detect off-target effects of an increase in gene copy number by the gTOW technique: the total cellular protein level measured in the total cell lysate did not differ in the Single and Multi conditions . An example of this observation is shown in S2A and S2C Fig . Quantification of fold change of the protein levels was carried out as shown in S2 Fig . The protein levels of the dosage-compensated genes were 0 . 2–0 . 6-fold ( Fig 1C ) , when their copy numbers were 15–27 copies ( S3 Fig ) . The dosage compensations are performed by post-transcriptional regulation because mRNA levels from the endogenous locus did not change even when the copy numbers were increased ( Fig 1D ) . We thus identified five genes with dosage compensation via post-transcriptional mechanisms . To verify the experimental setup for measuring only the endogenous protein levels , we measured the level of a target protein expressed from both the genome and plasmid . The experimental setup is the same with that used for the analysis of endogenous protein except that the plasmid encodes each of the TAP-tagged target proteins ( S4A Fig ) . We measured the total TAP-tagged protein levels ( S4B and S4C Fig ) and the plasmid copy numbers ( S4D Fig ) and calculated the fold change of the protein levels per gene copy ( S4E Fig ) . This analysis showed dosage compensation of all the five genes identified by the chromosome I screen when considering both endogenous and exogenous protein levels ( S4F Fig ) . The fold change values were very similar with those calculated from the endogenous protein levels . Thus , we conclude that the experimental setup shown in Fig 1A , whereby we detect the TAP-tagged protein expressed from the genomic locus , can capture dosage compensation . We further verified the experimental setup using green fluorescent protein ( GFP ) tag in order to assess the dependency of dosage compensation on the TAP tag . We used the yeast strains in which the GFP tag is integrated into the 3´-region of each target gene and measured the expression levels of GFP-tagged target proteins upon an increase in gene copy number . Western blot analysis for the dosage-compensated proteins Rbg1 and Mtw1 and the uncompensated protein Pop8 showed reduced levels of Rbg1 and Mtw1 but not Pop8 in the Multi condition ( S5 Fig ) . Because the similar degree of the compensation was observed between the analyses using the TAP and GFP tags , dosage compensation is not a TAP-tag-mediated phenomenon . Given that dosage compensation is performed by post-transcriptional mechanisms ( Fig 1D ) , the deceleration of protein synthesis and/or the acceleration of protein degradation should be the mechanisms of dosage compensation ( S6 Fig ) . We first examined the contribution of protein degradation by focusing on the ubiquitin–proteasome system , a major selective degradation pathway . We used cim5-1 strain as a proteasome-defective mutant [23] to test whether the compensation is not observed in this mutant . As shown in Fig 2A and 2B , the dosage compensations of Rbg1 , Mtw1 , and Erp2 were significantly weaker in cim5-1 than in wild-type cells ( CIM5 ) . The compensations of Pop5 and Saw1 also tended to be weaker in cim5-1 mutant , although the difference was not statistically significant ( S7 Fig ) . The mRNA levels of these genes in cim5-1 and CIM5 cells did not differ ( S8 Fig ) . To further verify the participation of the ubiquitin–proteasome system in dosage compensation , we examined the ubiquitination of the compensated proteins . The TAP-tagged proteins were immunoprecipitated with IgG-coated beads and cleaved with tobacco etch virus ( TEV ) protease , and the cleaved proteins were analyzed by Western blotting using anti-ubiquitin antibody ( Fig 2C ) . Because the expression levels of the dosage-compensated proteins and the pull-down efficiency were different among the samples ( Fig 2D ) , we normalized the ubiquitination level by dividing it by loading amount of immunoprecipitated proteins as described in Fig 2E . We compared the amount of the TAP-tagged proteins captured on the beads before and after TEV cleavage , which reflects the amount of immunoprecipitates analyzed by Western blotting for ubiquitinated proteins . This analysis showed a tendency to accumulate the greater amount of ubiquitinated proteins in cim5-1 cells upon the Multi condition ( Fig 2F ) . These results strongly suggest that protein degradation by the ubiquitin–proteasome system is the main mechanism of dosage compensation . We also examined the contribution of translational control to dosage compensation . A high compensation level of Pop5 in cim5-1 cells ( Fig 2B ) prompted us to measure the translational efficiency change upon an increase in POP5 copy number . We performed ribosome profiling and RNA-seq and measured translation rate comparing between the Single and Multi conditions of POP5 gene copy number . While a high copy number of POP5 led to an increase in its mRNA expression ( Fig 3A and 3C ) , the ribosome density per mRNA was not changed ( Fig 3B and 3C ) . The RNA-seq analysis also indicates that an increase in POP5 copy number by the gTOW technique specifically increased its mRNA level and did not induce off-target effects on mRNA expression of the other genes . Therefore , we conclude that translational efficiency is not responsible for dosage compensation , at least in the case of Pop5 . Residual proteasome activity in cim5-1 mutant or alternative systems may specifically degrade Pop5 protein upon an increase in its gene copy number . We noted that all the five dosage-compensated genes identified by the chromosome I screen encode subunits of protein complexes , as listed in Table 1 . To investigate the relationship between dosage compensation and complex subunits , we analyzed other subunits of the complexes . As shown in Fig 4 , we found that six of seven subunits of the RNase MRP and nuclear RNase P complexes , NSL1 in the MIND complex , and EMP24 in the Erp2 complex were compensated at the protein level but not at the mRNA level . Quantification showed that the degree of compensation is very similar among the six subunits of the RNase MRP and nuclear RNase P complexes ( Fig 4B ) . As listed in Table 1 , we tested an additional 12 subunit genes and identified 7 dosage-compensated ones . This ratio is significantly higher than that identified in the initial screening ( 5 out of 54 genes ) ( p < 10−9 , chi-square test ) , although not all subunit genes are compensated . Thus , we speculated that dosage compensation predominantly targets complex subunits . As shown above , dosage compensation may be performed mainly through protein degradation and target predominantly complex subunits . We thus hypothesized that accelerated degradation of excess subunits that failed to construct a stable complex is the nature of dosage compensation . To examine this , we focused on the Rbg1–Tma46 complex as a model complex . Our working hypothesis is that when a subunit is overexpressed , there are two pools of subunit , the unstable pool that has not found a dimerization partner and the stable pool that is in a complex ( Fig 5 ) . The unstable pool is present but very small in the native condition where a large fraction of Rbg1 molecules are stable and a stoichiometric balance between Rbg1 and Tma46 is in the steady state . In contrast , when Rbg1 is overexpressed , the unstable pool of Rbg1 is predominant . In the unstable pool , accelerated degradation of excess subunits should be observed . We first assessed the degradation of Rbg1 upon its overexpression by measuring the amount of Rbg1 after treating cells with a translational inhibitor , cycloheximide ( CHX ) . The CHX chase assay showed accelerated degradation of Rbg1 when its gene copy number was increased ( Fig 6A and 6B ) , as we expected . We next tested the effect of a loss and a high copy number of TMA46 on Rbg1 expression . In tma46Δ strain , the Rbg1 expression was reduced to less than 0 . 5-fold ( Fig 6C and S9A Fig ) . On the other hand , the amount of Rbg1 was increased more than 1 . 3-fold when the TMA46 copy number was increased in wild-type cells ( Fig 6D and S9B Fig ) . These compensations are performed post-transcriptionally because the RBG1 mRNA levels were not changed in these conditions ( Fig 6C and 6D ) . We further examined whether dosage compensation directly contributes to a higher or lower levels of the resulting complexes . The levels of the Rbg1–Tma46 complex were assessed by Native-PAGE followed by immunoblotting . This analysis confirmed that the complex was almost not detected in tma46Δ strain ( Fig 6E ) . In wild-type cells , the levels of the TAP-tagged version of the Rbg1–Tma46 complex decreased and increased upon an increase in RBG1 and TMA46 copy numbers , respectively ( Fig 6F ) . These changes in the complex levels are consistent with the changes in the Rbg1 monomer levels in the same conditions . Therefore , we conclude that Rbg1 stability is modulated depending on the dosage balance against the partner molecule Tma46 and that dosage compensation affects not only subunit levels but also complex levels .
This study extends our understanding of the rescue mechanism for perturbations causing the breakdown of biological systems . Our results demonstrate that protein-level dosage compensation is responsible for robust expression of subunit genes under genetic perturbations . Correction of the subunit levels is performed at the final step in gene expression by protein degradation rather than earlier steps , mRNA transcription/degradation or translation . These results suggest that dosage compensation at the post-translational level is a critical step to mask the fragility caused by an increase in gene copy number . Furthermore , our findings in the context of systems biology provide a new foundation for the robustness of cellular systems . The robustness in cellular systems to gene copy number changes has been investigated mainly using two approaches: generating aneuploidy of specific chromosomes [12 , 18] and introducing a plasmid carrying an individual target gene [17] . The generation of aneuploid cells containing one extra chromosome doubles the number of genes in the additional chromosome . Several recent studies using aneuploid yeast and mammalian cells have revealed fragility of cellular systems against gene copy number increase in a genome-wide manner [12 , 18] . The use of a multicopy plasmid carrying an individual target gene dramatically increases its copy number . A particular method for this approach is based on the gTOW technique [17] . The genome-wide gTOW analysis has revealed over 80% of the yeast genome with more than 100 copies of an upper limit of gene copy number . The impact of an increase in gene copy number on cell fitness differs between doubled number of genes in an extra chromosome and many copies of a single gene . Previous studies have demonstrated that aneuploidy-induced proteotoxic stress causes cell fragility leading to growth impairment [10 , 13 , 29] . Because aneuploid yeast strains are very sensitive to perturbations at the RNA and protein levels , aneuploidy-induced proteotoxicity affects a wide range of biological processes . On the other hand , overexpression of most individual genes does not inhibit growth of wild-type yeast strain [17 , 30] . Thus , the gTOW technique allows us to study mechanisms for buffering against genetic perturbations by focusing on individual target genes in normal physiological condition . We expect that exploring the effects of an increase in individual gene copy number will identify novel mechanisms for maintaining cellular homeostasis . Indeed , a very recent study has shown that the fragility of aneuploid cells is caused by many genes on single additional chromosomes but not by duplicated dosage-sensitive genes that were identified by the gTOW analysis [31] . We first developed a screening method based on the gTOW technique to estimate how much of the genome is subjected to dosage compensation ( Fig 1A ) . Our screen of chromosome I showed that 5 out of 54 genes are regulated by the compensation ( Fig 1B ) , which estimates that dosage compensation confers robustness to 10% of the genome for buffering perturbed gene expression . Interestingly , all screened genes encode subunits of different complexes ( Table 1 ) and , for 17 subunits included in these complexes , 70% ( 12 subunits ) are subjected to dosage compensation ( Fig 4 and Table 1 ) . This result is in agreement with previous findings that protein levels of duplicated genes encoding complex subunits are reduced in aneuploid yeast strains [10] . However , Mtw1 and Rpp1 , the dosage-compensated proteins identified in this study , are not compensated in aneuploid cells [12] . This difference may result from aneuploidy-specific physiological conditions associated with proteotoxicity [32] . Given that the biological function of dosage compensation is to maintain subunit stoichiometry , this result explains our previous observation that cellular systems are very fragile to subunit gene overexpression [17] . This is also consistent with previous observations that the stoichiometric imbalance caused by aneuploidy strongly correlates with impaired cell growth [18 , 33] . Similarly , our data support a classical hypothesis called the balance hypothesis that predicts deleterious effects due to imbalanced subunit stoichiometry [34] . Recent studies investigating the robust formation of protein complexes have elucidated the location where subunits are translated [35 , 36] , the timing when subunits are assembled into complexes [37] , and the mechanisms by which subunit stoichiometry is maintained [38 , 39] . Li et al . found a proportional synthesis strategy whereby protein synthesis rates of complex subunits correlate with subunit stoichiometry [39] . This strategy guarantees stoichiometry of some well-characterized complexes , with a small number of exceptions synthesized in excess . In agreement with previous studies [29 , 38 , 40–43] , we also identify proteasomal degradation as a mechanism of dosage compensation . We further provide direct evidence for the ubiquitination of the individual dosage-compensated proteins ( Fig 2C ) . Thus , this study enhances our understanding of dosage compensation as a general mechanism for the fine-tuning of subunit levels . Protein-level dosage compensation might occur cotranslationally for the following reasons: ( i ) Subunits are assembled into complexes cotranslationally [37] . ( ii ) A large proportion of the proteome is cotranslationally ubiquitinated [44 , 45] . ( iii ) The degradation of subunits via an N-terminal degradation signal at the nascent chain level has been supported by experimental evidence [38] . In addition , autophagy might be included because higher expression of autophagy-related proteins has been detected in aneuploid mammalian cells [15 , 18] . We show no evidence for a contribution of translational efficiency to the compensation of Pop5 protein ( Fig 3B and 3C ) . This result supports the robust translational efficiency of duplicated genes in aneuploid yeast strains [12 , 33] . However , it should be noted that an increase in a single gene to approximately 20 copies does not result in a decrease in ribosome occupancy for its mRNAs ( S3 Fig , Fig 3B and 3C ) . We speculate that translational efficiency is not responsible for dosage compensation and that translation is quite robust against genetic perturbations caused by an increase in gene copy number . Although our screen of chromosome I suggests that the dosage-compensated genes encoding complex subunits constitute approximately 10% of the genome , subunit genes constitute 33% of the yeast genome . This suggests that there are other rules to distinguish between the compensated subunits and the uncompensated ones . Pop8 might be helpful for further characterization of the dosage compensation mechanism since the compensation level of only Pop8 differed from those of all other tested subunits of RNase MRP and nuclear RNase P complexes ( Fig 4 ) . Pop8 has the smallest number of interacting partners in these complexes , although the other subunits have at least two or more potential partners [26 , 46] . Therefore , Pop8 is suggested to be located at the peripheral region of these complexes . It is also known that only depletion of the Pop8 does not result in deleterious effects on RNase MRP function [46–51] . A similar observation in a different protein complex , oligosaccharyl transferase ( OST ) , was recently reported [42] . The OST complex consists of nine subunits , including the functionally redundant Ost3 or Ost6 components , which are potentially the last subunit assembled into the complex . Overexpression of Ost3 or Ost6 does not lead to reduction of its protein level , whereas many of the other subunits show accelerated degradation upon their overexpression . Moreover , deletion of the Ost3 or Ost6 gene does not affect the protein level of the other subunits and results in only a small decrease in enzyme activity of the OST complex [42 , 52 , 53] . As listed above , characteristic features with similarities between Pop8 and Ost3 or Ost6 include the order of assembly , number of interactions , and responsibility for the function of each complex . Consideration of these features seems to provide other rules to determine the complex subunits predominantly regulated by dosage compensation . As shown in Fig 6 , we show that the compensation of Rbg1 is performed in a stoichiometry-dependent manner between gene dosage of RBG1 and TMA46 . This bidirectional regulation of Rbg1 level may reflect changes in its degradation rate ( Fig 6A and 6B ) . These results are analogous to bidirectional changes of Cog1 level upon overexpression of itself or its partner subunits: Cog2 , Cog3 , and Cog4 [38] . Although dosage compensation has been postulated to contribute to the levels of subunits and also resulting complexes , there might be no direct evidence for changes in the complex levels . Our study provides direct experimental evidence that dosage compensation of Rbg1 affects the levels of the Rbg1–Tma46 complex under genetic perturbations ( Fig 6E and 6F ) . We conclude by noting that subunit stoichiometry potentially has a broad impact on robustness in cellular systems because of the fact that numerous biological processes are dependent on protein complexes . Furthermore , studies of mechanisms behind stoichiometry maintenance might be important for understanding diseases related to gene copy number alterations . For example , a recent study suggests that a set of specific genes on trisomic chromosome 21 have a causal effect on Down syndrome [54] . Again , our approach based on the gTOW technique for measuring robustness in cellular systems provides a fundamental framework for the quantitative assessment of cell robustness .
The yeast strain BY4741 ( MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 ) [55] was used for the screening , ribosome profiling , and protein complex analysis . The W303-1B ( MATα ade2-1 his3-11 , 15 leu2-3 , 112 trp1-1 ura3-1 can1-100 ) [56] and CMY765 ( MATα cim5-1 ura3-52 leu2Δ1 his3Δ200 ) [23] strains were used for the analysis of the ubiquitin–proteasome system . The tma46Δ strain ( MATa tma46Δ::KanMX his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 ) was also used for the protein complex analysis . TAP-tagged or GFP-tagged strains ( BY4741 background ) and tma46Δ strain were obtained from Thermo Scientific . These strains were transformed with empty vector pTOWug2-836 or pTOW40836 or the same vector carrying the gene of interest . Transformation of the yeast strains was performed by the lithium acetate method [57] . The transformants were grown at 30°C in SC medium lacking the indicated amino acids . The copy number of each gene was measured using the gTOW technique , as described previously [17] . Briefly , single colonies of yeast cells carrying pTOW plasmids were cultivated in a 96-well plate containing 200 μL of SC–Ura medium for 4 days at 30°C , and then , 5 μL of the culture was inoculated into 200 μL of fresh SC–Ura medium . After culturing for 50 h at 30°C , the cells were harvested by filtration followed by DNA extraction with zymolyase treatment . The extracts were subjected to real-time quantitative PCR with Lightcycler 480 ( Roche ) using SYBR Green I Master ( Roche ) to quantify the expression of LEU3 from the chromosome and leu2d gene from pTOW plasmids . The resulting copy number of the pTOW plasmid carrying each target gene was calculated according to the method described previously [19] . Yeast cells grown in the appropriate medium were harvested at log-phase and subsequently total RNA was extracted using the hot phenol method [58] . Contaminating genomic DNA was removed and reverse transcription was carried out with PrimeScript RT reagent Kit with gDNA Eraser ( TaKaRa ) according to the manufacturer’s instructions . The generated cDNA was amplified by real-time quantitative PCR with Lightcycler 480 using SYBR Green I Master . Quantification of TAP tag and ACT1 mRNA expression was performed with the following primers to amplify TAP-tag and ACT1 gene on the chromosome: TAP-tag-forward ( 5´-AATTTCATAGCCGTCTCAGCA-3´ ) ; TAP-tag-reverse ( 5´-CTCGCTAGCAGTAGTTGGAATATCA-3´ ) ; ACT1-forward ( 5´-TGCAAACCGCTGCTCAA-3´ ) ; and ACT1-reverse ( 5´-TCCTTACGGACATCGACATCA-3´ ) . The fold change of mRNA levels was calculated as previously described [11] . Yeast cells were grown in 2 mL of the appropriate medium and subcultured in 3 mL of fresh medium . The optical density at 600 nm ( OD600 ) was measured and 2 OD600 units were harvested at log-phase . The cells were treated with 1 mL of 0 . 2 N NaOH for 5 min at room temperature and then were suspended in 2× NuPAGE LDS Sample Buffer ( Invitrogen ) and heated at 70°C for 10 min . The supernatant corresponding to 0 . 5 OD600 units was labeled with EzLabel FluoroNeo ( ATTO ) and subjected to polyacrylamide gel electrophoresis with lithium dodecyl sulfate ( SDS-PAGE ) , followed by Western blotting with PAP ( Sigma-Aldrich ) ( 1:2000 ) or an anti-GFP antibody ( Roche ) ( 1:1000 ) and peroxidase-conjugated secondary antibody ( Nichirei Biosciences ) ( 1:1000 ) . We used NuPAGE 4%–12% Bis-Tris Gel ( Invitrogen ) for SDS-PAGE and iBlot Transfer Stack PVDF membrane ( Invitrogen ) for Western blotting . Chemiluminescence was induced by SuperSignal West Femto Maximum Sensitivity Substrate ( Thermo Scientific ) and detected using LAS-4000 image analyzer ( Fujifilm ) and ImageQuant LAS 4000 ( GE Healthcare ) . The band intensity was quantified using ImageQuant TL ( GE Healthcare ) , and the fold change of protein levels was calculated as shown in S2 Fig according to a previously described method [11] . TAP-tagged strains carrying pTOW plasmid were cultivated in 100 mL of SC–Ura medium . The whole cells were harvested at log-phase and lysed with glass beads in 750 μL of lysis buffer [20 mM HEPES , 2 mM EDTA , 100 mM NaCl , 20% glycerol , 0 . 05% IGEPAL CA-630 ( Sigma-Aldrich ) , Protease Inhibitor Cocktail , EDTA-Free ( Thermo Scientific ) ] with 20 mM N-ethylmaleimide . The supernatant was immunoprecipitated using Dynabeads coated with pan-mouse IgG ( Life Technologies ) , as described previously [59] . In short , the supernatant was incubated with 40 μL of Dynabeads in a Thermomixer Comfort ( Eppendorf ) at 21°C for 2 h with shaking at 1300 rpm . The Dynabeads were washed one time with the lysis buffer and three times with the lysis buffer containing 150 mM NaCl and suspended in 16 μL of AcTEV buffer ( Invitrogen ) containing 1 mM DTT . Before TEV cleavage , for Western blot analysis of TAP-tagged protein , 2 μL of the suspension was removed and suspended in 10 μL of 2× NuPAGE LDS Sample Buffer and heated at 65°C for 20 min . The remaining Dynabeads were then treated with 1 μL ( 10 units ) of AcTEV protease ( Invitrogen ) in a Thermomixer Comfort at 4°C for 16 h with shaking at 1300 rpm . The supernatant was subjected to Western blotting with polyclonal rabbit anti-ubiquitin antibody ( DAKO ) ( 1:500 ) as primary antibody and peroxidase-conjugated secondary antibody ( Nichirei Biosciences ) . After TEV cleavage , the Dynabeads were suspended in 14 μL of 2× NuPAGE LDS Sample Buffer and heated at 65°C for 20 min , and 2 μL of the extracts were mixed with 8 μL of 2× NuPAGE LDS Sample Buffer and analyzed by Western blotting with PAP . Detection of chemiluminescence was performed as described above . Yeast cells were grown to log-phase in SC–Ura , and 0 . 5 OD600 units were harvested for time point 0 . Then , CHX was added to a final concentration of 200 μg/mL . Cells were harvested after 1 , 2 , 4 , and 6 h of CHX treatment , followed by total protein extraction in 2× NuPAGE LDS Sample Buffer . The supernatant corresponding to 0 . 1 OD600 units was analyzed by Western blotting against the TAP tag as described above . The protein level at each time point was calculated as the intensity of Rbg1-TAP from Western blot divided by that of the 50-kDa protein , corresponding to enolase , from SDS-PAGE . The relative level was calculated by dividing the protein level at each time point by that at time point 0 . Yeast cells BY4741 expressing POP5-TAP from a single genomic locus and carrying pTOWug2-836 or pTOWug2-POP5 were grown in 150 mL of SC–Ura at 30°C with vigorous shaking . These cells were grown from an initial OD600 of approximately 0 . 2 to OD600 around 0 . 7 , and the cells were then harvested by vacuum filtration . The cell pellet was immediately immersed in a 50 mL conical tube filled with liquid nitrogen and 2 mL of lysis buffer [10 mM Tris-HCl ( pH 7 . 0 ) , 10 mM Tris-HCl ( pH 8 . 0 ) , 150 mM NaCl , 5 mM MgCl2 , 1 mM DTT , 1% Triton X-100 , 200 μg/mL CHX , 25 U/mL Turbo DNase ( Invitrogen ) ] was dripped into the tube . Extracts were prepared as previously described [21] , except that the frozen cells were pulverized with a mixer mill at 30 Hz . The total amount of RNA in the extracts was quantified using RiboGreen ( Invitrogen ) , and then , 50 μg of total RNA was diluted to 300 μL with the lysis buffer . The sample was subjected to preparation of ribosome footprints according to a previously described method [60] . Briefly , total RNA was treated with RNase I ( Epicentre ) , and then the ribosomal pellet was collected by sucrose cushion centrifugation . RNA was recovered from the pellet with TRIzol ( Life Technologies ) and purified with Direct-zol RNA MiniPrep ( Zymo ) , followed by isopropanol precipitation . The resulting RNA was subjected to gel electrophoresis , and then , the 26–34-nucleotides regions were excised . The size-selected fragments were subjected to dephosphorylation with T4 PNK ( New England Biolabs ) and linker ligation with T4 Rnl2 ( New England Biolabs ) . Ribosomal RNA was depleted from the sample using Ribo-Zero Magnetic Gold Kit for yeast ( Epicentre ) . Reverse transcription was carried out with Protoscript II ( New England Biolabs ) on the rRNA-depleted sample . The reverse transcription product was then separated by gel electrophoresis , and the full-length product was excised . The size-selected product was circularized with CircLigaseII ( Epicentre ) . The circularized DNA was amplified by 6 , 8 , 10 , 12 , and 14 cycles of PCR with Phusion polymerase ( New England Biolabs ) . The PCR products were loaded on gel , and the products of eight cycles were excised . The quality of the PCR product was assessed using Agilent 2200 TapeStation ( Agilent Technologies ) . Deep sequencing ( 50 bp , single-end reads ) was then performed on the Illumina HiSeq 4000 ( Illumina ) . RNA-seq libraries were generated using TruSeq Standard Total RNA Library Prep Kit ( Illumina ) from total RNA prepared as described above , and then , deep sequencing was performed in the same run with ribosome footprint sequencing . The profiling analysis was performed according to the method previously described [60 , 61] with modifications for the analysis of budding yeast profiling . In short , rRNA sequences were aligned to a set of budding yeast rRNA sequences , and then , non-rRNA reads were aligned to the budding yeast transcriptome . A-site offsets of ribosome footprints and mRNA fragments were estimated from 13 to 17 nucleotides for each read length of 26–30 nucleotides and 15 nucleotides for 22–51 nucleotides , respectively . The mapped reads excluding the first 15 codons and last 5 codons were counted based on the A-site offsets . DESeq was used to calculate fold change of RNA expression and translational efficiency [62] . Ribosome profiling and RNA-seq data analysis did not distinguish the reads from endogenous or exogenous POP5 copies . Yeast cells were grown to log-phase in 6 mL of the appropriate medium and 5 OD600 units were harvested . The cells were washed with 1 mL of sterile water and lysed with glass beads in 250 μL of Digitonin buffer [1% Digitonin ( Invitrogen ) , 1× NativePAGE Sample Buffer ( Invitrogen ) , Protease Inhibitor Cocktail , EDTA-Free] . The supernatant corresponding to 0 . 2 OD600 units was mixed with NativePAGE 5% G-250 Sample Additive ( Invitrogen ) ( final concentration 0 . 25% ) and loaded on NativePAGE 4–16% Bis-Tris Gel ( Invitrogen ) . The native gel electrophoresis was performed at room temperature with NativePAGE Running Buffer Kit ( Invitrogen ) according to the manufacturer’s instructions . After electrophoresis , the gel was treated with SDS buffer [1× NuPAGE MOPS SDS Running Buffer ( Invitrogen ) , 1% SDS] for 15 min . The gel was washed five times with 1× NuPAGE MOPS SDS Running Buffer , and then , blotted onto PVDF membrane using the iBlot system . After blotting , the membrane was washed with methanol for 5 min for three times , rinsed with PBST [1× PBS , 0 . 1% Tween 20] for three times , and washed in PBST for 10 min . The membrane was blocked with 4% skim milk in PBST for 1 h at room temperature before incubation with PAP ( 1:4000 ) in the same condition . Chemiluminescence was induced and detected as described above . The membrane was stained with CBB-R250 after immunoblotting . | Cells are exposed to environmental changes leading to fluctuations in biological processes . For example , changes in gene copy number are a source of such fluctuations . An increase in gene copy number generally leads to a linear increase in the amount of protein; however , a small number of genes do not show a proportional increase in protein level . We investigated how many of the genes exhibit this nonlinearity between gene copy number and protein level . Our screen of chromosome I suggests that genes with such nonlinear relationships constitute approximately 10% of the genome and consist predominantly of subunits of multi-protein complexes . Because previous studies showed that an imbalance of complex subunits is very toxic for cell growth , a function of the nonlinear relationship may be to correct the balance of complex subunits . We also investigated the underlying mechanisms of the nonlinearity by focusing on protein synthesis and degradation . Our data indicate that protein degradation , but not synthesis , is responsible for maintaining a balance of complex subunits . Thus , this study provides insight into the mechanisms for coping with the fluctuations in biological processes . | [
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| 2017 | Post-Translational Dosage Compensation Buffers Genetic Perturbations to Stoichiometry of Protein Complexes |
The incidence of leptospirosis , a neglected zoonotic disease , is uncertain in Tanzania and much of sub-Saharan Africa , resulting in scarce data on which to prioritize resources for public health interventions and disease control . In this study , we estimate the incidence of leptospirosis in two districts in the Kilimanjaro Region of Tanzania . We conducted a population-based household health care utilization survey in two districts in the Kilimanjaro Region of Tanzania and identified leptospirosis cases at two hospital-based fever sentinel surveillance sites in the Kilimanjaro Region . We used multipliers derived from the health care utilization survey and case numbers from hospital-based surveillance to calculate the incidence of leptospirosis . A total of 810 households were enrolled in the health care utilization survey and multipliers were derived based on responses to questions about health care seeking in the event of febrile illness . Of patients enrolled in fever surveillance over a 1 year period and residing in the 2 districts , 42 ( 7 . 14% ) of 588 met the case definition for confirmed or probable leptospirosis . After applying multipliers to account for hospital selection , test sensitivity , and study enrollment , we estimated the overall incidence of leptospirosis ranges from 75–102 cases per 100 , 000 persons annually . We calculated a high incidence of leptospirosis in two districts in the Kilimanjaro Region of Tanzania , where leptospirosis incidence was previously unknown . Multiplier methods , such as used in this study , may be a feasible method of improving availability of incidence estimates for neglected diseases , such as leptospirosis , in resource constrained settings .
Incidence estimates of infectious diseases are crucial for determining burden of disease and prioritizing resources for disease control . However , these estimates are often unavailable in resource constrained settings , resulting in scarce data on which to base recommendations for public health interventions . Active population-based surveillance , using door-to-door visits in the community , is an ideal method for measuring infectious disease incidence , but active surveillance is limited in many areas due to its requisite investment of time and resources . Previous studies have described methods for extrapolating data from hospital based surveillance and population-based surveys of health care seeking behavior to estimate disease incidence in a population [1]–[5] . This method has facilitated disease incidence estimates in populations in resource constrained settings where these data were previously unavailable . The incidence of leptospirosis , a neglected , poverty-associated zoonosis found worldwide , is uncertain in sub-Saharan Africa [6] . Several studies in sub-Saharan African countries have shown that leptospirosis may comprise a substantial proportion of acute febrile illness [7]–[11] . However , population-based incidence estimates are lacking with the exception of studies from the Seychelles showing a high annual incidence of 60–101 cases per 100 , 000 persons [12] , [13] . The lack of data is likely the consequence of limited access to laboratories with leptospirosis diagnostic capability , low clinician awareness of the disease , often nonspecific clinical features of leptospirosis , and lack of surveillance infrastructure . As a result , in sub-Saharan Africa public health measures for leptospirosis prevention and control have not been prioritized , and leptospirosis remains a neglected cause of febrile illness . In this study , we estimate leptospirosis incidence in two districts in the Kilimanjaro Region of Tanzania using data from hospital based surveillance and multipliers derived from a population-based household health care utilization survey .
This study was conducted in the Kilimanjaro Region in northern Tanzania . The household survey was done in 2 districts in the Kilimanjaro Region , Moshi Rural ( population 401 , 369 ) and Moshi Urban ( population 143 , 799 ) ( Figure 1 ) . Febrile illness surveillance was conducted at 2 hospitals in Moshi , Kilimanjaro Christian Medical Centre ( KCMC ) and Mawenzi Regional Hospital ( MRH ) . These hospitals serve as major providers of care for residents of Moshi Urban and Moshi Rural . KCMC and MRH are located a diagonal distance of 3 . 5 km apart and a driving distance of approximately 5 . 5 km apart . KCMC is a 458 bed tertiary referral hospital that serves several regions in northern Tanzania , and MRH is a 300 bed regional hospital that serves the Kilimanjaro Region . As part of a comprehensive study of the etiology of febrile illness in northern Tanzania , adult and pediatric inpatients at KCMC and adult inpatients at MRH were prospectively enrolled from September 17 , 2007 through August 31 , 2008 . Methods and results have been previously described [15]–[17] . Patients admitted to the adult medicine wards , aged ≥13 years , were eligible to participate if they had an oral temperature of ≥38 . 0°C and had been admitted for <24 hours . Pediatric inpatients , aged ≥2 months to <13 years , were eligible if they had a history of fever in the past 48 hours , an axillary temperature of ≥37 . 5°C or a rectal temperature of ≥38 . 0°C and had been admitted for <24 hours . Demographic information , including the participant's district and village of residence , was collected . Participants were asked whether they had been referred from another inpatient hospital . Acute serum was drawn and archived , and all participants were asked to return 4–6 weeks after enrollment to submit a convalescent serum sample . Acute and convalescent serum samples were sent to the United States Centers for Disease Control and Prevention ( CDC ) for serologic analysis for leptospirosis . Incidence was estimated with the use of multipliers derived from the health care utilization survey and fever surveillance . Multipliers account for leptospirosis cases that were potentially missed in the stages of reporting ( Figure 2 ) and are the multiplicative inverse of the relevant proportions . We calculated the ‘KCMC multiplier’ and ‘MRH multiplier’ to account for health care seeking preferences and cases potentially missed due to selection of health care providers or options not under surveillance . In order to evaluate the sensitivity of our estimates , the ‘KCMC multiplier’ and ‘MRH multiplier’ were derived based on head of household responses to the 2 distinct survey questions: ‘What is the name of the health care facility with an inpatient ward where you/your family would go if you/your family had fever ? ’ and ‘What will you do if a member of this household in x age group has elevated body temperature for ≥3 days ? . ’ We selected the first and second choice responses to ‘elevated body temperature for ≥3 days’ as most representative of where patients sufficiently ill to warrant hospital admission would seek care . If the head of household's first choice for care in the event of ‘elevated body temperature for ≥3 days’ was KCMC and second choice was MRH , then we only counted the first choice . Since KCMC is a tertiary referral hospital , patients who would elect to present to KCMC first would be unlikely to subsequently be seen at MRH for the same illness . In addition , we calculated a ‘referral adjustment’ to adjust for patients transferred to KCMC from another inpatient hospital given that transfer may not reflect a patient's preference of health care facility . We calculated an ‘enrollment multiplier’ to account for patients who were eligible but not enrolled in fever surveillance for any reason . We calculated a ‘time multiplier’ to account for fever surveillance enrollment 5 ( 71 . 43% ) of 7 days of the week . We also calculated a ‘paired sera multiplier’ to account for patients in fever surveillance that did not have acute and convalescent serum samples tested , and therefore could not meet criteria for confirmed leptospirosis . This multiplier was applied to the incidence estimates involving only confirmed cases . We calculated a ‘MAT sensitivity multiplier’ to account for the sensitivity of the diagnostic test . MAT sensitivity on paired sera was estimated to approach 100% , while sensitivity on acute serum only was estimated at 48 . 7% , and sensitivity on convalescent serum only was estimated at 93 . 8% [23] . Case numbers were also adjusted to account for MAT specificity of approximately 97 . 3% [23] . Multiplier derivations based on study results are presented in detail in the results . Incidence was calculated by age group as follows: age 0 to <5 years , age 5 to <15 years , and age ≥15 years . We used the 2002 Tanzania National Census , the most recent population data available for Tanzania , to determine the population of Moshi Urban and Moshi Rural for the specified age groups [14] . Data were entered using the Cardiff Teleform system ( Cardiff , Inc . , Vista , CA , USA ) into an Access database ( Microsoft Corp , Redmond , WA ) . Incidence calculations were done using Microsoft Excel 2010 ( Microsoft Corp . Redmond , WA ) spreadsheets . Other analyses were performed using STATA , version 10 . 1 ( STATACorp , College Station , TX ) and Epi Info 7 , version 7 . 1 . 2 . 0 ( CDC , Atlanta , GA ) . Pearson's chi-square was used to compare the health care utilization study population with the census population . All p values are 2 sided and evaluated for statistical significance at the 0 . 05 significance level . This study was approved by the KCMC Research Ethics Committee , the Tanzania National Institutes for Medical Research National Research Ethics Coordinating Committee , and the Institutional Review Boards of Duke University Medical Center , the CDC , and the International Vaccine Institute . All study participants provided written informed consent .
In the health care utilization study , 810 households were enrolled; no selected household refused participation . Responses represented a total of 3919 household members . Table 1 shows the demographics of the health care utilization study population compared to the general census population of the two districts . All households had at least one member aged ≥15 years; 361 had ≥1 member aged 5 years to <15 years; 156 had ≥1 child aged 1 year to <5 years; and 42 had ≥1 infant <1 year . Responses for the <1 year and 1 to <5 years age groups were combined for analysis due to the small number of responses in the <1 year age group as well as to more closely match the age intervals of Tanzania census data . After combining these age groups , the <1 to 5 years age group had 198 responses; 16 households had members of both age groups for which responses to both questions were included . Aside from this exception , a response for a given age group was counted only once per household , regardless of the number of household members in that age group . A total of 870 inpatients were enrolled at KCMC and MRH . Participant characteristics have been described elsewhere [15]–[17] . Residence in Moshi Urban or Moshi Rural Districts was reported by 588 ( 67 . 59% ) participants . Of those residing in the study area , 315 ( 53 . 57% ) of 588 had paired sera tested , 222 ( 37 . 76% ) had only acute serum tested , and 28 ( 4 . 76% ) had only convalescent serum tested . Of those with paired sera tested , 23 ( 7 . 30% ) of 315 met the case definition for confirmed leptospirosis . Of those with ≥1 serum sample tested , and not classified as confirmed leptospirosis , 19 ( 3 . 51% ) of 542 met the definition of probable leptospirosis . Case numbers by age group and enrollment site are shown in Table 3 . Incidence calculations using the 2 distinct fever-related questions as well as different leptospirosis case definitions are shown in Table 3 . Leptospirosis incidence in Moshi Urban and Moshi Rural Districts by age group is estimated as follows: 0 to <5 years , 175–288 cases per 100 , 000 persons/year; 5 to <15 years , 149–161 cases per 100 , 000 persons/year; ≥15 years , 33–59 cases per 100 , 000 persons/year . Overall annual leptospirosis incidence in Moshi Urban and Moshi Rural is estimated at 97 to 102 cases per 100 , 000 persons based on responses to ‘elevated body temperature ≥3 days’ and 75 to 85 cases per 100 , 000 persons based on responses to ‘health care facility with an inpatient ward where you/your family would go if you/your family had fever ? ’ . Our best estimate of overall incidence , including the most comprehensive use of data derived from the question about ‘elevated body temperature ≥3 days’ and using a leptospirosis case definition including both confirmed and probable cases , is 102 cases per 100 , 000 persons/year .
We calculated a high incidence of leptospirosis in 2 districts of the Kilimanjaro Region in Tanzania . Despite its high incidence , leptospirosis remains an under-recognized cause of febrile illness in Africa , resulting in a lack of resources dedicated to defining risk factors and implementing public health control measures . The high estimated incidence underscores the importance of prioritizing further research into the epidemiology of leptospirosis in Africa . There is an urgent need for rapid diagnostic tests that are sensitive and specific as well as improved surveillance in order to better assess leptospirosis case fatality and disease burden across different types of health care facilities in Africa . An approach similar to ours , using health facility based surveillance and health care utilization surveys , is a practical method that may be feasible across multiple sites with limited resources to improve leptospirosis incidence data . | Leptospirosis is a zoonotic infection that occurs worldwide and is caused by a spirochete , Leptospira spp . The incidence of leptospirosis is unknown in most of sub-Saharan Africa , including Tanzania . Incidence estimates are important in prioritizing resources for disease prevention and control . In this study , we calculated leptospirosis incidence in 2 districts in the Kilimanjaro Region of Tanzania using a multiplier method . We used responses from a population-based survey that asked where participants and their household members would seek health care in the event of fever along with the number of leptospirosis cases found at 2 hospitals under surveillance to calculate estimated incidence . We calculated a high incidence of leptospirosis in the study area that was previously unrecognized . This has important implications for prioritizing further research and consideration of public health control measures for leptospirosis in Tanzania . | [
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| 2013 | Estimating Leptospirosis Incidence Using Hospital-Based Surveillance and a Population-Based Health Care Utilization Survey in Tanzania |
Prion diseases are incurable neurodegenerative disorders in which the normal cellular prion protein ( PrPC ) converts into a misfolded isoform ( PrPSc ) with unique biochemical and structural properties that correlate with disease . In humans , prion disorders , such as Creutzfeldt-Jakob disease , present typically with a sporadic origin , where unknown mechanisms lead to the spontaneous misfolding and deposition of wild type PrP . To shed light on how wild-type PrP undergoes conformational changes and which are the cellular components involved in this process , we analyzed the dynamics of wild-type PrP from hamster in transgenic flies . In young flies , PrP demonstrates properties of the benign PrPC; in older flies , PrP misfolds , acquires biochemical and structural properties of PrPSc , and induces spongiform degeneration of brain neurons . Aged flies accumulate insoluble PrP that resists high concentrations of denaturing agents and contains PrPSc-specific conformational epitopes . In contrast to PrPSc from mammals , PrP is proteinase-sensitive in flies . Thus , wild-type PrP rapidly converts in vivo into a neurotoxic , protease-sensitive isoform distinct from prototypical PrPSc . Next , we investigated the role of molecular chaperones in PrP misfolding in vivo . Remarkably , Hsp70 prevents the accumulation of PrPSc-like conformers and protects against PrP-dependent neurodegeneration . This protective activity involves the direct interaction between Hsp70 and PrP , which may occur in active membrane microdomains such as lipid rafts , where we detected Hsp70 . These results highlight the ability of wild-type PrP to spontaneously convert in vivo into a protease-sensitive isoform that is neurotoxic , supporting the idea that protease-resistant PrPSc is not required for pathology . Moreover , we identify a new role for Hsp70 in the accumulation of misfolded PrP . Overall , we provide new insight into the mechanisms of spontaneous accumulation of neurotoxic PrP and uncover the potential therapeutic role of Hsp70 in treating these devastating disorders .
The prion protein ( PrP ) appears to be an essential element in the pathogenesis of an incurable class of neurological disorders called transmissible spongiform encephalopathies ( TSE ) or prion diseases . These protein deposition disorders can present with sporadic , inherited or infectious origins and lead to dementia , motor dysfunction , and inevitably , death [1] . Regardless of the origin of TSE , conversion of the normal cellular prion protein ( PrPC ) into its pathological scrapie isoform ( PrPSc ) seems to be the fundamental process underlying the pathogenesis of prion diseases [2] . PrP is a membrane-anchored glycoprotein highly enriched in the brain with a unique ability to undergo conformational changes . PrPSc can be distinguished from PrPC by its partial resistance to heat , denaturing agents and protease digestion , its insolubility in non-ionic detergents , and its fibrillar aggregation [2] . Moreover , deposition of PrPSc in the brain is associated with cerebral damage , including spongiform degeneration and neuronal loss . According to the “protein-only” hypothesis , PrPSc transmits the disease by propagating its abnormal conformation using PrPC as a substrate by autocatalytic mechanisms [2] , [3] . It is not clear , though , what other proteins or cellular components are critical for PrP conversion . The unique infectious aspects of prion diseases have received substantial attention due to the scare of the “mad cow’ epidemics of the 1980's . However , the sporadic disease accounts for 80–85% of all prion disorders in humans [4] . In patients with sporadic Creutzfeldt-Jakob disease ( CJD ) , wild type PrPC converts into typical protease-resistant PrPSc by mechanisms largely unknown . It has been accepted , though , that intrinsic biochemical properties encoded into the amino acid sequence of PrP are the basis for its conformational changes . Indeed , transgenic mice overexpressing wild type PrP display neuronal loss , astroglyosis , and PrP deposition [5] , [6] . Although the role of PrPSc in transmission has been thoroughly documented , it is not clear whether PrPSc is the neurotoxic isoform of PrP . PrP conformers that do not share all the biochemical properties of PrPSc may be responsible for neuropathology in TSE [7] , [8] . But the biochemical isolation of this neurotoxic conformer , referred to as PrP* , PrPtoxic or PrPL ( lethal ) , has been challenging , so far . Given the interest in the infectious aspects of prions , the elucidation of the cellular mechanisms involved in spontaneous PrP misfolding and PrP-dependent neurotoxicity has progressed at a slower pace . Recent studies suggest that both endoplasmic reticulum ( ER ) and cellular stress may play an important role in prion diseases [9] . In fact , PrP misfolding can induce ER stress , which in turn triggers a mechanism of defense characterized by the activation of the unfolded protein response ( UPR ) and the upregulation of various molecular chaperones . For instance , the ER chaperones Grp58 , Grp78/BiP and Grp94 , and the heat shock protein Hsp70 are upregulated in the brain of patients affected with CJD and animals infected with scrapie [10]–[12] . However , the protective activity of these molecular chaperones against PrP neurodegeneration has not been assessed in vivo . In this paper , we have focused on three relevant aspects of PrP biology: Can wild type PrP spontaneously convert in vivo ? What is the nature of the neurotoxic PrP species ? And , do molecular chaperones play a role in PrP misfolding and aggregation ? To answer these questions we expressed wild type PrP from Syrian Golden Hamster ( HaPrP ) in transgenic flies . The initial isolation of PrP came from hamsters [13] and much knowledge about the biochemical and structural properties of hamster PrPSc has accumulated over the last 20 years . Since flies do not posses a PrP orthologue , this is a good host system to understand the consequences of expressing mammalian PrP in a genetically tractable model . However , modeling prion diseases in flies has proved challenging [14] , [15] . We report here that wild type PrP expressed in Drosophila neurons progressively misfolds , acquires biochemical features of PrPSc and induces spongiform degeneration of brain neurons . Remarkably , the molecular chaperone Hsp70 directly interacts with PrP , prevents the accumulation of misfolded isoforms and reduces its neurotoxicity in neurons of the fly brain . These results suggest that Hsp70 upregulation might be of therapeutic interest in prion diseases .
We created transgenic flies expressing wild type HaPrP , identified strong , moderate and weak HaPrP lines in western blot ( Figure 1A ) , and confirmed that observation by quantitative RT-PCR ( Figure S1A ) . Fly-expressed PrP ( Tg-PrP ) migrated in a compact band of 28 KDa , unlike control PrPC from a healthy hamster ( Figure 1A ) . PrPC typically produces three distinct bands ( 35–28 kDa ) due to the presence of two facultative N-glycosylation sites that yield di- , mono- and unglycosylated PrP fractions ( Figure 1A , B ) . It is well known that glycosylation in flies involves the addition of very small sugar chains and single sugars [16] . Running the fly brain extracts in a high-density gel allowed the separation of three bands , although the higher band showed weaker intensity ( Figure 1B ) . Thus , albeit with slight differences in the glycosylation pattern , PrP is normally processed in Drosophila . To investigate whether wild type PrP expression could cause neuronal dysfunction in flies , we expressed strong and moderate PrP transgenes or control transgenes in motor neurons . As shown in Figure 1C , control males expressing membrane-bound CD8-GFP or cytoplasmic LacZ performed well in climbing assays over 10 days and stopped climbing at around day 25 . Interestingly , the strong PrP line triggered a severe locomotor dysfunction only three days after eclosion ( measured at 50% of climbing ability ) and by day 6 no climbing ability was registered ( Figure 1C ) . We observed a similar result in males expressing the moderate PrP line in motor neurons , although the locomotor dysfunction occurred at day 4 ( 50% of climbing ability ) and the fast decline continued until day 10 . Since these flies exhibited an early locomotor dysfunction , we wondered if PrP was affecting motor neuron development . To answer this question , we initiated PrP expression in young adult flies also carrying a temperature-sensitive allele of the Gal4 inhibitor Gal80 [17] , thus , preventing PrP expression in developing motor neurons . A temperature shift to the restrictive temperature ( Gal80 inactive ) in newly eclosed flies ( day 1 ) initiated PrP transcription in mature neurons , which also led to a fast locomotor dysfunction ( Figure 1C ) . In contrast , sibling flies raised at the permissive temperature ( Gal80 active , no PrP expression ) behaved as control flies . Hence , PrP induces rapid neurotoxicity in motor neurons , and this early neuronal dysfunction is not caused by neurodevelopmental defects . The progressive neurotoxicity of wild type HaPrP in motor neurons seemed to disagree with a previous report in which wild type PrP from mouse ( mPrP ) did not induce neurodegeneration [18] . However , the authors clarified later that they had compared a weak wild type mPrP line with a strong mutant mPrP line ( Correction , J . Neurosci . , 2008 , Vol . 28 ) . To determine the ability of wild type mPrP to induce degenerative phenotypes , we tested mPrP and HaPrP lines in the same conditions . First , we determined the relative expression levels of the mPrP and HaPrP transcripts by quantitative RT-PCR . We identified mPrP lines expressing slightly lower levels ( P1 ) and twice as much ( J1 ) than our strong HaPrP line ( Figure S1A ) . These mPrP lines induced a strong locomotor dysfunction similar to the defects caused by HaPrP ( Figure S1A ) . While the P1 line showed a slightly delayed locomotor dysfunction compared to HaPrP ( at 50% climbing ) , the J1 line showed a more aggressive phenotype . Therefore , the onset and progression of the locomotor dysfunction correlated with the expression levels of wild type mPrP . These results support the ability of wild type PrP from both mouse and hamster to induce neurotoxic effects . Spongiform degeneration is the neuropathological hallmark of TSE . In the brain of scrapie-infected hamsters , spongiosis is the consequence of vacuolation of cell bodies and processes ( Figure 2A ) . To investigate the ability of wild type PrP to induce vacuolar pathology in transgenic flies , we expressed HaPrP in all brain neurons and incubated these flies for 1 or 30 days . Young Tg-PrP flies showed well-preserved architecture of the neuropile and the cortex , which contains the cell bodies of the brain neurons ( Figure 2C ) . In contrast , 30 day-old flies displayed large holes in the brain and the optic lobes ( Figure 2B ) . Large and small vacuoles localized to both the neuropile and the cortex ( Figure 2D ) . Additionally , the cortex is much thinner in the older flies , suggesting that a significant neuronal loss occurred . To document the vacuolar pathology at a subcellular level , we performed ultrastructural analysis of the fly brains . While young Tg-PrP flies exhibited normal cellular organization ( Figure 2E ) , older Tg-PrP flies clearly showed cytosolic vacuolation , nuclear condensation and abnormal membrane folding ( Figure 2F ) . Therefore , accumulation of wild type PrP for 30 days leads to severe spongiform vacuolar degeneration of Drosophila brain neurons , a hallmark of prion neuropathology . A classical finding in TSE is the misfolding of PrPC into new conformers that are insoluble in non-ionic detergents [2] . To determine if the neurodegeneration described in the fly brain correlated with PrP misfolding , we assessed PrP solubility in mammalian and fly brains . For this , we treated hamster and fly brain extracts with a non-ionic detergent ( sarkosyl ) and Na-phosphotungstate ( NaPTA ) , a reagent that promotes specific precipitation of PrPSc [19] . Then , the soluble and insoluble fractions were resolved by western blot . As expected , control PrPC from a healthy hamster was only detected in the sarkosyl/NaPTA soluble fraction , while PrPSc from a scrapie-infected hamster accumulated in the insoluble fraction ( Figure 3A ) . We next analyzed the biochemical properties of Tg-PrP expressed in a subset of brain neurons ( the mushroom bodies , see Figure 5C ) in young flies and flies aged for 40 days . Western blots showed that Tg-PrP was mostly soluble in young flies , while Tg-PrP showed marked insolubility in 40-day old flies ( Figure 3B ) . To determine the specificity of these changes in PrP properties , we examined the solubility of another exogenous protein , a cytosolic form of bacterial β-Galactosidase . β-Galactosidase demonstrated complete solubility in both young and older flies ( Figure S2 ) . This result argues against the possibility that the insolubility of PrP in aged flies could be due to the deterioration of cellular homeostasis systems or to the overexpression of high , non-physiological levels of any exogenous protein . Next , we confirmed the fibrillar state of Tg-PrP by using a conformation-dependent antibody ( OC ) that detects common epitopes in fibrils of different types of amyloids [20] . To preserve the OC-reactive epitopes , we generated brain homogenates without detergents and , then , ran the samples by denaturing PAGE . Immuno-detection with the OC antibody recognized a high molecular weight band in Tg-PrP flies , but not in control flies ( Figure 3C ) , consistent with the range of fibrillar structures recognized by this fibril-specific antibody [20] . We also quantified OC reactivity by ELISA and found that aged Tg-PrP flies produced a three-fold higher signal compared to younger flies and control flies ( Figure 3D ) . In summary , the biochemical study of Tg-PrP indicates that wild type PrP progressively misfolds and forms insoluble , fibrillar conformers that could be responsible for the neurodegenerative phenotype . PrP exhibits an unusual flexibility that allows it to acquire a number of conformations both in vivo and in vitro [21] . To better understand the conformation accumulated in flies , we compared the resistance of Tg-PrP and PrPSc from hamster to denaturing agents . PrPSc is highly resistant to heat , urea and guanidine thiocyanate; however , high concentration of chemical denaturants can destabilize the PrPSc conformation , rendering it soluble . We subjected extracts from scrapie-infected hamsters and heads from old flies to a gradient of guanidine thiocyanate and then tested for PrP solubility . Both PrPSc from hamster and Tg-PrP from old flies exhibited remarkable resistance to high concentrations of guanidinium ( up to 1 . 5 M ) ( Figure 3E ) . Interestingly , both PrP samples were solubilized at 2 M guanidinium . The comparable guanidinium sensitivity of PrPSc and Tg-PrP suggests that the conformation acquired by PrP in transgenic flies may be similar to that of PrPSc from infected hamsters . To further characterize the conformation of fly-expressed PrP , we used the 15B3 antibody [22] , which discriminates normal ( PrPC ) from disease-specific ( PrPSc ) conformations in bovine , sheep , rodents and human CJD ( Figure 3F ) . Then , we performed immunoprecipitation with 15B3 in either control ( Tg-LacZ ) or Tg-PrP flies . 15B3 did not cross-react with brain extract from Tg-LacZ flies , but recognized a small amount of PrPSc-like conformers in young Tg-PrP flies . Interestingly , 40-day old Tg-PrP flies produced a much larger amount of immunoprecipitated 15B3 conformers ( Figure 3F ) . Thus , Tg-PrP flies spontaneously and progressively accumulated biologically relevant conformations that share specific epitopes with PrPSc . One of the most typical features of infectious PrPSc is its resistance to high concentrations of proteinase K ( PK ) and the production of a protease resistant core of smaller size ( PrP27–30 ) [2] . We subjected brain extracts from PrP transgenic flies to mild PK digestions and , then , we resolved the products by western blot using a small pore membrane ( . 2 µm ) for increased sensibility . In these conditions we could observe several differences between the extracts from young and old flies , but no PK-resistant core ( shift ) was detected . Still , Tg-PrP showed a relative increase in PK resistance in the older flies , consistent with the accumulation of misfolded PrP ( Figure 3G ) . Older flies also accumulated a new band in the non-digested sample just below the lowest band expected for Tg-PrP , suggesting that this was a degradation product accumulated over time . Thus , although transgenic flies did not produce PrP conformers with the complete biochemical properties of PrPSc , our results are consistent with other PrP conformations that might be relevant in disease [8] . Among the chaperone proteins Hsp70 has the exceptional ability to correct the misfolding of several amyloidogenic proteins involved in neurodegenerative diseases [23] , [24] . However , very little is known about its potential role in extracellular amyloid diseases such as TSE . To test the ability of Hsp70 to functionally interact with PrP , we took advantage of the construct expressing human Hsp70 shown to rescue polyglutamine and α-Synuclein neurotoxicity in flies [25] , [26] . We overexpressed PrP and Hsp70 in brain neurons and , then , investigated whether Hsp70 elicits changes on the conformation , turnover and/or stability of Tg-PrP . Surprisingly , Hsp70/PrP flies accumulated less total PrP than control flies GFP/PrP ( Figure 4A ) . Densitometry of three independent experiments indicated that flies co-expressing Hsp70 accumulated 40% less PrP ( Figure 4D ) . Moreover , this reduction in the levels of PrP was exerted post-translationally since Hsp70 did not interfere with the production or stability of PrP transcripts ( Figure 4B ) . Consistent with this result , flies co-expressing PrP and a dominant negative variant of constitutive Hsp70 ( Heat shock cognate 4 , Hsc4-dn ) accumulated 35% more PrP ( Figure 4C , D ) , supporting a role for Hsp70 in PrP biology . Then , we examined whether Hsp70 promoted the elimination of specific PrP conformations using the 15B3 conformational antibody . While young GFP/PrP ( control ) flies accumulated a small amount of 15B3 immunoreactive species , young Hsp70/PrP flies did not accumulate 15B3-positive epitopes ( Figure 4E ) . Moreover , older Hsp70/PrP flies accumulated much lower levels of 15B3 immunoreactive species than the control GFP/PrP flies ( Figure 4E ) . These observations suggest that Hsp70 prevents the accumulation and/or promotes the degradation of specific PrP conformers , and support a role for Hsp70 in regulating PrP conformation . We followed these experiments by assessing the ability of Hsp70 to protect against PrP-dependent neurotoxicity . First , we wondered if Hsp70 could protect against the vacuolar degeneration of brain neurons induced by Tg-PrP . For this , we created flies that exhibited an intermediate spongiform phenotype by using a moderate PrP line . The rationale for this moderate phenotype was to provide sensitive conditions to detect Hsp70 neuroprotection . Flies expressing moderate PrP levels in all neurons showed a mixed phenotype at day 30 , where 44% of cells were undergoing vacuolar changes , while the rest exhibited preserved cytoplasm ( Figure 5A ) . The nuclei of most of these neurons were condensed , suggesting that most cells were undergoing apoptosis . In contrast , flies co-expressing Hsp70 and PrP exhibited very few vacuolated cells ( 7% ) and their nuclei were normal , suggesting that Hsp70 prevents spongiform degeneration and cell death ( Figure 5B ) . Next , we analyzed the mushroom bodies , which are the dorsal ( α lobe ) and medial ( β and γ lobes ) projections of the Kenyon cells ( Figure 5C , D ) . As expected , young flies co-expressing GFP with a strong PrP line and 40 day-old control flies showed normal mushroom body morphology ( Figure 5D , E ) . However , 40 day-old GFP/PrP flies displayed prominent degeneration of α lobes ( Figure 5F , H ) . Remarkably , flies co-expressing Hsp70 and PrP demonstrated robust protection of mushroom body projections ( Figure 5G , H ) . These results further confirmed the ability of Hsp70 to prevent the progressive degeneration of neuronal structures . Finally , we tested the protective activity of Hsp70 in functional assays . For this , we established a moderate locomotor phenotype by inducing a ubiquitous , albeit weak , expression of PrP . Under these conditions , the flies expressing only PrP displayed a steady decline in climbing ability over 20 days , with a 50% climbing activity at day 7 ( Figure 6A ) . Flies expressing PrP and Hsp70 also showed a steady , but less pronounced decline , with a 50% climbing activity at day 13 ( Figure 6A ) . From day 5 to day 31 the differences in climbing ability were highly significant . We further characterized the protective activity of Hsp70 by analyzing the movement of these flies at day 20 . Groupscan can identify and track multiple flies that enter a custom arena , records the movement of all the flies in the arena and produces parameters characteristic of each population ( Figure 6B ) . To document the protective activity of Hsp70 , we measured the number of flies that occupied the top and bottom halves of the vial after 8 seconds . Flies expressing PrP alone were never detected in the top arena , while only three out of 22 flies entered the bottom arena . These observations indicated that most of the flies stayed in the floor of the vial because they could not climb vertically ( Figure 6B , C ) . In contrast , 40% of the PrP/Hsp70 flies occupied the top arena and only one fly out of 15 stayed in the floor of the vial ( Figure 6B , C ) . To determine if the speed of these flies could be a more sensitive parameter to describe the protective activity of Hsp70 , we measured the average speed per fly in arenas that occupy the whole vial . PrP/Hsp70 flies performed much better than PrP-only flies , exhibiting a speed ten times higher ( Figure 6D ) . Fly distribution and speed clearly illustrated the improved locomotor ability of the flies co-expressing Hsp70 and PrP . Overall , the protective effect of Hsp70 against the locomotor dysfunction , the spongiform vacuolation and the axonal degeneration suggests that Hsp70 effectively protects both neural morphology and activity against PrP neurotoxicity in vivo . To better understand how Hsp70 exerts its chaperone activity on PrP , we evaluated the possibility that Hsp70 could interact directly with PrP . For this , we performed pull-down and co-immunoprecipitation assays in flies co-expressing PrP and Hsp70 . For the pull-down we prepared active His-Hsp70 coated beads in a spin column and tested the binding of Tg-PrP from brain homogenates of young and old flies . Then , we resolved the interacting fraction in western blot and assessed PrP Immunoreactivity . Tg-PrP from both young and old flies interacted with Hsp70 in the column , but the amount of PrP recovered from older flies was several fold higher ( Figure 7A ) . This result confirmed the interaction of Hsp70 with specific PrP conformers that accumulate in older flies , possibly misfolded PrP . To determine the biological relevance of this in vitro interaction , we next performed co-immunoprecipitation assays . As hypothesized , Hsp70 co-immunoprecipitated with PrP using anti-PrP coated beads in brain extracts from older Hsp70/PrP flies , but was not detected in extracts from flies expressing only Hsp70 ( Figure 7B ) . In addition , Hsp70 was not precipitated when beads were coated with an unrelated antibody ( anti-β-Galactosidase ) ( Figure 7B ) . To test the physiological relevance of this interaction , we added ATP to the binding reaction to induce Hsp70 cycling , resulting in the release of its substrate . Interestingly , Hsp70 was not detected in the eluted fraction in the presence of ATP ( Figure 7B ) . Then , we confirmed the interaction between Hsp70 and PrP by performing the reverse immunoprecipitation with anti-Hsp70 coated beads . In this case , PrP immunoprecipitated with Hsp70 in flies co-expressing both transgenes , but not in flies expressing only PrP or in beads coated with a control antibody ( Figure 7C ) . Similarly , when ATP was added to the reaction , the binding of Hsp70 and PrP was reversed ( Figure 7C ) . Combined , these results strongly suggest that Hsp70 exerts its protective activity by direct interaction with PrP . These intriguing results presented a clear problem: where does the interaction between Hsp70 and PrP take place ? Hsp70 is the major cytosolic chaperone , while PrP is a secreted glycoprotein attached to the extracellular membrane . Hence , the chances for these two proteins to physically interact seemed limited . Therefore , we wondered if we could identify the subcellular domain where Hsp70 and PrP interact . Recent reports show that Hsp70 exhibits a remarkable ability to translocate to different cellular compartments , including the extracellular space [27] . To determine if we could detect Hsp70 in membranous domains , we first separated the cytosolic and microsomal fractions . Microsomes contain membrane vesicles from all cellular compartments , including ER , Golgi , secretory and recycling vesicles , and plasma membrane . Since PrP is processed in the ER and Golgi and is anchored to the membrane , it accumulated solely in the microsomal fraction ( Figure 7D ) . As expected , endogenous Hsp70 localized in the cytosolic fraction in young Tg-PrP flies ( Figure 7D ) . However , when we analyzed the distribution of Hsp70 in older Tg-PrP flies , a significant amount of endogenous Hsp70 was present in the microsomal fraction . This observation suggests that Hsp70 can translocate to membranous domains in response to the accumulation of specific PrP conformers in aged animals . A relevant cellular microdomain for PrP biology is the lipid raft or detergent-resistant membrane ( DRM ) , an specialized plasma membrane domain involved in critical cellular functions , such as trafficking , signalling and protein sorting . Moreover , the lipid raft has been proposed as the site for PrP conversion [28] . To determine if Hsp70 is present in this key plasma membrane domain , we performed a fractionation of fly brain extracts in Optiprep gradients containing 1% Triton X-100 . Under these conditions , lipid rafts float to the top fractions , where they are visible as a white fatty material . We confirmed , first , the localization of PrP to the lipid raft , corresponding to floating fraction 3 ( Figure 7E ) . PrP is also detected in the bottom fractions , but its presence in the specialized membranes of fraction 3 is highly relevant . The quality of the separation was assessed by detecting the synaptic protein Syntaxin ( a lipid raft marker ) in fraction 3 ( Figure 7F ) and by the absence of the Na+/K+ ATPase , a transmembrane ion pump enriched in non-lipid raft membranes ( Figure 7G ) . Interestingly , a significant amount of Hsp70 was also present in fraction 3 ( Figure 7H ) . Thus , Hsp70 and PrP co-localize in a biologically active microdomain of the membrane that is also the site for PrP conversion , where Hsp70 can interfere with PrP misfolding and promote PrP degradation .
Considerable attention has been devoted in the last 25 years to define the chemical nature of prions and their transmissibility . However , less is known about the cellular mechanisms that participate in PrP misfolding , how prions actually damage the central nervous system and how this process can be prevented . To understand the mechanisms regulating spontaneous PrP misfolding , we described how the biochemical properties of wild type PrP change over time in transgenic flies . Early on , Tg-PrP is mostly soluble and accumulates very little misfolded conformers . In contrast , Tg-PrP from older flies is mostly insoluble , fibrillar , resistant to high concentrations of guanidinium and is recognized by a PrPSc-specific conformational antibody . These new features suggest that wild type PrP progressively misfolds and accumulates in a conformational state that shares several properties with PrPSc . However , this PrP conformer is protease sensitive , which clearly distinguishes it from prototypical PrPSc . Hence , Tg-PrP acquires a conformation consistent with PrP isoforms previously described in both experimental animals and patients [29] , [30] . These PrP* , PrPtoxic , PrPL or protease-sensitive PrPSc conformers have been interpreted as PrPSc byproducts or , alternatively , they could be immature metabolic intermediaries of PrPSc [8] , [29] . A possible factor in the formation of a PrP* conformer in flies may be the short incubations assayed in these animals ( 30–40 days ) . Other explanations could include the lack of co-factors necessary to promote the PrPSc conformation ( conversion factor ) or the presence of molecules that prevent the formation of PrPSc ( anti-conversion factor ) . It is not clear , thus , why flies accumulate this specific PrP conformer or whether flies could produce PrPSc through genetic modification of the cellular environment . These relevant aspects of PrP biology can be further studied in Tg-PrP flies through genetic studies . We describe here the formation of a neurotoxic PrP conformer that leads to typical spongiform vacuolation of brain neurons . But , what have we learned about the mechanisms of PrP neurotoxicity ? Conversion of PrPC to PrPSc is central to prion pathogenesis because Prnp null mice and mice in which PrP expression is knocked-out after infection are resistant to disease [31] , [32] . However , increasing evidence argues against the neurotoxicity of PrPSc because significant pathology and/or clinical dysfunction can develop with little accumulation of protease-resistant PrPSc in rodent models of TSE [33] , [34] . Moreover , a new prion disease in humans has been associated to protease-sensitive PrP [30] . Thus , it is not clear whether specific conformers are associated with neurodegeneration . Our data , though , support the hypothesis that PrP* or PrPL conformers induce deleterious effects by gain-of-function mechanisms since neurotoxicity in flies correlates with the progressive accumulation of novel , protease-sensitive PrPSc-like conformers [8] . We still do not understand how these conformers accumulate in flies . But according to the “templated toxic intermediate” model of J . Collinge , a high rate of conversion of PrPC to PrPL and a low rate of maturation of PrPL to PrPSc would favor the accumulation of neurotoxic conformers [29] . Transgenic flies seem to lack the maturation phase and , thus , only accumulate PrPL , resulting in strong neurotoxicity . These results also suggest that neurotoxic PrP can form independently of the typical PrPSc pathway and may represent a stable conformer with its own kinetics . Once the PrPL conformers accumulate , they can exert neurotoxicity by sequestration of cellular proteins , inhibition of the cellular clearance machinery ( molecular chaperones , the Ubiquitin-Proteasome Complex [UPC] ) , and/or induction of ER stress and the UPR , among other mechanisms . These deleterious effects of wild type PrP in flies are consistent with the brain and muscle defects observed in transgenic mice that overexpress wild type PrP [5] , [6] , [35] . Moreover , the ability of wild type PrP to misfold into a neurotoxic conformer fits nicely with the “permissive templating” hypothesis , which proposes that the quantity of the normal protein influences the risk of sporadic diseases , including TSE and Alzheimer and Parkinson's diseases [36] . So , can these new PrP* conformers generated in Drosophila be considered prions ? Based on the classic definition , which includes transmissibility , PrP* does not share all properties of prions since it is not protease resistant , an important feature for PrP infectivity . However , transmissibility has been achieved with protease sensitive material in some instances [34] . Consequently , some authors propose that prions should be defined based on disease-inducing activity , not on their resistance to protease digestion [33] . A recent report by Chiesa and col . described the spontaneous accumulation of misfolded , neurotoxic PrP in transgenic mice overexpressing wild type PrP [37] . The properties of PrP in these mice is very similar to that described here in transgenic flies expressing wild type PrP . Since brains extracts of these mice were not infectious , it may be safe to assume that Tg-PrP from flies will not be infectious either . However , we will know the answer to this question once our ongoing experiments are finalized . Hsp70 is one of the most potent molecular chaperones and has been shown to prevent misfolding of α-Synuclein and expanded polyglutamine proteins in transgenic flies [23] , [25] . Hsp70 also prevents neurodegeneration in mouse models of spinocerebellar ataxias and spinal and bulbar muscular atrophy [38] , [39] . These protein misfolding disorders are characterized by the presence of nuclear or cytosolic aggregates , where the direct activity of Hsp70 is possible . In contrast , no role has been proposed , so far , for Hsp70 and other cytosolic chaperones in extracellular amyloids such as Amyloid-β and PrP . Probing the protective activity of Hsp70 in PrP-expressing transgenic flies , we found that Hsp70 prevents the accumulation of neurotoxic , PrPSc-like conformers , and involves the direct binding of Hsp70 and PrP . Supporting this idea , the direct interaction of Hsp70 to cytosolic PrP ( cytPrP ) prevents apoptosis in cultured neurons [40] . Since the physiological relevance of cytPrP is unclear , a key question in PrP biology is whether Hsp70 can interact with the normal membrane-tethered PrP . Under stress conditions Hsp70 can move across membranous structures and into organelles [41] . Indeed , Hsp70 can be released into the extra-cellular space via exosomes [27] and can also pull proteins across membranes [42] . Furthermore , Hsp70 has been detected in lipid rafts in normal cells , a plasma membrane microdomain critical for PrP biology , while stress conditions exacerbate this distribution of Hsp70 [43] . In this study we show that Hsp70 can localize to cellular vesicles ( microsomes ) and , more specifically , to lipid rafts , providing a physical site for its interaction with PrP . We also present a mechanistic model for the neuroprotective activity of Hsp70 through the interaction with PrP in a key cellular compartment in which PrP misfolding might be occurring . This activity of Hsp70 may prevent or revert PrP conformational changes , while promoting the degradation of misfolded conformers through the UPC . These results agree with and may explain the observation that Hsp70 levels are elevated in patients affected with CJD and in animal models of TSE [10] , [11] . It is possible that the Hsp70/PrP interaction is mediated by Hsp70 co-chaperones , such as Hsp40 . Hsp40 directly binds substrates and presents them to the catalytic site of Hsp70 [42] . Thus , chaperone complexes that contain Hsp70 could bind PrP and directly modulate PrP conformation , stability and/or degradation in concert with the ubiquitin-proteasome complex . It would be interesting , therefore , to test if other families of chaperones , including the chaperonins ( Hsp60's ) and the small chaperones ( Hsp20's ) , also regulate PrP misfolding . Regardless of the mechanism mediating Hsp70 protection , this is the first evidence that a molecular chaperone can directly protect against PrP neurotoxicity in vivo .
The open reading frame of the Syrian golden hamster Prnp gene was isolated by PCR amplification from genomic DNA . EcoRI and NotI restriction sites were included in the primers ( 5′-GAATTCATCATGGCGAACCTTAGCTACTG-3′ and 5′-GCGGCCGCTCATCCCACCATCAGGAAGATG-3′ ) to facilitate cloning into the Drosophila pUAST vector [44] . The resulting construct ( UAS:HaPrP ) was injected into yw embryos and seven single-insertion lines were created . UAS flies expressing human Hsp70 ( HSPA1L ) , Drosophila Hsc4-dn ( HSC4-K71S ) , the reporter strains UAS:LacZ and UAS:CD8-GFP , and the mushroom body ( OK107-Gal4 ) , motor neuron ( BG380-Gal4 ) and ubiquitous ( da-Gal4 , Act-Gal4 ) drivers were obtained from the Bloomington Drosophila Stock Center . Two strong mPrP strains were provided by S . Supattapone [18] . Homozygous females for the drivers were crossed with males bearing either HaPrP-M6 ( strong ) and HaPrP-M9 ( moderate ) combined with Hsp70 , CD8-GFP or LacZ transgenes . To balance Gal4 activity , control ( CD8-GFP/LacZ ) and experimental ( CD8-GFP/PrP and Hsp70/PrP ) progenies always carried two UAS transgenes . The crosses and their respective progenies were kept at 28°C unless otherwise indicated . Flies expressing PrP throughout the brain under the control of da-GAL4 were collected at 1 and 30 days after eclosion , along with sibling control flies . Plastic embedding was prepared as described [45] , then semithin sections were cut at 1 µm and stained with toluidine blue . Ultrahin sections were cut at 70 nm and stained with uranyl acetate ( 1 h ) and lead citrate ( 15 min ) . Paraffin brain sections ( 6 µm ) from sick hamsters and H&E staining were performed as described [46] . Whole-mount immunohistochemistry was conducted as described [45] using the anti-HaPrP antibody 3F4 ( 1∶1 , 500 , Signet ) and anti-human Hsp70 antibody ( 1∶2 , 000 , StressGen ) . The anti-Mouse-Cy3 ( Molecular Probes ) and anti-Rabbit-FITC ( Sigma ) antibodies were used at 1∶600 and images were collected in a LSM510 confocal microscope . Flies carrying HaPrP-M6 , HaPrP-M9 , mPrP-P1 , mPrP-J1 or control constructs were crossed with the BG380-Gal4 driver and the progeny was subjected to climbing assays [47] . Flies also carrying the transcriptional repressor Gal80TS were raised at 18°C and the adults were placed at either 25°C ( no expression ) or 30°C ( high PrP expression ) upon eclosion [17] . For Hsp70 activity , we crossed a milder driver ( Act-Gal4 ) with HaPrP-M9 or HaPrP-M9; h-Hsp70 flies . Briefly , 30 newborn adult males were placed in empty vials and forced to the bottom by firmly tapping against the surface . After 8 seconds , the number of flies that climb above 5 cm was recorded . This was repeated 8 times every 1 or 2 days for 30 days . Climbing ability was plotted as a function of age . For software-assisted analysis , we recorded the climbing for 10 seconds and the videos were analyzed with Groupscan ( Cleveristics ) . Experimental arenas ( single or split in half ) were defined to cover the surface of the vials ( except the bottom and the stopper ) . Speed per active fly was calculated every frame ( 1 sec = 30 frames ) and flies were considered active at 5 mm/sec . Data was exported to Excel for statistical analysis . To quantify the levels of PrP transcripts expressed from hamster or mouse PrP transgenes , we performed real-time RT-PCR assays using the SYBR green fluorescent reagent . Total RNA was isolated ( Trizol , Invitrogen ) from fly heads expressing PrP under the control of the OK107 driver . DNA traces were eliminated with Turbo DNAse ( Ambion ) . Real-time PCR reactions were done using the ABI PRISM 7700 system ( Applied Biosystems ) and the relative amounts of mRNAs were calculated by amplifying RNA Pol II mRNA in the same reactions . For Hsp70 experiments , PrP transcripts were quantified in GFP/PrP or Hsp70/PrP flies . Plotted values were obtained from three independent reactions and arbitrarily normalized against one of the lines tested . Ten to twenty fly heads from each relevant genotype were used for brain extracts . Fly heads were homogenized in 30 µl of PBS containing 150 mM NaCl , 1% Triton X-100 , 4 mM EDTA and Complete Protease Inhibitors ( Roche ) . 10% brain homogenates ( w/v ) from healthy and sick hamsters were prepared as described [46] . Protein extracts were fractionated by SDS-PAGE under reducing conditions , electroblotted into nitrocellulose membranes and probed against 3F4 and β-tubulin ( 1∶200 , 000 , Sigma ) antibodies . To detect fibrillar conformations , the extraction was carried out as above , but without detergent , separated by denaturing PAGE and incubated with the OC antibody at 1∶ 5 , 000 [20] . For ELISA , 6 µL of fly head homogenate in coating buffer ( 0 . 1 M Sodium bicarbonate , pH 9 . 6 ) were placed in 96-well plates , 96 µL of coating buffer were added followed by 2 hour incubation at 37°C . After washing and blocking , 100 µL of OC antibody ( 1∶3000 ) were added per well and incubated 1 h at 37°C . Wells were washed again followed by an incubation with 100 µL of anti-rabbit HRP ( 1∶2000 ) for 1 hour at 37°C . After washing , 100 µL of TMB-1 ( KPL ) were added to each well and incubated at room temperature . When the color developed , the reaction was stopped with 100 µL of HCl 1 M and read at 450 nm . As positive control we used pre-aggregated amyloid-β fibers using published procedures [20] . NaPTA precipitation was conducted as outlined [19] , except that the final volume was scaled down to 60 µl and equivalent amounts of fly and hamster extracts were processed . Supernatant , pellet and an equivalent aliquot of the total fraction were then analyzed by Western blots . Homogenates ( Da-Gal/PrP-M6 ) from young ( day 1 ) and old ( day 30 ) flies were incubated with PK concentrations from 0 to 5 µg/ml for 30 min at 4°C . The digestions were stopped by adding 2 mM PMSF and the samples were resolved in 12% SDS gels , transferred to a . 2 µm nitrocellulose membrane and stained for 3F4 immunoreactivity . PrPSc-specific conformations were detected in hamster and fly brain extracts using the 15B3 immunoprecipitation kit ( Prionics AG , Switzerland ) [22] . For the direct interaction between PrP and Hsp70 , Pull-down assays were conducted with 6His-tagged recombinant human Hsp70 from StressMarq ( SPR-103B ) . The ProFound Pull-down PolyHis protein∶protein interaction kit ( PIERCE ) was used according to the manufacturer , except that bait immobilization to cobalt chelate beads was performed for 4 h and subsequent incubation with PrP-containing extracts was conducted in the presence of 0 . 25% BSA . Co-immunoprecipitation assays were conducted using Dynabeads M-280 Tosylactivated coupled to the 3F4 , Monoclonal Hsp70 ( StressGen ) or β-Galactosidase ( Sigma ) antibodies as specified by the manufacturer ( Invitrogen ) . Where indicated , ATP was also added to the binding reactions at 10 mM . Immunoprecipitated proteins were subjected to western blot using anti-human Hsp70 polyclonal antibody ( 1∶10 , 000 ) or 3F4 for the reverse experiments . For microsome preparations , forty heads were homogenized in 70 µL of BIB buffer ( 320 nm sucrose , 0 . 5 mM EGTA , 10 mM Tris pH 7 . 8 , 1 mM DTT and 1× protease inhibitor ) . Samples were centrifuged 30 min at 5 , 000 rpm at 4°C to eliminate debris . Supernatants were subjected to a second centrifugation for 1 h at 20 , 000 rpm at 4°C . Supernatants ( cytoplasmic fraction ) were recovered and pellets ( microsomes ) were resuspended in BIB buffer to run a Western blot . Detergent-resistant membranes ( DRMs ) or lipid rafts were prepared as described [48] with few modifications . Briefly , 50 fly heads were lysed in 250 µl of cold TNET buffer ( 100 mM Tris , pH 7 . 5 , 150 mM NaCl , 2 mM EGTA , 1% Triton X-100 , 1× protease inhibitor ) using a mini glass homogenizer and incubated in ice for 30 min . After debris removal , 200 µl of crude extract were mixed with 400 µl of 60% Optiprep™ in 5 mL ultracentrifuge tubes and overlaid with 1 . 8 mL of 30% Optiprep and 600 µl of 5% Optiprep . Gradients were spun in a Sorvall S52-ST rotor at 139 , 000×g for 5 h at 4°C . Ten 300 µl fractions were collected from the top and analyzed by western blotting after methanol precipitation . The anti-α Subunit of the Na+/K+ ATPase ( 1∶ 100 , 000 ) and Syntaxin ( 1∶ 50 ) antibodies were obtained from the Developmental Studies Hybridroma Center . The significance of the differences between PrP and Hsp70/PrP flies in climbing assays was determined by Chi-square with 7 degrees of freedom ( 8 points per day ) . The average speed was tested by a two-tailed t-student . Statistical significance was considered below 1% of chance . | Creutzfeldt-Jakob disease is a type of dementia caused by the deposition of the prion protein in the brain . This disorder belongs to a unique class of degenerative diseases that includes mad-cow disease in bovine and scrapie in sheep . An abnormal form of the prion protein is not only responsible for the disease in several mammals , but is also an infectious agent that can transmit the disease within or across species . To shed light on how the prion protein changes from its normal to the disease-causing form , we expressed the prion protein from hamster in transgenic flies . We observed that the prion protein progressively converts to the pathological form and induces neuronal loss in the brain . Thus , the prion protein experiences its typical transition from normal to disease-causing form in flies . This behavior gave us the opportunity to investigate whether other proteins can regulate such transition . We found that the stress-related protein Hsp70 prevents the accumulation of abnormal prion protein and prevents neuronal loss . We also determined that Hsp70 directly interacts with the prion protein in specific membrane domains . Overall , our studies provide new insight into the mechanisms that regulate the accumulation of abnormal prion protein . This discovery could have therapeutic applications in treating these devastating disorders . | [
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"Introduction",
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"Materials",
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"Methods"
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| [
"genetics",
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| 2009 | In Vivo Generation of Neurotoxic Prion Protein: Role for Hsp70 in Accumulation of Misfolded Isoforms |
Aphids are economically important pests that display exceptional variation in host range . The determinants of diverse aphid host ranges are not well understood , but it is likely that molecular interactions are involved . With significant progress being made towards understanding host responses upon aphid attack , the mechanisms underlying non-host resistance remain to be elucidated . Here , we investigated and compared Arabidopsis thaliana host and non-host responses to aphids at the transcriptional level using three different aphid species , Myzus persicae , Myzus cerasi and Rhopalosiphum pisum . Gene expression analyses revealed a high level of overlap in the overall gene expression changes during the host and non-host interactions with regards to the sets of genes differentially expressed and the direction of expression changes . Despite this overlap in transcriptional responses across interactions , there was a stronger repression of genes involved in metabolism and oxidative responses specifically during the host interaction with M . persicae . In addition , we identified a set of genes with opposite gene expression patterns during the host versus non-host interactions . Aphid performance assays on Arabidopsis mutants that were selected based on our transcriptome analyses identified novel genes contributing to host susceptibility , host defences during interactions with M . persicae as well to non-host resistance against R . padi . Understanding how plants respond to aphid species that differ in their ability to infest plant species , and identifying the genes and signaling pathways involved , is essential for the development of novel and durable aphid control in crop plants .
Aphids are hemipteran insects that display exceptional variation in host range . While some aphid species , such as Myzus persicae ( green peach aphid ) , are able to infest plants in over 40 families , including many important crops , closely related Myzus cerasi ( black cherry aphid ) is only able to infest a limited number of hosts within one or two plant families [1] . Other species , for example Rhopalosiphum padi ( bird-cherry oat aphid ) , are limited to cereal crops . The underlying mechanism of aphid host range is not well understood , but is likely determined by complex molecular interactions in both host and aphid species . Upon landing on a leaf surface , insects perceive several types of plant structures and volatiles that can indicate host suitability [2] . On host plants , aphids are able to establish a phloem-feeding site upon probing , using their specialized mouthparts called stylets . However , it has been reported that aphids exhibit probing behavior regardless of the plant species they land on , and thus regardless of host suitability [3 , 4 , 5 , 6] . Interestingly , it has been suggested that aphids have increased probing rates in non-host interactions , which explains the higher virus transmission rates by aphids reported on non-host plant species [2] . These observations imply that there is an opportunity for molecular interactions to take place during both aphid-host and non-host interactions . Perhaps as a results of these molecular interactions , aphids are either unable to reach the phloem or unable to successfully feed from the phloem of non-host plants . To date most research with regards to plant defence signaling has been focused on compatible interactions , and in particular on the Arabidopsis thaliana–M . persicae interaction . As reviewed in detail by Louis and Shah [7] , SA ( salicylic acid ) - , JA ( jasmonic acid ) - , ET ( ethylene ) - and ABA ( abscisic acid ) -signaling pathways are all involved in host defence responses against aphids , but their exact role is still not clear and may vary among plant species . Secondary metabolites are also known to be important in host defences against aphids . For example , PAD4 ( phytoalexin-deficient 4 ) , a lipase-like protein , and PAD3 , a cytochrome P450 that is involved in formation of camalexin , are both important in Arabidopsis defences against M . persicae [8] , [9] , [10] . Also , glucosinolates , which increase upon aphid feeding , reduce Arabidopsis susceptibility to aphids [11] . More recently , evidence for the involvement of PAMP ( Pathogen Associated Molecular Pattern ) -triggered immunity ( PTI ) in plant-aphid interactions has emerged . Work by Prince et al . [12] showed that BAK1 ( Brassinosteroid insensitive 1-associated receptor kinase 1 ) , which functions as a co-receptor for PRRs ( pattern recognition receptors ) to trigger PTI , may be involved in non-host resistance to aphids . More specifically , survival rates of Acyrthosiphon pisum ( pea aphid ) were increased on Arabidopsis bak1-5 mutants compared to wild type plants three to four days upon aphid challenge suggesting that BAK1 contributes to non-host resistance . Although it is possible that unidentified PRRs recognize conserved aphid molecules to trigger PTI , molecules from aphid-associated organisms such as bacteria , viruses or fungi could also be recognized . Bacterial GroEL is present in aphid saliva , among several other bacterial proteins , and activates PTI-like responses that reduce aphid virulence [13] , [14] . Another layer of defences involved in aphid recognition involves NB-LRR ( nucleotide-binding site leucine-rich repeat ) proteins . In several plant-aphid systems , resistance ( R ) proteins have been identified that confer resistance to specific aphid biotypes and have a typical CC-NB-LRR structure , similar to R proteins conferring resistance to plant pathogens [15] . Another plant response activated during the interaction with herbivorous insects is the production of Reactive Oxygen Species ( ROS ) . Diuraphis noxia ( Russian Wheat Aphid ) triggers production of ROS in resistant wheat lines , while a slight increase was also observed in susceptible lines [16] , which may reflect the activation of a hypersensitive response ( HR ) . However , several studies in dicots have indicated ROS-signaling also is activated in compatible interactions . Arabidopsis gene expression analyses upon infestation with Brevicoryne brassicae ( cabbage aphid ) showed differential expression changes of genes involved in the oxidative stress response and the generation of ROS as early as 6 hours post aphid challenge and highest expression of these genes 24 hours post insect challenge [17] . Kerchev et al . [18] provided evidence for activation of oxidative responses in the potato-M . persicae interaction 48 hours after challenging plants with aphids . Also in pea , an oxidative response , including the production of ROS , was observed upon host interaction with A . pisum [19] . By using the dye DCFH-DA ( dichlorodihydro-fluorescein diacetate ) , the ROS burst associated with aphid attack was observed and showed a peak in ROS production at 24 hours post aphid challenge . The production of ROS upon plant parasite attack involves NADPH-oxidases . In Arabidopsis at least two NADPH-oxidase isoforms , AtRbohD and AtRbohF , are involved in the production of ROS upon interaction with an avirulent Pseudomonas syringae pv . tomato strain and the oomycete pathogen Hyaloperonospora parasitica [20] . While AtRbohD plays a more pronounced role in ROS production than AtRbohF , the latter shows more involvement in the control of cell death triggered by plant pathogens . Interestingly , AtRbohD contributes to plant defences against aphids as reflected by increased susceptibility of the atrbohD-3 knockout mutant to M . persicae [21] . Moreover , this mutant also showed reduced ROS levels upon treatment with aphid-derived extract containing elicitor ( s ) [12] . Whether AtRbohF also contributes to plant defences against aphids , and whether AtRbohD and AtRbohF are involved in non-host responses to aphids remains to be investigated . Nonhost resistance to plant pathogens involves recognition events and activation of plant immunity , which can be suppressed and/or evaded by effector repertoires in compatible interactions [22] , [23] . Although important progress has been made in understanding how plants respond to aphids in compatible interactions , there is a need to investigate and compare how plants respond to aphids during non-host interactions . Here , we aimed to characterize Arabidopsis responses during host and non-host interactions with three different aphid species , M . persicae , M . cerasi and R . padi . Arabidopsis is not considered a host for M . cerasi and R . padi based on available literature , but is a host for the broad host range aphid M . persicae . To gain insight into overall plant responses to these aphids , we performed transcriptome analyses , which revealed high levels of similarity in Arabidopsis transcriptional changes as a consequence of the different aphid interactions , with the exception of a relatively small set of genes . We used the transcriptome data to select genes for further characterization with regards to their contribution to plant-aphid interactions and identified several genes involved in host susceptibility to M . persicae and M . cerasi and non-host resistance to R . padi .
Aphid probing generally takes place in non-host interactions and is responsible for the high transmission rates of viruses by aphids on non-host plant species ( Harrington et al . , 1986 ) . To test whether Myzus cerasi and Rhopalosiphum padi probe the leaf surface during the interaction with Arabidopsis we assessed leaves challenged with these aphid species as well as Myzus persicae for the presence of autofluorescence , indicative of damaged epidermal cells . This showed that indeed probing takes place during the different interactions with aphids by the presence of puncture sites , surrounded by autofluorescence ( Fig 1A ) . In addition , we performed acid fuchsin staining , which provides a pink staining of aphid stylet sheath proteins , to visualize aphid stylet pathways in leaf tissue . This confirmed stylet pathways were present in Arabidopsis leaves upon challenge with the three different aphid species ( S1 Fig ) . Finally , we used trypan blue staining to visualize plant cell death . This showed that all aphid species were able to cause cell death , either due to damage or activation of plant defences , during the interaction ( S1 Fig ) . Importantly , these observations indicate that transient but yet intimate associations take place in both host and non-host interactions that allow for molecular interactions to occur . Unexpectedly , while performing the aphid probing assays we noticed that M . cerasi was able to reproduce on Arabidopsis . To further determine and compare the colonization rates of the different aphid species on Arabidopsis we allowed aphids to infest plants over a 14-day period , starting with 2 ( age-synchronized ) adults on day one . Fourteen days later the total aphid population per plant was counted , including all adults and nymphs . For M . persicae , the population consisted on average of 23 aphids per plant , and as expected , R . padi was unable to survive and reproduce ( Fig 1B ) . Remarkably , the M . cerasi population consisted of around 8 aphids per plant , indicating that under our growth room conditions this species was able to colonize Arabidopsis to a relatively low level compared to M . persicae ( Fig 1B ) . Similar infestation experiments of cress plants showed the M . cerasi population on this host plant consisted of around 28 aphids on average ( S1 Fig ) . Although M . cerasi has not been reported on Arabidopsis , our observation suggests that this aphid is able to utilize this plant species as a host under greenhouse conditions . Potential host ranges as determined under laboratory conditions have been reported to differ from actual host ranges in the field for several insect pests , which may reflect the impact of environmental factors on plant susceptibility and insect behavior and predation [24 , 25 , 26] . We will refer to the Arabidopsis-M . cerasi interaction as a “poor-host interaction” in this manuscript . To further investigate Arabidopsis host , poor-host , and non-host responses to M . persicae , M . cerasi and R . padi , respectively , we performed microarray analyses using Agilent Arabidopsis 4×44K arrays . Plants were challenged with the different aphid species and above ground plant tissues were harvested after 0 , 3 , 6 and 24 hours . We identified 874 genes that displayed significant differential expression in at least one of the aphid treatments compared to the no-aphid control . Based on the gene expression profiles of these genes , we identified three main gene clusters ( Fig 2 ) . Cluster A groups together 275 genes up-regulated at 6h and 24h , cluster B comprises 306 genes up-regulated at 24h and mostly down-regulated at 3 and 6 hours , and cluster C contains 293 genes that are mainly down-regulated at 24h ( Fig 2 , S1 Table ) . Interestingly , the overall transcriptome changes with regard to direction of changes are quite similar among different aphid treatments . However , at 3h and 6h , the downregulation of a number of genes in cluster A and B is more pronounced during the host interaction with M . persicae than during the poor-host or non-host interactions with M . cerasi or R . padi , respectively . We performed Gene Ontology ( GO ) analyses of the genes within the three different clusters to assess whether there was an association with specific predicted gene functions . For cluster A , the main predicted gene functions were in transcriptional processes , and for cluster B main functions were related to metabolism , including ROS metabolism ( S2 Table ) . Although there was no obvious main functional category for cluster C , this cluster contained several genes involved in cell wall-related processes ( S2 Table ) . We used pairwise analyses of the set of 874 genes to identify down- and up-regulated genes per aphid species treatment per timepoint as compared to the no-aphid control ( Fig 3A and 3B ) . The number of genes down-regulated during the host interaction with M . persicae was higher than the number of genes down-regulated during the poor-host and non-host interactions , especially at the 3h and 6h timepoint ( Fig 3A , S3 Table ) . Most of the genes significantly down-regulated during the M . cerasi and R . padi interactions were also down-regulated during interaction with M . persicae pointing to overlap in gene regulation taking place during the different types of interactions ( Fig 3A , S3 Table ) . GO analyses of genes significantly down-regulated during all interactions revealed an overrepresentation of genes related to abiotic and biotic stress , such as those encoding small heat shock proteins ( SHSPs ) or proteins interacting with SHSP at the 3h timepoint . ( S4 and S5 Tables ) . More diverse functions were found for genes commonly down-regulated at the 24h timepoint , with an overrepresentation of genes predicted to function in transcriptional processes . We assessed whether the gene sets specifically down-regulated during the interaction with M . persicae showed a similar direction of regulation during the interactions with M . cerasi and R . padi by applying a log2 fold change = 0 . 2 cut off . This showed that at 3h and 24h timepoints 50% ( 55/110 ) and just over 70% ( 41/56 ) of genes , respectively , showed consistent repression of gene expression for all interactions ( S3 Table ) . When taking into account only the M . persicae and R . padi interactions these percentages increased to around 75% ( 82/110 ) for the 3h timepoint . However , for the 6h timepoint we only found around 20% ( 29/134 ) of M . persicae down-regulated genes to show consistent changes across all interactions , and this percentage increased to just over 65% when taking into account the M . persicae and R . padi data only ( S3 Table ) . Functional predictions suggest that many of the genes significantly down-regulated only during the M . persicae interaction at both 3h and 6h are involved in ROS metabolism , but also in metabolic processes , including those related to glucosinolate biosynthesis ( S3 and S6 Tables ) . When assessing genes up-regulated across interactions we also found overlap in gene sets ( Fig 3B , S3 Table ) . Some of the genes commonly up-regulated at 3h were predicted to be related to cell wall functions and growth , while those at the 6h and 24h timepoints were mainly predicted to be involved in transcriptional processes and stress-related responses , respectively ( S3 and S6 Tables ) . However , despite this overlap we also found that more genes were significantly affected during the interaction with M . persicae at 24h post aphid challenge when compared to the other aphid interactions ( Fig 3B , S3 Table ) . In addition , there was more overlap in genes differentially up-regulated during both the M . persicae and M . cerasi interactions at this timepoint than during both the M . persicae and R . padi interactions ( Fig 3B ) . We then looked whether the genes specifically up-regulated during the M . persicae interaction where affected in the same direction during other interactions . Using a log2 fold change = 0 . 2 value cut-off , we found that for the 3h and 24h timepoint nearly 45% ( 8/17 ) and just over 70% ( 69/96 ) , respectively , were up-regulated across interactions ( S3 Table ) . When comparing the M . persicae and R . padi data only , these percentages increased to around 75% ( 13/17 ) for the 3h timepoint ( S3 Table ) . In contrast , at the 6h timepoint only 40% ( 24/60 ) of the genes were affected in an upward direction across interactions ( S3 Table ) . Functional predictions showed that those genes significantly and specifically up-regulated by M . persicae at 6h were likely involved in plant abiotic stress responses , hormone signalling , or metabolic processes ( S3 and S6 Tables ) . Overall these data suggest that Arabidopsis responses to M . persicae are stronger than the responses to the other species , but also that some gene sets may be specifically down-regulated during the host interaction , especially at early timepoints . Global analyses revealed that there were sets of genes with significant differential expression in host but not non-host interactions and vice versa , as well as genes showing opposite gene expression changes in different interactions ( S1 and S3 Tables ) . To further look into these gene sets , we analyzed in more detail the one-way ANOVA results of the 874 genes differentially expressed in our experimental set-up . We compared gene sets differentially up- or down-regulated or unaffected during interaction with M . persicae with similar genes sets for the M . cerasi or R . padi interactions to look for genes specifically differentially expressed during either the host , poor-host or non-host interaction and also applied volcano plot filtering . A total of 96 genes showed either opposite gene expression patterns when comparing two different interactions to the no-aphid control or were only differentially expressed in host , poor-host or non-host interaction ( S2–S4 Figs and S7–S9 Tables ) . Functional predictions of these 96 genes showed an overrepresentation of genes involved in metabolic processes , including glucosinolate biosynthesis , and ROS production ( S10 Table ) . Some genes were specifically up- or down-regulated in only one or two of the interactions ( S7 Table ) . For example , two genes predicted to encode glucosinolate S-oxygenases were down-regulated during interactions with M . persicae and R . padi at 6h ( only significantly in the M . persicae interaction ) , but were not affected during interaction with M . cerasi at this timepoint . This suggests some plant defence responses may be differentially regulated during interactions with the different aphid species . The list of 96 genes with different gene expression profiles included a total of 11 genes identified by volcano plot filtering , including genes encoding LEA ( Late Embryogenesis Abundant ) proteins ( AT3G02480 , AT1G52690 ) , a transferase ( AT5G38130 ) , an oxidoreductase ( AT5G24140 ) , PIN5 ( PIN-formed 5 ) ( AT5G16530 ) , a benzodiazepine-related receptor ( AT2G47770 ) , TBL26 ( Trichome Birefringence-Like protein ) ( AT4G01080 ) , and TAT3 ( tyrosine aminotransferase 3 ) ( AT2G24850 ) ( Fig 4 ) . To confirm gene expression profiles we performed quantitative RT-PCR for these 11 genes . Although expression profiles across all timepoints were confirmed for most genes , for three genes ( hypothetical gene 1 , 2 and 3 ) we did not confirm opposite gene expression profiles by qRT-PCR ( S5 Fig ) . However , for hypothetical genes 2 and 3 we observed different gene expression profiles across interactions , with expression being affected specifically during the M . persicae but not R . padi interaction at the 6h and 24h timepoint , respectively ( S5 Fig ) . We also performed qRT-PCR analyses on 3 genes ( WRKY38 , VSP1 ( Vegetative Storage Protein 1 ) , and a Gln-amidotransferase ) that were selected based on gene expression profiles during host versus non-host interactions ( S7 Table ) , and two genes ( hypothetical gene 4 and MIOX4 ( Myo-Inositol Oxygenase 4 ) that showed interesting patterns of gene expression , but were not identified by our statistical analyses as differentially expressed . Overall , qRT-PCR results were in line with the microarray analyses for these additional 5 genes ( S5 Fig ) . For MIOX4 qRT-PCR results revealed more pronounced gene expression differences across interactions than found by microarray analyses , especially for the 24h timepoint ( S5 Fig ) . By applying stringent statistical analyses to select genes with different gene expression profiles we therefore most likely missed some genes of interest . We were interested in investigating whether genes with different gene expression changes during the different type of aphid interactions contributed to host and non-host plant defences against the aphids . Therefore , we selected knock-out lines for 8 of the 11 genes identified by volcano plot filtering , as well as the 5 genes selected based on their gene expression profiles during host versus non-host interactions ( S11 Table ) . This set included a mutant line for hypothetical gene 1 , for which we were unable to verify differential gene expression across treatments by qRT-PCR . Upon confirming T-DNA or transposon insertions ( S6 Fig ) , we subjected these lines to aphid performance assays . For M . cerasi and M . persicae we assessed aphid performance by measuring nymph production over 10 days , whereas for R . padi , which does not reproduce on Arabidopsis , we measured adult aphid survival over 6 days . The overall reproduction of M . cerasi was very low in our experiments , reflective of poor-host suitability to this species ( Figs 5 and 6 ) . A slight reduction in performance was observed for M . cerasi on several knock-out lines ( Gln-amidotransferase , pin5 , miox4 ) ( Fig 5 ) . Interestingly , M . persicae showed a significant reduction in reproduction on the pin5 , miox4 , sqp2 , hypothetical gene 2 , tat3 and Gln-amidotransferase lines indicating that the regulation of these genes is important for virulence in host interactions ( Fig 5 ) . Possibly these genes encode aphid susceptibility factors or aphids require a tight regulation of the processes these genes are involved in . Non-host resistance to R . padi was not affected in these mutants ( S7 Fig ) . Aphid survival assays with R . padi identified several lines affected in non-host resistance to this aphid species . More specifically , we observed increased survival of R . padi on the vsp1 mutant , indicating VSP1 ( vegetative storage protein 1 ) contributes to non-host resistance against this aphid ( Fig 6A and 6B ) . While on Col-0 plants R . padi survival was around 50% between 3 and 4 days of the assays , on the vsp1 mutant survival was around 80% , which was significantly higher . Aphids did not survive beyond 6 days on either the wild-type or vsp1 mutant plants . The vsp1 line showed a significant increase in progeny of M . persicae , indicating that VSP1 also contributes to host defences against this aphid species ( Fig 6C ) . Another interesting observation was that R . padi showed decreased survival on a mutant affected in the expression of an ABA-responsive gene , which is a member of the LEA gene family ( Fig 6D and 6E ) . Between 3 and 4 days of the assays , aphid survival on wild-type plants was around 50% , but reduced to about 7% on the mutant . We did not observe any difference in susceptibility of this mutant to M . persicae as compared to the Col-0 wild-type ( Fig 6F ) . This may indicate that this LEA gene negatively regulates plant defences to specific aphid species . Remaining lines were not affected in susceptibility or non-host resistance ( S8 Fig ) . Our results show that several of the genes identified by their differential gene expression profiles during the host , poor-host , and non-host interactions play an important role during plant-aphid interactions . Our transcriptome analyses revealed that genes involved in ROS metabolism were repressed more strongly upon interaction with the aphid species M . persicae as compared to the poor-host and non-host interactions . To further investigate the role of ROS production in plant-aphid interactions , we used the dye DCFH-DA to assess ROS accumulation over a 24-hour timecourse experiment . Leaves were challenged with five R . padi , M . cerasi and M . persicae aphids , or no aphids ( control ) and subjected to staining after 0 , 3 , 6 , 12 and 24 hours . Leaves were treated with DCFH-DA and analyzed by confocal microscopy to measure the accumulation of ROS . Across repeated experiments we observed an accumulation of fluorescence , indicative of ROS production , peaking at 3 and 24 hours post aphid challenge in host , but mainly at 24 hours in non-host or poor-host interactions ( Figs 7 , S9 ) . Moreover , at 24 hours ROS production was more pronounced in poor-host and non-host interactions with M . cerasi and R . padi . To determine whether accumulation was possibly the result of an active process or recognition of the aphid exoskeleton , we challenged leaves with aphid moults , containing chitin , and assessed ROS production at 24 hours post challenge . We did not observe an increased accumulation of ROS in these experiments ( S9 Fig ) , suggesting that activation of oxidative responses is not due to recognition of chitin at the leaf surface but rather requires aphid feeding and/or probing to take place . The production of ROS is dependent on NADPH oxidases . Two of these , AtRbohD and AtRbohF were previously shown to be involved in plant responses to biotic stress [20] . We challenged atrbohD-3 and atrbohF-3 knockout lines with R . padi , M . cerasi and M . persicae and monitored aphid population size over 10 days to determine if AtRbohD and AtRbohF contribute to plant defences in host and non-host interactions . Both M . cerasi ( poor-host interaction ) and M . persicae ( host interaction ) showed an increase in population size on the atrbohF-3 , but not the atrbohD-3 line , compared to the wild-type control ( Fig 8A ) . As expected R . padi did not reproduce on either the wild type or knockout lines . However , when assessing adult survival we found that 90% of aphids were still alive between day 3 and 4 on the atrbohF-3 line as opposed to 60% on the wild-type Col-0 plants ( Fig 8B and 8C ) . No difference in aphid survival was observed on the atrbohD-3 line . These results indicate that AtRbohF , but not AtRbohD , contributes to plant defences in the different types of aphid interactions and that this NADPH oxidase contributes to non-host resistance against aphids . To determine whether the atrboh mutants were able to generate ROS during aphid interactions , we performed DCFH-DA staining of leaves at 3h and 24h post aphid challenge . Although ROS levels were strongly reduced in both the atrbohD-3 and atrbohF-3 mutants for all interactions , the amount of ROS was more strongly reduced in the atrbohF-3 mutant than in the atrbohD-3 mutant , especially at the 3h timepoint ( Figs 8D , S10 ) . These data point to different roles of NADPH-oxidases in the production of ROS during plant-aphid interactions .
Here , we provide novel insights into plant responses during interaction with three different aphid species and identified several genes involved in host susceptibility or immunity to M . persicae as well as non-host resistance against R . padi . Gene expression analyses across timepoints and interactions showed high levels of overlap in transcriptional responses but also revealed genes that were differentially regulated . There are several possibilities that may explain the relatively small number of genes differentially affected during the different aphid interactions . It is possible that 1 ) only a relatively small number of plant genes affect host range , and/or 2 ) that changes at the protein level rather than transcript level are important in host versus non-host defences against aphids , and/or that 3 ) in addition to a set of plants genes , aphid genes play a key role in determining aphid host range . In addition , the stronger repression of plant transcriptional responses at early timepoints upon interaction with M . persicae as compared to the other interactions could reflect host transcriptional reprogramming by this aphid species to suppress host defences and enable infestation . It is likely that aphid effectors secreted with saliva into host plants not only target protein functions , but also manipulate host processes by targeting regulation of gene expression . Our microarray analyses provided limited evidence for activation of PAMP-responsive genes during the different plant-aphid interactions . We assessed expression profiles of several genes that are activated upon PAMP treatment . This showed that RLK1 ( receptor-like kinase 1 ) and RLK5 ( receptor-like kinase 5 ) were both down-regulated at 3h , but showed similar profiles across interactions ( S1 Table ) . Also , YLS9 ( AT2G35980 ) was highly upregulated during all interactions at 24h , while PH1 ( AT1G35140 ) was down-regulated at 3h and 24h . FRK1 , CYP81F2 and WRKY22 were not significantly affected upon aphid challenge . Overall , we did not observe any specific gene expression profiles of PAMP-responsive genes for non-host versus host interactions . Possibly , other unknown signaling pathways may be involved in the recognition of aphids or aphid-associated organisms . Arabidopsis mutant analyses revealed several genes that are important for aphid virulence and contribute to susceptibility to M . persicae and/or M . cerasi . PIN5 is an endoplasmic reticulum ( ER ) -localized transporter involved in regulating auxin influx into this organelle to regulate auxin homeostasis [27] . The role of auxin signaling in plant-aphid interactions is unknown , but possibly tight regulation of auxin is important for aphid virulence . Also , the UPR ( untranslated protein response ) , which is triggered by ER stress , is reduced in pin5 mutants [28] . Possibly this response is important for activation of defences against aphids and dealing with biotic stress . One of the other genes , MIOX4 , is highly expressed in Arabidopsis flowering tissues , and is potentially involved in the synthesis of cell wall polysaccharides [29] . This enzyme can convert myo-inositol to D-glucuronic acid , which is a major precursor for cell wall polysaccharides . MIOX4 is also highly upregulated in syncytia during infection by the plant parasitic nematode Heterodera schachtii [30] . A miox quadruple knockout mutant ( Δmiox1/2/4/5 ) , showed reduced susceptibility to nematodes . However , cell wall composition seems unaffected in this mutant possibly due to up-regulation of another polysaccharide synthesis pathway [31] and it was suggested that an increase in metabolites , including galactinol , may be responsible for the effects on nematode infection [32] . Whether metabolites are similarly affected in the miox4 mutant , remains to be investigated . VSP1 , which contributed to both host defences against M . persicae and non-host resistance to R . padi , encodes a putative acid phophatase . Although VSP1 transcripts are jasmonate-inducible [33] , the observed down- and up-regulation during the host and non-host interactions , respectively , is not necessarily linked to repression or activation of JA-signaling . For example , other JA-responsive genes , like LOX3 , and SEN1 show induction across interactions ( S1 Table ) . The JA-responsive marker PDF1 . 2 was also induced across all aphid interactions , but due to replicate-to-replicate variation this induction was not statistically significant ( https://www . ebi . ac . uk/arrayexpress/E-MTAB-3223 ) . A close relative of VSP1 , VSP2 , showed a similar gene expression profile to that of VSP1 , despite not being identified as differentially expressed between aphid interactions using volcano filtering ( S1 Table ) . Interestingly , VSP2 recombinant protein has anti-insect activity when added to an artificial diet provided to several insect species [34] . It is therefore possible that VSP1 has a direct and general anti-insect activity . Alternatively , VSP1 may have broader a role in plant defences , which could explain why its expression is induced by biotic and abiotic stress [33] , [35] . Two LEA genes were differently regulated across interactions , one of which increased non-host resistance to R . padi . The LEA family contains many different members that are likely involved in cell stress tolerance and osmoregulation [36] , [37] , [38] , [39] . Some of these proteins have been implicated in biotic stress responses [40] . More specifically , overexpression of ZmLEA3 increased the hypersensitive response triggered by avirulent P . syringae pv tomato in transgenic tobacco . AtLEA5 also was implicated in biotic stress as its overexpression in Arabidopsis reduced virulence of the fungal pathogen Botrytis cinerea as well as virulent P . syringae pv tomato [41] . Interestingly , this LEA member is involved in oxidative stress tolerance [42] . Also , NDR1 ( non-race-specific disease resistance 1 ) , which is important for signaling during ETI triggered by R proteins , shares homology to a member of the LEA protein family , LEA14 [43] . Although the role of members of the LEA family in biotic and abiotic stress is largely unknown , our work shows some LEA proteins may be involved in regulating non-host resistance to aphids . M . cerasi performance was less affected than M . persicae or R . padi on select Arabidopsis mutants . However , M . cerasi reproduction rates on Arabidopsis were low due to poor host suitability , resulting in small numbers of nymphs being produced . It is possible that different molecular mechanisms are important for M . cerasi virulence than for M . persicae or R . padi . Production of reactive oxygen species ( ROS ) is one of the early plant defence responses upon interaction with pathogens [44] . Here we used DCFH-DA as a tool for visualizing the accumulation of ROS and provide us insight into the overall accumulation of ROS upon aphid challenge . One limitation of this approach is that it does not allow to distinguish between intra- and extracellular pools of ROS [45] . However , plasma membrane NADPH-oxidases are involved in the apoplastic production of ROS . Therefore , our observations that ROS production upon aphid interaction was ( partially ) dependent on NADPH-oxidases , and that one of these NADPH-oxidases contributed to plant defences against aphids suggest that at least the production of apoplastic ROS is involved . The very early ROS response ( 3h post challenge ) observed mainly and consistently in the presence of M . persicae ( host interaction ) , may be due to early recognition events . Herbivore feeding can cause mechanical damage resulting in an increased accumulation of ROS [46] . However , aphids are considered stealthy herbivores that cause little damage to plant cells . Despite this we did notice significant damage to epidermal cells when assessing aphid probes ( Figs 1A , S1 ) , which could be responsible for activation of wound-like defences . Alternatively , aphid saliva , containing elicitors , could be responsible for the activation of oxidative stress at early timepoints . In either case , the difference in ROS production at the very early timepoint between the host and non-host interactions could reflect differences in damage caused by the aphids due to probing , or differences in saliva or elicitor delivery . The more pronounced ROS response in the non-host and poor-host interactions at the 24h timepoint may reflect a stronger activation of plant defences . Indeed , the enhanced susceptibility of the atrbohF-3 mutant to M . persicae , but importantly the reduced level of non-host resistance of this mutant to R . padi , suggest that the Arabidopsis oxidative response negatively impacts aphid performance regardless of the aphid species it is interacting with . However , ROS accumulation at 24h post aphid challenge was reduced in the atrbohF-3 as well as the atrbohD-3 mutant , suggesting that AtRbohF contributes to host and non-host defences through a mechanism other than ROS production , or that AtRbohF and AtRbohD are involved in different oxidative responses . Interestingly , Chaouch et al . [47] showed the AtRbohF is involved in the intracellular oxidative responses upon pathogen attack to regulate defences and metabolic process , and identified AtRbohF specific functions . Both SA and camalexin accumulation are reduced in atrbohF-3 , but not atrbohD-3 , mutants upon challenge with the plant pathogen P . syringae pv tomato DC3000 [47] . Arabidopsis mutants that are unable to produce camalexin are more susceptible to M . persicae [10] , pointing to a role of camalexin in plant defences against aphids . It is possible that differences in camalexin levels in the atrbohF mutant affect defences in both host and non-host interactions . SA has also been implicated in plant-aphid interactions , but the exact role of SA-mediated defences remains unclear . It has been speculated that the activation of SA-signaling pathways by aphids counters the activation of JA-dependent defence responses that are effective against aphids [48] . However , high levels of SA-signaling do not necessarily correspond with increased susceptibility to aphids [49] . We did not observe a change in aphid virulence on the atrbohD-3 knockout mutant , which is in contrast to what has been reported by Miller et al . [21] , where M . persicae was more virulent on this particular mutant . Although we used the same available atrbohD-3 mutant line in our experimental set-up as Miller et al [21] , plants were grown under short-day conditions rather than under constant light . Not only can constant light conditions impact aphid reproduction [50] , but also plant physiology [51] . We did , however , observe a reduction in ROS production in the atrbohD-3 mutant upon aphid challenge , in line with a role of AtRbohD in ROS production upon treatment with aphid-derived extract [12] . However , ROS production has been reported in atrbohD-3 and atrbohF-3 mutants indicating other genes may contribute to oxidative responses [52] . We used three different aphid species in our study , representing host- , poor-host and non-host interactions . Although our work is an important step forward in characterizing and comparing host and non-host plant responses to aphids , it will be important to investigate whether our observations extend to similar interactions with other aphid species . For example , Arabidopsis PEN1 ( PENETRATION1 ) and PEN2 differ in their contribution to non-host resistance against a range of fungal and oomycete pathogens with PEN1 showing a higher level of specificity [53] . Further characterization of the plant genes identified here is needed to determine whether they contribute to host and non-host interactions on a broader scale . With aphids being a major economic pest it is essential to understand the molecular basis of host susceptibility and host range . Understanding how plants respond to different aphid species that differ in their ability to infest plant species , and identifying the genes and signaling pathways involved , is essential for the development of novel and durable aphid control in crop plants . Overall our work contributes to a better understanding of the molecular mechanisms underlying host and non-host interactions with aphids . Further characterization of plant genes important for host susceptibility or non-host resistance will be needed to investigate whether their functions extend to other plant-aphid interactions and to reveal the plant cellular processes involved in determining aphid virulence and host range .
The aphids used in this study are M . persicae ( genotype O , kindly provided by Dr . B Fenton ) , M . cerasi ( collected from cherry trees in Dundee , UK ) and R . padi ( kindly provided by Dr . B Fenton ) . M . persicae aphids were reared on oilseed rape ( Brassica napus ) , M . cerasi on American land cress ( Barbarea verna ) and R . padi on barley ( Hordeum vulgare L . ) . The insects were maintained in cages in controlled conditions at 20°C under 16h of light . Aphids were age-synchronized on host plants . For M . persicae the host was B . napus , for M . cerasi American land cress and for R . padi this was barley . Two 8-day old adults were moved to four-week old Arabidopsis plants . Plants were individually caged and aphids were counted 14 days later . For colonization of cress by M . cerasi a similar experiment was performed at a later timepoint under the same conditions . Probing was assessed using confocal laser microscopy using an excitation at 488 nm and emission ranges of 500–530 nm ( 793 of master gain ) and 650–690 nm ( 590 of master gain ) . Arabidopsis thaliana Col-0 plants were grown under short day conditions ( 8h light ( ±80μmoles . m-2 . s-1 /16h dark ) at 22°C ( light ) / 20°C ( dark ) , 70% humidity . Plants were sown on Levington's M2 compost with 4 mm grit ( 8:1 ) . Individual four-week old plants were challenged with 25 mixed-age apterous aphids and enclosed in a mesh-covered cylindrical cage . Control plants ( non-infested ) , were placed in cages in parallel . We used 15 plants per aphid treatment , per timepoint , for each replicate and performed three biological replicates . For each aphid treatment , and per replicate , all above ground tissues from 15 plants were collected and pooled after 3h , 6h or 24h and flash-frozen in liquid nitrogen . Samples were ground in liquid nitrogen and total RNA was extracted using TRIzol Reagent ( Invitrogen , Life Technologies , Carlsbad , CA , USA ) as described by the manufacturer . The quality of RNA was assessed using the Agilent Bioanalyzer . For microarray analyses , slides were hybridized with three biological replicates per aphid treatment per timepoint . The microarray experimental design and dataset can be accessed at ArrayExpress ( https://www . ebi . ac . uk/arrayexpress/E-MTAB-3223; accession #E-MTAB-3223 ) . The Low Input Quick Amp Labeling kit ( Agilent Technologies , Santa-Clara , CA , USA ) was used according to the manufacturer’s instructions to amplify and label target RNA . Arabidopsis v4 Gene Expression Microarrays ( Agilent Technologies ) containing 43 , 803 probes were used ( 36 in total ) . Single-colour hybridization and washing of the slides were performed according to the manufacturer’s protocols ( Agilent Technologies; One-Color Microarray-Based Gene Expression Analysis , version 6 . 5 ) . An Agilent Technologies G2505B scanner was used to scan the hybridized slides at resolution of 5 μm at 532 nm ( Cy3 ) . Data were extracted from each microarray using Feature Extraction software ( Agilent Technologies version 10 . 7 . 3 . 1 ) with default settings and subsequently data were imported into GeneSpring ( version 7 . 3; Agilent Technologies ) software for analyses . One-way ANOVA ( Bonferroni correction , p-value ≤ 0 . 05 ) was used to identify genes differentially expressed across different treatments compared to the non-infestation controls ( S3 and S4 Figs , S9 Table ) . To identify genes with opposing gene expression profiles among treatments , we performed volcano plot filtering ( fold change ≥ 2 . 0 , t-test p-value ≤ 0 . 05 ) ( S2 Fig , S8 Table ) . We used BioMaps software available on the Virtual Plant web platform , version 1 . 3 , ( http://virtualplant . bio . nyu . edu/cgi-bin/vpweb/ , [54] ) to analyse gene ontologies ( GO ) and functional annotations from the Munich Information Center for Protein Sequences ( MIPS ) [55] . For these analyses we selected the TAIR/TIGR ( The Arabidopsis Information Resource/ The Institute for Genomic Research ) database and applied the Fisher Exact Test ( with FDR ( False discovery rate ) correction , p-value ≤ 0 . 01 ) . Real-time qPCR was performed on a Chromo4 System ( Bio-Rad , Hercules , CA , U . S . A . ) with Opticon 3 . 1 software , as follows: 95°C for 10 min followed by 44 cycles of 95°C for 15 s and 60°C for 1 min , with the qPCR MasterMix sybR green ( Applied Biosystems , Life Technologies , Carlsbad , CA , USA ) . Primer efficiency ( E ) was evaluated on a slope of a standard curve generated using a serial dilution ( 4 dilution points-2 fold dilution ) of the mixed sample ( E = 10^ ( -1/slope ) -1 ) . Each sample reaction was run in triplicate . Cycle threshold ( Ct ) values were normalized to the average Ct of three housekeeping genes , elongation factor EF1α ( AT1G07920-AtEF1α elongation factor ) , actin 2 ( AT3G18780 ) , and ubiquitin 22 ( AT5G10790-carboxyl-terminal hydrolase 22 ) . The expression of these three genes was unaffected in our microarray analyses across treatments , and ACT2 has been previously used as a reference gene in qPCR experiments on Arabidopsis infested with M . persicae[10] . Expression levels were quantified by the efficiency calibrated method following this equation ratio = EsampleΔCtsample/ EcalibratorΔCtcalibrator ) . Primers used are summarized in S12 Table . Arabidopsis leaves were exposed to 5 adult aphids for 24 hours . Aphids were maintained on leaves using mesh covered clip cages . Leaves were harvested and cleared in 70% ethanol for at least 48 hours . For fuchsin staining , cleared leaves were soaked in a fuchsin acid solution ( 0 , 035% in acetic acid:water , 1:3V ) for 2 minutes at room temperature , mounted on a glass slide in 100% glycerol and analyzed directly using a light microscope directly . For trypan blue staining , leaves were boiled in Trypan blue solution ( lactophenol solution/ EtOH 100% 1:1V , Trypan blue 0 , 02% ) for 3 minutes [56] , followed by a 15 minutes incubation at room temperature . Samples were cleared in chloral hydrate solution ( 1g/mL ) for 36 hours , washed twice in 50% of glycerol , and analyzed using a light microscope . We used the dye 2’ , 7’-dichlorofluorescein diacetate ( DCFH-DA , Sigma–Aldrich , St . Louis , MO , USA ) to determine levels of ROS production in Arabidopsis leaves using a protocol previously detailed by Mai et al [19] . Detached leaves of 4-weeks old plants , were placed into a 96-wells plate containing 1% water agar . Detached leaves were exposed to 5 aphids for 0 ( control ) , 3 , 6 , 12 or 24 hours . We used 5 detached leaves per treatment per replicate and performed three replicates for timecourse experiments including the 3 and 24h timepoints only , and two replicates for timecourse experiments including the 3 , 6 , 12 and 24h timepoints . Leaves were collected and submerged in 300μM DCFH-DA in 50mM potassium phosphate buffer ( pH 7 . 4 ) and incubated overnight in the dark . Leaves were washed twice with potassium phosphate buffer for at least 1min and analyzed using a Zeiss LSM 710 confocal microscope ( Carl Zeiss , Jena , Germany ) . Images were converted by the LSM Image Browser software , ZEN 2011 , Blue edition ( Carl Zeiss ) into JPEG files and ROS production was quantified with ImageJ . Graphs were generated for each individual biological replicate as relative values varied per replicate . All images , including those converted for ImageJ analyses , have been deposited in DRYAD ( dryad . 18b29 ) [57] . All mutants used in this study were in the ecotype Columbia background and were obtained from the NASC ( Nottingham Arabidopsis Stock Centre ) . S11 Table summarizes all mutants used in our study . T-DNA or transposon insertions were confirmed by PCR on genomic DNA and in the case of the AtRbohD and AtRbohF mutants also on cDNA . Primers are listed in S12 Table . Plants were grown under short day conditions ( 8h light/16h dark ) at 22°C ( light ) / 20°C ( dark ) , 70% humidity . Four-week old plants were challenged with two 8-day old aphids ( age-synchronized ) for the M . persicae treatment and with two similar-size adult aphids for M . cerasi treatments . For R . padi , plants were challenged with five similar-size apterous aphids . We used 10 plants per treatment per replicate and the experiment was repeated three times . For M . persicae and M . cerasi aphid progeny were counted after 10 days . For R . padi , aphid survival was assessed from the 2nd to 6th day . To compare the survival rates we took the average number of aphids alive on day 3 and day 4 of the experiment . We performed three biological replicates of aphid performance assays . Statistical analyses were performed using two-tailed Student's t-test . | Aphids are phloem-feeding insects that cause feeding damage and transmit plant viruses to many crops . While most aphid species are restricted to one or few host plants , some aphids can infest a wide range of plant species . These insects spend a considerable time on non-hosts , where they probe the leaf tissue and secrete saliva , but for unknown reasons are unable to ingest phloem sap . This suggests that aphids interact with non-host plants at the molecular level , but potentially do not suppress plant defences and/or promote the release of nutrients . We compared gene expression of plants during host and non-host interactions with aphids to identify genes involved in immunity . We found significant overlap in the plant responses to aphids regardless of the type of interaction . Despite this , we identified a set of genes specifically affected during host or non-host interactions with specific aphid species . In addition , we showed that several of these genes contribute to host and/or non-host immunity . These findings are important , as they advance our understanding of the plant cellular processes involved in host and non-host responses against insect pests . Understanding mechanisms of host and non-host resistance to plant parasites is essential for development of novel control strategies . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
]
| []
| 2015 | Characterization of Arabidopsis Transcriptional Responses to Different Aphid Species Reveals Genes that Contribute to Host Susceptibility and Non-host Resistance |
Fluctuations in the temporal durations of sensory signals constitute a major source of variability within natural stimulus ensembles . The neuronal mechanisms through which sensory systems can stabilize perception against such fluctuations are largely unknown . An intriguing instantiation of such robustness occurs in human speech perception , which relies critically on temporal acoustic cues that are embedded in signals with highly variable duration . Across different instances of natural speech , auditory cues can undergo temporal warping that ranges from 2-fold compression to 2-fold dilation without significant perceptual impairment . Here , we report that time-warp–invariant neuronal processing can be subserved by the shunting action of synaptic conductances that automatically rescales the effective integration time of postsynaptic neurons . We propose a novel spike-based learning rule for synaptic conductances that adjusts the degree of synaptic shunting to the temporal processing requirements of a given task . Applying this general biophysical mechanism to the example of speech processing , we propose a neuronal network model for time-warp–invariant word discrimination and demonstrate its excellent performance on a standard benchmark speech-recognition task . Our results demonstrate the important functional role of synaptic conductances in spike-based neuronal information processing and learning . The biophysics of temporal integration at neuronal membranes can endow sensory pathways with powerful time-warp–invariant computational capabilities .
Robustness of neuronal information processing to temporal warping of natural stimuli poses a difficult computational challenge to the brain [1]–[9] . This is particularly true for auditory stimuli , which often carry perceptually relevant information in fine differences between temporal cues [10] , [11] . For instance in speech , perceptual discriminations between consonants often rely on differences in voice onset times , burst durations , or durations of spectral transitions [12] , [13] . A striking feature of human performance on such tasks is that it is resilient to a large temporal variability in the absolute timing of these cues . Specifically , changes in speaking rate in ongoing natural speech introduce temporal warping of the acoustic signal on a scale of hundreds of milliseconds , encompassing temporal distortions of acoustic cues that range from 2-fold compression to 2-fold dilation [14] , [15] . Figure 1 shows examples of time warp in natural speech . The utterance of the word “one” in ( A ) is compressed by nearly a factor of one-half relative to the utterance shown in ( B ) , causing a concomitant compression in the duration of prominent spectral features , such as the transitions of the peaks in the frequency spectra . Notably , the pattern of temporal warping in speech can vary within a single utterance on a scale of hundreds of milliseconds . For example , the local time warp of the word “eight” in ( C ) relative to ( D ) , reverses from compression in the initial and final segments to strong dilation of the gap between them . Although it has long been demonstrated that speech perception in humans normalizes durations of temporal cues to the rate of speech [2] , [16]–[18] , the neural mechanisms underlying this perceptual constancy have remained mysterious . A general solution of the time-warp problem is to undo stimulus rate variations by comodulating the internal “perceptual” clock of a sensory processing system . This clock should run slowly when the rate of the incoming signal is low and embedded temporal cues are dilated , but accelerate when the rate is fast and the temporal cues are compressed . Here , we propose a neural implementation of this solution , exploiting a basic biophysical property of synaptic inputs , namely , that in addition to charging the postsynaptic neuronal membrane , synaptic conductances modulate its effective time constant . To utilize this mechanism for time-warp robust information processing in the context of a particular perceptual task , synaptic peak conductances at the site of temporal cue integration need to be adjusted to match the range of incoming spike rates . We show that such adjustments can be achieved by a novel conductance-based supervised learning rule . We first demonstrate the computational power of the proposed mechanism by testing our neuron model on a synthetic instantiation of a generic time-warp–invariant neuronal computation , namely , time-warp–invariant classification of random spike latency patterns . We then present a novel neuronal network model for word recognition and show that it yields excellent performance on a benchmark speech-recognition task , comparable to that achieved by highly elaborate , biologically implausible state-of-the-art speech-recognition algorithms .
Whereas the net current flow into a neuron is determined by the balance between excitatory and inhibitory synaptic inputs , both types of inputs increase the total synaptic conductance , which in turn modulates the effective integration time of the postsynaptic cell [19]–[21] ( an effect known as synaptic shunting ) . Specifically , when the total synaptic conductance of a neuron is large relative to the resting conductance ( leak ) and is generated by linear summation of incoming synaptic events , the neuron's effective integration time scales inversely to the rate of inputs spikes . Hence , the shunting action of synaptic conductances can counter variations in afferent spike rates by automatically rescaling the effective integration time of the postsynaptic neuron . We implement this mechanism in a leaky integrate-and-fire model neuron driven by N exponentially decaying synaptic conductances . Here , denotes the peak conductance of the ith synapse in units of sec−1 , and τs is the synaptic time constant . The total synaptic current , measured at rest , is given bywhere denotes the reversal potential of the ith synapse relative to resting potential and ti denote the arrival times of the spikes of the ith afferent . The factor β denotes a global scaling of all incoming spike times; β = 1 is the unwarped inputs . The total synaptic conductance , Gsyn ( t , β ) , is For fast synapses , the total synaptic current is essentially a train of pulses , each of which occurs at the time of an incoming spike and delivers a total charge of . Changing the rate of the incoming spikes will induce a corresponding change in the timing of these pulses but not their charge . Therefore , ignoring the effect of time warp on the time scale of τs , which is short relative to the time scale of voltage modulations , the total synaptic current obeys the following time-warp scaling relation , Isyn ( βt , β ) = β−1Isyn ( t , 1 ) . A similar scaling relation holds for the total synaptic conductance . The evolution in time of the subthreshold voltage is given by ( 1 ) Thus , V integrates the synaptic current with an effective time constant whose inverse is 1/τeff = gleak+Gsyn ( t , β ) . If the contribution of Gsyn is significantly larger than the leak conductance , then 1/τeff is rescaled by time-warp similar to Gsyn and Isyn , and , hence , the solution of Equation 1 is approximately time-warp invariant , namely , V ( βt , β ) = V ( t , 1 ) . This result is illustrated in Figure 2 , which compares the voltage traces induced by a random spike pattern for β = 1 and β = 0 . 5 . To perform time-warp–invariant tasks , peak synaptic conductances must be in the range of values appropriate for the statistics of the stimulus ensemble of the given task . To achieve this , we have devised a novel spike-based learning rule for synaptic conductances , the conductance-based tempotron . This model neuron learns to discriminate between two classes of spatiotemporal input spike patterns . The tempotron's classification rule requires it to fire at least one spike in response to each of its target stimuli but to remain silent when driven by a stimulus from the null class . Spike patterns from both classes are iteratively presented to the neuron , and peak synaptic conductances are modified after each error trial by an amount proportional to their contribution to the maximum value of the postsynaptic potential over time ( see Materials and Methods ) . This contribution is sensitive to the time courses of the total conductance and voltage of the postsynaptic neuron . Therefore , the conductance-based tempotron learns to adjust , not only the magnitude of the synaptic inputs , but also its effective integration time to the statistics of the task at hand . We first quantified the time-warp robustness of the conductance-based tempotron on a synthetic discrimination task . We randomly assigned 1 , 250 spike pattern templates to target and null classes . The templates consisted of 500 afferents , each firing once at a fixed time chosen randomly from a uniform distribution between 0 and 500 ms . Upon each presentation during training and testing , the templates underwent global temporal warping by a random factor β ranging from compression by 1/βmax to dilation by βmax ( see Materials and Methods ) . Consistent with the psychophysical range , βmax was varied between 1 and 2 . 5 . Remarkably , with physiologically plausible parameters , the error frequency remained almost zero up to βmax≈2 ( Figure 3A , blue curve ) . Importantly , the performance of the conductance-based tempotron showed little change when the temporal warping applied to the spike templates was dynamic ( see Materials and Methods ) ( Figure 3A ) . The time-warp robustness of the neural classification depends on the resting membrane time constant τm and the synaptic time constant τs . Increases in τm or decreases in τs both enhance the dominance of shunting in governing the cell's effective time constant . As a result , the performance for βmax = 2 . 5 improved with increasing τm ( Figure 3B , left ) and decreasing τs ( Figure 3B , right ) . The time-warp robustness of the conductance-based tempotron was also reflected in the shape of its subthreshold voltage traces ( Figure 3C , top row ) and generalized to novel spike templates with the same input statistics that were not used during training ( Figure 3C , second row ) . Synaptic conductances were crucial in generating the neuron's robustness to temporal warping . Athough an analogous neuron model with a fixed integration time , the current-based tempotron [22] ( see Materials and Methods ) also performed the task perfectly in the absence of time-warp ( βmax = 1 ) ; its error frequency was sensitive even to modest temporal warping and deteriorated further when the applied time warp was dynamic ( Figure 3A , red curve ) . Similarly , the voltage traces of this current-based neuron showed strong dependence on the degree of temporal warping applied to an input spike train ( Figure 3C , bottom trace pair ) . Finally , the error frequency of the current-based neuron at βmax = 2 . 5 showed only negligible improvement upon varying the values of the membrane and synaptic time constants ( Figure 3B ) , highlighting the limited capabilities of fixed neural kinetics to subserve time-warp–invariant spike-pattern classification . Note that in the present classification task , the degree of time-warp robustness depends also on the learning load , i . e . , number of patterns that have to classified by a neuron ( unpublished data ) . A given degree of time warp translates into a finite range of distortions of the intracellular voltage traces . If these distortions remain smaller than the margins separating the neuronal firing threshold and the intracellular peak voltages , a neuron's classification will be time-warp invariant . Since the maximal possible margins increase with decreasing learning load , time-warp invariance can be traded for storage capacity . This tradeoff is governed by the susceptibility of the voltage traces to time warp . If the susceptibility is high , as in the current-based tempotron , robustness to time warp comes at the expense of a substantial reduction in storage capacity . If it is low , as in the conductance-based tempotron , time-warp invariance can be achieved even when operating close to the neuron's maximal storage capacity for unwarped patterns . In the conductance-based tempotron , synaptic conductances controlled , not only the effective integration time of the neuron , but also the temporal selectivity of the synaptic update during learning . The tempotron learning rule modifies only the efficacies of the synapses that were activated in a temporal window prior to the peak in the postsynaptic voltage trace . However , the width of this temporal plasticity window is not fixed but depends on the effective integration time of the postsynaptic neuron at the time of each synaptic update trial , which in turn varies with the input firing rate at each trial and the strength of the peak synaptic conductances at this stage of learning ( Figure 4 ) . During epochs of high conductance ( warm colors ) , only synapses that fired shortly before the voltage maximum were appreciably modified . In contrast , when the membrane conductance was low ( cool colors ) , the plasticity window was broad . The ability of the plasticity window to adjust to the effective time constant of the postsynaptic voltage is crucial for the success of the learning . As is evident from Figure 4 , the membrane's effective time constant varies considerably during the learning epochs; hence , a plasticity rule that does not take this into account fails to credit appropriately the different synapses . The evolution of synaptic peak conductances during learning was driven by task requirements . When we replaced the temporal warping of the spike templates by random Gaussian jitter [22] ( see Materials and Methods ) , conductance-based tempotrons that had acquired high synaptic peak conductances during initial training on the time-warp task readjusted their synaptic peak conductances to low values ( Figure 5 , inset ) . The concomitant increase in their effective integration time constants from roughly 10 ms to 50 ms improved the neurons' ability to average out the temporal spike jitter and substantially enhanced their task performance ( Figure 5 ) . To address time-warp–invariant speech processing , we studied a neuronal module that learns to perform word-recognition tasks . Our model consists of two auditory processing stages . The first stage ( Figure 6 ) consists of an afferent population of neurons that convert incoming acoustic signals into spike patterns by encoding the occurrences of elementary spectrotemporal events . This layer forms a 2-dimensional tonotopy-intensity auditory map . Each of its afferents generates spikes by performing an onset or offset threshold operation on the power of the acoustic signal in a given frequency band . Whereas an onset afferent elicits a spike whenever the log signal power crosses its threshold level from below , offset afferents encode the occurrences of downward crossings ( see Materials and Methods ) ( cf . also [6] , [23] ) . Different on and off neurons coding for the same frequency band differ in their threshold value , reflecting a systematic variation in their intensity tuning . The second , downstream , layer consists of neurons with plastic synaptic peak conductances that are governed by the conductance-based tempotron plasticity rule . These neurons are trained to perform word discrimination tasks . We tested this model on a digit-recognition benchmark task with the TI46 database [24] . We trained each of the 20 conductance-based tempotrons of the second layer to perform a distinct gender-specific binary classification , requiring it to fire in response to utterances of one digit and speaker gender , and to remain quiescent for all other stimuli . After training , the majority of these digit detector neurons ( 70% ) achieved perfect classification of the test set , and the remaining ones performed their task with a low error ( Table 1 ) . Based on the spiking activity of this small population of digit detector neurons , a full digit classifier ( see Materials and Methods ) that weighted spikes according to each detector's individual performance , achieved an overall word error rate of 0 . 0017 . This performance matches the error rates of state-of-the-art artificial speech-recognition systems such as the Hidden Markov model–based Sphinx-4 and HTK , which yield error rates of 0 . 0017 [25] and 0 . 0012 [26] , respectively , on the same benchmark . To reveal qualitatively some of the mechanisms used by our digit detector neurons to selectively detect their target word , we compared the learned synaptic distributions ( Figure 7A ) of two digit detector neurons ( “one” and “four” ) to the average spectrograms of each neuron's target stimuli aligned to the times of its output spikes ( Figure 7B; see Materials and Methods ) . The spectrotemporal features that preceeded the output spikes ( time zero , grey vertical lines ) corresponded to the frequency-specific onset and offset selectivity of the excitatory afferents ( Figure 7A , warm colors ) . These examples demonstrate how the patterned excitatory and inhibitory inputs from both onset and offset neurons are tuned to features of the speech signal . For instance , a prominent feature in the averaged spectrogram of the word “one” ( male speakers ) was the increase in onset time of the power in the low-frequency channels with the frequency of the channel ( Figure 7B , left , channels 1–16 ) . This gradual onset was encoded by a diagonal band of excitatory onset afferents whose thresholds decreased with increasing frequency ( Figure 7A , left ) . By compensating for the temporal lag between the different lower-frequency channels , this arrangement ensured a strong excitatory drive when a target stimulus was presented to the neuron . The spectrotemporal feature learned by the word “four” ( male speakers ) detector neuron combined decreasing power in the low-frequency range with rising power in the mid-frequency range ( Figure 7B , right ) . This feature was encoded by synaptic efficacies through a combination of excitatory offset afferents in the low-frequency range ( Figure 7A , right , channels 1–11 ) and excitatory onset afferents in the mid-frequency range ( channels 12–19 ) . Excitatory synaptic populations were complemented by inhibitory inputs ( Figure 7A , blue patches ) that prevented spiking in response to null stimuli and also increased the total synaptic conductance . The substantial differences between the mean spike-triggered voltage traces for target stimuli ( Figure 7C , blue ) and the mean maximum-triggered voltage traces for null stimuli ( red ) underline the high target word selectivity of the learned synaptic distributions as well as the relatively short temporal extend of the learned target features . In the examples shown , the average position of the neural decision relative to the target stimuli varied from early to late ( Figure 7B , left vs . right ) . This important degree of freedom stems from the fact that the tempotron decision rule does not constrain the time of the neural decision . As a result , the learning process in each neuron can select the spectrotemporal target features from any time window within the word . The selection of the target feature by the learning takes into account both the requirement of triggering output spikes in response to target stimuli as well as the demand to remain silent during null stimuli . Thus , for each target neuron , the selected features reflect the statistics of both the target and the null stimuli . We have performed several tests designed to assess the ability of the model word detector neurons to perform well on new input sets , different in statistics from the trained database . First , we assessed the ability of the neurons to generalize to unfamiliar speakers and dialects . After training the model with the TI46 database , as described above , we measured its digit-recognition performance on utterances from another database , the TIDIGITS database [27] , which includes speech samples from a variety of English dialects ( see Materials and Methods ) . This test has been done without any retraining of the network synapses . The resulting word error rate of 0 . 0949 compares favorably to the performance of the HTK system , which resulted in an error rate of 0 . 2156 when subjected to the same generalization test ( see Materials and Methods ) . Across all dialects , our model performed perfectly for roughly one-quarter of all speakers and with at most one error for half of them . Within the best dialect group , an error of at most one word was achieved for as many as 80% of the speakers ( Table S1 ) . These results underline the ability of our neuronal word-recognition model to generalize to unfamiliar speakers across a wide range of different unfamiliar dialects . An interesting question is whether our model neurons are able to generalize their performance to novel time-warped versions of the trained inputs . To address this question , we have tested their performance on randomly generated time-warped versions of the input spikes corresponding to the trained word utterances , without retraining . As shown in Figure 8 , the neurons exhibited considerable time-warp–robust performance on the digit-recognition task . For instance , the errors for the “one” ( Figure 8A , black line ) and “four” ( blue line ) detector neurons ( cf . Figure 7 ) were insensitive to a 2-fold time warp of the input spike trains . The “seven” detector neuron ( male , red line ) showed higher sensitivity to such warping; nevertheless , its error rate remained low . Consistent with the proposed role of synaptic conductances , the degree of time-warp robustness was correlated with the total synaptic conductance , here quantified through the mean effective integration time τeff ( Figure 8B ) . Additionally , the mean voltage traces induced by the target stimuli ( Figure 8C , lower traces ) showed a substantially smaller sensitivity to temporal warping than their current-based analogs ( see Materials and Methods ) ( Figure 8C , upper traces ) . We also found that our model word detector neurons are robust to the introduction of spike failures in their input patterns . For each neuron , we have measured its performance on inputs which were corrupted by randomly deleting a fraction of the incoming spikes , again without retraining . For the majority of neurons , the error percentage increased by less than 0 . 01% for each percent increase in spike failures ( Figure 9 ) . This high robustness reflects the fact that each classification is based on integrating information from many presynaptic sources .
The proposed conductance-based time-rescaling mechanism is based on the biophysical property of neurons that their effective integration time is shaped by synaptic conductances and therefore can be modulated by the firing rate of its afferents . To utilize these modulations for time-warp–invariant processing , a central requirement is a large evoked total synaptic conductance that dominates the effective integration time constant of the postsynaptic cell through shunting . In our speech-processing model , large synaptic conductances with a median value of a 3-fold leak conductance across all digit detector neurons ( cf . Figure 8B ) result from a combination of excitatory and inhibitory inputs . This is consistent with high total synaptic conductances , comprising excitation and inhibition , that have been observed in several regions of cortex [28] including auditory [29] , [30] , visual [31] , [32] , and also prefrontal [33] , [34] ( but see ref . [35] ) . Our model predicts that in cortical sensory areas , the time-rescaled intracellular voltage traces ( cf . Figure 3C ) , and consequently , also the rescaled spiking responses of neurons that operate in the proposed fashion , remain invariant under temporal warping of the neurons' input spike patterns . These predictions can be tested by intra- and extracellular recordings of neuronal responses to temporally warped sensory stimuli . A large total synaptic conductance is associated with a substantial reduction in a neuron's effective integration time relative to its resting value . Therefore , the resting membrane time constant of a neuron that implements the automatic time-rescaling mechanism must substantially exceed the temporal resolution that is required by a given processing task . Because the word-recognition benchmark task used here comprises whole-word stimuli that favored effective time constants on the order of several tens of milliseconds , we used a resting membrane time constant of τm = 100 ms . Whereas values of this order have been reported in hippocampus [36] and cerebellum [21] , [37] , it exceeds current estimates for neocortical neurons , which range between 10 and 30 ms [35] , [38] , [39] . Note , however , that the correspondence of our passive membrane model and the experimental values that typically include contributions from various voltage-dependent conductances is not straightforward . Our model predicts that neurons specialized for time-warp–invariant processing at the whole-word level have relatively long resting membrane time constants . It is likely that the auditory system solves the problem of time-warp–invariant processing of the sound signal primarily at the level of shorter speech segments such as phonemes . This is supported by evidence that primary auditory cortex has a special role in speech processing at a resolution of milliseconds to tens of milliseconds [11]–[13] . Our mechanism would enable time-warp–invariant processing of phonetic segments with resting membrane time constants in the range of tens of milliseconds , and much shorter effective integration times . The proposed neuronal time-rescaling mechanism assumes linear summation of synaptic conductances . This assumption is challenged by the presence of voltage-dependent conductances in neuronal membranes . Since the potential implications for our model depend on the specific nonlinearity induced by a cell-type–specific composition of different ionic channels , it is hard to evaluate the overall effect on our model in general terms . Nevertheless , because of its immanence , we expect the conductance-based time-rescaling mechanism to cope gracefully with moderate levels of nonlinearity . As an example , we tested its behavior in the presence of an h-like conductance ( see Materials and Methods ) that opposes conductance changes induced by depolarizing excitatory synaptic inputs and is active at the resting potential . As expected , we found that physiological levels of h-conductances resulted in only moderate impairment of the automatic time-rescaling mechanism ( Figure S1 ) . For the sake of simplicity as well as numerical efficiency , we have assumed symmetric roles of excitation and inhibition in our model architecture . We have checked that this assumption is not crucial for the operation of the automatic time-rescaling mechanism and the learning of time-warped random latency patterns . Specifically , we have implemented the random latency classification task for a control architecture in which all synapses were confined to be excitatory except a single global inhibitory input that , mimicking a global inhibitory network , received a separate copy of all incoming spikes . In this architecture , all spike patterns have to be encoded by the excitatory synaptic population , and the role of inhibition is reduced to a global signal that has equal strength for all input patterns . Due to the limitations of this architecture , this model showed some reduction of storage capacity relative to the symmetric case , but the automatic time-rescaling mechanism remained intact . For a time-warp scale of βmax = 2 . 5 ( cf . Figure 3 ) , the global inhibition model roughly matched the performance of the symmetric model when the learning load was lowered to 1 . 5 spike patterns per synapse , with an error fraction of 0 . 18% . To utilize synaptic conductances as efficient controls of the neuron's clock , the peak synaptic conductances must be plastic so that they adjust to the range of integration times relevant for a given perceptual task . This was achieved in our model by our novel supervised spike-based learning rule . This plasticity posits that the temporal window during which pre- and postsynaptic activity interact continuously adapts to the effective integration time of the postsynaptic cell ( Figure 4 ) . The polarity of synaptic changes is determined by a supervisory signal that we hypothesize to be realized through neuromodulatory control [22] . Because present experimental measurements of spike-timing–dependent synaptic plasticity rules have assumed an unsupervised setting , i . e . , have not controlled for neuromodulatory signals ( but see [40] ) , existing results do not directly apply to our model . Nevertheless , recent data have revealed complex interactions between the statistics of pre- and postsynaptic spiking activity and the expression of synaptic changes [41]–[44] . Our model offers a novel computational rationale for such interactions , predicting that for fixed supervisory signaling , the temporal window of plasticity shrinks with growing levels of postsynaptic shunting . One challenge for the biological implementation of the tempotron learning rule is the need to compute the time of the maximum of the postsynaptic voltage . We have previously shown for a current-based neuron model that this temporally global operation can be approximated by temporally local computations that are based on the postsynaptic voltage traces following input spikes [22] . We have extended this approach to plastic synaptic conductances and checked that the resulting biologically plausible implementation of conductance-based tempotron learning can readily subserve time-warp–invariant classification of spike patterns . Specifically , in this implementation , the induction of synaptic plasticity is controled by the correlation of the postsynaptic voltage and a synaptic learning kernel ( see Materials and Methods ) whose temporal extend is controlled by the average conductance throughout a given error trial . A synaptic peak conductance is changed by a uniform amount whenever this correlation exceeds a fixed plasticity induction threshold . When tested on the time-warped latency patterns with βmax = 2 . 5 ( cf . Figure 3 ) , the correlation-based tempotron roughly matched the voltage maximum–based version at a reduced learning load of 1 . 5 patterns per synapse with an error fractions of 0 . 35% . In our model , dynamic time-warp–invariant capabilities become avaliable through a conductance-based learning rule that tunes the shunting action of synaptic conductances . This learning rule enables neurons to adjust the degree of synaptic shunting to the requirements of a given processing task . As a result , our model can naturally encompass a continuum of functional specializations ranging from neurons that are sensitive to absolute stimulus durations by employing low total synaptic conductances , to time-warp–invariant feature detectors that operate in a high-conductance regime . In the context of auditory processing , such a functional segregation into neurons with slower and faster effective integration times is reminiscent of reports suggesting that rapid temporal processing in time frames of tens of milliseconds is localized in left lateralized language areas , whereas processing of slower temporal features is attributed to right hemispheric areas [45]–[47] . Although anatomical and morphological asymmetries between left and right human auditory cortices are well documented [48] , it remains to be seen whether these differences form the physiological substrate for a left lateralized implementation of the proposed time-rescaling mechanism . Consistent with this picture , the general tradeoff between high temporal resolution and robustness to temporal jitter that is predicted by our model ( Figure 5 ) parallels reports of the vulnerability of the lateralizion of language processing with respect to background acoustic noise [49] as well as to abnormal timing of auditory brainstem responses [50] . The architecture of our speech-processing model encompasses two auditory processing stages . The first stage transforms acoustic signals into spatiotemporal patterns of spikes . To engage the proposed automatic time-rescaling mechanism , the population rate of spikes elicited in this afferent layer must track variations in the rate of incoming speech . Such behavior emerges naturally in a sparse coding scheme in which each neuron responds transiently to the occurrences of a specific acoustic event within the auditory input . As a result , variations in the rate of acoustic events are directly translated into concomitant variations in the population rate of elicited spikes . In our model , the elementary acoustic events correspond to onset and offset threshold crossings of signal power within specific frequency channels . Such frequency-tuned onset and offset responses featuring a wide range of dynamic thresholds have been observed in the inferior colliculus ( IC ) of the auditory midbrain [51] . This nucleus is the site of convergence of projections from the majority of lower auditory nuclei and is often referred to as the interface between the lower brain stem auditory pathways and the auditory cortex . Correspondingly , we hypothesize that the layer of time-warp–invariant feature detector neurons in our model implements neurons located downstream of the IC , most probably in primary auditory cortex . Current studies on the functional role of the auditory periphery in speech perception and its pathologies have been limited by the lack of biologically plausible neuronal readout architectures; a limitation overcome by our model , which allows evaluation of specific components of the auditory pathway in a functional context . Psychoacoustic studies have indicated that the neural mechanism underlying the perceptual normalization of temporal speech cues is involuntary , i . e . , it is cognitively impenetrable [16] , controlled by physical rather than perceived speaking rate [17] , confined to a temporally local context [2] , [18] , not specific to speech sounds [52] , and already operational in prearticulate infants [53] . The proposed conductance-based time-rescaling mechanism is consistent with these constraints . Moreover , our model posits a direct functional relation between high synaptic conductances and the time-warp robustness of human speech perception . This relation gives rise to a novel mechanistic hypothesis explaining the impaired capabilities of elderly listeners to process time-compressed speech [54] , [55] . We hypothesize that the down-regulation of inhibitory neurotransmitter systems in aging mammalian auditory pathways [56] , [57] limits the total synaptic conductance and therefore prevents the time-rescaling mechanism from generating short , effective time constants through synaptic shunting . Furthermore , our model implies that comprehension deficits in older adults should be linked specifically to the processing of phonetic segments that contain fast time-compressed temporal cues . Our hypothesis is consistent with two interrelated lines of evidence . First , comprehension difficulties of time-compressed speech in older adults are more likely a consequence of an age-related decline in central auditory processing than attributes of a general cognitive slowing [56] , [58] . Second , recent reports have indicated that recognition differences between young and elderly listeners originate mainly from the temporal compression of consonants , which often feature rapid spectral transitions , but not from steady-state segments [54] , [55] , [58] of speech . Finally , our hypothesis posits that speaking rate–induced shifts in perceptual category boundaries [2] , [16] , [17] should be age-dependent , i . e . , their magnitude should decrease with increasing listener age . This prediction is straightforwardly testable within established psychoacoustic paradigms . In a previous neuronal model of time-warp–invariant speech processing [5] , [6] , sequences of acoustic events are converted into patterns of transiently matching firing rates in subsets of neurons within a population , which trigger synchronous firing in a downstream readout circuit . The identity of neurons whose firing rates converge to an identical value during an input pattern , and hence also the pattern of synchrony emerging in the readout layer , depends only on the relative timing of the events , not on the absolute duration of the auditory signal . However , for this model to recognize multiple input patterns , the convergence of firing rates is only approximate . Therefore , the resulting time-warp robustness is limited and , as in our model , dependent on the learning load . Testing this model on our synthetic classification task ( cf . Figure 3 ) indicated a substantially smaller storage capacity than is realizable by the conductance-based tempotron ( Text S1 ) . An additional disadvantage of this approach is that it copes only with global ( uniform ) temporal warping . Invariant processing of dynamic time warp as is exhibited by natural speech ( cf . Figure 1C and 1D ) is more challenging since it requires robustness to local temporal distortions of a certain statistical character . Established algorithms that can cope with dynamically time-warped signals are typically based on minimizing the deviation between an observed signal and a stored reference template [59]–[61] . These algorithms are computationally expensive and lack biologically plausible neuronal implementations . By contrast , our conductance-based time-rescaling mechanism results naturally from the biophysical properties of input integration at the neuronal membrane and does not require dedicated computational resources . Importantly , our model does not rely on a comparison between the incoming signal and a stored reference template . Rather , after synaptic conductances have adjusted to the statistics of a given stimulus ensemble , the mechanism generalizes and automatically stabilizes neuronal voltage responses against dynamic time warp even when processing novel stimuli ( cf . Figure 3C ) . The architecture of our neuronal model also fundamentally departs from the decades-old layout of Hidden Markov Model–based artificial speech-recognition systems , which employ probabilistic models of state sequences . These systems are hard to reconcile with the biological reality of neuronal system architecture , dynamics , and plasticity . The similarity in performance between our model and such state-of-the-art systems on a small vocabulary task highlights the powerful processing capabilities of spike-based neural representations and computation . Although the present work focuses on the concrete and well-documented example of time-warp robustness in the context of neural speech processing , the proposed mechanism of automatic rescaling of integration time is general and applies also to other problems of neuronal temporal processing such as birdsong recognition [3] , insect communication [9] , and other ethologically important natural auditory signals . Moreover , robustness of neuronal processing to temporal distortions of spike patterns is not only important for the processing of stimulus time dependencies , but also in the context of spike-timing–based neuronal codes in which the precise temporal structure of spiking activity encodes information about nontemporal physical stimulus dimensions [62] . Evidence for such temporal neural codes have been reported in the visual [63]–[65] , auditory [66] , and somatosensory [67] , as well as the olfactory [68] pathways . As a result , we expect mechanisms of time-warp–invariant processing to also play a role in generating perceptual constancies along nontemporal stimulus dimensions such as contrast invariance in vision or concentration invariance in olfaction [4] . Finally , time warp has also been described in intrinsically generated brain signals . Specifically , the replay of hippocampal and cortical spiking activity at variable temporal warping [69] , [70] suggests that our model has applicability beyond sensory processing , possibly also encompassing memory storage and retrieval .
Numerical simulations of the conductance-based tempotron were based on exact integration [71] of the conductance-based voltage dynamics defined in Equation 1 . With the membrane capacitance set to 1 , the resting membrane time constant in this model is τm = 1/gleak . Implementing an integrate-and-fire neuron model , an output spike was elicited when V ( t ) crossed the firing threshold Vthr . After a spike at tspike , the voltage is smoothly reset to the resting value by shunting all synaptic inputs that arrive after tspike ( cf . [22] ) . We used Vthr = 1 , Vrest = 0 , and reversal potentials and for excitatory and inhibitory conductances , respectively . Unless stated otherwise , the resting membrane time constant was set to τm = 100 ms throughout our work [20] . For the synaptic time constant , we used τs = 1 ms for the random latency task , which minimized the error of the current-based neuron , and to τs = 5 ms in the speech-recognition tasks . To check the effect of nonsynaptic voltage-dependent conductances on the automatic time-rescaling mechanism , we implemented an h-like current Ih after [72] as a noninactivating current with HH-type dynamics of the form Here , is the maximal h-conductance , with reversal potential and m is its voltage-dependent activation variable with kineticswhereand The voltage dependence of the rate constants α and β were described by the formwith parameters aα = −39 . 015 s−1 , bα = −259 . 925 s−1 , kα = 1 . 77926 and aβ = 365 . 85 s−1 , bβ = −2853 . 25 s−1 , kβ = −1 . 28889 . In Figure S1 , we quantified the effect of the h-conductance on the fidelity of the time-rescaling mechanism by measuring the time-warp–induced distortions of neuronal voltage traces for different values of the maximal h-conductance . Specifically , for a given value of and a time warp β , we measure the voltage traces and and their standard deviations across time σ1 and σβ , respectively . We define the time-warp distortion index as the mean magnitude of the time-warp–induced voltage difference across time normalized by the mean standard deviation , , In Figure S1 , values of are normalized by Λ ( 0 , β ) . The voltage traces were generated by random latency patterns and uniform synaptic peak conductances as used in Figure 2 . As increasing values of depolarized the neuron's resting potential , excitatory and inhibitory synaptic conductances were balanced separately for each value of . In the current-based tempotron that was implemented as described in [22] , each input spike evoked an exponentially decaying synaptic current that gave rise to a postsynaptic potential with a fixed temporal profile . In Figure 8C ( upper row ) , voltage traces of a current-based analog of a conductance-based tempotron with learned synaptic conductances , reversal potentials , and effective membrane integration time τeff ( cf . Figure 8B ) were computed by setting the synaptic efficacies ωi of the current-based neuron to and its membrane time constant to τm = τeff . The resulting current-based voltage traces were scaled such that for each pair of models , the mean voltage maxima for unwarped stimuli ( β = 1 ) were equal . Following [22] , changes in the synaptic peak conductance of the ith synapse after an error trial were given by the gradient of the postsynaptic potential , , at the time of its maximal value tmax . To compute the synaptic update for a given error trial , the exact solution of Equation 1 was differentiated with respect to and evaluated at tmax , which was determined numerically for each error trial . Whenever a synaptic peak conductance attempted to cross to a negative value , its reversal potential was switched . A voltage correlation-based approximation of tempotron learning was implemented by extending the approach in [22] such that the change in the synaptic peak conductance of the ith synapse due to a spike at time ti was governed by the correlation of the postsynaptic potential V ( t ) with a synaptic learning kernel Klearn ( t ) = ( exp ( −t/τlearn ) −exp ( −t/τs ) ) / ( τlearn−τs ) . The two time constants of the synaptic learning kernel were the synaptic time constant τs and the learning time constant , which was determined by the time-averaged synaptic conductance of the current error trial and approximated the effective membrane time constant during that trial . The voltage maximum operation was approximated by thresholding νi , yieldingfor changes of excitatory conductances on target and null patterns , respectively , and changes with the reversed polarity , ±1 , for inhibitory conductances . The plasticity induction threshold was set to κ = 0 . 45 . As in [22] , we employed a momentum heuristic to accelerate learning in all learning rules . In this scheme , synaptic updates consisted , not only of the correction , which was given by the learning rule and the learning rate λ , but also incorporated a fraction μ of the previous synaptic change . Hence , . We used an adaptive learning rate that decreased from its initial value λini as the number of learning cycles l grew , λ = λini/ ( 1+10−4 ( l−1 ) ) . A learning cycle corresponded to one iteration through the batch of templates in the random latency task or the training set in the speech task . Global time warp was implemented by multiplying all firing times of a spike template by a constant scaling factor β . In Figure 3A , random global time warp between compression by 1/βmax and dilation by βmax was generated by setting β = exp ( qln ( βmax ) ) with q drawn from a uniform distribution between −1 and 1 for each presentation . Dynamic time warp was implemented by scaling successive interspike intervals tj−tj−1 of a given template with a time-dependent warping factor , such that warped spike times with and . The time-dependent factor resulted from an equilibrated Ornstein-Uhlenbeck process ξ ( t ) with a relaxation time of τ = 200 ms that was rescaled by the complementary error function erfc to transform the normal distribution of ξ ( t ) into a uniform distribution over [−1 1] at each t . To ensure that the symmetry of excitation and inhibition in our model architecture was not crucial for the time-warp–invariant processing of spike patterns , we implemented a control architecture in which all afferents were confined to be excitatory , except one additional inhibitory synapse , which mimicked the effect of a global inhibitory network . In the time-warped random latency task , spike patterns were fed into the excitatory population as before; however , in addition , the inhibitory synapse received a copy of each incoming spike . All synaptic peak conductances were plastic and controlled by the conductance-based tempotron rule . In this model , synaptic sign changes were prohibited . Spike time jitter [22] was implemented by adding independent Gaussian noise with zero mean and a standard deviation of 5 ms to each spike of a template before each presentation . Sound signals were normalized to unit peak amplitude and converted into spectrograms over NFTT = 129 linearly spaced frequencies fj = fmin+j ( fmax+fmin ) / ( NFTT+1 ) ( j = 1… NFTT ) between fmin = 130 Hz and fmax = 5 , 400 Hz by a sliding fast Fourier transform with a window size of 256 samples and a temporal step size of 1 ms . The resulting spectrograms were filtered into Nf = 32 logarithmically spaced Mel frequency channels by overlapping triangular frequency kernels . Specifically , Nf+2 linearly spaced frequencies given by hj = hmin+j ( hmax−hmin ) / ( Nf+1 ) with j = 0…Nf+1 and hmax , min = 2 , 595log ( 1+fmax , min/700 ) were transformed to a Mel frequency scale between fmin and fmax . Based on these , signals in Nf channels resulted from triangular frequency filters over intervals with center peaks at . After normalization of the resulting Mel spectrogram SMel to unit peak amplitude , the logarithm was taken through log ( SMel = ε ) −log ( ε ) with ε = 10−5 and the signal in each frequency channel smoothed in time by a Gaussian kernel with a time constant of 10 ms . Spikes were generated by thresholding of the resulting signals by a total of 31 onset and offset threshold-crossing detector units . Whereas each onset afferent emitted a spike whenever the signal crossed its threshold in the upward direction , offset afferents fired when the signal dropped below the threshold from above . For each frequency channel and each utterance , threshold levels for onset and offset afferents were set relative to the maximum signal over time to and . For , onset and offset afferents were reduced to a single afferent whose spikes encoded the time of the maximum signal for a given frequency channel . We used the digit subset of the TI46 Word speech database [24] . This clear speech dataset comprises 26 isolated utterances of each English digit from zero to nine spoken by 16 adult speakers ( eight male and eight female ) . The data is partitioned into a fixed training set , comprising 10 utterances per digit and speaker , and a fixed test set holding the remaining 16 utterances per digit and speaker . We also tested our neuronal word-recognition model on the adult speaker , isolated-digit subset of the TIDIGITS database [27] . This subset comprises two utterances per digit and speaker , i . e . , a total of 20 utterances from 225 adult speakers ( 111 male and 114 female ) , that are dialectically balanced across 21 dialectical regions ( tiling the continental United States ) . Because the TI46 database only provides utterances of the word “zero” for the digit 0 , we excluded the utterances of “oh” from our TIDIGITS sample . Based on the spiking activity of all binary digit detector neurons , a full digit classifier was implemented by ranking the digit detectors according to their individual task performances . As a result , a given stimulus was classified as the target digit of the most reliable of all responding digit detector neurons . If all neurons remained silent , a stimulus was classified as the target digit of the least reliable neuron . To preserve the timing relations between the learned spectrotemporal features and the target words , we refrained from correcting the spike-triggered stimuli for stimulus autocorrelations [73] . Test errors in the speech tasks were substantially reduced by training with a Gaussian spike jitter with a standard deviation of σ added to the input spikes as well a symmetric threshold margin v that required the maximum postsynaptic voltage on target stimuli to exceed Vthr+v and to remain below Vthr−v during null stimuli . Values of λini , μ , σ , and v were optimized on a 4-dimensional grid . Because for each grid point , only short runs over maximally 200 cycles were performed , we also varied the mean values of initial Gaussian distributions of the excitatory and inhibitory synaptic peak conductances , keeping their standard deviations fixed at 0 . 001 . The reported performances are based on the solutions that had the smallest errors fractions over the test set . If not unique , we selected the solution with the highest robustness to time warp ( cf . Figure 8B ) . Note that this naive optimization of the training parameters did not maintain a separate holdout test set for cross-validation and might therefore overestimate the true generalization performance . We adopted this optimization scheme from [25] , [26] to ensure comparability of the resulting performance measures . HTK generalization performance was tested with the HTK package version 3 . 4 . 1 [74] with front-end and HMM model parameters following [26] . Specifically , speech data from the TI46 and TIDIGITS databases were converted to 13 Mel-cepstral coefficients ( including the 0th order coefficient ) along with their first and second derivatives at a frame rate of 5 ms . Mel-coefficients were computed over 30 channels in 25-ms windows with zero mean normalization enabled ( TARGETKIND = MFCC_D_A_Z_0 ) . In addition , the following parameters were set: USEHAMMING = T; PREEMPCOEF = 0 . 97; and CEPLIFTER = 22 . Ten HMM models , one for each digit plus one HMM model for silence , were used . Each model consisted of five states ( including the the two terminal states ) with eight Gaussian mixtures per state and left-to-right ( no skip ) transition topology . | The brain has a robust ability to process sensory stimuli , even when those stimuli are warped in time . The most prominent example of such perceptual robustness occurs in speech communication . Rates of speech can be highly variable both within and across speakers , yet our perceptions of words remain stable . The neuronal mechanisms that subserve invariance to time warping without compromising our ability to discriminate between fine temporal cues have puzzled neuroscientists for several decades . Here , we describe a cellular process whereby auditory neurons recalibrate , on the fly , their perceptual clocks and allows them effectively to correct for temporal fluctuations in the rate of incoming sensory events . We demonstrate that this basic biophysical mechanism allows simple neural architectures to solve a standard benchmark speech-recognition task with near perfect performance . This proposed mechanism for time-warp–invariant neural processing leads to novel hypotheses about the origin of speech perception pathologies . | [
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| 2009 | Time-Warp–Invariant Neuronal Processing |
Neuronal avalanches are a form of spontaneous activity widely observed in cortical slices and other types of nervous tissue , both in vivo and in vitro . They are characterized by irregular , isolated population bursts when many neurons fire together , where the number of spikes per burst obeys a power law distribution . We simulate , using the Gillespie algorithm , a model of neuronal avalanches based on stochastic single neurons . The network consists of excitatory and inhibitory neurons , first with all-to-all connectivity and later with random sparse connectivity . Analyzing our model using the system size expansion , we show that the model obeys the standard Wilson-Cowan equations for large network sizes ( neurons ) . When excitation and inhibition are closely balanced , networks of thousands of neurons exhibit irregular synchronous activity , including the characteristic power law distribution of avalanche size . We show that these avalanches are due to the balanced network having weakly stable functionally feedforward dynamics , which amplifies some small fluctuations into the large population bursts . Balanced networks are thought to underlie a variety of observed network behaviours and have useful computational properties , such as responding quickly to changes in input . Thus , the appearance of avalanches in such functionally feedforward networks indicates that avalanches may be a simple consequence of a widely present network structure , when neuron dynamics are noisy . An important implication is that a network need not be “critical” for the production of avalanches , so experimentally observed power laws in burst size may be a signature of noisy functionally feedforward structure rather than of , for example , self-organized criticality .
In what follows we provide a theory for the formation of avalances using a stochastic version of the sigmoid rate model originally introduced to represent individual neural activity [8] . We call this the stochastic rate model [9]–[11] . Each neuron spikes with a probability per unit time dependent on its total synaptic input , while the resulting spiking activity decays at a constant rate . The stochastic nature of the model allows for efficient simulation via the Gillespie algorithm [12] , an event-driven method . We extend the stochastic rate model to explicitly deal with coupled excitatory and inhibitory populations . We show that this model , with appropriate connectivity , produces avalanches in an all-to-all connected network of excitatory and inhibitory neurons when a parameter is increased . We call this parameter the feedforward strength , [13] , since it measures the extent to which our recurrent network functions analogously to a feedforward network . Analytically , we show that the stochastic rate model may be treated as a stochastic perturbation of the deterministic Wilson-Cowan equations [14] , [15] . The stochastic rate model produces avalanches in a range of network sizes , for example thousands of neurons , depending on the parameters; in the limit of large network size , the model obeys the Wilson-Cowan equations exactly , which do not themselves produce avalanches . This analysis allows us to address the relation of avalanche dynamics to other parameters , in particular the network size and the external input to the network , showing that these dynamics are robust to wide-ranging variations in these parameters . Finally we obtain avalanche dynamics in a network with random sparse connectivity by generalizing the notion of feedforward strength .
The stochastic rate model treats neurons as coupled , continuous-time , two-state Markov processes ( figure 1A ) ; this may be seen as analogous to a deterministic neuron with very noisy synaptic input , but is agnostic about the source of the noise . Each neuron can exist in either the active state , representing a neuron firing an action potential and its accompanying refractory period , or a quiescent state , representing a neuron at rest . In order to fully describe this two-state Markov process , it is only necessary to specify the transition rates between the two states . The transition probability for the neuron to decay from active to quiescent ( right arrow of figure 1A ) is ( 1 ) as , where represents the decay rate of the active state of the neuron . The transition probability for the neuron to spike ( left arrow in figure 1A ) , i . e . change from quiescent to active , is ( 2 ) ( 3 ) as . Here is the response function , giving the firing rate as a function of input , and the total synaptic input to neuron , a sum of external input and network input , where are the weights of the synapses , and the activity variable if the th neuron is active at time and zero otherwise . Although there is no explicit refractory state in the model , in all simulations , , corresponding to an active state with a time constant of ( 1 for the action potential plus 9 to approximate a refractory period where neurons are hyperpolarized ) . This choice of constrains neuronal firing rates to be no greater than 100 Hz . All neurons are chosen to have the same response function , ( 4 ) As shown in figure 1B , this standard choice of response function models a neuron's firing rate as zero if it is below threshold , growing close to linearly with the synaptic input as it passes threshold , and then saturating at a maximum rate further above threshold . Since we are studying spontaneous activity in this study , external input is positive but small , so that even in the absence of any network activity , some neurons have a non-zero firing rate . We next consider networks of excitatory and inhibitory neurons , initially with all-to-all connectivity depending only on the cell type; at the end of the results section we address how our findings extend to sparse or inhomogenous connectivities . The outgoing synaptic weight from each excitatory neuron to each excitatory neuron is , from excitatory to inhibitory is , from inhibitory to excitatory is , and from inhibitory to inhibitory is . The effect is of one excitatory and one inhibitory population , connected with strengths shown in figure 2A . The network's stochastic evolution can be thought of as a random walk between states with excitatory and inhibitory neurons active , where the number of active neurons can increase or decrease only by one at a time , causing the state to wander around on a lattice as shown in figure 2C . Solid lines show movements out of the state and dashed lines movements into . The rightwards ( upwards ) arrow is the result of a single excitatory ( inhibitory ) neuron firing in response to its synaptic input . The leftwards ( downwards ) arrow is associated with the decay of an excitatory ( inhibitory ) neuron from active to quiescent , reflecting the single neuron dynamics shown in figure 1A . To treat this analytically , we consider the probability that there are excitatory , and inhibitory neurons active at time . The random walk on the lattice depicted in in figure 2C is reflected by evolving dynamically in time for each state . The probability evolves according to the master equation ( 19 ) . The equation and its derivation are detailed in methods; in fact the equation contains exactly the same information as figure 2C . This is a generalization of the one population master equation for the stochastic rate model introduced in [9] . Note here that , in the case of identical single neurons and all-to-all connectivity the population-level master equation is an exact description of the network evolution; if the single neuron parameters and the connection strengths were drawn from probability distributions , we would have to average over these distributions to get an approximate population-level master equation . We use the Gillespie algorithm [12] , an event-driven method of exact simulation , for all simulations of the master equation ( see methods ) . We now investigate the range of parameters for which the stochastic model exhibits a transition from independent firing to irregular bursts of synchronous activity , i . e . to avalanches . We vary both inhibitory synaptic strength and the excitatory strength , while fixing the difference between them , . We keep the other parameters constant; as we will later show , this has the effect of leaving the deterministic equilibrium or fixed point unchanged . As shown in figure 3A , when the total synaptic strength is small , firing rates fluctuate weakly about the fixed point predicted by the deterministic Wilson-Cowan equations , meaning that the neurons fire asynchronously . The neurons fire roughly as independent Poisson processes , as shown by their approximately exponential inter-spike-interval distribution in the insets to figure 3D . The distribution of burst sizes shown in figure 3D fits a geometric distribution consistent with independent Poisson firing , explained in methods . As we increase the synaptic input , fluctuations in the firing rate grow , and we begin to see large and long-lived downwards fluctuations away from the deterministic value of the firing rate , at random times , shown in figure 3B–C . Episodes of near-zero firing interpose between episodes of collective firing of many neurons across the network . Looking at the statistics of these irregular bursts of synchronous activity , we find that the distribution of burst sizes , measured in number of spikes , approaches a power law distribution as the firing becomes more synchronized , shown in figures 3E–F . This is therefore a candidate mechanism for neuronal avalanches [2] , [4] , [5] . In figure 3F , we see that the size distribution conforms to a power law for avalanche sizes between roughly 5 and 500 spikes . Testing the goodness of fit using ordinary least-squares linear regression on the bilogarithmically transformed co-ordinates , the test of significance used in [2] , we find the value was 0 . 968 . However , recent research has shown that to be is an inappropriate and unreliable method for detecting power laws [16] , a point we return to in the discussion . Using the maximum likelihood estimator developed in [16] ( see methods ) we find an exponent of 1 . 62 . However , the goodness of fit test also developed in [16] , we reject the null hypothesis that the sample is drawn from an exact power law , for its entire range , with . Considering the population activity , ( i . e . the proportion active per population , as opposed to the spike firing rate ) , figure 3G–I show that the activity also becomes increasingly prone to large fluctuations towards zero , despite the associated deterministic Wilson-Cowan equations having an unchanging single stable fixed point . We illuminate this behaviour with the help of the system size expansion [17]–[20] , a standard technique from stochastic chemical kinetics , reviewed in Text S1 . The inspiration for this comes from a Gaussian approximation: if the neurons were to fire independently of each other , then the total activity in each population would be Gaussian with mean proportional to and standard deviation proportional to . Accordingly , we model the number of neurons active at a given time as the sum of a deterministic component , scaled by , and a stochastic perturbation , scaled by , so that ( 5 ) The deterministic terms obey the Wilson-Cowan equations ( 6 ) where and are , respectively the ( time-averaged ) proportions of excitatory and inhibitory neurons active in a given time bin , [see [14]] , and now the total synaptic inputs are the same to both populations , , where is external input . The fluctuation variables obey a linear stochastic differential equation ( 7 ) to order , where the matrix is the Jacobian of ( 6 ) calculated at the deterministic trajectory , and and are independent white-noise variables whose amplitude is also calcuated via the deterministic trajectory . Since this equation is linear , the fluctuations are approximately Gaussian for large . Notice that in figure 3G the trajectory of the master equation closely tracks the trajectory of the Wilson-Cowan equations ( 6 ) . In the case of independent firing , the fluctuation term is small , but we see in figures 3H–I that as the network transitions to synchronous firing the fluctuations dominate and the stochastic trajectories move away from those for the deterministic system . It is is easier to understand the dynamics by making a change of variables; to motivate this change of variables , note that large fluctuations tend to occur increasingly as inhibition approaches excitation , . This is sometimes called a balanced network [13] , [21] , [22] , in the sense that inhibition balances excitation . In this case , we can express the synaptic input in terms of the mean and difference of the excitatory and inhibitory population activities , and note that the neuronal response is highly sensitive to changes in the difference and relatively insensitive to changes in the mean , described schematically in figure 2B . More precisely , if ( 8 ) then the total synaptic input is ( 9 ) where . From ( 9 ) we deduce that , in the balanced case where , the input is much more sensitive to changes in the difference than in the mean . Accordingly , we make a linear change of variables from to . As shown in Text S1 , this leads to the more transparent deterministic equations ( 10 ) ( 11 ) with unique stable solution . The factor of in ( 11 ) means that at the fixed point , and that close to the fixed point is only weakly sensitive to changes in . Since , and depends on the sum of the weights only through the term which is zero at the fixed point , in fact the fixed point is left unchanged by varying the sum while keeping the difference constant . This is why the fixed point is the same in figures 3G–I . In these new variables the linear noise approximation [see Text S1] is expressed as ( 12 ) where , , , and and are again independent white-noise variables . The Jacobian matrix ( 13 ) is upper-triangular , and has eigenvalues and . If is small and positive , so are the eigenvalue magnitudes and . To see this , note that is the sum of two small terms and ; the extra term in is also small if is small , since . Thus , the fixed point is weakly stable , and like the location of the fixed point , its linear stability depends on the weights only via the difference . The off-diagonal term has been called a hidden feedforward term [13] , [23]–[25] , feedforward because fluctuations in feed into the evolution of but not vice versa , and hidden because a change of variables is required to see this structure , not obviously present in the network connectivity ( figure 2B ) . The Jacobian , with small eigenvalues but a large off-diagonal term , leads to the amplification of small values of into transient increases in whose magnitude increases with . This effect is called balanced amplification in [13]; it may also be thought of as a shear flow in the phase plane , and is characterized by the nullclines crossing at a shallow angle . In figures 3G–I , one can see that the nullclines become closer to parallel as the feedforward term increases . In a noisy system , the functionally feedforward mechanism means that small spontaneous fluctuations in are amplified into transient increases in whose size increases with . An appropriate combination of the noise being strong enough , the feedforward term being large enough , and the eigenvalue damping the fluctuations being small enough , leads to large sustained fluctuations in . We may make this more explicit by examining the variance of the activity , calculated in Text S1 , from the linear noise approximation as ( 14 ) Fluctuations predicted by the linear noise approximation grow with the strength of the functionally feedforward term , and also grow as the eigenvalues and go to zero . We may relate the above findings to the fluctuations in firing rate found in simulations , by observing how the mean and standard deviation of the time-binned spike count varies as we increase the feedforward strength . We time bin the spike counts into bins of width , so that the number of spikes in the th bin is . Then the normalized firing rate is and the normalized standard deviation is . In figure 4A we see that as the feedforward strength increases , the standard deviation initially increases sharply . Meanwhile , the mean firing rate drops , and continues to drop even as the standard deviation saturates . The effect of this is that the coefficient of variation ( CV ) , , which measures the typical size of the fluctuations relative to the mean , increases , initially rapidly but later more slowly , as shown in figure 4B . ( Note that this is the CV of the time-binned spike counts , not the much studied CV of the inter-spike interval . ) The linear noise approximation , via equation ( 14 ) , predicts the increase in the standard deviation with . Although the linear noise approximation predicts no change in the mean , correction terms at the next order , , indicate that the mean decreases as increases ( see Text S1 ) . This leads to the counterintuitive observation that the deterministic fixed point does not even accurately describe the mean value of the stochastic system when fluctuations are large . Another prediction from ( 14 ) is that the fluctuations become small as increases , in particular causing the firing rate to return to its deterministic limit . In figure 5 we show the effect of varying the size of the network . Fluctuations do indeed die away at large size , and the firing rate barely fluctuates for neurons per population; however , irregular bursts are still observed in networks with size of up to neurons per population . This indicates that , although the stochastic Wilson-Cowan model has as its large-scale limit the deterministic Wilson-Cowan equations , the network size may need to be extremely large for the deterministic equations to accurately describe its behavior . We have found spontaneous dynamics organized into irregular synchronous bursts in neural networks with very weak constant input . To shed light on how networks of neurons process information , we want to know what happens when the input varies . In the simplest case - where input to every neuron is identical , but may change over time - a change in the magnitude of alone may be sufficient to cause the network to move from irregular to regular behaviour , shown in figure 6 . Here a change in the input strength makes the fixed point more stable , so decreases the extent to which the network at the fixed point is functionally feedforward . We can see this by tracking the changes caused in the Jacobian matrix ( 12 ) at the fixed point with respect to the mean and difference co-ordinates . Increasing the external input results in an increase in the synaptic input , both directly as appears in the sum , and indirectly as it causes the fixed point to increase . This causes the eigenvalue to become more negative , increasingly the stability of the fixed point . Since the response function saturates , so has a decreasing derivative , the other eigenvalue also becomes more negative as input increases . Similarly the feedforward term decreases . In other words , when input is high , spontaneous internal network correlations quickly decrease . This quick response to an increase in input is a computationally desirable property previously observed in balanced networks [13] , [21] , [22] . The effects of altering various parameters of the model starting from independent firing are summarized in table 1 , where an increase in the coefficient of variation means that fluctuations are proportionately greater , or that the dynamics are more avalanche-like . The number of synapses per neuron in cortex is believed to be at most [26] , so only networks with fewer than neurons could have anything approaching all-to-all connectivity; larger networks in cortex must be sparsely connected . Our results so far deal with all-to-all connected networks , so it is reasonable to ask whether or not a sparsely connected network could produce avalanches via the same mechanism . The answer is yes: we are able to generate random sparse matrices with weakly stable fixed points and high functional feedforward connectivity which exhibit large fluctuations grouped into avalanches , as shown in figure 7 . We used the same single-neuron parameters and response function as the all-to-all case , changing only the connectivity matrix . To make this matrix , we generated random sparse positive matrices with large eigenvalues , and with small eigenvalues , so that the weight matrix ( 15 ) is random , sparse and obey's Dale's principle that every column , representing the synaptic weights outwards from a single neuron , is either all excitatory or all inhibitory [27] . The details of how to construct such a weight matrix are given in methods . The condition that the eigenvalues of are much smaller than those of is analogous to the population condition in the all-to-all case . As in the all-to-all case , this sparsely connected network has a single stable fixed point , and a change of variables to the mean and difference of the activities leads to the Jacobian at the fixed point having small negative eigenvalues and large off-diagonal elements causing strong functionally feedforward dynamics . We conclude that homogenous all-to-all connectivity , which has the effect of averaging the population activity at the input to every neuron , is not a requirement for strongly synchronized fluctuations grouped into avalanches . The same mechanism produces similar fluctuations in an inhomogenous network if the functional feedforward strength is large enough .
Although simplified models are commonly used to study neural network dynamics , the question remains whether a given simplification is appropriate for modeling the network at hand . Our model neurons , which are stochastic switches , are so simple as to make it difficult to relate their parameters precisely to the cells being modeled , although not as difficult as for a purely population-based model . Two-state Markov processes have been previously used for modeling neurons at longer timescales , for example the states representing a zero or nonzero firing rate in studies of attractor networks [28] , or up and down states in cortex in studies of repeating patterns of activity [29] , contrasting with our use of a state transition to represent a single spike . Such simple stochastic models may produce qualitatively the same network dynamics as more biophysically detailed models , while their simplicity enables them to give insight into the mechanisms of emergent phenomena [14] , [30]; we expect that further research will show the same to hold for our model . In addition , it would be interesting to see if functionally feedforward connectivity could produce avalanche dynamics at much longer timescales via the model of Roxin et al . [29] . Another concern is that the time scales in our simulations reflect the time scales in cortex . For example our cellular firing rates are at the high end of those observed in cortex in the asynchronous case . One simple way to adjust our model is to place a time constant in front of the time derivative term in the master equation , or equivalently to scale all the transition rates by , thus slowing down the entire simulation , including firing rates , by a constant factor . One could also scale the transition rates differently for each population , since excitatory neurons tend to have lower firing rates than inhibitory neurons in cortex [31] . Another way to slow down the rate of occurence of avalanches without changing the single-neuron parameters is , by increasing the size of the simulated network to match the size of a cortical slice , so decreasing the effective noise strength which is proportional to the square root of the size . Since the avalanches are noise-driven fluctuations , with appropriate adjustments to the connectivity parameters this would make the time between avalanches longer . The lack of conduction delays in our model raises another issue with the time scales: the delay in activation of one neuron by another is accounted for solely by the random exponential time to spiking , thus meaning that a postsynaptic spike may follow a presynaptic spike at a delay shorter than is reasonable for causality in cortex . We would expect the introduction of delays to slow down the network dynamics , and also be relatively straightforward to simulate as an adaptation of the Gillespie algorithm to account for delays already exists [32] . As neurons in larger networks are more likely to be far apart , we might expect conduction delays to play a bigger role in larger , spatially distributed networks . Although we showed that self-organization is not needed to maintain avalanching dynamics in a network , this begs the question , what kind of self-organization can put the network in a regime where it produces avalanches ? In cortical cultures from layers 2/3 of the rat , avalanche-like dynamics emerge after 6–8 days [3]; similarly , in cultured networks of dissociated rat hippocampal neurons , avalanche dynamics emerge after 3–4 weeks [5] . Feedforward connectivity requires the sum of excitatory and inhibitory synaptic inputs to be on average much greater than the difference , and we would expect it to take time to develop extensive enough connectivity for the total to be large . An extension of our model to involve slow modification of network properties , for example by synaptic plasticity , would be needed to account fully for these experimental results . If the proposed mechanism of functionally feedforward connectivity generates neuronal avalanches in an experimental system , it should be possible to probe that system in ways analogous to varying the parameters in our model . For example , the model predicts no activity in the absence of external input , since the only fixed point of the model is the origin . If the network topology already exhibits strong feedforward strength , then the addition of small concentrations of an excitant would effectively increase the external input parameter , so shifting the fixed point away from the origin and causing avalanches . This was in fact the method used by Beggs & Plenz [2] , who added NMDA ( N-methyl-D-aspartic acid ) to produce avalanches in cortical slices and cultures . If too much NMDA is added , however , then we expect an excess of excitation , so that the near balance of excitation and inhibition responsible for the strong feedforward strength of the network would be disrupted and avalanches would no longer occur . A small increase in extracellular effectively increases both excitatory and inhibitory synaptic weights , thereby increasing the feedforwardness while keeping the difference relatively unchanged , leading to increased burst frequency in our model . This suggests that an experimental preparation could be studied near the avalanche transition by titrating with . If the network were in a state where is slightly positive , as in the simulations performed here , then further addition of a small amount of an inhibitory antagonist such as bicuculline ( a antagonist ) would weaken , thereby increasing the difference and leading counterintuitively to decreased burst frequency after the addition of an inhibitory blocker . If the synaptic weights were initially elevated by increasing extracellular , this would ensure the feedforwardness to be much larger than the difference , so that weakening would make a proportionately larger change to the difference . This may be the effect at work in [33] , where adding and bicuculline together produced a lower overall burst frequency than adding potassium alone , in a slice preparation of rat hippocampus . If it were possible to add carefully co-ordinated amounts of an inhibitory blocker and an excitatory blocker , the model raises the possibility that a network , by becoming less functionally feedforward , could have higher mean firing rates but fewer bursts . In general , if there are pharmacological manipulations corresponding to varying the parameters as shown in table 1 , we expect the coefficient of variation of the firing rate , our proxy for the strength of avalanche dynamics , to move accordingly . This study of neural network dynamics shows that the stochastic rate model may be viewed as a stochastic generalization of the Wilson-Cowan equations . In this context neither specific types of neural connectivity , nor tuning or self-organization to criticality , are necessary for the emergence of avalanche dynamics , namely spontaneous network bursts with power-law distributed burst sizes . What is important is that the net difference between excitation and inhibition should be small compared to the sum of excitation and inhibition , so that the network effectively has feedforward structure . Small random fluctuations , here provided by stochastic single neurons , are amplified by the functional feedforward structure into bursts involving many neurons across the network . Analogous deterministic models with functionally feedforward structure do not produce avalanches . Thus stochastic functionally feedforward networks are a sufficient and general condition for the emergence of avalanche dynamics , and a mechanism for the spontaneous production of network bursts .
Here we show how to derive the master equation governing the evolution of the network state , visualized in figure 2C . We consider active excitatory neurons , each becoming inactive at rate . This causes a flow of rate out of the state proportional to , hence a term . Similarly the flow into from , caused by one of active excitatory neurons becoming inactive at rate , gives a term . The net effect is a contribution ( 16 ) In state , there are quiescent excitatory neurons , each prepared to spike at rate , leading to a term , where the total input is ( 17 ) Correspondingly , the flow into the state from due to excitatory spikes is given by . The total contribution from excitatory spikes is then ( 18 ) There are analogous terms for the decay of active inhibitory neurons and the spiking of quiescent inhibitory neurons . Putting this together , the probability evolves according to the master equation ( 19 ) We simulate the entire network as a single continuous-time Markov process , using Gillespie's exact stochastic simulation algorithm [12] . The most general form of this starts with the single-neuron transition rates , that for the th neuron being: ( 20 ) The algorithm takes the state of the network , i . e . each neuron is specified as being either active or quiescent , and proceeds as: In the case of homogenous all-to-all networks , if one only wants to simulate the number of neurons active in each population , one may simplify this algorithm along the lines of Gillespie's original presentation for a well-mixed chemical system , since the upwards transition rates would be identical for all neurons in a population . The simplified algorithm uses much less memory and runs considerably faster . The Gillespie algorithm is event-driven [41] in the sense that the simulation time is moved on only when the network state is updated , and the time intervals are random variables dependent upon the network state . It is then necessary to store only a vector of transition times and a corresponding vector of which neuron transitioned at each time . In the case of fluctuating firing rates found in avalanche dynamics , the algorithm , by its definition , adapts its time-steps to the firing rates , which can be a computational advantage . All simulations were performed in Matlab 7 . 1 ( Mathworks , Natick , MA ) . To produce plots of the mean firing rate , we counted the number of spikes in timebins of width , and smoothed the signals by convolving with a Gaussian of width . The phase-plane figures ( 3G–I ) show an approximation to the proportion active: since active neurons decay at rate , we may calculate the activity from the spike times as . The mean firing rate , plotted in figure 4 and over the raster plots ( figures 3A–C etc . ) , and the activity , plotted in the phase plane figures ( 3G–I ) and used in the calculations , are closely related . Due to the single-neuron dynamics described in ( 2 ) , the firing rate , which is the rate of transitions from active to quiescent per neuron per second , is in the all-to-all case . We define a neuronal avalanche as a sequence of spikes such that no two consecutive spikes in the avalanche are separated by a time greater than . The size of an avalanche is defined as the total number of spikes belonging to the sequence . Clearly , if is small , then avalanche sizes will be small . Indeed , in the limiting case that is smaller than the minimum time interval between any two consecutive spikes in the network , each spike becomes its own avalanche , so all avalanches have size unity . Similarly , if is chosen to be large , then avalanches will be large . Again consider a limiting case , where is on the order of the entire simulation time . Then all of the spikes belong to a single avalanche . We estimate an appropriate , as the average time interval between consecutive spikes in the network [2] , [42] . More precisely , let be the ordered sequence of spike times in the network , then ( 21 ) This is the same as the total number of spikes in the simulation divided by total simulation time . We fit two distributions to the avalanche size . Firstly , if each neuron spikes independently as a Poisson process , then the entire network fires as a Poisson process , with a rate . Then , the distribution of avalanche size is ( 22 ) which is a geometric distribution with parameter . This is the red line in figure 3D–F . It has been hypothesized that avalanche size distributions are consistent with a power law distribution , with the size given by ( 23 ) for some reasonably large range of . Note that this means the distribution is linear in bilogarithmic coordinates . The best fitting power law distribution to the avalanche size data was obtained by using a maximum likelihood estimator ( MLE ) for the slope of a power law probability distribution for discrete data ( avalanche sizes are integer values only ) ; the derivation and uses of this of this estimator are clearly explained by Clauset et al [16] . According to the MLE the slope is given by the equation ( 24 ) where is the number of avalanches greater than size and is the size of the avalanche . We take = 10 . This is the blue line in figure 3D–F . Note that this is a different method for obtaining slope values than the more common ordinary least squares linear regression analysis ( LRA ) of the bilogarithmically transformed data . LRA is based on the assumption that the noise in the dependent variable is independent for each value of the independent variable and normally distributed . Although this is true when the dependent variable is the probability of a certain size avalanche , it does not hold after the bilogarithmic transformation . The transformed probability distribution has log-normally distributed noise , and so a calculation of the slope from LRA methods can give spurious results [16] , and a biased estimate of the avalanche slope . Here we show how to make the sparse matrix with functionally feedforward connectivity; the construction is closely related to the supplementary information from [13] . For a network with excitatory and inhibitory neurons , we make a connectivity matrix ( 25 ) The matrices and are created from random orthogonal matrices and and sparse diagonal matrices and , by ( 26 ) Since and have sparse diagonal entries , we ensure that is sparse . By choosing the non-zero diagonal components of to be much smaller than those of , we pick the eigenvalues of to be much smaller than those of ; this condition is analogous to the population condition in the all-to-all case , and means that there will be a large feedforward component to the network dynamics . The fact that and are orthonormal means that both and are normal , i . e . their eigenvectors are mutually orthogonal . Next we recover and from their sum and difference in the obvious way , but adjust any negative elements of these to zero so that the resulting matrix ( 25 ) obeys Dale's principle . This perturbation of and leads to a perturbation of and , making them no longer exactly normal . A normal matrix satisfies , and we may measure the deviation from normalcy of by taking the Frobenius norm , i . e . the sum of the squares of the elements , of . For the particular matrices under study this deviation from normalcy is very small , remaining less than for both matrices after the perturbation . Now we introduce a generalized Wilson-Cowan equation for the vector of neural activities so that ( 27 ) where we interpret the response function as the diagonal operator . This set of equations has a single fixed point for the given weight matrix , due to the symmetry in input currents , . Accordingly , we change variables to sum and difference modes ( 28 ) so that equations ( 27 ) become ( 29 ) where the synaptic input is . If we replace with the population average , the Jacobian of the system at the fixed point is approximated by ( 30 ) which is upper triangular since is diagonal . It can be shown that since the elements of are large , the off-diagonal elements of this matrix will be much larger than the diagonal ones , leading to strong feedforward dynamics . It should be noted that if the net input current to each neuron is the same at the fixed point , as it is in the all-to-all case , then , and ( 30 ) becomes exact . | Networks of neurons display a broad variety of behavior that nonetheless can often be described in very simple statistical terms . Here we explain the basis of one particularly striking statistical rule: that in many systems , the likelihood that groups of neurons burst , or fire together , is linked to the number of neurons involved , or size of the burst , by a power law . The wide-spread presence of these so-called avalanches has been taken to mean that neuronal networks in general operate near criticality , the boundary between two different global behaviors . We model these neuronal avalanches within the context of a network of noisy excitatory and inhibitory neurons interconnected by several different connection rules . We find that neuronal avalanches arise in our model only when excitatory and inhibitory connections are balanced in such a way that small fluctuations in the difference of population activities feed forward into large fluctuations in the sum of activities , creating avalanches . In contrast with the notion that the ubiquity of neuronal avalanches implies that neuronal networks operate near criticality , our work shows that avalanches are ubiquitous because they arise naturally from a network structure , the noisy balanced network , which underlies a wide variety of models . | [
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| 2010 | Avalanches in a Stochastic Model of Spiking Neurons |
Epigenomes commonly refer to the sequence of presence/absence of specific epigenetic marks along eukaryotic chromatin . Complete histone-borne epigenomes have now been described at single-nucleosome resolution from various organisms , tissues , developmental stages , or diseases , yet their intra-species natural variation has never been investigated . We describe here that the epigenomic sequence of histone H3 acetylation at Lysine 14 ( H3K14ac ) differs greatly between two unrelated strains of the yeast Saccharomyces cerevisiae . Using single-nucleosome chromatin immunoprecipitation and mapping , we interrogated 58 , 694 nucleosomes and found that 5 , 442 of them differed in their level of H3K14 acetylation , at a false discovery rate ( FDR ) of 0 . 0001 . These Single Nucleosome Epi-Polymorphisms ( SNEPs ) were enriched at regulatory sites and conserved non-coding DNA sequences . Surprisingly , higher acetylation in one strain did not imply higher expression of the relevant gene . However , SNEPs were enriched in genes of high transcriptional variability and one SNEP was associated with the strength of gene activation upon stimulation . Our observations suggest a high level of inter-individual epigenomic variation in natural populations , with essential questions on the origin of this diversity and its relevance to gene x environment interactions .
Divergence of DNA sequences between individuals has been the basis of genetics for half a century . More recently , epimutations were identified where inter-individual differences resided in DNA methylation patterns rather than in the DNA sequence itself , with notable consequences on imprinting and phenotypes [1]–[4] . In addition to DNA methylation , nucleosome positioning and post-translational modifications of histone tails have received increasing interest as they can regulate gene activity and genome dynamics [5] . A wealth of stimulating research has been conducted on these modifications , leading to a more and more precise characterization of the machineries remodeling them ( such as acetyl- or methyl-transferases ) , of the pathways regulating these machineries ( such as environmental cues ) , of the factors recognizing these modifications ( such as bromo- and chromo-domain containing proteins ) , and of the consequences of these interactions on cellular outcomes ( such as cellular differentiation or disease ) [6] . In addition , the genomic distributions of these histone marks have been described in various organisms and cell types [7]–[11] , raising the hope to understand or predict outcomes of eukaryotic cells from the sequence of their epigenomes . Many laboratories are therefore intensively studying if and how information can be coded by epigenomes [12] , [13] . Whether epigenomic sequences vary in natural populations has only been poorly investigated . Recent studies showed a rather abundant natural epigenetic variation of methylated DNA in plants , which was shown to correlate to transcriptional differences and to be additively inherited [3] , [14] . In addition , cases of allele-specific histone modifications have been reported [15]–[17] . But a detailed comparison of histone-tail epigenomes has been lacking . Using two unrelated strains of the yeast Saccharomyces cerevisiae as a model system , we provide here a first estimate of this variability for one histone post-translational modification at a single-nucleosome resolution . The number of epi-polymorphisms was high , with notable enrichment in regions of conserved DNA sequences and numerous cases where a precise ( isolated ) nucleosome was targeted . This variability was not correlated to differential transcription but to the degree of transcriptional response to perturbations . Our observations provide a basis for population epigenomics and raise essential questions on the origin of this diversity and its contribution to inter-individual variability in the response to environmental changes .
To provide a first estimate of nucleosome-level epigenomic diversity , we used two unrelated strains of the yeast S . cerevisiae ( BY and RM ) as a model system . These strains were previously used to investigate natural genetic variability within S . cerevisiae for various phenotypes such as cellular morphology , sensitivity to drugs , gene expression or telomere length [18]–[21] . BY is a commonly used laboratory strain , it is isogenic to S288c which derives from a clone isolated from a rotten fig in California . RM ( also called RM11-1a ) derives from an isolate collected in a Californian vineyard by Robert Mortimer [22] . We compared them with respect to nucleosome positions as well as epigenomic sequence of one histone tail modification . Nucleosome positions were mapped using whole-genome 4-bp resolution tiling microarrays [23] , [24] . Of the 6 , 553 , 600 probes of the microarray , 2 , 801 , 885 and 2 , 570 , 638 had a single perfect match on BY and RM genome , respectively . Only signals from these probes were used for analysis , averaging ∼34 reliable probes per nucleosome . We aligned the two assembled genome sequences and used probe positions to fit a Hidden Markov Model ( HMM ) for inference of nucleosome positioning in each strain , as previously described [25] ( Figure 1A ) . Note that the HMM algorithm was run on BY and RM datasets independently . Positioning looked similar between the two strains and was in very good agreement with a previously published atlas of positions [24] . We systematically aligned nucleosomes between the two strains ( see Methods ) and found that positioning was generally well conserved: The distance between BY and RM midpoints was smaller than 19 nucleotides in 75% of all nucleosomes; and the overlap covered at least 78% of BY nucleosome length in 80% of alignments ( Figure S1 ) . Nucleosomal occupancy was also conserved except at specific regions near heterochromatin sites ( telomeres and rDNA repeat ) ( Figure S2 ) . Comparison of SNP densities in linkers versus nucleosomal DNA was consistent with the results obtained when using the atlas of Lee et al . [24] ( Text S1 ) . We examined in more details occupancy around transcription start sites and found the stereotyped nucleosome-depleted regions flanked by well-positioned nucleosomes ( Figure 1B and Figure S3 ) . The typical nucleosome depletion at transcription end sites [26] was also observed in both strains ( Figure S3 ) . We clustered promoters according to their nucleosome signature in the BY strain only , and used the resulting gene order to plot occupancy data in BY and RM as heatmaps , as well as differential gene expression known from previous studies ( Figure 1B ) . The similarity of the occupancy profiles of the two strains contrasted with the large extent of transcriptional differences ( Figure 1B and 1C ) . We then searched for nucleosomes bearing differential levels of a specific histone post-translational modification . By analogy to nucleotide polymorphisms , we called these nucleosomes ‘Single Nucleosome Epi-Polymorphisms’ ( SNEP ) . We chose acetylation of lysine 14 of histone H3 because it was reported to be largely distributed over the genome and not restricted to specific regulatory positions [9] , [27] . Hereafter , ‘BYac’ and ‘RMac’ SNEP will refer to nucleosomes where H3K14 is preferentially acetylated in BY and RM , respectively . To detect such nucleosomes , we used ChIP-CHIP [7] and we developed a custom algorithm for data analysis . First , only probes that had a single perfect match on both BY and RM genomes were retained . This precaution is important as DNA polymorphisms can greatly affect hybridization intensities . For each pair of aligned nucleosomes , probes that were not entirely covered by both BY and RM nucleosomes were also removed and a dedicated analysis of variance was applied ( see Methods ) . The underlying linear model integrated both nucleosome mapping and chromatin immuno-precipitation experiments , which enabled to decouple the call for SNEPs from strain differences in occupancy intensity . This method identified 5 , 442 H3K14ac SNEPs at nominal P-value <9 . 27×10−6 which corresponded to a False Discovery Rate [28] ( FDR ) of 0 . 0001 . This list was used in all further analysis described here . SNEPs were distributed all over the genome , with few particular hotspots ( Figure 2A ) . Epigenetic variability was very high , as these highly significant SNEPs were found in nearly 10% of nucleosomes interrogated . At the commonly used level of FDR = 0 . 01 , 25 . 3% of nucleosomes were significant SNEPs , and further relaxing the detection threshold to FDR = 0 . 2 listed 31 , 854 SNEPs . We can therefore assume that about 40% of the chromatin is variable for this epigenetic mark between the two strains . In most cases , SNEPs were not detected as all-or-none nucleosomal acetylation , but as a quantitative difference between the two strains . The degree of inter-strain difference varied between SNEPs ( Figure S4 ) , with most cases displaying a 1 . 2 to 1 . 5 fold difference . Intriguingly , the acetylation difference was more pronounced in BYac SNEP ( 918 SNEPs at >1 . 5 fold ) than in RMac SNEPs ( 274 SNEPs at >1 . 5 fold ) . Because some highly polymorphic DNA features are associated with chromatin silencing , specific cases of histone acetylation epi-polymorphisms could be expected . One example is the rDNA locus , a repetitive sequence silenced by the Sir2 histone deacetylase [29] , which is 15 . 6 Kb longer in RM than in BY . This higher repeat length could better recruit deacetylase activity and generate BYac SNEP in the vicinity of the repeat . Consistently , we saw a significant enrichment of BYac SNEPs in the region directly upstream rDNA ( Figure 2B ) . Other examples are Ty retrotransposons . They differ greatly between natural strains , their epigenetic effect on nearby gene expression has long been observed [30] and their active LTR promoters are known to recruit the SAGA histone acetyltransferase [31] . Thus , nucleosomes residing near a Ty element in one strain but not in the other may harbor acetylation epi-polymorphisms . Consistently , BYac SNEPs were significantly enriched near BY Ty insertions ( Figure 2B ) . If such large position effects were the general source of H3K14ac epipolymorphisms , one would expect SNEPs to cluster together at particular hot spots . We clearly observed local correlations , as epipolymorphisms were 7 times more frequent than expected by chance among nucleosomes adjacent to SNEPs , and this effect could span over 10 nucleosomes upstream and downstream of a SNEP ( Figure 2C ) . However , the majority ( 55% ) of SNEPs were limited to a single nucleosome . This is unlikely to be a detection limitation , as 994 SNEPs had both flanking nucleosomes still scoring non-polymorphic at P<0 . 01 . Thus , local correlation seems to be limited and epi-polymorphisms are frequently distributed on specific nucleosomes . SNEPs were not uniformly distributed along genes . The averaged H3K14 acetylation profile of both strains was consistent with previous descriptions [7] , [27] , with enrichment downstream transcription start sites and decreased acetylation at the end of transcribed sequences ( Figure 2D ) . However , strikingly , BYac SNEPs were abundant upstream TSS and around TES , while RMac SNEPs marked the second half of transcribed regions . These patterns were also visible when selecting only SNEPs with strong effect ( >1 . 4-fold acetylation difference ) . This could result from a better recruitment of Rpd3S deacetylase behind elongating RNA polymerase II [32] , as signs of elongation impairments were previously seen in RM [33] . It is important to note that this pattern of SNEP distribution reflects an average tendency , and that several genes present a totally different epigenetic pattern . For example , the NDE2 gene did not have BYac SNEP in promoter nor in terminator region , but had RMac SNEPs at the beginning of its coding region ( Figure S5 ) . Finally , RMac SNEPs were slightly more frequent than BYac SNEPs ( 58 . 5% versus 41 . 5% ) . Since acetylation of H3K14 is known to be associated with high transcription levels [7] , [27] , [34] , its inter-strain variability could simply reflect inter-strain differences in gene expression . Transcriptional variation between BY and RM has been extensively studied in the same growth conditions as here [20] , thus allowing direct examination of this possibility . We considered three regions at the beginning , middle and end of genes , and computed in each one the average log-ratio of H3K14 acetylation between the two strains . In all three regions this ratio was clearly not correlated to expression differences ( Figure 3A ) . Consistently , SNEPs acetylated in the strain with highest gene expression were not over-represented in any of the three regions ( Figure 3B ) . The two strains therefore have a high degree of divergence at both transcriptomic and epigenomic levels but with no apparent connection between the two . If not correlated to expression differences , do SNEPs have any functional implication ? If so , one might expect them to target nucleosomes located at critical positions for gene regulation , such as nucleosomes containing a transcription factor binding site . In favor of this , and in accordance with the distribution pattern described above , we found a striking ( 3 . 2-fold ) enrichment of BYac SNEPs in nucleosomes that fully covered a conserved regulatory site [35] ( Figure 3C ) . BYac SNEPs were also abundant in conserved non-coding regions regardless of regulatory sites ( Figure 3C ) . In contrast , RMac SNEPs were poorly present at these conserved regions ( Figure 3C ) , which is consistent with their enrichment within protein coding regions ( Figure 2D ) . The abundance of BYac SNEPs at conserved regulatory sites indicates that genetic and epigenetic polymorphisms can be complementary , the latter providing diversity where the former is more constrained . Are genes with high inter-strain variability in gene expression the same genes as those having high epigenomic variability ? Although SNEP acetylation was not associated to higher gene expression , it remained possible that genes with high expression changes contained more SNEPs than others . We examined this possibility by ranking genes according to their BY/RM expression fold-change and by counting their SNEP content ( Figure S6A ) . This showed that indeed , SNEPs were more frequent in genes showing high inter-strain transcriptional differences . Several studies have examined the evolvability of yeast gene expression levels . For example , when comparing 4 yeast species across 5 different stressful environments , Tirosh et al . showed that genes can have very different inter-species expression divergence [36] . Similarly , Landry et al . showed that S . cerevisiae genes greatly differ in their divergence of expression across independent mutation accumulation lineages [37] . To see if SNEP abundance was correlated with expression evolvability beyond the scope of the BY and RM strains , we used these datasets to rank genes either by their expression divergence [36] or by their mutational variance [37] . This showed an unambiguous association between SNEP frequency and expression variability ( Figure S6B and S6C ) . The extent of gene x environment interactions in the control of gene expression has been thoroughly estimated by Smith et al . who used the same BY and RM strains as here and compared their transcriptomes between two different steady-state environments: growth in glucose and growth in ethanol [38] . Using this dataset , we examined if SNEP frequency in genes was associated with the level of genotype x environment interaction in the gene's expression level ( Figure S7 ) . We found that BYac but not RMac SNEPs were more frequent in genes with high genotype x environment interaction , with no correlation between the direction of the SNEP ( which strain is acetylated ) and the direction of the interaction ( which strain shows the highest change between glucose and ethanol growth ) . SNEP acetylation was therefore not predictive of the amplitude of expression change between the two different environments . However , it is important to note that these two environments were stable and this dataset did not correspond to the dynamic response to an environmental change . Expression variability within a given strain background has also been studied in a broad sense by estimating the extent of variation across a large compendium of environmental conditions and/or specific genetic perturbations [36] . This “transcriptional plasticity” varies greatly among genes . For example , housekeeping genes display very low plasticity as they present stable expression across many conditions . This plasticity was previously associated with expression evolvability [36] , [37] and nucleosome occupancy at promoter regions [39] . Using the values previously compiled [36] , we found that SNEPs were enriched in genes displaying high transcriptional plasticity ( Figure 4A ) . This enrichment was also visible when considering only SNEPs with strong effect ( 1 . 4-fold acetylation difference ) . Genes with at least one H3K14ac SNEP had significantly higher plasticity than genes with no SNEP ( t-test P = 3 . 6×10−7 and 1 . 2×10−6 for BYac and RMac SNEP , respectively ) . Finally , to see if SNEPs were more frequent among nucleosomes known to be evicted upon an environmental change , we used previous maps of nucleosome positioning in normal and stress conditions [26] and counted SNEPs among 147 remodeled nucleosomes and 61 , 623 unperturbed ones . Although this dataset represents only one environmental change and the remodeling of relatively few nucleosomes , a significant 1 . 7 fold enrichment of SNEPs was seen among these ‘mobile’ nucleosomes ( P = 0 . 01 , Chi-square ) . We reasoned that SNEPs could influence the dynamics of activation or repression . Intuitively , an acetylated nucleosome may be more rapidly evicted than a non-acetylated one upon promoter activation [40] . We noticed one SNEP where association with a differential dynamic response could be tested experimentally . A nucleosome contained a binding site for transcription factor Hsf1 ( Heat Shock Factor 1 ) in the promoter region of the AHA1 gene , which codes for a co-chaperone of Hsp90 known to be activated upon heat-shock [41] . This nucleosome had similar positioning in BY and RM but was acetylated at H3K14 in BY only ( Figure 4B ) , while the DNA sequence of Hsf1-binding site was fully conserved between the two strains . Notably , other nucleosomes of the region were acetylated in both strains . We exposed BY and RM cells to heat shock and monitored AHA1 mRNA by real-time quantitative RT-PCR ( Figure 4C ) . Gene induction was unambiguously more pronounced in BY than RM . This marked difference was not observed when quantifying mRNA from three other HSP genes ( SSA3 , FES1 and CPR6 ) lacking SNEP ( Figure S8 ) . This example illustrates how one SNEP can be associated with gene activation differences upon an environmental change .
We observed that nucleosome positioning at promoter regions was similar between two unrelated strains of S . cerevisiae . Because these strains have a large extent of transcriptional differences , this argues that differences in nucleosome occupancy profiles are not a major source of intra-species variation in gene expression . In contrast , the epigenomic profile of H3K14ac was highly variable and this variability targeted specific nucleosomes . The presence/absence of a modification at a particular nucleosome in a given cell is , by definition , a discrete state . However , we observed quantitative acetylation differences that were often subtle ( 1 . 2 to 1 . 5 fold ) . This is likely due to high cell-to-cell heterogeneity and high dynamics of the acetylated state: all states from billions of cells were averaged in our samples , and no dynamical information was acquired over time . It is therefore important to interpret SNEPs as differences in the overall acetylation level across a cell population and not as a uniform epigenotype shared by all cells of the sample . Natural epigenetic variation was previously reported at the level of methylated DNA ( meDNA ) , particularly in plants [3] , [14] . In this case also , differences were not necessarily discrete but often continuous . Important properties of SNEPs distinguish them from meDNA epi-polymorphisms . Methylated epi-alleles were predictive of lower gene expression [14] but SNEPs with reduced acetylation were not . In addition , no evidence was reported on a possible role of meDNA variation on the dynamics of gene activation . Since histone-tail modifications are known to be highly reversible and dynamic , the basis and the origin of SNEPs remain to be further investigated . We observed that the two strains had different overall patterns of acetylation along genes , with a preferential acetylation near TSS and TES in the BY strain , while the RM strain had enriched acetylation in the second half of transcribed regions . This pattern difference accounted for many SNEPs and may result from trans-acting factors that act differentially in the two strains . However , 1806 SNEPs could not be attributed to this general inter-strain difference . Focusing on these SNEPs only , we looked again at their genomic distribution , their potential correlation to expression divergence and enrichment in genes with high plasticity ( Figure S9 ) . All conclusions made in our study were retrieved for this subset of SNEPs . Thus , the differential pattern of acetylation does not explain the general SNEP properties . Nucleosomal epi-polymorphisms may offer an alternative to irreversible nucleotide mutations . How BYac SNEPs accumulated at regulatory regions of conserved DNA is unclear . As mentioned above , it may occurred with the fixation of a trans-acting variation . Alternatively , accumulation may have occurred as a drift during laboratory culture conditions where fitness selection poorly applied . Future experiments examining a third wild strain will help determine if one of the two patterns is more ‘common’ , if the stronger effect ( acetylation fold-change ) of BYac SNEP is peculiar , and if the abundance of SNEPs is similar in various pairwise comparisons of strains . What is the origin of this epigenomic variability ? E . Richards proposed a classification of epigenotypes based on their dependency on DNA variation [4] , where the obligatory , pure and facilitated qualifications relate to genetic controls that are full , absent or incomplete , respectively . Following this terminology , obligatory SNEPs may result from genetic factors acting in cis or in trans . Known cis-regulations are exemplified by position effects of transposable elements , rDNA repeats or telomeric sequences . Trans-acting genotypes may reside in histone acetyl-transferase or de-acetylase machineries , or in upstream regulatory factors . Such obligatory SNEPs could have been fixed together with their genetic determinants . In contrast , if some SNEPs are pure ( independent of genotype ) they likely result from their direct selection . As SNEPs seem to relate to the dynamics rather than the steady-state levels of gene expression , this selection may act through the ability to respond to environmental changes ( the Baldwin effect ) . Also , interactions between epigenotypes and genotypes are expected since histone acetylation can modulate the buffering of cryptic genetic variations [42] , [43] . Acetylation of Lysine 14 of histone H3 at the beginning of protein-coding sequences has unambiguously been associated to high transcriptional activity in several studies [7] , [27] , [34] . It is therefore surprising that a preferential acetylation in one strain is not accompanied by a higher gene expression . This illustrates the complexity by which the various layers of inter-strain molecular differences are connected . Previous studies showed that DNA polymorphisms act on transcripts abundance in a complex manner [20] , with a large extent of gene x environment effects [38] and that this genetic control was largely distinct from the control of protein levels [44] . Our results show that chromatin histone-borne modifications provide yet another layer of diversity , with non-trivial connections to genotypes and transcripts levels . SNEP identification and characterization provide a basis for population epigenetics of histone-borne modifications , and future quantitative epigenetics studies such as previously suggested [45] , [46] will define the nature of these dependencies , and their relevance to the control of complex traits . The abundance of SNEPs in highly-responsive genes and our observation that one SNEP correlated with the dynamics of gene activation upon stimulation suggest a contribution to gene x environment interactions . This is in full agreement with a previous report describing the contribution of H3K27me3 at the FLC locus of Arabidopsis to natural variation in cold-induced acceleration of flowering [17] . Except in such rare cases , gene-by-environment interactions have only been studied in the context of DNA variation . Integrating epigenotyping of histone marks in these investigations will likely better explain how individuals differ in their response to environmental changes . In particular , attempts to predict and optimize the response to specific treatments is at the heart of personalized medicine . Chemical inhibitors of histone deacetylase are used in anti-cancer therapies and seem promising to fight other diseases [47] , and ChIP-SEQ technologies [9] will soon provide clinicians with epigenotyping possibilities . Our results suggest that histone modification profiles of human individuals may greatly differ , with likely consequences on treatment outcome .
Yeast strains used were BY4716 ( MATalpha , laboratory [48] ) and RM11-1a ( MATa , derived from wild isolate [20] ) . We processed six BY and six RM independent cultures for H3K14ac ChIP , plus three BY and three RM independent cultures for nucleosome mapping , totalling 18 microarray hybridizations . Cells were grown to exponential phase in synthetic medium with 2% glucose ( SDall ) as in Brem et al . [20] . We followed the protocol of Liu et al . [7] for both nucleosomal DNA isolation and ChIP , except that incubation time with micrococcal nuclease ( Worthington Biochemical ) prior to immunopurification was increased to 30 min at 37°C to obtain mononucleosomes . ChIP was performed using 3 µl of anti-H3K14Ac polyclonal antibody ( Upstate , 07–353 ) . For H3K14ac , efficiency was controlled by quantifying acetylation at the MAT locus by real-time quantitative PCR . This locus , as opposed to the silenced HML and HMR loci , is acetylated [49] and since BY and RM have opposite signs , we expect ChIP to be enriched for HMLalpha1 sequence in the case of BY and HMRa1 sequence in the case of RM ( Figure S11 ) . Real-time quantitative PCR was performed on a LightCycler 1 . 5 ( Roche ) using FastStart DNA Master Plus SYBR GREEN I kit ( Roche ) . Primer pairs were 5′- AAATGTCTTGTCTTCTCTGCTC-3′ and 5′-ACTGTTG<@ ? show=[fo] ? >CGCGAAGTAGT-3′ for HMLalpha1 and 5′-AAGAGCCCAAAGGGAAAATC-3′ and 5′-AGGCTTTGCTTTCTTCTA-3′ for HMRa1 . ChIP and non-immunoprecipitated DNA fragments were linearly amplified using T-7 based in-vitro transcription as described previously [7] with few modifications . Briefly , the reaction mixture of 28 . 5 µl contained 18 µl template DNA , 5 . 2 µl 5x TdT buffer ( Roche ) , 0 . 68 mM CoCl2 ( Roche ) , 4 . 2 µM dTTP , 0 . 36 µM ddCTP , and 40 U terminal transferase ( NEB ) . It was incubated at 37°C for 20 minutes and then stopped by adding 5 µl of 0 . 5 M EDTA ( pH 8 . 0 ) . Products were purified using Qiagen MinElute reaction cleanup kit and eluted in 20 µl nuclease free water , then concentrated to a 8 µl volume by Speed Vacuum centrifugation . The following were added: 0 . 6 µl of 25 µM T7- A18B primer , 1 µl of NEB buffer ( 2 ) and 0 . 4 µl of 5 mM dNTPs and the following thermal cycles were applied; 94°C for 2 min , decreasing to 35°C at -1°C/sec , hold down at 35°C for 2 min and decreasing to 25°C at -0 . 5°C/sec . Immediately after , 0 . 4 µl of Klenow enzyme ( NEB ) were added to the samples which were incubated at 37°C for 90 min . The reaction was halted by adding 5 µl of 0 . 5 M EDTA ( pH 8 . 0 ) . Products were purified using Qiagen MinElute reaction cleanup kit and eluted in 20 µl nuclease free water . The eluted samples were concentrated to a final volume of 5 µl . The IVT reaction mixture contained 5 µl nuclease free water , 2 µl 10X reaction buffer and 2 µl enzyme mix of MEGAshortscript® T7 kit ( Ambion ) , 6 µl Labeling NTP mix from Affymetrix , and 5 µl T-7 tailed DNA and incubated at 37°C for 16 hrs . Amplified RNAs were purified using RNeasy Mini kit ( Qiagen ) and eluted in 50 µl of nuclease free water . RNAs ( ≥15 µg ) were hybridized to GeneChip S . cerevisiae Tiling Array from Affymetrix [23] following manufacturer protocol . An isolated colony was picked to inoculate 4 ml SDall medium and incubated at 30°C with 220 rpm shaking for 12 to 16 h . This culture was used as a starter to inoculate 2 ml SDall medium at 0 . 1 OD600 , which was grown for 6 hrs at 30°C with shaking . 1 . 5 ml of culture were then transferred to a microcentrifuge tube , incubated at 30°C in a water bath for 10 min , and incubated at 37°C for the times indicated on Figure 4 . Cells were immediately harvested by centrifugation , re-suspended in 700 µl of TES buffer ( 10 mM Tris-HCl ( pH 7 . 4 ) , 10 mM EDTA and 0 . 5% SDS ) , snap frozen in liquid nitrogen and stored at −80°C . The experiment was conducted on BY and RM simultaneously , and repeated four times at different days . Total RNA was extracted using the following procedure: 700 µl of room temperature phenol was added to the cell extract , mixed well by vortexing and incubated at 65°C for 20 minutes . Extract was then snap frozen in liquid nitrogen for 1 min , thawed at room temperature , centrifuged at 13000 rpm for 5 min and the upper aqueous phase was transferred to a fresh microcentrifuge tube . Once again , 700 µl of room temperature phenol was added , mixed well and centrifuged at 13000 rpm for 5 min . The upper aqueous phase was transferred to a fresh tube , 700 µl of chloroform was added , mixed well and centrifuged at 13000 rpm for 5 min , the upper aqueous phase was purified using RNeasy mini kit ( Qiagen ) and eluted in 50 µl nuclease free water . RNA was precipitated by adding 50 µl of 3 M NaAc , and 1 . 25 ml of ice-cold ethyl alcohol to the purified samples followed by incubation at −20°C for 30 min . RNA was pelleted by centrifugation at 13000 rpm for 5 min , washed once with ice-cold 70% ethyl alcohol at 13000 rpm for 5 min and re-suspended in 50 µl of nuclease water . RNA concentration was quantified based on spectral absorbance using a NanoDrop ND-1000 Spectrophotometer . Reverse transcription and real time quantitative PCR were performed on a Stratagene MX3000P real-time PCR system using the Superscript III Platinum SYBR Green One-Step qRT-PCR kit from Invitrogen following manufacturer's protocol . Primers were 5′-GTCTGTTTCGTCCATTGAAGG-3′ and 5′- GTCCTTAGAGTCCACGTGTCC-3′ for AHA1 , 5′-ATGGATTCTGAGGTTGCTGC- 3′ and 5′-TGGGAAGACAGCACGAGGAG-3′ for ACT1 , 5′-GATGCAAAGAGATTAGAAACAGCG -3′ and 5′-GCCTTCCAACTCCTTTTGTCTA -3′ for SSA3 , 5′-GATGAAGAACTACGTGCTGCTG-3′ and 5′-GCTTCGCAGACCATTGTCG-3′ for FES1 and 5′-CATTCCTTCTATCCATGGCC-3′ and 5′-GCTTCCCGTCCAAATGAG-3′ for CPR6 . Amplification efficiencies and relative quantification of AHA1/ACT1 , SSA3/ACT1 , FES1/ACT1 and CPR6/ACT1 ratios were calculated as described by Pfaffl [50] . Genome sequences of S288c ( isogenic to BY ) and RM were downloaded in December 2007 from NCBI ( ftp://ftp . ncbi . nih . gov/genomes/Saccharomyces_cerevisiae ) and the Broad Institute ( http://www . broad . mit . edu/annotation/genome/saccharomyces_cerevisiae/Home . html ) , respectively . The RM genome 8X assembly originates from whole genome shotgun and consists of 17 high-quality supercontigs ( hqSC hence after ) totalizing 11 . 7 Mb . The17 hqSC of RM were aligned on the 16 nuclear chromosome sequences of BY by using the nucmer algorithm implemented in MUMmer version 3 . 0 [51] with options –maxgap = 1000 –mincluster = 50 considering BY chromosomes as the references and RM hqSCs as the queries . The output of nucmer was then filtered and formatted using the delta-filter and show-coords programs of the MUMmer package . At this stage , the output of our alignment pipeline consisted on a list of clusters of perfect matches between regions of BY chromosomes and RM hqSCs . We implemented an automatic rule to relate RM hqSCs to BY chromosomes by maximizing the coverage and alignment quality chromosome by chromosome . We then dynamically resolved overlapping clusters of perfect matches in order to get the longest aligned fragments of RM hqSCs along each BY chromosome . Visual inspections were also used in a few cases in order to define optimal boundaries of alignments . Detailed results of this genome alignment process as well as the hybrid shell/perl script used to do the genome alignment are available upon request . Polymorphisms between BY and RM were detected by base substitution in the final alignment . Since base calling information were not available for RM sequences , we assumed that quality was reasonable and uniform along the RM genome sequence . From the 54 , 039 polymorphisms found , a few targeted repeated sequences ( in both genomes as annotated by RepeatMasker ( open-3 . 1 . 9 , Smit , AFA , Hubley , R & Green , P . RepeatMaskerOpen-3 . 0 . 1996–2004 http://www . repeatmasker . org ) and were thus excluded , leading to a final core set of 52 , 280 polymorphisms consisting in 47 , 011 SNPs ( 90% ) , 2 , 448 insertions ( 4 . 5% ) and 2 , 821 deletions ( 5 . 5% ) . These 2 , 448 insertions corresponded to 238 , 087 bp that were absent in RM while the 2 , 821 deletions corresponded to 80 , 349 bp absent in BY . This discrepancy between BY and RM insertions is mainly due to the heterogeneous content of Ty elements between both genomes ( see below ) . BY gene annotations were extracted from chromosomal features defined at NCBI website ( ftp://genome-ftp . stanford . edu/pub/yeast/chromosomal_feature/saccharomyces_cerevisiae . gff ) . For RM , predicted gene set was downloaded from the Broad Institute website ( http://www . broad . mit . edu/annotation/genome/saccharomyces_cerevisiae/Downloads . html ) . which were obtained using a combination of mapped ORFs from SGD predictions ( http://www . yeastgenome . org ) , Glimmer [52] and GeneMark [53] . More details on this automated gene prediction pipeline are posted at http://www . broad . mit . edu/annotation/genome/saccharomyces_cerevisiae/GeneFinding . html#prediction . This original annotation file consisted of 5695 gene loci . 83 were dubious and discarded , as their annotated coding sequence did not code for a protein . 80 other predictions were also discarded because they fell in unaligned regions and could not be mapped to BY . Orthologs were identified using Blastp from WUBlast version 2 . 0 ( Gish , W . ( 1996–2004 ) http://blast . wustl . edu ) by aligning protein sequences of the 5532 remaining RM genes against the protein sequences of 6608 BY genes , and selecting reciprocal matches that fulfilled all following criteria: i ) E-value <1 . e-5 , ii ) percentage of identity >40% and iii ) match length >75% of both protein sequences . This way , 5200 RM genes were called orthologs of BY genes , and 328 genes were RM-specific . Transcript boundaries for BY genes were obtained from the complete published set of experimentally detected transcripts [23] , [54] available at http://www . ebi . ac . uk/huber-srv/actinomycinD . Only transcript segments overlapping >50% of a non-dubious annotated coding region on the 5′ end were retained . This resulted in 4 , 714 verified transcription segments . We then mapped the BY transcript boundaries of ortholog genes on the RM genome , leading to 4 , 612 transcription segments on RM . We extracted the DNA sequences of the 50 active Ty elements annotated in the BY genome ( as defined in the NCBI chromosomal features , leading to 31 Ty1 , 13 Ty2 , 2 Ty3 , 3 Ty4 and 1 Ty5 ) and blasted them onto the 17 RM supercontigs . We found 10 matches on RM with 10 Ty2 elements ( percentage of identity >90% and match length >95% ) . No match was found with any other Ty elements even after relaxing these criteria , indicating that Ty elements populating the RM genome are Ty2 only . Of these 10 Ty2 elements found in RM , 3 were located at the same place in BY , 6 were located elsewhere in RM ( leading to 6 deletions ( insertions ) in BY ( RM ) ) and 1 replaced a Ty1 element of BY . Conserved Regulatory Sites ( CRS ) [35] were downloaded from ( http://fraenkel . mit . edu/improved_map/p001_c3 . gff ) . Conserved Non-Coding Sites were obtained from the UCSC website ( http://genome . ucsc . edu/ ) , using data from table phastConsElements for track MostConserved , and were then defined as the intersection between conserved regions and non-coding regions . Gene expression values of Brem et al . [20] were downloaded from NCBI GEO site ( http://www . ncbi . nlm . nih . gov/sites/entrez ? db=gds , dataset GSE1990 ) . Regions where nucleosome ( s ) were remodeled upon heat-shock were extracted from whole-genome nucleosome maps of Shivaswamy et al . [26] filtered for nucleosomes of normalized score <0 . 2 . They were defined as chunks of at least 145 consecutive nucleotides ( average size of a nucleosome ) covered by a nucleosome in only one condition ( unstressed or heat-shock ) . Using our BY atlas of nucleosome positions ( see below ) , 147 nucleosomes ( ∼0 . 2% ) were then said to be remodeled if they lied entirely within a remodeled region . The 25 bp array probes were mapped on BY and RM genomes using MUMmer . For each strain , we kept only probes that had unique perfect match on the genome ( 2 , 801 , 885 probes for BY and 2 , 570 , 638 probes for RM , overlap: 2 , 491 , 913 probes ) . For nucleosome mapping ( see below ) we used only the 3 array replicates per strain . Since informative probe sets differed between the two strains , normalization was done separately for each strain and a log2 transformation of probe signals was applied before normalization [55] . For SNEP identification ( see below ) , only the subset of RM probes that can be mapped within ±3 bp of the corresponding probe in BY was kept to insure that at every positions the same probe is used between the two strains . In addition , since any DNA polymorphism would bias hybridization efficiency , we discarded probes containing at least one BY/RM polymorphism , keeping a final core set of 2 , 356 , 676 probes . Then the full dataset ( 18 arrays ) was log2-transformed and quantile-normalized all together using only this set of probes with dual perfect match . We positioned nucleosomes in each strain separately using only the 3 dedicated replicates per strain . We then implemented a custom version of the Hidden Markov Model devised by Yuan et al . [25] . Our HMM implementation was similar to the one used by Lee et al . [24] , except than we did not train the HMM on specific regions but used sliding windows as in Yuan et al . in order to remove unpredictable trends in the hybridization signal . Thus , independent run of the HMM were successively applied in window of 1 kb ( i . e . ∼250 probes ) all along the genome . The model parameters and posteriors of all windows containing a fixed probe were then averaged and used for a global computation of both state probabilities and most-likely states ( among well-positioned nucleosome , fuzzy nucleosome and linker ) . As we used only probes with unique and perfect matches and that regions with high SNP density between BY and RM can lead locally to low probe coverage in RM , we also allowed the HMM to deal with missing data . State probabilities and most-likely states of “missing probes” were computed in the same way than for observed probes , taking advantage of neighboring observed information . The most-likely nucleosome occupancy profiles of the two strains ( as obtained from the Viterbi algorithm on each chromosome ) were aligned according to the genome alignments . The BY genome was the reference and the positions of RM nucleosomes on this reference were obtained from the coordinates of RM nucleosomal sequence fragments on the BY genome . Once RM nucleosomes were “mapped” on BY , we used a dynamic algorithm to align RM and BY nucleosomes . The algorithm works chromosome-by-chromosome as follows: More stringent assumptions can be used to define “unambiguously aligned” nucleosomes without significant changes on the final results ( data not shown ) . This strategy aligned 64 , 294 nucleosomes between the two strains , i . e . ∼95% of RM nucleosomes . Finally , in order to evaluate locally the quality of our alignment , for each pair of aligned nucleosomes we computed their likelihood as L ( aligned ) = Bn . Rn where Bn and Rn are the probabilities that the corresponding probes belong to a nucleosome in BY and RM , respectively . Similarly , we computed the likelihood for insertion and deletion of a nucleosome ( with respect to BY ) as L ( insertion ) = Bn*Rl and L ( deletion ) = Bl*Rn where Bl and Rl are the probabilities that the corresponding probes belong to a linker in BY and RM , respectively . Within each strain , the probability of each state ( nucleosome or linker ) was derived from probe-level posterior probabilities ( as estimated by our HMM , and merging fuzzy and well-positioned nucleosome states into a single class ) averaged over probes covering the target region . To generate Figure 1B and 1C , we first divided the +/−300 bp region around the TSS of each transcript into 60 bins of equal size ( 10 bp ) . We then computed the average nucleosome occupancy in each bin by averaging the posterior probabilities to be a nucleosome ( output by our HMM and summing posteriors from well-positioned and fuzzy nucleosomal states ) of the probes within the bin . We then applied K-means clustering ( with kmeans function implemented in the base package of R ) using the Euclidean distance metric and 25 repetitions for each number of cluster tested ( 1< = K< = 10 ) . Visual inspection together with standard clustering validity measures ( e . g . ratio of variance within clusters and variance between clusters ) were used to choose the optimal number of clusters ( K = 6 ) . To screen for SNEPs , we considered only pairs of aligned nucleosomes sharing at least 15 microarray probes , which was the case of 97% of aligned pairs . Following previous linear models validated for transcripts quantification [56] , we applied to each pair the following analysis of variance ( ANOVA ) : where yijkl is the log2 normalized hybridization intensity of probe k in replicate l for strain i ( BY or RM ) in experiment type j ( nucleosome positioning or ChIP-ChIP ) , u is the global mean of the signal , ai is the strain effect ( BY or RM ) , bj is the experiment type effect ( nucleosome positioning or ChIP-Chip ) , ck is the probe effect , dij is the interaction term between strain and experiment type and eijkl is the residual . We reasoned that if a nucleosome carries the modification then the corresponding DNA is present in both ChIP and nucleosomal positioning samples and signal expectancies should not differ between experiment types ( bj = 0 ) . However , if a nucleosome carries the modification in only one strain , then a significant interaction should be seen between experiment type and strain ( dij ≠ 0 ) ( Figure S12 ) . We therefore used an F-statistic to test ( H0: dij = 0 vs . HA:dij ≠0 ) and derived nucleosome-level P-values . A striking enrichment of low P-values was observed ( Figure S13 ) . We applied the false discovery rate ( FDR ) control procedure [28] to compute a genome-wide cutoff from our sorted vector of 58 , 694 P-values . EMBL ArrayExpress accession number E-MEXP-1777 . Processed data files and the C source code of NucleoMiner ( for Unix-based platforms ) are available on our web site http://www . ens-lyon . fr/LBMC/gisv/snep/ | Nucleosomes are the basic units of chromatin , with part of the long DNA molecule wrapped around a multiprotein core , which makes unpacked chromatin often portrayed as a string of pearls . This string can carry three types of sequences: DNA , methyl groups on cytosines , and , on every pearl , the presence-or-absence of histone post-translational modifications such as acetylation of lysines ( nucleosomal epigenome ) . These latter sequences can change dynamically , and the mechanisms involved are heavily studied as they participate in many physiological processes ( pluripotency , disease… ) . However , nothing is known about the natural diversity of nucleosomal epigenomes in natural populations . As a model , we compared two unrelated yeast strains for their epigenome of one histone modification . We found a high divergence , which was enriched at regulatory sites and often carried on specific nucleosomes . Although this nucleosome modification is usually associated with high transcription , higher acetylation in one strain did not necessarily imply higher expression of the corresponding gene . However , one nucleosomal variation was associated with a stronger gene activation upon stimulation . These results suggest that nucleosomal epigenomes largely differ between individuals , raising essential questions on the origin of these differences and their contribution to personal responses to environmental changes ( such as clinical treatments ) . | [
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| 2010 | Natural Single-Nucleosome Epi-Polymorphisms in Yeast |
Metabolism , the conversion of nutrients into usable energy and biochemical building blocks , is an essential feature of all cells . The genetic factors responsible for inter-individual metabolic variability remain poorly understood . To investigate genetic causes of metabolome variation , we measured the concentrations of 74 metabolites across 100 segregants from a Saccharomyces cerevisiae cross by liquid chromatography-tandem mass spectrometry . We found 52 quantitative trait loci for 34 metabolites . These included linkages due to overt changes in metabolic genes , e . g . , linking pyrimidine intermediates to the deletion of ura3 . They also included linkages not directly related to metabolic enzymes , such as those for five central carbon metabolites to ira2 , a Ras/PKA pathway regulator , and for the metabolites , S-adenosyl-methionine and S-adenosyl-homocysteine to slt2 , a MAP kinase involved in cell wall integrity . The variant of ira2 that elevates metabolite levels also increases glucose uptake and ethanol secretion . These results highlight specific examples of genetic variability , including in genes without prior known metabolic regulatory function , that impact yeast metabolism .
Inter-individual differences in metabolism are of substantial biological importance . In humans , they underlie susceptibility to type II diabetes [1] , obesity [2] and Crohn's disease [3] , while in yeast , they contribute to the flavor profile of wine [4] and to the efficiency of ethanol generation [5] , [6] . Accordingly , there has been growing interest in identifying the genetic loci responsible for inter-individual metabolome differences . Over the past decade , the relationship between the metabolome and the genome has been increasingly studied , most thoroughly in the plant community [7]–[10] . Initial investigations followed metabolomic alterations in response to gene knockouts [8] , [11]–[14] , and this analysis has proven valuable for annotating gene functions [15] . Of late , decoding metabolic variation due to natural perturbations using quantitative genetics [16] has garnered increasing interest . Quantitative trait locus ( QTL ) studies have been performed on enzyme activities and metabolite concentrations in plants with greatest success for secondary metabolites [17]–[25] . Association of metabolite abundance variation with unsuspected genetic determinants has demonstrated the potential of metabolite QTL ( mQTL ) analysis for identifying genes with previously unknown enzymatic roles [17] . Metabolomic methods have been applied to determine how levels of metabolites are associated with gene segregation across intercrosses of mice , A . thaliana and yeast [26]–[28] . This has demonstrated that there is substantial genetic variation in primary and secondary metabolites , and this variation is governed by the segregation of relatively few mQTL hot spots [27] , [28] whose epistatic interaction further shapes the metabolome [27] . These mQTL hot spots generally coincide with known eQTL hot spots , highlighting the extensive pleiotropy of these regions . While these studies have been able to associate regions of the genome with metabolic alterations , the residual unexplained heritability of these studies can be extensive , raising important questions about the power and reproducibility of QTL and mQTL analysis . Furthermore , the resolution of 100–200 F2 intercrosses is limited and identifying genetic associations has typically entailed identifying a locus of interest and reporting on the proximity to pathway-related enzymes , without searching rigorously for other linked genes that might play a regulatory role . With the goal of discovering potential novel regulators of primary metabolism , we examined 74 metabolites involved in highly conserved core metabolic pathways of central carbon metabolism and nucleotide and amino acid biosynthesis . We found 52 significant linkages and experimentally verified the genes underlying three major linkage hot spots , including two linked genes responsible for altering S-adenosyl-methionine levels , neither with known metabolic roles . Additionally , we compared our metabolite results with the expression QTL results for the same cross [29] and discovered six overlapping hot spots . The largest mQTL hot spot is shared with the largest hot spot in the transcript data , and is caused by polymorphisms in a global regulator of cell signaling , ira2 . Interestingly , while the expression QTLs linked to ira2 were enriched for central metabolic enzymes , the variant of ira2 that promoted high metabolite concentrations favored low enzyme transcript levels . This dichotomy can be explained because ira2-linked transcripts are primarily involved in oxidative metabolism , while linked metabolites are mainly associated with fermentation . These findings reveal the utility of mQTL analysis for identifying metabolic regulatory mechanisms .
We tested for linkage with R/qtl [38] and used permutations to establish that a LOD score of 3 . 4 corresponded to an empirical FDR of 10% . Of the 74 compounds tested , 34 showed at least one significant linkage ( metabolite quantitative trait locus or mQTL; Table S1 ) . The majority of these compounds ( 21 of 34 ) had one mQTL , 9 had two mQTLs , three had three mQTLs and one had four mQTLs , for a total of 52 detected mQTLs . Almost all the compounds for which mQTLs were detected differed significantly between the parental strains at an FDR of 5% ( 29 of 34 ) . For 24 compounds that differed significantly between the parental strains , we did not detect mQTLs , most likely due to complex underlying genetics , with multiple loci of small effect . All compounds found to have significant mQTLs were primarily intracellular ( as levels in biological samples were much greater than in media ) . The mQTLs were not uniformly distributed along the genome; rather , most fell within 8 “hot spots” with 3 or more compounds linking to each ( Figures 1 and 2 , Materials and Methods ) . To improve the power and thoroughness of this analysis ( as well as a subsequent analysis of heritability and mQTL effect size ) , 42 ion peaks ( 20 mQTLs ) with a defined but unknown structural identity , were included . The observation of such hot spots , previously seen for other classes of traits , implies the presence of underlying polymorphisms with broad effects on the metabolome . We compared the metabolite linkage results with those for transcript abundance in the same cross [29] . Transcript linkages also cluster in hot spots , and the hot spots for metabolites and transcripts show a significant overlap in location , with six of eight metabolite hot spots also corresponding with transcript hot spots ( p 0 . 0001 , based on permutation test ) ( Figure 2 ) . Two metabolite hot spots did not have a corresponding eQTL hot spot: m8 on chromosome XVI ( linked to levels of ribose-phosphate , aspartate and glutamate ) and hot spot m5 on chromosome VIII ( linked to levels of S-adenosyl-homocysteine , S-adenosyl-methionine , and thiamine ) . The absence of eQTL hot spots at these locations could be explained by underlying variants with effects on metabolism but not on transcript abundance , or by false negatives in the eQTL hot spot results , which could arise from variants with effects on only a few transcripts . Hot spot m5 is especially interesting since regulation of the methionine cycle is poorly understood in eukaryotes despite being implicated in cardiovascular disease [39] , [40] . It will be discussed in greater depth below . To determine whether changes in metabolites tend to be linked to genes with known roles in metabolism , we carried out functional enrichment analysis of genes located in mQTL confidence intervals . Genes were classified as “metabolic” based on inclusion in the iMM904 metabolism model [41] . The mQTL confidence intervals were found to be modestly but significantly enriched for metabolic genes . 471 out of a total of 904 metabolic genes in the yeast genome partially or completely overlapped with an mQTL 95% confidence interval . This is far greater than would be expected by chance , based upon permutation analysis ( Figure S1; p 0 . 001 ) . Each mQTL confidence interval was also examined specifically for the presence of metabolic genes in the same pathway as the linked metabolite ( Table S2 ) . Over half ( 31/52 ) of the confidence intervals were found to contain at least one metabolic gene from one of the pathways involving the linked metabolite . Levels of five metabolites linked to a hot spot on chromosome V: orotate , orotidine , orotidine-5′-phosphate , UDP-D-glucose , and UDP-N-acetyl-glucosamine . All five are intermediates or products of pyrimidine biosynthesis ( Figure 3 ) . Ura3 , a pyrimidine biosynthesis gene which carries an engineered deletion in the RM strain , is contained within the hot spot and lies within the 95% mQTL confidence intervals for all five compounds ( Figure S2 ) . Compounds upstream of ura3 in the pathway show the greatest differences in abundance ( as much as 128-fold ) , and particularly strong linkages ( Figure 3 ) . To confirm that this mQTL hot spot was governed by segregation of the engineered ura3 deletion , ura3 , this RM allele was inserted into a BY background and metabolomic differences between BY and BYura3 were assessed . Using a two-tailed t-test , two compounds were found to differ between these two conditions at a 0 . 05 FDR . These two compounds , orotate and orotidine-5′phosphate , are both associated with this mQTL hot spot; the deletion resulted in a 16 and 43-fold increase in their accumulation respectively . These results demonstrate that our approach can link changes in metabolite levels to a polymorphism ( in this case , an engineered one ) in a gene known to participate in the biosynthesis of the relevant metabolites . The mQTL hot spot on chromosome VIII ( m5 ) is linked to levels of three metabolites: thiamine , S-adenosyl-methionine ( SAM ) , and S-adenosyl-homocysteine ( SAH ) ( Table S1 ) . The overlap among 95% confidence intervals of the mQTLs for these compounds covers a region containing all or part of 14 genes ( Figure S3 ) . None of the genes in this region have a known connection with the sulfur-assimilation pathway . We identified slt2 as a candidate for further evaluation due to the presence of a two amino acid indel polymorphism between BY and RM in a polyglutamine track; variation in the number of glutamines in this track has previously been implicated in stress response [42] . Segregants inheriting the RM allele of slt2 had significantly higher levels of SAM and SAH ( Figure 4 ) . To test the allelic effect of slt2 , we created allele-replacement strains in both parental backgrounds and compared metabolite levels to those in the parent strains ( Figure 5 ) . In the BY background , the RM allele of slt2 did not raise SAH levels above the limit of detection , nor did it result in a significant change for SAM ( p = 0 . 1598 ) . However , in the RM background , the BY allele of slt2 resulted in a three-fold decrease for both SAM and SAH ( Figure 5; p 0 . 001 ) . The difference in the effects of the allele swaps in the two backgrounds implies an interaction between the allelic status of slt2 and other loci . We considered the possibility that the effect of this locus is due to polymorphisms in multiple linked genes . We investigated a nearby gene , erc1 , due to the presence of an indel polymorphism that causes a frameshift which alters 37 residues and extends the peptide by 43 amino acids in the RM background . Erc1 has also been shown to have an effect on SAM levels when overexpressed in saké strains of S . cerevisiae [43]–[45] . Erc1 is located 3 kb ( approximately 1 cM ) from slt2 , and thus the alleles of the two genes segregate together as a haplotype . We used the slt2 allele replacement strains to create strains in which both genes were replaced with the alternative alleles . In the BY background , replacing both slt2 and erc1 with the RM alleles led to a significant increase in SAM ( p = 0 . 019 ) compared to the original BY strain , but the level of SAM was still much lower than in RM ( Figure 5 ) . In the RM background , replacing both genes with the BY alleles led to significantly lower levels of both metabolites compared to either the original RM strain or to the slt2 replacement alone ( p 0 . 001 for all comparisons ) . These results suggest that polymorphisms in both slt2 and erc1 alter the levels of SAM-cycle compounds in these strains , with other undetected loci also playing a role in the observed variation . A mQTL hot spot on chromosome XV ( m6 ) is linked to five central carbon metabolites: glucose-6-phosphate ( G6P ) and its isomers ( which were not distinguished by the LC-MS method used here ) , fructose-1 , 6-bisphosphate ( FBP ) , sedoheptulose 7-phosphate ( S7P ) , dihydroxyacetone phosphate ( DHAP ) , and ( iso ) citrate . The overlap among the 95% confidence intervals of the mQTL for each compound covers a region containing all or part of 13 genes ( Figure S4 ) . We focused on ira2 as a candidate gene because it has a known function as a regulator of the Ras/PKA pathway [46] , a known effector of glycolytic flux [47] , and because we previously showed that polymorphisms in ira2 underlie a major eQTL hot spot ( t16 ) at the same locus in this cross [29] , [48] . Ira2 is a Ras-related GTPase [46] , [49] , [50] , with ira2-catalyzed GTP hydrolysis leading to inactivation of Ras . The eQTL expression patterns suggested that ira2 is hypoactive in the BY strain . Segregants that inherit the BY allele of ira2 showed higher levels of all five linked metabolites than those that inherit the RM allele ( Figure 6 ) . To test the allelic effect of ira2 , we compared metabolite levels of ira2 allele-replacement strains in both backgrounds [29] to the original parent strains ( for FBP , see Figure 7; for other metabolites , see Figure S5 ) . In the RM background , the BY allele of ira2 led to significantly higher levels of three compounds ( p 0 . 01 for sedoheptulose-7-phosphate , FBP , DHAP ) . In the BY background , the RM allele of ira2 led to significantly lower levels of all five metabolites ( p 0 . 05 ) . These results demonstrate that polymorphisms in ira2 contribute to the observed variation in these five central metabolites . Metabolites can accumulate due to either increased production or decreased consumption . To distinguish whether the BY allele of ira2 was enhancing central carbon metabolic flux versus inhibiting metabolite consumption , we analyzed glucose uptake in the BY and RM parent strains , as well as in ira2 allele-replacement strains in both backgrounds . Glucose uptake rate did not differ significantly between the two parental strains . In the two allele-replacement strains , however , glucose uptake diverged markedly . In the RM background , the BY allele of ira2 led to 45% faster glucose uptake , whereas in the BY background , the RM allele led to a 20% decrease ( Figure 7 ) . The main fermentative product of glucose is ethanol , so the rate of ethanol excretion in ira2 allele-swap strains was measured using 1H NMR . In either background , the BY allele of ira2 led to a significant increases in ethanol excretion ( p 0 . 05 ) . These results demonstrate that polymorphisms in ira2 control central carbon metabolic flux , with the BY allele inducing both higher metabolite levels and fluxes . In the parental strains , the metabolic flux impact of the ira2 polymorphism is presumably offset by differences at other loci . Because polymorphisms in ira2 result in differences in expression of 1300 genes [29] , we considered whether expression differences in central carbon metabolism genes might underlie the observed metabolic changes . Of 70 known central carbon metabolism genes ( i . e . , those with roles in glycolysis , pentose phosphate pathway , citric acid cycle , and oxidative phosphorylation from yeastgenome . org ) , 32 genes' expression linked to the ira2 locus in glucose media ( Table S3 ) . This significantly exceeds the number of linkages expected for a random set of genes ( p 0 . 01 , Fischer's exact test ) . Remarkably , of the 32 linked genes , 28 are less highly expressed in the BY strain , which has higher levels of G6P , FBP , S7P , DHAP , and ( iso ) citrate . Thus , paradoxically , the BY allele of ira2 promotes higher central carbon metabolite levels while repressing central carbon metabolism gene expression . Insight into this paradox is provided by the nature of the regulated genes: 28 of the 32 central carbon metabolism genes that link to ira2 tend to be more highly expressed in ethanol than in glucose [29]; i . e . , the primary transcriptional regulatory role of ira2 seems to be in enhancing expression of genes required for respiratory growth . In contrast , with the exception of ( iso ) citrate , the linked metabolites are indicative of active fermentation . The accumulation of ( iso ) citrate in the BY strain is consistent with the lower expression of the primary isocitrate consuming enzyme ( idh1 ) from the BY allele of ira2 . Taken together with the data showing that the BY allele of ira2 promotes glucose fermentation , one obtains a coherent view: ira2 activity is lower in the BY strain . This leads to decreased expression of genes required for respiration , more need for fermentative ATP production , and higher levels of the glycolytic intermediates G6P , FBP , and DHAP . We can only relate metabolite abundance variation to genetic heterogeneity across segregants when there is substantial genetic variation affecting metabolite levels in the first place . Previous estimates of broad-sense heritability [51] in A . thaliana have suggested moderate heritability of metabolite traits across globally-distributed strains [20] , while segregants showed substantially lower heritability of metabolite traits than expression traits ( an average of 25% and 65% respectively ) [27] , [52] . We found extensive heritable variation of metabolite abundance in this study , with an average broad-sense heritability of 62% . This indicates that there are likely larger metabolic differences segregating between BY & RM than within the Bay Sha A . thaliana cross . Greater levels of heritability across metabolites are associated with an increased number of detected mQTLs ( p = 0 . 014 ) ; this is evident in Figure 8 , which shows linkage numbers as a function of heritability . The effects of these QTLs can be seen by determining the fraction of the variance in metabolite abundance that is explained using QTL genotypes ( Figure 9 ) . Effect sizes and the total fraction of heritability explained vary greatly across metabolites , with some mQTLs explaining the vast majority of genetic variation , others collectively explaining a sizable portion through the joint additive effects of multiple loci and others still explaining little of the total variance . The large fraction of unexplained metabolite abundance heritability could be owing to two factors: insufficient power to detect multiple loci of small effect , or the non-additive interaction between loci [27] , [53] .
We used high-throughput metabolite phenotyping in a cross of two divergent strains of yeast to find 52 linkages for 34 metabolites . We have detected linkages for a majority of compounds with significant differences between parental strains , as well as for a few compounds without such differences . Many metabolites show transgressive segregation , with levels in progeny strains outside the range of the parents; the parental strains likely carry alleles with opposing effects , with some segregants that inherit combinations of alleles that result in extreme metabolite levels , as has been observed for transcript levels [32] . Such opposing effects in the parent strains were also evident in control of glycolytic flux , which is similar in the parental strains , but diverges upon an ira2 allele swap . Ira2 is a regulator of cell signaling , not metabolism per se . Nevertheless , allelic differences in ira2 have a broad impact on central carbon metabolism at the level of transcripts , metabolites and flux . The hypoactive variant of ira2 found in the BY strain is associated with decreased expression of oxidative metabolism transcripts , higher levels of citrate , glycolytic intermediates , and sedoheptulose-7-phosphate , as well as higher glycolytic flux . These observations are consistent with active ira2 inducing oxidative metabolic genes , which in turn decrease the glycolytic flux required to fulfill ATP production . This raises the intriguing possibility that , due to the efficiency of oxidative ATP production , the extent of residual oxidative phosphorylation during yeast fermentative growth is a major determinant of glycolytic flux . More direct inhibition of glycolysis by the BY variant of ira2 , e . g . , through inhibition of phosphofructokinase-2 , is also a possibility . Perhaps the most exciting use of yeast mQTL mapping is to discover novel regulators of metabolism . In this respect , we have found linkages between levels of SAM and SAH and two proteins , slt2 and erc1 , with no previously known metabolic regulatory role . These two proteins interestingly segregate as a complex haplotype . SAM and SAH are key metabolites from the perspective of epigenetics; they are substrates and products , respectively , in DNA and histone methylation . Through epigenetics or other mechanisms , SAM and SAH may impact a broad range of diseases , e . g . , of the cardiovascular system [39] , [40] , liver [54] , or brain [55]–[57] . Slt2 is part of a MAP kinase cascade responsible for maintaining cell wall integrity , and thus contributing to fitness during osmotic stress . Erc1 was identified for conferring ethionine resistance [42]–[45] , [58]–[60] . While SAM and SAH ( as well as a thiamine , which also links to the same locus ) , are notable for containing sulfur , neither slt2 nor erc1 is regulated transcriptionally in response to sulfur availability [61] , [62] . Both sulfur metabolites and slt2 have been associated with the cell cycle ( in the case of slt2 , via the cell cycle transcription factors swi4 and swi6 ) [63]–[67] . The molecular mechanism by which slt2 and erc1 polymorphisms regulate SAM and SAH levels remains , however , to be elucidated . The discovery of the underlying mechanisms , may in turn , inform the overall interplay between metabolism , epigenetics , and the cycle cell . Thus , mQTL analysis provides a powerful tool for integrative systems biology . The BY RM cross utilized in this work has been previously used to characterize metabolite abundance variation with quantitative NMR in Zhu et al . 2012 [28] . While the designs of these studies are very similar , the use of LC-MS in our study , as well as different experimental procedures , resulted in substantial differences in the observed mQTL hot spots , allowing us to expand upon and provide an alternative explanation for the basis of some of these controlling regions . Of the 56 metabolites reported in our study , 27 were also quantified in Zhu et al . , and of the 16 metabolites for which Zhu detected significant linkage , 12 were shared between the two studies . Three hot spots are shared between these two studies: those which we have shown are due to variation in ura3 , slt2/erc1 , and ira2 . In Zhu et al . , the ura3 auxotrophy was implicated through its linkage with orotate and dihydroorotate elevation; we have confirmed these effects both statistically and through direct experimental manipulation of ura3 , and also expanded them to other metabolites in the pathway . In both studies , SAM and SAH were linked to the slt2/erc1 locus , but Zhu et al . did not discuss this hot spot , and they did not identify or propose underlying genes . Zhu et al . also mapped the abundance of glycerol , lysine , tyrosine and trehalose to the region containing ira2 and pmh7 . They concluded that variation in pmh7 was the causal source of these metabolic alterations , but this conclusion was based on a weak knockout phenotype , rather than on an allele replacement . Of these metabolites , we were only able to quantify lysine , which was not linked to this region in our study . It is therefore difficult to determine whether ira2 and phm7 function as a complex locus , similar to slt2/erc1 , with both genes playing a role in variation of the same or different sets of metabolites , or whether ira2 is the only gene in the region that influences metabolite variation . The remaining mQTL hot spots of Zhu et al . were associated with amino acid metabolism and were not observed in our study , perhaps because of differences in growth conditions: synthetic compete medium in Zhu et al . vs . supplemented minimal medium in this study . Such mQTL hot spot dependence on growth conditions would be analogous to gene-environment interaction eQTLs ( gxeQTL ) previously identified in the BY RM cross [29] . This observation suggests that mQTL analysis under a variety of growth conditions could be an important method for discovering novel metabolic regulatory mechanisms .
We used strains generated from the cross between BY4716 ( MAT lys2 ) and RM11-1a ( MATa leu2 ura3 ) ; these strains have been extensively studied for a variety of quantitative phenotypes [29]–[34] , [68] . Growth medium was comprised of 6 . 7 g/L Yeast Nitrogen Base ( YNB ) without amino acids , 2% ( w/v ) glucose as the sole carbon source , and was supplemented with leucine , lysine and uracil ( final concentrations 100 mg/L , 30 mg/L , 20 mg/L respectively ) to complement the strain auxotrophies . Yeast were grown in this medium using a filter culture technique that enables rapid sampling of metabolism without perturbation of the cultured cells [35] . In brief , strains were grown aerobically in liquid minimal medium to an 0 . 1 , at which point 5 mL of the culture was transferred by filtration to the surface of an 82 mm , 0 . 45 m pore size nylon membrane , which was subsequently placed atop a medium-loaded agarose plate as described in Brauer et al . [35] . The filter cultures were grown aerobically to mid-log phase ( in 5 mL wash = 0 . 2–0 . 6 , for 3–5 hr , approximately 2–4 doublings ) before metabolism quenching and metabolome extraction . All growth was at 30C . Cultures were grown in triplicate , with two filters used for metabolite extraction and the third filter for OD measurement . The cell-loaded filter membrane was quenched by placing it cell-side down in 2 mL of acetonitrile/methanol/water ( 40∶40∶20 ) at C . After 15 min , residual cells were rinsed off of the filter and the 2 mL cell-extraction solvent mixture was centrifuged at 13 , 200 rpm for 5 minutes at 4°C to generate a clear supernatant . 90L of this clear metabolome extract was mixed with 10L of a mixture of isotope-labeled internal standards to yield an analysis-ready sample . Samples were stored at C until analysis , which was completed within 24 h of sample generation . Two different LC separations were coupled by electrospray ionization ( ESI ) to Thermo TSQ Quantum triple quadrupole mass spectrometers operating in multiple reaction monitoring ( MRM ) mode . Positive-mode ESI was coupled to hydrophilic interaction chromatography ( HILIC ) on an aminopropyl column; negative-mode ESI was coupled to reversed-phase chromatography with an amine-based ion pairing agent [69] , [70] . Raw LC-MS/MS data from both runs were analyzed using the MAVEN software [71] . The results of this automated analysis were manually verified in all cases . Peak quantitation was based on the average of the top three points in the peak . For linkage analysis , compounds detected in fewer than 25% of samples were discarded; for the remaining compounds , when signal was not detectable , raw ion counts were floored to 32 , which is approximately the lower limit of detection . Duplicate samples of the same strain were averaged and then divided by the associated OD at extraction to normalize for any sample-size differences . Each day the RM11-1a strain was also run under this method . To correct for inter-day variance in raw signal intensities , log-ratios between segregant and the same-day RM values were used for each compound . For each compound's abundance data , an ANOVA of the form phenotype strain was performed in R using the aov function to compute p-values . These p-values were then false-discovery-rate corrected to assess statistical significance . Tests for mode of inheritance were conducted according to the formulae laid out in Brem & Kruglyak [32] . To determine which metabolites may appear abundant by virtue of the extraction procedure , we compared metabolite levels from mock extracted cells to the parental strains using a one-tailed t-test and we found six compounds at levels comparable to biological samples . Four of these metabolites were included in the media as vitamins or supplements: leucine/isoleucine , nicotinate ( ) , pantothenate ( ) , and 4-Pyridoxic acid ( a catabolite ) . Two additional metabolites had elevated levels that likely resulted from systematic contamination: deoxyribose-phosphate and D-glucono--lactone-6-phosphate . No QTLs were associated with any of these compounds , so their inclusion should not impact our subsequent analysis . We used genotypes at 2 , 820 SNP markers that were previously genotyped in individual segregants [32] , giving an average spacing between markers of 4 . 3 kb or 1 . 5 cM . With over 100 segregants , we would expect to see an average of more than one recombination event between adjacent marker pairs in this cross . Linkage analysis was performed using the qtl package in R [38] . We used the normal model and nonparametric method , assessing significance through the built-in permutation test . We computed 100 permutations of the qtl profile for every metabolite; linkage scores that were in the top 10% of this set were considered significant . This cutoff differs for each metabolite , ranging from a LOD score of 3 . 14 to 3 . 58 with an average of 3 . 35 . We calculated confidence intervals using the bayesint function with a probabilit y of 0 . 95 . This is generally considered more conservative than intervals calculated based on a 1 . 5 LOD drop; secondary peaks on the same chromosome will result in larger intervals . The allele replacement strains for IRA2 , SLT2 , and ERC1 were constructed according to methods laid out in Gray et al . [72] and Smith et al . [29] . The strains used were BY4724 ( MATa LYS2 URA3 ) , BY4724 , BY , BY , ACY753 ( an RM MATa URA3 ) , and RM , RM , RM . Allele swap strains were compared to their parental strain using paired t-tests . Confidence intervals for each QTL were computed as described above . Using the intervals package in R and the position and name of metabolic genes from Mo et al . [41] , we created a dataset of all metabolic genes in the S . cerevisiae genome . The intervals_overlap ( ) command returned how many and which metabolic genes fully or partially overlaped with our confidence intervals . To compute significance for all confidence intervals , we randomly permuted the position of the intervals 10 , 000 times , each time recording the total number of metabolic genes contained in the intervals . To look at pathway-specific metabolic genes for each metabolite , we compared the SGD list of genes in all pathways for that metabolite with the list of all metabolic genes in that metabolite's confidence interval ( pathway information was downloaded from Yeast Biochemical Database available at Saccharomyces gene database http://www . yeastgenome . org/biocyc on 29 September 2009 ) . For metabolites with multiple linkages , each confidence interval was examined separately . All transcript data was taken from Smith and Kruglyak [29] , using only the data for glucose-grown cells . For comparing linkage location , the genome was broken into 10 kb bins and the peak of each linkage ( transcript and metabolite ) was assigned to a bin . A bin was considered to have an excess of linkages if the number exceeded the number expected by chance by Poisson distribution . Given the number of metabolite-linkages ( 52 ) and bins ( 1216 ) we have = 0 . 0428 , and we used a Bonferroni corrected significance ( p 4 . 11*10-5 ) ; this resulted in significance for any bin that linked to three or more metabolites . For transcript-linkages = 4 . 151 and the significant hot spots are defined by have 14 or more linkages . Hot spots in immediately adjacent bins were accepted as part of the same hot spot . When comparing hot spots between the datasets , they were considered shared only if they inhabited the same linkage bin . For each metabolite , segregants with two quantifiable biological replicates were isolated and the variance within replicates was compared to the total across all samples . This is effectively subtracting the environmental variance from the total phenotypic variance to yield the genetic variance . The ratio of genetic variance to phenotypic variance is the broad sense heritability ( equation 1 ) ( 1 ) The association between the number of QTLs found for a metabolite and the metabolite's heritability was found by modeling the number of detected QTLs as an approximately poisson trait and predicting this value using poisson regression . | Many traits , from human height to E . coli growth rate , quantitatively vary across members of a species . Among the most medically and agriculturally important traits are levels of cellular metabolites , such as cholesterol levels in humans or starch in food crops . Metabolic variation in yeast also holds practical importance with some Saccharomyces strains better suited to making ethanol for biofuel and others tailored to making flavorful wine . This metabolic heterogeneity can be used to gain insight into general principles of metabolic regulation which effect metabolite abundance in eukaryotes . To this end , we examined inter-strain differences in metabolism in over 100 closely related S . cerevisiae strains . We identified over 50 genetic loci that control the levels of specific metabolites , including not only loci that encode metabolic enzymes , but also those that encode global cellular regulators . For example , differences in the sequence of ira2 , an inhibitor of Ras , lead to differences in central carbon metabolite levels , and polymorphisms in slt2 , a poorly characterized MAP kinase , alter levels of sulfur-containing metabolites . These findings provide insights into the mechanisms cells use to control metabolite concentrations . | [
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| 2014 | Genetic Basis of Metabolome Variation in Yeast |
Kaposi's sarcoma ( KS ) is caused by infection with Kaposi's sarcoma-associated herpesvirus ( KSHV ) . The virus expresses unique microRNAs ( miRNAs ) , but the targets and functions of these miRNAs are not completely understood . In order to identify human targets of viral miRNAs , we measured protein expression changes caused by multiple KSHV miRNAs using pulsed stable labeling with amino acids in cell culture ( pSILAC ) in primary endothelial cells . This led to the identification of multiple human genes that are repressed at the protein level , but not at the miRNA level . Further analysis also identified that KSHV miRNAs can modulate activity or expression of upstream regulatory factors , resulting in suppressed activation of a protein involved in leukocyte recruitment ( ICAM1 ) following lysophosphatidic acid treatment , as well as up-regulation of a pro-angiogenic protein ( HIF1α ) , and up-regulation of a protein involved in stimulating angiogenesis ( HMOX1 ) . This study aids in our understanding of miRNA mechanisms of repression and miRNA contributions to viral pathogenesis .
At our current understanding , the herpesvirus family is the only viral family expressing multiple miRNAs . Kaposi's sarcoma-associated herpesvirus ( human herpesvirus 8 ) expresses 12 pre-miRNAs [1] , [2] , [3] , [4] . These miRNAs are encoded in the latency locus of the KSHV genome and all KSHV miRNAs are expressed during latency . This discovery presented the possibility that KSHV expresses miRNAs to modulate host gene expression by a mechanism that would avoid generating additional viral proteins , which could be detected by the host immune system . Although many groups have been successful in detecting viral miRNA expression , our understanding of the functions of the viral miRNAs has been limited due to the small number of validated miRNA target genes . Previously identified human targets include thrombospondin [4] , BACH-1 [5] , [6] , BCL-2 associated factor [7] , MICB [8] , musculoaponeurotic fibrosarcoma oncogene homolog [9] , IκBα [10] , Rbl2 [11] , p21 [12] , caspase 3 [13] , TWEAKR [14] , TGFβR2 [15] , and other targets . These targets represent host genes involved in angiogenesis , transcription regulation , immune evasion , NF-κB regulation , epigenetic modifications , apoptosis and cell cycle regulation . Recently , a number of other host targets have been identified by purifying RNA-induced silencing complexes and analyzing associated nucleic acids [16] [17] , [18] in primary effusion cell lines , which represents a recent addition to the technologies used to identify miRNA targets . Gene expression studies to discover targets repressed by viral miRNAs in primary endothelial cells have been limited . Previous methods for miRNA target prediction include measuring changes at the mRNA level in response to miRNAs using microarrays and bioinformatic methods to search for limited sequence complementarity [4] , [7] . The human targets of miRNAs that will be detected depend on the expression profiling methods utilized and the mechanisms of miRNA-mediated repression [19] . If a miRNA is inhibiting gene expression by stimulating deadenylation and destabilization of the mRNA target , then gene expression microarrays can be successful in identifying targets . However , miRNAs may repress gene expression of some targets by inhibiting translation and mRNA expression profiling may miss miRNA targets that are repressed at the protein level , but not at the mRNA level . One method to detect these types of targets is by measuring changes in protein expression in the presence of specific miRNAs . Stable isotope labeling of amino acids in cell culture ( SILAC ) coupled with tandem mass spectrometry has been used recently to study the effects of miRNAs on protein expression [20] , [21] , [22] , [23] . In this report , the pulsed SILAC method was employed to focus on changes in newly translated proteins in the presence of KSHV miRNAs . Here , we report the discovery of human targets of viral miRNAs using this technology in primary human endothelial cells , a relevant cell type for KSHV infection . We found that specific miRNAs can inhibit expression of a protein involved in immune response and can stimulate expression of two proteins known to stimulate angiogenesis ( a key hallmark of Kaposi's sarcoma ) .
Since sixteen miRNAs were introduced into HUVECs simultaneously during the SILAC assay , the analysis of potential miRNA targets and protein expression was complex , even though these experiments were biologically relevant to the expression of all miRNAs during normal viral infection . Bioinformatic programs are commonly used to identify complementary sequences between miRNAs and their potential targets . We used TargetScan [25] to search for seed-matching sequences in the 3′ untranslated regions ( UTRs ) of transcripts corresponding to proteins that were identified in the SILAC analysis . An initial analysis of genes included in both the SILAC and TargetScan datasets separated the genes into two sets , one with at least one TargetScan site ( 847 genes ) and another set of corresponding transcripts which did not have any TargetScan sites ( 424 genes ) ( Figure 2A ) . This revealed that the fraction of proteins containing at least one predicted miRNA target site ( in the corresponding transcript's 3′UTR ) was larger in the set of proteins that were strongly repressed ( Figure 2B ) . Approximately 60% of proteins that were not repressed ( log2>0 ) had at least one seed-matching site in their corresponding 3′UTR , suggesting an over 60% false positive rate of detection using seed matching alone . However , those proteins whose transcripts have seed-matching sites tend to have lower expression in the presence of KSHV miRNAs , as do the proteins from mRNAs with multiple seed-matching sites ( Figure 2C–D ) . Repressed proteins detected in the SILAC analysis can represent direct targets of KSHV miRNAs , as well as indirect targets . In order to determine if these repressed genes are directly targeted by KSHV miRNAs , we chose six genes based only on protein expression changes to test in standard 3′UTR luciferase reporter assays . Using full 3′ UTRs , we determined that all six of the 3′UTR luciferase reporters tested ( GRB2 , ROCK2 , STAT3 , HMGCS1 , TSPAN3 , AKAP9 ) are significantly inhibited by at least one KSHV miRNA ( Figure 3A ) , but TSPAN3 repression was the weakest of the six 3′UTRs tested . Interestingly , GRB2 was also recently described as a target of KSHV miRNAs [17] . Additionally , we mapped the specific site targeted by a KSHV miRNA for two of these targets , ROCK2 and HMGCS1 ( Figure 3B–C ) . Luciferase reporters shown in Figure 3A contained 3′UTRs downstream of a firefly luciferase gene and reporters shown in Figure 3B–C had 3′UTRs downstream of a renilla luciferase gene . Different transcription rates , half lives of luciferase enzymes , and cloned 3′UTR context may have been responsible for certain variations in the repression of the same 3′UTR in different reporter plasmids . The mutation of predicted sites significantly relieved miRNA-mediated repression for both miRNA targets ( Figure 3B–C ) . Together , these results suggest the 3′UTRs of these six genes identified in the SILAC screen contain sequences targeted directly by KSHV miRNAs . Using two-color quantitative Western blotting , we assayed sixteen mature miRNAs for their ability to modulate endogenous protein expression of four ( of the six ) luciferase-validated target genes in primary endothelial cells . All four proteins tested , GRB2 , ROCK2 , STAT3 ( alpha and beta isoforms ) and HMGCS1 , were inhibited significantly by at least one miRNA ( Figure 4A ) . Furthermore , the protein expression from the majority of the individual genes tested was inhibited significantly by multiple miRNAs . For example , GRB2 protein expression was repressed by miR-K4-3p , -K4-5p , and -K9* . We observed an overall correlation between the miRNAs that repress the 3′UTR reporter and the miRNAs that decrease the steady-state levels of endogenous protein . This supports the pulsed SILAC strategy as a method of discovering miRNA targets . It is also important to determine target protein expression levels in the context of viral infection . We observed significant repression of four miRNA targets , including a particularly robust inhibition of HMGCS1 in de novo infected HUVECs compared with mock infected cells ( Figure 4B ) . The repression of HMGCS1 protein after infection was similar to the protein expression changes in the pSILAC data ( Figure 1E ) and cells transfected with miR-K11 mimics ( Figure 4A ) . Repression after de novo infection validates that these targets are repressed in the context of physiological levels of viral miRNAs during infection . An additional use of the proteomic data is to address the question of how miRNAs repress gene expression . Whether miRNA-induced gene expression changes are reflected primarily at the mRNA or the protein level may lead to a better understanding of miRNA repression mechanisms . Using the same transfected cells from the proteomic screening , we also analyzed the mRNA expression profiles using microarrays ( Figure 4C , Table S1 ) . All of the protein expression changes in Figure 1D were combined with mRNA expression changes from microarray analysis and plotted in Figure 4C . The protein and mRNA expression changes of the six newly validated miRNA targets were analyzed and for all six of these target genes the changes at the protein level were more pronounced than at the mRNA level ( Figure 4C ) . These findings justified the additional focus on protein expression changes to predict miRNA targets , which may be missed by solely measuring changes at the mRNA level ( depending on the mRNA expression change cutoff values used ) . Identifying potential miRNA targets is an initial step to elucidate the functions of KSHV miRNAs . One of the validated miRNA targets , Rho-associated , coiled-coil containing protein kinase 2 ( ROCK2 ) has been shown to be largely responsible for lysophosphatidic acid ( LPA ) -induced intercellular adhesion molecule 1 ( ICAM1 ) expression in HUVECs [26] . ICAM1 is essential for the recruitment and transmigration of leukocytes to sites of inflammation [27] . Therefore , we hypothesized that KSHV miRNA-mediated knockdown of ROCK2 would contribute to the decrease of ICAM1 expression induced by LPA as part of a host immune evasion strategy during latency . HUVECs were transfected with individual or combinations of KSHV miRNAs or siRNAs targeting ROCK2 , treated with LPA , and harvested at 48 h post-transfection . The whole cell lysates were analyzed for relative changes in ROCK2 and ICAM1 protein expression by quantitative Western blot analysis . In LPA-treated cells , ROCK2 protein was sufficiently repressed by both miR-K4-3p and siROCK2 , but not reproducibly by miR-K10a . We observed an average 6-fold increase of ICAM1 protein expression upon treatment with LPA ( data not shown ) . While there was a significant decrease in ICAM1 protein expression from LPA-treated cells also transfected with miR-K4-3p or siROCK2 , there was a much more robust repression of ICAM1 expression by miR-K10a transfection ( Figure 5A ) . Based on these results , we hypothesized that miR-K10a represses ICAM1 up-regulation through a ROCK2-independent mechanism . It was known that STAT3 can activate ICAM1 expression [28] , [29] , [30] and LPA treatment induces STAT3 phosphorylation [31] . We confirmed an increase in phospho-STAT3 ( Tyr705 ) using Western blot analysis upon LPA treatment and found decreased levels of phospho-STAT3 ( Tyr705 ) in the presence of miR-K10a ( Figure 5B ) . While repression of total STAT3 protein levels with miR-K10a transfection in the absence of LPA was variable , STAT3 protein levels were repressed in LPA-treated cells upon transfection with miR-K10a mimics compared to control mimics . TargetScan analysis found three potential miR-K10a binding sites in the STAT3 3′UTR ( Table S2 ) , and luciferase assays with the STAT3 3′UTR confirmed direct repression by miR-K10a ( Figure 5C ) . This suggested a potential role of STAT3 in the repression of ICAM1 in LPA-treated endothelial cells that is independent of ROCK2 . Additionally , we observed strong repression of ICAM1 after de novo KSHV infection in HUVECs ( Figure 5D ) . To determine if KSHV miRNAs play a role in this repression , HUVECs were transfected with miRNA inhibitors to miR-K4-3p and miR-K10a , then infected with KSHV , and analyzed for ICAM1 protein expression three days after infection . ICAM1 protein expression is modestly elevated ( likely due to incomplete inhibition of target miRNAs ) in HUVECs transfected with miR-K4-3p and miR-K10a inhibitors ( Figure 5E ) . Together , these results show that KSHV miRNAs decrease LPA-stimulated ICAM1 expression and are at least partially responsible for ICAM1 repression during KSHV infection in primary endothelial cells , which could potentially minimize recruitment of leukocytes to areas of KSHV infection . Our initial focus was to identify direct miRNA target genes by focusing on genes that were repressed in the presence of the viral miRNAs . However , we were intrigued by the increased protein production of heme oxygenase 1 ( HMOX1 , log2 = 2 . 03 ) and biliverdin reductase ( BLVRA , log2 = 1 . 99 ) in the presence of KSHV miRNAs . These proteins are important factors in oxidative stress and heme metabolism [32] . HMOX1 protein was previously described to be upregulated upon infection with KSHV [33] . Because miRNAs usually work through suppressing gene expression , these results suggested that some KSHV miRNAs may work through modulating protein expression of factors regulating HMOX1 and BLVRA protein expression . An analysis of promoters corresponding to the up-regulated proteins ( top 5% ) revealed that HIF1α binding sites were enriched in this set of up-regulated genes ( p-value = 0 . 0005 ) . Closer inspection revealed both HMOX1 and BLVRA are transcriptional targets of HIF1α [34] , [35] . We sought to determine if specific miRNAs could influence HIF1α expression or activity . Inducing hypoxia in 293 cells ( also HUVECs , data not shown ) with the addition of a hypoxia mimic , cobalt chloride ( data not shown ) , or inducing hypoxia with incubation in 1% oxygen , showed that miR-K7 can induce a 5-fold activation of endogenous HIF1α protein levels ( Figure 6A ) . We also observed that miR-K7 can increase HIF1α transcriptional activity through assays using a HIF-responsive luciferase reporter ( Figure 6B ) . Quantitative PCR data did not detect a significant change in HIF1α mRNA levels ( Figure 6C ) , suggesting transcription rates are not affected by miR-K7 . HIF1α protein is constitutively produced , but destroyed in cells growing in normoxic conditions . We suspected that miR-K7 might increase HIF1α protein levels by repressing an inhibitor of HIF1α protein expression . We investigated the changes in protein expression of four inhibitors of HIF1α , including hypoxia-inducible factor 1-alpha inhibitor ( HIF1AN ) , egl nine homolog 1 ( PHD2/EGLN1 ) , von Hippel-Lindau tumor suppressor ( VHL ) , and tumor protein p53 ( TP53 ) , but we did not detect significant changes ( Figure 6D ) . However , another protein , ring-box 1/E3 ubiquitin protein ligase ( RBX1 ) , has been shown to mediate ubiquitination and degradation of HIF1α [36] . Protein levels of RBX1 were modestly repressed in hypoxic cells transfected with miR-K7 mimic compared to the negative control miRNA mimic ( Figure 6D ) . It was unknown if RBX1 is a direct target of miR-K7 , but RBX1 was found in miRNA target detection screens ( CLIP assays ) in KSHV-infected cells [17] , [18] . These data suggested that RBX1 may play a partial role in miR-K7 upregulation of HIF1α protein levels during hypoxia , but it remains likely that up-regulation of HIF1α is due to changes in expression of multiple genes that remain to be determined . Taken together , these results suggest miR-K7 may repress additional inhibitors of HIF1α protein expression . In normoxia , HMOX1 protein expression was not induced by miR-K7 ( Figure 6E ) . Furthermore , the increase in HMOX1 protein expression detected in the SILAC analysis ( in normoxia ) was likely not due to increased HIF1α protein levels , but rather repression of a repressor of HMOX1 . In addition to positive regulation by HIF1α , HMOX1 was also known to be repressed by BTB and CNC homology 1 , basic leucine zipper transcription factor 1 ( BACH1 ) which is a known target of miR-K11 [5] , [6] . Under normoxia and miR-K11 expression , we observed an expected repression of BACH1 and a robust 4 . 5-fold activation of HMOX1 protein expression ( Figure 6E ) . These results suggest up-regulation of HMOX1 by miR-K11 is achieved by repression of BACH1 during normoxia . In addition to determining the roles of miRNAs through the study of individual target genes , the analysis of predicted target gene functions could highlight cellular pathways and biological processes that miRNAs regulate during infection . Furthermore , repressed gene expression could be the result of direct or indirect consequences of miRNAs , but both classes of targets may influence KSHV-infected cells . Analysis of the biological processes enriched in the most repressed ( five percent ) proteins showed that many of these repressed proteins are involved in translation , cytoskeleton , cell cycle , chromatin modification and angiogenesis ( Figure 7 ) . While it is currently unknown how many of these repressed proteins are direct miRNA targets , this analysis points to certain cellular functions important to KSHV pathogenesis that KSHV miRNAs are targeting , directly or indirectly .
In order to understand miRNA functions , it is critical to identify their targets , so we can increase our knowledge of cellular pathways that are important for infection and pathogenesis . Genome-wide studies have been conducted analyzing the Argonaute-associated mRNAs ( CLIP assays ) in B cells [16] , [17] , [18] , and the microarray and proteomic screening for miRNA-induced gene expression changes in primary endothelial cells from this report represent a complimentary dataset for elucidating viral miRNA functions . Indeed , integration of miRNA targets from CLIP methods and other expression studies will continue to be useful for identifying miRNA target sites , as well as >those CLIP hits that are repressed at the mRNA and/or protein level . Compared with other approaches to discover miRNA targets , current mass spectrometry methods are able to query a lower number of gene products . Despite this limitation , this current study has identified repression of multiple novel and previously validated miRNA targets ( THBS1 , GRB2 ) . Additionally , gene expression studies can reveal direct and indirect miRNA targets , both of which are important for virus-host interactions . By inspecting gene expression changes at both the mRNA and protein level , we have demonstrated that multiple miRNA targets are likely missed using microarrays since the miRNA target may only be repressed at the level of translation . This finding is relevant given the conflicting reports about the predominant mechanism and order of repression mechanisms [37] that are utilized by miRNAs to modulate gene expression , whether that be mRNA level repression [38] or translation inhibition [39] , [40] . In this study , validated miRNA targets AKAP9 , STAT3 , and GRB2 proteins were significantly repressed , but microarray results indicated mRNA levels were not reduced in the presence of KSHV miRNA mimics . The protein SH3-domain GRB2-like endophilin B1 ( SH3GLB1 ) was the second most inhibited protein , but the mRNA levels were relatively unchanged ( log2 0 . 03 ) . Interestingly , previous reports have shown that SH3GLB1 functions as a tumor suppressor and pro-apoptotic factor [41] , [42] . Given our findings , this proteomic method is clearly an important start to discover novel miRNA targets . Furthermore , we have also shown novel functions of viral miRNAs involved in cellular pathways important to KSHV pathogenesis , including ICAM1 repression , HMOX1 up-regulation and HIF1α up-regulation . Previous studies have indicated that ROCK2 is involved in a pro-inflammatory pathway induced by lysophosphatidic acid ( LPA ) that results in the up-regulation of intercellular adhesion molecule 1 ( ICAM1 ) on the surface of endothelial cells [26] . ICAM1 binds with lymphocyte function-associated antigen 1 ( LFA-1 ) and leads to the recruitment and transmigration of leukocytes . Interestingly , ICAM1 is downregulated from the cell surface and degraded through a well-described mechanism by the KSHV lytic protein , K5 , which can cause a decrease in the recruitment of helper T cells [27] , [43] , [44] . Furthermore , a previous study [45] and this report have also shown a decrease in ICAM1 expression during latent de novo infection of endothelial cells . We discovered that KSHV miRNAs , miR-K10a and miR-K4-3p , repress ICAM1 expression after induction by LPA , likely through ROCK2 and STAT3-associated pathways . Our data indicate that miR-K10a may be inhibiting LPA induction of ICAM1 by multiple mechanisms . First , the repression of a direct or indirect miRNA target of miR-K10a may be partially responsible for the decrease in LPA-induced STAT3 phosphorylation . HITS-CLIP data [18] showed the kinase PTK2B/FAK as a hit for miR-K10a alone , and , interestingly , PTK2B/FAK is thought to be responsible for phosphorylation of STAT3 in LPA-treated cells [31] . Although STAT3 protein levels can be repressed by miR-K6-5p , unlike miR-K10a , it is not predicted to target the kinase ( PTK2B/FAK ) and it remains to be determined if miR-K6-5p can repress LPA-activation of ICAM1 . Second , miR-K10a may directly inhibit STAT3α total protein levels in LPA-treated cells , as suggested by the results from the STAT3 3′UTR luciferase assays with miR-K10a . While others [45] have shown that low levels of the KSHV protein K5 can still down-regulate ICAM1 expression , we believe it is likely that during latent infection , the inhibition of ICAM1 is also due to the viral miRNAs , miR-K4-3p and miR-K10a . However , further studies are required to further elucidate the contributions of viral protein and viral miRNA-mediated repression of ICAM1 . HIF1α can activate transcription of VEGF and other factors involved in angiogenesis [46] , which raises the possibility that KSHV miRNAs may influence the angiogenic environment in KSHV-infected endothelial cells . Since miR-K7 increases HIF1α protein levels , but did not inhibit some major repressors of HIF1α ( Figure 6 ) , this suggests miR-K7 is working through an alternative pathway . We also observed a modest decrease in RBX1 when HIF1α is upregulated and the combined data suggest that there may be an underappreciated mechanism regulating HIF1α protein levels . Others have reported an increase in HIF1α activity with KSHV infection [47] , [48] , [49] . This increased activity is likely due to contributions from both viral proteins and viral miRNAs . Interestingly , analysis using MetaCore software reveals human genes involved in translation initiation are enriched in the proteins repressed by KSHV miRNAs in endothelial cells . This class of translation initiation genes was also enriched in predicted miRNA targets from both KSHV and EBV miRNAs in co-infected latent BC1 cells [17] , [50] . By contrast , lytic viral infections have been known to repress host translation inhibition [51] and others report that translation is activated upon KSHV lytic reactivation [52] . Together , these results suggest KSHV may play a complex role in influencing translation during latency and lytic infection . This investigation into HIF1α regulation by miRNAs was raised by the fact that the HIF1α transcriptional target heme oxygenase ( HMOX1 ) is strongly upregulated in this proteomic screen and in KSHV infected cells in a previous report [33] . It was also found that increased HMOX1 activity stimulated proliferation of KSHV-infected endothelial cells [33] . Both heme oxygenase I ( HMOX1 ) and bilverdin reductase ( BLVRA ) are strongly up-regulated in the presence of KSHV miRNAs in our study , and both of these gene products can protect endothelial cells from oxidative stress [53] . This also suggests certain KSHV miRNAs may protect cells from oxidative stress , by inhibiting BACH1 from repressing HMOX1 expression . Increased HMOX1 activity also correlates with increased angiogenesis [54] , [55] , [56] , [57] . Taken together , KSHV miRNA induction of HMOX1 can potentially protect cells from oxidative stress and increase proliferation and angiogenesis . In summary , the SILAC method revealed miRNA targets and discovered ways in which KSHV miRNAs can influence proliferation , angiogenesis , and immune evasion . More in-depth studies are needed to fully understand the significance of selected human genes targeted for repression by viral miRNAs .
293 cells were maintained in Dulbecco's modified Eagle's medium ( DMEM ) containing 10% fetal bovine serum ( FBS ) and 1× penicillin and streptomycin ( Pen Strep ) glutamine solution ( Gibco ) . Primary human umbilical vein endothelial cells ( HUVECs; Lonza ) were maintained in EGM-2 ( Lonza ) for up to five passages . Locked nucleic acids were from Exiqon . Synthetic KSHV miRNA mimics and a non-targeting miRNA ( control ) were from Ambion ( Sequences in Supplemental Information ) . HUVECs were seeded at 2×105 cells/well in a 6-well plate , transfected by using 1 . 5 µl/well DharmaFECT 1 reagent ( Dharmacon ) and 10 nM KSHV miRNA , and harvested at 48 h posttransfection ( hpt ) . ON-TARGETplus SMARTpool small interfering RNAs ( siRNAs ) targeting ROCK2 and an ON-TARGETplus nontargeting pool were obtained from Dharmacon . For ICAM1 experiments , cells were then serum starved overnight in basal media ( EBM-2 ) with 25% EGM-2 and , 40 hours post-transfection , treated with LPA ( 50 µM , Enzo ) for 8 hours . Cells were harvested at 48 hr . post-transfection and lysed in RIPA . For SILAC experiments , HUVECs were transfected ( total miRNA concentration was 10 nM ) in T75 flasks for 6 hr . and then split into new flask with medium-heavy ( with 13C6-L-arginine and D4-L-lysine ) or heavy ( 13C615N4 L-arginine and 13C615N2 L-lysine ) SILAC media as described [20] , except the media was also supplemented with endothelial growth factors ( Bulletkit , Lonza ) . After 30 hr . post-transfection , cells were harvested from flasks , counted , and equal number of cells from each condition were combined and frozen . Frozen cell pellet containing equal amount of control ( neg miRNA ) and experimental ( KSHV miRNAs ) cells were suspended in 100 µl of 25 mM ammonium bicarbonate buffer ( pH 8 . 4 ) . The cells were lysed by brief sonication and the proteins were denatured by heating the protein lysate at 95°C for 5 min . Protein concentration was estimated using standard BCA assay ( Pierce ) and the lysate was subjected to trypsin ( enzyme to protein ratio 1∶100 ) digestion overnight at 37°C . The tryptic digest was lyophilized and reconstituted in 25% ACN/0 . 1% FA ( 100 µl ) and fractionated using strong cation exchange ( SCX ) liquid chromatography into 96 fractions . The fractions were pooled on the basis of the intensity profile into 45 fractions , vacuum dried and reconstituted in 12 µL of 0 . 1% formic acid prior to nano-flow reversed-phase liquid chromatography mass spectrometry analysis . NanoRPLC–MS/MS analysis was performed using an Agilent 1100 nanoflow LC system coupled with hybrid linear ion trap-fourier transform ion cyclotron resonance ( LIT-FTICR ) mass spectrometer ( LTQ FT Ultra ) ( ThermoElectron , San Jose , CA ) . The system was connected to a 75 µm i . d . ×360 mm o . d . ×10 cm long fused silica microcapillary column ( Polymicro Technologies , Phoenix , AZ ) packed in-house with 5 µm , 300 Å pore size C-18 silica-bonded stationary RP particles ( Vydac , Hysperia , CA ) . The LC mobile phase A was 0 . 1% formic acid in water and B was 0 . 1% formic acid in acetonitrile . After the peptide sample injection , gradient elution was performed under the following conditions: 2% B at 500 nL/min in 30 min; a linear increase of 2–42% B at 250 nL/min in 40 min; 42–98% B at 250 nL/min in 10 min; and 98% at 500 nL/min for 18 min . The LIT-FTICR-MS was operated in the profile mode with 50 , 000 resolution for FTMS scans and followed by the data-dependent MS/MS scans where the seven most abundant peptide molecular ions in each FTMS scan were sequentially selected for collision-induced dissociation ( CID ) using a normalized collision energy of 35% . Dynamic exclusion was applied to minimize repeated selection of peptides previously selected for CID . The capillary temperature and electrospray voltage were set to 160°C and 1 . 7 kV , respectively . The raw LC-MS/MS data obtained from FT-LTQ was analyzed by MaxQuant ( version 1 . 0 . 13 . 13 ) for peptide identification and quantification . MS/MS peak list from individual RAW files were generated using the Quant module of the MaxQuant software and protein identification was performed using MASCOT against a decoy human database . Oxidation of methionine was searched as a variable modification . The false discovery rate was set at 1% for peptide and protein identification . Peptide peak intensities were used to determine the relative abundance ratio of “heavy” labeled proteins to “medium” labeled proteins . Unlabeled peptides were not used for further analysis . The ratio of “heavy” to “medium” proteins represents the fold change values reported ( Figure 1D-E , Tables S1 , S2 ) . Raw data files from pSILAC from both technical replicates were combined and then processed in MaxQuant to improve the coverage and the number of peptides found per protein . The Spearman correlation coefficient between protein expression changes for the two biological replicates is 0 . 51 ( and 0 . 43 Pearson correlation coefficient ) . The Spearman correlation coefficient between mRNA expression changes for the two biological replicates is 0 . 54 ( and 0 . 57 Pearson correlation coefficient ) . Due to the limited amount of sample obtained from the primary cells , equal amount of heavy ( H ) and medium ( M ) labeled cells were mixed prior to processing of the samples . To verify that there was no labeling bias , an MA plot ( M = log2 ( H ) −log2 ( M ) , A = ½ ( log2 ( H ) +log2 ( M ) ) was performed followed by Lowess curve analysis on the transformed data . Figure S4 shows that the Lowess regression line is almost straight around zero horizontal line , demonstrating no labeling bias in the H and M labeling . RNA was purified using Tri reagent ( Ambion ) and RNA quality was determined using a Bioanalyzer 2100 ( Agilent ) . Agilent arrays were performed and analyzed using Agilent Feature Extraction Software and Genespring GX as previously [7] . HUVEC microarray data was deposited to NCBI GEO database , accession number GSE43640 . Total cell protein was harvested from cell pellets by using RIPA lysis buffer ( Sigma ) supplemented with 1× Halt protease and phosphatase inhibitor cocktail ( Thermo Scientific ) . Cells were lysed on ice for 10 min , and cell debris was removed by centrifugation at 13 , 000 rpm for 10 min . Nuclear extracts for HIF1α blots were prepared using NE-PER ( Pierce ) . The Li-Cor Odyssey system was used for the detection and quantitation of protein bands . The following primary antibodies were used: rabbit anti-TWEAKR ( 4403 , Cell Signaling ) , rabbit anti-BCLAF1 ( Bethyl ) , goat anti-BACH1 ( SC-14700 , Santa Cruz ) , mouse anti-GAPDH ( sigma ) , anti-STAT3 ( 9132S , Cell Signaling ) , rabbit anti-HMGCS1 ( sc-33829 , Santa Cruz ) , rabbit anti-GRB2 ( 3972S , Cell Signaling ) , rabbit anti-ICAM1 ( 4915 , Cell Signaling ) , rabbit anti-ROCK2 ( sc-5561 , Santa Cruz ) , mouse anti-HIF1α ( NB100-105 , Novus ) and mouse anti-actin ( AC-74 , catalog number A5316; Sigma ) antibodies . The following secondary antibodies conjugated to infrared ( IR ) fluorescing dyes were obtained from Li-Cor: goat anti-rabbit antibody IR800CW , goat anti-mouse antibody IR680 , and goat anti-mouse antibody IR800CW . Protein band intensities were calculated and background corrected using ImageStudio ( Li-Cor ) . Results are normalized to actin levels , relative to levels in mock-infected or negative-control miRNA conditions . Full-length 3′UTR assays were performed as previously described [58] . Assays in Figure 3A used 3′UTR firefly luciferase reporters and were contransfected with a control renilla luciferase reporter under the control of a thymidine kinase promoter . Assays in Figure 3B–C used 3′UTRs cloned into a dual luciferase reporter . The 3′UTRs were cloned downstream of the renilla luciferase gene reporter . Luciferase values were normalized to an internal luciferase reporter and to parental vectors lacking cloned 3′UTRs . The hypoxia-inducible factor ( HIF ) luciferase reporter has five HIF-responsive elements in the promoter upstream of firefly luciferase reporter gene ( Panomics ) . Mutations of the predicted miRNA binding sites within the 3′UTRs of ROCK2 and HMGCS1 were performed as previously described [58] using the following primers and their reverse compliments: 5′-GCAGGCCTGCAAATACTGGCACAGAAATATAATCATACACCTTATTAACGGTGA-3′ for HMGCS1 and 5′-CTATGAAAGCAGTCATTATTCAAGGTGATCGTAAAGATCCAGTGAAAACAAGACTGAAATAT-3 for ROCK2 mut1 and 5′-TTACGCAGGACATTCTTGCCGTAAAGACATGATCCCAGATAAGTGTGTGT-3′ for ROCK2 mut2 . HUVECs were transfected as in SILAC experiments ( mixture of 16 mimics , total concentration of mimics was 10 nM ) . Each Ago2 immunoprecipitation was performed from individual T75 flasks using an Ago2 antibody ( 20 µl per 1 ml immunoprecipitation of diluted lysate , Cell Signaling #2897 ) , and the Magna RIP System ( Millipore ) . Purified RNA was subjected to TaqMan MicroRNA Reverse Transcription Kit . Mature miRNA levels were determined using Taqman MicroRNA Assays and viral miRNAs were normalized to human miR-21 levels using the ΔΔCt method . Note uninfected and untransfected HUVECs ( control ) had no detectable miR-K12-1 , but displayed average threshold cycles of 35 for miR-K12-7 and 37 cycles for miR-K12-11 . Uniprot IDs from pSILAC data , Agilent microarray probe IDs , and TargetScan v5 . 0 Refseq IDs were mapped to Ensembl gene IDs . Data integration was performed using Ensembl gene IDs . TargetScan sites “8mer” , “7mer-m8” , and “7mer-1A” were included , but 6mer sites were not included . Additional seed matching information is provided in Table S2 . Data similar to Table S2 was used to calculate the number of seed matching sites per 3′UTR . The empirical cumulative distribution function ( ecdf ) was performed using R ( http://CRAN . R-project . org/ ) . Promoter analysis of hypoxia-inducible factor responsive elements was performed using ExPlain ( Biobase ) using up-regulated ( top 5% ) proteins compared to a control set of genes normally expressed in HUVECs . At least three biological replicates were used for each analysis and the mean and standard deviation were used in T-tests . Changes were considered statistically significant when P<0 . 05 . ENSG00000134318 ENSG00000168610 ENSG00000140391 ENSG00000127914 ENSG00000112972 ENSG00000177885 | Kaposi's sarcoma-associated herpesvirus is the virus associated with multiple proliferative disorders , including Kaposi's sarcoma , primary effusion lymphoma and multicentric Castleman's disease . This virus expresses small nucleic acids ( with sequences distinct from other organisms ) , called microRNAs , that can limit expression of specific genes . Currently , we only know a few validated targets of these viral microRNAs and the mechanisms of microRNA-mediated repression are still being actively debated . We used a method to look at protein expression changes induced by these viral microRNAs to better understand microRNA targets and functions . The method we describe here found microRNA targets that are missed by other approaches . In addition to identifying previous microRNA targets and discovering new microRNA targets , we found the function of specific viral microRNAs to be associated with immune evasion and the expansion of blood vessel networks , a hallmark of Kaposi's sarcoma . The results may be a resource for those studying microRNAs from other organisms , and furthermore , the microRNA functions described provide mechanistic insight into viral pathogenesis and immune evasion . | [
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| 2013 | Proteomic Screening of Human Targets of Viral microRNAs Reveals Functions Associated with Immune Evasion and Angiogenesis |
Sub-Saharan Africa harbors the majority of the global burden of malaria and schistosomiasis infections . The co-endemicity of these two tropical diseases has prompted investigation into the mechanisms of coinfection , particularly the competing immunological responses associated with each disease . Epidemiological studies have shown that infection with Schistosoma mansoni is associated with a greater malaria incidence among school-age children . We developed a co-epidemic model of malaria and S . mansoni transmission dynamics which takes into account key epidemiological interaction between the two diseases in terms of elevated malaria incidence among individuals with S . mansoni high egg output . The model was parameterized for S . mansoni high-risk endemic communities , using epidemiological and clinical data of the interaction between S . mansoni and malaria among children in sub-Saharan Africa . We evaluated the potential impact of the S . mansoni–malaria interaction and mass treatment of schistosomiasis on malaria prevalence in co-endemic communities . Our results suggest that in the absence of mass drug administration of praziquantel , the interaction between S . mansoni and malaria may reduce the effectiveness of malaria treatment for curtailing malaria transmission , in S . mansoni high-risk endemic communities . However , when malaria treatment is used in combination with praziquantel , mass praziquantel administration may increase the effectiveness of malaria control intervention strategy for reducing malaria prevalence in malaria- S . mansoni co-endemic communities . Schistosomiasis treatment and control programmes in regions where S . mansoni and malaria are highly prevalent may have indirect benefits on reducing malaria transmission as a result of disease interactions . In particular , mass praziquantel administration may not only have the direct benefit of reducing schistosomiasis infection , it may also reduce malaria transmission and disease burden .
Malaria is highly endemic throughout sub-Saharan Africa in which 85% of global malaria cases and 90% of malaria deaths occur [1] . Schistosoma mansoni ( the causative agent of intestinal schistosomiasis ) is likewise prevalent in many sub-Saharan African countries [2] , [3] , accounting for approximately one-third of the total cases of schistosomiasis in the region [4] . Both malaria and intestinal schistosomiasis share similar epidemiological distributions and present challenges to public health and socio-economic development throughout these regions [5] . Due to their coendemicity , there has been increased investigation into the interactive pathology between malaria and S . mansoni [6]–[9] . Heavy S . mansoni infections have been found to be associated with a significant increase in the incidence of malaria among school-age children [6] . While the mechanism responsible for the exacerbation of malaria in individuals infected with S . mansoni is not yet fully understood [7] , [9] , the interactions between the two diseases are possibly driven by countering effects the parasites have on immunological cytokines [10] , [11]; that is , S . mansoni may alter the balance between Th1 and Th2 type immune responses [12]–[14] which reduces immunological control of malaria , although other mechanisms are possible . Artemisinin-based combination therapies ( ACT ) are increasingly used as first-line treatment against malaria in sub-Saharan Africa [15] , [16] . ACT is an efficacious drug regimen that reduces the risk of malaria-induced morbidity and mortality as well as malaria transmission from humans to vectors [17] , [18] . For the control of schistosomiasis , current World Health Organization ( WHO ) guidelines recommend frequent mass administration of praziquantel , a highly effective and relatively inexpensive anti-schistosomal agent [19] , to school-age children or to entire communities depending on schistosomiasis prevalence and available resources [20] . However , the adoption of mass praziquantel administration remains suboptimal in sub-Saharan Africa mainly due to limited drug availability , even as the schistosomiasis disease burden continues to rise [21] . Mass praziquantel treatment coverage and compliance may vary substantially from one schistosomiasis endemic area to another . We evaluated how S . mansoni infection and mass praziquantel administration may affect the dynamics and control of malaria in co-endemic communities . In the absence of field studies that directly measure the effect of schistosomiasis control on malaria transmission and progression [6] , [22]–[25] , we address this question by using epidemiological and clinical findings that estimate the elevation in relative risk of malaria attributable to S . mansoni infection [6] to develop a mathematical model of the joint dynamics of malaria and S . mansoni among children . We use this model to evaluate the inter-dependent impact of S . mansoni on malaria infection and the potential impact of schistosomiasis and malaria treatment for reducing malaria transmission .
We developed a mathematical model of the interplay between malaria and S . mansoni . Malaria transmission was modeled as follows [26]: At each point in time people can be in one of six infectious states – susceptible ( S ) , treated symptomatic disease ( T ) , untreated symptomatic disease ( D ) , asymptomatic patent infection ( A ) , asymptomatic sub-patent infection ( U ) and protected by a period of prophylaxis from treatment ( P ) . We assumed that individuals entered the model susceptible and become infected at a rate determined by the force of infection in the population given by , where represents the biting rate on humans by a female mosquito , is the density of mosquitoes per human , is the probability of successful human inoculation upon an infectious bite , and the proportion of infectious mosquitoes in the vector population . Upon infection , individuals either develop symptomatic disease ( with a probability Φ ) or develop patent asymptomatic infection ( 1−Φ ) . Those who develop symptomatic disease have a fixed probability ( fT ) of being treated successfully with an ACT ( T ) , in which case they clear infection at a rate rT and enter a period of prophylactic protection ( P ) before returning ( rP ) to being susceptible to new infection . Those who fail treatment ( 1−fT ) are assumed to eventually clear disease ( D ) and become patently asymptomatic ( A ) at rate rD . From patent asymptomatic infection , individuals will move to a sub-patent stage ( U ) at a rate rA and then clear infection at rate rU and individuals return to being susceptible . The force of infection of malaria on the mosquito population , , was given by the product of host biting rate per mosquito , probability of mosquito infection upon biting an infectious human , and the proportion of infected individuals at each infectious stage ( D , A , U ) . The intensity of malaria transmission is represented as the annual entomological inoculation rate ( AEIR ) , defined as the product of the human biting rate of mosquitoes and the proportion of mosquitoes that are infectious . AEIR is measured in the number of infective bites per person per year ( ibpy ) . Here malaria prevalence refers to any level of parasitaemia rather than symptomatic disease alone . For S . mansoni transmission , we assumed that at each point in time people can be in one of three states – susceptible ( S ) , infected with low egg output ( IL ) , and infected with high egg output ( IH ) [27] . Likelihood of schistosomiasis transmission from humans to snails depends on worm burden and mean egg production per worm . For the sake of simplicity , egg production was not explicitly modeled . However consistent with previous schistosomiasis modeling studies , we used transmission rates that implicitly account for egg production rate per worm , contact with infested waters , and probability of worm establishment per contact [27] , [28] . We assumed that individuals entered the model susceptible and become infected with an initially low egg output at a transmission rate . Individuals with low egg output may then transition to high egg output at a transmission rate , where determines the rate of transition to a high egg output from a low egg output relative to . We assumed the individuals infected with low egg output infect susceptible snails at a transmission rate , and individuals with high egg output infect snails at a transmission rate , where is the relative increase of transmission rate to snails for high egg output individuals relative to low egg output individuals . Because rates of schistosomiasis reinfection are very high in endemic areas , we assumed that there is no natural recovery for S . mansoni infected individuals , and that without treatment infected individuals with a high egg output will not transition to a low egg output . We incorporated annual praziquantel treatment into the model by assuming that treatment has an efficacy of 70% [29] , [30] . We assumed that upon treatment , 70% of individuals with low egg output will recover from infection , and 70% of individuals with high egg output will either recover from infection or have their egg output reduced to a low level , such that 40% of treated high egg output will recover from infection , while 30% will have their egg output reduced to a low level [29] , [30] . We evaluated the potential impact of deworming through mass drug administration with praziquantel on malaria prevalence by considering different levels of treatment coverage ranging from 30–80% . Individuals can be infected with malaria only , S . mansoni only , or dually infected with malaria and S . mansoni . The model captures the epidemiological interaction between the two diseases in terms of S . mansoni enhancing susceptibility to malaria denoted as and parameterized from epidemiological field data [6] ( Table 1 ) . We focused on communities in which malaria and S . mansoni are co-endemic , and considered variation in malaria transmission intensity by varying the AEIR from 1 ibpy to 500 ibpy [31] . We present results obtained at endemic equilibrium . A detailed description of the model is given in the Supplement Material and an overview of parameters and values used to generate the model outcomes are given in Table 1 .
By comparing malaria prevalence in the presence and absence of S . mansoni co-endemicity , we showed that the impact of schistosomiasis co-infection on increasing malaria prevalence was higher in areas of low malaria transmission than areas of high malaria transmission ( Figures 1 & S1 ) . For example , disease interaction was shown to increase malaria prevalence by 3 . 0–4 . 5% for an AEIR of 10 ibpy and by 0 . 6–1 . 5% for an AEIR of 100 ibpy , depending on malaria treatment coverage , ranging from 30–90% ( Figure 1 ) . The effect of S . mansoni co-infection on malaria prevalence plateaued from 100 ibpy upwards ( Figure 1 ) . We also found that the interaction between malaria and S . mansoni may reduce the effectiveness of malaria treatment for decreasing malaria prevalence ( Figure 2 ) . For an AEIR of 100 ibpy , S . mansoni co-infection was shown to decrease the proportional reduction of malaria prevalence due to treatment by 1 . 3% for 90% treatment coverage , 1% for 60% treatment coverage , and 0 . 5% for 30% treatment coverage ( Figure 2A ) . For 90% malaria treatment coverage , S . mansoni co-infection increases symptomatic malaria episodes by 29 episodes per 100 people annually , by 45 episodes per 100 people annually for 60% treatment coverage , and 93 episodes per 100 people annually for 30% treatment coverage ( Figure 2B ) . For an AEIR of 10 ibpy , disease interaction was shown to decrease the proportional reduction of malaria prevalence due to treatment by 2 . 5% for 90% treatment coverage , 1 . 4% for 60% treatment coverage , and by 0 . 6% for 30% treatment coverage ( Figure 2A ) . For 90% malaria treatment coverage S . mansoni co-infection increases symptomatic malaria episodes by 11 episodes per 100 people annually , by 16 episodes per 100 people annually for 60% treatment coverage , and 21 episodes per 100 people annually for 30% treatment coverage ( Figure 2B ) . When ACT is used in combination with annual mass praziquantel administration , we showed that the intervention was more effective in reducing malaria prevalence and that this effectiveness increases both with the coverage of praziquantel and with the increased susceptibility to malaria infection due to S . mansoni ( Figure 3 ) . This increase in effectiveness was more pronounced in areas of low malaria transmission intensity ( Figure 3A ) than in areas of high transmission intensity ( Figure 3B ) . The interaction between S . mansoni and malaria generated an additional indirect benefit for mass praziquantel administration by reducing malaria prevalence ( Figure 3 ) .
We developed a co-epidemic model of malaria and S . mansoni transmission dynamics to take into account elevated susceptibility to malaria mediated by S . mansoni infection . We used this model to investigate the potential effect of malaria-S . mansoni interaction on the effectiveness of ACT and mass praziquantel administration for schistosomiasis for reducing malaria prevalence in co-endemic communities . Our results suggested that co-infection with schistosomiasis in low malaria transmission settings increases malaria prevalence . We further showed that in the absence of mass praziquantel administration , the interaction between S . mansoni and malaria may have contributed to reductions in population-level effectiveness of malaria treatment in areas of stable malaria transmission . In regions of low malaria treatment coverage , co-infection with schistosomiasis led to the greatest increase in per person malaria episodes , independent of whether malaria transmission was high or low . Our finding is consistent with epidemiological observations and laboratory studies that have suggested that presence of S . mansoni infections may affect the efficacy of malaria control measures , including a potential vaccine in co-endemic communities [9] , [32] . The interaction between the two diseases may increase the health benefits of mass praziquantel administration by generating the additional indirect benefit of reducing malaria transmission in co-endemic communities . Our results indicated that this benefit was particularly strong in low malaria transmission regions that experienced increased malaria susceptibility due to schistosomiasis co-infection . Malaria is associated with a Th1 immune response [12] , while S . mansoni infection is associated with a Th2 response and had been demonstrated to impair immune responses to malaria [11] , [13] . By reducing S . mansoni worm burden of infected individuals , praziquantel treatment may reduce the Th2 immune response associated with S . mansoni infection which may in turn result in a shift in the Th1/Th2 immune balance [14] , [33] towards the Th1 response that protects against malaria parasite . Though our study focused on Plasmodium falciparum , our results may be applicable to other forms of malaria such as Plasmodium ovale and Plasmodium vivax , which may also interact with S . mansoni . Prototype vaccines for both malaria [34] and S . mansoni intestinal schistosomiasis [35] are under development , such that the two vaccines could be co-formulated or combined [36] . Our results suggest that a co-formulated or combined vaccine may be more efficacious in reducing malaria transmission in S . mansoni endemic communities than a vaccine targeting malaria alone . In addition to increasing malaria incidence , clinical studies have shown that malaria–S . mansoni co-infection may exacerbate clinical manifestations of both diseases [14] , [33] , [37] . These additional impacts were not factored into our model , making our predictions of the effectiveness of joint programs of ACT and praziquantel conservative . Our model also did not account for malaria age-dependent immunity [26] , [38] . Age-dependent malaria immunity is less important among children than adults , however , and it is even less relevant in areas of low malaria transmission [26] , [38] , [39] . We anticipate that accounting for age-dependent malaria immunity would only have a marginal quantitative effect on our results , such that the findings would remain qualitatively unchanged . Malaria and S . mansoni may differ in their distribution of disease intensity , prevalence , and morbidity , with some portion of the population being at higher risk than others [33] . Therefore , the magnitude of the interaction between malaria and S . mansoni on malaria transmission dynamics may vary from one risk group to another . Given that data on risk group specific interaction between malaria and S . mansoni are not available , our model only accounted for elevated malaria susceptibility from S . mansoni high egg output . Future studies could account for heterogeneity in malaria intensity and prevalence . Currently , there is debate surrounding the extent and direction of the effects of malaria and co-infection with different helminth species on human hosts [7] , [33] , [40] . Apparent contradictions arising from clinical and epidemiological studies may be resolved by the possibility of species-specific effects of helminth infections on malaria [7] , [40] , [41] . As well as qualitatively different interactions for different worm burdens For example , Ascaris has been associated with protection from severe malaria complications [7] . Conversely , epidemiological studies have suggested that hookworm elevates malaria prevalence [42] and exacerbates malaria-induced anaemia [22] , [43] . Similarly , S . mansoni has been shown to be associated with increased malaria incidence [6] and exacerbation of hepatosplenomegaly [37] , [44] and anemia [45] in individuals co-infected with malaria . It has also been reported that children with low ( but not high ) S . haematobium infection intensity co-infected with malaria have significantly lower P . falciparum parasitemia than worm-free individuals [46] . This observation implies that the interaction between P . falciparum and S . haematobium may have contributed to lower malaria prevalence in S . haematobium low risk endemic communities , but that the reverse could be the case in S . haematobium high risk communities . Additionally , malaria-schistosomiasis coinfection may have opposite effect on malaria transmission in S . haematobium compare to S . mansoni endemic communities . Therefore , in S . mansoni - S . haematobium co-endemic communities [47] , [48] , schistosomiasis control may have a very complex impact on malaria transmission . Further studies are needed on the interaction of S . mansoni and S . haematobium and their potential impact on malaria transmission . Future transmission models on this topic could also account for worm mating probability and density dependent effects on egg output per worm , which can affect schistosomiasis transmission [49] , [50] . Polyparasite helminth infections and malaria co-infection are widespread throughout sub-Saharan Africa [22] , [51] , [52] . Therefore , studies investigating how co-infection affects the course of each infection , as well as immune responses , are fundamental to understand the potential additional benefits or perverse effects of mass drug administration and control programmes for tropical diseases . There are myriad examples of parasitic co-endemicity and co-infections affecting health outcomes in sub-Saharan Africa . For example malaria and hookworm co-infections [22] , [53] as well as and S . mansoni and hookworm co-infections [54] can lead to severe anemia . A new modeling study on the interaction between lymphatic filariasis and malaria that takes into account increase in vector mortality due to lymphatic filariasis prevalence in mosquito and antagonistic Th1/Th2 immune response in co-infected host has shown that control strategies that reduce lymphatic filariasis transmission could potentially increase malaria prevalence [55] . Similarly , some studies have indicated that antimalarial bednets may reduce transmission from lymphatic filariasis transmitted by anopheles mosquitoes [56] , [57] . In addition , S . haematobium is interacting with HIV by increasing susceptibility to HIV infection through lesions and inflammation of genital track and immunomodulation effects [58] . Two large studies in Zimbabwe and Tanzania found that women with genital schistosomiasis have a 3–4 fold increased odds of having HIV compared to women without genital schistosomiasis [59] , [60] . Subsequent models have shown that female genital schistosomiasis ( caused by S . haematobium ) control strategies could reduce HIV transmission [61] , [62] , in co-endemic communities . One of the limitations of our study was that we did not examine the relationship between S . haematobium and malaria . Future studies could investigate the interaction between malaria and S . haematobium , as well as other helminths including hookworm . Such studies could also investigate low risk schistosomiasis communities where , because of the low rate of schistosomiasis reinfection , the sequential order of infection between malaria and schistosomiasis may impact the co-infections of schistosomiasis on malaria transmission . Clinical studies have shown that ACT used in combination with praziquantel may reduce both the malaria and the schistosomiasis health burden in co-infected individuals [63]–[66] , and that artemisinin-based therapy may have indirect benefits for reducing schistosomiasis health burden [63] . Additional drug interaction studies may be required if ACT and praziquantel are combined for purposes of mass drug administration . In an experimental rat model of clonorchiasis , combinations of praziquantel and artemisinins produced both synergistic and antagonistic effects depending on the doses administered [67] . In humans infected with S . japonicum in China , it was noted that the combination of artemether and praziquantel chemotherapy did not improve treatment efficacy relative to praziquantel alone [68] , while in Africa ( Cote d'Ivoire ) the addition of mefloquine-artesunate did not increase the efficacy of praziquantel against S . haematobium infection [69] . Integrating mass screening and treatment for malaria using ACT with mass drug administration of praziquantel could contribute to reducing both malaria and schistosomiaisis transmission in sub-Saharan Africa . Therefore , future studies would investigate the complementary effects of ACTs and mass praziquantel administration for reducing both malaria and schistosomiasis transmission in co-endemic communities . Immunological studies have suggested that praziquantel treatment in malaria-schistosomiasis co-endemic communities may alter the immune response of treated individuals , making them less susceptible to malaria infection [70] . However , more studies are needed to confirm this impact of praziquantel treatment . Our results suggest that in S . mansoni endemic areas , mass treatment of schistosomiasis may not only have a direct benefit of reducing schistosomiasis infection , it may also reduce malaria prevalence and disease burden . This reduction of malaria prevalence was higher in areas of low malaria transmission intensity , but less pronounced in areas of high transmission intensity ( AEIR greater than 100 ibpy ) . Additional epidemiological and clinical data on malaria–S . mansoni co-infection to determine influence on immune responses and duration of malaria infection are needed to fully evaluate the potential effects of S . mansoni and schistosomiasis control strategies on malaria . | Malaria and Schistosoma mansoni are co-endemic in many regions of sub-Saharan Africa . Evidence from clinical and epidemiological studies support the hypothesis that concurrent infection with S . mansoni is associated with greater malaria incidence among school-age children . We use mathematical modeling to evaluate the epidemiological impact of S . mansoni infection on malaria transmission in sub-Saharan Africa . Using epidemiological data on the increased risk of malaria incidence in S . mansoni endemic communities from Senegal , we developed a co-epidemic model of malaria and S . mansoni transmission dynamics to address key epidemiological interactions between the two diseases . Parameterizing our model for S . mansoni high-risk endemic communities , we show that the interaction between S . mansoni and malaria may reduce the effectiveness of malaria treatment for curtailing malaria transmission . Moreover , we show that in addition to reducing schistosomiasis health burden , mass praziquantel administration will generate indirect benefit in terms of reducing malaria transmission and disease burden in S . mansoni–malaria co-endemic communities . Our findings indicate the possible benefit of scaling up schistosomiasis control efforts in sub-Saharan Africa , and especially in areas were S . mansoni and malaria are highly prevalent . | [
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| 2014 | Impact of Schistosoma mansoni on Malaria Transmission in Sub-Saharan Africa |
The master regulators of the cell cycle are cyclin-dependent kinases ( Cdks ) , which influence the function of a myriad of proteins via phosphorylation . Mitotic Cdk1 is activated by A-type , as well as B1- and B2-type , cyclins . However , the role of a third , conserved cyclin B family member , cyclin B3 , is less well defined . Here , we show that Caenorhabditis elegans CYB-3 has essential and distinct functions from cyclin B1 and B2 in the early embryo . CYB-3 is required for the timely execution of a number of cell cycle events including completion of the MII meiotic division of the oocyte nucleus , pronuclear migration , centrosome maturation , mitotic chromosome condensation and congression , and , most strikingly , progression through the metaphase-to-anaphase transition . Our experiments reveal that the extended metaphase delay in CYB-3–depleted embryos is dependent on an intact spindle assembly checkpoint ( SAC ) and results in salient defects in the architecture of holocentric metaphase chromosomes . Furthermore , genetically increasing or decreasing dynein activity results in the respective suppression or enhancement of CYB-3–dependent defects in cell cycle progression . Altogether , these data reveal that CYB-3 plays a unique , essential role in the cell cycle including promoting mitotic dynein functionality and alleviation of a SAC–dependent block in anaphase chromosome segregation .
The eukaryotic cell cycle is driven by the temporally controlled activation of cyclin-dependent kinases ( CDKs ) in association with their requisite cofactors , the cyclins [1] . The expression and stability of individual cyclins is coordinated with specific cell cycle stages . For instance , cyclin E is expressed as cells enter G1 and is degraded in early S phase , while cyclin B levels rise in G2 and fall at the metaphase-to-anaphase transition [1] . Cyclins not only contribute to the temporal activation of specific CDKs at particular cell cycle transitions , but also appear to provide substrate specificity [2] . As cells prepare to enter mitosis , cyclin B/Cdk1 complexes phosphorylate a host of substrates leading to chromosome condensation , centrosome maturation , and nuclear envelope breakdown [3] . During this period , the chromosome/microtubule interface , the kinetochore , is constructed from several protein complexes that are coordinately built at the centromere , an epigenetically defined chromosomal location [4] . In budding yeast , the centromere consists of a defined 125 base-pair sequence , while in fission yeast and higher eukaryotes centromeres are heterochromatin rich and are not identified by specific nucleotide sequences . Other organisms , including C . elegans , have holocentric chromosomes with kinetochores along their entire length [5] . Despite these differences , all eukaryotic centromeres harbor specialized nucleosomes wherein the canonical histone H3 is replaced by the centromere-specific histone CENP-A/CenH3 [6] . The raison d'être for mitosis is the equal partitioning of replicated genetic material to each daughter cell . Hence , progression through mitosis is inextricably linked to the state of kinetochore-microtubule attachment . To be properly segregated , each pair of sister chromatids must be attached to the mitotic spindle in a bipolar fashion [7] . Once bipolar attachment is achieved , the cohesed sister centromeres and kinetochores are under tension; stretching occurs between sister centromeres and within kinetochores [8] . The spindle assembly checkpoint ( SAC ) monitors this process and is exquisitely sensitive to the attachment and tension state of individual kinetochores . The SAC delays the metaphase-anaphase transition via inhibition of the anaphase-promoting complex ( APC ) until all chromosomes are attached and are under tension . The SAC consists of several components , including the Bub- and Mad-related proteins first identified in genetic screens in budding yeast , and is influenced by the Mps1 , Polo , and Aurora B kinases [7] . Unattached kinetochores recruit Mad2 [9] , [10] , while the Polo and Aurora B kinases monitor tension [11] , [12] . Aurora B is localized to the inner centromere where it destabilizes inappropriate kinetochore-microtubule interactions via phosphorylation of microtubule-associated proteins , including Ndc80/Hec1 , MCAK , and Kif2 [13]–[16] . This activity releases kinetochore-microtubules , resulting in “free” kinetochores that can undergo reattachment [17] . It has become increasingly clear that once a cell engages a checkpoint such as the SAC , the checkpoint must be shut-off or silenced once the checkpoint is satisfied ( i . e . , all chromosomes are attached and under tension ) [18] . Inter-centromeric and intra-kinetochore stretching resulting from bipolar attachment appears to limit the interaction between Aurora B and its substrates at the outer kinetochore , resulting in the stabilization of bipolar attachments [19] . In addition , the minus-end directed protein dynein is required for SAC silencing as it strips Mad2 and other checkpoint proteins from kinetochores and traffics them along kinetochore-microtubules to centrosomes [20]–[23] . When dynein function is compromised , the APC remains inhibited and the metaphase-to-anaphase transition is delayed , even when all chromosomes are properly attached . A key target of the APC is cyclin B , a mitotic-specific Cdk1 partner . Mammals have three B-type cyclins -B1 , B2 , and B3- which appear to have both overlapping and specific functions [24] . While cyclins B1 and B2 are highly similar , B3 forms a distinct sub-family with more sequence conservation among B3 proteins from divergent species than with B1 and B2 cyclins from the same species [25] . While human B1 and B2 cyclins are highly expressed in dividing cells , B3 is found at much lower levels [24] . However , human B3 is also highly expressed in male and female meiotic germ cells [25] , [26] . In Drosophila , a cyclin B3/CycB3 mutant is female sterile yet viable [27] . RNAi experiments also revealed that cycB3 is not essential for mitosis , but does share a partially redundant function with cycB to promote timely anaphase entry [28] . To date , a specific , functional role for cyclin B3 in mitosis has not been revealed . C . elegans harbors four partially redundant cyclin B family members [29] , [30] . While previous studies revealed a role for CYB-3 in progression through meiosis II and the oocyte-embryo transition [31]–[33] , here we demonstrate that loss of CYB-3 leads to specific defects in multiple dynein-related mitotic processes . Strikingly , CYB-3 depletion leads to an unprecedented C . elegans mitotic phenotype: a persistent block in the initiation of anaphase chromosome segregation . The experiments herein reveal the nature of this phenotype and lead to a working model whereby CYB-3 genetically promotes mitotic dynein functionality and is required to satisfy the spindle assembly checkpoint .
The first mitotic division of C . elegans embryogenesis occurs after fertilization and the completion of the meiotic divisions of the oocyte nucleus . Upon extrusion of the second polar body , the maternal pronucleus migrates towards the paternal pronucleus at the posterior end of the embryo . As their chromosomes condense , the two pronuclei join and traverse toward the center of the embryo while the growing mitotic spindle undergoes a rotation to align with the long axis . Nuclear envelope breakdown and microtubule attachment ensue , culminating with chromosome alignment at the metaphase plate followed by immediate anaphase sister chromatid segregation , cleavage furrow ingression , and mitotic exit [34] . To assess the role of C . elegans CYB-3 in these processes , young hermaphrodites ( L4 larvae ) were fed bacteria expressing cyb-3 dsRNA . This RNAi treatment resulted in efficient CYB-3 depletion ( Figure S1A and Text S1 ) and fully penetrant embryonic lethality . To fully address this phenotype , progression through the meiotic divisions and early embryogenesis were monitored by live imaging of fertilized oocytes and embryos expressing either GFP::Histone H2B; GFP::γ-tubulin ( TH32 ) [35] or mCherry::Histone H2B; GFP::α-tubulin ( OD57 ) [36] to visualize chromosomes , centrosomes , and/or spindle microtubules . As in controls , the maternal nucleus of newly fertilized cyb-3 ( RNAi ) oocytes underwent an apparently normal first meiotic division followed by extrusion of the first polar body at the anterior end of the embryo ( Videos S1 , S2 ) . Likewise , both types of embryos generated a second meiotic spindle with chromosomes aligned at the metaphase plate . However , in the majority of cyb-3 ( RNAi ) embryos , anaphase II did not occur . Sister chromatids failed to separate from one another and a second polar body was not extruded ( Videos S1 , S2 ) ; similar findings were recently reported [30] . In many of these embryos , the meiotic spindle “floated” away from the anterior cortex and ultimately disassembled in the anterior third of the embryo ( Video S2 ) . This MII defect resulted in either multiple maternal pronuclei or a single diploid pronucleus ( Videos S3 , S4 , S5 , S6 ) . Upon completion of the two meiotic divisions in wild-type cells , the maternal pronucleus migrates toward the male pronucleus , which is positioned at the posterior end of the embryo . The maternal pronucleus migrates in two distinct phases , with an initial slow velocity until it reaches approximately 40% of embryo length ( EL ) from the anterior end ( Anterior: 0%; Posterior: 100% ) [22] , [37] . The migration rate then increases significantly ( fast phase ) . Since the paternal pronucleus migrates toward the anterior , the two pronuclei meet at approximately 70% of EL [37] . Compared to control , both phases of maternal pronuclear migration were approximately two-fold slower in cyb-3 ( RNAi ) embryos ( Figure 1A; Videos S3 , S4 , S5 , S6 ) . In addition , paternal pronuclear migration toward the anterior was greatly reduced , resulting in pronuclear meeting ( PNM ) occurring significantly closer to the embryo posterior ( Figure 1B , 1D , and Figure 3C; Videos S4 , S6 ) . C . elegans oocytes are devoid of centrioles and centrosomes [38] . Therefore , the centriole donated by the sperm is the sole mitotic organizing center ( MTOC ) in the newly fertilized one-cell embryo [39] . The paternal centriole duplicates upon completion of the meiotic divisions of the oocyte nucleus . As the maternal pronucleus migrates , the centrioles recruit pericentriolar material and separate away from one another along the surface of the paternal pronucleus . Concurrently , condensation of the maternal and paternal pronuclei occurs in a synchronous manner . We noted that the maturing centrosomes in cyb-3 ( RNAi ) embryos were much smaller compared to controls . At the time of PNM , cyb-3 ( RNAi ) centrosomes were approximately two-fold smaller than control centrosomes ( Figure 1C , 1D; Videos S3 , S4 ) . However , by nuclear envelope breakdown ( NEB ) , there was no appreciable difference in centrosome size between CYB-3-depleted embryos and controls . Curiously , condensation of the paternal and maternal pronuclei was asynchronous in cyb-3 ( RNAi ) embryos; condensation of the paternal pronucleus was significantly delayed with respect to the maternal pronucleus ( Figure 1D; Videos S3 , S4 ) . However , as with centrosome size , condensation of the paternal pronucleus also “caught up” to control levels by NEB ( Figure 1D , Figure 2 , and Figure S2; Videos S3 , S4 ) . These defects are not likely to be secondary consequences of a failure to undergo MII anaphase since other MII defective mutants do not display these phenotypes [40] . Asynchrony of pronuclear condensation is a feature of mutants that fail to undergo pronuclear migration [37] . However , it is the maternal pronucleus that is delayed in these cases . This delay is thought to be due to the increased distance between a stationary maternal pronucleus and centrosome-based signals that promote mitotic entry [41] , [42] . The role of CYB-3 in regulating centrosome maturation and differential mitotic entry of maternal and paternal pronuclei is an exciting question that will be addressed in detail in a forthcoming manuscript ( Deyter et al . , in preparation ) . We quantified the duration of the first mitotic division in OD57 embryos treated with control or cyb-3 ( RNAi ) using specific mitotic landmarks as follows: Prophase: interval between pronuclear meeting ( PNM; the initial joining of the maternal and paternal pronuclei ) and nuclear envelope breakdown ( NEB; the absence of clearly demarcated nucleoplasm surrounded by a nuclear envelope ) ; Prometaphase: interval between NEB and chromosome congression to the metaphase plate; Metaphase: interval between complete ( or nearly complete ) congression and the initiation of anaphase chromosome segregation; Anaphase: interval between the initiation of chromosome segregation and the beginning of chromosome decondensation; Telophase/mitotic exit: interval between the initiation of chromosome decondensation and centrosome breakdown . Fixed-cell and live imaging revealed that prophase and prometaphase were approximately two-to-three fold longer in cyb-3 ( RNAi ) embryos compared to control , and chromosome congression was often incomplete ( Figure 3; Videos S5 , S6 ) . 30% of cyb-3 ( RNAi ) embryos had at least one chromosome that initially congressed to the metaphase plate but subsequently underwent movement towards the centrosome , followed by re-alignment in the majority of embryos ( Figure 3A , 3B; Video S6 ) . cyb-3 ( RNAi ) mitotic spindles also had an abnormal appearance , with microtubule bundles appearing to be pinched at the centrosomes rather than the more spread out , straight microtubules of control spindles ( Figure 3A ) . The centrosome-centrosome distance at metaphase was also much greater ( Figure 3A and below ) . However , the most striking and unusual phenotype was the prolonged metaphase delay ( Figure 3 , Figure S3; Video S6 ) . While metaphase was the shortest mitotic stage in control embryos ( Figure 3C , 3D , Figure S3 , Video S5 ) , loss of CYB-3 resulted in a prolonged metaphase delay characterized by the persistence of aligned , condensed chromosomes even after other cell cycle events had proceeded ( i . e . , spindle disassembly ) ( Figure S3 , Video S6 ) . The pinched spindle pole phenotype appears to be a function of time spent in metaphase since it becomes more apparent over the course of the delay ( Video S6 ) . Since anaphase chromosome segregation and telophase decondensation did not occur , metaphase in cyb-3 ( RNAi ) embryos was defined as the continued alignment of chromosomes at the metaphase plate until centrosome breakdown ( Figure S3 , Video S6 ) . In these embryos , cleavage furrow ingression occurred while chromosomes remained condensed and aligned at the metaphase plate . Indeed , the cleavage furrow often “cut” these chromosomes depending on their position relative to the furrow ( Video S6 ) . In addition , centrosomes in cyb-3 ( RNAi ) embryos were disassembled only to reform , separate , and nucleate microtubules in the presence of aligned chromosomes ( Video S6 ) . These results indicate that the absence of chromosome segregation does not prevent other cell cycle events from proceeding . To address whether the persistent metaphase delay is a secondary consequence of the failure of the oocyte nucleus to undergo the MII meiotic division , we assayed cell cycle progression in the relatively rare cyb-3 ( RNAi ) embryos with two extruded polar bodies , which is indicative of complete MI and MII divisions . All of these embryos ( n = 7 ) displayed metaphase delays comparable to cyb-3 ( RNAi ) embryos with single polar bodies ( Figure S4 ) . Hence , the failure to undergo mitotic anaphase chromosome segregation does not correlate with increased embryonic ploidy or a failure to undergo the MII division . These results are consistent with the absence of prolonged mitotic metaphase delays in other genetic conditions that disrupt the meiotic divisions of the oocyte nucleus and/or polar body extrusion [43]–[46] . CYB-3 is one of four B-type cyclins in C . elegans [30] . The other three Cyclin B proteins include CYB-1 , the closest homolog to mammalian B1 , and two B2-like proteins . cyb-1 , cyb-2 . 1 , and cyb-2 . 2 are highly similar to one another and were targeted for RNAi elimination via microinjection of a single dsRNA ( Figure S1B ) . As recently described [30] , the meiotic divisions were aberrant in cyb-1&2 ( RNAi ) embryos ( data not shown ) . However , in sharp contrast to cyb-3 ( RNAi ) , cyb-1&2 ( RNAi ) mitotic chromosomes did not align to a metaphase plate but still underwent anaphase ( Figure S3A; Video S7 ) . Surprisingly , the interval between NEB and the onset of anaphase spindle elongation in CYB-1&2-depleted embryos was similar to controls ( control: avg . = 161±26 seconds , number of embryos ( n ) = 11; cyb-1&2 ( RNAi ) : avg . = 124±20 seconds , n = 7 ) , suggesting that there were no appreciable delays in prometaphase or the metaphase-to-anaphase transition . In conclusion , embryos depleted of CYB-3 exhibit a phenotype distinct from that caused by co-depletion of CYB-1 and CYB-2 . Since the spindle assembly checkpoint ( SAC ) delays the metaphase-to-anaphase transition in the presence of unattached kinetochores or defective microtubule attachments , we asked whether the prolonged metaphase delay in cyb-3 ( RNAi ) embryos was dependent on a functional SAC . Hence , OD57 embryos co-depleted of CeMad1/MDF-1 [47] and CYB-3 were subjected to live imaging ( Figure 3C , 3D; Videos S8 , S9 , S10 ) . As controls , cyb-3 and mdf-1 dsRNA-expressing bacteria were diluted with control bacteria ( see Materials and Methods ) . Consistent with previous reports , mdf-1+control ( RNAi ) did not result in any apparent defects in the timing or execution of mitosis as compared to control ( RNAi ) embryos ( Figure 3C , 3D; Video S9 ) [48] . The mitotic defects of cyb-3+control ( RNAi ) embryos were indistinguishable from undiluted cyb-3 ( RNAi ) ( Figure 3C , 3D , and Figure S3; Videos S6 , S8 ) . MDF-1 contributed to the prometaphase delay in cyb-3 ( RNAi ) embryos , since the duration of prometaphase in cyb-3+mdf-1 ( RNAi ) embryos was shortened compared to cyb-3+control ( RNAi ) ; however , this interval remained lengthened as compared to control ( RNAi ) ( Figure 3C , 3D; Videos S5 , S8 , S10 ) . Strikingly , cyb-3+mdf-1 ( RNAi ) embryos entered anaphase after a brief metaphase delay , suggesting that the SAC is required for the prolonged metaphase in cyb-3 ( RNAi ) embryos ( Figure 3C , 3D; Videos S8 , S10 ) . Indeed , co-depletion of CYB-3 and other SAC proteins ( CeMad3/SAN-1 and CeBub1/BUB-1 [49] , [50] ) also resulted in anaphase onset ( Videos S11 , S12 ) . To confirm these results , homozygous unc-46 ( e177 ) ; mdf-1 ( gk2 ) L4 hermaphrodite offspring ( F1 ) from unc-46 ( e177 ) ;mdf-1 ( gk2 ) heterozygous mothers were fed control or cyb-3 dsRNA-expressing bacteria . unc-46 ( e177 ) is a recessive linked visible marker for homozygous mdf-1 ( gk2 ) animals [47] . gk2 is a strong loss-of-function deletion allele of mdf-1 [47] . F1 mdf-1 ( gk2 ) homozygotes are viable but display a low level of sterility ( 23% ) , while the majority of F2 mdf-1 ( gk2 ) progeny arrest as embryos or larvae [47] . Embryos of RNAi-treated F1 unc-46 ( e177 ) ;mdf-1 ( gk2 ) or unc-46 ( e177 ) hermaphrodites were fixed , immunostained with kinetochore ( CeBub1/BUB-1 ) [10] and spindle ( α-tubulin ) antibodies , and the number of one-cell embryos in mitotic metaphase versus other cell cycle stages was counted ( Table 1 ) . 100% of cyb-3 ( RNAi ) ;unc-46 ( e177 ) one-cell embryos were in mitotic metaphase and none in anaphase , while 50% of cyb-3 ( RNAi ) ;unc-46 ( e177 ) mdf-1 ( gk2 ) one-cell embryos were in mitotic metaphase and 36% were in anaphase ( Table 1 ) . These data are consistent with the RNAi experiments described above where depletion of MDF-1 results in a significant but not complete reduction in the duration of the extended metaphase in cyb-3 ( RNAi ) embryos and permits anaphase onset . Given that CYB-3-depleted embryos display chromosome condensation defects ( Figure 1D , Figure 2 , Figure S2 ) , mitotic progression of embryos depleted of the condensin subunit SMC-4 [51] was assessed to determine whether condensation defects also lead to significant delays in mitotic progression . These experiments revealed that smc-4 ( RNAi ) embryos , although highly defective with respect to chromosome condensation , do not display significant delays at any mitotic stage ( Figure 3C , 3D; Video S13 ) . These results suggest that the mitotic delay in cyb-3 ( RNAi ) embryos is not a secondary consequence of chromosome condensation defects . C . elegans chromosomes are holocentric , providing a large centromere advantageous for studying changes in kinetochore structure and centromere resolution [52] . Given that the organization of kinetochore microtubules was altered in cyb-3 ( RNAi ) embryos , we tested whether kinetochore architecture was also changed . Hence , control and cyb-3 ( RNAi ) embryos were fixed and co-stained with antibodies recognizing two kinetochore proteins , CeCENP-F/HCP-1 [53] and CeBub1/BUB-1 [10] ( Figure 4A ) . In wild-type cells , sister chromatids are resolved from one another in prophase , resulting in paired kinetochores oriented to opposite spindle poles [54] . This geometry lessens the probability of kinetochores interacting with microtubules emanating from the wrong spindle pole . Sister chromatid resolution occurred in both control and CYB-3-depleted embryos , as evidenced by parallel stripes of BUB-1 and HCP-1 staining on prophase chromosomes ( Figure 4A , arrowheads ) . This kinetochore geometry was maintained in both types of embryos through prometaphase . At metaphase , 100% of control embryos had two clearly defined stripes of BUB-1 and HCP-1 staining , as well as kinetochore microtubule ( K-Mt ) staining ( Figure 4A , arrows ) . However , the majority ( >80% ) of cyb-3 ( RNAi ) embryos had no clear BUB-1 or HCP-1 kinetochore stripes , and no BUB-1 or HCP-1 localization to metaphase K-Mts ( Figure 4A ) . Rather , BUB-1 and HCP-1 staining appeared to be “twisted” and was coincident with the body of the metaphase chromosomes ( Figure 4A ) . Immunostaining with additional kinetochore-specific antibodies ( e . g . , α-KNL-2 ) [55] , as well as live imaging of GFP::KBP-4Ndc80 ( strain OD11 ) transgenic embryos [56] , confirmed these results ( data not shown ) . As in other organisms , the C . elegans kinetochore is built on centromeric chromatin containing the histone variant CENP-A ( CeHCP-3 ) [57] . To determine whether the altered metaphase kinetochore architecture in CYB-3-depleted embryos coincided with changes in centromere geometry , fixed embryos were co-stained with BUB-1 and HCP-3 antibodies . These experiments revealed that HCP-3 behaves identically to BUB-1 and HCP-1 , suggesting that metaphase kinetochores and underlying centromeres are equally affected by the loss of CYB-3 ( Figure 4B ) . Similar results were obtained upon live imaging of GFP::HCP-3;mCherry::H2B embryos ( strain JS9670 ) [55] ( data not shown ) . Since the prolonged metaphase delay in cyb-3 ( RNAi ) embryos is dependent on the spindle assembly checkpoint , we determined whether depletion of SAC components affected kinetochore geometry . As above , the majority of one-cell embryos treated with cyb-3+control ( RNAi ) displayed “twisted” kinetochores ( Figure 4C ) . The twisting appears to increase over the course of the delay since it correlates with the severity of the pinched spindle pole phenotype ( compare the two cyb-3;control ( RNAi ) embryos in Figure 4C ) . Co-depletion of CeMad1/MDF-1 resulted in a suppression of both phenotypes , with 65% of co-depleted one-cell embryos displaying two distinct stripes of BUB-1 staining and normal spindle morphology ( Figure 4C ) . Note that while BUB-1 was localized to metaphase K-Mts in control and mdf-1+control ( RNAi ) embryos , no K-Mt BUB-1 localization was apparent in cyb-3 ( RNAi ) or cyb-3+mdf-1 ( RNAi ) embryos . The quality of kinetochore-microtubule attachments in C . elegans is directly reflected by the timing and rate of spindle pole separation . C . elegans chromosomes do not undergo anaphase A movements [35]; therefore , cortical pulling forces on centrosomes and astral microtubules prior to anaphase are countered by bipolar kinetochore-microtubule attachments to cohesed sister chromosomes . When kinetochore-microtubule attachments are defective , spindle poles separate immediately upon NEB as there are no forces counteracting astral microtubule-based pulling of centrosomes to the cell cortex [35] . For instance , in embryos depleted of the core kinetochore protein KNL-1 , the distance between centrosomes rapidly increases immediately after NEB ( Figure 4D and [58] ) . Interestingly , the centrosome-centrosome distance in cyb-3 ( RNAi ) embryos was significantly greater than control embryos prior to NEB ( Figure 4D ) , indicating that CYB-3 is likely to be affecting processes other than or in addition to kinetochore-microtubule interactions ( see below ) . Indeed , this premature spindle pole separation was not affected by abrogation of the spindle assembly checkpoint ( Figure 4D ) . Spindle length in cyb-3 ( RNAi ) embryos is stabilized within 60 seconds after NEB at the same length ( 14 . 5 µm±0 . 73 ( SEM ) ; n = 6 ) as the metaphase-to-anaphase transition spindle in control embryos ( 15 . 7 µm±0 . 56 ( SEM ) ; n = 6; p = 0 . 2 ) ( 180 seconds post-NEB ) ( Figure 4D ) . Spindles in embryos co-depleted of CYB-3+MDF-1 behaved similarly to cyb-3 ( RNAi ) spindles until the centrosomes of the former separated coincident with anaphase chromosome segregation ( approximately 240 seconds post-NEB ) ( Figure 4D ) . Mitotic spindles in SMC-4-depleted embryos undergo a brief premature spindle pole separation just after NEB , but then the centrosome-centrosome distance increases at the same rate as spindles in control embryos ( Figure 4D ) . Hence , the premature spindle pole separation phenotype of CYB-3-depleted embryos is not likely to be a consequence of chromosome condensation defects . Altogether , these data indicate that loss of CYB-3 results in very early , pre-NEB centrosome separation , perhaps due to abnormalities in the attachment of centrosomes to the nuclear envelope ( see Discussion ) . cyb-3 ( RNAi ) spindles then stabilize at the same length as control metaphase spindles ( 180 seconds post-NEB ) , indicating that kinetochore-microtubule interactions reach levels that balance cortical pulling forces similarly to control spindles . This balance could also be achieved if kinetochore-microtubule interactions were compromised coincident with a diminution of cortical pulling forces . However , spindle pole separation and sister chromatid segregation in cyb-3+mdf-1 ( RNAi ) embryos are not consistent with this latter model . While we cannot rule out the presence of underlying spindle abnormalities or assembly defects , these data reveal that CYB-3-depleted embryos are capable of generating at least grossly functional kinetochore-microtubule attachments . The spindle assembly checkpoint delays anaphase entry until all chromosomes achieve bipolar attachment to the mitotic spindle [4] . In mammalian cells , this delay can be several hours [9] . However , C . elegans SAC-dependent mitotic delays are transient . The worm SAC mediates a modest two-fold increase in the interval between NEB and anaphase onset , even when kinetochore-microtubule attachments are severely compromised by treatment with the microtubule inhibitor nocodazole [50] . The complete SAC-dependent abrogation of anaphase spindle elongation and chromosome segregation in CYB-3-depleted cells suggests that loss of CYB-3 results in a much “stronger” and/or persistent checkpoint response . Since the checkpoint protein CeMad2/MFD-2 is recruited to unattached chromosomes and is “stripped” from kinetochores upon microtubule attachment and checkpoint satisfaction [9] , [10] , [20] , we examined the localization of GFP::MDF-2 in living C . elegans embryos ( strain OD110 ) treated with control or cyb-3 ( RNAi ) . As previously reported , GFP::MDF-2 localizes to prophase and prometaphase nuclei but is not apparent on metaphase kinetochores in control embryos ( Figure 5A; Videos S14 , S15 and [10] ) . Interestingly , in cyb-3 ( RNAi ) embryos , GFP::MDF-2 accumulated on chromosomes beginning in prophase and remained on chromosomes throughout the prolonged metaphase in these cells ( Figure 5A; Videos S16 , S17 ) . In SMC-4-depleted embryos , GFP::MDF-2 behaved similarly to control cells , indicating that reduced chromosome condensation does not lead to the retention of MDF-2 on metaphase chromosomes ( Figure 5A: Videos S18 , S19 ) . A second hallmark of an engaged SAC is the accumulation of phospho-specific epitopes recognized by the 3F3/2 antibody [11] , [59] , [60] , which is thought to correlate with reduced tension within and across paired sister kinetochores [8] , [61] . As expected , 3F3/2 immunostaining of chromosomes increased upon taxol treatment ( Figure S5 ) , indicating that this antibody recognizes epitopes in C . elegans that are sensitive to microtubule dynamics . 3F3/2 immunostaining accumulated around prophase and prometaphase chromosomes in control and cyb-3 ( RNAi ) treated embryos ( Figure 5B and data not shown ) . While staining was absent in control cells at metaphase , it accumulated to high levels on metaphase chromosomes in cyb-3 ( RNAi ) embryos , consistent with persistent SAC signaling . The ability of checkpoints to halt cell cycle progression in response to DNA damage and spindle assembly defects is well established [7] , [62] . In the past few years , it has become apparent that cells must not only satisfy these checkpoints ( e . g . , attach all chromosomes ) but also actively silence these checkpoints once the damage or defects have been repaired [18] , [63] , [64] . The minus-end directed microtubule motor dynein contributes to SAC inactivation by trafficking SAC components from kinetochores along K-Mts to centrosomes [20] , [23] . Since SAC proteins accumulate on metaphase chromosomes in CYB-3-depleted cells , we wondered whether dynein was appropriately localized in cyb-3 ( RNAi ) embryos; hence , we examined dynein behavior in C . elegans embryos harboring a GFP-tagged dynein heavy chain ( GFP::DHC-1 ) transgene ( strain OD203 ) [65] . In control embryos , GFP::DHC-1 localized to the nuclear periphery in prophase and was associated with chromosomes upon nuclear envelope breakdown ( Figure 5C; Videos S20 , S21 ) . At metaphase , kinetochore and K-Mt localization was evident . At anaphase , GFP::DHC-1 was no longer detectable at kinetochores but was still apparent on K-Mts and centrosomes ( Figure 5C , Videos S20 , S21 ) . A similar pattern was found in embryos treated with smc-4 ( RNAi ) or mdf-1+control ( RNAi ) ( Figure 5C , Videos S22 , S23 , S24 , S25 ) . In cyb-3 ( RNAi ) embryos , GFP::DHC-1 localized to the nuclear periphery and centrosomes during prophase and also accumulated at kinetochores during prometaphase and metaphase as in controls ( Figure 5C , Videos S26 , S27 ) . Strikingly , there was little or no apparent localization to K-Mts or centrosomes at anytime after NEB . In contrast , GFP::DHC-1 was readily apparent on kinetochores in embryos co-depleted of MDF-1+CYB-3 but disappeared just prior to anaphase initiation; no localization to K-Mts or centrosomes was apparent ( Figure 5C , Videos S28 , S29 ) . Immunostaining with an antibody specific for Dynactin/DNC-1 led to similar results ( Figure S6 ) . The inability of dynein and dynein-related proteins to associate with the mitotic spindle in cyb-3 ( RNAi ) embryos could reflect a global defect in microtubule-associated proteins ( MAPs ) binding to K-Mts . However , the CeBimC/BMK-1 kinesin [66] localizes to K-Mts in both control and cyb-3 ( RNAi ) embryos ( Figure S7 ) , indicating that K-Mts in CYB-3-depleted embryos are not inaccessible to microtubule motors . Altogether , these data are consistent with a model whereby loss of CYB-3 leads to persistent chromosomal SAC signaling , characterized by a failure of dynein and SAC proteins to mobilize from kinetochores to K-Mts and centrosomes , leading to a robust block in anaphase chromosome segregation . One possible mechanistic model that explains our findings is that CYB-3 directly or indirectly promotes dynein activity with respect to SAC satisfaction and/or silencing . One prediction of this model is that increasing dynein activity should alleviate SAC signaling in cyb-3 ( RNAi ) embryos , leading to timely anaphase entry . A recent study in C . elegans revealed that specific dynein light chains negatively regulate the activity of the dynein heavy chain ( DHC-1 ) [67] . Although loss of the light chain DYLT-1 leads to no discernible phenotype in a wild-type background , DYLT-1 depletion rescues the lethality of a temperature-sensitive ( ts ) dhc-1 allele [67] . Hence , we determined whether co-depleting DYLT-1 with CYB-3 would affect the ability of these cells to enter anaphase . To increase the sensitivity of our assay , we diluted cyb-3 dsRNA bacteria 20-fold with control or control+dylt-1 dsRNA bacteria and fed these mixtures to young adult OD57 hermaphrodites . Embryos treated with diluted cyb-3 ( RNAi ) ( 20x dilution with control bacteria ) experienced significant metaphase delays , but ultimately underwent anaphase chromosome segregation approximately 90 seconds after control embryos ( Figure 6A , 6B; Video S30 ) . However , diluting cyb-3 dsRNA bacteria 20x with control+dylt-1 dsRNA bacteria completely abrogated this delay , leading to anaphase onset coincident with controls ( 180 seconds post-NEB; Videos S31 , S32 ) . Furthermore , while kinetochore twisting was readily apparent in embryos treated with dilute cyb-3 ( RNAi ) , this phenotype was rescued by concomitant loss of DYLT-1 ( Figure 6C ) . As described above , the distance between mitotic centrosomes in CYB-3-deficient embryos begins to increase well before NEB , and this distance remains significantly greater than in wild-type embryos until stabilizing with the same spacing as centrosomes of wild-type spindles at the metaphase-to-anaphase transition ( Figure 4D and Figure 6D ) . Interestingly , loss of DYLT-1 rescued the premature centrosome separation phenotype of embryos treated with dilute cyb-3 ( RNAi ) , both before and after NEB ( Figure 6D ) . Altogether , the rescue of these abnormalities and abrogation of the metaphase delay by modulating dynein functionality reveal that cyb-3 genetically interacts with components of the dynein motor complex and support a model whereby CYB-3 promotes the functionality of mitotic dynein with respect to spindle assembly and mitotic progression . If cyb-3 genetically promotes dynein activity , then we predict that dynein impairment would enhance cyb-3 ( RNAi ) phenotypes . Hence we utilized a dhc-1 ( ts ) allele to test this model . Embryos from dhc-1 ( ts ) hermaphrodites reared at semi-permissive temperatures ( 22°C and 24°C ) were fed cyb-3 dsRNA bacteria diluted 20x with control bacteria . Embryos were fixed after 24 hours on dsRNA bacteria and the number of one-cell embryos at different stages of mitosis was counted ( Figure 7 ) . With respect to embryos reared at 22°C , there were no statistically significant differences in the number of one-cell embryos in prometaphase , metaphase , or anaphase between wild-type or dhc-1 ( ts ) embryos treated with control ( RNAi ) or wild-type embryos treated with diluted cyb-3 ( RNAi ) . However , in dhc-1 ( ts ) embryos treated with diluted cyb-3 ( RNAi ) , there was a significant increase in the number of prometaphase embryos and a concomitant decrease in the number of anaphase embryos ( Figure 7 ) . Embryos reared at 24°C revealed similar distributions with the exception that dhc-1 ( ts ) embryos treated with control ( RNAi ) also showed a significant increase in the number of prometaphase embryos and a decrease in anaphase embryos ( Figure 7 ) . Since DHC-1 inhibition slows the rate of prometaphase ( Figure 7 and [22] ) , the increase in the number of prometaphase embryos from animals co-depleted of CYB-3 and DHC-1 is satisfyingly consistent with a model whereby CYB-3 plays a critical , positive role in the regulation of dynein during mitosis .
Here , we report that C . elegans CYB-3 plays an essential role in the timing and execution of many mitotic events in the early embryo , including pronuclear migration , chromosome condensation , centrosome maturation , spindle pole separation , chromosome congression , and alleviation of a SAC-dependent block in the initiation of anaphase chromosome segregation . In addition , genetic experiments are consistent with cyb-3 acting as a direct or indirect positive regulator of mitotic dynein functionality . Given that other cyclins have a variety of targets , it is not surprising that CYB-3 affects a number of different cellular events . A commonality of many of these processes is that they are dynein-dependent . The significantly slower migration rate of the female pronucleus and the failure of the male pronucleus to move from the embryo posterior in CYB-3-depleted embryos are strikingly similar to the pronuclear defects of C . elegans embryos harboring a temperature-sensitive allele of the dynein heavy chain dhc-1 [22] . Moreover , centrosome attachment to the nuclear envelope is also dynein-dependent [68]–[70] . The pre-NEB increase in centrosome-centrosome distance in cyb-3 ( RNAi ) embryos and rescue of this phenotype by modulation of dynein activity suggest that centrosome attachment to the nuclear envelope is compromised in CYB-3-deficient embryos . Consequently , the shortening of the post-NEB centrosome-centrosome distance in cyb-3;dylt-1 ( RNAi ) compared to cyb-3 ( RNAi ) embryos may be a secondary consequence of stronger centrosome-nuclear envelope attachments and thus abrogation of pre-NEB separation . Alternatively , it may reflect the more timely formation of , or more robust , kinetochore-microtubule attachments than in CYB-3-depleted embryos . Unfortunately , our data neither allow us to distinguish between these two possibilities nor address whether modulating dynein activity also affected CYB-3-dependent prophase events since diluted cyb-3 ( RNAi ) embryos did not display consistent defects in early mitosis . Experiments to titrate the amount of CYB-3 and dynein activity required for different mitotic events are underway . Although loss of CYB-3 affects a number of processes , the most striking abnormality is the persistent SAC-dependent delay in the initiation of anaphase chromosome segregation . To our knowledge , this is an unprecedented phenotype in the early C . elegans embryo . Several groups have reported that the C . elegans SAC is relatively weak and can only mount , at most , a two-to-three fold delay in the metaphase-to-anaphase transition under all conditions tested , including severe spindle damage after nocodazole exposure [10] , [47] , [48] , [50] . Two potential mechanisms to explain the unusual duration of the SAC-dependent metaphase delay in CYB-3-depleted embryos are: 1 ) loss of CYB-3 results in rare or very specific spindle defects that engage the SAC more persistently than other mitotic spindle abnormalities reported to date , or 2 ) CYB-3 is required for SAC inactivation or silencing . Loss of CYB-3 leads to gross defects in kinetochore and kinetochore-microtubule architecture . We posit that the twisted centromeres and kinetochores are the result of multiple cycles of microtubule attachment and detachment occurring during the metaphase delay . Microtubule attachment appears to play a role since prophase kinetochores are not affected by CYB-3 depletion and the twisting correlates with increased AIR-2/Aurora B activity ( G . M . R . D , unpublished ) , which is congruent with increased kinetochore-microtubule turnover . This twisting is phenotypically distinct from that seen in embryos depleted of the condensin proteins SMC-4 and HCP-6 [71] ( G . M . R . D , unpublished ) and is consistent with findings that centromeres and kinetochores are not elastic [72] . These twisted kinetochores could potentially lead to unusually persistent SAC signaling and a prolonged metaphase delay . However , our data suggest that kinetochore-microtubule attachments are stabilized or reach a steady state since the centrosome-centrosome distance in cyb-3 ( RNAi ) embryos reaches and is maintained at the same length as control spindles at the metaphase-to-anaphase transition . Furthermore , when the metaphase delay is abrogated by loss of the SAC or DYLT-1 , twisting is not apparent and sister chromatid separation readily occurs , suggesting that kinetochore-microtubule attachments are made and are at least partially functional . Hence , although CYB-3 loss may very well lead to spindle assembly defects that engage the SAC , the unusual persistence of SAC signaling in this circumstance and not in embryos with qualitatively more severe spindle and chromosome segregation defects is not easily reconciled . A second potential mechanism , and one we favor , is that among other essential mitotic functions , CYB-3 is required to inactivate or silence the SAC . Our data are consistent with a model whereby CYB-3 participates in SAC silencing by either directly or indirectly affecting the ability of dynein to strip SAC components from kinetochores . The dynein motor has been implicated in SAC silencing in mammals , C . elegans , and Drosophila [20]–[22] , [73] . Dynein , dynein-regulatory proteins , and SAC components all accumulate on metaphase kinetochores but do not appear to transfer to K-Mts or to centrosomes in CYB-3-depleted embryos , consistent with a conserved role for dynein in SAC silencing [20]–[22] , [73] . Suppression of the metaphase delay by depleting a dynein inhibitor supports a working model that CYB-3 is a positive regulator of dynein vis-à-vis SAC silencing . Furthermore , a role for CYB-3 in SAC silencing is more easily reconciled with the rare and unusually persistent metaphase delay in CYB-3-depleted embryos . While a plethora of proteins are required for spindle assembly [74] , relatively few have been shown to be required for SAC silencing [18] , [20] , [21] , [75] , [76] . Hence , while loss of many different proteins leads to spindle defects and transient engagement of the SAC in the C . elegans embryo , many fewer would be necessary to turn off the SAC and allow cell cycle progression . It will be very interesting to determine whether proteins implicated in SAC silencing in other systems , such as the phosphatase PP1 [75] , [77] , also lead to prolonged SAC-dependent metaphase delays in the C . elegans embryo . One puzzling aspect of the cyb-3 loss-of-function phenotype is that despite a complete inhibition of sister chromatid separation and chromosome decondensation , centrosomes breakdown and duplicate with the same timing as mitotic centrosomes in wild-type cells ( i . e . , at the same time relative to NEB ) . Engagement of the SAC should inhibit all aspects of cell cycle progression . Strikingly , an uncoupling of the nuclear and centrosome cell cycle occurred upon depletion of Drosophila mitotic cyclins [28] , [78] . While mitotic entry was inhibited , centrosomes continued to duplicate with the same timing as the wild-type cell cycle . Centrosome duplication even occurred in the presence of an inhibitor of the anaphase promoting complex ( APC ) . In the presence of mitotic cyclins , the same inhibitor led to a block in both the nuclear and centrosome cell cycle [78] . These results suggest that loss of mitotic cyclins eliminates the dependence of the centrosome cycle on an active APC , which is consistent with our findings that centrosome breakdown and duplication continues in the absence of CYB-3 despite an engaged SAC . Recent reports further support a model whereby cyclins and cyclin-dependent kinases “entrain” other cell cycle events and the dependency of these events is disrupted when cyclin or CDK activity is compromised [79] , [80] . Altogether , our data demonstrate that CYB-3 plays a distinct , non-redundant role in mitosis by influencing dynein-dependent mitotic processes . That CYB-3 depletion does not mirror all dynein/DHC-1 loss-of-function phenotypes may reflect a requirement for CYB-3 in some dynein-related processes but not others , or may indicate that different processes require varying doses of dynein activity . This hypothesis is supported by the isolation of hypomorphic dynein alleles that display a range and severity of defects [22] , [68] , [81] . The simplest of multiple possible mechanistic relationships between CYB-3 and dynein would be the direct phosphorylation of dynein subunits by a CYB-3/CDK-1 holoenzyme . In mammalian cells , cyclin B3 associates with both Cdk1 and Cdk2 [24] , but a second report suggests that human cyclin B3 binds exclusively with Cdk2; however , this association does not result in detectable kinase activity [25] . C . elegans CYB-3 associates with CDK-1 in vitro , and CYB-3 complexes display H1 kinase activity; H1 is commonly used as a Cdk1 substrate [30] , [33] . Interestingly , a recent study revealed that direct phosphorylation of the human dynein light intermediate chain ( LIC1 ) by Cdk1 activates dynein and promotes Mad2 removal from the kinetochore , leading to SAC inactivation and anaphase progression [23] . Cdk1 complexes isolated from cell extracts phosphorylated LIC1 , and while the authors did not identify the specific cyclin cofactor , our results suggest that this phosphorylation may be specifically due to a Cdk1/Cyclin B3 complex . However , of the four Cdk1 phosphorylation sites in LIC [23] , only one is partially conserved in the C . elegans ortholog , DLI-1 . Furthermore , unlike CYB-3 and DHC-1 , DLI-1 does not appear to be required for the MII division of the oocyte nucleus [82] , suggesting that if DLI-1 is a direct CYB-3/CDK-1 target , then there are certain to be additional substrates . Biochemical studies to address which , if any , of the 13 dynein subunits in C . elegans are directly phosphorylated by CYB-3/CDK-1 and the functional consequence of these phosphorylation events on mitotic progression are important investigations for the future .
C . elegans strains were maintained at 15°C–25°C [83] . The following strains were used: N2 ( C . elegans wild type , DR subclone of CB original ( Tc1 pattern I ) ) [83] , OD57 ( unc-119 ( ed3 ) ; ltIs37 [pAA64: pie-1p::mCherry::his-58+ unc-119 ( + ) ]; ltIs25 [pAZ132; pie-1p::GFP::tba-2+ unc-119 ( + ) ] ) [36] , [84] , CB177 ( unc-46 ( e177 ) V ) [83] , KR3627 ( unc-46 ( e177 ) mdf-1 ( gk2 ) V/nT1[let-X] IV;V ) [85] , OD110 ( unc-119 ( ed3 ) III; ltIs52 [pOD379; pie-1/GFP::Y69A2AR . 30; unc-119 ( + ) ]; ltIs37 [pAA64;pie-1/mCherry::his-58; unc-119 ( + ) ] IV ) [10] , OD203 ( unc-119 ( ed3 ) III; orls17 [dhc-1::GFP::dhc-1; unc-119 ( + ) ]; ltIs37 [pAA64; pie- 1/mCherry::his-58; unc-119 ( + ) ] IV ) [65] , [86] , OD11 ( unc-119 ( ed3 ) III; ltIs7 [pIC41; pie-1/GFP-TEV-STag::kbp-4; unc-119 ( + ) ]/+ ) [65] , TH32 ( unc-119 ( ed3 ) III; ruIs32 [pAZ132; pie-1/GFP::his-58; unc-119 ( + ) ] III; ddIs6 [pie-1/GFP::tbg-1; unc-119 ( + ) ]V ) [87] , EU828 ( dhc-1 ( or195 ) I ) [22] . To create the GFP::HCP-3; mCherry::Histone H2B strain ( JS967 ) , OD101 [55] and OD56 [10] strains were crossed and animals homozygous for the pie-1/GFP::hcp-3 and pie-1/mCherry::his-58 transgenes were isolated . RNAi plasmids for cyb-3 , mdf-1 , san-1 , bub-1 , smc-4 , knl-1 , and dylt-1 were obtained from the Geneservice Ltd . C . elegans feeding library [88] . The L4440 RNAi vector was used as an RNAi control ( control ) . To deplete CYB-3 alone , a three ml LB + 100 µg/µl ampicillin liquid culture was seeded with a single colony of HT115 bacteria transformed with the cyb-3 ( RNAi ) L4440 plasmid and shaken overnight ( O/N ) at 37°C . The next day , the O/N culture was expanded to 50 ml with the same media and grown until the OD600 of the culture was between 0 . 6–0 . 8 ( ∼ two hours ) . IPTG was added to a final concentration of 1 mM and the culture was grown an additional three hours at 37°C to induce cyb-3 dsRNA expression . The culture was then centrifuged at 5000 rpm for 10 minutes , the pellet was resuspended in 800 µl LB , and 200 µl of the suspension plated on nematode growth ( NG ) media containing 100 µg/µl ampicillin and three mM ITPG ( NG/AMP/IPTG ) . Plates were incubated at 37°C O/N and then seeded with L4 larvae . Seeded plates were incubated at 25°C O/N and embryos from the young adult worms ( L4+24 hours ) were utilized for experiments . To co-deplete CYB-3 and MDF-1 , SAN-1 , or BUB-1 , the induction conditions were as described above . However , after resuspension of the pellets in 800 µl LB , 200 µl of each suspension ( i . e . , cyb-3 and mdf-1 dsRNA-expressing bacteria ) were thoroughly mixed and transferred to NG/AMP/IPTG plates , incubated at 37°C O/N , and then seeded with L4 larvae . To generate highly dilute cyb-3 ( RNAi ) conditions for dylt-1 and dhc-1 ( ts ) experiments , control and cyb-3 dsRNA expressing bacteria were induced , pelleted , and resuspended as above . 10 µl cyb-3 ( RNAi ) bacteria were thoroughly mixed with 190 µl control or dylt-1 ( RNAi ) bacteria in a 15 ml conical tube and briefly centrifuged at low speed . The pellet was resuspended in the supernatant and plated as above . For cyb-1&2 ( RNAi ) experiments , sense and anti-sense mRNAs corresponding to ZC168 . 4 ( CYB-1 ) were transcribed from linearized templates using a T7 in vitro transcription kit ( Ambion , Austin , TX ) . Complementary RNAs were mixed , heated at 90°C for five minutes , and annealed at room temperature ( RT ) . cyb-3 dsRNA was also generated in this manner for direct comparison of injected animals . dsRNAs were injected into the gonads of OD57 L4 larvae and the injected animals incubated at 25°C O/N . Embryos from adult hermaphrodites were fixed and stained as previously described [89] . Primary antibodies used were α-tubulin ( Sigma , St . Louis , MO ) , HCP-1 [53] , BUB-1 [90] , HCP-3 [90] , 3F3/2 ( Boston Biologicals , Boston , MA ) [60] , DNC-1 [91] , and BMK-1 [66] . Secondary antibodies were: Alexa Fluor® 488 goat anti-mouse IgG or IgM , and Alexa Fluor® 555 goat anti-rabbit IgG ( both at 1∶1000 ) ( Invitrogen Molecular Probes , Eugene , OR ) . For HCP-3 and BUB-1 co-staining experiments , HCP-3 and BUB-1 antibodies were directly conjugated to fluorophores utilizing the Zenon Tricolor Rabbit IgG labeling kit ( Invitrogen Molecular Probes , Eugene , OR ) as per the manufacturer's instructions . The labeled antibodies were incubated on slides with fixed embryos for three hours at RT . Slides were washed three times with PBSTb ( PBS , 0 . 1% TritonX-100 , 0 . 1% BSA ) and mounted with ProLong Gold with DAPI ( Invitrogen Molecular Probes , Eugene , OR ) . Immunofluorescent images were acquired on a Nikon 2000U inverted microscope equipped with a Photometrics Coolsnap HQ camera . Metamorph software was used for image acquisition . Z-sections were acquired at 0 . 2 µm steps using a 60X/1 . 49 NA objective . Z-stacks were projected and deconvolved for 10 iterations using Autodeblur ( Autoquant Media Cybernetics , Bethesda , MD ) . Images were processed for figures using Adobe Photoshop . For live imaging , embryos cut from RNAi-treated hermaphrodites ( 24 hours post-RNAi exposure ) were mounted on 2% agarose pads and imaged using a spinning disk confocal ( Perkin Elmer , Waltham , MA ) attached to a Nikon TE2000U inverted microscope . Images were acquired using an ORCA-ER digital camera ( Hamamatsu , Bridgewater , NJ ) and a 60×1 . 45 NA Plan Apo VC lens . Ultraview software ( Perkin Elmer ) was used to control the confocal , microscope , and camera . Images were captured at 30 second intervals; Z-sections were 1 µm . For condensation assays , condensation of male pronucleui in TH32 RNAi-treated embryos were imaged and the condensation parameter calculated as previously described [87] . Image J software ( http://rsbweb . nih . gov/ij ) was used to measure centrosome size , centrosome-centrosome distance , and pronuclear migration rates . | Every time a cell divides in two , the genetic material , DNA , is copied; each copied chromosome is referred to as a pair of sister chromatids . Each chromatid must be cleanly separated from its sister so that each daughter cell inherits the same DNA complement as the starting cell . The mitotic spindle is a cellular machine that physically separates the sister chromatids from one another . The chromatids are attached to the spindle at kinetochores , which are structures built at specific sites ( centromeres ) on each chromatid . The cell monitors the attachment of each chromatid and blocks their separation until they are all properly attached . This process is called the spindle assembly checkpoint ( SAC ) . Here we report that loss of an evolutionarily conserved cell cycle regulator , Cyclin B3/CYB-3 , results in an unusual and strikingly persistent SAC–dependent delay in sister chromatid separation . Furthermore , CYB-3 promotes the activity of a cellular motor , dynein , in this and other mitotic processes . Altogether , our results indicate that Cyclin B3 genetically interacts with mitotic dynein and is absolutely required to satisfy a SAC–dependent inhibition in sister chromatid separation . | [
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| 2010 | Caenorhabditis elegans Cyclin B3 Is Required for Multiple Mitotic Processes Including Alleviation of a Spindle Checkpoint–Dependent Block in Anaphase Chromosome Segregation |
The Alliance for the Global Elimination of Trachoma has set the target for eliminating trachoma as a public health problem by 2020 . However , challenges remain , including socio-cultural issues . Districts in Northern Tanzania , predominantly inhabited by the Maasai ethnic group , remain endemic for trachoma . We explored socio-cultural factors that may impact the elimination of trachoma . This study was nested within a larger ethnographic study of trachoma among Maasai in Northern Tanzania . We used stratified random sampling and semi-structured interviews to examine knowledge and understanding . Interviews were conducted and recorded in Maa , by a native Maa speaking trained interviewer . Transcripts were translated into English . A framework method for a content analysis was used . There was awareness of trachoma and basic symptoms . Yet understanding of etiology and prevention was poor . Trachoma was attributed to pollen , dust , and smoke . Water was recognized as beneficial , but seen as treatment and not prevention . Traditional medicines were most often used for treating conjunctival inflammation , with the most common being a rough leaf used to scratch the inside of the eyelid until it bleeds . Knowledge of mass drug administration ( MDA ) was inconsistent , although many thought it helped the community , but it was perceived as only for children and the sick . Many participants reported not taking azithromycin and some had no recollection of MDA six months earlier . There was little connection between childhood infection , trichiasis and related blindness . Trichiasis was often seen as a problem of old women , and treated locally by epilation . Understanding indigenous knowledge may help guide control programs , tailor them to local contexts , address local beliefs and dispel misunderstandings . There is an essential need to understand the social , cultural and political context of the target community to deliver effective programs . Despite limited knowledge , the community recognized trachoma as a public health problem . Results have implications for disease control programs in other marginalized communities .
Trachoma is the commonest infectious cause of blindness worldwide , caused by chlamydia trachomatis and remains a significant public health concern . This neglected tropical disease ( NTD ) tends to mostly impact poor and underdeveloped areas . Current estimates indicate about 182 million people live in trachoma endemic areas and is the cause of blindness or visual impairment of 1 . 9 million people in 42 countries [1] . The clinical features of trachoma are divided into those related to ‘active’ disease which characterizes episodes of infection and are most common in children under 10 years; and those associated with scarring . Early stages of trachoma are characterized by follicles and inflammation in the conjunctiva of the upper eyelid . Contraction of scar tissues causes eyelids to turn inward ( entropion ) . Trichiasis occurs when eyelashes touch the eyeball . Eventually a number of factors including corneal trauma and secondary infection can lead to blindness [2] . While prevalence of active trachoma is fairly similar across sexes; women tend to have more scarring , more trichiasis and subsequently more loss of vision likely due to greater life-time exposure to infection from young children [3] . There is a dearth of information related to trachoma among marginalized ethnic groups including the Maasai of Tanzania . The Maasai are semi-nomadic pastoralists predominantly spanning the central border of Tanzania and Kenya . The traditional lifestyle of the Maasai is changing with reduced access to land for grazing and changes in weather leading to a more semi-nomadic or even agricultural based lifestyle . Maasai are facing challenges that may negatively impact health including increased drought , poor access to major roads and education , substandard health services and on-going land disputes [4–6] . Food insecurity was found to be severe and vaccination coverage the lowest among the Maasai when compared to five other tribes in Northern Tanzania [7] . For trachoma , baseline surveys in 2006 , reported trachomatous inflammation-follicular ( TF ) prevalence of 57 . 6% in children aged 1–9 years [8] . More recent studies showed the prevalence of conjunctival follicles , papillary inflammation and scarring among a cohort of children aged 6–10 years in a predominantly Maasai community in northern Tanzania , was 33 . 6% , 31 . 6% and 28 . 5% , respectively [9] . Risk factors for trachoma span environmental , socio-economic and behavioral factors . Risk factors for trachoma include limited access and use of water [10]; limited face washing [11–13]; poor sanitation [14 , 15]; and crowding [16] . However , in marginalized communities such as the Maasai , communal living and poor economic , social and environmental conditions are a challenge to maintaining proper hygiene [17] . Control of trachoma is based on the SAFE Strategy , established by WHO in 1997 under the Alliance for the Global Elimination of Trachoma by 2020 ( GET 2020 ) . SAFE includes four public health interventions: Surgery for trachomatous trichiasis; Antibiotic treatment to eliminate the infection; Facial cleanliness promoting hygiene to reduce transmission; and Environmental change which includes management of human and animal feces , cleanliness to reduce flies , crowding and access to water [18] . SAFE has not been fully implemented in this context . Mass drug administration of azithromycin has been conducted in all high trachoma endemic Maasai districts in Tanzania in accordance with the National NTD Control Program and WHO , although impact surveys are still ongoing . Surgical camps to reduce the backlog of trichiasis cases have been ongoing in Maasai districts with the support of several international implementing partners and the National NTD Control Program . However , programs addressing facial cleanliness and environmental change components of SAFE are limited particularly in Maasai communities . Trachoma control interventions require community understanding of trachoma and behavior change . Furthermore , it is important to consider the community’s perspective to account for socio-cultural factors that may guide the design of effective control interventions and increase uptake of the SAFE strategy . The aim of this study was to explore the knowledge and understanding of the nature of trachoma including pathology , progression of disease , risk factors , prevention and treatment among a trachoma endemic Maasai community . These findings can help guide more effective public health approaches to implementation of the SAFE strategy in Maasai communities .
This study was conducted in Sinya Ward in Longido District , of Northern Tanzania . Sinya is located in the plains between Mt Kilimanjaro and Mt Meru . Sinya is comprised of three villages , Il Donyo , Leremeta , and Endonyoemali; with a total population of 4285 . The community is nearly all Maasai most of whom have permanent bomas , in the village . A boma is a Maasai homestead headed by one male , consisting of houses for each of his wives and their children . A boma can range in size of 10–70 people but on average is approximately 40 people . There are a few non-Maasai , Ormeek , staying in Sinya for the purpose of government work in the schools and dispensaries and for trade . The main source of livelihood has been traditional livestock production in this purely pastoralist community . Of 107 bomas in Sinya , five bomas were randomly selected in each of the three villages to achieve a sample size of 30 participants . For the purpose of this study a boma was considered a household since decisions are made by the male head of the boma . A boma also physically acts as a household in that it is a fenced enclosure of all homes or huts of the wives which are systematically placed in order of marriage . It was expected that in this traditional , isolated , Maasai community with little variation in lifestyles , 30 participants representing all three villages and different bomas would be a representative sample of the larger community . Census data was collected by the lead author for the 15 selected bomas . Each boma was visited by the lead author and together with the male head of the boma a list of all people ages 18–50 residing at the boma was documented . Using the census data , one male and one female aged 18–50 years were randomly selected to be interviewed from each of the 15 bomas . An internet-based sample builder was used to randomly select five men and women from each boma ( www . randomizer . org ) . If the first randomly selected person was not available , the next person in the randomization list was approached to participate . As the researchers had conducted the census at each boma , most selected participants were already familiar with the researchers and a rapport had been established . Semi-structured interviews were conducted from October to December 2016 with participants in Maa ( Maasai language ) by a native Maa speaking interviewer in a conversation-like manner . Interviews were conducted in a private setting , typically under a tree , at the participant’s home with only the participant , interviewer and principal investigator present . The interview guide consisted of socio-demographic information and open-ended questions on experiences , knowledge and understanding of the nature of trachoma including pathology , progression of disease , risk factors , experiences , treatment , prevention and blindness . Interviews were audio recorded and later transcribed and translated from Maa to English ( S1 Text ) . Participants were asked about local treatment for trachoma and a list of plants in Maa was compiled . Two Maasai field assistants identified and photographed the plants in the field . The list of Maa plant names and photographs was used by a botanist to identify the botanical names . Transcription was done directly from Maa to English; some transcripts were corrected to ensure more understandable English while assuring meaning was not changed . English transcripts were entered into NVIVO 11 Software . Initial interpretation included familiarization of the data and review of reflective notes . Data were coded by lead author , TM , using the interview guide as a framework and verified by author SL . A framework method for content analysis [19] was used and descriptive findings reported . Open coding was conducted on five transcripts to confirm there were no emerging codes to be included in the analysis . Codes were grouped into themes and compared against the interview guide ( S1 Table ) . Themes reflected the key topics from the interview guide . Using a coding framework , data were charted into a framework matrix ( S2 Table ) . Impressions and interpretation of the framework matrix were discussed with the native speaking interviewer and coauthors . The findings reported include the key high-level codes . Constant comparative analysis was done comparing responses between genders and within bomas . Quotes presented are used to show dominant views from the interviews . Not all interviewees views are represented but rather more overarching themes included . This study was approved by the Ethics Committees of the National Institute for Medical Research , Tanzania and the London School of Hygiene & Tropical Medicine , United Kingdom . Informed consent was obtained from all participants in Maa and a witness was present for illiterate participants . Permission to digitally record interviews was obtained from each participant . Permission from the male boma elder of the 15 selected bomas was also obtained .
A total of 28 adults , 15 women and 13 men , from the 15 study bomas in three villages were interviewed . There were no men available for interview at two bomas due to seasonal migration in search for good pastures and business travel . Despite data saturation being reached , researchers continued to conduct interviews to assure a sense of inclusivity in the community . The participant ages ranged from 18 to 49 years . Exact age was unknown to the majority of participants as they do not maintain documentation of date of birth nor track their ages . Estimated age ranges of participants were as follows: 18–29 years ( n = 10 ) , 30–39 years ( n = 9 ) , 40–49 years ( n = 9 ) . Education level of participants were as follows: no formal education ( n = 22 ) , attended primary school ( n = 5 ) , attended secondary school ( n = 1 ) . All were conducted in a private setting , however for one interview a husband [8–1] insisted that he and his wife [8–2] be present for each other’s interview . The Maa term enaoji is a condition of the eye associated with irritation specific to the eyelid . Some mentioned white spots on the inner surface of the eyelids , possibly follicles . The most commonly reported symptoms of enaoji described included discharge ( sometimes described as heavy and yellow ) , swollen eyelids , redness and itching . Some described pain , light sensitivity , inability to open the eye and an overall ill feeling throughout the body . Regardless of the associated symptoms , enaoji was most often explained as being specific to the eyelids . It was often reported to affect young children and some said it occurs within a few days after birth . Some described other symptoms which were not necessarily related to trachoma nor to the local interpretation of enaoji . Participants were all aware of a condition in which eye lashes turn inward and touch the eye ball , trichiasis , although they have no Maa term for it . Trichiasis was considered a normal condition that occurs with age particularly in women . None of the participants were able to link this with childhood eye infections or enaoji although after some probing they agreed such a link is plausible . When asked about what causes trachoma , many attributed it to pollen , dust , smoke and climate conditions that seem to vary with the year . Some mentioned “year of the eyes” in which some years there are a lot of eye problems compared to other years . Some participants attributed enaoji as a result of magic or a curse being inflicted by someone . A link to flies was described by many participants but the mechanism was not clear . Some thought the flies had to bite the eye or a part of the body or the fly gets into the nose and goes up to the eyes causing enaoji . Others said babies were born with dead flies in their eyes which caused eye problems . Some discussed that when it rains and when there is more milk around during calving season , there are more flies and that was when more children were getting enaoji . Only one participant mentioned bacteria and flies as a vector . He was in secondary school , and had the highest level of education of all participants interviewed: Methods to prevent infection were not mentioned by any participants . When asked if facial cleanliness can prevent trachoma , only a few respondents said that “it helps” but they were unable to elaborate more . Many described it as a treatment for yellow discharge , pain or irritation rather than for prevention . Although many reported being given information on cleanliness at clinics , most were unsure of the links between a clean environment and preventing trachoma and hence not convinced to follow the advice . Many said that hospitals help prevent disease or “only God can help” . Most participants said western medicine is better than local medicines or they are equally effective . Collecting local medicines from trees , plants and shrubs is part of Maasai women’s daily activities . Women prepare tea for their family from local medicines each morning depending on several factors including the weather , activities family members are involved in ( ie , grazing , setting out on a long journey ) , food availability and current illnesses . Some said they go to hospitals if the local medicines do not help . A few participants believed that local medicines are better . Most participants , however , reported using local medicines or veterinary medicines ( such as penicillin and oxytetracyline ) for eye problems , mostly because of poor access to western treatments . These included roots , leaves and bark of various trees and plants ( Table 1 ) , or other household products ( Table 2 ) . In most cases eye treatment for children is administered by women and the most common local treatments for enaoji are brewing medicines for eye drops , direct application of liquid from leaves or scratching the eyelids with a rough leaf . For the later practice , women take the leaf of the plant , Grewia bicolor , ( Fig 1 ) and rub the inside of the inverted eyelid until it bleeds . Although trichiasis was not considered a health condition that can be treated with surgery , most described the use a U-shaped iron , olputet , for epilation of eye lashes . Only one participant talked about surgery an old woman in her boma previously had , however on probing it seemed to not be related to trichiasis . Mass drug administration of azithromycin was conducted in Sinya in 2015 and 2016 . Most participants recalled the MDA although a few were completely unaware of the program . Most reported that the drugs were probably effective because after the distribution there were fewer eye infections in the community although most were unsure how it helps . Some reported side effects either they or others had including diarrhoea , vomiting and dizziness . Some also reported uncertainty and lack of trust in the drugs: When asked what causes blindness , most responses were that it is a result of aging . Some other causes mentioned included trichiasis , untreated eye diseases and God . If someone becomes blind at a young age , it was attributed to someone using witchcraft on them . A few women and a man talked about it as a curse for not paying traditional birth attendants after the birth of their child . Despite the traditional beliefs on causes , it did not negatively impact on how they treated the blind person . All participants remarked that blindness is a serious disability for the individual as they are unable to attend to their daily activities without the assistance of others . They all said it was a significant burden on others in the boma in regards to taking on their responsibilities at the boma including cooking and cleaning and assisting them to the toilet and other basic needs . The economic burden was also mentioned . Although blindness was considered a burden on the community , all participants discussed supporting blind people in their daily activities . Some family members , including children , were appointed as caretakers . There was little difference in responses between men and women . Women tended to be more descriptive in the symptoms of enaoji as well as the practice of scratching eyelids for treatment . Women were more likely to discuss side effects and rumours of MDA . Analysis of the data between the man and woman within a boma , showed similar responses . This consistency of responses within a boma indicates sharing of knowledge and perceptions around health between men and women .
This is the first study to examine perceptions and experiences of trachoma among the Maasai in Tanzania . Additionally , this was the first study to identify a local treatment used in treatment of swollen eyelids among the Maasai . In particular the commonly practiced scratching of eyelids with a rough leaf has not been documented . Children’s eyelids are scratched until they bleed . This may potentially lead to secondary scarring related to this local practice . Further studies are needed to explore the effects of local treatment on scarring and progression of disease . This study found prevention of blindness is important to Maasai . They discussed the social and economic burdens of blind people in the community . Possibly if health education in the community included the connections of childhood infections , trichiasis , and blindness , the community would place greater value on the information to prevent this disability . It is important to understand the indigenous knowledge of disease to guide effective control programs . Therefore , further ethnographic research with an in-depth focus on this communities’ beliefs , practices and relationships with health care is needed . The National NTD Control Programme has put resources into behaviour change interventions for trachoma control . These findings will provide further understanding of the community to tailor interventions more appropriate for Maasai . Additional research is needed to further explore the effect of a multi-level behaviour change intervention on sustained behaviour change for improved F and E practices among marginalized communities such as the Maasai . While this study focused on the Maasai in Tanzania , the results may contribute to the broader knowledge base and approach to improving control programs for other marginalized communities . | While global efforts to control and ultimately eliminate trachoma have been successful in many contexts , it has proven to be more challenging in many societies . Due to social , political or economic vulnerabilities , the approach to delivering global health programmes to some marginalized communities requires a more social science perspective . The Maasai , semi-nomadic pastoralists predominantly spanning the central border of Tanzania and Kenya , are one such community in which trachoma is endemic despite efforts to deliver interventions . Trachoma control interventions require community understanding of trachoma and behavior change . Furthermore , it is important to consider the community’s perspective to account for socio-cultural factors that may guide the design of effective control programmes and increase uptake of the interventions . This paper explores the knowledge and understanding of the nature of trachoma including pathology , progression of disease , risk factors , prevention and treatment among a trachoma endemic Maasai community . These findings can help guide more effective public health approaches to implementation of trachoma and other disease control interventions in Maasai communities . | [
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| 2019 | Knowledge, perceptions and experiences of trachoma among Maasai in Tanzania: Implications for prevention and control |
Anti-leishmanial drug regimens that include a single dose AmBisome® could be suitable for eastern African patients with symptomatic visceral leishmaniasis ( VL ) but the appropriate single dose is unknown . A multi-centre , open-label , non-inferiority , randomized controlled trial with an adaptive design , was conducted to compare the efficacy and safety of a single dose and multiple doses of AmBisome® for the treatment of VL in eastern Africa . The primary efficacy endpoint was definitive cure ( DC ) at 6 months . Symptomatic patients with parasitologically-confirmed , non-severe VL , received a single dose of AmBisome® 7 . 5 mg/kg body weight or multiple doses , 7 times 3 mg/kg on days 1–5 , 14 , and 21 . If interim analyses , evaluated 30 days after the start of treatment following 40 or 80 patients , showed the single dose gave significantly poorer parasite clearance than multiple doses at the 5% significance level , the single dose was increased by 2·5 mg/kg . In a sub-set of patients , parasite clearance was measured by quantitative reverse transcriptase ( qRT ) PCR . The trial was terminated after the third interim analysis because of low efficacy of both regimens . Based on the intention-to-treat population , DC was 85% ( 95%CI 73–93% ) , 40% ( 95%CI 19–64% ) , and 58% ( 95%CI 41–73% ) in patients treated with multiple doses ( n = 63 ) , and single doses of 7·5 ( n = 21 ) or 10 mg/kg ( n = 40 ) , respectively . qRT-PCR suggested superior parasite clearance with multiple doses as early as day 3 . Safety data accorded with the drug label . The tested AmBisome® regimens would not be suitable for VL treatment across eastern Africa . An optimal single dose regimen was not identified . www . clinicaltrials . gov NCT00832208
Visceral leishmaniasis ( VL ) is a life-threatening disease and a major health burden in developing countries [1] , [2] , [3] . WHO estimates there are approximately 0 . 2–0 . 4 million cases of VL annually; and more than 90% of global VL cases occur in six countries: India , Bangladesh , Sudan , South Sudan , Ethiopia and Brazil [1] . In eastern Africa approximately 30 , 000 people develop symptomatic VL and 4 , 000 die every year [3] , [4] . For decades , the mainstay of VL treatment in eastern Africa has been antimonials such as sodium stibogluconate ( SSG ) , but this treatment is cardiotoxic [5] and requires a 4-week hospitalisation imposing a huge economic burden on families [6] . Monotherapy with intramuscular paromomycin ( PM ) for 3 weeks was shown to be less efficacious in eastern Africa [7] than in Asia [8] , but a 17-day treatment with a combination of SSG and PM showed good efficacy and is now recommended as first-line treatment by WHO . However , this treatment also requires a relatively long treatment course and twice daily injections [8] . Currently , the safest anti-leishmanial drug is AmBisome® , a liposomal amphotericin B formulation with significantly diminished renal toxicity [9] . In trials in India , cure rates of around 90% were obtained with single AmBisome® doses of 5 mg/kg [9] . In addition , 95% efficacy was achieved with higher single doses ( 10 mg/kg ) or when used in combination with miltefosine or paromomycin [10] . Although licensed and recommended for first-line treatment of VL in immunocompetent patients [11] , Ambisome® use in eastern Africa has been mostly limited to second line treatment in a few centres mainly due to its high cost and cold storage requirements [12] . A small study with AmBisome® conducted in Kenya indicated higher doses were required than had been used in studies in India . Doses of 2 mg/kg given 3 , 5 , or 7 times to groups of 10 patients resulted in cure rates of 20% , 90% , and 100% respectively [13] . The aim of this study was to determine the minimum efficacious and safe single dose for the likely future use of the drug as part of a shorter course of treatment regimen for eastern African VL patients . The trial was undertaken with the goal of ultimately identifying a short and simplified treatment regimen that includes AmBisome® . Such a regimen will improve patient compliance and will have the advantage of a reduced cost .
The study was designed as a multi-centre , open-label , non-inferiority , randomized controlled trial , using a sequential-step design to evaluate the efficacy and safety of a single dose treatment regimen of intravenous AmBisome® ( either 7 . 5 , 10 . 0 , 12 . 5 or 15 . 0 mg/kg body weight ) compared to the reference multiple dose regimen currently approved in the USA: 3 mg/kg body weight on days 1 to 5 , 14 , and 21 . The single dose tested in the first cohort was 7·5 mg/kg body weight ( figure 1 , the consort flowchart ) . Two interim analyses were planned , after enrolment of 20 and 40 patients per arm , for early detection of inefficacious single doses , based on parasite clearance at day 30 ( figure 2 ) and/or worsening clinical conditions . If the stopping rule was met , the single dose was increased by 2·5 mg/kg and recruitment into the two arms restarted . The multiple dose treatment remained the same throughout . Non-responders to treatment were considered treatment failures and received rescue medication ( multiple dose AmBisome® regimen for single-dose failures and SSG for multiple dose failures ) . Patients with age of at least 4 years , confirmed HIV-negative , parasitologically-confirmed non-severe VL , were enrolled in three centres: ( 1 ) Gondar University Hospital , Amhara Regional State , Northern Ethiopia; ( 2 ) Arba Minch Hospital , Gamo Gofa Zone , Southern Nations , Nationalities and Peoples Regional State , Southern Ethiopia; and per protocol amendment ( 3 ) Ministry of Health Hospital , Kassab , Gedaref State , Eastern Sudan from May 2009 to September 2010 . Exclusion criteria were signs/symptoms of severe VL ( patients who were very weak , unable to walk , bleeding , jaundiced , suffering from sepsis and other concomitant infections/illnesses ) ; anti-leishmanial or unlicensed investigational treatments within six months; underlying chronic disease such as severe cardiac , pulmonary , renal , or hepatic impairment; serum creatinine outside the normal range; liver function tests more than 3 times the normal range; platelet count less than 40 , 000/mm3; known alcohol abuse; pregnancy or lactation; concomitant acute drug usage for malaria and bacterial infection; pneumonia within last 7 days; known hypersensitivity to AmBisome® or amphotericin B; any other condition which may invalidate the trial . On pre-specified treatment days , AmBisome® dosage was calculated according to body weight . Preparation included reconstitution with sterile water for injection and filtration according to the manufacturer's instructions . Administration was by slow intravenous infusion in 5% dextrose solution . During infusion , patients were closely observed with regular monitoring of vital signs ( blood pressure and pulse ) . A test dose was administered in the first 10 minutes of infusion , and the patients carefully observed for 30 minutes . The time of treatment and dosage was recorded . Patients were randomized to receive either treatment using a computer-generated randomisation list , stratified by site . Individual treatment allocations were placed in sealed , opaque envelopes which were opened after a patient had been entered into the trial . It was not possible to blind patients or treating physicians due to the nature of the intervention . Primary ( definitive/final cure ) and secondary ( initial cure ) efficacy endpoints were determined by parasitological assessment , as the most objective and consistent method across treatment centres at six months follow-up and at Day 30 , respectively . Day 30 was considered as the end of treatment time point for initial cure assessment . Definitive cure was defined as absence of parasites in tissue aspirates ( bone marrow , lymph node or spleen ) with no relapse of signs and symptoms of VL during six months follow-up . Initial cure was defined on day 30 by absence of parasites in tissue aspirates . For patients with detectable parasites at initial cure assessment , clinical and biological assessments were used , in addition to parasitological results , to ascertain the need for rescue treatment at the end of study treatment at the discretion of the treating clinician , according to the protocol . Patients with a presence of parasites at initial assessment of cure but who were clinically well were invited to return after one month to further assess their status and need for rescue treatment . Patients who did not clear parasites at end of treatment but did by six months follow-up , in the absence of a requirement for additional rescue treatment , were classified as treatment successes at six months as patients who were slow responders , in line with previous trials in the region [7] . Assessment of safety involved monitoring vital signs , documentation of patients' complaints about the treatments , and haematological and biochemical measurements evaluated on days 2 to 5 , 7 , 14 , 21 and 30 , and at 3 and 6 months . Treatment emergent adverse events ( TEAE ) were classified according to the Medical Dictionary for Regulatory Activities ( MedDRA ) . Treatment emergent events were those with onset between day 1 of treatment and day 60 inclusive . Peripheral Blood Parasitaemia: In Kassab , per protocol amendment , peripheral blood samples were analysed for parasite load in a subset of 5 consenting patients in each of the 10 mg/kg single dose and multiple dose arms . For this , a validated quantitative reverse-transcriptase polymerase-chain-reaction ( qRT-PCR ) method targeting Leishmania 18S ribosomal RNA was used [15] . Genetic material was extracted using a modified Boom-method [15] , [16] , [17] . qRT-PCR analysis using a Bio-Rad CFX-96 real-time machine ( Bio-Rad , Veenendaal , the Netherlands ) was performed at Koninklijk Instituut voor de Tropen ( KIT ) . For the primary endpoint comparison , 120 patients per arm would provide 80% power to detect non-inferiority within a margin of 10% , assuming 95% cure in the reference arm , a one-sided alpha of 0·05 and 15% loss-to-follow-up . In interim analyses , 20 patients per arm would provide 90% power to detect a difference of at least 35% in parasite clearance rate at day 30 , assuming 95% cure in the reference arm and a two-sided alpha of 0 . 05 . With 40 patients per arm , there would be 90% power to detect a difference of at least 25% under the same assumptions . Interim analyses were based on day 30 cure in the Intention-to-Treat ( ITT ) population . Decision-making at each interim analysis was based on a test of difference between the parasite clearance rates in the single dose arm and multiple dose arms . If the single dose arm showed significantly poorer efficacy ( P<0 . 05 ) , the single-dose was increased prior to re-starting recruitment . Patients allocated to the multiple-dose arm for discontinued single-dose comparisons were not included in comparisons of higher single doses . Key assumptions for the planned final analysis were not met due to low efficacy in the multi-dose arm and the trial was terminated prematurely ( see Results ) . Cumulative data for each treatment regimen were used to calculate the percentage of patients cured , with exact binomial 95% confidence intervals ( CI ) , at day 30 and 6 months follow-up in ITT and per-protocol ( PP ) analysis populations . Patients with missing outcome data were excluded from analyses . For safety , the number and percentage of patients per arm experiencing adverse events ( AEs ) were summarised , for AEs with cumulative incidence higher than 10% . For parasite clearance from peripheral blood , a linear mixed effects regression model using the natural log-transformed parasite loads was applied to estimate the time to clear 50% and 90% of parasites for each individual . Model performance and significance were assessed by analysis of variance ( ANOVA ) .
The most commonly presenting VL symptoms were fever and weight loss , followed by loss of appetite , abdominal swelling , and cough; less commonly observed were epistaxis , diarrhoea , and skin lesions . Other characteristics of patients at entry to the trial are summarized in Table 1 . Overall , 82% of patients were male and about half were children . About two thirds of patients were underweight or severely underweight . Mean haemoglobin concentrations were <8·0 g/dl , and anaemia was common , but neither baseline laboratory parameters nor vital signs suggested any major difference among dose groups . Baseline characteristics were generally comparable in the multiple and the 10 mg/kg single dose group , whereas the smaller 7·5 mg/kg dose group showed some imbalances: on average , patients in this group were older and accordingly had higher body weight and larger spleen size , but also had the highest baseline parasitaemia . Patients were younger in Kassab ( 10·5±5·0 years ) than in Gondar ( 20·9±6·3 ) or Arba Minch ( 17·4±10·2 in ) and more often female in Kassab ( 33·3% ) than in Gondar ( 10·5% ) or Arba Minch ( 12% ) . In Arba Minch , they were more often normal weight ( 42·0% ) than in Gondar ( 21·1% ) or Kassab ( 28·6% ) . In the first section of Table 2 , parasite clearance rates at day 30 are shown for the three interim analyses . Summary data for the parasite clearance rate at day 30 and the cure rate at 6 months are shown for all patients and for those treated at each of the 3 centres . The IC and DC rates with the standard multiple dose treatment were both 85% . IC rates with single doses of 7·5 and 10 mg/kg were 50% and 73% , respectively; and DC rates were lower , at 40% and 58% , respectively . However , there were variations in treatment response between treatment centres , with poor efficacy in Kassab and Gondar , particularly with single doses . By contrast , at Arba Minch , the multiple doses as well as the single dose of 10 mg/kg resulted in complete cure , and treatment failures were observed with the 7·5 mg/kg dose only . All non-responders were cured after receiving rescue medication . For the first interim analysis , comparing the 7·5 mg/kg single dose to the multiple dose regimen with 20 and 18 patients per arm , respectively , in the two Ethiopian sites , the stopping rule was met ( Table 2: Fisher's exact test , p = 0·015 ) . The single dose was increased to 10 mg/kg , and recruitment restarted at both Ethiopian sites and in an additional site , Kassab , Sudan . There was no significant difference in efficacy found at the next interim analysis comparing 10 mg/kg to the multiple dose arm ( p = 0·748 ) , but when 44 patients had been recruited into the multiple dose and 40 patients into the 10 mg/kg single-dose arm , the third interim analysis indicated unexpectedly low initial cure rates in both arms; 84% in the multiple dose and 73% in the single-dose arm . The stopping rule was not met ( chi-squared test , p = 0 . 196 ) , but based on the observed poor efficacy overall , and following discussions with the Data Safety and Monitoring Board ( DSMB ) and investigators , the sponsor terminated the trial . At that time , a total of 124 patients had been enrolled into the trial; 63 had received the multiple dose regimen , 20 patients received a single dose of 7·5 mg/kg dose and 41 patients received a single dose of 10 mg/kg ( figure 1 ) . TEAE were common regardless of dose regimen . Severity was mostly mild or moderate and only about 2% of TEAE were rated severe , mostly with respect to laboratory measurements . There was one non-fatal SAE , a pneumonia deemed unlikely related to the drug , and one death due to snakebite . AEs for which relatedness could not be excluded are listed in Table 3 . These potential adverse drug reactions were seen in all the three dose groups and occurred in both the multiple and the higher single dose groups with similar frequencies . At baseline , semi-quantitative microscopy counts on bone marrow aspirates correlated well with parasite loads in peripheral blood when assessed by qRT-PCR ( R2: 0·77 , p<0·01 ) . Mean natural log-normalized parasite loads ( P ) were comparable in the single and multiple dose groups ( 6·4 lnP/mL , 95%CI: 4·6–8·2 versus 5·1 lnP/mL , 95%CI: 3·3–6·8; p = 0·358 ) at baseline . Three out of the 5 patients of each group had baseline blood parasite loads >50 per mL and these had clearance rates assessed and modelled over the first 7 days . Mean parasite clearance rates were significantly different between the single and the multiple dose group ( 0·35 per day , 95%CI: 0·00–0·70 versus 1·14 per day , 95%CI: 0·78–1·50; p = 0·012 ) as early as day 3 ( Figure 3 ) , corresponding to mean parasite elimination half-lives of 1·97 days ( 95% CI: 0·99–278 ) for the single-dose group and 0·61 days ( 95%CI: 0·46–0·89 ) for the multiple dose group . Time required for 90% parasite clearance for single-dose and multiple-dose groups was estimated at 6·55 ( 95% CI: 3·29–923 ) and 2·02 ( 95% CI: 1·53–2·95 ) days , respectively . One patient in the single dose group had a low blood parasite load at baseline , which increased until day 30 , but was no longer detectable after rescue treatment .
AmBisome® in single doses of up to 10 mg/kg is suboptimal for eastern African VL patients . A higher dose of 21 mg/kg administered in multiple doses was also far less effective than anticipated . In a small number of patients , no treatment failures occurred in patients from south Ethiopia treated in Arba Minch Hospital either with 10 mg/kg single dose or 21 mg/kg divided doses . The tested AmBisome® regimens would not be suitable for VL treatment across eastern Africa , and an optimal single dose that could be included in shorter and simplified treatment regimens of anti-leishmanial drugs across all VL endemic regions of eastern Africa was not identified . Efficacy of AmBisome®for the treatment of VL in eastern Africa was variable and overall lower than in India . | Visceral leishmaniasis is a potentially fatal disease which affects 0 . 2–0 . 4 million people every year , principally in South-East Asia , Latin America or Eastern Africa . Currently the safest drug in use is AmBisome® , which cures 90% of patients in India at 5 mg/kg , and is even more effective at higher doses ( 10 mg/kg ) or in combination with miltefosine or paromomycin . These regimens have been shown to be equally cost-effective in India . However , the drug requires a cold chain for storage and reconstitution prior to injection . Although it is licensed for use in eastern Africa , in practice it is mainly used as a second-line treatment . A small study carried out in Kenya indicated that a higher dose is necessary in eastern Africa in contrast to Asia . This study aimed to determine the minimum single dose that is safe and effective for treatment of eastern African VL patients so as to be used in simplified treatment regimens . However , the tested regimens were found to be ineffective , and an optimal single dose that could potentially be used in simplified treatment regimens was not identified . | [
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| 2014 | Safety and Efficacy of Single Dose versus Multiple Doses of AmBisome® for Treatment of Visceral Leishmaniasis in Eastern Africa: A Randomised Trial |
Receptor tyrosine kinases ( RTKs ) typically contain multiple autophosphorylation sites in their cytoplasmic domains . Once activated , these autophosphorylation sites can recruit downstream signaling proteins containing Src homology 2 ( SH2 ) and phosphotyrosine-binding ( PTB ) domains , which recognize phosphotyrosine-containing short linear motifs ( SLiMs ) . These domains and SLiMs have polyspecific or promiscuous binding activities . Thus , multiple signaling proteins may compete for binding to a common SLiM and vice versa . To investigate the effects of competition on RTK signaling , we used a rule-based modeling approach to develop and analyze models for ligand-induced recruitment of SH2/PTB domain-containing proteins to autophosphorylation sites in the insulin-like growth factor 1 ( IGF1 ) receptor ( IGF1R ) . Models were parameterized using published datasets reporting protein copy numbers and site-specific binding affinities . Simulations were facilitated by a novel application of model restructuration , to reduce redundancy in rule-derived equations . We compare predictions obtained via numerical simulation of the model to those obtained through simple prediction methods , such as through an analytical approximation , or ranking by copy number and/or KD value , and find that the simple methods are unable to recapitulate the predictions of numerical simulations . We created 45 cell line-specific models that demonstrate how early events in IGF1R signaling depend on the protein abundance profile of a cell . Simulations , facilitated by model restructuration , identified pairs of IGF1R binding partners that are recruited in anti-correlated and correlated fashions , despite no inclusion of cooperativity in our models . This work shows that the outcome of competition depends on the physicochemical parameters that characterize pairwise interactions , as well as network properties , including network connectivity and the relative abundances of competitors .
Cellular regulatory networks encompass conserved patterns of protein-protein interactions , which have been called network motifs [1 , 2] . One key network motif is the phosphotyrosine-based reader/writer/eraser motif [3 , 4] , which is found in many important human cell signaling systems , including systems downstream of antigen , cytokine , and hormone receptors . The motif consists of 1 ) a tyrosine residue within a protein , 2 ) a kinase that phosphorylates the tyrosine ( termed a writer ) , 3 ) a phosphatase that dephosphorylates the phosphotyrosine product of kinase activity ( termed an eraser ) , and 4 ) a protein containing a Src homology 2 ( SH2 ) and/or phosphotyrosine binding ( PTB ) domain ( termed a reader ) . SH2 and PTB domains are conserved modular structural units consisting of approximately 100 amino acids that have the ability to recognize and bind phosphotyrosine-containing short linear motifs ( SLiMs ) [5 , 6] . Binding properties of SLiMs are determined by regions generally spanning three to ten amino acids [6] . SH2 and PTB domains have preferences for specific SLiMs , which confers a degree of specificity in phosphotyrosine signaling [7] . However , interactions are characterized by equilibrium dissociation constants ( KD values ) that fall roughly between 1 and 10 μM , a range not thought to be large enough to confer a high degree of specificity [8–11] . Thus , neither SH2/PTB domains nor SH2/PTB domain-binding SLiMs have exclusive and unique binding partners . Rather , instances of these domains and SLiMs each have the potential to interact with multiple binding partners , a property that is sometimes referred to as binding promiscuity [12–14] . The consequences of binding promiscuity are not well understood . It has been suggested that promiscuity may necessitate a strengthening of reader/writer/eraser signaling specificity by factors such as compartmentalization or secondary protein-protein interactions beyond the characteristic phosphotyrosine-SH2/PTB domain interaction [15] . However , the specificity requirements of cellular information processing are unclear . It may be that limited specificity is sufficient for the function of the reader/writer/eraser motif , and it may even be that numerous interactions are not merely tolerated but somehow important for proper functioning [16] . In any case , a byproduct of binding promiscuity is competition . Competition arises as a factor that influences reader/writer/eraser motif function because we expect that only a single SH2/PTB domain can physically interact with only one phosphotyrosine at a time . Thus , the recruitment of an SH2/PTB domain to a phosphotyrosine may be significantly influenced by the presence of other interaction partners [12] . Which SH2/PTB domain containing proteins are bound first may even influence the ability of potential competitors to bind , through a protective shielding effect [17] . Importantly , competition is a network property , making mathematical models for network behavior potentially valuable for integrating information about relative abundances of SH2/PTB domains and phosphotyrosines and affinities of pairwise interactions and for reasoning about the effects of competition [18] . Here , we formulated a series of models to investigate signaling by a well-studied receptor tyrosine kinase ( RTK ) , the insulin-like growth factor 1 ( IGF1 ) receptor ( IGF1R ) . In normal cells , IGF1R signaling regulates differentiation , proliferation , motility , and anti-apoptosis; however , IGF1R signaling has also been strongly linked to the progression of cancer [19 , 20] , with mutations affecting the IGF1R or the related insulin receptor ( INSR ) signaling pathway noted in 43% of stomach cancer tumors [21] and activated IGF1R detected in 50% of breast cancers across multiple subtypes [21 , 22] . Furthermore , loss of IGF1R signaling in pancreatic β cells has been observed to promote diabetes [23 , 24] . Models capable of predicting IGF1R signaling pathway behavior could aid in the realization of effective treatment strategies for IGF1R-driven disease . Mass-action models of cell signaling typically focus on a limited subset of signaling proteins [25 , 26] or group a set of binding partners together [27] because of the challenge of accounting for a combinatorial number of phosphoforms and protein complexes with traditional modeling approaches [28] . For an IGF1R homodimer with six phosphotyrosines per monomer , if each phosphotyrosine is limited to one interaction , there are three possible states ( unphosphorylated/unbound , phosphorylated/unbound , or phosphorylated/bound ) for each of the 12 phosphotyrosines in the homodimer . Overall , there are on the order of 105 unique dimer configurations that can be populated . This complexity naturally follows from biological mechanisms and presents a challenge to those who wish to develop and analyze mathematical models to better understand cell signaling . Rule-based modeling was developed to cope with combinatorial complexity [29] . This approach allows reactions to be defined in terms of rules describing molecular interactions mediated by specific parts of proteins . A list of rules can be used to computationally generate the entire network of molecular states and reactions implied by the interactions represented by the rules . Such a comprehensive specification might be needed to simulate a network through numerical integration of a system of ordinary differential equations ( ODEs ) [30 , 31] . However , the combinatorial explosion in number of potentially populated chemical species that occurs as more interactions are considered often leads to models with a very large number of mostly redundant states that cannot be efficiently derived from rules or efficiently simulated with ODE-based approaches . For an example of redundancy , consider a receptor with n tyrosine sites that can be either unphosphorylated or phosphorylated . Describing changes to every possible configuration of a receptor would require 2n ODEs . However , if the state of one tyrosine residue does not influence the state of others , then the same system of interactions could be fully captured with only 2n equations . One way to overcome the combinatorial explosion problem is with network-free simulation algorithms that avoid the explicit specification or derivation of all possible states [32–36] . A second option is model reduction , in which an approximate model is derived by neglecting sparsely populated species [37] . With this approach , a network and equations must be derivable from rules , then the derived network and equations are simplified according to the results of simulation . In this report , we applied a method of restructuring a model formulation to reduce state redundancy , which allows the model to be simulated with network-based algorithms . Strategies similar to the restructuration approaches employed here have been previously described [38–43] . In contrast to model reduction , model restructuration does not entail approximation to arrive at a simpler model form . We applied a rule-based approach to formulate mathematical models for early events in IGF1R signaling . We modeled IGF1 binding to IGF1R based on work by Kiselyov et al . [44] , which we built upon by considering the full-scale interaction network of IGF1 , IGF1R , and a set of IGF1R binding partners . We leveraged the availability of datasets characterizing interaction affinities between IGF1R and a subset of the human complement of SH2/PTB domains [45 , 46] . Importantly , we demonstrate that naive predictors of signaling protein recruitment , including binding affinity , copy number , and simple analytical expressions for equilibrium binding , are unable to recapitulate predictions obtained via simulations . Using cell line-specific measurements of protein copy numbers , we extended the model to make predictions for IGF1R binding partner recruitment across diverse cell lines . Thus , this work considers the effects of competition for phosphotyrosine sites , differences in binding affinity , and the impacts of cell line-specific protein abundance profiles to rank the importance of downstream IGF1R signaling partners .
We modeled IGF1-IGF1R interactions based on the harmonic oscillator ( HO ) mechanism proposed by Kiselyov et al . [44] and supported by subsequent structural analyses [47–49] . IGF1R molecules exist in pre-formed dimeric complexes , each of which contain two IGF1 binding pockets that are considered to be functionally equivalent ( Fig 1A ) [50] . Each binding pocket contains two dissimilar IGF1 binding sites , termed Site 1 ( S1 ) and Site 2 ( S2 ) . A binding pocket can accommodate at most one IGF1 molecule , so a dimeric complex can simultaneously engage at most two IGF1 molecules . According to the HO mechanism , an IGF1 molecule first engages S1 or S2 in one of the two binding pockets of a dimer . Sites S1 and S2 are known to have differing affinities for ligand binding [47 , 51] , such that there exists a primary route toward receptor crosslinking whereby the high affinity site is bound first , as well as a secondary route whereby the ligand binds the lower affinity site first [44] . After binding one site ( most likely the high affinity site ) , the other IGF1 binding surface may then engage the remaining site in the pocket . The two sites in the pocket are crosslinked when both binding surfaces of one IGF1 molecule simultaneously occupy S1 and S2 ( Fig 1B ) . Crosslinking stabilizes the active conformation , wherein transmembrane and intracellular regions of the receptor are close together [48 , 52] , making the receptor competent for autophosphorylation of intracellular phosphotyrosine sites . When one pocket is crosslinked , the S1 and S2 sites of the other pocket are located too far apart to permit ligand engagement of both sites . Recent crystallographic data have led to the proposal of an alternative mechanism for IGF1-IGF1R binding , which has been termed the induced fit mechanism [51] . In this picture of IGF1 binding , receptors are generally present in a closed conformation , wherein incoming ligand cannot interact with either S1 or S2 sites with high affinity . In contrast , the HO mechanism assumes that a proportion of receptors are present in an open conformation , with ligand capable of binding to either S1 or S2 . Receptor activation via the induced fit mechanism is initiated when ligand interacts with the binding pocket in a metastable manner . The structural data suggest that this pre-complex includes IGF1 interaction with S1 and putative residues contained in S2 . Once the pre-complex is formed , the receptor may relax and shift to an open conformation in which IGF1 can crosslink S1 and S2 . The induced fit mechanism was proposed after analysis of crystal structures of unbound IGF1R as well as IGF1 in complex with S1 in IGF1R . However , the crystallization chaperone is derived from the monoclonal antibody ( mAb ) 24–60 , which is known to inhibit IGF1 binding to IGF1R [51 , 53] . Therefore , it is unclear how the reported structures supporting the induced fit mechanism reflect physiological conditions , and further study is needed to determine the in vivo conformations of IGF1R important for interaction with IGF1 . For the models presented here , we consider that IGF1 molecules are capable of reversibly associating with and crosslinking one of the two binding pockets , modulating IGF1R receptor transition between inactive and active conformations . Ultimately , our model preserves the major features of both the HO and induced fit mechanisms , each of which entails two steps to achieve activation of IGF1R . In either mechanism , IGF1 initially engages IGF1R , either by binding one site ( HO mechanism ) or by forming a pre-complex ( induced fit mechanism ) . Then , the ligand-receptor complex is stabilized in a conformation competent for autophosphorylation , either via completion of crosslinking of the bound IGF1 ( HO mechanism ) or by conformational change of ligand and receptor ( induced fit mechanism ) . IGF1R possesses six autophosphorylation sites: Y973 , Y980 , Y1161 , Y1165 , Y1166 , and Y1346 ( Fig 1B ) . Note that this list of sites does not correspond to sites considered in some previous studies [46 , 54] , as we excluded Y1280 and Y1281 . The literature suggests that Y1280 and Y1281 are not in fact autophosphorylated ( as indicated in some databases ) , but rather , are important in coordinating binding to the surrounding serines [55 , 56] . Fig 1B illustrates the mapping of IGF1R signaling proteins to the phosphotyrosines that they are capable of binding . The possible IGF1-IGF1R interactions shown in Fig 2A give rise to nine distinct dimeric complexes , of which three represent crosslinked receptors ( Fig 2B ) . Tyrosine phosphorylation ( Fig 2C ) and dephosphorylation ( Fig 2D ) are each modeled as a one-step process ( i . e . , a first-order reaction ) . For all six tyrosines , we apply the same pseudo first-order phosphorylation rate constant ( 0 . 5 s-1 ) and dephosphorylation rate constant ( 0 . 1 s-1 ) . These simplifications are acceptable based on the common assumption that neither tyrosine kinases nor phosphatases are saturated . The concentration of IGF1R considered in the model is in the nM range; RTKs have been observed to become saturated when concentrations are in the μM range [57] . The half-life of a phosphorylated tyrosine residue is on the order of seconds [58] , which indicates excess phosphatase activity . Once a tyrosine site is phosphorylated , it can recruit its respective SH2/PTB domain-containing binding partner ( s ) ( Fig 2E ) . Dephosphorylation is only permitted if a site is not occupied by a signaling protein . IGF1-IGF1R interactions are characterized by a cyclic reaction scheme with eight reactions ( Fig 2A ) . The kinetics of forward ( binding ) reactions are characterized by the rate constants a1 , a2 , a'1 , and a'2 , where a'1 , and a'2 characterize the rate of crosslinking of S1 and S2 . The corresponding dissociation rate constants are d1 , d2 , d'1 , and d'2 . We take the view that the oscillatory movements of IGF1R dimers are spontaneous , meaning driven purely by thermal fluctuations without any dissipation of energy ( e . g . , no consumption of ATP ) . Thus , the IGF1-IGF1R cyclic reaction scheme must satisfy the principle of detailed balance [59 , 60] . In other words , the rate constants of the cyclic reaction scheme cannot be specified independently , and are related by the following constraint: ( a1d1 ) ( a′2d′2 ) = ( a′1d′1 ) ( a2d2 ) ( 1 ) To estimate the values of the rate constants , we used BioNetFit [61] and IGF1-IGF1R ligand dissociation data and equilibrium binding data presented in Ref . [44] . We estimated the values of a1 , a2 , a'1 , d1 , d2 , d'1 , and d'2 . The remaining rate constant , a'2 , was determined by the detailed balance constraint of Eq 1 . We did not assume d1 = d'1 or d2 = d'2 . Kiselyov et al . found improved fits without the assumption that these pairs of dissociation rate constants are equivalent , but concluded that the inclusion of the equivalency constraint only marginally detracted from the quality of the fit . We found that , with the inclusion of the detailed balance constraint , acceptable fits ( χ2 ≤ 3 . 5 ) could not be achieved with d1 = d'1 and d2 = d'2 . The curves generated with our best-fit parameter estimates are plotted in Fig 3 . Note that the assignment of S1 and S2 is arbitrary , such that equivalent fits and simulation results are obtained when swapping the parameters of the two paths ( i . e . , swapping a1 with a2 , d1 with d2 , a'1 with a'2 , and d'1 with d'2 ) . From our best-fit parameters in Table 1 , we estimate site-specific KD values to be 13 nM and 180 nM , comparable to the 9 nM and 490 nM estimated by Kiselyov et al . [44] and the value of 39 nM for IGF1 binding IGF1RΔβ ( an ectodomain-only construct ) measured by Xu et al . [51] . Furthermore , we estimated apparent KD values using the simplification presented by Ullah et al . [62] . With this approach , the two-step pathway comprising initial ligand binding followed by crosslinking is approximated as a one-step reaction . We determined an apparent KD value of 0 . 61 nM using the best-fit parameter set . This estimate is similar to the 0 . 12 nM reported by Kiselyov et al . [44] and subsequently used to fit an induced fit binding model in the study of Xu et al . [51] . The IGF1R model with a natural formulation of rules has tremendous combinatorial complexity , which presents a significant challenge when performing simulations . There are three possible configurations in which a receptor dimer can be crosslinked ( Fig 2B ) . Every receptor monomer has six tyrosine sites , or 12 sites in each receptor dimer , each of which can be unphosphorylated , phosphorylated but unbound , or phosphorylated and bound to an SH2/PTB domain-containing protein . With nine possible configurations of IGF1 bound to IGF1R ( Fig 2B ) , a coarse estimation of the total number of possible unique species ( avoiding double counting of symmetrical species ) is ≈ 9 × ( 312 + 36 ) /2 = 8×105 . It is impractical to generate and simulate the full reaction network between these species . To address this issue , we have optimized the formulation of rules , through a process we term restructuration , to obtain a rule set that implies an equivalent network with minimal state redundancy . We have outlined the approach used to restructure the IGF1R signaling model in Materials and Methods , and provided a detailed overview of restructuration in S1 File . After restructuring , the IGF1R model contains 577 species , or < 0 . 07% of the number of species implied by the natural formulation . Which signaling proteins are most often recruited to IGF1R depends on many factors , including relative protein expression levels ( S1 Table ) , relative binding affinities ( S2 Table ) , and competition for each of the six autophosphorylation sites . We first analyzed signaling protein binding in a HeLa S3 cellular background , parameterized with proteomics data from Kulak et al . [63] . Out of 18 possible binding partners , only ITK and ZAP70 are not expressed in HeLa S3 cells ( S1 Table ) . Fig 4A shows the model prediction for the temporal recruitment of SH2/PTB-domain containing proteins in HeLa S3 cells in response to 1 nM IGF1 stimulation . Fig 4B shows the steady-state recruitment at various doses of IGF1 stimulation . Simulations indicate strong recruitment ( 17–22% of IGF1R bound at steady-state with 1 nM stimulation ) of SHC1 , CRKL , ABL2 , and STAT1 and modest recruitment ( 7–12% of IGF1R bound ) of VAV2 , YES1 , VAV2 , and RASA1 . The adapter protein SHC1 is predicted to be the most highly recruited protein in HeLa S3 cells . On IGF1R , SHC1 binds only to pY980 , a site that also recruits IRS1 and ZAP70 . As ZAP70 is not expressed in HeLa S3 cells , the only competitor for SHC1 in this cellular background is IRS1 . The strong recruitment of SHC1 is due to both its high copy number ( 105/cell ) ( two orders of magnitude higher than that of IRS1 , see S1 Table ) and its strong affinity for this site ( 20-fold stronger than IRS1 , see S2 Table ) . Next , we consider STAT1 , CRKL , and ABL2 , which all display strong IGF1R recruitment . STAT1 and CRKL are the two most abundant binding partners in HeLa S3 cells , with relatively high concentrations ( 105 copies per cell ) , whereas ABL2 is the ninth most abundant signaling protein , with an abundance on the order of 104 copies per cell . STAT1 binds pY1161 , which also recruits PIK3R3 , SRC , YES1 , SYK , and BLK . Despite these five competitors , recruitment of STAT1 is strong because the five competing proteins have relatively low expression levels ( S1 Table ) . Like STAT1 , CRKL competes with multiple proteins that are expressed at relatively low levels for binding to pY1166 and pY973 . High recruitment of ABL2 can be explained by the strong affinity of ABL2 for pY973 , which is approximately 40-fold greater than that of CRKL . We observe that the rank order predicted by numerical simulations is distinct from that obtained through ranking by KD , copy number , or the copy number/KD ratio ( Fig 4C ) , suggesting that competition , protein abundances , and binding affinities all contribute to the relative recruitment of downstream partners in an unintuitive manner . We further evaluated whether simple methods could return the same predictions obtained from simulations by deriving analytical expressions of equilibrium binding ( S3 File , Eq . S1–S9 ) . These expressions roughly estimate how the rank ordering of binding partners is related to factors affecting binding partner recruitment , including copy number and binding affinity . This approach is similar to that proposed by Maslov and Ispolatov [64] . To derive analytical expressions for estimating recruitment , we exploit a key simplification that assumes each IGF1R binds a single partner at a time . In the rule-based model , we allow for recruitment of multiple binding partners to different pY sites , which is consistent with the findings of a recent study where multi-site phosphorylation of EGFR was directly observed through and single-molecule fluorescence microscopy [65] . However , another study using related technology found that ligand-activated EGFR is infrequently multi-phosphorylated for the sites probed [66] . Thus , RTKs may in some cases bind only a single downstream signaling partner at a time , so an acceptable analytical approximation for these cases may be obtained by the application of this assumption . The concentration of protein bound to IGF1R can be expressed as a function of the total protein concentration , the free , active IGF1R concentration , and the measured binding affinity between the protein and the receptor . Because each active receptor is assumed to bind only one signaling protein at a time , this relationship can be represented by the following equation ( derived in S3 File as Eq . S9 ) : XiBound≈R⋅XiTotKi+R ( 2 ) In Eq 2 , XiBound represents the concentration of the ith protein species that is bound by IGF1R , XiTot represents the total concentration in the system of the ith protein species , Ki is the apparent dissociation constant for partner Xi , and R is the concentration of free , active IGF1R . To account for competitive binding , we need to calculate the free active IGF1R concentration , R , from the following balance equation for the total active IGF1R ( RTot ) , see S3 File . Eqs 2 and 3 provide a means to rank IGF1R binding partners . There are six inconsistencies in rank order when comparing predictions from numerical and analytical methods ( Fig 4C ) . Differences between the analytical and numerical rankings are minimized as the concentration of free active IGF1R in the analytical model increases , which suggests that these discrepancies arise from the assumption that each active RTK can bind only one binding partner at a time , described in more detail in S3 File . We also compared quantitative predictions from numerical simulations to those from the analytical approximation ( S3 Fig ) . There is a strong positive correlation between the steady-state number of bound molecules predicted with each of the methods ( Pearson’s r = 0 . 93 with a p value of 10−8 for HeLa S3 , obtained using R software’s cor . test function ) , which provides support for use of the analytical approach for generating rough predictions . However , the degree of agreement between the approaches depends on the specific protein considered . Overall , we conclude that it is difficult to capture the predictions of the rule-based model solely via simple metrics like copy number or binding affinity , or even with our analytical expressions . We investigated whether competition among the signaling proteins could be influenced by cell type-specific differences in protein copy number . In addition to the HeLa S3 cell model discussed above , we parameterized 44 other models to represent diverse cell types . Eleven human cell lines were studied by Geiger et al . [67] . An additional 31 models were formulated for cell lines within the NCI-60 panel derived from human tumors and maintained by the National Cancer Institute . We developed models for only the NCI-60 cell lines in which IGF1R was detected [68] . HeLa Kyoto proteomics data were from Hein et al . [69] . We also considered one non-human cell line , the immortal murine myoblast cell line C2C12 [70] , to allow for comparison with available binding data [71] . For each cell line model , we simulated the steady-state pattern of recruitment of signaling proteins to IGF1R in response to stimulation with 1 nM IGF1 . Across all cell lines , the most highly recruited proteins were predicted to be CRKL , STAT1 , RASA1 , and YES1 ( Fig 5 ) . The ranking from numerical simulations for each cell line is provided in S2 File along with predicted ranks from four simple ranking metrics: analytical approximation , copy number , equilibrium dissociation constant ( KD ) , and copy number divided by KD . YES1 was the top-ranked binding partner in 38% of cell lines ( Fig 5B ) . It has been suggested that YES1’s SH2 domain is remarkably promiscuous [45] , such that the high recruitment predicted by our model may not accurately reflect recruitment in vivo . YES1 and other proteins in the SRC class of tyrosine kinases have been found to be highly connected , a factor which may contribute to oncogenic potential [46] . However , promiscuity does not necessarily indicate that YES1 is not a legitimate IGF1R binding partner . Indeed , the literature offers evidence that YES1’s SH2 domain does exhibit specificity . For example , YES1 reportedly binds to pY1161 and pY1165 with KD values of approximately 0 . 9 μM , but was not strongly recruited to the other IGF1R tyrosine sites [46] . Similarly , YES1 is recruited to three of the eight sites in the INSR with KD values less than 1 μM . In ErbB-family RTKs , including EGFR , ErbB2 , ErbB3 , and ErbB4 , YES1 was either not detected as an interacting partner or bound weakly , with a KD greater than 2 μM [11] . These observations suggest that YES1 is a bona fide binding partner relevant for IGF1R signaling , but further investigation is needed . Hanke et al . [71] analyzed protein recruitment to IGF1R in the mouse myoblast cell line C2C12 via a pulldown assay . To our knowledge , Hanke et al . ’s study provides the best data available to aid in evaluating predictions from our IGF1R binding model . Their procedure , in which peptides ( 15-mers ) spanning each of the murine IGF1R phosphotyrosine residues were incubated with cell lysate , allows binding partners to compete for individual phosphotyrosines . The ratios of proteins detected with and without each phosphorylated tyrosine were reported . Unfortunately , this normalized recruitment data cannot provide a ranking of the most highly recruited binding partners , especially because each phosphotyrosine was considered separately . However , the pulldown data does provide a picture of detectable binding partners , which can be compared to model predictions . The most highly recruited protein in C2C12 simulations was RASA1 , a GTPase-activating protein ( GAP ) for RAS . This protein was also detected in the pulldown assay , with a reported 8-fold increase in binding between phosphorylated and unphosphorylated Y1283 in murine IGF1R . Other proteins that were predicted to bind in simulations and detected by the pulldown assay include the PI3K regulatory α and β subunits ( PIK3R1 and PIK3R2 ) , which displayed 15-fold and 9-fold increase in binding , respectively , to pY1352 over Y1352 . Differences between the C2C12 model predictions and pulldown assay results arise because our models were designed to include only detectable interactions between human IGF1R and SH2/PTB domain-containing proteins . We kept binding affinities consistent across all cell line-specific models , including in simulations with the murine C2C12 cell line , an assumption based on previous findings that binding affinities are often evolutionarily conserved across eukaryotic organisms [72] . However , this approach resulted in exclusion from the model of many binding partners detected in the murine pulldown assay . For instance , human PLCG2 , not PLCG1 , has a detectable affinity for IGF1R [45 , 46] , so only PLCG2 was included as a potential binding partner in the model , yet expression of PLCG2 in C2C12 was negligible [70] . Therefore , the model parameterized with protein copy numbers for C2C12 cannot predict binding of either PLCG1 or PLCG2 . Likewise , GRB2 , SHP2 , PIK3CA , and PIK3CB were captured in the murine pulldown assay , but were not included in the models due to lack of detectable IGF1R binding in humans [45 , 46] . Thus , in this case human binding affinity data are not sufficient for predicting all binding partners in mouse myoblasts . The discrepancies between model predictions and experimental evidence could perhaps be rectified if KD data for murine SH2/PTB domain interactions with IGF1R were to become available . However , pulldown assays are not ideally suited for the task of evaluating rank order predictions . One intriguing experimental approach that could be useful for this purpose is mass-spectrometry ( MS ) -based phosphoproteomics , which was recently used by Jadwin et al . [17] to measure phosphorylation of specific phosphosites in full-length EGFR . Most pY sites are bound to an SH2/PTB-domain containing protein , so quantifying which sites are highly phosphorylated could validate predictions of which partners are highly bound . To determine whether cell line-specific IGF1R recruitment is indicative of tissue of origin , we performed hierarchical clustering to group cell lines according to similarity in protein recruitment profiles ( Fig 6 ) . In some cases , we do observe that similar tissue types clustered together . The HeLa S3 cell lines profiled by Kulak et al . [63] and the HeLa Kyoto cell line profiled by Hein et al . [69] were grouped together . Five out of six melanomas were co-clustered: M14 , SKMEL28 , UACC257 , MALME3M , and SKMEL5 . All of these cell lines had high recruitment of STAT1 and moderate recruitment of YES1 . We also observe co-clustering of ovarian cancer cell lines OVCAR5 , IGROV1 , and OVCAR4 , which had high predicted recruitment of YES1 . In examining the cell line clusters , we note that the source of the data contributes to similarity in predicted recruitment profiles . Eight out of the 11 cell lines profiled by Geiger et al . [67] are grouped together: RKO , U2OS , GAMG , LnCap , K562 , Jurkat , HEK293 , and HepG2 . These cell lines stem from disparate tumor or tissue types , but they were all predicted to have very high recruitment of CRKL ( more than one copy of CRKL bound to each molecule of IGF1R ) coupled with moderately high recruitment of either SRC , SHC1 , or RASA1 . Similarly , of the seven cell lines that were profiled with higher coverage by Gholami et al . [68] , four were clustered together: the ovarian carcinoma SKOV3 , non-small cell lung cancer H460 , the prostatic adenocarcinoma PC3 , and the acute lymphoblastic leukemia CCRFCEM . This result indicates that , on the basis of IGF1R signaling , these four cell lines are predicted to be more similar to one another than to cell lines from the same tissue type . However , we suspect that clustering is highlighting an artifact in the data . Most of the NCI-60 cell lines have only low coverage proteomics data available; therefore , copy numbers derived from these data may not accurately represent the protein abundance profiles of these cell lines . Thus , while it is clear that IGF1R binding partner recruitment is influenced by protein copy number , the quality of the proteomics data used to parameterize the models is critical in using simulations to generate reliable predictions . We will set aside concerns with the quality of the available proteomics datasets for the sake for demonstrating the capabilities of cell line-specific models . To further interrogate the mutual dependency among signaling proteins , we developed population-level models for IGF1R signaling . The population-level models are designed to account for natural cell-to-cell variability in protein levels present in a clonal population of cells [73] . This analysis produces a distribution of recruitment profiles across 5 , 000 cells in each simulated population , thereby allowing us to ascertain whether the recruitment of any pair of binding proteins is correlated . Fig 7 shows a matrix of the correlations between each pair of binding partners for the HeLa S3 , U251 , MCF7 , and GAMG population-level models ( correlations for all other cell lines are in S4 and S5 Figs ) . The degree of positive or negative correlation between any two proteins may arise from direct competition for receptor binding or via indirect effects , as discussed below . In the HeLa S3 model , the moderate negative correlation ( Pearson’s r = -0 . 34 ) between VAV2 and STAT1 arises from their direct competition for pY1161 . Both proteins are present at relatively large and comparable abundances ( 105 copies per cell ) , bind only to pY1161 with similar affinities , and therefore effectively antagonize each other . Other cell lines with a negative correlation between VAV2 and STAT1 included the leukemias CCRFCEM and SR , the melanoma M14 , the non-small cell lung cancers H226 and H460 , and the prostatic adenocarcinoma PC3 . Negative correlations are also observed with discrepancies in binding affinity or copy number between competing pairs . The weak negative correlation between CRKL and ABL2 in HeLa S3 cells ( Pearson’s r = -0 . 23 ) can be explained by competition for binding to pY973; these proteins have similar abundances , but ABL2 binds pY973 with approximately 40-fold higher affinity . Slight variations in protein copy number contribute to differing patterns of correlations across cell lines . Cell line-specific differences are especially evident when considering competition for pY1161 , to which nine different signaling proteins can bind . The pair of proteins displaying high negative correlations in the largest number of cell lines was YES1 and STAT1 , which compete for binding pY1161 and were negatively correlated in 19 cell lines , including MCF7 ( Fig 7 ) . The average Pearson’s r for YES1/STAT1 was -0 . 55±0 . 18 across these 19 cell lines . In each of these cell lines , the abundances of YES1 and STAT1 were within an order of magnitude of one another , and other proteins that bind pY1161 were either not expressed or expressed at a low level in comparison to YES1/STAT1 . In 14 cell lines , where STAT1 had low expression , and SRC expression was comparable to that of YES1 , strong negative correlations between YES1 and SRC were evident , with an average Pearson’s r = -0 . 67±0 . 21 across these 14 cell lines ( see correlations for MCF7 and GAMG in Fig 7 ) . Interestingly , positive correlations between binding partners are observed in several of the cell line-specific population-level simulations , despite there being no cooperative binding explicitly considered in the models . These positive correlations arise through an indirect mechanism . In four cells lines , including the gliomas U251 and SNB19 , the renal cell carcinoma TK10 , and the colorectal carcinoma HCC2998 , a pattern of positive correlations ( Pearson’s r ≥ 0 . 4 ) was identified for every pairwise combination of STAT1 , VAV2 , and YES1 ( see Fig 7 for U251 ) . Furthermore , we identified strong negative correlations between SRC and STAT1 , VAV2 , and YES1 in these cell lines . These four proteins compete for binding to pY1161 , but in these cellular backgrounds , SRC is expressed much more highly ( 107 copies per cell ) than STAT1 , VAV2 , or YES1 ( 102–106 copies per cell ) . Thus , stochastic decreases in expression of higher abundance competitors results in a net increase in the opportunity for binding lower abundance proteins , observable as a positive correlation between lower-abundance binding partners . In other words , positive correlations arise when a pair or group of proteins are similarly affected by a mutual competitor .
In cell signaling , many receptors , including IGF1R , are capable of activating multiple signaling pathways in response to a common input signal . Mathematical modeling provides an avenue for predicting which subset of possible interactions are more likely to occur in a combinatorially complex network [74] . Because of the wealth of empirical data available , IGF1-IGF1R signaling represents an ideal case for study of competition in cell signaling . Here , we developed mechanistic models of IGF1R signaling to provide a method for determining how competitive protein interactions play a role in early events in receptor-mediated signaling . Our analysis shows that differences in signaling protein expression can be expected to impact the rank order of protein recruitment . A protein with higher abundance and lower binding affinity in a given cellular background can outcompete other proteins with lower concentrations and higher affinities . Thus , a given extracellular input can preferentially activate different cellular functions in diverse cellular backgrounds . We adopted the rule-based modeling approach to develop models of IGF1R signaling . Given the large reaction network size implied by the rules as naturally formulated , simulation was a critical challenge . We addressed this challenge through model restructuration , meaning optimization of rule formulation so as to obtain a smaller rule-implied reaction network size without introducing approximations . We applied three strategies to restructure the model: decoupling , bunching , and scaling , the first two of which are related to previously described methods of model transformation ( see S1 File ) . Through these fairly straightforward transformations , we were able to enumerate equations describing IGF1-IGF1R binding and signaling protein recruitment that could be efficiently integrated with an ODE solver . Similar strategies could be useful in simulating similar biochemical systems . We demonstrated the difficulty of using naive prediction methods , such as copy number , binding affinity , or simple analytical expressions , to predict even the rank order of signaling protein recruitment . This is important , because it is still tempting to draw conclusions about recruitment profiles or important drug targets using measurements of only binding affinity or only RNA/protein abundance . Indeed , it is not unheard of in the literature to present recruitment profiles derived solely from KD values [46 , 54] , or to profess a phenotypic impact based on omics data [68] . While speculations based on simple metrics are not necessarily incorrect , our results show that a more detailed model exhibits nuances even in steady-state behavior that simple ranking metrics cannot recapitulate . Therefore , to best decipher early events in receptor signaling , it is recommended to build and simulate mechanistically-detailed models that incorporate experimentally determined parameters and/or are parameterized by fitting to experimental data . Mechanistic models of receptor signaling allow for consideration of the impacts of competition for binding phosphotyrosine residues , differences in binding affinity between SH2/PTB domains and pY sites , and differences in signaling protein abundance . We propose these models as reasonable first approximations of early events in IGF1R signaling . Our predicted rankings can be justified because we expect all signaling proteins to have a comparable number of alternate binding partners . The study of Koytiger et al . [46] shows that each of the SH2/PTB domain-containing proteins considered here could bind an average of 76±35 phosphotyrosine sites in RTKs , with a range between 28 for VAV2 and 131 for SYK ( excluding IRS1 , which was only detected to bind INSR ) . However , in the scenarios simulated here , we are considering a case where IGF1 is the only input stimulus , such that other RTKs are not activated and therefore not competing for the recruitment of these binding partners . We further evaluated the number of alternate binding partners by querying the String Database [75] for known interactions , and found that each of the IGF1R binding partners participates in a highly connected network , with an average of 43±9 edges in a network comprising one signaling protein and its ten closest partners ( minimum of 27 edges in the BLK network and maximum of 55 for the PIK3R1 and IRS1 networks ) . A number of the alternate partners interact with multiple IGF1R signaling proteins . After longer durations , differences in the number of possible alternate binding partners will impact the rank order of binding , as will differences in copy number of alternate binding partners , and differences in binding affinity for various interactions [76] . Given the extensive crosstalk present in metazoan signaling networks [77] , we expect that additional layers in the signaling network will maintain the same degree of complexity , such that the need for modeling and numerical simulation only increases as higher order effects are considered . Elevated IGF1R signaling has been implicated in many cancers , but thus far strategies to inhibit IGF1R have proven ineffective [78–80] . For instance , phase II or III trials evaluating the use of the anti-IGF1R mAbs ganitumab , dalotuzumab , and figitumumab were halted after showing no discernible anti-tumor activity or survival benefit [79] . Tyrosine kinase inhibitors ( TKIs ) such as linsitinib are able to inhibit IGF1R as well as the INSR , leading to toxicities from metabolic complications such as hyperglycemia [81] . Prolonged treatment of breast cancer with IGF1R TKIs has also been observed to promote resistance via activation of an alternate RTK , TYRO3 [80 , 82] . One burgeoning strategy is to target the IGF1R network rather than the receptor [80 , 83] . Simulations of our models could provide a rationale for selecting targets in the network that are more likely to be successful for treating cancers driven by IGF1R signaling . Here , we demonstrated the capabilities of the IGF1R model by developing cell line-specific models that used proteomics data available from diverse human cancer cell lines , including leukemias , melanomas , and breast , colon , prostate , renal , lung , bone , central nervous system , cervical , and ovarian cancers . Depending on the protein abundance profiles of these cell lines , our models generated predictions of the downstream information relays that would be preferentially activated . Many FDA-approved cancer drugs inhibit signaling proteins either directly recruited to IGF1R or downstream of the recruited proteins; thus , as different binding partners are more highly recruited in different cellular backgrounds , the most effective molecularly targeted drugs would likewise be cell type- or tumor-specific . For example , CRKL was predicted to be the most highly recruited protein across the cell lines evaluated here . CRKL activates MAPK signaling as well as AKT signaling via PI3K [84 , 85] , so we hypothesize that an effective approach against many cancers might be to use a combination of AKT and/or ERK inhibitors . Treatment strategies could be adjusted for tumors displaying rare recruitment profiles , such as the highly ranked recruitment of PLCG2 predicted for the non-small cell lung cancer H460 . Looking downstream of PLCG2 in the KEGG pathway database [86] , we hypothesize that a tumor with high recruitment of PLCG2 could be successfully treated with combinations of COX- , ERK- , and PKC-inhibitors . Another potential strategy would be to use an IGF1R mAb or TKI in combination with drugs targeting molecules downstream of the most highly ranked binding partners . Interestingly , our models do not predict highly ranked recruitment of the insulin receptor substrate 1 ( IRS1 ) , a canonical binding partner of INSR and IGF1R [87 , 88] . The average predicted rank of IRS1 recruitment across cell lines was 13±1 . 8 ( Fig 5A ) . IRS1 competes primarily with SHC1 for binding to phosphotyrosine sites in the juxtamembrane region of the receptors . Although the KD for IRS1 binding to IGF1R is not well-established , the PTB domain of IRS1 has an affinity for INSR phosphosites that is approximately 20-fold weaker than that of SHC1’s PTB domain [89] , and protein copy numbers for IRS1 tended to be two to three orders of magnitude lower than those of SHC1 ( S1 Table ) . Therefore , our models overwhelmingly predict recruitment of SHC1 over IRS1 . How then , might IRS1 be recruited to IGF1R and INSR ? One explanation could be that the ratio of SHC1 to IRS1 affinity for INSR is not in fact preserved for IGF1R . The amino acid sequences of INSR and IGF1R are not identical in the regions proximal to the respective tyrosines recruiting IRS1 and SHC1 , which could promote differences in binding affinity . Disparity in the ratio of SHC1 to IRS1 affinity for these similar receptors could be a mechanism for divergence in the biological function of these receptors . Indeed , IRS1 could actually bind IGF1R with extraordinarily strong affinity , in the nanomolar range , as has been reported in one study employing surface plasmon resonance technology [90] . An alternate explanation for the lack of IRS1 recruitment predicted by our models might be attributed to the role of IRS1’s pleckstrin homology ( PH ) domain , which has been shown to be important for receptor recruitment [91] . The mechanism by which the PH domain contributes to recruitment is somewhat ambiguous; only about 10–15% of human PH domains are believed to bind phospholipids with strong affinity [92] , and other evidence suggests that the IRS1 PH domain binds negatively charged regions of proteins [91] . Nonetheless , it is generally agreed that PH domains enhance membrane localization [93 , 94] , so it is conceivable that IRS1’s PH domain leads to a higher effective concentration of IRS1 near the juxtamembrane region of receptors , sufficient to enable IRS1 to compete with cytoplasmic SHC1 . Surprisingly , we observed that the study from which proteomics data were taken was predictive of clustering of results from cell line-specific model simulations . This finding was concerning , because we would expect binding partner recruitment to be influenced by cell type , and not by the lab collecting the data . The models here were parameterized with shotgun proteomics data , which are evidently not well-suited for this purpose . Issues with shotgun proteomics data could emerge from biases introduced during sample preparation , inadequate coverage , or a limited ability to accurately quantify lower-abundance peptides [95–97] . Our approach here unintentionally serves as an example of how batch effects in proteomics data can be identified . By fitting data to a common model and clustering results , it becomes possible to identify datasets that are potentially problematic . Going forward , mechanistic modeling efforts may benefit from the increasing availability of data from targeted proteomics methods , such as that generated by Shi et al . [98] . Targeted proteomics allows for higher-confidence quantification of a selected subset of proteins , with higher sensitivity and reproducibility than shotgun proteomics [96 , 99] . To the best of our knowledge , such data are not yet available for IGF1R . The idea that protein expression variability can significantly impact signal transduction has been previously proposed , including in studies of the EGFR , ErbB1 , AKT , and ERK signaling networks [98 , 100–102] . Our analysis potentially explains the seemingly redundant wiring of networks with multiple signaling pathways , as follows . The models presented here provide a testable hypothesis that a receptor does not discriminate among potential downstream signaling pathways . Rather , pathway selectivity is the consequence of expression levels of recruited signaling proteins and the competition among them for receptor binding domains . Therefore , gene regulatory mechanisms , by affecting protein copy numbers , can modulate the function of cell signaling networks .
We first developed a generic model for early events in IGF1R signaling that includes the following processes: IGF1 binding to IGF1R , autophosphorylation of IGF1R , dephosphorylation of phosphotyrosine sites , and reversible recruitment of signaling proteins with SH2 and PTB domains to IGF1R via interactions with phosphotyrosines . The generic model was specified using the BioNetGen language ( BNGL ) [30] . All model specification files are available in S1 Compressed/ZIP File Archive . Rules were written for binding interactions between six phosphorylated tyrosines in IGF1R and the SH2/PTB domains of IGF1R binding partners characterized in the studies of Gordus et al . [45] and Koytiger et al . [46] . The following is an example of a rule for the binding of the PTB domain of one such binding partner , SHC1 , to phosphorylated tyrosine residue 980 of IGF1R: SHC1 ( PTB ) +IGF1R ( Y980~P ) <->SHC1 ( PTB ! 1 ) . IGF1R ( Y980~P ! 1 ) ka , kd The above rule includes two rate constants , one for binding ( ka ) and one for unbinding ( kd ) , where mass-action kinetics are assumed . The kinase domain of each IGF1R monomer in a dimer was considered to be active if the dimer was crosslinked by IGF1 . Phosphorylation and dephosphorylation of the receptor were modeled as first-order reactions . We use the HUGO Gene Nomenclature Committee [103] approved gene symbols when referring to signaling proteins , where here the gene symbol in uppercase , non-italic font indicates a gene product . KD values for binding of signaling proteins are from Gordus et al . [45] and Koytiger et al . [46] , with the exception of IRS1 , for which no detectable IGF1R binding was found in the aforementioned studies . As there is much evidence to suggest that IRS1 does interact directly with IGF1R [87 , 88] , we approximated a KD value based on quantification of interactions with INSR . Farooq et al . [89] found that the affinity of SHC1 recruitment to INSR at pY972 was approximately 20-fold stronger than that of IRS1 . We assumed the same relation holds between SHC1 and IRS1 binding to IGF1R’s pY980 , and set a KD for IRS1 equal to 20-fold higher than that of SHC1 binding . Association rate constants ( ka ) for signaling protein binding were set to 1×106 M-1s-1 , and dissociation rate constants were calculated using the formula kd = ka×KD . Some proteins have multiple SH2 domains reported as binding to the same tyrosine site , such as the two SH2 domains in PIK3R2 reported to bind pY1346 [46] . For these proteins , we assumed that the domain with the higher binding affinity would dominate binding and accordingly used the lower of the KD values in the model . We do not consider the possibility of two-point attachment of proteins with multiple SH2/PTB domains to adjacent phosphotyrosines in IGF1R . As a simplification , we do not consider receptor trafficking . The ligand dissociation curves to which we fit our model were obtained at low temperatures , which is expected to reduce recycling [44] . The models formulated in this study are not intended to capture the dynamics of later IGF1R signaling events . We do not consider any interactions between the binding partners of IGF1R themselves , nor do we attempt to predict the activation and subsequent activity of the binding partners after recruitment to IGF1R . We used three strategies to restructure the natural model formulation and reduce state redundancy: decoupling , bunching , and scaling ( S2 Fig ) . These strategies are described in further detail in S1 File . 1 . Decoupling . The state of each phosphotyrosine site can be decoupled from the others because the phosphorylation dynamics of each site are assumed to be independent . If the state of one tyrosine site is not affected by the states of other sites , we can consider each of the six individual tyrosine sites as separate entities . 2 . Bunching . The configurations of the S1 and S2 binding sites in a dimeric IGF1 receptor can be considered together as one binding pocket . Bunching the S1 and S2 sites is acceptable because the possible states for one site depend on the state of the other site in the pocket; both sites cannot simultaneously be bound to separate ligands . Specifically , the configuration of the S1-S2 binding pocket can either be 1 ) both sites are free , 2 ) one of the S1-S2 pair is bound to a ligand , or 3 ) both S1 and S2 sites in the pocket are bound to the same ligand ( crosslinked ) . The same argument applies to the other binding pocket in the dimeric receptor . Using this representation , we grouped the individual states of four Si’s into states of two pairs of binding pockets . 3 . Scaling . After bunching IGF1 binding sites and decoupling tyrosine sites , the model accounts for six forms of receptor , with two identical tyrosine sites associated with each dimeric receptor form . Because the two sites in the same dimer form are assumed to act independently , one can construct a model as if there are six forms of receptor , each of which has only one tyrosine site . To preserve mass-action kinetics , one must increase the abundance of the ligand and receptor each by a factor of two and reduce the second-order rate constants characterizing interactions between the ligands and receptors ( a1 and a2 ) by a factor of two . Restructuration implies a model form that is equivalent to the natural formulation of a model , but with the elimination of many redundant states . As such , our restructured model is equivalent to the original model from which it was derived . We discuss this equivalency mathematically in S1 File , and through numerical simulation in S1 Fig . Parameter estimation was performed with the BioNetFit software package [61] . BioNetFit is a tool designed to fit BNGL-formatted models to data through use of an evolutionary algorithm . Evolutionary algorithms are population-based metaheuristics inspired by nature that involve independent evaluation of parameter sets across parameter space and perturbation of the parameters over multiple population generations . While fitting the IGF1R model to experimental data , we found and fixed a number of minor bugs in the BioNetFit code base . The modified source code for the version used in this work , v1 . 01 , is available at https://github . com/RuleWorld/BioNetFit . To ensure that the initial set of parameter values effectively sample parameter space where distinct parameters may span multiple orders of magnitude , we normalize the parameters through the use of multiplicative factors so that each parameter can be perturbed on the same scale ( see S2 Compressed/ZIP File Archive ) . Finally , we used a bootstrapping procedure that is embedded in BioNetFit to characterize the uncertainty of the fitted parameters . This procedure samples the experimental data points with replacement and refits the model to the bootstrap sample [104] . The 2 . 5% and 97 . 5% percentiles of observed parameters from 2 , 000 independent fits to bootstrap samples were used to create the 95% confidence interval . To obtain the confidence interval for parameter a'2 , we calculated the value of a'2 using Eq 1 for each of the 2 , 000 bootstrapping fits , and calculated the confidence interval using percentiles in the same manner as for the other parameters . The generic IGF1R signaling model can be explicitly parameterized as a cell line-specific model . To define a cell line-specific model , we incorporate signaling protein copy numbers consistent with the corresponding cell type from datasets available in the literature ( S1 and S2 Tables ) . To make a model representing IGF1R signaling in a HeLa S3 cell , we specify abundances of the signaling proteins consistent with their reported copy numbers in HeLa S3 cells [63] . Hein et al . [69] provided absolute copy numbers for HeLa Kyoto cells . Deshmukh et al . [70] reported normalized relative abundances for C2C12 cells . Geiger et al . [67] quantified protein abundances in A549 , GAMG , HEK293 , HeLa S3 , HEPG2 , Jurkat , K562 , LnCap , MCF7 , RKO , and U2OS cell lines . Models for the NCI-60 cell lines were parameterized with data from Gholami et al . [68] . We used the higher coverage deep proteomics data for the seven cell lines for which these data were available: MCF7 , M14 , COLO205 , CCRFCEM , U251 , H460 , PC3 , SKOV3 , and RXF393 . Of the remaining NCI-60 cell lines , 24 had IGF1R expression that was nonzero . The proteomics profiling data were used to parameterize models for these cell lines . To calculate copy numbers from relative abundances , we assumed the same copy number of IGF1R as reported by Kulak et al . [63] and scaled the other copy numbers accordingly . This assumption is acceptable because we are interested in determining only the relative rank ordering of binding partner recruitment . With the exception of the protein copy numbers , the model assumes other parameters of the generic model remain the same across different cell types . We expanded the IGF1R signaling models into population-level models , which account for intrinsic and extrinsic contributions to cell-to-cell variability in expression of IGF1R and IGF1R-binding proteins across populations of clonal cells . In the population-level models , the copy number of any protein i in cell k is a value sampled from a log normal distribution: Xk , i = eμi+σiN . Here , μi is the mean and σi is the standard deviation . The mean μi represents the natural logarithm of the population-averaged copy number of the corresponding protein used in single-cell IGF1R signaling models . The population averaged copy numbers are listed in S1 Table . We assumed the same standard deviation σi = 0 . 2 for all proteins , a value consistent with experimentally determined cell-to-cell variability in protein copy number [105 , 106] . N is a random variable sampled from the standard normal distribution . Scripts used to generate the population models are provided in S3 Compressed/ZIP File Archive . For the restructured model , BioNetGen was used to generate a system of ODEs . We numerically integrated the resulting system of equations using CVODE/SUNDIALS [107 , 108] , which is included in the BioNetGen package . For the natural model formulation , because of the intractability of generating the large reaction network described by model rules , we performed network-free simulation using NFsim [35] . Recruitment data were obtained from steady-state simulations in individual cell line models . We normalized recruitment of each protein as the ratio of the absolute number of bound copies of the protein over the total amount of IGF1R . Some proteins can bind to IGF1R in multiple locations , so the ratio can be greater than one . Hierarchical clustering of cell lines and binding proteins according to normalized recruitment was performed with Python 2 . 7 and the clustermap function of Seaborn [109] . Clustering linkage was per Ward’s method [110] . Ranks in Fig 4 and S2 File were determined using five different metrics , which are based on either 1 ) copy number information only , 2 ) KD values only , 3 ) ratios of copy number to KD values only , 4 ) the rule-based model simulation for single cells , which includes protein copy numbers , site-specific KD values , and accounts for competition for phosphotyrosine sites , and 5 ) the analytical approximation , which includes copy number , KD values and a simplified treatment of competition in which only one binding partner can interact with an IGF1R dimer at one time ( S3 File ) . To calculate correlations between the recruitment of signaling protein pairs , we considered a population of 5 , 000 cells for each of the cell line-specific population-level models . In the models , the cells were stimulated with 1 nM IGF1 . We created the correlation matrices in Fig 7 , S4 Fig and S5 Fig based on steady-state recruitment of proteins to IGF1R . For a given protein i∈{1 , … , m} , where m = 18 , we collected its steady-state IGF1R binding Yk , i in cell k∈{1 , … , n} , where n = 5 , 000 . We calculated the mean receptor recruitment of the protein as yi=∑k=1nyk , i and the standard deviation follows as σi={∑k=1n ( Yk , i−yi ) 2/ ( n−1 ) } . Finally , the Pearson's correlation coefficient between each pair of proteins i and j is pi , j=cov ( Yi , Yj ) σiσj , where cov ( Yi , Yj ) =∑k=1n ( Yk , i−yi ) ( Yk , j−yj ) . We used SciPy to calculate correlation coefficients from population-level data . | Cells rely on networks of interacting biomolecules to sense and respond to environmental perturbations and signals . However , it is unclear how information is processed to generate appropriate and specific responses to signals , especially given that these networks tend to share many components . For example , receptors that detect distinct ligands and regulate distinct cellular activities commonly interact with overlapping sets of downstream signaling proteins . Here , to investigate the downstream signaling of a well-studied receptor tyrosine kinase ( RTK ) , the insulin-like growth factor 1 ( IGF1 ) receptor ( IGF1R ) , we formulated and analyzed 45 cell line-specific mathematical models , which account for recruitment of 18 different binding partners to six sites of receptor autophosphorylation in IGF1R . The models were parameterized using available protein copy number and site-specific affinity measurements , and restructured to allow for network generation . We find that recruitment is influenced by the protein abundance profile of a cell , with different patterns of recruitment in different cell lines . Furthermore , in a given cell line , we find that pairs of IGF1R binding partners may be recruited in a correlated or anti-correlated fashion . We demonstrate that the simulations of the model have greater predictive power than protein copy number and/or binding affinity data , and that even a simple analytical model cannot reproduce the predicted recruitment ranking obtained via simulations . These findings represent testable predictions and indicate that the outputs of IGF1R signaling depend on cell line-specific properties in addition to the properties that are intrinsic to the biomolecules involved . | [
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| 2019 | Modeling cell line-specific recruitment of signaling proteins to the insulin-like growth factor 1 receptor |
Human excreta is a low cost source of nutrients vital to plant growth , but also a source of pathogens transmissible to people and animals . We investigated the cost-savings and infection risk of soil transmitted helminths ( STHs ) in four scenarios where farmers used either inorganic fertilizer or fresh/composted human excreta supplemented by inorganic fertilizer to meet the nutrient requirements of rice paddies in the Red River Delta , Vietnam . Our study included two main components: 1 ) a risk estimate of STH infection for farmers who handle fresh excreta , determined by systematic review and meta-analysis; and 2 ) a cost estimate of fertilizing rice paddies , determined by nutrient assessment of excreta , a retailer survey of inorganic fertilizer costs , and a literature review to identify region-specific inputs . Our findings suggest that farmers who reuse fresh excreta are 1 . 24 ( 95% CI: 1 . 13–1 . 37 , p-value<0 . 001 ) times more likely to be infected with any STH than those who do not handle excreta or who compost appropriately , and that risk varies by STH type ( Ascaris lumbricoides RR = 1 . 17 , 95% CI = 0 . 87–1 . 58 , p-value = 0 . 29; Hookworm RR = 1 . 02 , 95% CI = 0 . 50–2 . 06 , p-value = 0 . 96; Trichuris trichiura RR = 1 . 38 , 95% CI = 0 . 79–2 . 42 , p-value = 0 . 26 ) . Average cost-savings were highest for farmers using fresh excreta ( 847 , 000 VND ) followed by those who composted for 6 months as recommended by the WHO ( 312 , 000 VND ) and those who composted for a shorter time ( 5 months ) with lime supplementation ( 37 , 000 VND/yr ) ; however , this study did not assess healthcare costs of treating acute or chronic STH infections in the target group . Our study provides evidence that farmers in the Red River Delta are able to use a renewable and locally available resource to their economic advantage , while minimizing the risk of STH infection .
Application of human excreta onto rice paddies as fertilizer is a common practice in northern Vietnam , where many farmers use single or double vault latrines , lack access to wastewater infrastructure , and have variable access to commercial inorganic fertilizers [1] . Using organic waste to fertilize fields has clear benefits for crop yield [2]; however , this practice increases certain health risks for farmers and consumers , such as infection by soil transmitted helminths ( STHs ) [3 , 4] . The STH group includes Ascaris lumbricoides , Trichuris trichiura , and hookworm spp . , which are intestinal parasites that spread between people when sanitation is inadequate or when good hygiene is not practiced [4] . People are infected when they accidentally ingest infective eggs or when their skin contacts infective larvae in contaminated soil . These parasites are particularly prevalent in regions with warm , moist climates , and are included in the category of tropical neglected diseases associated with poverty . World Health Organization ( WHO ) guidelines recommend that farmers compost human excreta for six months prior to application in order to inactivate STH eggs and larvae , and thereby reduce spread between people [5] . This practice is not feasible for all Vietnamese farmers , in particular those who harvest multiple crops per year or have single vault latrines that lack a chamber for long-term excreta storage . Current evidence suggests that only one-third of farmers who use human excreta follow the six-month recommendation [6] , and that STH infection remains an occupational hazard associated with handling human excreta [3] . It is common practice for household members to add a handful of kitchen ash after using a latrine , as this reduces smell . A recent study characterizing A . lumbricoides egg die-off during excreta composting suggests that adding lime reliably accelerates egg inactivation so that WHO criteria for safe handling ( <1 viable egg/g total solids ) are met by 153 days [7] . Ascarid eggs can survive longer periods in adverse environmental conditions than other STHs , and for that reason we chose A . lumbricoides die-off as a proxy for overall STH die-off [8] . Rice farmers in some agricultural regions of Vietnam have shifted their source of fertilizer from human excreta to commercial inorganic products , either wholly or in part . It is unclear whether this trend will become universal as not all farmers are able to afford or access commercial fertilizer , and others consider human excreta a superior source of long-term nutrition for plants and soil [9] . Inorganic fertilizers are primarily imported , and their costs are influenced by a wide range of factors , including energy prices [10] . Using human waste to fertilize crops is recognized as a way to decrease household expenditures; however , it is unclear how costs and health risks associated with STH infection interact . The goal of this study was to compare the costs and STH risk associated with fertilizing rice paddies in the Red River Delta ( RRD ) .
The RRD encompasses eight provinces and two major urban municipalities ( Hanoi and Haiphong ) in northern Vietnam . The RRD is an agriculturally intense area that produces approximately 15% of the national annual rice output [11] . Throughout the region , farmers use various combinations of human excreta , inorganic fertilizers , and animal manure to replenish soil nutrients and maximize rice yield . To generate cost estimates , we chose four fertilization scenarios: ( A ) Fresh human excreta ( ≤ 139 day storage without lime ) ; ( B ) Composted human excreta ( 153 day storage with 10% lime as per [7] ) ; ( C ) Composted human excreta ( 181 day storage without lime; WHO standard [5] ) ; ( D ) Inorganic fertilizer . Although three scenarios ( A-C ) involved human excreta , we assumed that only farmers who handled fresh excreta ( A ) would experience STH infection risk , as the composting scenarios ( B and C ) met WHO standards for helminth inactivation . Risk of STH infection for Vietnamese farmers handling fresh excreta was evaluated by systematic review and meta-analysis . Our economic analysis of the four scenarios included the direct costs incurred for composting human excreta ( i . e . lime ) and supplementing excreta with inorganic fertilizers . Capital costs ( i . e . cost to build a double vault latrine ) were not included because differences in factors such as materials and design cause costs to vary substantially in the RRD , and would add a high level of uncertainty to our analysis . To estimate the direct costs , we determined nutrient content of organic fertilizer scenarios , conducted a retailer survey of inorganic fertilizers in the study area , and collected economic inputs from published sources specific to the RRD ( e . g . household size , excreta production per household , annual harvest frequency , average paddy size ) .
This study adds to current knowledge about the opportunities and risks associated with reusing human excreta to fertilize rice plants in one region of Vietnam . Our finding that handling fresh excreta increases the risk of STH infection in farmers ( RR = 1 . 24 , 95% CI: 1 . 13–1 . 37 ) emphasizes the importance of adequately treating excreta to inactivate STH life stages . Furthermore , the risk is not limited to farmers as fresh excreta reuse facilitates STH spread to other commune residents , and ultimately to consumers , through food , water , and environmental transmission routes . This practice is one factor contributing to the high prevalence of A . lumbricoides ( 44 . 4%; N = 34 million ) , T . trichiura ( 23 . 1%; N = 17 . 6 million ) and hookworm ( 28 . 6%; N = 21 . 8 million ) infections in Vietnam [23] . Our study did not assess healthcare costs associated with STH prevention , treatment , or chronic disability . Individuals with low intensity infections are often asymptomatic; however , those with high intensity infections can experience a variety of acute or chronic conditions ( e . g . diarrhea , abdominal discomfort , anemia and rectal prolapse ) that reduce quality of life and may require costly medical interventions to treat [4] . Long-term sequelae of chronic infections , such as impaired cognitive development and growth faltering , can negatively impact lifelong earnings and contribute to the cycle of poverty in low resource communities . Although our study showed that farmers using fresh excreta benefitted from the largest cost-savings in fertilizer expenditure , the direct and indirect societal costs incurred due to prolonged STH infection would likely outweigh these savings . Despite laws that prohibit use of human excreta for agriculture in Vietnam , this practice remains common among certain farming groups [24] . Human excreta is perceived as more valuable than animal manure due to differences in dietary protein content , and it is believed to improve soil structure more sustainably than inorganic fertilizers [9] . Although many farmers compost excreta , WHO recommendations for hygienic composting are not commonly followed , as farmers harvest multiple crops per year and are unwilling or unable to store excreta for six months prior to use [5 , 6] . Some misperceptions about the reasons for safe composting might influence farmer willingness to use fresh excreta . For example , focus group participants in the RRD emphasized ease of application and benefits to soil structure , rather than the benefits of composting to protect human health [1] . Our alternative to the WHO standard , composting for 153 days with 10% lime to accelerate STH inactivation , was not a reasonable alternative for farmers prioritizing cost savings . However , for farmers less concerned about cost savings , the 10% lime compost strategy could be further accelerated to 111 days by adding aeration to latrines , which would allow excreta to be safely handled at more frequent intervals [7] . Human excreta use in crop agriculture was previously estimated to represent 83 million USD in fertilizer import savings to the Vietnamese economy [6] , which is one-fifth of the 2014 net expenditure on inorganic fertilizer importation ( 384 million USD ) [25] . Our cost analysis indicated that farmers could save 37 , 000–847 , 000 VND/yr ( 1 . 48–37 . 28 USD , $2017 ) [26] by using human excreta . While these savings might appear low , they represent 1–22% of a farmer’s average annual income in the RRD [27] . Furthermore , the savings could represent a higher percent of annual income in regions that are less fertile , where rice yields are lower , or in remote locations where transportation challenges result in higher commercial fertilizer costs . Another report , suggesting that household excreta traded on the domestic market could contribute up to 15% of household income for those in the lowest income quintile , is in line with our analysis [6] . Therefore , it is unlikely that low-income rural farmers would be willing to universally replace organic fertilizers of human origin with inorganic commercial fertilizers . This was previously demonstrated by farmers who were given non-composting latrines and who ultimately broke the seals open to access the excreta [24] . Our nutrient analysis of human excreta originating from the RRD and composted over time demonstrated excreta to be an adequate organic source of phosphorus and potassium , but not nitrogen , for plant growth . Therefore , all of the scenarios using human excreta ( A-C ) required additional inorganic fertilizer in order to meet the recommendations for optimizing rice yield . It is not clear how far outside Vietnam these results should be extrapolated as differences in dietary intake directly influence NPK excretion , and soil supplementation requirements vary regionally . Furthermore , our analysis was based on total excreta collected in a double vault latrine , rather than waste separated into liquid and solid components , as occurs in some other regions that use excreta . However , beyond the immediate economic and agricultural gains to reusing excreta , there are global benefits to nutrient recycling . It is estimated that the demand of phosphate rock will outweigh supply by the mid-21st century , which has important consequences for food security as phosphorus is essential for plant growth [28] . As access to a hygienic toilet ( flush , pour flush , sulabh or double vault latrine ) is still regionally variable in Vietnam ( 61 . 6–96 . 7% of homes containing a latrine ) , an opportunity currently exists to optimize nutrient recovery infrastructure in homes requiring sanitation upgrades [27] . Our systematic review and meta-analysis found a statistically significant higher risk of infection with any STH among Vietnamese farmers who use human excreta , and highlighted the limited volume of evidence to describe this association . Only four studies met our inclusion criteria , despite searching academic and grey-literature sources in Vietnamese and English . Of these , only three were included in meta-analysis due to poor reporting quality . Out of a possible score of 13 , two studies achieved a quality score of 50% or lower . Our findings showed that studies often did not report descriptions of appropriate sample size determinations , confounders controlled for , sample size for positive exposure and/or outcomes , as well as risk estimates or associated p-values . However , aside from one study , all studies were from the RRD study area , included participants of similar age and gender , and estimated exposure and outcomes using similar methods . Each study had slightly different definitions for agricultural use of human excreta , and therefore our meta-analysis included both individuals whose primary occupation was rice farming , but also those who worked with human excreta in other ways aside from direct field application . Inclusion of Yajima et al . , 2009 in the meta-analyses of both T . trichiura and A . lumbricoides produced significant heterogeneity in the estimates of infection risk . This study had a small sample size and very low prevalence of STHs ( i . e . one case of A . lumbricoides detected ) , leading to low risk ratios and wide confidence intervals . The study did not provide information on approaches used to measure or control for potential confounders , further adding to the difficulty in interpretation of protective properties of human excreta use in STH . Pooled results by STH type revealed the lowest risk for hookworm infection , and significant variation in studies combined in meta-analysis for this outcome . It was not possible to examine potential factors contributing to heterogeneity in hookworm risk estimates due inclusion of only two studies in meta-analysis . Therefore , in order to better understand factors influencing infection and to substantiate the limited body of evidence on STH risk in the RRD of Vietnam , additional research employing high methodological rigour is warranted . Although our study attempts to represent the typical situation in the RRD , much of our data comes from Ha Nam province exclusively which may differ in relevant ways from other RRD provinces . The estimate of risk was limited by the number of estimates reported in the literature and we assumed that STH risk was equal in scenarios B-C . Human excreta was collected from various households and mixed before analysis . Thus , results may not accurately reflect the nutrient and moisture content of unmixed excreta if collected and analysed from time of defecation . Costs related to the removal of excess human excreta , latrine construction and maintenance , and personal safety equipment were not explored .
Our study confirmed that human excreta is a significant and sustainable source of nutrients needed for crop fertilization . Its use as agricultural fertilizer , a common practice in Vietnam , offers direct benefits to rice farmers . Human health , agricultural productivity , household earnings are optimized when farmers follow WHO standards for excreta use and government standards for crop fertilization; however current policies prohibit excreta use altogether and therefore may need to be revisited . Furthermore , our results suggest that farmers and the Vietnamese economy would benefit by forward thinking public health messaging promoting STH prevention , such as safe excreta handling strategies , personal protective equipment ( e . g . gloves and boots ) and regular anthelmintic prophylaxis , rather than an outright ban on excreta use . This study highlights agricultural policies needing further attention , and demonstrates the value of promoting research that provides innovative solutions for safely and economically extracting nutrients from human excreta . | Each year , hundreds of millions of people worldwide are infected with intestinal worms spread by contaminated soil , also known as soil transmitted helminths ( STHs ) . These worms are most common in tropical climates in areas lacking good hygiene and sanitation , and negatively impact child development , quality of life , and economic wellbeing . Reuse of human excreta for fertilizer is a common practice in many low to middle income countries because farmers require a low cost source of nutrients to grow food crops eaten by people and animals . Excreta can contain microbes , such as STHs , that cause disease in people; however , composting is a known method of killing STHs . Therefore , our goal was to determine if Vietnamese rice farmers involved in this practice are at higher risk of STH infection , and to calculate the amount of money saved by farmers composting for different lengths of time , and supplementing with various commercial fertilizers . We suggest that farmers compost excreta for six months to reduce disease exposure and optimize household savings . Optimizing practices to improve food production and protect farmer health is critical for poverty alleviation in low to middle income countries . | [
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| 2017 | Turning poop into profit: Cost-effectiveness and soil transmitted helminth infection risk associated with human excreta reuse in Vietnam |
Corpus allatum ( CA ) ablation results in juvenile hormone ( JH ) deficiency and pupal lethality in Drosophila . The fly CA produces and releases three sesquiterpenoid hormones: JH III bisepoxide ( JHB3 ) , JH III , and methyl farnesoate ( MF ) . In the whole body extracts , MF is the most abundant sesquiterpenoid , followed by JHB3 and JH III . Knockout of JH acid methyl transferase ( jhamt ) did not result in lethality; it decreased biosynthesis of JHB3 , but MF biosynthesis was not affected . RNAi-mediated reduction of 3-hydroxy-3-methylglutaryl CoA reductase ( hmgcr ) expression in the CA decreased biosynthesis and titers of the three sesquiterpenoids , resulting in partial lethality . Reducing hmgcr expression in the CA of the jhamt mutant further decreased MF titer to a very low level , and caused complete lethality . JH III , JHB3 , and MF function through Met and Gce , the two JH receptors , and induce expression of Kr-h1 , a JH primary-response gene . As well , a portion of MF is converted to JHB3 in the hemolymph or peripheral tissues . Topical application of JHB3 , JH III , or MF precluded lethality in JH-deficient animals , but not in the Met gce double mutant . Taken together , these experiments show that MF is produced by the larval CA and released into the hemolymph , from where it exerts its anti-metamorphic effects indirectly after conversion to JHB3 , as well as acting as a hormone itself through the two JH receptors , Met and Gce .
Juvenile hormones ( JHs ) are members of a family of sesquiterpenoid compounds synthesized primarily by the corpus allatum ( CA ) of insects . Several forms of JH have been identified , including JH 0 , JH I , 4-methyl JH I , JH II , JH III , JH bisepoxide ( JHB3 ) and JH skipped bisepoxide . JH III is found in most insect orders , whereas JH 0 , JH I , and JH II are exclusive to Lepidoptera [1] . JHB3 is unique to higher Diptera , such as the fruit fly , Drosophila melanogaster [2] , and JH skipped bisepoxide has been described in Heteroptera [3] . Methyl farnesoate ( MF ) is the major sesquiterpenoid identified in the hemolymph of crustaceans , in which it might play the role of a JH [4] . MF lacks the epoxide moiety present in other JHs , and it is usually considered as an immediate precursor of JH III in Insecta [1] . The potential role of MF as a true JH in insects has been an issue of a long-standing debate; it has JH activity in the Drosophila white puparial bioassays and is abundant in the hemolymph of several insects [5–10] . The biosynthetic pathway of JH III in the CA of insects involves 13 discrete enzymatic reactions and is conventionally divided into early and late steps ( S1 Fig ) [1] . The early steps follow the mevalonic acid pathway to form farnesyl pyrophosphate [11] . 3-hydroxy-3-methylglutaryl CoA reductase ( HMGCR ) , the rate-limiting enzyme for mevalonic acid biosynthesis in mammals , is also an important enzyme in the early steps of JH biosynthesis [11] . In the late steps of JH III biosynthesis , farnesyl pyrophosphate is sequentially transformed to farnesol , farnesal and farnesoic acid ( FA ) [1] . The order of the last two biosynthetic steps , methyl esterification and epoxidation , catalyzed by a JH acid ( JHA ) methyltransferase ( JHAMT ) and a P450 epoxidase , differs among insect species: epoxidation precedes methylation in Lepidoptera , whereas epoxidation follows methylation in Diptera , Orthoptera , Dictyoptera , Coleoptera and probably most other insect orders [12–17] . The Drosophila CA produces and releases three sesquiterpenoids: JHB3 , JH III , and MF [2 , 9 , 10 , 18 , 19] . However , the entire JH biosynthetic pathway in Drosophila has not been well defined to date . One major function of JH is to inhibit action of the molting hormone ( 20-hydroxyecdysone , 20E ) for preventing metamorphosis during the larval molts [1] . In JH-deficient animals in which the CA is genetically ablated , JH prevents 20E-triggered apoptosis of the larval fat body [20 , 21] and precocious differentiation of the optic lobe in the adult brain [22] in Drosophila . JH serves an equally important function , regulating various aspects of reproductive maturation in most insects [1] . For example , incomplete ablation of the CA results in a partial deficiency of JH with an associated reduction in reproductive capacity in Drosophila [23] . The recent discovery that the JH-resistance gene , Methoprene-tolerant ( Met ) , plays a critical role in insect metamorphosis has been followed by a rapid increase in our understanding of JH signaling [24] . Met and Gce , two paralogous bHLH transcription factors in Drosophila , are involved in JH action [25 , 26] . Although both the Met and gce null mutants are viable , the Met gce double mutant dies during the larval-pupal transition [21] , similar to that observed in JH-deficient animals [20 , 22] . Functionally , Met and Gce mediate JH action to prevent the 20E-triggered metamorphic events [20–22] . Moreover , Met and Gce bind to JH at physiological concentrations in vitro [27 , 28] , suggesting that they are JH receptors . In parallel , Met is also involved in JH action as a receptor in the red flour beetle , Tribolium castaneum [28 , 29] . Downstream of Met , the anti-metamorphic action of JH is transduced by Krüppel-homolog 1 ( Kr-h1 ) , a transcription factor involved in JH action . A number of studies in Drosophila [21 , 30 , 31] and several other insect species [24] have shown that Kr-h1 is a JH primary-response gene . As shown in previous studies [20 , 22] , genetic ablation of the CA results in JH deficiency and pupal lethality in Drosophila . To further clarify the roles of JHs in Drosophila , we generated a jhamt mutant . Surprisingly , the jhamt mutant is viable and its MF biosynthesis was not affected . Further , MF was demonstrated to exert crucial roles for completion of Drosophila metamorphosis , by both acting directly as a JH and indirectly after conversion to JHB3 .
Genetic ablation of the CA results in JH deficiency and pupal lethality in Drosophila [20 , 22] , while traces of the CA cells are often still present in the ring gland ( RG ) of the ablated animals during the early larval stages . To further clarify the roles of JHB3 , JH III , and MF in Drosophila , we generated a jhamt mutant , which was expected to disrupt the JH biosynthetic pathway and to result in lethality at pupal or earlier stages . The ends-out gene targeting method was utilized to replace the entire jhamt open reading frame with the white gene via homologous recombination [32] ( Fig . 1A ) . Three independent jhamt mutant lines ( jhamt1 , jhamt2 , and jhamt3 ) were obtained and validated by PCR analysis of genomic DNA ( Fig . 1B ) . The mRNA of jhamt was not detectable in the CA of the jhamt mutants at 3 h after the initiation of wandering ( 3h AIW ) , a time when JH titer [10] , JH biosynthesis [2] and jhamt mRNA levels [13] are high ( Fig . 1C ) . Immunohistochemical studies revealed the absence of JHAMT protein in the CA of the jhamt mutants at 3h AIW ( Fig . 1D and 1D’ ) . Taken together , these studies showed that jhamt1 and jhamt2 are null alleles . For consistence , jhamt2 was used in all the subsequent studies . JH-dependent phenotypes were evaluated in jhamt2 in comparison with w1118 , the wild type fly used to generate jhamt mutants . Approximately 10% of jhamt2 larvae died during the larval stage , with the rest surviving to adulthood ( Fig . 1E ) . In addition , the initiation of wandering was delayed for about 4 hours in jhamt2 larvae ( S2A Fig ) , whereas body weight was not affected ( S2B Fig ) . The fecundity of jhamt2 adult females decreased by about 80% , whereas topical application of methoprene ( 0 . 5×10-3 μmol per female ) partially restored fecundity ( S2C Fig ) . The ovary size of the 6-day-old jhamt2 virgin females was significantly reduced . However , methoprene partially restored ovary growth ( S2C’ Fig ) . The CA-specific Aug21-GAL4 was used for genetic ablation of the CA in previous studies [20 , 22] . We performed a genetic rescue experiment with Aug21-GAL4 driving UAS-jhamt overexpression in a jhamt2 background . Importantly , fecundity and ovary growth of jhamt2/jhamt2; Aug21-GAL4>UAS-jhamt were restored to similar levels to those in w1118 ( S2D and S2D’ Fig ) , showing that the reproductive capacity in jhamt2 was fully rescued by CA-specific jhamt overexpression . Overall , the phenotypic changes in jhamt2 were similar to those described for Aug21-GAL4>UAS-reaper::UAS-hid animals , in which the CA is incompletely ablated and JH is partially deficient [23] . However , jhamt2 showed less robust effects than those observed in JH-deficient Aug21-GAL4>UAS-Grim ( Aug21>Grim ) animals , in which CA activity is efficiently disrupted [20 , 22] . To verify whether jhamt2 might be only partially JH-deficient , we measured the activity of methyltransferase in the brain-RG complexes isolated from 3h AIW larvae using either FA or JHA as substrates [14 , 20 , 23 , 33] . In w1118 larvae , the methyltransferase activity using FA as substrate was at least 10-fold higher than that using JHA ( Fig . 2A ) . In jhamt2 larvae , the activity of methyltransferase using JHA as the substrate was similar to that of wild-type glands , whereas the activity of methyltransferase using FA as the substrate decreased by 90% when compared to that in wild-type glands ( Fig . 2A ) . Using the radiochemical assay followed by thin layer chromatography analysis , we studied the biosynthesis of JHB3 , JH III , and MF by the brain-RG complexes dissected from 3h AIW larvae . As previously reported [2 , 18 , 19] , JHB3 was the most abundant product released by wild-type glands , the amount of MF released was about half that of JHB3 , whereas JH III was produced at the lowest rate . Remarkably , although JHB3 biosynthesis in jhamt2 larval glands decreased by 75% when compared to that in wild-type glands , the rates of JH III and MF biosynthesis were not affected ( Fig . 2B ) . Finally , using a recently developed HPLC-FD protocol [34] , we measured whole body titers of JHB3 , JH III , and MF in 3h AIW larvae . In w1118 larvae , MF was the most abundant sesquiterpenoid ( ~670 fmol/larva ) , followed by JHB3 ( ~18 fmol/larva ) and JH III ( ~2 . 5 fmol/larva ) ( Fig . 2C–2C” ) . Although JHB3 showed higher biosynthetic rates , MF showed a higher titer in the larvae , suggesting that MF could be more stable than JHB3 in the body . Whole body titers of JHB3 , JH III , and MF in jhamt2 larvae decreased by approximately 70% , 50% , and 30% ( no statistical difference ) to their respective control levels ( Fig . 2C–2C” ) . Our data thus suggest that 1 ) jhamt is critical for JHB3 biosynthesis , but not for the biosynthesis of MF and JH III , and 2 ) the highly abundant MF might play important roles during Drosophila metamorphosis . To better understand the relation between the JH-deficient lethal phenotypes and the biosynthesis of the three sesquiterpenoids by the larval CA , we further explored the effect of additional loss-of-function of enzymes in the JH biosynthetic pathway . Drosophila CG10527 is an ortholog of a crustacean FA methyltransferase [35] , which has been reported as not involved in JH biosynthesis in Drosophila [33 , 36] . We generated a jhamt CG10527 double mutant , jhamt2 CG10527187 ( S3 Fig ) . Mutation of CG10527 in a jhamt2 background did not increase JH-deficient phenotypes ( S4 Fig ) , confirming that CG10527 is not involved in FA or JHA methylation in Drosophila . Different promoters can be used to drive CA-specific expression in Drosophila . We have previously shown that jhamt-GAL4 has a more robust CA-specific expression than Aug21-GAL4 [21] . Therefore , we generated jhamt-GAL4>UAS-GFP flies , which exhibited strong CA-specific expression of GFP ( Fig . 3A and 3A’ ) . As expected , similar to jhamt2/jhamt2; Aug21-GAL4>UAS-jhamt , fecundity and ovary growth of jhamt2/jhamt2; jhamt-GAL4>UAS-jhamt were restored to levels similar to those in w1118 ( S2D and S2D’ Fig ) . We then generated Aug21-GAL4>UAS-hmgcr dsRNA and jhamt-GAL4>UAS-hmgcr dsRNA animals , in which hmgcr expression is specifically reduced in the CA by RNAi . As detected by quantitative real-time PCR ( qPCR ) , hmgcr expression in the brain-RG complexes at 3h AIW decreased by ~35% in Aug21-GAL4>UAS-hmgcr dsRNA animals and ~50% in jhamt-GAL4>UAS-hmgcr dsRNA animals ( S5A Fig ) . Lethality of ~55% and ~70% was observed in Aug21-GAL4>UAS-hmgcr dsRNA ( S5B Fig ) and jhamt-GAL4>UAS-hmgcr dsRNA animals ( Fig . 3B ) , respectively . Moreover , the lethality in jhamt2/jhamt2; Aug21-GAL4>UAS-hmgcr dsRNA was about 93% ( S5C Fig ) , whereas 100% lethality before adult emergence was observed in jhamt2/jhamt2; jhamt-GAL4>UAS-hmgcr dsRNA ( Fig . 3B’ ) . Most jhamt2/jhamt2; jhamt-GAL4>UAS-hmgcr dsRNA animals died during the pupal stage ( 60% ) , exhibiting a variety of developmental defects ( Fig . 3C ) . These data not only confirmed that jhamt-GAL4 has a more robust CA-specific expression than Aug21-GAL4 , but also demonstrated that reduction of hmgcr expression in the CA in a jhamt2 background causes stronger lethal phenotypes than the jhamt mutant alone . Overall , these experiments suggest that reduction of hmgcr expression in the CA in a jhamt2 background decreases biosynthesis and titers of the three sesquiterpenoids to very low levels , resulting in complete lethality . In the following experiments , jhamt-GAL4>UAS-hmgcr dsRNA ( hmgcrRNAi ) and jhamt2/jhamt2; jhamt-GAL4>UAS-hmgcr dsRNA ( jhamt2 hmgcrRNAi ) were used to further confirm the above hypothesis . We measured JH biosynthesis in larval brain-RG complexes isolated from four different lines at 3h AIW: w1118 , jhamt2 , hmgcrRNAi , and jhamt2 hmgcrRNAi . In comparison with the w1118 larvae , JHB3 biosynthesis decreased by 75% in jhamt2 and hmgcrRNAi larvae and by more than 90% in jhamt2 hmgcrRNAi larvae . JH III biosynthesis was not altered in jhamt2 larvae , but decreased by 30–40% in hmgcrRNAi and jhamt2 hmgcrRNAi larvae . MF biosynthesis was not altered in jhamt2 larvae , but decreased to about 50% in hmgcrRNAi and jhamt2 hmgcrRNAi larvae ( Fig . 4A ) . We also measured titers of the three sesquiterpenoids in the whole larval bodies of the four above mentioned genotypes at 3h AIW . In comparison with the w1118 larvae , JHB3 titer decreased by 60–70% in jhamt2 , hmgcrRNAi , and jhamt2 hmgcrRNAi larvae ( Fig . 4B ) . JH III titer decreased by 50% in jhamt2 larvae , whereas it decreased by 70–75% in hmgcrRNAi and jhamt2 hmgcrRNAi larvae ( Fig . 4B’ ) . MF titer decreased by 30% ( not statistically significant difference ) in jhamt2 larvae , whereas the decrease was approximately 40% in hmgcrRNAi larvae ( Fig . 4B” ) . Interestingly , MF titer decreased by 98% in jhamt2 hmgcrRNAi larvae ( Fig . 4B” ) , implying that most of MF is converted to JHs in jhamt2 hmgcrRNAi larvae . Overall , these experiments suggest that the three sesquiterpenoids synthesized and released by the larval CA are required for Drosophila to survive to adulthood; in particular , that the very abundant MF plays essential anti-metamorphic roles during Drosophila development ( Table 1 ) . To further understand the anti-metamorphic roles of each of the three sesquiterpenoids synthesized by the larval CA , we performed a series of experiments by treating JH-deficient animals with methoprene or sesquiterpenoids to evaluate their ability to prevent lethality , as well as their efficiency in inducing expression of the JH-responsive gene Kr-h1 . Topical application of high doses of methoprene , JHB3 , JH III , and MF ( 0 . 5×10-2 μmol per larva ) to third instar larvae when JH titers are low ( at 96h AEL: 96 hours after egg laying ) [10] was able to decrease mortality significantly ( 40–75% ) in the two JH-deficient animals ( Aug21>Grim and jhamt2 hmgcrRNAi ) . By contrast , neither methoprene nor sesquiterpenoids ( 0 . 5×10-2 μmol per larva ) prevented the lethality of Met27 gce2 . 5k ( Fig . 5A ) . Additional experiments were performed on jhamt2 hmgcrRNAi to evaluate the dose-responses for methoprene and the three sesquiterpenoids in preventing lethality . These compounds showed significant effects at 0 . 5×10-4 μmol/larva , with MF being the most effective , followed by JH III , methoprene , and JHB3 . At higher doses ( 0 . 5×10-3 and 0 . 5×10-2 μmol/larva ) , only the effects of JHB3 and JH III continued to increase ( Fig . 5B ) . qPCR was utilized to examine whether MF acts through Met/Gce to induce Kr-h1 expression [20 , 30 , 31] . Methoprene and the three sesquiterpenoids induced Kr-h1 expression in both Drosophila Kc cells ( 1×10-10~-6 M ) ( Fig . 5C ) and cultured fat body tissues isolated from w1118 larvae at 96h AEL ( 1×10-7 M ) ( Fig . 5D , left panel ) ; although induction with MF was weaker than JHB3 and JH III . We also determined Kr-h1 mRNA levels in jhamt2 larvae , wherein JHB3 biosynthesis but not MF biosynthesis is reduced ( Fig . 2A ) . Kr-h1 expression was normal in 3h AIW jhamt2 larvae , indicating that the other two sesquiterpenoids ( in particular the very abundant MF ) were sufficient to induce Kr-h1 expression to control levels . In contrast , as previously reported [21] , in Met27 gce2 . 5k larvae , Kr-h1 mRNA levels were reduced by about 95% when compared to its levels in w1118 larvae ( Fig . 5E ) . As expected , methoprene and the three sesquiterpenoids failed to induce Kr-h1 expression in cultured fat body tissues isolated from Met27 gce2 . 5k larvae at 96h AEL ( Fig . 5D , right panel ) . These data from in vitro and in vivo experiments revealed that , in addition to JHB3 and JH III , MF also has an anti-metamorphic or “JH-like” role in Drosophila larvae , acting through Met/Gce to induce Kr-h1 expression . We then extended our study to Tribolium , in which JH III directly induces heterodimerization of the JH receptor ( TcMet ) and its partner ( TcSRC ) in mouse embryonic fibroblast 3T3 cells [37] . Here we found that MF also induced heterodimerization of TctMet and TcSRC in 3T3 cells in a dose-dependent manner , although its induction ability was weaker than JH III ( Fig . 5F ) . This experiment provides strong evidence that MF acts as a hormone itself through a direct interaction with the JH receptor Met in Tribolium , supporting the above findings in Drosophila . Finally , we examined whether once released by the CA , MF could be converted to JHB3 or JH III in the fly hemolymph or peripheral tissues . The jhamt2 hmgcrRNAi larvae were topically treated with acetone or MF ( 0 . 5×10-2 μmol per larva ) at 108h AEL , and the three sesquiterpenoid titers were measured at 3h AIW ( about 24 hours after treatment ) . While JH III titer did not change , MF and JHB3 titers in the MF-treated animals increased approximately 9- and 7-fold respectively when compared to control animals treated with acetone ( Fig . 5G–5G” ) . The topical application experiments showed that a portion of the exogenous MF was converted to JHB3 in the hemolymph or peripheral tissues , consistent with the results obtained from jhamt2 ( Fig . 2C–2C” ) and jhamt2 hmgcrRNAi larvae ( Fig . 4B–4B” ) . We conclude that MF is required for completion of Drosophila metamorphosis , playing a dual role: as a JHB3 precursor and as a hormone ( Fig . 6 ) .
This study ( Table 1; Figs 1–4 ) confirmed and expanded previous studies , showing that genetic ablation of the CA caused JH deficiency and pupal lethality in Drosophila [20 , 22] . Knockdown and/or knockout of enzymes in the early and late steps of the JH biosynthetic pathway generated different phenotypes depending on the background of the animals: 1 ) null mutation of jhamt resulted in significant decrease in JHB3 biosynthesis , as well as JHB3 and JH III titers , without compromising development and survival , 2 ) RNAi-mediated reduction of hmgcr expression in the CA decreased biosynthesis and titers of the three sesquiterpenoids produced by the larval CA , resulting in partial lethality , and 3 ) RNAi-mediated reduction of hmgcr expression in the CA of the jhamt mutant further decreased JHB3 biosynthesis and MF titer , leading to complete lethality . These results lead us to conclude that only dramatic decreases in biosynthesis of the three sesquiterpenoids resulted in very low circulating titers and caused complete lethality in the two JH-deficient animals ( Aug21>grim and jhamt2 hmgcrRNAi ) . Moreover , the requirement of the three sesquiterpenoids for Drosophila metamorphosis was further strengthened by the rescue experiments in the two JH-deficient animals ( Fig . 5A and 5B ) , showing that JHB3 , JH III , and MF are able to functionally replace one another . Although accepted as the anti-metamorphic hormone in Crustacea , the potential role of MF as a true JH in Insecta has been an issue of a long-standing debate [1 , 4 , 24 , 37] . Our experiments provide additional evidence that supports the anti-metamorphic or “JH-like” role of MF in Drosophila , including: 1 ) the fact that MF is released by the CA and is the most abundant sesquiterpenoid present in extracts of larval body , 2 ) the ability to phenocopy anti-metamorphic roles following topical application to JH-deficient animals ( “rescue” experiments ) , 3 ) the capability to act through the JH receptors ( Met and Gce ) and induce a dose-dependent expression of Kr-h1 , a JH primary-response anti-metamorphic gene , and 4 ) the conversion to JHB3 in the hemolymph or peripheral tissues . The presence of high circulating MF levels has been previously described in Drosophila larvae [9 , 10] , as well as the production of MF by the larval brain-RG complexes [3] . MF might also play an anti-metamorphic role during early larval development in Bombyx; high levels of MF might exist in Bombyx dimolting , a P450 epoxidase mutant , that contains no detectable JH I , JH II , and JH III in the hemolymph [16] . The ability of MF to phenocopy anti-metamorphic roles has been previously established in the white puparia JH bioassay [6 , 7] . The importance of MF during Drosophila metamorphosis was validated by the RNAi-mediated reduction of hmgcr expression in the CA of the jhamt mutant , in which only MF was further decreased leading to complete lethality ( Table 1 ) ; as well as by the observation that JHB3 , JH III and MF efficiently precluded lethality in two JH-deficient lines . It has been suggested that MF could play anti-metamorphic roles acting through ultraspiracle ( USP , an ortholog of the retinoid X receptor and a molecular partner of the 20E receptor , EcR ) [9] . On the other hand , MF efficiently competes with JH III for binding to Met and Gce in Drosophila [28] , MF directly induces heterodimerization of Met and SRC of Crustacea in mammalian cells [38] , and MF induces Kr-h1 promoter activity in mammalian cells in the presence of Bombyx Met and SRC [39] . We validated and expanded those results , showing that MF induces a dose-dependent Kr-h1 expression in Drosophila cell lines and fat body tissues isolated from JH-deficient animals ( Fig . 5C–5E ) . Moreover , MF induces heterodimerization of Met and SRC of Tribolium in mammalian 3T3 cells in a dose-dependent manner ( Fig . 5F ) . Data included in this paper show that MF acts through Met/Gce ( Fig . 5C–5F ) , but not USP ( S6 Fig ) , at least in the induction of Kr-h1 expression and Met-SRC heterodimerization . Finally we showed that MF can be converted in the hemolymph or peripheral tissues to other active JHs in Drosophila . In jhamt2 larvae , JHB3 biosynthesis is dramatically reduced and MF and JH III biosynthesis are unaffected ( Fig . 2B ) , whereas whole body titers of JHB3 , JH III , and MF decreased by approximately 70% , 50% , and 30% ( no statistical difference ) relative to their respective control levels ( Fig . 2C–2C” ) . The decrease in whole body levels of MF could be the consequence of a portion of the MF pool undergoing conversion to JHB3 in jhamt2 larvae . In comparison with hmgcrRNAi larvae , JHB3 biosynthesis is further reduced in jhamt2 hmgcrRNAi larvae , whereas the biosynthesis of MF and JH III is unaffected ( Fig . 4A ) . Similarly , although MF titer decreased to almost zero in jhamt2 hmgcrRNAi larvae , JHB3 and JH III titers remained at the same levels ( Fig . 4B–4B” ) , suggesting again that most of MF is converted to JHB3 in jhamt2 hmgcrRNAi larvae . The possibility that MF can be converted to other JHs was further confirmed by topical application of MF to jhamt2 hmgcrRNAi larvae ( Fig . 5G–5G” ) . We conclude that MF plays a dual role in regulating Drosophila metamorphosis: through its conversion to JHB3 , as well as through its role as a bona fide juvenoid ( Fig . 6 ) . Was MF the ancestral ‘JH’ of Arthropods ? Ongoing studies of the metabolic pathways for JH biosynthesis and degradation in other Arthropods , including Myriapods and Chelicerates , indicate that these groups all possess the requisite enzymes to produce at least MF . In particular , these groups all appear to possess a JHAMT ortholog , indicating that MF may have been synthesized and functional in these groups . These groups also possess enzymes known to be involved in the degradation of the sesquiterpenoids , as well as binding proteins [40 , 41] . At present , it is unknown if these groups possess a functional member of the CYP family of cytochrome P450 enzymes that would be responsible for the epoxidation of MF . The apparent absence of this enzyme in crustaceans and possibly in Drosophila argues for the importance of MF in the regulation of metamorphosis . These studies suggest that the ‘JH’ signaling pathway has deep evolutionary roots [40 , 41] and our present results on Drosophila support such a view . These authors also suggest that the pathway “might have evolved together with the emergence of the exoskeleton” . This suggestion highlights the importance of MF , particularly in metamorphosis . During evolution in arthropods , MF maintains its anti-metamorphic role from crustaceans to insects and probably across the phylum . Subsequently , different JHs emerged in different orders of insects . Diversification of the JH ( s ) might contribute to variation and novelty during arthropod evolution . The co-existence of three JHs and two JH receptors in a single organism makes Drosophila a complicated but fascinating system for studying the JH signal transduction pathway , from both molecular and evolutionary perspectives . Compared with other insects producing only JH III , the last two steps of the JH biosynthetic pathway in Drosophila are much more ambiguous . We propose a JH biosynthetic pathway in which FA is the common precursor for JHB3 , JH III , and MF in Drosophila ( Fig . 6 ) . Our previous studies [19] and the data included in this paper ( Fig . 2 ) show that overexpression and mutation of jhamt increased and decreased JHB3 biosynthesis , respectively , but did not affect the production of JH III and MF , suggesting that JHAMT is responsible only for JHB3 biosynthesis in the CA . Moreover , mutation of jhamt significantly decreased the activity of methyltransferase using FA but not JHA as substrate , implying the existence of one or more additional methyltransferases converting FA into MF and JHA into JH III in the CA of Drosophila larvae . It has been suggested that the lack of a clear ortholog of a P450 epoxidase in Drosophila might be explained on the basis of the different chemistry of the fly JHs [15] . The CYP15 of higher flies could have evolved to allow the epoxidation at both the 6 , 7 and 10 , 11 double bonds , and this evolution resulted in such significant changes so that the sequence is no longer recognizable as a CYP15 . A global analysis of CYP enzymes in Drosophila revealed specific expression of CYP6G2 in the CA [42] , but whether it functions as a P450 epoxidase is currently unknown . One possibility is that CYP6G2 preferably epoxidizes FA to 6 , 7; 10 , 11-epoxyfarnesoic acid ( JHB3 acid ) rather than 6 , 7-epoxyfarnesoic acid ( JHA ) , resulting in a much higher JHB3 biosynthesis ratio compared to the JH III biosynthesis ratio . Moreover , we found that a portion of MF was converted to JHB3 in the hemolymph or peripheral tissues ( Fig . 2 , 4 , 6 ) , presumably by an uncharacterized P450 epoxidase . The identification of the methyltransferases and P450 epoxidases that are involved in the last two steps of JH biosynthesis in Drosophila remains as a future challenge .
To generate the jhamt mutant , we used the homologous recombination—mediated ends-out gene targeting technique [32] . Two genomic DNA fragments flanking the jhamt ( CG17330 ) coding region were amplified by PCR . The upstream flanking region ( 4245-bp length: -4212 bp to +33 bp from the translational start site of jhamt ) was cloned into the pw25 plasmid using the Not I ( jhamt-5’end-Not I ) and Acc65 I ( jhamt-5’end-Acc65 I ) restriction sites introduced by PCR primers . Subsequently , the downstream flanking region ( 3977-bp length: +1050 bp to +5027 bp from the start site of the jhamt gene ) was cloned into the above generated vector using the Asc I ( jhamt-3’end-Asc I ) and BsiW I ( jhamt-3’end-BsiW I ) restriction sites . The resulting construct of pw25-jhamt ( Fig . 1A ) was used to generate transgenic flies using P-element-mediated germline transformation . Then , the pw25-jhamt transgenic flies were crossed with yw; p{70FLP}23 p{70I-SceI}4A/TM6 to generate the jhamt knock-out strains ( jhamt1 , jhamt2 , and jhamt3 ) ( Fig . 1B and 1C ) . Primers used here and elsewhere are listed in S1 Table . The putative promoter sequence ( 2540-bp length: -2544 bp to-4 bp , from the translational start site of jhamt ) of jhamt was amplified as a Sac II-BamH I fragment , and cloned into the pChsGAL4 plasmid to generate the jhamt-GAL4 construct . The jhamt-GAL4 transgenic flies were then produced . w1118 , Aug21-GAL4 , Act-GAL4 , UAS-GFP , UAS-grim , CG10527187 , Met27 , gce2 . 5k , and Met27 gce2 . 5k were reported previously [14 , 20 , 21 , 31 , 33] . Multiple UAS-hmgcr dsRNA lines ( stock number 11635 is reported ) were obtained from the Vienna Drosophila RNAi Center . RNAi lines were also obtained from the Bloomington Drosophila Stock Center , and similar results were obtained . Other flies used in this paper were generated by recombination . All fly strains in this paper were grown at 25°C on standard cornmeal/molasses/agar medium . For genomic DNA PCR , genomic DNA was extracted from flies using phenol-chloroform-isoamyl alcohol . To confirm the jhamt mutants and the jhamt2 CG10527187 double mutants , genomic DNA PCR was performed with 4 primer pairs , including jhamt-1 and jhamt-2 ( 689-bp length ) , jhamt-3 and jhamt-4 ( 812-bp length ) , jhamt-1 and jhamt-5 ( 671-bp length ) , and jhamt-6 and jhamt-4 ( 1259-bp length ) ( Fig . 1A and 1B ) . To identify and confirm the CG10527187 mutation in the jhamt2 CG10527187 double mutant , genomic DNA PCR were performed with primer pairs CG10527-F and CG10527-R ( 1968-bp for wild type and ~600-bp for the CG10527187 mutant ) ( S3 Fig ) . For reverse transcription PCR , a primer pair jhamt-7 and jhamt-8 ( 405-bp ) were used to detect jhamt mRNA expression from the brain-RG complexes isolated from larvae at 3hAIW ( Fig . 1C ) . qPCR was performed as previously described [14 , 20 , 21 , 31 , 33] . DmCG10527 rat polyclonal antibody [33] was used to conduct the Western blot analysis of the brain-RG complexes isolated from larvae at 3hAIW . The tubulin mouse monoclonal antibody ( #AT819 , Beyotime , China ) was used as an internal control . For detecting JHAMT in the CA by immunohistochemistry , the brain-RG complexes were dissected from larvae at the EW stage . The Drosophila JHAMT rabbit polyclonal antibody ( 1:100 ) [13] and the FITC-conjugated Affinipure Goat Anti-Rabbit IgG secondary antibody ( Jackson ImmunoResearch Inc . ) were used , and the fluorescence signals were captured with an Olympus IX71 invert fluorescence microscope ( Japan ) [14 , 20 , 31] . Methoprene ( Service Chemical Inc . , Germany ) , JH III ( Sigma-Aldrich ) , and MF ( Echelon ) were purchased . JHB3 was synthesized from MF using m-chloroperbenzoic acid in dichloromethane ( Sigma-Aldrich ) [19] . For rescue of fertility of jhamt2 , newly eclosed females were placed in vials with standard medium; after 24 hours , virgin females were topically treated with acetone-dissolved methoprene ( 0 . 5 μl × 10-3 M per female ) [21 , 23] . For rescue of pupal lethality of Aug21>grim and jhamt2 hmgcrRNAi , methoprene , JHB3 , JH III , and MF ( 0 . 5 μl × 10-9~-2 M per larva ) were dissolved in acetone and topically applied to the larvae at 96h AIW [14 , 20 , 21 , 31 , 33] . For inducing Kr-h1 expression in w1118 and Met27 gce2 . 5k , fat body tissues were isolated at 96h AIW and treated with methoprene , JHB3 , JH III , and MF ( 1×10-6 M; DMSO as a control ) for 30 min . For testing the conversion of MF to other JHs , the jhamt2 hmgcrRNAi larvae were topically treated with acetone or MF ( 0 . 5×10-2 μmol per larva ) at 108h AEL , and the three sesquiterpenoids titers were measured at 3hAIW ( about 24 hours after treatment ) . For inducing Kr-h1 expression in Drosophila Kc cells cultured in Schneider’s medium , the cells were treated with methoprene , JHB3 , JH III , and MF ( 1×10-11~-6 M; DMSO as a control ) for 30 min [31] . Using the T7 RiboMAX Express RNAi System ( Promega ) , dsRNAs of USP and EGFP ( as a control ) were synthesized . Reduction of gene expression by RNAi in Kc cells was performed by transfecting dsRNAs using Effectene at a final concentration of 1 μg/ml dsRNA . The transfected cells were cultured for 48 h and treated with MF ( 1×10-6 M; DMSO as a control ) for 30 min [31] . 3T3 cells were grown at 37°C with 5% CO2 in a DMEM ( life technology ) containing 10% fetal bovine serum . For transfection experiments , 50 , 000 cells/well were seeded in a 48-well plate . On the following day , the cells were transiently transfected with 67 ng each of receptor/partner and 200 ng each of pFRLUC reporter construct , using a “Polyfect” transfection reagent ( Qiagen ) . After 4 hours , different final concentration of MF ( 0 . 4 , 2 , 10 and 50 μM ) were added to the wells along with DMEM medium with 20% FBS as well . DMSO and 10 μM JH III were used as a negative and positive control , respectively . After 24 hours exposure to the ligands , cells were washed with PBS , 60 μl of reporter lysis buffer was added to each well and luciferase reporter activity was measured using the luciferase reporter assay system from Promega ( Madison , WI ) . To standardize the luciferase activity , protein concentration in cells from well was determined using the Bradford reagent . Details on the constructs GAL4:TcMet in the pBIND vector and TcSRC in the pACT vector , as well as JH III treatment experiments were published previously [37] . S-Adenosyl-L-methionine ( SAM ) was purchased from Sigma-Aldrich and S-Adenosyl-L-[methyl-3H] methionine ( 370GBq mmol , 10 Ci/mmol ) from Perkin-Elmer Life Sciences ( Waltham ) . Methyltransferase activity in the brain-RG complexes isolated from larvae at 3hAIW was measured with JHA and FA as substrates , as described previously [14 , 20 , 23 , 33] . L-[Metyl-3H] methionine ( 2 . 92–3 . 70 TBq/mmol ) was purchased from Perkin-Elmer Life Sciences and TLC plates ( 20×20 cm2 plastic plate coated with silica gel F254 ) from Merck KgaA ( Germany ) . JH biosynthesis in the brain-RG complexes was detected using the radiochemical assay followed by thin layer chromatography analysis as reported previously [18 , 19 , 37] . JH titers from the whole bodies of each genotype were determined using the recently developed HPLC-FD protocol [34] . Experimental data were analyzed with the Student’s t-test and ANOVA . t-test: * , p<0 . 05; ** , p<0 . 01 . ANOVA: the bars labeled with different lowercase letters are significantly different ( p<0 . 05 ) . Throughout the paper , values are represented as the mean ± standard deviation of at least five independent experiments . | Methyl farnesoate ( MF ) is the immediate precursor of juvenile hormone ( JH ) III in the JH biosynthetic pathway , and lacks the epoxide moiety characteristic of JHs . The potential role of MF as a JH in arthropods has been an issue of a long-standing debate . In this report , comprehensive molecular genetics studies demonstrated that MF plays a dual role in regulating Drosophila metamorphosis . MF is produced by the larval CA and released into the hemolymph , from where it exerted its anti-metamorphic effects indirectly after conversion to JHB3 , as well as acting as a hormone itself through a direct interaction with Met and Gce , the two JH receptors . | [
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| 2015 | Methyl Farnesoate Plays a Dual Role in Regulating Drosophila Metamorphosis |
The genetic basis of most heritable traits is complex . Inhibitory compounds and their effects in model organisms have been used in many studies to gain insights into the genetic architecture underlying quantitative traits . However , the differential effect of compound concentration has not been studied in detail . In this study , we used a large segregant panel from a cross between two genetically divergent yeast strains , BY4724 ( a laboratory strain ) and RM11_1a ( a vineyard strain ) , to study the genetic basis of variation in response to different doses of a drug . Linkage analysis revealed that the genetic architecture of resistance to the small-molecule therapeutic drug haloperidol is highly dose-dependent . Some of the loci identified had effects only at low doses of haloperidol , while other loci had effects primarily at higher concentrations of the drug . We show that a major QTL affecting resistance across all concentrations of haloperidol is caused by polymorphisms in SWH1 , a homologue of human oxysterol binding protein . We identify a complex set of interactions among the alleles of the genes SWH1 , MKT1 , and IRA2 that are most pronounced at a haloperidol dose of 200 µM and are only observed when the remainder of the genome is of the RM background . Our results provide further insight into the genetic basis of drug resistance .
The budding yeast Saccharomyces cerevisiae has become a powerful model for elucidating fundamental principles and mechanisms of complex trait genetics [1] . Many quantitative trait loci ( QTL ) – and the causal genes underlying these loci – have been identified for diverse biological processes , including gene expression [2]–[4] , high-temperature growth [5]–[8] , DNA damage repair [9] , sporulation efficiency [10]–[12] , and drug sensitivity [7] , [13] , [14] . In studies of chemical resistance traits , compound concentrations with the highest heritability are typically selected for further analysis [15] . However , the extent to which the genetic architecture underlying the response to a drug is specific to the drug dose is a major open question . Following initial observations of complex and dose-dependent inheritance patterns of the response to the small molecule haloperidol , we set out to investigate the genetic basis of haloperidol resistance as a function of dose . Haloperidol is a psychoactive drug that binds to dopamine and serotonin receptors in humans [16] , and is widely used for treating schizophrenia . In Saccharomyces cerevisiae ( which does not contain the pharmacologically relevant haloperidol targets ) , haloperidol exerts effects on vesicle transport and amino acid metabolism [17] , demonstrating perturbations of fundamental cellular physiology upon exposure to the drug . Haloperidol , a cationic amphiphilic drug , has been shown at concentrations of 10–200 µM to cause defects in phospholipid metabolism/transport [18] , [19] and trigger autophagy upon accumulation [20] in yeast , and to result in degradation of membranes [21] in vitro . Haloperidol was also found to inhibit both sterol Δ8 , 7 isomerase ( Erg2 ) and C-14 reductase ( Erg24 ) activities in yeast [22] , [23] . An early biochemical study showed that haloperidol binds to Erg2 in yeast , and causes decreased ergosterol levels [23] . Erg2 functions in the ergosterol biosynthesis pathway , suggesting haloperidol's interference with sterol metabolism and trafficking . Here , we used a large panel of 1008 segregants from a cross between a laboratory strain BY4724 ( hereafter referred to as BY ) and a vineyard strain RM11-1a ( hereafter RM ) to study yeast growth in haloperidol . We identified a total of nine genomic loci associated with resistance to haloperidol with different dose-specificity . We further identified SWH1 as a major gene contributing to resistance to haloperidol at all concentrations , and showed that variants within its oxysterol binding protein ( OBP ) -like domain are responsible for resistance . We also showed that variants in MKT1 and IRA2 underlie loci that have effects predominantly at high haloperidol concentrations , and found complex , background-dependent genetic interactions among the allelic states of SWH1 , MKT1 , and IRA2 . This study sheds light on the contribution of QTL-dosage interaction to chemical resistance in yeast , and the complexity of the underlying sources of variation in quantitative traits .
To assess the biological effects of haloperidol in S . cerevisiae , we examined susceptibility of the laboratory strain BY carrying gene deletions erg2Δ , erg24Δ , or erg4Δ to haloperidol in rich medium ( Fig . 1A ) . Erg2 , Erg24 , and Erg4 make up three important steps in the ergosterol biosynthesis pathway in yeast [24] . BY erg2Δ and BY erg24Δ strains had growth defects in rich medium , but neither was completely resistant to haloperidol . Erg4 catalyzes the final step in the ergosterol biosynthesis pathway , and it has been shown that erg4Δ mutants lack detectable levels of ergosterol [25] . Deleting ERG4 did not eliminate the sensitivity to haloperidol ( Fig . 1A ) ; thus , haloperidol has biological effects other than those on the ergosterol pathway [17] . Similar to previous observations with other cationic amphiphilic drugs [18] , [20] , we found sensitivity to haloperidol to be pH dependent ( Fig . 1A ) . Acidic pH ( = 4 . 3 ) [26] completely rescued growth in the presence of 150 µM haloperidol ( Fig . 1A ) . pH related phenotypes are often indicative of vacuole-related defects [27] . Staining of the vacuole and vacuolar membrane of haloperidol-treated cells showed that vacuoles were intact ( Fig . 1B ) . Measuring acidity with fluorescent dye quinacrine ( which accumulates in acidic compartments ) revealed decreased vacuolar acidity upon longer exposure to haloperidol ( Fig . 1B ) . Quinacrine efficiently labeled the cytoplasm in the presence of haloperidol , suggesting that proton-pumping mechanisms are impaired ( Fig . 1B ) . We compared growth of BY and RM in the presence of different concentrations of haloperidol ( 0–240 µM ) . Although RM has higher baseline growth in the absence of haloperidol , it is more sensitive to haloperidol at concentrations ranging from 40 to 160 µM ( Fig . 2A ) . We also tested segregants from a cross between BY and RM and found that resistance to haloperidol showed transgressive segregation , with some progeny exhibiting phenotypes more extreme than either parent ( Fig . 2B ) . For instance , haloperidol concentration of 200 µM completely inhibited the growth of both parental strains at 48 hours , but ∼8 . 5% of segregants were able to grow . A formal statistical test for transgressive segregation [28] showed that it was significant at all measured concentrations of haloperidol between 40 µM and 200 µM ( p<0 . 0001 in all cases; see Methods for details ) . We sought to further understand the genetics underlying growth in the presence of haloperidol through QTL mapping . We carried out linkage analysis in a panel of 1008 BY-RM segregants [15] for growth at five different concentrations of haloperidol ( 40 , 80 , 120 , 160 , 200 µM ) and identified nine distinct significant QTL ( Fig . 3A ) . At the major locus on the right arm of chromosome I , the allele from RM ( the sensitive parent ) promoted growth in the presence of haloperidol , consistent with our observation of transgressive segregation ( Fig . 2B ) . RM alleles at loci on chromosomes V , XII and XV also confer greater resistance , while BY alleles confer higher resistance at the remaining five loci ( Fig . 3 ) . Some of the loci were detected only at certain doses of haloperidol , and the effect sizes of most loci were dose-dependent ( Fig . 3 ) . For instance , the loci on chromosomes VII , XII , and XIII were only detected at the two lower doses , while the effects of loci on chromosomes XIV and XV primarily manifested at the higher doses ( Fig . 3B ) . Most loci had undetectable or weak effects at 200 µM , because few segregants grew at this dose . We quantified the amount of variation explained by these nine loci by fitting a linear model with additive QTL . This model explained between 35 . 9% and 54 . 7% of the total phenotypic variance at concentrations 40–160 µM ( S1 Table ) . The locus on chromosome I ( right arm ) alone explained 21 . 4% , 26 . 7% , 21 . 4% , 11 . 6% of the variance at these four doses , respectively . The major QTL on the right arm of chromosome I had significant additive effects at all concentrations of haloperidol . The confidence interval of this peak contained the gene SWH1 ( also known as OSH1 ) , which encodes a yeast homologue of the mammalian oxysterol-binding protein ( OSBP ) [29] . OSBPs are a family of proteins with the ability to bind oxysterols [30] , [31] , which are oxidized derivatives of sterols in the cell . We sequenced the coding region of the BY and RM alleles of SWH1 and identified 13 synonymous single nucleotide polymorphisms ( SNPs ) , 9 non-synonymous SNPs , and a 6 base pair indel between the two alleles ( S2 Table , Fig . 4E ) . According to Pfam alignments with the amino acid sequence of Osh4 ( which recently had its crystal structure solved [32] ) , Swh1 contains ankyrin repeats , a pleckstrin-homology-protein-like domain ( PH ) , and an oxysterol-binding-protein-like domain ( OBP ) at the C-terminus [29] . Three of the non-synonymous SNPs between BY and RM are located in the OBP domain , five are in the linker region between the PH domain and OBP domain , and one is in the PH domain ( Fig . 4E ) . To test whether SWH1 allelic variation caused differences in growth at different haloperidol concentrations , we conducted a reciprocal hemizygosity analysis [5] . The BY/RM hybrids carrying either only the BY or only the RM allele of SWH1 grew differently in the presence of haloperidol , demonstrating that SWH1 contributes to the variable response ( Fig . 4A ) . Specifically , the hybrid carrying only the RM allele of SWH1 ( swh1BYΔ/SWH1RM ) showed a higher growth rate compared to the hybrid carrying only the BY allele ( SWH1BY/swh1RMΔ ) . Thus , SWH1RM is the resistant allele relative to SWH1BY , confirming the QTL results . We found that deletion of SWH1 in both BY and RM haploid backgrounds conferred higher growth rates across the response range to haloperidol ( Fig . 4B ) . This illustrates that SWH1 loss of function leads to greater haloperidol resistance . To gain some insight into the relative function of the BY and RM alleles of SWH1 , we examined the growth rates of the BY/RM hybrid carrying none , either , or both BY and RM copies of SWH1 ( Fig . 4A ) . With the BY allele of SWH1 intact in the hybrid , little difference in growth rate was observed with or without the RM allele , indicating that a single copy of the BY allele of SWH1 is sufficient for function . Next , comparing the growth rates of the SWH1 hemizygotes ( swh1BYΔ/SWH1RM or SWH1BY/swh1RMΔ ) relative to the deletion ( swh1BY Δ/swh1RM Δ ) , growth of swh1BYΔ/SWH1RM was more similar to swh1BY Δ/swh1RM Δ ( Fig . 4A ) . These results demonstrate that the RM allele of SWH1 is the less functional of the two . However , the RM allele is not a complete loss-of-function , as deleting the RM allele of SWH1 still increased haloperidol resistance ( Fig . 4B ) . Analysis in the BY and RM haploids and their hybrid illustrated that reducing SWH1 function leads to haloperidol resistance . To explicitly test the effect of SWH1 polymorphisms on haloperidol resistance , we swapped the SWH1 coding region in both BY and RM ( replacing the coding region with the copy from the other strain ) . Introducing the functional BY allele into the RM background slightly reduced resistance to haloperidol , whereas having the RM allele of SWH1 in BY increased resistance . The results from allele replacements in BY and RM haploids demonstrated that SWH1 affects resistance to haloperidol in both genetic backgrounds ( Fig . 4C ) . To gain further insight into the mechanism of resistance to haloperidol , we looked more specifically at the polymorphisms between BY and RM in the SWH1 gene . Among all 22 SNPs residing in the coding region of SWH1 , three of the non-synonymous SNPs between BY and RM ( D1020G , S1085L , and I1098V ) are located in the OBP domain ( Fig . 4E , S2 Table ) . We replaced the OBP domain of SWH1 in BY with the counterpart from RM ( hereafter BY SWH1OBP-RM ) and tested resistance to haloperidol between the replacement strains ( Fig . 4D ) . BY SWH1OBP-RM fully recapitulated the increased resistance to haloperidol achieved by replacing the entire coding region of SWH1 in BY with the RM allele . According to structure-based alignments of Swh1 with the crystal structure of full length Osh4 in yeast [32] , D1020G lies within β-sheets ( β14 - β15 ) that form a hydrophobic tunnel , which can bind one sterol molecule [32] . Therefore , we speculate that D1020G in RM may result in an altered structural form of the binding pocket and reduce Swh1 activity . Among the loci identified for haloperidol resistance , those on chromosomes XIV and XV became the major QTL at higher doses of the drug . IRA2 , a gene previously identified to contain variants underlying differences in gene expression and metabolite levels [33] , [34] , resides within the locus on chromosome XV . IRA2 encodes a GTPase activating protein that inhibits RAS , which mediates cellular responses in nutrient limiting conditions via the Ras/PKA pathway [35]–[37] . Analyzing allele replacement strains for IRA2 [34] , we saw that in both parental backgrounds , the allelic state of IRA2 influenced resistance to haloperidol . The IRA2RM allele in both BY and RM genetic backgrounds was more resistant to the drug ( Fig . 5A ) . The locus on chromosome XIV is a QTL hotspot identified in many chemical stresses , and as a QTL for growth in rich medium [14] , [15] , [38] . It contains the gene MKT1 , which encodes a protein member of a complex involved in HO regulation [39] . A laboratory strain allele of MKT1 has been shown to influence gene expression [34] , DNA replication stress [40] and mitochondrial genome stability [41] . Using BY and RM allele replacement strains from [41] , we confirmed MKT1 as the gene underlying the chromosome XIV locus ( Fig . 5B ) . We further found that MKT1 only had an effect in the RM background: replacing MKT1RM with MKT1BY led to near complete resistance , while the reciprocal allele swap had little effect on BY ( Fig . 5B ) . The observation that the BY allele of MKT1 led to greater growth in haloperidol is the opposite of what has been seen for growth in other conditions in previous complex trait studies , which found the MKT1BY allele to be deleterious for growth in the absence of functional mitochondria and in the presence of the drug 4-NQO [9] , [41] . To quantify the amount of phenotypic variance explained by genetic variation , we calculated broad- and narrow-sense heritability [15] at the five concentrations of haloperidol ( Table 1 ) . Narrow-sense heritability ranged from 0 . 56 to 0 . 71 at doses from 40–160 µM and decreased dramatically at 200 µM . Broad-sense heritability was consistently high ( >∼75% ) at all doses . Differences between broad- and narrow-sense heritability suggest that non-additive interactions contribute to phenotypic variance [15] . We tested for statistical interactions between additive QTL detected in at least one haloperidol concentration , and found 9 , 10 , 3 , 5 and 5 significant pair-wise QTL interactions ( out of 36 possible locus pairs ) at the five doses ( Bonferroni-corrected p<0 . 005 , S1 Table , S3 Table ) . Incorporating the corresponding significant two-way QTL interactions in the QTL model at each dose explained an additional 11 . 0% , 6 . 6% , 1 . 7% , 7 . 8% , and 7 . 2% of phenotypic variance at 40 µM , 80 µM , 120 µM , 160 µM , and 200 µM haloperidol , respectively ( S3 Table ) . At 40 µM , the additional variance explained accounted for most of the difference between broad- and narrow-sense heritability ( 14 . 6% ) , and at 80 µM half of this difference ( 12 . 9% ) was captured ( Table 1 ) . However , at higher doses , taking into account two-way interactions explained little of the differences between broad- and narrow-sense heritability ( Table 1 ) , suggesting the presence of higher-order interactions , interactions between loci with no detectable main effects , or other non-additive contributions to broad-sense heritability . We detected significant pair-wise interactions at 160 µM and 200 µM among all pairs of loci on chromosomes I , XIV , and XV that correspond to SWH1 , MKT1 , and IRA2 ( S1 fig . , S3 Table ) . To further explore these interactions , we generated allele replacement strains in both BY and RM carrying all 8 combinations of SWH1 , MKT1 , and IRA2 alleles ( 16 total strains ) , and measured their growth at 200 µM haloperidol . We tested these allelic effects and their interactions using analysis of variance ( ANOVA ) and found that the pairwise interaction terms were not significant , but all locus pairs had a significant interaction effect with the genetic background ( S4 Table ) . We therefore performed ANOVA in the BY and RM background separately . The pair-wise interactions among SWH1 , MKT1 , and IRA2 were all significant in the RM background , but none were significant in the BY background ( Table 2 , S4 , S5 , and S6 Table ) . These results suggest that complex interactions among the three tested alleles ( SWH1 , MKT1 , IRA2 ) and the genetic background determine resistance to haloperidol . In the RM background , the allelic state of SWH1 influenced the effect of MKT1 . Introduction of MKT1BY dramatically increased growth , but only in the presence of SWH1RM ( Fig . 6A ) . We also found interactions between the alleles of SWH1 and IRA2 in the RM background ( Fig . 6B ) . IRA2RM increased growth in RM when it carried SWH1RM and MKT1BY ( Fig . 6B , left panel ) , but reduced growth when it carried SWH1BY and MKT1RM ( Fig . 6 , right panel ) . Further , the allelic state of SWH1 influenced the direction of effect for IRA2 . IRA2BY promoted growth in the presence of the BY allele of SWH1 ( Fig . 6C , left panel ) , but reduced growth in the presence of the RM allele of SWH1 ( Fig . 6C , right panel ) . Growth of both BY and RM in haloperidol was completely rescued by the genotype combination MKT1BY , IRA2RM and SWH1RM ( Fig . 6 ) . However , MKT1RM only caused sensitivity to haloperidol in RM ( Fig . 6B , right panel ) , even in the presence of SWH1RM and IRA2RM . We therefore conclude that MKT1RM , in combination with other unidentified factors in the RM background contribute to the sensitivity of RM to haloperidol .
The genetic architectures of chemical resistance in yeast range from relatively simple ( involving a single locus ) to highly complex ( >20 loci ) [9] , [13]–[15] . These studies typically tested only one dose per compound . Here , we explored the full dose response range of the small molecule drug haloperidol to dissect the genetic architecture of dose-response variation in S . cerevisiae . We have shown that loci underlying haloperidol resistance have dose-dependent effects . We identified QTL that showed effects only at low doses of haloperidol , and loci that showed significant effects primarily at higher concentrations of the drug . Our study demonstrates QTL-dosage interaction within the response range of a single drug , and provides new insight into the complex genetic basis of drug resistance in yeast . We identified SWH1 ( OSH1 ) to be the causal gene underlying the largest effect locus in response to haloperidol . Swh1 is a protein similar to the mammalian oxysterol-binding protein and targets to both the Golgi and the nucleus-vacuole junction in yeast [42] . Swh1 that associates with the nucleus-vacuole junction has been shown to act as a substrate for a degradation process , named the piecemeal microautophagy of the nucleus ( PMN ) [43] . Our observation that variants within the OBP domain of Swh1 contribute to resistance to haloperidol suggests that cellular transport , perhaps of sterol-related molecules , is affected in the presence of haloperidol . Cationic amphiphilic drugs have been linked to phospholipidosis and cellular membrane damage [19] , and our identification of Swh1 suggests a potential role for oxysterol binding proteins in these defects . We found that after 6 hours of exposure to haloperidol , yeast vacuoles were enlarged , with the cytoplasm more acidic than the vacuoles , suggesting that haloperidol leads to vacuole dysfunction and further linking Swh1 , vacuole functions , and haloperidol resistance . The same locus was previously linked to growth in E6 berbamine , cobalt chloride , copper sulphate , and neomycin [13] , [15]; it also overlaps with a QTL hotspot in response to a panel of small-molecule therapeutic drugs [13] , suggesting that this locus has pleiotropic effects . In S . cerevisiae , there are seven OSBP homologues ( OSH1-7 ) [44] . Previous studies of the yeast OSH genes suggested that the seven oxysterol-binding proteins shared at least one essential role in the cell ( only deletion of all seven genes is lethal ) , and their functions have significant overlap [44] . We have here provided genetic evidence that Swh1 functions are related to resistance to haloperidol . BY and RM display variation in both the coding and non-coding regions of the remaining six OSH genes . These six OSH genes do not lie in the detected QTL intervals , suggesting that the variants within these genes may lack effects on growth in the presence of haloperidol , either because they do not alter gene function or because only SWH1 has an effect on growth in the presence of haloperidol . Further studies are required to tease apart the specific functions of the individual yeast OSH genes . We showed that polymorphisms in MKT1 contribute to yeast growth in the presence of high concentrations of haloperidol . MKT1 is also a hotspot identified in eQTL [34] , protein QTL [45]–[47] , and drug resistance studies [13] in yeast . The BY ( isogenic derivative of S288c ) allele of MKT1 , which is not present in other strain backgrounds , was previously shown to reduce formation of petite colonies and compromise growth of petite cells [41] . Lipophilic cations can pass through phospholipid membranes , especially those with a large transmembrane potential , such as the mitochondrial inner membrane . This leads to the accumulation of these drugs in the mitochondrial matrix , inducing mitochondrial respiration inhibition [48] . The observation that the BY allele of MKT1 confers resistance to haloperidol suggests that haloperidol may compromise mitochondrial integrity . Variants in IRA2 also contribute to haloperidol resistance . The RM allele of IRA2 inhibits the Ras/PKA pathway more strongly than the BY allele [34] . Since PKA inhibits Msn2/Msn4 , the major transcription factors in stress response [49] , [50] , the RM allele of IRA2 is predicted to lead to stronger stress response , suggesting that stronger stress response may be advantageous at high haloperidol concentrations . In this study , we demonstrated complex interactions among the alleles of SWH1 , MKT1 , and IRA2 in the RM background at 200 µM haloperidol . Pair-wise interactions between identified loci explained the majority of the difference between broad- and narrow-sense heritability at 40 µM haloperidol , but not at higher doses , suggesting higher order interactions or other non-additive contributions . Previous studies in yeast using sporulation efficiency as a model for complex traits [10] , [11] revealed linkage between small- and large-effect QTL , as well as interactions among these QTL . Small-effect QTL were found to depend on the allelic status of the large-effect QTL [11] , which is similar to our observation that the effects of IRA2 and MKT1 were dependent on the allele of SWH1 – the gene underlying the large effect QTL . Through allele replacement analyses , we found that the interactions between SWH1 , MKT1 , and IRA2 were present in the RM background but absent in the BY background , illustrating the value of studying genetically diverse strains . Haloperidol and many antidepressants are cationic amphiphilic drugs that accumulate in membranes in the absence of their specific targets [51] . SWH1 is functionally related to sterol trafficking and the membrane system , and underlies the QTL detected throughout the entire dose response in haloperidol . The identification of MKT1 and IRA2 at higher concentrations of haloperidol suggests the effects of other cellular processes and stress responses . Given the current knowledge on the functions of these identified genes , the interactions between SWH1 , MKT1 , and IRA2 could reflect underlying mechanisms that connect the membrane system , sterol metabolism , and stress response . The as yet unidentified genes underlying the remaining QTL may provide further insight into the mechanisms of action of haloperidol .
S . cerevisiae strains BY4724 and RM11-1a derived strains were used in this study . The panel of 1008 prototrophic segregants derived from BY ( MATa ) and RM ( MATα hoΔ::HphMX4 flo8Δ::NatMX4 AMN1BY ) was previously generated [15] . Allele replacement strains were constructed via the Delitto Perfetto approach using the CORE cassette [52] . This two-step process was performed by first inserting a URA3-KanMX4 cassette from pCORE to generate yfgΔ::URA3-KanMX4; then the region of interest was amplified through high-fidelity PCR from the donor strain , and inserted to replace the URA3-KanMX4 cassette . The loss of the URA3-KanMX4 cassette was selected via 5-Fluoroorotic Acid ( 5-FOA ) counter selection of URA3 and further selected via loss of G418 resistance . Single colonies were isolated at each step , cassette insertions were confirmed via PCR , and allele replacements were sequenced to verify the presence of the correct allele . Transformations were performed by the standard lithium acetate method . All gene sequences were obtained from the Saccharomyces Genome Database ( http://www . yeastgenome . org/ ) . All DNA sequencing related to strain construction and confirmation was performed through standard dideoxy methods . Cultures were grown in rich medium ( YPD , 1% yeast extract , 2% peptone and 2% glucose ) . YPD liquid media and agar plates were made as described [53] . SPO++ was used for sporulation ( http://www . genomics . princeton . edu/dunham/sporulationdissection . htm ) . Selection plates for strain construction were made with YPD containing the respective drugs at standard doses . Haloperidol was purchased from Sigma ( Sigma H1512 ) . All drugs in this study are dissolved in DMSO . Because BY and RM exhibit growth defects only at DMSO concentrations >3% ( v/v% ) , DMSO concentrations in all experiments were kept at <1% . Drug selection agar plates were made with Nunc OmniTray ( Thermo Scientific 264728 ) . 50 mL of YPD with drug concentrations specified were poured into each tray , the trays were placed on a flat surface to solidify in order to obtain best pinning results . All trays contained the same final DMSO concentration . Each experiment was performed with the same batch of YPD . Each plate was made 6 times to allow testing 2 full replicates of the entire segregant panel in 2 different layout configurations . Segregants were pinned on to each agar tray in 384 well format . Haloperidol concentrations for agar plates were selected to be 40 , 80 , 120 , 160 , and 200 µM . These concentrations capture the growth differences between BY and RM , yet maintain enough colony growth to allow QTL mapping . The 1008 segregant panel are stored at −80°C in 96 well format . They were inoculated in YPD and cultured in 384-well plates for ∼48 hours or until saturation . Two configurations were used when converting from 96 to 384 well format , to control for position effect and growth differences due to neighboring cells ( including blank controls ) . Culture plates were then fully resuspended and pinned onto corresponding agar plates with 384 long pins using Singer RoToR . The pinned agar plates were incubated at 30°C for 48–72 hr ( as specified ) and scanned with an Epson 700 transparency scanner . TIFF images ( 400 dpi ) were processed for end-point colony size and effective colony radius was used as proxy for growth [15] . In order to control for both intrinsic growth rate differences and plate position effects , end point effective colony radiuses ( as described in “Yeast colony growth measurement” ) were normalized for growth on control media ( YPD supplemented with same amounts of DMSO as solvent control ) through fitting a regression for effect of growth that were in the same layout configuration on YPD . Residuals were used for QTL mapping . Linkage was determined by calculating LOD scores for each genotype marker using both Haley-Knott regression and non-parametric linkage mapping with the R/qtl package . QTL were called at a LOD cutoff of 3 . Significance was further determined by 1000 permutations of phenotypic values , and re-calculation of LOD scores . Yeast cells were inoculated in rich medium in 96-well plates ( Costar 3370 ) and incubated at 30°C until saturation . 1% ( v/v% ) of saturated culture was used in fresh medium ( with or without drug ) for growth rate measurement ( starting optical density OD <0 . 05 ) . Growth curves were recorded using Synergy 2 Multi-Mode Microplate Reader ( BioTek Instruments ) at 30°C with continuous fast linear shaking ( 100 µL/well ) . OD600 were collected at 15-minute intervals for up to 24 hours . Growth curves were spline fitted , and the maximum fitted slope during logarithmic phase was used as maximum growth rate . Each strain/condition was performed in triplicate . Growth rates are shown as the mean ± sstandard deviation . A t-test was performed between samples in comparison to obtain p-values . All data fitting and comparison were performed in R ( http://www . r-project . org/ ) . Thirty-six replicates each of sixteen replacement strains ( BY background: SWH1RM , MKT1RM , IRA2RM , IRA2RM SWH1RM , IRA2RM MKT1RM , SWH1RM MKT1RM , SWH1RM IRA2RM MKT1RM; RM background: SWH1BY , MKT1BY , IRA2BY , MKT1BY SWH1BY , MKT1BY IRA2BY , SWH1BY IRA2BY , SWH1BY MKT1BY IRA2BY ) and wild type progenitor BY and RM strains were spotted onto YPD agar supplemented with 0 , 40 , 80 , 120 , 160 , 200 µM haloperidol . Plates were incubated at 30°C for ∼72 hr , then scanned as described above in “Yeast colony growth measurement” . Colony radiuses were extracted after image processing . The effect of replacements or replacement combinations was compared to their otherwise isogenic progenitor through analysis of variance ( ANOVA ) . The analysis was conducted in R . The test for transgression was adapted from [28] . Briefly , segregants and parents were tabulated , and the pooled variance was calculated . Segregants that were 2 standard deviations above the mean of the high parent or below the mean of the low parent were counted . The null model was constructed by pooling the segregants and parents , and the null parents and null segregants were randomly sampled from this pool . Significance was determined based on resampling 10 , 000 times the pooled null model . Yeast cells in early to mid log-phase were divided into two cultures of equal volume . Haloperidol ( final concentration 150 µM ) was added to one half , while an equal volume of DMSO as solvent control was added to the other half . The cultures were incubated at 30°C on a shaker for ∼2 hr . Then , 2×106 cells were harvested from each culture for subsequent staining . Quinacrine ( Sigma Q3251 ) staining of vacuoles was performed as described in [54] . Harvested cells were washed once in buffered YPD ( supplemented with 100 mM HEPES , pH 7 . 6 ) , resuspended in 100 µL of the same buffered medium and quinacrine at a final concentration of 200 µM . Cell suspensions were incubated at 30°C for 10min and placed on ice for 5min . Cells were pelleted , washed twice , resuspended with ice-old 100 mM HEPES , pH 7 . 6 buffer containing 2% glucose and kept on ice until imaging . Carboxy-DCFDA ( Yeast Vacuole Marker Sampler Kit , Molecular Probes Y-7531 ) staining was performed according to kit instructions . Briefly , harvested cells were washed and resuspended in 50 mM sodium citrate buffer , pH 5 . 0 , containing 2% glucose . Carboxy-DCFDA at a final concentration of 10 µM was added to the cell suspension followed by incubation at room temperature for 15–30min . For FM4-64 ( Molecular Probes , T-3166 ) staining [55] , harvested cells were resuspended in 50 µL YPD with 1 µL FM4-64 stock solution ( 1 . 6 µM in DMSO ) and incubated at 30°C for 20min . Cells were washed subsequently with 1 mL YPD at room temperature and resuspended in 5 mL YPD to recover at 30°C on a shaker for 90–120min . Recovered cells were washed once in 1 mL sterile ddH2O and resuspended in 200–500 µL YNB for imaging . All imaging was performed within 30min of staining on an Olympus IX81 inverted fluorescence microscope [56] using a 100× oil objective . Quinacrine and carboxy-DCFDA staining were visualized with Chroma SP101v2 ( FITC ) , and FM4-64 with Chroma 49008 ( mCherry TexasRed ) filter sets . Images were acquired using Slidebook 5 . 0 digital image acquisition software ( Intelligent Imaging Innovations ) and processed using ImageJ version 1 . 46r . | Variation in response to a drug can be determined by many factors . In the model organism baker's yeast , many studies of chemical resistance traits have uncovered a complex genetic basis of such resistance . However , an in-depth study of how drug dose alters the effects of underlying genetic factors is lacking . Here , we employed linkage analysis to map the specific genetic loci underlying response to haloperidol , a small molecule therapeutic drug , using a large panel of segregants from a cross between two genetically divergent yeast strains BY ( a laboratory strain ) and RM ( a vineyard strain ) . We found that loci associated with haloperidol resistance are dose-dependent . We also showed that variants in the oxysterol-binding-protein-like domain of the gene SWH1 underlie the major locus detected at all doses of haloperidol . Genetic interactions among genes SWH1 , MKT1 , and IRA2 in the RM background contribute to the differential response at high concentrations of haloperidol . | [
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| 2014 | Genetic Basis of Haloperidol Resistance in Saccharomyces cerevisiae Is Complex and Dose Dependent |
Testosterone concentrations in men are associated with cardiovascular morbidity , osteoporosis , and mortality and are affected by age , smoking , and obesity . Because of serum testosterone's high heritability , we performed a meta-analysis of genome-wide association data in 8 , 938 men from seven cohorts and followed up the genome-wide significant findings in one in silico ( n = 871 ) and two de novo replication cohorts ( n = 4 , 620 ) to identify genetic loci significantly associated with serum testosterone concentration in men . All these loci were also associated with low serum testosterone concentration defined as <300 ng/dl . Two single-nucleotide polymorphisms at the sex hormone-binding globulin ( SHBG ) locus ( 17p13-p12 ) were identified as independently associated with serum testosterone concentration ( rs12150660 , p = 1 . 2×10−41 and rs6258 , p = 2 . 3×10−22 ) . Subjects with ≥3 risk alleles of these variants had 6 . 5-fold higher risk of having low serum testosterone than subjects with no risk allele . The rs5934505 polymorphism near FAM9B on the X chromosome was also associated with testosterone concentrations ( p = 5 . 6×10−16 ) . The rs6258 polymorphism in exon 4 of SHBG affected SHBG's affinity for binding testosterone and the measured free testosterone fraction ( p<0 . 01 ) . Genetic variants in the SHBG locus and on the X chromosome are associated with a substantial variation in testosterone concentrations and increased risk of low testosterone . rs6258 is the first reported SHBG polymorphism , which affects testosterone binding to SHBG and the free testosterone fraction and could therefore influence the calculation of free testosterone using law-of-mass-action equation .
Testosterone , the most important testicular androgen in men , is largely bound to two plasma proteins . Most of the circulating testosterone ( ∼50–60% ) is bound with high affinity to sex hormone-binding globulin ( SHBG ) , while a smaller fraction ( 40–50% ) is bound loosely to albumin , and 1–3% is unbound and termed free testosterone [1] . In prospective cohort studies , low serum testosterone concentrations are associated with cardiovascular morbidity , metabolic syndrome [2] , [3] , dyslipidemia [4] , hypertension [5] , type 2 diabetes mellitus [6] , stroke [7] , atherosclerosis [8]–[10] , osteoporosis , sarcopenia , and increased mortality risk [11]–[13] . Thus , there is growing evidence that serum testosterone is a valuable biomarker of men's overall health status . Since age , body mass index ( BMI ) , and smoking are known to affect serum testosterone concentrations [14] , we used these parameters as common set of covariates in all association models . Studies in male twins indicate that there is a strong heritability of serum testosterone , with genetic factors accounting for 65% of the variation in serum testosterone [15] . However , the genetic determinants of serum testosterone and the genetic risk factors for low concentrations are poorly understood . Given the current gap in knowledge of the genetic factors that contribute to the inter-individual variability in serum testosterone concentration in men we conducted a meta-analysis of genome-wide association studies ( GWAS ) . This two-stage meta-analysis included data from 14 , 429 Caucasian men from 10 independent cohorts within the Cohorts for Heart and Aging Research in Genomic Epidemiology ( CHARGE ) consortium . In stage one , the discovery stage , genome-wide association data from seven cohorts were meta-analyzed ( n = 8 , 938 ) and all genome-wide significant findings that fulfilled the criteria described in the methods section were followed up in the three replication cohorts: one in silico replication cohort ( n = 871 ) and two replication cohorts with de novo genotyping ( n = 4 , 620 ) . All association analyses of the discovery stage were conducted both with and without additional adjustment for serum SHBG concentrations . Our primary aim was to identify genetic variants reproducibly associated with serum testosterone concentrations in men , evaluated as a continuous trait . We also assessed whether the lead single-nucleotide polymorphisms ( SNPs ) from the continuous trait analyses had a significant influence on the risk of having low serum testosterone , defined as <300 ng/dl [16] . This level is slightly lower than that suggested recently by Wu et al . [11 nmol/l = 317 ng/dl] as one of the clinical criteria for late onset hypogonadism [17] .
We performed a GWAS of serum testosterone concentrations , investigating ∼2 . 5 million SNPs in 8 , 938 men of Caucasian ancestry , 18 to 98 years , from seven cohorts . Genome-wide significant SNPs were found in the discovery analysis at one locus on chromosome 17 ( 17p13-p12 ) using the criteria described in the methods . The strongest association was found for rs12150660 ( p = 1 . 9×10−17 ) , located 11 . 5 kb upstream of the major transcription start site of sex hormone-binding globulin ( SHBG ) , with a minor allele frequency ( MAF ) of 23% ( Table 1 [SNPs rs12150660 and rs6258] , Figure 1A and Figures S1A , S2 and S3 ) . Tests for independently associated SNPs with serum testosterone in this region revealed a second SNP , rs6258 ( p = 4 . 1×10−14 ) , which represents a missense ( P→L ) polymorphism located in exon 4 of SHBG ( Table 1 [SNPs rs12150660 and rs6258] , Figure 1B ) and which had a MAF of 2% . Based on HapMap release 22 ( CEU ) , the r2 between rs12150660 and rs6258 was 0 . 004 . To validate the independence of these two SNPs , conditional meta-analysis of the discovery cohorts including both rs12150660 and rs6258 in an additive genetic linear model adjusted for covariates was calculated . Because the associations remained significant and mostly unchanged ( rs12150660 , p = 7 . 0×10−14; rs6258 , p = 1 . 6×10−13 ) , both SNPs were independently associated with serum testosterone concentrations . No additional autosomal locus fulfilled the criteria for genome-wide significance . The associations of rs12150660 and rs6258 were confirmed in the three replication cohorts ( in silico replication in YFS and de novo replication in MrOS Sweden and EMAS ) , demonstrating a combined p-value in the discovery and the replication cohorts of 1 . 2×10−41 and 2 . 3×10−22 , respectively ( Table 1 [SNPs rs12150660 and rs6258] ) . Both SNPs showed considerable heterogeneity of results across the studies as measured by the I2 statistic [18] . The I2 values for the discovery meta-analysis using the untransformed total testosterone values were 76 . 7% and 81 . 6% for rs12150660 and rs6258 , respectively . The heterogeneity was reduced to 39 . 3% and 75 . 5% for rs12150660 and rs6258 , respectively , by meta-analysing the z-score based untransformed total testosterone values and to 30 . 9% and 78 . 0% , respectively , by meta-analysing the inverse-normal transformed testosterone values . For rs12150660 , a substantial amount of heterogeneity could be explained by phenotypic variation among the cohorts , whereas for rs6258 one cohort ( InCHIANTI ) showed consistent opposite effect directions in all models used . To take into account this heterogeneity , we additionally calculated a random effects model for untransformed total testosterone values . The association for rs12150660 remained genome-wide significant in the combined discovery and replication stage meta-analysis , the association for rs6258 reached genome-wide significance after excluding the InCHIANTI cohort ( Table S3 ) . In Table 2 , the serum testosterone concentrations according to genotype are given for the three replication cohorts . As expected , mean serum testosterone concentrations were found to be lower in men with GG than in those with TT genotype for rs12150660 . Similarly , men with the CT genotype for rs6258 had lower serum testosterone concentrations than those with CC genotype . The TT genotype of rs6258 was extremely rare and only found in two subjects in the replication cohorts . The two autosomal SNPs identified by GWAS had a significant influence on the risk of having low serum testosterone ( serum testosterone <300 ng/dl ) in both the discovery and the replication cohorts with a combined odds ratio ( OR ) per minor allele of 0 . 72 ( 95% CI , 0 . 65 – 0 . 79 ) and 2 . 7 ( 95% CI , 2 . 1 – 3 . 5 ) for rs12150660 and rs6258 , respectively ( Figure 2A ) . We analyzed the combined effect of the two SNPs on the risk of having low serum testosterone concentrations according to the number of combined risk alleles for rs12150660 ( G ) and rs6258 ( T ) in the three replication cohorts ( MrOS Sweden , EMAS , and YFS ) . The risk of having low serum testosterone concentrations increased by the number of risk alleles with an OR of 1 . 62 ( 95% CI , 1 . 41 – 1 . 86 ) for each risk allele ( Figure S4 ) . Low serum testosterone concentrations were 6 . 5-times more prevalent in men with ≥3 risk alleles ( 30 . 1% prevalence of low serum testosterone ) compared to men without any risk allele ( 4 . 6% prevalence of low serum testosterone; Figure 2B ) . As SNP rs12150660 is located 11 . 5 kb upstream of SHBG and SNP rs6258 is non-synonymous and located in exon 4 of SHBG , we evaluated the influence of these polymorphisms on SHBG concentrations . Both of these polymorphisms demonstrated a significant association with SHBG concentrations in both the discovery and replication cohorts ( Table 1 [SNPs rs12150660 and rs6258] ) . However , even after adjusting for SHBG concentrations , the associations between these two SNPs and serum testosterone concentrations were still significant ( p = 9 . 0×10−8 for rs12150660 and p = 4 . 5×10−7 for rs6258 ) . Free testosterone calculated using law-of-mass-action equation was not associated with either of the two polymorphisms ( Table 1 [SNPs rs12150660 and rs6258] ) . As serum testosterone and SHBG are highly correlated ( e . g . , in MrOS Sweden rs = 0 . 53 ) , variations in SHBG concentration might have influenced the observed associations of serum testosterone with other non-SHBG-related loci . Therefore , we performed an additional SHBG-adjusted genome-wide meta-analysis among the discovery cohorts , wherein none of the non-SHBG-related autosomal SNPs reached genome-wide significance ( Figure S1B ) . As rs6258 is non-synonymous ( P156L ) and located in exon 4 of SHBG , we evaluated the serum SHBG steroid-binding capacity of the different rs6258 genotypes . As shown in Figure S5 , serum SHBG from CT but not CC subjects had a lower steroid-binding capacity than expected from values obtained by an SHBG immunoassay ( p = 0 . 003 ) . Therefore , we analyzed the SHBG affinity for testosterone using Scatchard plots of SHBG in serum of men with the rs6258 genotype ( Figure 3A ) , and revealed ( Figure 3B ) a higher mean dissociation constant ( Kd ) indicative of a lower affinity in CT ( Kd = 4 . 5 nM ) and TT ( Kd = 4 . 9 nM ) individuals than in CC individuals ( Kd = 2 . 8 nM ) . Recombinant SHBG corresponding to the T genotype demonstrated a higher dissociation constant ( lower affinity ) compared with recombinant SHBG corresponding to the C genotype ( T genotype Kd 2 . 5 nM; C genotype Kd 1 . 2 nM , Figure 3C ) . In addition , the free testosterone fraction measured by an equilibrium dialysis method was 22% higher ( p = 1 . 4×10−5 ) in serum from CT subjects than in serum from CC subjects ( Figure 3D ) . Imputed values for X chromosome-located SNPs were available for the two larger discovery cohorts ( SHIP and FHS; n = 5 , 067 ) . We performed meta-analyses of imputed X chromosome SNPs for serum testosterone concentrations both with and without SHBG adjustment , revealing one genome-wide significant association for SNP rs5934505 ( p = 8 . 5×10−9 ) in the SHBG-adjusted model ( Table 1 [SNP rs5934505] and Figures S1B and S3 ) . This SNP was confirmed in the two replication cohorts with de novo genotyping ( MrOS Sweden p = 3 . 6×10−3; EMAS p = 1 . 5×10−7 ) . Meta-analysis of discovery and replication cohorts resulted in a combined p-value of 5 . 6×10−16 . The rs5934505 SNP is located in a CNV-insertion area ( Xp22 ) , 145 kb upstream of the family with sequence similarity 9 , member A ( FAM9A ) and 79 kb downstream of the family with sequence similarity 9 , member B ( FAM9B ) ( Figure 1C ) . In addition , rs5934505 is located 214 kb upstream of Kallmann syndrome 1 sequence ( KAL1 ) . SNP rs5934505 was associated with serum testosterone without SHBG-adjustment ( combined p-value of 1 . 7×10−9 ) and with free testosterone ( combined p-value of 6 . 7×10−15 ) , but not with SHBG ( Table 1 [SNP rs5934505] ) . The mean serum testosterone and calculated free testosterone but not SHBG concentrations were lower in men with T genotype than in those with C genotype for rs5934505 ( Table 2 ) .
This GWAS revealed novel genetic variants that significantly affect circulating testosterone concentrations in men . The presence of three or more risk alleles for the two polymorphisms in the SHBG loci resulted in markedly decreased testosterone concentrations compared to men with two or less risk alleles . Importantly , one of the identified genetic variations was associated with an alteration in SHBG's binding affinity for testosterone and the measured free testosterone fraction . In addition , we identified a locus on the X chromosome influencing serum testosterone concentrations . The genetic contribution of the polymorphisms to testosterone concentrations reported here is substantial; as a reference for comparison , the effect of these polymorphisms on testosterone concentrations in men is similar or greater than that for known risk factors such as age , smoking , and BMI [19] , [20] . These findings improve our understanding of the genetic factors that affect serum testosterone concentrations and contribute to the variation in testosterone concentrations in men . These polymorphisms may assist in the identification of men at risk of low serum testosterone , although the clinical usefulness of these findings remains to be established . As rs12150660 and rs6258 were strongly associated with SHBG concentrations , both SNPs may at least partly affect total testosterone concentrations by modulating SHBG concentrations . Our findings that rs6258 substantially affects SHBG binding affinity and the measured free testosterone fraction raise questions about the use of a single consensus value for SHBG's dissociation constant in the law of mass action equations used to calculate free testosterone concentrations . As emphasized by the Endocrine Society's expert panel on androgen deficiency syndromes , low testosterone concentrations alone should not necessarily be viewed as evidence of androgen deficiency [16] . Whether rs593405 near the FAM9B and KAL1 genes on Xp22 renders men susceptible to the increased risk of androgen deficiency remains to be determined . Further studies are required to determine the impact of these genetic variations on sex steroid-related disorders , including osteoporosis , cardiovascular diseases , prostate cancer , and male infertility [21] . Our studies add to the evidence that genetic variations within the SHBG gene may explain some of the inter-individual differences in SHBG concentrations . Our finding that SNP rs6258 results in the production of an SHBG variant with reduced affinity for testosterone provides an explanation for the association between rs6258 and low serum testosterone concentrations . This is the first described genetic variant associated with altered SHBG binding for testosterone . As rs6258 is non-synonymous ( P156L ) , located in exon 4 of SHBG and associated with altered SHBG binding for testosterone and free testosterone fraction , rs6258 is likely a functional polymorphism with impact on testosterone binding to SHBG as well as testosterone bioavailability and action at target tissue level . The SNP rs12150660 that is strongly associated with testosterone concentrations is located 11 . 5 kb upstream of the coding sequence for SHBG mRNA production in the liver . However , it still resides within the human SHBG locus because several other alternative exon 1 sequences are located up to ∼13 kb upstream of the exon 1 sequence that encodes the secretion signal polypeptide of the SHBG precursor in the liver [22] . There are no obvious nuclear protein binding sites within the sequences spanning SNP rs12150660 , and it remains to be determined whether this SNP disrupts a cis-element that directly influences SHBG transcription . We have found that rs12150660 is in strong LD ( r2 = 0 . 89 ) with another common SNP ( rs1799941 ) in the SHBG proximal promoter that was shown to be associated with serum SHBG concentrations [23]–[25] . Thus , it is highly likely that only one of these polymorphisms is actually functional and therefore both SNPs represent the same signal . It should also be noted that rs1799941 is linked to the number of TAAAA repeats within an Alu sequence upstream of SHBG promoter [26] and that the rs1799941 ( A allele ) is linked with the presence of six TAAAA repeats in this location which has been reported to be associated with higher SHBG concentrations [27] . In addition , while there does not appear to be any putative transcriptional factor binding sites with the sequence comprising rs12150660 , it remains to be determined whether rs12150660 or these other associated SNPs in the SHBG gene are functionally important or simply represent proxies of SHBG and testosterone concentrations in men . Our meta-analyses of imputed X chromosome SNPs revealed one genome-wide significant association for SNP rs5934505 , located in a CNV-insertion area ( Xp22 ) , 145 kb upstream of family with sequence similarity 9 , member A ( FAM9A ) and 79 kb downstream of family with sequence similarity 9 , member B ( FAM9B ) . Both genes , FAM9A and FAM9B , are expressed exclusively in the testis [28] and described here for the first time to be associated with total as well as free testosterone concentrations . rs5934505 is located 214 kb upstream of Kallmann syndrome 1 sequence ( KAL1 ) . Although the Kallmann syndrome , a type of hypogonadotropic hypogonadism associated with anosmia and other congenital anomalies , has been linked to mutations in the KAL1 gene on the X chromosome , only 11–14% of Caucasian patients with hypogonadotropic hypogonadism have detectable KAL1 mutations [29] , reflecting the considerable genetic heterogeneity of this syndrome . The strengths of our study include a discovery sample size of 8 , 938 men , which allowed us at the threshold α = 5×10−8 , a 90% power to detect SNPs accounting for 0 . 5% of the total variance in serum testosterone concentrations , and 99% power to detect SNPs accounting for 1% of the total variance . The SNPs rs12150660 , rs6258 , and rs5934505 explained 2 . 3% , 0 . 9% , and 0 . 6% , respectively , of the variance in serum testosterone concentrations when evaluated in the MrOS Sweden replication cohort . Future meta-analyses including larger samples will probably reveal additional loci associated with serum testosterone . Further research into the functional significance of these variants will be needed to enable the translation of these findings into the mechanisms of sex steroid-related diseases and strategies for risk assessment . As the causal or etiological role of these polymorphisms in the genesis of low testosterone has not been established , the reported polymorphisms associated with low serum testosterone concentration may be viewed currently as risk markers rather than causal risk factors . In conclusion , genetic variants in the SHBG locus and on the X chromosome are associated with a substantial variation in testosterone concentrations and increased risk of low testosterone in men . Further studies are needed to determine the impact of these genetic variations on sex hormone-related disorders . rs6258 is the first reported SHBG polymorphism , which affects testosterone binding to SHBG and the free testosterone fraction and could therefore influence the calculation of free testosterone using law-of-mass-action equation .
The discovery stage of the GWAS included 8 , 938 Caucasian men of European descent drawn from seven epidemiological cohorts: the Framingham Heart Study ( FHS ) , the Study of Health in Pomerania ( SHIP ) , the Gothenburg Osteoporosis and Obesity Determinants ( GOOD ) study , the Cooperative Health Research in the Region of Augsburg ( KORA ) study , the Health , Aging and Body Composition ( HEALTH ABC ) study , the Rotterdam Study ( RS1 ) , and the Invecchiare in Chianti ( InCHIANTI ) ( Table S1 ) . The replication stage consisted of 4 , 620 men from two epidemiological cohorts ( the European Male Ageing Study [EMAS] and the Osteoporotic Fractures in Men [MrOS] Sweden study ) for de novo genotyping of the top SNPs and one additional cohort ( the Young Finns Study , [YFS , n = 871] ) with genome-wide association data available and joining the study after stage one was finished for in silico replication ( Table S2 ) . Exclusion criteria included chemical or surgical castration and/or medications affecting sex hormones such as steroid 5-alpha reductase inhibitors , and sex hormone antagonists . All studies were approved by local ethics committees and all participants provided written informed consent . Characteristics of the study samples and detailed descriptions of the participating cohorts , genotyping methods , quality control , and imputation procedures are provided in Text S1 . Altogether , ∼2 . 5 million SNPs , imputed using the HapMapII CEU population , were tested for association with serum testosterone in the discovery stage . Genome-wide association analyses using an additive genetic linear regression model adjusted for age , BMI , and current smoking were conducted twice within each of the discovery cohorts using serum testosterone expressed as ng/dl , as well as inverse-normal transformed serum testosterone as outcomes . To examine the robustness of the discovery results and to reduce the risk of spurious associations due to possible testosterone measurement heterogeneity between the individual cohorts , three different types of meta-analyses were performed in the discovery stage: 1 ) an inverse-variance weighted fixed effect model; 2 ) a z-score based analysis of the untransformed serum testosterone concentrations; and 3 ) a z-score based meta-analysis of the inverse-normal transformed values . Model 1 ) was used as main analysis since it allowed the computation of effect estimates , whereas the other two analysis models were used for verification and quality control checks of the main findings . All meta-analyses were performed using METAL ( www . sph . umich . edu/csg/abecasis/metal/ ) . The random effects model of the two SHBG locus SNPs was calculated using the R-package metafor ( www . r-project . org ) . Imputed genotypes were analyzed in all cohorts taking the genotype uncertainties into account . Genomic control was applied to each individual cohort's results and to the discovery stage meta-analysis to correct p-values for potential effects of mild population stratification . The estimated genomic control lambda was low for both the individual cohorts ( range of λGC: 1 . 00–1 . 07 ) and the meta-analyses ( range of λGC: 1 . 01–1 . 02 ) , suggesting little residual confounding due to population stratification ( Figure S2 ) . To reduce the variance on serum testosterone induced by SHBG concentration , the GWAS included a genome-wide test for association of untransformed serum testosterone concentrations adjusted for age , BMI , current smoking , SHBG and SHBG2 concentrations , again using both an inverse-variance weighted fixed effect as main analysis and a z-score based meta-analysis for quality control purposes . A threshold of p<5×10−8 was established a priori as the level for genome-wide significance in the discovery analyses [30] . SNPs that reached genome-wide significance in the inverse-variance weighted meta-analysis of untransformed serum testosterone concentrations with or without adjustment for SHBG and which had association results in at least five of the seven cohorts ( for chr X: two cohorts with data available ) were selected for further analyses . Notably , all autosomal SNPs that fulfilled these criteria also reached genome-wide significance in the other two types of meta-analyses . From these SNPs , all independent SNPs were taken to the replication stage . We also assessed whether the lead SNPs from the continuous trait analyses were associated with low serum testosterone concentration ( defined as <300 ng/dl [16]; this level is slightly lower than that suggested recently by Wu et al [11 nmol/l = 317 ng/dl] [17] ) by binary logistic regression including the same covariates in the model used for the main analysis and meta-analyzing the within-cohort results using inverse-variance weighted fixed-effect model . The KORA cohort was not included in the meta-analyses of low serum testosterone as testosterone was measured using plasma in this cohort . We determined the number of low serum testosterone concentration risk alleles ( 0 to 4 ) for the two lead SNPs of the SHBG locus in each individual and assessed the risk of low serum testosterone concentrations in the three replication cohorts ( MrOS Sweden , EMAS , and YFS ) using a trend test . Since only two subjects in the replication cohorts had four risk alleles , individuals having three and four risk alleles were grouped into one category to obtain more reliable effect estimates during the subsequent analyses . Details of test for independence , SHBG related analysis of the top SNPs and quality control steps performed can be found in Text S1 . Methods for the measurement of serum testosterone and SHBG are given in Text S1 . Calculated free testosterone was for all subjects with both testosterone and SHBG available ( n = 13833; Table 1 and Table 2 ) calculated by using a modified law of mass action equation , as described by Mazer [31] . The concentrations of testosterone and SHBG and a fixed value for SHBG's dissociation constant were used in these calculations . Free testosterone fraction was measured by an equilibrium dialysis method in 87 subjects with the CC genotype and 32 subjects with the CT genotype of rs6258 ( Figure 3D ) [32] . Detailed description of the free testosterone fraction measurements is provided in Text S1 . In experiments evaluating SHBG binding capacity , serum SHBG concentrations were determined by two-site immunofluorometric assay ( PerkinElmer Life Sciences , Turku , Finland ) [33] , or by a steroid-binding capacity assay [34] . For steroid-binding assays , serum samples were pre-incubated with dextran-coated charcoal ( DCC ) to remove endogenous steroids , prior to incubation with either [3H]5α-dihydrotestosterone ( [3H]DHT; specific activity 50 Ci/mmol ) or [3H]testosterone ( specific activity 40 Ci/mmol ) , bound from free [3H]steroid were separated using DCC as the separation reagent [34] . The steroid-binding properties of SHBG in diluted serum samples or tissue culture medium were determined by Scatchard analysis [34] . For the expression of SHBG protein , wild type ( corresponding to the C genotype of rs6258 ) and rs6258 ( corresponding to the T genotype of rs6258 ) SHBG cDNAs in the pRC/CMV expression vector were transfected into CHO cells , and G418 was used for selection of stably transfected cells . At near confluence , cells were washed with PBS and cultured in serum-free SFM4CHO medium ( Thermo Scientific HyClone , Logan , UT ) for four days before the SHBG-containing medium was harvested . | Testosterone is the most important testicular androgen in men . Low serum testosterone concentrations are associated with cardiovascular morbidity , metabolic syndrome , type 2 diabetes mellitus , atherosclerosis , osteoporosis , sarcopenia , and increased mortality risk . Thus , there is growing evidence that serum testosterone is a valuable biomarker of men's overall health status . Studies in male twins indicate that there is a strong heritability of serum testosterone . Here we perform a large-scale genome-wide association study to examine the effects of common genetic variants on serum testosterone concentrations . By examining 14 , 429 men , we show that genetic variants in the sex hormone-binding globulin ( SHBG ) locus and on the X chromosome are associated with a substantial variation in serum testosterone concentrations and increased risk of low testosterone . The reported associations may now be used in order to better understand the functional background of recently identified disease associations related to low testosterone . Importantly , we identified the first known genetic variant , which affects SHBG's affinity for binding testosterone and the free testosterone fraction and could therefore influence the calculation of free testosterone . This finding suggests that individual-based SHBG-testosterone affinity constants are required depending on the genotype of this single-nucleotide polymorphism . | [
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| 2011 | Genetic Determinants of Serum Testosterone Concentrations in Men |
BAHD1 is a vertebrate protein that promotes heterochromatin formation and gene repression in association with several epigenetic regulators . However , its physiological roles remain unknown . Here , we demonstrate that ablation of the Bahd1 gene results in hypocholesterolemia , hypoglycemia and decreased body fat in mice . It also causes placental growth restriction with a drop of trophoblast glycogen cells , a reduction of fetal weight and a high neonatal mortality rate . By intersecting transcriptome data from murine Bahd1 knockout ( KO ) placentas at stages E16 . 5 and E18 . 5 of gestation , Bahd1-KO embryonic fibroblasts , and human cells stably expressing BAHD1 , we also show that changes in BAHD1 levels alter expression of steroid/lipid metabolism genes . Biochemical analysis of the BAHD1-associated multiprotein complex identifies MIER proteins as novel partners of BAHD1 and suggests that BAHD1-MIER interaction forms a hub for histone deacetylases and methyltransferases , chromatin readers and transcription factors . We further show that overexpression of BAHD1 leads to an increase of MIER1 enrichment on the inactive X chromosome ( Xi ) . In addition , BAHD1 and MIER1/3 repress expression of the steroid hormone receptor genes ESR1 and PGR , both playing important roles in placental development and energy metabolism . Moreover , modulation of BAHD1 expression in HEK293 cells triggers epigenetic changes at the ESR1 locus . Together , these results identify BAHD1 as a core component of a chromatin-repressive complex regulating placental morphogenesis and body fat storage and suggest that its dysfunction may contribute to several human diseases .
Chromatin-based transcriptional repression is mediated by macromolecular complexes containing proteins involved in chromatin writing , reading , erasing and remodeling activities . The combinatorial assembly of subunits with transcription factors affects cell-specific gene expression in response to developmental , physiological or environmental stimuli . Chromatin-repressive complexes control key pathways during embryonic development and adult life; as a consequence , deregulation or abnormalities in their components can lead to a wide range of pathological processes [1 , 2] . The importance of chromatin-modifiers in development , cell differentiation and disease is well illustrated for three complexes containing the histone deacetylases HDAC1 and HDAC2: NuRD [3 , 4] , Sin3A [5] and CoREST [6 , 7] ( For reviews , see [2 , 8–10] ) . By a proteomic approach , we found that the Bromo-Adjacent-Homology domain-containing 1 ( BAHD1 ) protein co-purifies with HDAC1/2 , together with heterochromatin proteins HP1 and KAP1 ( or TRIM28 ) in human embryonic kidney ( HEK ) 293 cells , suggesting that BAHD1 is a core component of a novel HDAC1/2-associated complex [11] . BAHD1 also interacts with the Methyl-CpG-binding protein MBD1 and the H3K9 methyltransferases ( KMT ) SETDB1 [12] and SUV39H1 [13] and acts as a repressor , pointing to a role of BAHD1 in heterochromatin-mediated transcriptional repression [12] . In agreement with this , overexpression of BAHD1 in human cells induces large-scale chromatin condensation [12] and changes in the DNA methylation landscape [14] . BAHD1-associated heterochromatic domains lack acetyl histone H4 and partially overlap with HP1α , a marker of constitutive heterochromatin , and/or with H3 trimethylated at lysine 27 ( H3K27me3 ) , a marker of facultative heterochromatin [12] . Furthermore , when overexpressed in human female cells , BAHD1 is enriched at the inactive X chromosome ( Xi ) , a paradigm of facultative heterochromatin [12] . A study in mouse embryonic stem cells ( mESCs ) recently reported that BAHD1 , HDAC1 and HDAC2 are pulled-down by CDYL , a transcriptional co-repressor that may play a role in the maintenance of the Xi [15] . Taken together , these data suggest that BAHD1 is a component of HDAC1/2-associated complexes involved in a variety of epigenetic mechanisms . A single gene encodes BAHD1 in vertebrates and no ortholog is found in invertebrates or plants , suggesting that BAHD1 has vertebrate-specific functions . However , the low expression of BAHD1 in cell lines has hampered its functional characterization and , so far , the BAHD1 regulatory gene network is poorly characterized . We identified the insulin-like growth factor II ( IGF2 ) transcript and its antisense transcript ( IGF2AS ) as BAHD1 targets in HEK293 embryonic cells [12] . We also demonstrated that infection by a bacterial pathogen triggers BAHD1-mediated repression of Interferon-Stimulated Genes ( ISGs ) in epithelial cells [11] . However , apart from bacterial infection , signals that control the expression and/or activity of BAHD1 are unknown . The aim of the present study was to determine the physiological functions of BAHD1 . We show that disruption of the Bahd1 gene in the mouse leads to a placental growth defect associated with low birth weight and neonatal death , hypocholesterolemia and decreased body fat in surviving adults . Proteomic studies of BAHD1-associated proteins identify MIER proteins as novel BAHD1 partners . Our extensive characterization of the transcriptome strongly suggests that BAHD1-MIER complexes repress genes involved in the control of steroid/lipid metabolism both in mouse and human cells .
We searched for tissue-specific expression of the BAHD1 gene by a survey of referenced transcriptome datasets ( S1 Table ) and found that BAHD1 mRNA levels are low and do not vary much between tissues , when compared to a set of housekeeping or tissue-specific genes . This observation is consistent with the lack of detection of the endogenous BAHD1 protein in mammalian cultured cells and suggests that BAHD1 basal levels are low . In order to identify functions of BAHD1 in biological processes , we studied the physiological consequences of BAHD1 inactivation by performing a large-scale phenotyping of Bahd1 haplo-deficient ( Bahd1+/- ) mice [11] . The analysis was carried out on 18 heterozygous ( HET ) and 16 wild type ( WT ) littermates ( results are detailed in S1 Text and S2 Table ) . Mice were first fed a standard chow diet ( CD ) for 14 weeks and then a high fat/high carbohydrate diet ( HFHCD ) for 16 weeks . HET Bahd1+/- mice did not show any morphological , sensory or cardiac abnormality and no change in bone density , body weight and fat , when compared to WT littermates , and the blood chemistry and hematology parameters were within the normal range . However , some slight and gender-specific modifications of glucose or cholesterol parameters were observed in HET mutants . At the age of 10 weeks , HET male mice displayed a significant hypoglycemia when compared to WT mice ( Fig 1A ) . After the switch to HFHCD , blood levels of glucose remained lower in 30-week-old HET than in WT male mice , although difference did not then reach statistical significance ( S2A Table ) . In contrast to males , glycemia was not affected in female HET mice whatever the diet ( Fig 1A ) but female HET fed with HFHCD displayed a slight decrease in blood levels of total cholesterol , high-density lipoprotein ( HDL ) and low-density lipoprotein ( LDL ) compared to WT littermates ( Fig 1B ) . However , values were statistically significant only for LDL concentrations ( S2B Table ) . We next crossed Bahd1-heterozygous mice to evaluate potential metabolism defects in the Bahd1-null context . Most Bahd1-/- ( KO ) pups died within the first days of life , indicating that the Bahd1-null mutation leads to perinatal death . Only 5 Bahd1-/- out of 200 genotyped pups ( 2 . 5% ) survived birth . At six weeks , these 5 survivors , all males , displayed reduced sizes and weights compared to control littermates ( Fig 1C ) . After several months , they reached the length of Bahd1+/+ littermates ( Fig 1C and 1D ) , but kept a significant lower body weight ( Fig 1E ) , characterized by reduced fat and lean mass ( Fig 1F ) . Quantification of blood parameters highlighted lower levels of total cholesterol , HDL and LDL in plasma of Bahd1-/- mice than in Bahd1+/+ littermates at 3–5 months ( S1A Fig ) , 7 and 18 months of age ( Fig 1G and 1H ) . Glucose and leptin levels were also significantly lower in 18 month-old Bahd1-/- mice than in Bahd1+/+ littermates ( Fig 1H ) . The other measured blood parameters were not significantly different between KO and WT mice ( i . e . insulin , adiponectin , Fig 1G and 1H; triglycerides , free fatty acids , glycerol , aspartate and alanine amino transferases , urea , creatinine , albumin , glucagon , GIP , S1B Fig ) . Together , these results demonstrate a biological function of BAHD1 in controlling lipid and carbohydrate metabolism . The high neonatal mortality rate of BAHD1-KO pups suggested that BAHD1 could have important functions during fetal life . In order to address this question , we examined embryos just before birth at the embryonic day 18 . 5 ( E18 . 5 ) . Bahd1-/- fetuses were present at Mendelian frequency , had normal morphology , were alive and exhibited the breathing reflex . However , their weight was decreased by 30% ( Fig 2A ) when compared to WT or HET fetuses . In addition , Bahd1-/- embryos exhibited a smaller placenta , with a reduction of 30% in the circumference and 55% of the area ( Fig 2B ) , when compared to placentas of WT or HET embryos . At an earlier stage ( E16 . 5 ) , Bahd1-/- placentas were also smaller than Bahd1+/+ ones ( Fig 2B ) . Histology of placenta sections with hematoxylin and eosin ( HE ) staining showed that Bahd1-/- placentas comprised the two fetal compartments ( labyrinthine zone ( Lz ) and junctional zone ( Jz ) ) and the maternal compartment ( decidua basalis ( Db ) ) . However , the Lz surface area was significantly reduced and the Jz and Db were thinner in Bahd1-/- placentas than in control littermates ( Fig 2C ) . The junctional zone plays an important role in hormone synthesis , while the labyrinthine zone is critical for materno-foetal nutrient exchange [16 , 17] . The Jz comprises fetal spongiotrophoblast cells and trophoblast glycogen cells ( GCs ) . The exact origin and function of GCs is unknown , but they are believed to provide an important glucose supply for fetoplacental development . Evidence indicates that they differentiate early ( E . 6 . 5 ) in the ectoplacental cone at the origin of the Jz and migrate into the maternal decidua at about E12 . 5 [18 , 19] . We used periodic acid-Shiff ( PAS ) staining to examine if GCs were altered by the Bahd1 null mutation . PAS staining confirmed the reduced thickness of Jz and Db in Bahd1-/- relative to Bahd1+/+ placentas at E16 . 5 and showed a severe reduction in the number of PAS-positive GCs both in the Jz and the Db ( Fig 1D and S2A Fig ) . In contrast , histology of the fetal liver at E16 . 5 did not reveal any noticeable difference between Bahd1-KO and WT embryos ( S2B Fig ) . Together , these results indicate that BAHD1 is required for a normal placental development and intra-uterine fetal growth . BAHD1 mutants may die after birth from metabolic defects , or secondary to altered placental exchange prenatally , impairing the development of energy stores . To examine the consequence of BAHD1 deficiency on the placental transcriptome , we isolated RNA from Bahd1-/- placentas at stages E16 . 5 ( n = 6 per genotype ) and at stage E18 . 5 ( n = 3 per genotype ) to perform a comparative microarray analysis using Affymetrix mouse arrays . The Bahd1-null mutation altered expression of 397 and 1396 genes ( FDR-BH < 0 . 05 ) at E16 . 5 and at E18 . 5 , respectively , with a much higher proportion of up-regulated genes ( 70–80% ) than down-regulated genes ( 20–30% ) , consistent with a role for BAHD1 in transcriptional repression ( Fig 3A ) . 65% of genes deregulated in E16 . 5 Bahd1-KO placentas remained similarly deregulated at the E18 . 5 stage ( 214 up-regulated and 46 down-regulated genes , S3 Table ) . We used the DAVID software to classify genes based on Gene Ontology ( GO ) [20] and highlight the most significant biological processes that could be altered by BAHD1 deficiency . At the E16 . 5 stage , the most significant gene cluster ( P value < 5 . 10−4 ) was a group of 16 genes involved in steroid metabolic processes ( Fig 3B and S4A Table ) . Notably , 11 of these genes remained deregulated in Bahd1-/- E18 . 5 placentas: 7 genes up-regulated ( Apoc3 , Atp8b1 , Cyp11A1 , Insig2 , Osbpl5 , Pbx1 , VldlR ) and 4 genes down-regulated ( Fabp6 , Hsd17b2 , Hsd17b7 , LepR ) ( S3 Table ) . In addition , two steroid hormone receptor genes , the estrogen receptor alpha gene Esr1 and progesterone receptor gene Pgr , were up-regulated in Bahd1-/- placentas , both in males and females embryos and at both gestational stages , as confirmed by Real time quantitative PCR ( RT-qPCR ) analysis ( Fig 3C ) . Groups of genes involved in the response to oxidative stress , skeletal system development and blood vessel development were also over-represented in Bahd1-KO placentas ( S4 Table ) . Several of these genes are known to be involved in placental morphogenesis , such as Gja1/Cx43 , Cxcl14 , Mmp2 , Mmp14 , Runx1 ( up-regulated ) and Ada , Adm , Gcm1 , Gjb5 ( down-regulated ) . Altered expression of these genes in Bahd1-KO mice is consistent with an abnormal placentation . RT-qPCR analysis of a set of transcripts confirmed the transcriptome data ( Fig 3C and 3D ) . Several genes previously shown to be imprinted in the mouse during development or in the adult [21] were also up-regulated in Bahd1-KO placentas . Imprinted genes , expressed from only one of the parental alleles , play important functions during mammalian development , particularly in the placenta [22] . The parental conflict hypothesis of imprinting proposes that maternally− or paternally−expressed imprinted genes restrict or increase the energy expenditure , respectively [23] . We have previously shown that in human embryonic cells BAHD1 repressed IGF2 and IGF2AS [12] , which are imprinted in many tissues . BAHD1 deficiency did not affect Igf2 expression in murine placenta , but , the paternally−expressed Igf2as and ten maternally−expressed putative imprinted ( Ampd3 , Ano1 , Dcn , Gatm , Htra3 , Osbpl5 , Qpct , Tfpi2 , Tnfrsf23 and Wt1 ) were up-regulated in Bahd1-deficient E16 . 5 placentas , while one , Gpr1 , was down-regulated . RT-qPCR assays confirmed these results using several other imprinted genes as negative controls ( Fig 3E ) . Most of these genes are imprinted only in the placenta . However , determining placental specific imprinting can be confounded by maternally derived placental tissue and there is controversy over the imprinted status of some of these genes [24] . Strikingly , Htra3 , Tfpi2 , Ampd3 , Gatm , Osbpl5 , Qpct and Wnt1 were also consistently up-regulated in Bahd1-deficient E18 . 5 placentas ( S3 Table ) and may be functionally implicated in the impaired placental growth . Bahd1-KO fetuses exhibited a lower weight than Bahd1-WT . This phenotype could result from placental dysfunction but also from deregulation of BAHD1 target genes in intra-embryonic tissues . To address this point , we analyzed transcriptomes of mouse embryonic fibroblasts derived from Bahd1-WT and -KO embryos at stage E13 . 5 ( n = 3 per genotype ) . The results of this analysis revealed 353 up-regulated and 139 down-regulated genes ( FDR-BH < 0 . 05 ) in Bahd1-KO MEFs , when compared to Bahd1-WT MEFs ( Fig 3A ) . We focused on up-regulated genes , as these likely contain those genes directly repressed by BAHD1 . Strikingly , the most significant biological process associated with up-regulated genes in Bahd1-KO MEFs was sterol metabolism ( Fig 3B ) . However , genes associated with this process were different to those affected in Bahd1-KO placentas ( S4C Table ) , suggesting that BAHD1-dependent gene networks are tissue-specific even though they may have a common function . We next assessed whether BAHD1 has a similar role in repressing sterol metabolism genes in human cells , by comparing transcriptomes of HEK293 cells stably overexpressing BAHD1 ( HEK-BAHD1 ) to that of parental isogenic cells ( HEK-CT ) , in which the BAHD1 protein is undetectable [14] . This analysis identified 1148 transcripts ( FDR-BH < 0 . 05 ) that were down-regulated in cells constitutively expressing the BAHD1 repressor . Once again , GO term classification analysis of these transcripts highlighted sterol metabolism as the most significant biological process ( Fig 3B and S4D Table ) . Genes involved in lipid and hexose metabolism also grouped as significant clusters . Although biological functions of BAHD1-associated chromatin repressive complexes are likely to be cell type- and species-specific , and a transcriptome will identify direct and indirect gene expression changes , BAHD1 bona fide target genes may be identified by comparing different biological systems ( Fig 3F ) . Overlapping HEK-BAHD1 and Bahd1-KO E18 . 5 placenta transcriptomes identified 107 potential BAHD1 targets ( S5A Table and Fig 3F ) , the most significant gene groups where those involved in the regulation of hormone levels ( ALDH1A2 , BACE2 , CAMK2G , CRABP2 , LY6E , SCARB1 , SLC16A2 ) and lipid biosynthetic processes ( ALDH1A2 , CD81 , EBP , ELOVL7 , LASS4 , LPL , LPCAT2 , LTA4H , SCARB1 ) ( S4E Table ) . Overlapping HEK-BAHD1 and Bahd1-KO MEFs transcriptomes identified 44 potential BAHD1 target genes ( S5B Table and Fig 3F ) , the most statistically significant group in term of biological processes including genes involved in sterol/steroid metabolism ( DHCR24 , HMGCS1 , LDLR , NSDHL , SC4MOL , SREBF2; S4F Table ) . Only six genes were altered consistently between all the three transcriptome datasets ( S5C Table ) . Using a tetracycline-inducible BAHD1 HEK293 line ( HPT-BAHD1 cells , [11] ) , we confirmed that induction of BAHD1 expression ( S3 Fig ) is sufficient to repress five transcripts involved in lipid/steroid metabolism ( HMGCS1 , LDLR , NSDHL , CRABP2 and LASS4 ) ( Fig 3G ) . Of note , no imprinted gene was found in this analysis , indicating that the effect of the Bahd1-null mutation on this gene category is specific to the placenta . Altogether , these findings show that changes in BAHD1 levels alter expression of gene networks controlling steroid/lipid metabolism and hormone signaling pathways , which are key players in the development of the placenta and in control of energy in the body . Efforts to perform chromatin immunoprecipitation ( ChIP ) of BAHD1 in murine or human cells using custom [12] or commercial BAHD1 antibodies repeatedly failed , preventing straightforward identification of genomic loci targeted by BAHD1 ( BAHD1 ChIP is further discussed in S1 Text ) . To get further clues on BAHD1 function , we searched for novel BAHD1-associated proteins in cells stably expressing His6-Protein-C-tagged-BAHD1 ( HPT-BAHD1 ) by using tandem affinity chromatography purification ( TAP ) and Mass spectrometry ( MS ) , as described previously [11] . We found that ~10 proteins reproducibly co-purified with BAHD1 after two successive purification steps ( Fig 4A and S6 Table ) : the mesoderm induction early response ( MIER ) proteins ( MIER1 , MIER2 and MIER3 ) , HDAC1/2 , HP1γ/β , KAP1 , CDYL1 , KAP1 , PPP2R1A , RUVBL2 and DDX17 . Other polypeptides were consistently detected in the first-step purification ( HP1α , RUVBL1 , CDYL2 , DDX21 , G9a , CHD3 , S6 Table ) . Peptides matching with MBD1 were found in one TAP , in agreement with yeast two-hybrid , co-immunoprecipitation and GST pulldown assays that previously showed that BAHD1 interacts with MBD1 and HP1 [12] . Western blot analysis confirmed that BAHD1 co-purifies with MBD1 and HP1γ together with MIER1 , HDAC1 , HDAC2 , KAP1 and the H3K9 KMT G9a ( Fig 4B and S4 Fig ) . These data suggest that BAHD1 and MIER proteins are subunits of a macromolecular complex containing chromatin writers ( e . g . G9a ) , readers ( e . g . HP1α/β/γ , CDYL1/2 , MBD1 ) , erasers ( e . g . HDAC1/2 ) and remodelers ( e . g . CHD3 , RUVBL1/2 ) . We noticed that BAHD1 and MIER1 displayed conserved domains found in scaffolding proteins of other HDAC1/2 complexes: the metastasis-associated protein ( MTA ) subunits of NuRD [8 , 25] and RERE/atrophin-2 , a transcriptional repressor belonging to a poorly characterized HDAC1/2 complex [26–28] ( Fig 4C ) . BAHD1 , MTA and RERE share a Bromo-Adjacent Homology ( BAH ) domain , which is known to promote protein-protein interactions and binding to nucleosomes [25 , 29 , 30] , while MIER1 , MTA and RERE proteins contain juxtaposed ELM2 and SANT domains that recruit HDACs [25] . Thus , BAHD1 and MIER1 could cooperate to fulfill a function similar to that of MTA and RERE in other HDAC1/2 complexes ( Fig 4D ) . To investigate this hypothesis , we performed a series of co-immunoprecipitation experiments on HEK293-FT cells transiently expressing tagged-versions of BAHD1 from plasmid vectors , since endogenous BAHD1 is undetectable in HEK293 cells . Reciprocal co-IP assays with nuclear extracts confirmed association of endogenous MIER1 with V5-tagged BAHD1 in HEK293-FT cells and their co-immunoprecipitation with HDAC2 and HP1γ ( Fig 4E and S5 Fig ) . Likewise , BAHD1 tagged with the fluorescent protein citrine ( YFPc-BAHD1 ) pulled-down HDAC2 , MIER1 and HP1γ ( Fig 4F , lane 1 ) . BAHD1 harbors a N-terminal proline-rich region with the highest density of prolines found between residues 239 to 361 , a region termed the cPRR [31] , and a C-terminal BAH domain that interacts with the N-terminal tail of histone H3 [12] ( Fig 4C ) . To assess the requirement of specific protein domains in BAHD1 for productive interaction with MIER and HDACs , we expressed truncated forms of YFPc-BAHD1 in HEK293-FT cells . As shown in Fig 4F and S5E Fig , the deletion mutant YFPc-BAHD1-ΔcPRR lacking residues 239 to 361 co-immunoprecipitated with HDAC2 , MIER1 and HP1γ , as the full-length YFPc-BAHD1 . In contrast , the deletion mutant YFPc-BAHD1-ΔBAH lacking residues 592 to 780 encompassing the BAH624-780 domain failed to pull-down HDAC2 and MIER1 , while retaining the ability to bind HP1γ . We conclude that BAHD1 interaction with MIER1 and HDAC2 requires the integrity of the BAH domain of BAHD1 . We also found that varying BAHD1 amounts has an impact on MIER1 subcellular localization . First , MIER1 shifted from cytosolic to chromatin-bound fraction in response to induction of BAHD1 expression in HPT-BAHD1 cells ( Fig 5A ) . Second , microscopy experiments showed that BAHD1 overexpression increased MIER1 nuclear staining and induced enrichment of MIER1 at the heterochromatic inactive X chromosome ( Xi ) ( Fig 5B and 5C ) . This effect was not due to changes in MIER1 transcript levels ( S3 Fig ) . The fact that BAHD1 levels dictate the localization of MIER1 strongly suggests that BAHD1 and MIER1 form a stable chromatin-bound complex . This hypothesis is also supported by results from the Heard laboratory [15] showing that BAHD1 , MIER1/2 , HDAC1/2 and G9a co-immunoprecipitate with CDYL , a protein recruited to Xi in mouse ES cells and also found in our TAP assays ( S6 Table ) . We propose that BAHD1 and MIER are co-repressors that , like MTA subunits of NurD complexes [9] , exert their function through association with sequence-specific DNA binding transcription factors ( TFs ) . With the hypothesis that such TFs should regulate the same gene networks as BAHD1 , we used the Ingenuity Pathway Analysis Upstream Regulator software to predict TFs responsible for differential gene expression in BAHD1-deficient or -overexpressing cells . This analysis identified seven TFs that were consistently found as upstream regulators of a set of BAHD1-associated genes in all transcriptome datasets: ESR1 , ESR2 , EPAS1 ( HIF2α ) , PPARG , FOS , TP53 and SP1 ( S7 Table ) , the latter being previously reported to associate with BAHD1 [12] and MIER1 [32] . It is striking that these TFs have relevance both in placental development and regulation of genes involved in lipid/steroid metabolism ( [33–36] and other references in S1 Text ) . Since ESR1 has been shown to bind MIER1 [37] and to cooperate with SP1 [38] , we hypothesized that BAHD1-MIER could act as co-repressors for ESR1-mediated transcriptional regulation . Based on this assumption , BAHD1 should target the ESR1 gene itself , because ESR1 autoregulates its own expression [39] . Accordingly , ESR1 was up-regulated in Bahd1-KO placentas ( Fig 3C ) , as well as in HEK293-FT cells depleted of BAHD1 with siRNA ( Fig 6A ) . siRNA-mediated knockdown of individual MIER genes did not significantly change expression of ESR1 in HEK293-FT cells , but combined knockdown of MIER1 and MIER3 ( MIER2 was undetectable ) increased ESR1 expression ( Fig 6A ) . We also found that expression of PGR ( encoding the progesterone receptor ) , which has an ESR1 binding site in its proximal region [40] significantly increased in BAHD1- and MIER1/3-knockdown cells ( Fig 6A ) , whereas the androgen receptor transcript ( AR ) was unaffected . These results indicate that BAHD1 and MIER1/3 act as repressors for the steroid hormone receptor genes ESR1 and PGR . Human ESR1 forms a large and complex genetic unit that spans approximately 300 kb of chromosome 6 ( chr6 ) , of which 140 kb containing 8 protein-coding exons [41] . It is transcribed from at least seven promoters into multiple transcripts that vary in their 5’-UTRs and whose expression is tissue-specific . To find out whether the BAHD1-associated complexes targets ESR1 , we analyzed the DNA captured with BAHD1 and its associated partners in the TAP assays by using real-time PCR with primers targeting the proximal region of ESR1 at , or in the vicinity of ESR1-binding sites identified by ChIP-seq ( Fig 6B , [40] , ENCODE ChIP-seq data ) . Regions in C6orf211 ( Fig 6B ) and GAPDH loci were used as controls . Three sites upstream of ESR1 were identified as BAHD1 binding sites , when compared to input or control cell DNA ( E2 , B2 , B1; Fig 6C ) . To determine whether any change in the chromatin structure occur as a consequence BAHD1 binding at these sites , we tested the effect of BAHD1 depletion on histone H3 modifications at lysine 9 ( H3K9 ) by using a ChIP-qPCR assay . We observed a reproducible increase in histone H3K9 acetylation and decrease in H3K9 dimethylation ( H3K9me2 ) and trimethylation ( H3K9me3 ) in cells with siRNA−induced knockdown of BAHD1 expression , relative to cells treated with control siRNA ( Fig 6D and S6 Fig ) . This effect occurred at all sites of the ESR1 proximal region surveyed by PCR , but not at the GAPDH control site . This result strongly suggests that a BAHD1-associated complex containing HDACs and KMTs contributes to the epigenetic silencing of ESR1 . H3K9 methylation is often linked to DNA methylation and several BAHD1-associated partners are known to interact with DNA methyltransferases [42–45] . To test whether BAHD1 level also affects DNA methylation patterns at ESR1 , we exploited the DNA methylome datasets of HEK-CT and HEK-BAHD1 cells that we recently obtained by whole-genome bisulfite sequencing ( BS-seq ) [14] . In this study , we found that BAHD1 overexpression in HEK293 cells stimulates de novo DNA methylation in autosomes at ~80 , 000 regions that become hypermethylated when compared to control cells . These “BAHD1-DMRs” group into large ( 0 . 3–6 . 5 Mb ) chromosomal domains , termed BAHD1-Associated Domains ( BADs , [14] ) . We observed that one such BAD was present on chr6 in a large region containing the ESR1 locus ( Fig 6E ) . The high density of BAHD1-DMRs located at the ESR1 proximal and gene body regions suggests that BAHD1 overexpression stimulates large-scale epigenetic changes . Taken together , these results provide evidence that BAHD1-associated complexes induce histone and DNA modifications that shape repressive chromatin structures .
The functions of BAHD1-mediated chromatin reorganization in developmental and physiological processes were hitherto unknown . Deletion of Bahd1 in mice does not lead to embryonic lethality or anatomical malformations in fetuses . However , murine Bahd1-KO placentas are small and display histomorphological alterations , including reduced surface area of the labyrinthine zone and a thinner junctional zone and decidua . The lack of trophoblast glycogen cells is particularly striking , suggesting a failure in the differentiation program . The growth restriction of Bahd1-KO fetuses is likely to be secondary to defective placental nutrient exchange resulting from these placental abnormalities . In addition , as there is substantial deregulation of metabolic genes in Bahd1-KO MEFs , the perinatal death of Bahd1-KO pups likely results from metabolic defects in embryos . The rare mice surviving beyond birth are males with a lower weight than controls , associated with decreased fat mass and lower levels of circulating cholesterol , glucose and leptin . In light of our finding that the transcriptional co-repressor MIER1 is a key partner of BAHD1 , it is striking that Mier1 null mice also show decreased body weight and reduced levels of circulating glucose and cholesterol ( data from The Mouse Phenotyping Consortium and The Wellcome Trust Sanger Institute , references in S1 Text ) . This phenocopy is a strong argument in favor of BAHD1 and MIER1 cooperating to control the expression of genes involved in energy metabolism in somatic tissues . Separate studies have shown that MIER1 binds to HDAC1/2 , G9a and CDYL [27 , 28 , 46] , while BAHD1 interacts with proteins involved in heterochromatin formation ( e . g . HP1 , MBD1 ) and its overexpression is sufficient to compact chromatin [12] . We now bring evidence that BAHD1 and MIER act in partnership within a novel histone deacetylase complex involved in gene silencing . First , BAHD1 co-purifies with MIER , HDAC1/2 , G9a and CDYL in very different cellular models: human HEK293 cells , as shown here , and mouse ES cells [15] . Second , as in other subunits of HDAC1/2-associated complexes , MTAs of NuRD [8–10 , 25] ( Fig 4D ) and RERE [26–28] ( Fig 4C ) , a BAH domain is present in BAHD1 and HDAC-interacting ELM2-SANT domains are present in MIER [27 , 46] . We found that a truncation of the BAH domain in BAHD1 abrogates BAHD1 coimmunoprecipitation with MIER1 and HDAC2 . From this , we propose that MTA , RERE and BAHD1-MIER define related macromolecular complexes of the “NuRD superfamily” and to name “BAHD1 complexes” those containing a BAHD1 subunit , as BAHD1 has no homolog or isoform . MIER1 , MIER2 and MIER3 could be incorporated into distinct BAHD1 complexes leading to functional redundancy and/or context-dependent functions , as described for MTA1 , MTA2 and MTA3 . MIER1 is the most abundant partner of BAHD1 in HEK293 cells and its nuclear localization , including at the Xi , depends on BAHD1 expression levels . This translocation is likely to have functional consequences , as MIER1 nuclear/cytoplasmic distribution varies with cell type and stage of differentiation [47 , 48] . Bahd1- and Mier1-KO adult mice share common phenotypes but a prenatal growth restriction is not reported for Mier1-KO; hence , the BAHD1-associated regulatory gene network in embryonic tissues likely involves other partners than MIER1 . Genes altered in Bahd1-KO placentas or embryonic fibroblasts are mainly up-regulated compared to wild type controls , consistent with BAHD1 acting as a repressor . The most significant biological process associated with these genes , and with those repressed in BAHD1-overexpressing human HEK293 cells , is steroid metabolism . This functional convergence is striking and consistent with the observed hypocholesterolemia and hypolipidaemia in Bahd1-KO mice . We observed that genes associated with this biological function in Bahd1-KO MEFs and HEK-BAHD1 cells ( e . g . DHCR24 , HMGCS1 , LDLR , NSDHL , SREBF2 , SC4MOL ) mostly differ with those in Bahd1-KO murine placentas ( e . g . Apoc3 , Atp8b1 , Cyp11A1 , Insig2 , Osbpl5 , Pbx1 , VldlR ) . Thus , like many other chromatin repressors [2] , BAHD1 regulates distinct targets in different tissues and at different developmental stages . However , some of the BAHD1 bona fide target genes can be common to different biological systems , as exemplified by the steroid hormone receptor gene ESR1 . BAHD1 binds the proximal region of human ESR1 and represses ESR1 both in human cells and murine placentas . In addition , changes in BAHD1 expression levels in HEK293 cells alter the patterns of H3K9 modifications and DNA methylation at the ESR1 locus , which is consistent with the interaction of BAHD1 with HDACs , KMTs , HP1 and MBD1 and the crosstalk between histone and DNA modifications [49] . It is worth mentioning that MTA1 [50] and SIN3a [39] have also been shown to represses ESR1 in other cell types , indicating that different HDAC complexes control the epigenetic silencing of ESR1 . Recruitment of BAHD1 at specific sites of the genome might rely on the combinatorial assembly of BAHD1-MIER subunits with transcription factors . The product of ESR1 itself ( ESR1/ERα ) could be one such TF targeted by BAHD1 complexes because ESR1 autoregulates its own expression [39] and MIER1 has been shown to bind ESR1 [37] . In line with this hypothesis , ESR1 is predicted to control several genes differentially expressed in BAHD1-KO tissues , such as PGR . In addition , ESR1 cooperates with SP1 [38] , a TF known to bind BAHD1 and MIER1 [12 , 32] , and which is also predicted to drive BAHD1-associated transcriptional changes . Our analysis also suggests that BAHD1 could act as a co-repressor with PPARγ and HIF2α , which like ESR1 are known to play roles in the regulation of energy metabolism and placental cell differentiation [33–36] . This raises the plausible hypothesis that the BAHD1-associated chromatin complex could act as a transcriptional co-repressor in synergy with different TFs in the context of placental functions and lipid metabolism . Placenta morphogenesis depends on the correct balance of cytotrophoblast proliferation and differentiation , into either syncytiotrophoblast involved in nutrient/gas exchange or invasive extravillous trophoblast involved in establishment of blood flow to the placenta . It is proposed that ESR1 controls the proliferation of estrogen-dependent cells , while ESR2 controls their maturation , hence trophoblast differentiation is associated with the transition from Esr1 to Esr2 expression [51 , 52] . By maintaining unbalanced expression of Esr1 at an inappropriate time of the gestation , BAHD1 deficiency could disturb trophoblast differentiation . Pgr and Lepr genes are also deregulated in Bahd1-KO placentas; this should affect progesterone and leptin signaling , also important in placental development [53 , 54] . Therefore , BAHD1 could play a role in trophoblast differentiation , in particular in the formation of glycogen-producing cells , by controlling hormone signaling in a timely manner , particularly in the junctional zone , an important endocrine region . Knockdown of Bahd1 in the mouse placenta results in up-regulation of several other genes that also have relevance to placental development . Among them , Htra3 and Tfpi2 are two confirmed imprinted genes expressed from the maternally inherited allele [24] . Maternally expressed genes have been proposed to limit maternal resource provision; thus up-regulation of such genes is consistent with a placental growth restriction observed upon BAHD1 deficiency . In fact , HtrA3 and Tfpi2 are highly transcribed in the placenta and involved in the regulation of endothelial function , trophoblast migration and invasion [55–58] . Further studies will be required to explore whether BAHD1 directly targets these genes and contributes to their imprinting . With the importance of the placenta in the feto-maternal exchange processes , as well of steroid signaling in the body , alterations in the amounts or activity of BAHD1 may lead to various pathological processes . We previously identified a role for BAHD1 in infection of epithelial cells with the bacterial pathogen Listeria monocytogenes [11 , 31] . The fact that BAHD1 is involved in placental function now opens the possibility that BAHD1 contributes to the fetoplacental step of listeriosis , as L . monocytogenes has a tropism for the placenta . More generally , a connection between BAHD1 and placenta-associated pathologies , such as Intrauterine Growth Restriction and Pre-Eclampsia , should be carefully examined . BAHD1-mediated regulation of ESR1 is also enticing as ESR1 is a key regulator in a variety of biological processes and ESR1 deregulation has been implicated in several diseases , including breast cancer [59] . A shift from nuclear to cytoplasmic localization of MIER1 during breast cancer progression has been observed , suggesting that nuclear MIER1 contributes to the repression of genes involved in invasive breast carcinoma [37] . Similar to chromatin-repressive complexes Polycomb and NuRD [9 , 60] , BAHD1 could be implicated in the regulation of transcriptional events involved in diverse oncogenic pathways . Of note , insertion events in BAHD1 and MIER1 genes were identified in a screen for genes that cooperate with oncogenic KRAS ( G12D ) to accelerate tumorigenesis and promote progression in a mouse model of pancreatic ductal preneoplasia [61] . Our data also implicate BAHD1 in mammalian metabolic regulation and several BAHD1 candidate target genes have been associated with metabolic diseases in humans . For instance , genetic polymorphisms in CRABP2 are associated with changes in plasma cholesterol levels [62] , SNPs in LPL and LASS4 are associated with dyslipidemia [63–65] , changes in expression levels of HMGCS1 , LDLR and SC4MOL correlated with obesity-related type 2 diabetes and cardiovascular diseases [66] . Together , these results indicate that dysfunction of BAHD1 complexes could promote aberrant epigenetic phenomena at the origin of different disorders . Detailed understanding of how and where BAHD1 complexes establish repressive chromatin states could be instrumental for the development of new strategies for selective treatment of metabolic disorders in the future .
Mice were bred and maintained in the animal facilities of the Institut Clinique de la Souris ( ICS , Illkirch , France ) under pathogen-free conditions . The ICS facilities are licensed by the French Ministry of Agriculture ( agreement #A67-218-37 ) . All animal procedures were approved by the local ethical committee CREMEAS ( registered under the reference “C2EA– 35” ) , and were supervised by M . F . C . and O . W . who are qualified in compliance with the European Community guidelines for laboratory animal care and use ( 2010/63/UE Directive ) . Human cell lines derive from HEK293 cells ( ATCC CRL-1573 ) : the HPT-BAHD1 inducible line and its isogenic HPT-control are described in [11]; the HEK-BAHD1 constitutive line and its isogenic HEK-CT control are described in [14]; HEK293-FT are from Invitrogen ( ThermoFisher Scientific ) . Plasmid pcV5-BAHD1 ( BUG2289 ) , pYFPc ( also named pEYFP-Citrine-N1 , BUG2897 ) , pYFPc-BAHD1 ( BUG2897 ) and pYFPc-BAHD1-ΔBAH ( BUG2740 ) are described in [12] . pYFPc-BAHD1ΔcPRR ( BUG2897 ) is described in [31] . Antibodies were against BAHD1 ( Abcam , 46573 ) , MIER1 ( Sigma , HPA019589 ) , HDAC1 ( Abcam , ab7028 ) , HDAC2 ( Abcam , ab7029 ) , HP1γ ( Euromedex , 2MOD-1G6-AS ) , KAP1 ( Abcam , ab10483 ) , G9a ( MBL Cliniscience , D141-3 ) , MBD1 ( Abcam , ab3753 ) , tubulin α ( Santa Cruz , sc5546 ) , H3K9me2 ( Abcam ab1220 ) , H3K9me3 ( Abcam ab8898 ) , H3K9ac ( Upstate/Millipore 07–352 ) , V5 and V5-HRP ( Invitrogen R960-25 , R961-25 ) , GFP ( Mouse anti-GFP Sigma/Roche 11814460001 used in IP and Rabbit anti-GFP Santa-Cruz sc-8334 , used in WB , which both recognize YFPc ) and control IgG mouse ( Santa-Cruz sc-2025 ) and IgG rabbit ( Santa-Cruz sc-2027 ) . Fluorescent secondary antibodies were from Jackson ImmunoResearch or Molecular Probes , and HRP-conjugated secondary antibodies were from AbCys or Abcam ( IgG Veriblot for IP , ab131368 ) . siRNAs were purchased from Dharmacon ( ThermoFisher Scientific ) as follows: on-TARGETplus Non-targeting pool ( D-001810-10-20 ) , BAHD1 ( L-020357-01 ) , MIER1 ( M-014201-02 ) , MIER2 ( M-023917-01 ) , MIER3 ( M-015618-01 ) . Cells were transfected 72h with siRNA using Lipofectamine RNAimax ( Life Technologies , Grand Island , NY ) according to the manufacturer's instructions . Immunofluorescence and XIST FISH assays were as described in [12] . Bahd1+/- mice have been described in [11] . Mice were bred and maintained in the animal facilities of the Institut Clinique de la Souris ( ICS , Illkirch , France ) under pathogen-free conditions . Throughout the experiment mice were housed in the same climate-controlled stable with a 12h/12h dark-light cycle and handled identically . For wild type and Bahd1-/- production , Bahd1+/- mice were mated and the day on which a vaginal plug was found was designated 0 . 5 . Genotyping protocols are described in [11] . Knockout of Bahd1 expression was verified by RT-qPCR on placenta , MEF and embryo liver samples . Phenotyping methods of Bahd1 heterozygous and knockout mice and isolation of primary mouse embryonic fibroblasts are described in S1 Text . All the data are expressed as mean ± SE . Statistical analysis were performed using a one way ANOVA tests followed by a Fischer’s PLSD test with significance set at P<0 . 05 . * P<0 . 05 , ** P<0 . 01 , *** P<0 . 0001 . Total RNAs were extracted from Bahd1+/+ and Bahd1-/- placentas at E16 . 5 ( n = 6/genotype ) or E18 . 5 ( n = 3/genotype ) , as well as from Bahd1+/+ and Bahd1-/- MEFs ( n = 3/genotype ) and HEK-CT and HEK-BAHD1 cells ( n = 3/cell line ) using RNeasy Kit ( Qiagen ) , treated TURBO DNA-freeTM kit ( Ambion ) . RNA concentration and integrity were tested with RNA quality was monitored on Agilent RNA Pico LabChips ( Agilent Technologies , Palo Alto , CA ) . 100 ng of RNA per sample were used as templates for the synthesis of hybridization probes for Affymetrix GeneChip Microarrays ( Genechip HuGene 1 . 0 ST for HEK-CT and HEK-BAHD1 cells; Mouse gene 1 . 0 for E16 . 5 placentas; Mouse Exon 1 . 0 ST for E18 . 5 placentas and MEFs ) . Hybridization was carried out with biological replicates according to the expression analysis technical manual with wash and stain kit ( Affymetrix ) . Gene-level expression values were derived from the CEL file probe-level hybridization intensities using the model-based Robust Multichip Average algorithm ( RMA ) [67] . RMA performs normalization , background correction and data summarization . An analysis is performed using the LPE test [68] and a p-value threshold of p<0 . 05 is used as the criterion for expression . The estimated false discovery rate ( FDR ) of this analyze was calculated using the Benjamini Hochberg approach in order to correct for multiple comparisons . Results were annotated using information provided by Affymetrix . Full data sets were reduced by discarding genes with “EST” and “unknown” annotation labels . To generate functional clusters of genes , we used the DAVID program ( http://david . abcc . ncifcrf . gov , 2015 version; [20] ) for selected gene sets according to gene ontology ( GO ) of biological process categories . To search for transcriptional regulators driving the differential gene expression changes we used the Ingenuity Pathway Analysis Upstream Regulator software [69] . Datasets have been deposited in Gene Expression Omnibus ( GEO ) and are accessible through accession number , as follows: GEO series GSE51868 ( transcriptomes of HEK-CT and HEK-BAHD1 ) ; GSE53443 and GSE53442 ( transcriptome of Bahd1-WT and Bahd1-KO placentas at E16 . 5 and at E18 . 5 ) and GSE73816 ( transcriptome of Bahd1-WT and Bahd1-KO MEFs ) . Total RNA from HEK293-CT and HEK-BAHD1 cells , HEK293-FT cells treated 72h with siRNAs , or from mouse placentas or MEFs was extracted using the RNeasy Kit ( Qiagen ) , from three to six biological replicates . Genomic DNA was removed by treatment with TURBO DNA-freeTM kit ( Ambion ) . cDNAs were generated from 1 to 2 μg total RNA using the RT2-HT first strand kit ( Qiagen/SABiosciences ) . Quantitative Real-Time PCR was performed on Biorad MyiQ device ( Biorad ) , using SsoFast Evagreen supermix ( Biorad ) , as specified by the supplier . Each reaction was performed in triplicate . Data were analyzed by the ΔΔCt method . Target gene expression data were normalized to the relative expression of human GAPDH or mouse Gapdh ( and Hprt , for imprinted genes ) and YWHAZ was used as a control gene . Statistical significance of the difference in mean expression of genes was evaluated using the Student t test; a P value <0 . 05 was considered significant . Primer sets are provided in S1 Text . The TAP-TAG protocol to purify the partners of His6-Protein-C-tagged BAHD1 in HPT-BAHD1 cell is described in [11] . Modifications to this protocol , Mass spectrometry analysis and precipitation of the associated DNA are detailed in S1 Text . Immunoprecipitation ( IP ) of nuclear proteins was performed as described in [70] with the following modifications . Nuclear soluble and insoluble fractions were sonicated , mixed and incubated overnight at 4°C with 1–3μg of the indicated antibodies and then with Dynabeads Protein G ( #10004D , Novex ) at 4°C during 2h30 . IPs were washed 4 times with washing buffer ( Tris 20mM pH = 7 , 65 , NaCl 150mM , 0 , 05% IGEPAL , 2 , 5% Glycerol , 0 , 5mM EDTA , 0 , 6mM DTT ) . Samples were resuspended in 1X Laemmli and boiled at 95°C for 10 min . Proteins were separated on 8–10% SDS-PAGE gels , transfered to nitrocellulose membrane , probed with primary and secondary antibodies and detected by chemiluminescence ( SuperSignal West Femto Substrate #34094 , ThermoFisher Scientific ) . Blots were visualized on Films ( Amersham ) or using a ChemiDoc MP Imaging system ( Bio-Rad ) . ChIP of modified H3K9 were performed in three independent biological replicates , using the ChIP-IT Express Enzymatic Kit ( Active Motif ) according to manufacturers instructions . Briefly , HEK293-FT cells were grown in T150 flasks for 72h with either siRNA against BAHD1 or non-targeting control siRNA ( up to 70–90% confluency ) . One flask was kept for RNA extraction and quantification of BAHD1 knockdown by RT-qPCR . Cells from other flasks were harvested and cross-linked with a final concentration of 1% formaldehyde ( Sigma-Aldrich ) for 10 minutes . Fixation was stopped with 0 . 125 M glycine and cells were washed twice with ice-cold PBS . Collected cells were lysed and nuclei pellet were resuspended in shearing cocktail and incubated for digestion for 10 minutes at 37°C . Shearing efficiency was tested by agarose gel electrophoresis and DNA concentration was quantified with a Nanodrop 2000 ( ThermoFisher Scientific ) to normalize the quantity of chromatin per ChIP . For each ChIP , 3 μg of antibody were used . 10 μL of chromatin was kept as input and processed as ChIP samples . After washing and reverse crosslinking of precipitated samples , DNA was purified by two extractions with equal volumes of phenol:chloroform:isoamylalcohol ( 25:24:1 , pH = 8 ) , assisted by phase lock heavy gel tubes ( 5Prime ) , followed by ethanol precipitation . Pellets were washed once in 75% ethanol , then resuspended in 50 μL DNAse-free water . H3K9ac , H3K9me2 and H3K9me3 enrichment levels were measured by qPCR with primers matching in the ESR1 locus and the non-target control GAPDH region ( primer sets are provided in S1 Text ) . A standard curve was generated using 10% , 1% , 0 . 1% and 0 . 01% of input DNA . We first determined the fold enrichment of the ChIP sample relative to the IgG sample and then the effect of BAHD1 knockdown on H3K9 modifications was calculated as the ratio of enrichment in cells treated with BAHD1 siRNA to that in cells treated with control siRNA ( presented in Fig 6 and S6 Fig as a Log2 ratio ) . | The importance of epigenetics in regulation and dysfunction of metabolic pathways is increasingly recognized but the underlying mechanisms and molecular actors involved remain incompletely characterized . Here , we provide evidence that the heterochromatinization factor BAHD1 cooperates with MIER proteins to assemble chromatin-repressive complexes that control a network of metabolic genes involved in placental and fetal growth and in cholesterol homeostasis . | [
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| 2016 | Role of the BAHD1 Chromatin-Repressive Complex in Placental Development and Regulation of Steroid Metabolism |
Toxoplasma gondii is a zoonotic protozoan with a worldwide occurrence , but the determinants of the current pattern in the geographical distribution of T . gondii lineages and strains remain poorly understood . To test the influence of human trade on T . gondii populations , we conducted a population genetic study of 72 T . gondii animal isolates from Senegal , a West African country in which the ongoing inland progress of invasive murine hosts ( introduced in port cities of Senegal since the 16th century by European sailors ) is well described . Isolates were mainly collected on free-range poultry , which are considered as relevant bioindicators of T . gondii strain diversity in the domestic environment . Sampling was conducted in two port cities of Senegal ( Dakar and Saint-Louis ) and in one inland region ( Kedougou ) . Population genetic analyses using 15 microsatellite markers revealed different patterns between port cities where lineages non-virulent for mice ( type II , type III , and Africa 4 ) were predominant , and Kedougou where the mouse-virulent Africa 1 lineage was the most common . By considering the current spatial pattern in the inland progress of invasive rodents in Senegal , our results suggest that the invasive house mouse Mus musculus domesticus counter-selects the Africa 1 lineage in the invaded areas . The comparison of the microsatellite alleles of type II strains from Senegal to type II strains from other areas in Africa and Western Europe , using discriminant analysis of principal components and Network analysis , point to a mainly Western European origin of the type II lineage in Senegal . Collectively , these findings suggest that human-mediated intercontinental migrations of murine hosts are important vectors of T . gondii strains . Differential susceptibility of endemic and introduced murine hosts to various T . gondii strains probably determines the persistence of these strains in the environment , and therefore their availability for human and animal infection .
Toxoplasma gondii is a zoonotic protozoan with a worldwide distribution . Felids are the only final hosts and all other species of mammals and birds are intermediate hosts . Within the domestic cycle , infection can occur through the ingestion of few of the million oocysts shed in the environment by cats during the three to 14 days following their primary infection [1] . Infected hosts often develop persistent cysts in their tissue , which constitute the main source of infection for cats and an important potential source of infection for meat-consuming intermediate hosts , including humans . The genetic diversity of T . gondii strains shows a strong geographical structure [2–4] . This geographical pattern is of epidemiological importance because T . gondii genotype has often been associated with disease severity in immunocompetent individuals , especially in South America [5–10] . Few data are available for Africa , but some indirectly suggest a significant burden of ocular toxoplasmosis in West and Central Africa [11 , 12] . Evidence of an important role of human-mediated dispersal in shaping T . gondii population structure is supported by the intercontinental occurrence of some lineages [13] . Mainly , the remarkable success of the archetypal type II and type III lineages in global spread has been attributed to human exchanges through movements of infected livestock and involuntary dispersal of infected rodents via maritime or terrestrial routes [14 , 15] . The intensification of maritime trade since the sixteenth century has probably given strains of type II and type III lineages the opportunity to spread from Western Europe to new lands such as America and Australia , but also to West and Central Africa . [4 , 14 , 16] . Based on these assumptions , Lehmann et al . [14] speculated that the genetic background of T . gondii strains near ports that were active during early transatlantic trade should differ markedly from that in regions distant from such ports . The aim of the present study was to test this hypothesis in Senegal ( West Africa ) . Unlike North and East Africa where the intercontinental lineage type II , followed by type III , are by far the predominant lineages , West and Central Africa seem to be the refuge for a more autochthonous diversity of African T . gondii strains , mainly composed of strains of the Africa 1 lineage [4] . This pattern has been attributed to the more recent exposure of West and Central Africa to the influence of globalization compared to North and East Africa that have anciently been linked to Europe and Asia through privileged trade exchanges during the successive historical periods . West Africa appears therefore to be a suitable framework to test the influence of relatively recent human exchanges on T . gondii population structure . In this study , we compared the diversity of T . gondii strains circulating among domestic animals in the port cities of Saint-Louis and Dakar , with those circulating in the Kedougou region inland , which is located more than 500 kilometres from the coast . Most samples were collected on domestic poultry ( mainly chickens ) . These intermediate hosts live in the vicinity of human dwellings and are considered as good sentinels for T . gondii occurrence in the environment given that they feed on the ground and that they rarely become sick from T . gondii infection [17–19] . In order to evaluate the extent of gene flow between T . gondii populations from Senegal and other regions of the world through both terrestrial and maritime routes , we compared type II T . gondii strains from Senegal to those from other areas in Western Europe and Africa .
From April 2016 to April 2018 , three regions of Senegal were investigated for T . gondii strain isolation: the coastal regions of Dakar and Saint-Louis , which were founded during the colonial period by French sailors , and the inland region of Kedougou . In each of these regions , sampling was conducted in both urban and rural localities . Our sampling efforts focused mainly on backyard poultry raised around households and were occasionally completed by opportunist sampling of other domestic or wild animals when they were available . The geographic origin of each animal included in this study was checked by questioning the owners to insure that infection had occurred locally . All sampled households were georeferenced using a Juno SC GPS Data Collection PDA ( Trimble , California , USA ) . Blood samples were collected from poultry from the wing vein for serological screening . For animals sampled opportunistically , as in the case of home slaughter or for animals found freshly dead by the roadside , blood samples were collected during slaughtering or from blood clot in the heart during post-mortem examination . After separation by centrifugation , sera were tested for presence of antibodies against T . gondii using the modified agglutination test ( MAT ) with a seropositivity cut-off at 1:20 dilution titer [20] . Overall , 2 , 040 animals were sampled , the majority being chickens ( 79 . 5% ) and ducks ( 14 . 8% ) ( S1 Table ) . The total seroprevalence was 11 . 8% ( 241/2040; 95% confidence interval CI: 10 . 4%—13 . 2% ) . According to poultry’s availability for sale , a total of 122 seropositive domestic birds were purchased , brought alive to the Institut de Recherche pour le Développement ( Belair , Dakar ) and euthanized . Brain and heart samples were collected and kept at 4°C before being processed for parasite isolation . In addition , brain and/or heart samples of 33 others seropositive animals ( S1 Table ) were also kept at 4°C before processing . The isolation protocol was performed as reported previously [21] . Brain and heart samples of each animal were homogenized together using a blender in saline solution ( 0 . 9% NaCl ) containing 0 . 4% of trypsin and 40μg/ml gentamycin and incubated in a shaker water bath at 37°C for 90 min . The suspensions were filtered through two layers of gauze and washed three times by centrifugation for 10 min at 2600 rpm . The obtained digestates were then re-suspended in saline solution and treated with an antibiotic saline solution ( 1000 U/ml penicillin and 100 μg streptomycin/ml in saline solution ) . The digestates were intraperitoneally inoculated into three out-bred female Swiss Webster ( SW ) mice ( 1 mL/mice ) provided by the Institut Pasteur of Dakar . All inoculated mice were monitored daily for clinical signs of toxoplasmosis during four weeks . Ill mice developing ascites were punctured for peritoneal exudates to check for the presence of tachyzoites before being euthanized . After four weeks , surviving mice were tested for T . gondii antibodies by MAT serology ( cut-off at 1:20 serum dilution ) . Seropositive mice were euthanized and brain samples were homogenized with 1 ml of physiological solution for microscopic examination of tissue cysts . For each sample , 200 μl of peritoneal exudate or brain homogenate was stored at -20°C for DNA extraction . Live parasites were cryopreserved in liquid nitrogen with RPMI containing 10% FCS and 10% DMSO and were sent to the T . gondii Biological Resource Centre ( BRC ) , Limoges , France , ( http://www . toxocrb . com ) for strain preservation . The isolation protocol was approved and accepted by the Research Ethics Committee of Cheikh Anta Diop University in Senegal ( Registration numbers: 0232/2017/CAR/UCAD and 0278/2018/CAR/UCAD ) . Cryopreserved brain samples for which no tissue cysts could be observed were re-inoculated into SW mice in Limoges , France . After 4 weeks , mice that tested seropositive using MAT serology were euthanized . Their brains were aseptically sampled , rinsed in saline solution , placed in 1 ml of saline solution , and extruded through a 21-gauge needle several times , and then through a 23-gauge needle . Half of this suspension was treated by 1 ml of trypsin-EDTA solution ( pre-heated at 37°C ) , thoroughly shaken , and incubated at 37°C for 3 minutes to disrupt tissue-cysts . The obtained suspension was then re-extruded through a 25-gauge needle several times , washed in 5 ml of Iscove's Modified Dulbecco's Medium ( IMDM ) , resuspended in 1ml of IMDM , and inoculated in a Vero cell monolayer in a T75-flask . The culture medium was composed of IMDM treated with 1% of antibiotic saline solution ( 1000 U/ml penicillin and 100 μg streptomycin/ml in saline solution ) and enriched with 2% of fetal bovine serum ( FBS ) . Parasite growth was observed between one and four weeks post-initial inoculation . Animal experimentation conducted in Limoges was approved and accepted by the Ethics Committee for Animal Experimentation n°033 validated by the French Ministry of National Education , Higher Education and Research ( Registration numbers: APAFIS#14582-2018041010294175 v2 ) . All experimental procedures were conducted according to European guidelines for animal care ( ‘‘Journal Officiel des Communautés Européennes” , L358 , December 18 , 1986 ) . Total genomic DNA was extracted from 200μl of mice brain homogenates , mice ascites or supernatants of cell culture , using the QIAamp DNA MiniKit ( Qiagen , Courtaboeuf , France ) . For animal samples that did not infect laboratory mice , DNA extraction was performed directly on 200μl of animal tissue digestate . Toxoplasma gondii strains were genotyped using 15 microsatellite markers distributed on 11 of the 14 chromosomes composing T . gondii genome in a single multiplex PCR-assay , as described previously [22] . Those 15 loci included a combination of eight “typing” markers with low polymorphism ( TUB2 , W35 , TgM-A , B17 , B18 , M33 , IV . 1 and XI . 1 ) that show little or no variation within lineages and seven “fingerprinting” markers with high polymorphism ( M48 , M102 , N83 , N82 , AA , N61 , N60 ) that show significant variation within lineages [23] . For each strain successfully genotyped at some loci but not at others , each failed locus was amplified separately by simplex PCR ( to prevent primer competition ) using the same protocol as the multiplex PCR-assay . PCR products were sized using capillary electrophoresis on ABI PRISM 3130xl ( Applied Biosystems , Foster City , CA ) and the GenScan 500 ROX dye size standard ( Applied Biosystems ) . Results were analyzed using GeneMapper 5 . 0 software packages ( Applied Biosystems ) . To assign each strain to a clonal lineage , Senegalese multilocus genotypes ( MLGs ) were compared to those from reference strains representative of the main T . gondii clonal lineages previously described worldwide . Those reference strains are single nucleotide polymorphism ( SNP ) inferred lineages from previous studies , either based on multilocus sequence typing ( MLST ) , whole genome sequencing ( WGS ) , or multilocus restriction fragment length polymorphism ( RFLP ) analysis ( S2 Table ) . Assignment to a clonal lineage relied on the examination of the allelic combination at eight “typing” alleles that constitutes the lineage identity for each strain [22] . In order to further confirm the relationships of Senegalese MLGs with the reference T . gondii lineages , an unweighted pair group method with arithmetic mean ( UPGMA ) dendrogram was generated by including all MLGs from Senegal with a single reference MLG for each of the major clonal lineages that were identified worldwide ( S2 Table ) . This UPGMA dendrogram was produced using the BRUVO . BOOT function ( based on Bruvo’s genetic distance ) with 1 , 000 bootstrap replications , implemented in the “Poppr” package [24] in R version 3 . 4 . 0 . This package is specifically designed for analysis of clonal , sexual or admixed populations , that may not fit to basic assumptions of the Wright–Fisher model of populations , which implies panmixia and Hardy–Weinberg equilibrium . The software QGIS V2 . 14 . 14-Essen [25] was used to map the geographical distribution of the sampling locations and the corresponding genotypes . To estimate the occurrence of a geographical structure within T . gondii populations , an AMOVA was performed using GenAlEx 6 . 51 software package [26] . The individuals were grouped according to their geographical origin . The genetic differentiation between geographical populations was determined using a pairwise population test ( PHIPT ) . PHIPT , an analogue of the fixation index FST , suppresses the within-population variance and ranges from 0 ( no differentiation ) to 1 ( full differentiation ) . Levels of significance were determined by computing 10 , 000 random permutations . Genotypic diversity indices ( Stoddart and Taylor’s index; Simpson’s index; Evenness ) within each lineage identified by UPGMA and within each region were calculated using the “diversity_ci” function of the “Poppr” package which corrects diversity indices for sample size using rarefaction . This function was also run for linkage disequilibrium ( LD ) estimations for each lineage by the calculation of the index of association ( Ia ) and the standardized index of association ( rd ) with 1 , 000 permutations , the latter removing the dependency of Ia on the number of loci . In addition , HP-Rare 1 . 1 . [27] was used to calculate allelic richness and private allelic richness using a rarefaction procedure . Minimum spanning networks ( MSN ) based on Bruvo’s genetic distance were drawn using ‘‘Poppr” to visualize the relationships between T . gondii strains from Senegal and those from areas of Western Europe ( France and Portugal ) and Africa ( Egypt , Ethiopia and South Africa ) for each lineage ( refer to S3 Table for genotyping data of the collection of strains used for comparative analysis ) . Discriminant analysis of principal components ( DAPC ) was used to identify genetic populations within lineage using a nonparametric approach ( free from Hardy–Weinberg assumptions ) . In this model , genetic data were initially transformed using a principal components analysis ( PCA ) and subsequently clusters were identified using discriminant analysis ( DA ) . DAPC was performed using the adegenet package [28] implemented in R version 3 . 4 . 0 . A T . gondii strain was defined as virulent if it caused mortality in all infected mice within four weeks of bioassay or if all infected mice developed certain symptoms of acute toxoplasmosis ( diminished response to handling , immobility , rapid breathing and ruffled fur ) within the same period . The infecting T . gondii strain was considered as being of intermediate virulence if it caused acute infection in only a proportion of infected mice , and as non-virulent if all infected mice were asymptomatic at the end of the four weeks of monitoring . The humane endpoints of acute disease at which mice were euthanized were defined as ( 1 ) a state of fever ( ruffled fur and diminished response to handling ) for more than three consecutive days or ( 2 ) a state of prostration . The etiologic role of T . gondii in mortality or acute disease was confirmed by the observation of tachyzoites in peritoneal exudates punctured before euthanasia or death or in peritoneal washing made postmortem . The occurrence of a region-effect in mouse virulence was tested by Fisher’s exact test , adopting a 95% confidence interval .
The paucity of strains belonging to the type III , Africa 1 and Africa 4 lineages precluded performing extensive analysis of these lineages and hence our analyses focused on strains of type II lineage . Within the Minimum spanning networks ( MSN ) representing strains of type II lineage ( Fig 2 ) , Senegalese strains segregated from strains from African countries in most branches of the network and exhibited strong intermingling pattern with strains from Western Europe ( France and Portugal ) . Using model selection based on Bayesian information criterion ( BIC ) values , the optimal number of clusters was K = 5 among type II strains from Senegal , France , Portugal , Ethiopia , Egypt , and South Africa ( S2 Fig ) . Those five clusters were differentially distributed between geographical populations ( Fig 3 ) . DAPC 1 was mainly found in South Africa . All other DAPC clusters exhibited extensive geographical distribution in Western Europe and Africa although DAPC 2 and 3 were the predominant populations in both Ethiopia and Egypt , and DAPC 5 in Senegal , France and Portugal . The virulence of T . gondii isolates in bioassayed mice varied significantly between Dakar and Saint-Louis on one hand and Kedougou on the other hand . Virulent isolates were more prevalent in Kedougou region compared to Saint-Louis ( p-value < 0 . 001 ) and Dakar ( p-value < 0 . 001 ) where non-virulent isolates were predominant . Lineage assignment with the eight “typing” microsatellite markers was highly predictive of virulence . All type III and Africa 4 strains , and the large majority of type II strains ( 34/37 ) , caused asymptomatic infection in mice . In contrast , all mice infected by Africa 1 strains developed an acute and lethal toxoplasmosis .
In the present study , we found a significant differentiation between the T . gondii populations of the inland region of Kedougou and those of the port regions of Saint-Louis and Dakar . Most of T . gondii strains from Senegal could be unambiguously assigned to one of four clonal lineages: type II , type III , and Africa 1 lineages , in addition to a lineage inferred here for the first time from microsatellite analysis and designated as Africa 4 lineage . Strains belonging to this lineage were occasionally described in the literature by using RFLP markers under the genotype designation of ToxoDB#20 [29–32] . Within Senegal , although these four lineages had an extensive range of distribution and were found in all three regions ( except for Kedougou where type III strains were not found ) , they exhibited marked regional variations in their relative abundances . In the port regions of Saint-Louis and Dakar , strains of type II lineage , followed by strains of type III and Africa 4 lineages , constituted the large majority of T . gondii strains . At the opposite , Africa 1 was by far the predominant lineage in the inland region of Kedougou . LD tests were statistically significant for all four groups , although sample sizes for each cluster were relatively low . Usually , large sample sizes are necessary to have the statistical power to reject the null hypothesis of random mating unless LD is very strong . Reaching statistical significance in LD testing for such small sample sizes indicates the robustness of the clonal structure of T . gondii populations from Senegal . The high prevalence of strains of Africa 1 lineage in both urban and rural localities of Kedougou indicates that strains of this lineage migrate ( or have migrated in the past ) through terrestrial pathways between this region of Senegal and other regions of West Africa where Africa 1 is the predominant T . gondii lineage [4] . The scarce sampling of strains of Africa 1 lineage in Senegal and in other neighbouring African countries does not permit estimation of the magnitude of these migrations ( rare event or extensive migrations ) . The introduction of this lineage in this region may have been caused by livestock transhumance , which is a millenary practice in these areas of the world . In this context , an animal that was infected in some area could be slaughtered or die hundreds of kilometers away . The carcass and offal of this animal , if consumed by local cats , could lead to the introduction of T . gondii strains into new remote areas . A possible scenario is an introduction of this lineage in Kedougou during the sedentarization of populations of Fulani nomads arriving in successive waves in this region with their livestock herds from South Mali ( region of Bountou ) since the end of the thirteenth century [33] . It is also possible that wildlife played a role in the regional dissemination of this lineage . In this study , an Africa 1 strain was isolated from a wild fowl of genus Pternistis in the region of Kedougou . It is unknown whether this wild fowl got infected from a domestic source of infection in the vicinity of human dwellings or if this lineage extensively circulates among wildlife in these areas . This latter hypothesis would be consistent with an autochthonous occurrence of this lineage in Africa . Wild strains circulating in the Amazonian rainforest of French Guiana in South America are genetically divergent from those that infect humans in populated areas bordering the forest [34] and these wild strains have often been associated with more severe disease in immunocompetent patients [6 , 35] . This call for further research in Africa , through collecting more strains from wildlife and characterizing the sylvatic cycle of T . gondii in this continent . Concerning the Africa 4 lineage , its geographical pattern of distribution also suggests a terrestrial route of dissemination across an East-West axis linking Asia to Africa . Indeed , its RFLP equivalent ToxoDB#20 has been identified in China , Sri Lanka , Emirates , Egypt and Ethiopia [4 , 32] . In addition , the identification of strains of Africa 4 lineage in isolates of Malian and Gambian patients corroborates this scenario [4] . The caravans bringing diverse merchandise together with animals along the well-known Silk Road may have allowed the spread of this lineage between Asia and Northeast Africa [32 , 36 , 37] . From this point , dissemination in Africa through Trans-Saharan trade or livestock transhumance may explain the pattern observed in the distribution range of the Africa 4 lineage but more isolates of this lineage in Africa and especially from countries of the Sahelian belt are needed to support this hypothesis . In Senegal , deciphering the origin of type II and type III lineages may be more challenging due to their intercontinental occurrence . In addition to a putative terrestrial propagation of these lineages following the same path as the Africa 4 lineage , an introduction from Europe through maritime trade—mediated by the invasive house mouse Mus musculus domesticus and the black rat Rattus rattus—appears to be a reasonable hypothesis given the predominance of these two lineages in Europe . Our results suggest that T . gondii type II strains from Senegal are more related to those from Western Europe than those from other areas in Africa . The port cities of Saint-Louis and Dakar are believed to be the introduction points of invasive European rodents [38–40] , which may have allowed multiple transatlantic introductions of type II and type III strains in port localities . Later , the livestock chain linking inland regions to these urban poles [41] could have allowed gene flow between inland and coastal areas of the country . Since the 1930s , the development of the road infrastructure and the transport network has allowed a rapid inland dissemination of the invasive M . m . domesticus and R . rattus . Those species , which probably play a major role in T . gondii strains dissemination [14 , 16] , rely on fast human means of transport like trucks for terrestrial propagation [42] . In this context , the road network development probably increased migration opportunities for T . gondii and contributed to the homogenization of T . gondii populations between the connected nodes of this network as previously shown in Gabon [21] . Kedougou region has long remained isolated from the transport network and was opened up more recently by the construction of a national road linking this area to the rest of the road network since 1998 . This may have limited the exchanges between T . gondii populations of this region with those from other regions in comparison to the highly connected regions of Dakar and Saint-Louis . This assumption could explain the lower allelic and genotypic diversity found in the T . gondii populations of this region in comparison to the port regions . The occurrence of strains with lineages other than Africa 1 ( Africa 4 and type II ) only in urban localities of Kedougou—that are probably more exposed to exchanges through the road network than rural localities—seem to be in line with this assumption . In port regions of Senegal , if the high prevalence of type II lineage ( and to lesser extent of type III lineage ) can be attributed to an introduction of these lineages through transatlantic trade , the apparently higher prevalence of Africa 4 lineage compared to Africa 1 lineage in coastal regions is more unexpected . Although the success of spread and establishment of a given lineage may be subject to random processes , it is unlikely that this mechanism solely explain the higher prevalence of Africa 4 lineage over Africa 1 lineage in both Saint-Louis and Dakar . There is experimental evidence that Africa 4 , type II , and type III lineages differ markedly from Africa 1 lineage concerning mouse virulence . Africa 4 , type II and type III lineages are non-virulent for laboratory mice [8 , 30] unless the parasite inoculum is high , whereas Africa 1 lineage leads to lethal infection in all infected mice independently from the inoculated dose of parasites [21] . In the present study , although the dose-effect could not be controlled before mouse bioassay , results of virulence in SW mice were largely congruent with results of previous studies for each of the four T . gondii lineages considered here [21 , 29 , 30 , 43–45] . Importantly , a recent experimental study showed that Africa 1 lineage is also lethal for wild-derived house mice Mus musculus [46] . Given that M . m . domesticus is the predominant commensal rodent in Dakar and Saint-Louis [42] , we propose that this important T . gondii reservoir may favour the maintenance of non-virulent T . gondii strains in these regions , as it would die from infection by strains of the Africa 1 lineage . In line with this , results from models simulating transmission by Shwab et al . [16] support the notion that the house mouse eliminates highly virulent strains from its environment . In contrast , the native African Mastomys natalensis exhibits resistance to type I strains [47] , which share common virulence alleles with Africa 1 strains [48] . This native African rodent , being the predominant commensal species in Kedougou [42] , may consequently act as a competent reservoir for Africa 1 lineage in this region as it was previously demonstrated for other species of commensal small mammals from West Africa [49] . This may explain the contrasted geographical structure in T . gondii populations between coastal and inland regions in this study , which appear to correlate spatially with host resistance . The most important conclusion that can be drawn from our results is that the different patterns of virulence among T . gondii strains for various reservoir hosts may be a major bottleneck for domestic T . gondii strains , driving the persistence of only certain strains in the environment , then available for human and animal infection . In the context of our study in Senegal , the human-mediated invasion of the house mouse , in addition to its putative role in the introduction of type II and type III lineages in Senegal , may be responsible of the decline of T . gondii populations of Africa 1 lineage in invaded areas . Further research should be performed to confirm the occurrence of spatial correlation between T . gondii strain virulence and murine host resistance in different geographical areas . Africa 1 lineage is one of the most prevalent lineages in West Africa , where a high prevalence of ocular toxoplasmosis has been reported among patients from this region [11 , 12] . The possible involvement of Africa 1 lineage in this heightened incidence of ocular toxoplasmosis has been proposed in a recent review [4] . This hypothesis is supported by the genetic proximity between Africa 1 lineage and a number of strains from South America [50 , 51] , the continent that suffers from the highest burden of ocular toxoplasmosis [5 , 9 , 52] . By providing an accurate mapping of T . gondii lineages geographical distribution according to host species occurrence in Senegal , our findings offer a valuable framework for epidemiological studies aiming to identify the parasite determinants of ocular toxoplasmosis . | Toxoplasma gondii is a zoonotic protozoan with a worldwide distribution and which can infects virtually all warm-blooded species , including human . Clinical expression of human toxoplasmosis , as well as T . gondii strains diversity , exhibit contrasting patterns across geographic regions . The determinants of this geographical structure are poorly understood , but a growing body of evidence supports an important role of human-mediated migrations of T . gondii hosts in the intercontinental dissemination of some parasite lineages . The results of our study conducted in Senegal suggest that the invasive house mouse—which was introduced in the port cities of this country through maritime trade since colonial times—has a dramatic influence on the T . gondii populations of invaded areas . This important T . gondii reservoir seems to be a vector for the intercontinental migrations of T . gondii . In addition , it may have a role in the selection ( or the counter-selection ) of local T . gondii populations found in invaded areas . This study provides insights into the mechanisms shaping T . gondii populations , thereby determining which strains will be available for human and animal infection . | [
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| 2019 | The introduction of new hosts with human trade shapes the extant distribution of Toxoplasma gondii lineages |
Odor perception requires that each olfactory sensory neuron ( OSN ) class continuously express a single odorant receptor ( OR ) regardless of changes in the environment . However , little is known about the control of the robust , class-specific OR expression involved . Here , we investigate the cis-regulatory mechanisms and components that generate robust and OSN class-specific OR expression in Drosophila . Our results demonstrate that the spatial restriction of expression to a single OSN class is directed by clusters of transcription-factor DNA binding motifs . Our dissection of motif clusters of differing complexity demonstrates that structural components such as motif overlap and motif order integrate transcription factor combinations and chromatin status to form a spatially restricted pattern . We further demonstrate that changes in metabolism or temperature perturb the function of complex clusters . We show that the cooperative regulation between motifs around and within the cluster generates robust , class-specific OR expression .
The expression of developmental genes is regulated such that they are either on or off at the appropriate time and in the correct place . The expression patterns of these genes must be robust; they must be stable and resistant to changes in both external and internal environments [1] . Mechanisms underlying developmental buffering and resistance to temperature changes and mutation have been described . Redundant enhancers act together to support gene expression and robustness under adverse conditions [2] . microRNAs silence ectopically expressed transcripts and buffer steady-state gene expression by controlling the levels of repressors or activators [3] . For genes expressed in mature cells , the demand for robust expression and spatial regulation is even more pronounced . Neurons , for example , are remarkably robust: their function can be maintained for one hundred years , implying that gene function is also maintained during this time . How the high requirement for stability is integrated into the continuous gene regulation that occurs in mature cells is poorly understood . The high regulatory demands placed on the nervous system are typified by the olfactory sensory system , in which each olfactory sensory neuron ( OSN ) expresses only one olfactory receptor ( OR ) gene from its genomic repertoire of one hundred to one thousand ORs [4–6] . OSNs expressing the same OR project their axons to the same glomerulus in the brain and create a functional unit , the OSN class , that exists in both insects and mammals [6] . The restriction of OR expression to a single OSN class is crucial for the perception of odors as changes in OR expression pattern produce a mix of ORs in each OSN class , distorting the response properties of the class and thereby impairing odor detection [7] . Despite the difference in the number of ORs between mouse ( 1432 OR genes ) and Drosophila ( 62 OR genes ) , there are common themes in the regulation of OR expression in these model organisms . In addition to class-specific expression , ORs exhibit spatially restricted expression patterns in each olfactory tissue [8 , 9] . OR gene expression is regulated by a small number of transcription factors ( TFs ) [10–17] , and in Drosophila , these TFs regulate OR expression in a combinatorial fashion [16] . Directly upstream of each OR , there is a short cis-regulatory region sufficient for driving expression in OSNs but generally is insufficient for restricting expression to a single OSN class [16 , 18–20] . Searches for DNA binding motifs in OR cis-regulatory regions have not identified a direct regulatory “code” but , rather , an enrichment of motifs upstream of regulated OR genes [16] . Similarly , the identified TFs in Drosophila show little of the spatial and temporal specificity expected for combinatorial regulation , implying that it is neither the presence of a TF in an OSN nor the motif upstream of the OR that restricts expression to one OSN class . Class-specific OR expression is generated in Drosophila in part by input from upstream repressive regions [16 , 20] . In vertebrates , a single OR allele is expressed in each OSN class [21 , 22] . The expression of a functional vertebrate OR creates a negative feedback loop [23–25] that reduces the expression of the H3K9 demethylase Lsd1 [26] and locks the expressed OR allele into a stable , robust expression state while suppressing the expression of other ORs [27] . Without monoallelic expression , vertebrate OR expression is not stable and robust within a single OSN class [18] . Drosophila ORs , like the majority of genes , are not expressed monoallelically and lack a feedback system , suggesting that other mechanisms must exist to ensure robust , specific biallelic gene expression . Here , we address the cis-regulatory mechanisms that result in the precise and robust expression of Drosophila ORs . We utilize the short , well-defined cis-regulatory regions upstream of ORs that limit expression to a single class and the fact that projections from each OSN class form a stereotyped pattern , enabling the direct visualization of expression specificity . Our results demonstrate that structured motif clusters involving one to several TFs located directly upstream of the OR gene provide spatially restricted regulation to a single OSN class . We also show that cooperative gene regulation is a mechanism by which expression variability is buffered and the correct expression of ORs is ensured .
To identify putative motif patterns that regulate OR expression , we focused on the regulation of the Or85a gene . The Or85a upstream region lacks published DNA binding motifs for Acj6 , the sole TF regulating its expression . Acj6 and its vertebrate orthologs have two DNA binding domains , Hox and Pou , which bind two very different sequences: the Hox core motif ( AATTA; [30–32] ) and the Pou core motif ( TGCAA/T; [29 , 33] ) , respectively . Within the first 1000 bp upstream of Or85a , we identified 17 Pou and 7 Hox core motifs . Several of the Hox and Pou motifs exhibited a possible dimer arrangement . A search of all 32 analyzed OR upstream regions showed an array of similar Hox/Pou dimers with variations in the spacing between the motifs ( exemplified in Fig . 1A ) . Constructs with pairs of Acj6Hox/Pou dimers placed upstream of a synthetic minimal promoter fused to CD8:GFP did not induce expression ( Figs . 1B and S1B ) , indicating that motifs from other TFs or spatial arrangements support Acj6 dimer function . Three of the Acj6Hox/Pou motif dimers upstream of Or85a generated a condensed cluster ( Fig . 1A ) . To test whether the cluster was sufficient for expression in OSNs , we placed the cluster directly upstream of a minimal promoter fused to CD8:GFP . The Or85a Acj6 dimer cluster resulted in GFP expression specific to Ab2b OSN class neurons , which express Or85a and innervate DM5 ( Fig . 1B , C ) . All insertions of the transgene resulted in equally strong and specific expression ( S2 Table ) , demonstrating that the cluster is sufficient for expression in the correct OSN class , independent of the locus of insertion . Knockdown of acj6 abolished the expression of the construct ( Fig . 1D ) , showing that Acj6 likely binds the Hox/Pou dimers and the cluster then regulates the expression of Or85a . To identify the smallest regulatory unit sufficient for OSN expression , we generated synthetic motif constructs . Constructs with pairs of E-boxes did not induce expression ( Fig . 1E ) , indicating that E-boxes are insufficient for OR gene regulation . Onecut binds to Cut and Hox motifs spaced 2–6 bps apart [29] . A pattern scan revealed fixed Hox/Cut dimers upstream of 71% of the OR genes regulated by Onecut compared with 8% of those not regulated by Onecut , almost a tenfold enrichment . To investigate whether Hox/Cut dimers are sufficient to drive gene expression in OSNs with Onecut-regulated OR genes , we made constructs with two or six dimers directly upstream of a minimal promoter fused to CD8:GFP . The Onecut Hox/Cut dimers produced expression specific to Ab3a OSN class neurons , which express Or22a and innervate the DM5 glomerulus ( Fig . 1E ) . Interestingly , Or22a expression is regulated by Onecut [16] , and in our pattern scan , the closest Hox/Cut dimer to the consensus was found upstream of Or22a . Increasing the number of Hox/Cut dimers in the construct from 2 to 6 increased the expression level in Ab3a OSNs as well as the number of insertions that were expressed ( Fig . 1E and S2 Table ) . Knockdown of Onecut attenuated the expression driven by the sextet ( Fig . 1F ) , indicating a direct regulation by Onecut . Our results thus show that motif dimers specific to one TF provide sufficient regulatory information to specifically drive gene expression in a single OSN class . To identify how the combinatorial input stemming from multiple TFs regulates OR gene expression , we focused on Or59b , whose expression is regulated by three factors with known DNA binding properties: acj6 , Fer1 and pdm3 [16] ( S2 Fig . ) . Pdm3 binds to a Hox motif ( TAAT ) 2–3 bp upstream of a Pou motif ( TGCAA/T ) [34] . One Pdm3 Hox/Pou motif dimer was identified upstream of Or59b . Interestingly , the Pdm3 Pou motif overlapped with an E-box , the motif that binds bHLH proteins such as Fer1 , and was directly downstream of one of the two Acj6Hox motifs ( Fig . 2A ) , indicating that the motifs for all three TFs that regulate Or59b are clustered . This small , 36 bp cluster drove expression in between 30 and 50 OSNs in the proximal region of the antenna ( Fig . 2C ) . Analysis of the axonal projections to the antennal lobes showed that the Or59b cluster produced expression was confined to two OSN classes: Ab2a , which expresses Or59b and innervates the DM4 glomerulus , and Ab7b , which expresses Or67c and innervates the VC4 glomerulus ( Fig . 2B , C ) . Knockdown of the 3 TFs that regulate Or59b expression , acj6 , Fer1 and pdm3 resulted in the loss of Or59b cluster produced expression in both OSN classes ( Fig . 2D ) , implying that this TF combination does not segregate the Ab2a and Ab7a classes . We have previously shown that the co-repressor Atro represses Or59b expression specifically in the Ab7a OSN class [35] . Overexpression or knockdown of Atro did not attenuate reporter expression driven by the Or59b cluster in the Ab7a OSN class ( Fig . 2D ) , demonstrating that Atro represses Or59b expression via a mechanism that is separate from the cluster and that restricts OR expression to a single class . To investigate the regulatory function of each TF , we made constructs with mutations in the different motifs belonging to the Or59b cluster . Mutation of the E-box resulted in a total loss of reporter expression ( Figs . 3A and S3A ) , indicating that bHLH proteins induce its expression . Mutation of the Acj6 and Pdm3 Pou motif caused loss of expression , whereas mutation of each Hox motif produced ectopic expression ( Figs . 3A and S3A ) , indicating that the repressive or inductive function of Acj6 and Pdm3 is dictated by the Hox motif . Further genetic analyses demonstrated that pdm3 is downstream of acj6 and that both can either repress or activate the cluster function and expression ( S3B–S3C Fig . ) . To explore whether the cluster interprets the protein levels of Pdm3 and Acj6 , we manipulated the level of each factor . The expression of the cluster was sensitive to the loss of one copy of acj6 but not of pdm3 ( Fig . 3B ) . The loss of expression was rescued by lowering the copy number of both factors ( Fig . 3B ) , demonstrating that the ratio of Acj6 to Pdm3 creates a window of cluster function that limits Or59b expression to two OSN classes . To investigate whether cluster structure regulates expression , we rearranged the order of the motifs in the Or59b cluster . First , we moved the E-box 125 bp downstream the cluster , which disrupted expression ( Figs . 4B and S4A ) , demonstrating that the combinatorial clustering of the motifs was required for expression . Next , we addressed the regulatory function of the overlap between the Pou motif and the E-box . Moving the E-box upstream of the cluster caused ectopic expression in seven OSN classes , Ab1a , Ab2a , Ab3a , Ab5b , Ab7b , Ab8a , Ab8b and Ab10b ( Figs . 4C and S4A ) , indicating that the precise location of the E-box dictates a repressive function necessary for class-specific OR expression . To further address how TFs binding at the E-box and Pou motifs interact , we moved the E-box either one-half or a full DNA turn ( 5 or 10 bp , respectively , S1C Fig . ) that placed the TFs at different phases and sides of the DNA . Both constructs resulted in stereotyped ectopic expression in the seven OSN classes ( Figs . 4D , E and S4A ) . As both the Ebox and the Pou motifs were shown to be required for cluster function ( Fig . 3A ) , the above results indicate that occupancy of either of the two motifs interferes with the other and causes the repression of expression . Further genetic analyses placed Fer1 downstream of the Hox/Pou factors ( S4B–S4C Fig . ) . Together , these results demonstrate that the composition and relative positions of motifs within the cluster define and restrict the expression of Or59b . For proper odorant perception , OR expression must be active continuously and must be restricted to a single OSN class , despite changes in the environment . To investigate OR gene expression and class-specific transcription under conditions of environmental fluctuation , we first starved flies for three days . qPCR revealed that the mRNA levels of most ORs increased slightly upon starvation ( Fig . 5A ) . Starvation did not change the expression produced by reporter transgenes with the cis regulatory region between Or59b and the gene upstream fused to CD8:GFP ( Fig . 5B and S3 Table ) , showing that robust class-specific expression is encoded by the region directly upstream the gene . Interestingly , starvation attenuated the expression of the Or59b cluster ( Fig . 5B and S3 Table ) . These results show that the cluster lacks the regulatory information required to maintain class-specific expression during starvation . To further investigate the requirements for robust class-specific OR expression , we changed the physical environment of the flies . Flies are stressed by high ( >30°C ) and low ( <15°C ) temperatures [36] . We switched flies between 3 and 5 days old to low temperature ( 14°C ) and kept a control group at ambient temperature ( 24°C ) for 3 days . qPCR showed increased mRNA levels of most assayed ORs in flies exposed to low temperature compared with those kept at ambient temperature ( Fig . 5C ) . The Or59b cis-regulatory region drove reporter expression at both low and ambient temperatures ( Fig . 5D ) . Different cluster insertions produced similar expression phenotypes , with stable expression at the ambient temperature , but at low temperature , the cluster produced ectopic expression in several OSN classes in 12% , no expression in 33% and restricted class specific expression in 55% of the analyzed animals ( Fig . 5D and S3 Table ) . These results show that the cluster can support class-specific expression under ambient conditions , but the fine-tuned balance of TF assembly is perturbed in low-temperature or starvation conditions . Because the Or59b cluster produces weak expression compared with the Or59b reporter ( Fig . 2C ) , we investigated whether the level of Or59b expression generates robust class-specific expression . Two tandem copies of the Or59b cluster produced strong expression at ambient temperature and both loss and gain of expression phenotypes at low temperature ( Fig . 6A , D ) , demonstrating that expression level does not buffer against environmentally induced changes and that class specificity is maintained via a separate mechanism . Interestingly , environmental changes did not affect the function of the Or85a cluster ( Fig . 6B , D ) , suggesting that cooperative binding of one TF may be sufficient to drive robust class-specific expression . Of the three TFs known to regulate Or59b , Fer1 has 10 binding sites ( E-boxes ) outside the cluster that can cooperate with the cluster to regulate Or59b expression ( Fig . 2A ) . To test whether cooperating E-boxes might produce robust Or59b cluster expression , we generated a cluster with two E-boxes . The addition of an extra E-box led to stabilized expression in flies challenged with changes in temperature or food ( Fig . 6C , D ) . A count of GFP-positive OSNs showed that the cluster produced a varied number , from a few cells to over 60 positive cells per antenna , at 14°C ( Fig . 6E ) . The number of positive cells per antenna was fully rescued to the control number by the addition of an extra E-box , demonstrating that the cooperative function of single motifs around a cluster can stabilize the assembly and expression produced by the cluster . The variability of expression produced by different Or59b cluster insertions ( S2 Table ) and the general increase of OR expression upon environmental changes suggested a general epigenetic mechanism for regulation . In mice , H3K9 trimethylation , a marker of heterochromatin , is required for stable and robust class-specific OR expression [27 , 37] . To address whether changes in H3K9 trimethylation control Or59b cluster function , we introduced a mutant allele of su ( var ) 3–9 , the enzyme that trimethylates H3K9 [38] . Or59b reporters showed robust expression in su ( var ) 3–9 heterozygote flies ( Fig . 7A ) . By contrast , each Or59b cluster insertion showed both gain- and loss-of-expression phenotypes in su ( var ) 3–9+/− flies ( Fig . 7A ) , indicating that H3K9 methylation status modulates gene expression driven by the cluster . To address how TF assembly at the cluster interacts with the assembly of heterochromatin , we crossed the various cluster versions to the su ( var ) 3–9 mutant . In the heterozygote su ( var ) 3–9 background the ectopic expression of the Acj6Hox mutant cluster was lost ( Fig . 7C ) , indicating that the epigenetic status at the cluster determines the function of Acj6 and Pdm3 . Moreover , the attenuated expression of the Pou mutant cluster was weakly rescued in the heterozygote background ( Fig . 7B ) , indicating that the Hox/Pou TFs generate the open chromatin required for the induction of Or59b expression . Interestingly , the loss of expression by the mutated E-box was not rescued in su ( var ) 3–9 heterozygotes ( Fig . 7D ) , placing the heterochromatin regulation downstream of the Hox/Pou factors and upstream of the E-box . The loss of heterochromatin in the heterozygote background further induced expression of the cluster with the E-box displaced by 125 bp ( Fig . 7E ) , supporting the notion that Hox/Pou factors open chromatin and allow for the binding of bHLH proteins to the E-box ( modeled in Figs . 7G and S5 ) . As with the temperature- and starvation-induced phenotypes , the additional E-box construct rescued the su ( var ) 3–9 phenotypes ( Fig . 7F ) , implying that stabilization of TF binding at the E-boxes buffers the cluster function . Our results thus support a model in which the Hox- and Pou-binding proteins open chromatin and let bHLH proteins to bind the E-box that induce expression . The bHLH binding compete with the Hox/Pou proteins and cooperation between additional E-boxes stabilize bHLH binding and buffers Or59b expression from variation in epigenetic and environmental states ( Fig . 7G; regulatory models of each phenotype are presented in S5–S6 Figs ) .
The motifs identified in this study are short , are likely to have low affinity and are abundant in OR cis-regulatory regions . Nonetheless , these motifs are sufficient to regulate the restriction of OR expression to a single class . Our results further imply that low information value of a motif can be an advantage for several reasons . First , TF binding to low-affinity motifs requires cooperative input for stability , thus favoring combinatorial and patterned gene regulation . Second , a high on/off rate supports competition among TFs at overlapping motifs , which we show is crucial for the integration of combinatorial input from several TFs to restrict OR transcriptional output to a single class . Third , weak TF interactions with the motif facilitate direct chromatin regulation of the locus [39] . Finally , the level of degeneracy of each motif defines the role , and possibly the function , of the TF in each cluster , which increases the use and flexibility of a TF from a static activator to a modulator , as we showed for Acj6/pdm3 by demonstrating both repressive and inductive roles . Short motifs are evolutionarily unstable because they can be generated or lost with one or two mutations . The olfactory system is evolutionarily very plastic , with the continuous generation and loss of OR genes , implying that selection might regulate the birth and death of weak motifs . Even a complex cluster such as the Or59b cluster can only be found as a unit in the melanogaster clade and shows a large number of changes among species in the clade . Recently , it has been shown that the evolutionary stability of expression patterns differs between vital developmental genes and genes expressed only in mature cells [40 , 41] . Thus , one possibility is that short motifs are the product of an active selection process . Studies on the conservation of motifs upstream of genes expressed in different tissues and stages indicate that upstream genes expressed in adult tissues , such as the vertebrate liver [42 , 43] , are less conserved than those critical for developmental processes , such as invertebrate segmentation [44 , 45] , and organelle function , such as the Rfx regulation of genes conserved in cilia function [46] . It will therefore be interesting to determine whether the predictive use of conserved large ( >8 bps ) motifs is limited to the prediction of evolutionarily stable systems , such as development or organelle function , and whether the identification of motif patterns or clusters of core motifs will improve predictions regarding the regulatory function of non-vital genes expressed in mature cells . Cooperative regulation of clustered motifs has been shown in Drosophila to restrict regulation by broadly expressed TFs to regions of the embryo [47–49] . Our results show that the simplest switches are motif clusters recognized by one TF . Tandem synthetic consensus Acj6 motif dimers did not result in any expression , but the more complex Or85a Acj6 cluster did , indicating that the structure of the motif cluster can limit the function of a broadly expressed TF to regulate OR gene expression to a single OSN class . Interestingly , despite the fact that onecut is expressed in several OSN classes [16] , the cluster of Hox/cut motifs produced expression restricted to a certain class . As TFs with more than one DNA binding domain , such as Acj6 and Onecut ( with two binding domains each ) , can bind to multiple DNA motifs simultaneously [29 , 30] , the arrangement of motifs within a cluster can define expression pattern , and in this manner , a broadly expressed TF can produce a very restricted expression pattern . The integration of several TFs requires more complex clusters . The Or59b cluster integrates the function of 3 TFs , Acj6 and Pdm3 , which bind to two different Hox motifs and compete for one Pou motif , and Fer1 , which binds to an E-box that partly overlaps with the Pou motif . Our results show that the competition between Hox/Pou proteins and bHLH proteins allows the cluster to integrate the epigenetic status and the levels of Hox/Pou proteins , which can be summarized in the following model: the Hox/Pou TFs Acj6 and Pdm3 compete for the Pou motif , where Pdm3 likely opens chromatin and facilitates the binding of bHLH proteins to the E-box , which induces Or59b expression . As the bHLH proteins bind the E-box , binding of the Hox/Pou TFs to the cluster is destabilized , reducing the opening of chromatin . In this less-favorable chromatin environment , binding to the E-box is reduced and a steady state is generated . The generated steady state is sufficient to support expression in two OSN classes; the Atrophin complex represses expression in one of two classes , resulting in expression of the Or59b gene in a single OSN class . Various environmental challenges generated a stereotyped expression phenotype , indicating that a general molecular mechanism underlies robust OSN class expression . One cause of combined gain and loss of expression phenotypes is direct competition between a repressor and activator for one motif [50] . Our results demonstrate that it is the competition between the Hox/Pou TFs and bHLH proteins that generates unstable expression and that cooperative regulation of bHLH proteins bound to the E-box in the cluster and secondary E-boxes beyond the cluster stabilizes OR expression . This stabilization is likely a function of favoring bHLH binding to the E-box in the cluster , thereby reducing the need for Hox/Pou regulation and chromatin opening at the locus . Even in simple clusters with only one TF motif , cooperative function generates robust expression in the class . Interestingly , robustness to environmental changes during development has been shown to be produced by shadow enhancers [2] ( redundant cis-regulatory regions that together support expression [51] ) or homotypic motif clusters [52] , suggesting that cooperative regulation between motifs and clusters might be a general mechanism through which to maintain restricted gene expression .
The Pebbled-Gal4 ( Peb-Gal4 ) and acj66 mutants were kind gifts from Liqun Luo ( Stanford University , Stanford , CA , USA ) . The su ( var ) 3–906 mutant was a kind gift from Anita Öst ( Linköping University , Linköping , Sweden ) . The following fly lines were obtained from the Vienna Drosophila Center ( VDRC; Vienna , Austria; http://stockcenter . vdrc . at ) : Acj6-IR , Atro-IR , Fer1-IR , UAS-Atro , and UAS-Dcr2 . The following RNAi lines were obtained from the Transgenic RNAi Project ( TRiP; Harvard Medical School , Boston , MA , USA; http://www . flyrnai . org ) : Fer1-IR ( 27737; 50672 ) , Onecut-IR ( 29343 ) , Pdm3-IR ( 35726 , 26749 ) . The following fly lines were provided by the Bloomington Drosophila Stock Center ( BDSC; Indiana University , Bloomington , IN , USA; http://flystocks . bio . indiana . edu ) : w1118 , UAS-tub-Gal80ts , Pdm3MI03202 ( 37337 ) , Pdm3MI01072 ( 37552 ) . An online pattern search tool ( http://www . bioinformatics . org/sms2/dna_pattern . html ) was used to scan 1 kb upstream from the translational start site of each OR for 6 motifs recognized by 4 TFs: Acj6 ( Hox and Pou ) , Fer1 ( E-box ) , Onecut ( cut and Pou ) and Pdm3 ( Hox linked to Pou , only for Or59b ) ( S1 Table ) . All constructs were synthesized at Genescript and cloned into a transformation vector containing a synthetic TATA region fused to a single ORF that contained the mCD8 transmembrane domain , four tandem copies of GFP , and two c-myc epitope tags , as previously described ( Couto et al ) . The DNA constructs were injected into w1118 flies at BestGene , and six to 12 lines were analyzed per construct . Virgin flies of the RNAi line were mated with males containing Pebbled-GAL4 , UAS-Dicer2 , and the cluster transgenes . The crosses were set up and maintained at 24°C and 2–5 d after eclosure , flies were dissected , stained and scored for phenotypes . RNA interference lines for Acj6 , Atro , Fer1 , and Onecut were previously described [16 , 35] . Both pdm3 RNA interference lines produced identical phenotypes that were phenocopied by the Pdm3 mutant ( Pdm3MI01072 ) . All flies were raised on standard Drosophila culture medium at 24°C and collected 2–5 days after eclosion unless otherwise specified . w1118 flies were used as controls . In the experimental group , flies were transferred to new vials and maintained for 3 days at 14°C . In the starvation experiments , 2–5-day-old flies were kept in a vial with water-soaked filter paper for 3 days . Immunofluorescence was performed according to previously described methods [16] ) . The following primary antibodies were used: rabbit anti-GFP ( 1:2000 , TP-401; Torrey Pines Biolabs ) and mouse anti-nc82 ( 1:100; DSHB ) . Secondary antibodies were conjugated with Alexa Fluor 488 ( 1:500; Molecular Probes ) . Confocal microscopy images were collected on an LSM 700 ( Zeiss ) and analyzed using an LSM Image Browser . The numbers of co-expressing BP104 and GFP OSNs for different constructs were counted from the images . Adobe Photoshop CS4 ( Adobe Systems ) was used for image processing . Antennae were obtained with a sieve after freezing 2–5-day-old flies in liquid nitrogen . Total RNA from antennae was extracted with TRIzol reagent ( Invitrogen ) followed by purification with the RNeasy kit ( Qiagen ) . Quantitative PCR was conducted on an Applied Biosystems 7900HT real-time PCR system ( Life Technologies ) using the Power SYBR Green PCR master mix ( Applied Biosystems , Life Technologies ) and primer sets designed using Primer Express software v3 . 0 . 1 ( Integrated DNA Technologies ) . Tubulin was used as an internal control for the experiments . To amplify cDNA products and not genomic DNA , primers were designed to join the end of one exon with the beginning of the next exon . Quantitative PCR for each primer set was performed on both control and experimental samples for 40 cycles . Following amplification , melt curve analysis and ethidium bromide agarose gel electrophoresis were performed to evaluate the PCR products . The relative quantification of the fold change in mRNA expression was calculated using the 2−ΔΔCT threshold cycle method . | Our neurons can become over a hundred years old . Even if neurons are restructured and remodeled by their constant work of receiving , storing and sending information , they stay devoted to one single task and retain their identity for their whole life . How a neuron keeps its identity is not well understood . In the olfactory system , the identity of the olfactory sensory neuron ( OSN ) is a result of the expression of a single odorant receptor ( OR ) from a large receptor gene repertoire in the genome . Neurons that share an expressed receptor make a functional class . Here , we identify clusters of transcription factor binding motifs to be the smallest unit that drive expression in a single olfactory sensory neuron class . We further demonstrate that it is the structure of the cluster that determines the class specific expression . However , environmental stress , such as temperature changes or starvation , destabilizes the expression produced by the cluster . Our results demonstrate that stable expression is generated from redundant motifs outside the cluster and suggest that cooperative regulation generates robust expression of the genes that determine neuronal identity and function . | [
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| 2015 | Cis-Regulatory Mechanisms for Robust Olfactory Sensory Neuron Class-restricted Odorant Receptor Gene Expression in Drosophila |
Accumulation of unfolded proteins in the lumen of the endoplasmic reticulum ( ER ) causes ER stress . Snf1 , the Saccharomyces cerevisiae ortholog of AMP–activated protein kinase ( AMPK ) , plays a crucial role in the response to various environmental stresses . However , the role of Snf1 in ER stress response remains poorly understood . In this study , we characterize Snf1 as a negative regulator of Hog1 MAPK in ER stress response . The snf1 mutant cells showed the ER stress resistant phenotype . In contrast , Snf1-hyperactivated cells were sensitive to ER stress . Activated Hog1 levels were increased by snf1 mutation , although Snf1 hyperactivation interfered with Hog1 activation . Ssk1 , a specific activator of MAPKKK functioning upstream of Hog1 , was induced by ER stress , and its induction was inhibited in a manner dependent on Snf1 activity . Furthermore , we show that the SSK1 promoter is important not only for Snf1-modulated regulation of Ssk1 expression , but also for Ssk1 function in conferring ER stress tolerance . Our data suggest that Snf1 downregulates ER stress response signal mediated by Hog1 through negatively regulating expression of its specific activator Ssk1 at the transcriptional level . We also find that snf1 mutation upregulates the unfolded protein response ( UPR ) pathway , whereas Snf1 hyperactivation downregulates the UPR activity . Thus , Snf1 plays pleiotropic roles in ER stress response by negatively regulating the Hog1 MAPK pathway and the UPR pathway .
The endoplasmic reticulum ( ER ) is the cellular organelle responsible for the folding and modification of newly synthesized secretory or membrane proteins . Environmental or developmental changes which perturb ER homeostasis , or genetic alterations causing production of irreversibly misfolded proteins lead to an accumulation of unfolded and misfolded proteins within the ER . This condition , which is collectively termed ER stress , is toxic to cells and has been implicated in a variety of human pathologies , such as diabetes , cancer and neurodegeneration , including Alzheimer , Parkinson and Huntington disease [1 , 2] . Therefore , when ER stress is sensed , cells actuate adaptive signaling pathways to alleviate ER stress [1 , 3] . In the budding yeast Saccharomyces cerevisiae , the unfolded protein response ( UPR ) signaling pathway , composed of an ER transmembrane protein Ire1 and a transcriptional activator Hac1 , plays a principal role in ER stress response [1 , 3] . When activated by ER stress , Ire1 excises the translation-inhibitory intron from HAC1 mRNA , initiating splicing of HAC1 mRNA and consequent production of Hac1 protein . Hac1 induces expression of target genes , such as genes encoding chaperones and proteins functioning ER-associated degradation , thus increasing the protein folding capacity of the ER . Although the UPR is undoubtedly essential for yeast cells to alleviate ER stress , a previous genome-wide study [4] has predicted that not less than 100 genes are involved in response to ER stress . Therefore , it remains to be fully elucidated how ER stress response is precisely controlled . AMPK is evolutionarily conserved in eukaryotic cells and a key sensor of cellular energy status [5–7] . In Saccharomyces cerevisiae , a catalytic subunit of AMPK is encoded by the SNF1 gene ( S1 Fig ) . Similar to other members of the AMPK family , Snf1 forms a heterotrimeric complex with two regulatory subunits , the γ subunit Snf4 and one of the three alternative β subunits , Sip1 , Sip2 , or Gal83 [5] . The catalytic activity of Snf1 is regulated by phosphorylation at Thr-210 that is located in the activation loop of its kinase domain [8 , 9] . Three upstream kinases , Sak1 , Tos3 , and Elm1 , have been identified as kinases responsible for Snf1 activation [10–12] . Oppositely , Snf1 is inactivated by the Reg1-Glc7 protein phosphatase 1 complex; the catalytic subunit Glc7 is directed to Snf1 through the regulatory subunit Reg1 [13 , 14] . Besides critical roles in adaptation to glucose deprivation and utilization of alternative carbon sources to glucose , the Snf1 complex is involved in the response to environmental stresses , such as heat and oxidative stresses [5 , 15] . However , the role of Snf1 in ER stress response is as yet poorly understood . The budding yeast Hog1 , which is structurally highly similar to the mammalian p38 MAPK , was originally identified as a key protein kinase required for the adaptation of yeast cells to osmotic stress [16 , 17] . In osmotic stress response , the Sln1-Ypd1-Ssk1 multistep phosphorelay system , which is homologous to bacterial two-component systems , regulates the Hog1 MAPK cascade ( S2 Fig ) [16 , 17] . Under normal osmotic conditions , the membrane-associated histidine kinase Sln1 phosphorylates itself . The phosphate group is transferred to Ssk1 through the Ypd1 phosphotransmitter . Hyperosmotic stress inactivates Sln1 , resulting in downregulation of the phosphorylation level of Ssk1 . Dephosphorylated Ssk1 directly binds to and activates the Ssk2 and Ssk22 MAPKKKs , and consequently , leads to sequential activation of Pbs2 MAPKK and Hog1 MAPK . In addition to a pivotal role in osmotic stress response , Hog1 has been revealed as a regulator of a wide array of stress responses , including cold , heat and ER stresses [16–19] . In ER stress response , Hog1 is activated in an Ssk1-dependent manner [18] . However , the mechanisms that control Hog1 activity in ER stress response are still poorly understood . In this study , we identified Snf1 as a negative regulator of Hog1 in ER stress response . Cells lacking Snf1 have elevated levels of active Hog1 , whereas upregulation of Snf1 activity reduces Hog1 activation . ER stress induces expression of Ssk1 , but this induction is counteracted by Snf1 . These results indicate that Snf1 modulates Hog1 activation by controlling the expression level of its activator Ssk1 . We also demonstrated that loss of Snf1 leads to upregulation of the UPR pathway , whereas the UPR activity is downregulated in Snf1-activated cells . Thus , Snf1 negatively regulates the Hog1 MAPK pathway and the UPR pathway in ER stress response .
In order to test whether the Snf1 protein kinase regulates ER stress response , cells carrying snf1 deletion were plated on medium containing tunicamycin , a natural inhibitor of N-linked glycosylation that is widely employed as an inducer of ER stress . We unexpectedly found that compared to wild-type cells , the snf1 mutant was resistant to tunicamycin ( Fig 1A ) . To confirm that snf1 mutation caused tunicamycin resistance , we transformed the snf1 mutant with the plasmid that expresses SNF1 and tested the transformants for growth on medium containing tunicamycin . Expression of SNF1 significantly rescued the tunicamycin-resistant phenotype associated with the snf1 mutation ( Fig 1A and S3A Fig ) . To address the biological importance of Snf1 kinase activity , we generated a catalytically inactive form of Snf1 [Snf1 ( K84M ) ] , in which Lys-84 in the ATP-binding motif was mutated to methionine [8] . When Snf1 ( K84M ) was expressed in snf1 mutants , the tunicamycin resistance was not rescued ( Fig 1A ) . To examine the effect of Snf1 hyperactivation on ER stress response , we generated Snf1 ( G53R ) , a catalytically active form in which Gly-53 in the kinase domain has been mutated to arginine [8] . Expression of Snf1 ( G53R ) resulted in hypersensitivity to tunicamycin ( Fig 1B ) . These results indicate that Snf1 negatively regulates the response to ER stress in a manner dependent on its kinase activity . We next asked if the regulatory subunits of Snf1 are involved in ER stress response . We found that deletion of the SNF4 gene , which encodes the γ subunit of the Snf1 complex , caused increased resistance to tunicamycin ( Fig 1C ) . Furthermore , cells harboring both snf1 and snf4 mutations displayed ER stress tolerance indistinguishable from that observed in the snf1 single mutants ( Fig 1C ) , indicating that Snf1 and Snf4 act in the same pathway . We next examined whether the β subunits , Sip1 , Sip2 , and Gal83 , regulate ER stress response . We found that the sip1 sip2 gal83 triple mutant was resistant to tunicamycin , although neither of their single mutants exhibited the obvious tunicamycin-resistant phenotype ( Fig 1D ) . These results indicate that the Snf1 complex negatively regulates ER stress response . Snf1 is phosphorylated at Thr-210 and activated by exposure of cells to alkaline pH and oxidative stresses [15] . Therefore , we investigated whether Snf1 is activated by treatment with ER stress . Anti-phospho AMPK antibodies that recognize the phosphorylated , activated form of AMPK were used to monitor phosphorylation of Snf1 at Thr-210 in the budding yeast [12] . In wild-type cells , we could detect Snf1 phosphorylation under unstressed conditions ( Fig 2A ) . Treatment of cells with tunicamycin stimulated Snf1 phosphorylation within 1 . 5–3 hr , and its phosphorylation was persisted for at least 7 . 5 hr ( Fig 2A ) . Similar observation was seen when cells were exposed to dithiothreitol ( DTT ) , which causes ER stress by blocking disulfide bond formation in the ER ( Fig 2B ) . Thus , ER stress induces activation of Snf1 through phosphorylation at the Thr-210 residue . We next asked whether ER stress-induced Snf1 activation is mediated by the upstream kinases , Sak1 , Tos3 , and Elm1 . We found that sak1 tos3 elm1 triple mutations completely abolished activation of Snf1 both in the presence or absence of ER stress ( Fig 2C ) , although activated Snf1 levels were only slightly decreased in each single mutants ( S4A and S4B Fig ) . This result indicates that ER stress induces Snf1 activation in a manner dependent on the three redundant kinases . Snf1 is inactivated through dephosphorylation mediated by the Reg1-Glc7 phosphatase complex . We next investigated the role of the Reg1-Glc7 complex in ER stress-induced Snf1 activation . Because the glc7 deletion strain is lethal [20 , 21] , we used reg1 deletion . Both in the presence or absence of tunicamycin treatment , phosphorylated Snf1 levels were clearly upregulated by reg1 deletion ( Fig 2D ) , indicating that the Reg1-Glc7 protein phosphatase 1 acts to inactivate Snf1 in ER stress response . We next asked whether phosphorylation of Snf1 at Thr-210 is important for its function in ER stress response . We first examined the ability of Snf1 ( T210A ) , which contains a mutation of Thr-210 to Ala , to complement ER stress resistance associated with snf1 deletion . The Snf1 ( T210A ) mutant failed to complement the snf1 defect ( Fig 2E ) . We investigated whether the ER stress response involves the three upstream kinases and the phosphatase complex . We found that the sak1 tos3 elm1 triple mutant cells , which were defective in Snf1 activation , were resistant to tunicamycin ( Fig 2F ) . In contrast , reg1 mutant cells in which Snf1 activity is upregulated exhibited hypersensitivity to tunicamycin; however , the stress sensitivity of the reg1 mutant was completely suppressed by snf1 deletion ( Fig 2G ) . Similar results were obtained when DTT was used as an ER stressor ( Fig 2H ) . Taken together , these results demonstrate that Snf1 is activated by ER stress through phosphorylation at Thr-210 and then negatively regulates ER stress response . Next , we explored the mechanism underlying the effect of Snf1 on ER stress response . Previous analyses in Saccharomyces cerevisiae have revealed that the UPR , composed of Ire1 and Hac1 , is at the center of ER stress response [1 , 3] . Therefore , we investigated a potential role for Snf1 in regulating the UPR . Upon ER stress , activated Ire1 excises the translation-inhibitory intron from HAC1 mRNA , consequently producing Hac1 protein . Hac1 transcriptionally activates its target genes , including KAR2 and ERO1 . We first examined the kinetics of HAC1 mRNA splicing ( Fig 3A ) . In wild-type cells under unstressed conditions , the unspliced form of HAC1 mRNA ( HAC1u ) was robustly detected , but the spliced form ( HAC1s ) was rarely detectable . Treatment of cells with ER stress promoted splicing of HAC1 mRNA . The amount of HAC1s peaked 1 . 5 to 3 hr after DTT addition and gradually decreased thereafter . We next investigated the role of Snf1 in regulation of HAC1 mRNA splicing using the snf1 and reg1 mutant cells . We found that both in snf1 and reg1 mutant cells , HAC1 mRNA splicing was normally promoted in response to ER stress ( Fig 3A ) . We found that downregulation of HAC1 mRNA splicing was unaffected by snf1 mutation . On the other hand , in the reg1 mutant cells , HAC1s was decreased rapidly within 3 hr of DTT addition . Therefore , we compared the protein level of Hac1 between wild-type and the reg1 mutant cells ( Fig 3B ) . In wild-type cells , Hac1 protein was hardly detectable prior to ER stress treatment . Production of Hac1 was induced within 1 . 5 hr after exposure to DTT and subsequently downregulated . In the reg1 mutant cells , Hac1 production was induced at levels comparable to that of wild-type cells . However , the amount of Hac1 declined rapidly within 3 to 4 . 5 hr after DTT treatment . A rapid decrease in Hac1 protein was also seen when cells harboring reg1 mutation were exposed to tunicamycin ( S5A Fig ) . Consistent with the protein level of Hac1 , expression of the well-known Hac1 target genes , ERO1 and KAR2 , was reduced by reg1 mutation ( Fig 3E and S5B Fig ) . These UPR defects observed in the reg1 mutant could be significantly restored by snf1 mutation ( Figs 3A , 3C and 3E and S5B ) . These results suggest that Snf1 participates in downregulation of the UPR pathway . The observation that the UPR activity was downregulated by reg1 mutation prompted us to perform a detailed analysis of the UPR activity in the snf1 mutant cells . We found that the UPR activity under unstressed conditions was increased in the snf1 mutant cells . In the absence of ER stress , the level of HAC1s in cells harboring snf1 deletion was statistically higher than that in wild-type cells ( Fig 3A ) . Consistent with this , the snf1 mutant cells expressed a small amount of Hac1 protein even prior to treatment with ER stress ( Fig 3D ) . Furthermore , under unstressed conditions , both ERO1 and KAR2 mRNAs were statistically significantly increased in the snf1 mutant compared to wild-type cells ( Fig 3E and S5B Fig ) . Taken together , these results indicate that Snf1 negatively regulates the UPR pathway . The snf1 mutation significantly enhanced resistance against ER stress , although UPR upregulation caused by snf1 mutation was detected only under unstressed conditions . Therefore , additional mechanisms may contribute to ER stress resistance caused by snf1 mutation . In the budding yeast , Hog1 MAPK is activated by ER stress through phosphorylation at critical threonine and tyrosine residues located in the activation loop [16 , 17] , and is in fact required for protecting cells against ER stress [18 , 19] . Anti-phospho-p38 antibodies that recognize the phosphorylated form of mammalian p38 MAPK can be used to detect activated Hog1 in the budding yeast [22] . As observed previously [18] , western blot analysis with anti-phospho-p38 antibody marginally detected the activated form of Hog1 in wild-type cells and its abundance was increased by treatment of cells with DTT ( Fig 4A ) . To investigate the role of Snf1 in regulation of Hog1 activity , we monitored the activated form of Hog1 in the snf1 mutant following induction of ER stress . We found that activated Hog1 levels were increased in snf1 mutant cells both in the presence or absence of DTT treatment ( Fig 4A ) . Similar results were obtained when cells were exposed to tunicamycin ( S6A Fig ) . These results suggest that Snf1 has the inhibitory effect on Hog1 activation . The observation that Snf1 is activated by ER stress prompted us to test whether Snf1 acts to downregulate Hog1 activity during recovery from ER stress . We observed that Snf1 remained active even after removal of DTT from the medium ( Fig 4C ) . In contrast , DTT removal allowed reduction of Hog1 activity ( Fig 4B ) . However , Hog1 activation was prolonged in cells lacking Snf1 ( Fig 4B ) . These results suggest that ER stress-activated Snf1 participates in the process that Hog1 activity returns to the basal level . Furthermore , it is suggested that additional mechanisms function in Hog1 inactivation during recovery from ER stress , since Hog1 dephosphorylation after DTT removal was delayed , but occurred in snf1 mutant cells . Next , we examined the effect of Snf1 hyperactivation on ER stress-induced Hog1 activation . Strikingly , activation of Hog1 in response to DTT was diminished by reg1 mutation which increases Snf1 activity ( Fig 4D ) , and this defect could be entirely restored by snf1 mutation ( Fig 4E ) . Similar results were obtained when cells were exposed to tunicamycin ( Fig 4F and S6B Fig ) . Our finding that reg1 mutation interferes with Hog1 activation in response to ER stress strongly suggests that Snf1 acts as a negative regulator of Hog1 in ER stress response . Previous report showed that ER stress-activated Hog1 accumulated in the nucleus [18] . To investigate the effect of Snf1 on nuclear accumulation of Hog1 in response to ER stress , we used the strain which expresses Hog1 carboxyl-terminally tagged with GFP ( Fig 4G ) . As observed previously [18] , Hog1 was uniformly distributed in the nucleus and cytosol under normal conditions and became enriched in the nucleus after ER stress treatment . Loss of Snf1 slightly but significantly increased nuclear localization of Hog1 even in the absence of ER stress . In contrast , nuclear accumulation of Hog1 in response to ER stress was obviously decreased in the reg1 mutant cells; however , this defect was clearly suppressed by snf1 deletion . These observations support a role of Snf1 in negative regulation of Hog1 in ER stress response . It has been well-characterized that yeast cells activate Hog1 when exposed to hyperosmotic extracellular environments [16 , 17] . We therefore examines whether Snf1 might be involved in the osmotic stress response mediated by Hog1 . In wild-type cells , activated Hog1 is robustly detectable within 3 min of NaCl treatment and then rapidly decreases by 30 min ( Fig 4H ) . Hog1 activation in response to hyperosmotic stress appeared to be enhanced and reduced by snf1 and reg1 mutations , respectively ( Fig 4H ) . These alterations are probably attributed to a potential role of Snf1 in inhibiting Hog1 activation . Indeed , snf1 mutation elevated the basal activity of Hog1 ( Fig 4A ) , and reg1 mutation partially suppressed the lethality of SLN1- and YPD1-deleted cells in which Hog1 is constitutively activated ( see below ) . However , we could not find that reg1 mutation resulted in hypersensitivity to osmotic stress ( S6C Fig ) . Therefore , it remains obscure whether Snf1-mediated Hog1 regulation is physiologically important for osmotic stress response . We next examined whether enhanced ER stress resistance in the snf1 mutants is caused by Hog1 hyperactivation . We constructed the snf1 hog1 double mutants and test them for growth on medium containing tunicamycin ( Fig 5A ) . The snf1 hog1 double mutant was sensitive to tunicamycin . However , we also found that ER stress sensitive phenotype of the hog1 mutant could be partially suppressed by snf1 mutation . As snf1 mutation leads to upregulation of the UPR , we compared the effect of snf1 deletion in cells having a wild-type , hac1 , or hac1 hog1 background . The snf1 mutation modestly restored ER stress sensitivity caused by hac1 mutation ( Fig 5B ) . In contrast , the snf1 hog1 hac1 triple mutants exhibited hypersensitivity to tunicamycin , similar to the hog1 hac1 double mutants ( Fig 5C ) , indicating that Hog1 and UPR are key targets of Snf1 in ER stress response . As shown above , activities of the UPR and Hog1 pathways are upregulated by snf1 deletion , but downregulated by reg1 mutation which leads to Snf1 hyperactivation . These observations raised the possibility that Snf1 continuously regulates the UPR and Hog1 pathways . If this is true , we can observe the diminished Hog1 activation in hac1 mutant cells or the reduced UPR activity in hog1 mutant cells . First , we measured Hog1 activity in cells lacking Hac1 . However , we could not find that loss of Hac1 reduced Hog1 activation ( Fig 5D ) . We next monitored HAC1 mRNA splicing in hog1 mutant cells . In hog1 mutant cells , HAC1 mRNA splicing was normally induced , but retained longer than wild-type cells ( Fig 5E ) . This is consistent with a previous observation [18] and indicates that hog1 mutation does not reduce , but rather upregulates the UPR activity . Thus , the activities of the UPR and Hog1 pathways are independently regulated by Snf1 . We next investigated how Snf1 negatively regulates Hog1 in ER stress response . The dephosphorylation of MAPK by protein phosphatases is well-known as a common mechanism for the negative regulation of the signaling mediated by MAPK [23] . Hog1 is dephosphorylated and inactivated by Ptp2 tyrosine phosphatase [24 , 25] . Previously , it has been shown that loss of Ptp2 results in enhanced resistance to ER stress in a HOG1-dependent manner [19] . Therefore , we examined the relationship between Snf1 and Ptp2 . In the ptp2 mutant cells , basal activity of Hog1 was modestly increased and ER stress-induced Hog1 activation was significantly upregulated ( S7A Fig ) . We found that Hog1 activation was enhanced in the ptp2 snf1 double mutants compared with the ptp2 mutant cells ( S7A Fig ) , indicating that Snf1 negatively regulates Hog1 in ER stress response independently of Ptp2 . In ER stress response , signaling through the Hog1 pathway is controlled by the Sln1-Ypd1-Ssk1 phosphorelay system [18 , 19] . Disruption of the SLN1 gene results in lethality due to constitutive activation of Hog1 and , indeed , mutations in any of the four downstream genes , SSK1 , SSK2 , PBS2 , and HOG1 , suppress the sln1 lethality by blocking activation of Hog1 [26 , 27] . As shown above , Hog1 activity is considerably decreased in reg1 mutant cells in which Snf1 is hyperactivated . Therefore , we tested whether deletion of the REG1 gene suppresses the sln1 lethality . We found that reg1 mutation modestly suppressed the growth defect associated with sln1 deletion ( Fig 6A ) . Similarly , the lethality caused by ypd1 deletion was partially suppressed by reg1 mutation ( Fig 6B ) . However , loss of Snf1 interfered with the ability of reg1 mutation to suppress the ypd1 lethality ( S8A and S8B Fig ) . These results suggest that Snf1 regulates the component functioning downstream of Ypd1 in the Hog1 pathway . In order to identify the molecule that mediates the signaling from Snf1 to Hog1 , we examined the expression levels of components that act in the Hog1 pathway . We generated yeast strains carrying the carboxyl-terminally Myc-tagged genes , including SSK1 , SSK2 , SSK22 , and PBS2 , and analyzed their expression levels ( Figs 6C–6E and S8C–S8E ) . Among them , we found that Ssk1 expression is changed by treatment with ER stress and genetic modulation of Snf1 signaling . In wild-type cells , the protein abundance of Ssk1 is increased following exposure to DTT and tunicamycin , but not NaCl ( Fig 6C–6F ) , suggesting that ER stress specifically affects Ssk1 expression . The snf1 mutation moderately increased Ssk1 expression ( Fig 6D ) , suggesting that Ssk1 expression is negatively regulated by Snf1 . Next , we examined the effect of Snf1 hyperactivation on the expression level of Ssk1 . ER stress-mediated Ssk1 induction was effectively inhibited by reg1 mutation that leads to hyperactivation of Snf1 ( Fig 6E ) . This defect could be significantly restored by snf1 mutation ( Fig 6E ) . Similar results were obtained when cells were exposed to tunicamycin ( Fig 6F ) . These results suggest that Ssk1 expression is negatively regulated by Snf1 . We next examined the functional importance of Ssk1 in Snf1-mediated regulation of Hog1 activity . As shown previously [18] , activated Hog1 levels were significantly decreased in ssk1 mutant cells ( Fig 6G ) . This defect could not be suppressed by snf1 mutation ( Fig 6G ) , indicating that Ssk1 is important for Hog1 hyperactivation caused by snf1 mutation . We also asked whether Ssk1 is involved in enhanced ER stress resistance of the snf1 mutants . We found that ssk1 mutation rendered cells lacking Snf1 sensitive to tunicamycin ( Fig 6H ) . We also observed that the ssk1 snf1 double mutant cells were more resistant to ER stress than the ssk1 single mutants . ER stress tolerance of the ssk1 snf1 double mutants is probably due to increased UPR activity caused by snf1 mutation . Taken together , these results indicate that Snf1 inhibits Hog1 activation in response to ER stress by negatively regulating the expression level of Ssk1 . To explore the mechanism by which Snf1 regulates the expression level of Ssk1 , we measured the amount of SSK1 mRNA by qRT-PCR ( Fig 7A and S9A Fig ) . In wild-type cells , SSK1 mRNA is increased following exposure to ER stress . This induction seemed to be normal in snf1 mutant cells . On the other hand , reg1 deletion significantly inhibited the induction of SSK1 mRNA . This reg1 defect could be restored by snf1 mutation . These results indicate that Snf1 hyperactivation reduce the expression level of SSK1 mRNA . Numerous studies have revealed that Snf1 regulates the gene expression at the transcriptional level through phosphorylation of transcription factors [5 , 6] . This raised the possibility that Snf1 regulates SSK1 promoter activity . To test this possibility , we generated a PSSK1-GFP reporter , consisting of the 5' upstream region of the SSK1 gene to drive GFP expression ( Fig 7B ) . Wild-type cells harboring the PSSK1-GFP reporter displayed GFP expression in the absence of ER stress ( Fig 7C ) . GFP expression from the PSSK1-GFP reporter was increased following incubation with DTT ( Fig 7C ) . On the other hand , we observed that DTT treatment had no obvious effect on expression of GFP derived from the PMCM2-GFP reporter , in which the 5' upstream region of the MCM2 gene is fused to GFP ( Figs 7B and 7C and S9B ) . These results suggest that SSK1 promoter is activated by ER stress . Next , we tested whether PSSK1-GFP induction is regulated by the Snf1 pathway . In contrast to wild-type cells , PSSK1-GFP expression was barely induced by DTT in reg1 mutant cells ( Fig 7D ) . This reg1 defect could be significantly restored by snf1 mutation ( Fig 7D ) . These results strongly support the model in which Snf1 inhibits the activity of SSK1 promoter . We next examined whether SSK1 induction in response to ER stress is important for resistance to ER stress . To test this , we generated two constructs , PSSK1-SSK1 and PMCM2-SSK1 , which express SSK1 under the control of SSK1 and MCM2 promoters , respectively . Introduction of the PSSK1-SSK1 construct significantly rescued ER stress sensitive phenotype associated with ssk1 mutation ( Fig 7E ) . On the other hand , when the PMCM2-SSK1 construct was introduced into ssk1 mutant cells , ER stress sensitivity was less effectively rescued ( Fig 7E ) . This suggests that SSK1 induction via its promoter activation is important for protecting cells against ER stress . We also attempted to compare the ability of the PSSK1-SSK1 and PMCM2-SSK1 constructs to rescue the osmotic stress sensitivity caused by ssk1 mutation . In osmotic stress response , the Hog1 pathway is activated by the membrane protein Sho1 in addition to Ssk1 [26]; hence , as shown in S9C Fig , the ssk1 sho1 double mutants was sensitive to osmotic stress , although neither of their single mutants exhibited the obvious sensitivity to osmotic stress . Therefore , we transformed the ssk1 sho1 double mutant cells with the PSSK1-SSK1 and PMCM2-SSK1 constructs and tested the transformants for growth under hyperosmotic conditions . We found that the PMCM2-SSK1 construct could rescue the osmotic stress sensitivity caused by ssk1 sho1 mutations to same extent as the PSSK1-SSK1 construct ( Fig 7F ) . Thus , it is unlikely that the SSK1 promoter is involved in regulation of osmotic stress response . Taken together , the mechanisms underlying Hog1 activation mediated by Ssk1 are different between osmotic and ER stresses .
Here , we revealed that Snf1 inhibits Hog1 activity by downregulation of the expression level of SSK1 mRNA encoding an upstream activator of the Hog1 MAPK cascade . It is well-known that the dephosphorylation of MAPK by protein phosphatases is crucial for the negative regulation of the signaling mediated by MAPK [23] . The protein phosphatases , such as Ptc1 , Ptp2 , and Ptp3 , play an important role in Hog1 inactivation [16 , 17 , 24 , 25] . Previous report showed that Ptp2 and Ptp3 play the main and minor role , respectively , in negative regulation of Hog1 during ER stress response [19] . Nevertheless , why is Snf1 needed to function in downregulation of Hog1 activity ? It is possible that Snf1 coordinates ER stress response with other cellular responses , since Snf1 is activated in the response to various environmental stresses [5 , 6 , 15] . Indeed , it has been reported that ER stress sensitivity is enhanced under the extracellular environments in which Snf1 activity is known to be elevated [28] . Alternatively , Snf1 may function to inactivate Hog1 in the whole cell level . Previous study showed that upon exposure to ER stress , Hog1 not only translocates into the nucleus and regulates the gene expression , but also functions in activation of autophagy in the cytoplasm [18] . On the other hand , Ptp2 and Ptp3 phosphatases are localized in the nucleus and cytoplasm , respectively [29] . Therefore , it is anticipated that Hog1 activity and its related cellular responses are negatively regulated in a manner different from the nucleus and cytoplasm . In contrast , Snf1 interferes with the signal from Ssk1 to Hog1 MAPK cascade through negative regulation of Ssk1 expression . Therefore , Snf1 is expected to contribute to downregulation of Hog1 in the whole cell level . Our analyses showed that loss of Snf1 moderately increased Hog1 activity , while Snf1 hyperactivation caused by reg1 deletion effectively inhibited Hog1 activation . The existence of protein phosphatases for Hog1 may make apparently difficult to observe the effect of snf1 mutation on Hog1 activity . It is well-known that expression of protein phosphatases for MAPK is induced by environmental stresses [23] . In fact , we found that Ptp2 was induced in response to ER stress ( S7C Fig ) . Consistent with this , Hog1 inactivation after removal of ER stress was modestly delayed , but occurred in snf1 mutant cells . Thus , it is likely that intricate signaling networks regulate Hog1 activity during ER stress response . Therefore , detailed analyses of the relationships between Hog1-mediated ER stress responses and the function of each negative regulator for Hog1 will be important for further understanding how Hog1 activity is controlled during ER stress response . In this study , we observed that the snf1 mutant cells expressed Hac1 even in the absence of ER stress . On the other hand , the expression level of Hac1 in the presence of ER stress was rapidly decreased in Snf1-hyperactivated cells . These observations suggest that Snf1 acts as a negative regulator of the UPR pathway . Although the activation mechanisms of the UPR pathway has been well-studied , there are only a few reports about how the UPR is inactivated after ER stress . Previously , two groups demonstrated the importance of the phosphorylation state of Ire1 kinase domain in the attenuation of the UPR activity [30 , 31] . However , their proposed models are significantly different from each other . Therefore , the mechanisms by which the UPR is finally attenuated have yet to be elucidated . In the course of preparation of this manuscript , Casamayor and colleagues also reported that Snf1 is involved in yeast ER stress response [28] . Consistent with our findings , they showed that reg1 mutation results in increased sensitivity to ER stress in a Snf1-dependent manner . Interestingly , they proposed the model in which Snf1 plays an inhibitory role in attenuation of the UPR by regulating the oligomerization of Ire1 . In regard to the UPR activity in the reg1 mutant cells , their results are distinctly different from our observations: they showed that increased UPR activity after ER stress treatment was prolonged in the reg1 mutant cells; we found that in the reg1 mutant cells , the UPR activity declined rapidly during ER stress response . The reason for this discrepancy is not clear now . However , this phenotypic distinction may be attributed to the difference in genetic background: their strains were derivatives of BY4741 and DBY746; we used W303 derivatives . Indeed , we could observe that the snf1 mutant was resistant to tunicamycin , although they found no difference in ER stress sensitivity between wild-type and the snf1 mutant cells . Thus , further analyses should be needed to elucidate the molecular mechanism by which Snf1 regulates the UPR signaling pathway . We found here that snf1 mutation increases the activities of the Hog1 and UPR pathways and leads to resistance to ER stress . Numerous studies have revealed that improper hyperactivation of stress-responsive signaling pathways is deleterious to cells and organisms [16 , 23 , 32] . In fact , constitutive activation of Hog1 under unstressed conditions causes cell lethality [26 , 33] . We observed that cells deleted for both SNF1 and PTP2 genes showed a high basal activity of Hog1 , but remains viable ( S7B Fig ) . Thus , Hog1 activity in the ptp2 snf1 double mutant cells is not high enough to induce lethality . We found that compared with wild-type cells , the snf1 mutant cells possessed an increased Hog1 activity during ER stress response . However , it is noteworthy that a decline in the Hog1 activity occurred after removal of ER stress even in cells lacking Snf1 . This indicates that the Hog1 activity in the snf1 mutant cells is upregulated , but still remains under the control of the regulatory mechanisms . Therefore , it is possible that Hog1 upregulation caused by snf1 mutation is preferable for yeast cells to survive in the presence of ER stress . Similar view may be applied to the UPR activity . Previous studies revealed that perturbation of the mechanism for properly attenuating Ire1 activity results in reduction of cell viability in the presence of ER stress [30 , 31] . In the snf1 mutant cells , the basal activity of UPR is significantly higher than wild-type cells; however , attenuation of the UPR was nearly unaffected by snf1 mutation . Since the snf1 mutant cells possesses high , but adjustable , UPR activity , loss of Snf1 may be preferable for cells to survive under ER stress . Thus , Snf1 plays pleiotropic roles in negative regulation of ER stress response . The signaling through the Hog1 pathway is controlled by the Sln1-Ypd1-Ssk1 phosphorelay system [16 , 17] . In osmotic stress response , Sln1 inactivation is a key step of Hog1 activation . Under normal osmotic conditions , active Sln1 leads to Ssk1 phosphorylation . Hyperosmotic stress inactivates Sln1 , causing an increase of the dephosphorylated form of Ssk1 . This promotes the Ssk1-Ssk2/Ssk22 physical interaction and results in activation of the Hog1 MAPK cascade . Previous studies demonstrated that Ssk1 is implicated in regulation of Hog1 activity during ER stress response [18 , 19] . Little is understood , however , about the mechanism by which ER stress activates Hog1 . In this study , we demonstrated that the expression level of Ssk1 is increased during ER stress response , and that elevation of Ssk1 protein level is important for cells to survive under ER stress conditions . In contrast , Ssk1 expression remained unchanged upon osmotic stress . These findings suggest that the mechanisms for the regulation of Hog1 are different among these different types of stress . Based on our data , we propose the model in which Hog1 activation in response to ER stress involves upregulation of Ssk1 ( Fig 8 ) . In unstressed conditions , Ssk1 is phosphorylated and inactivated by the upstream phosphorelay system . In the presence of ER stress , increased expression of Ssk1 overwhelms the phosphorylation activity of upstream phosphorelay system , leading to accumulation of dephosphorylated Ssk1 and consequent activation of the Hog1 MAPK cascade . It is well-known that many protein kinases of the MAPKKK family can be activated by binding of their activators , similar to the budding yeast Ssk2 and Ssk22 MAPKKKs [23] . In unstimulated cells , MAPKKK is kept catalytically inactive through an autoinhibitory interaction between the regulatory domain and the kinase domain . Upon stimulation , binding of an activator protein leads to the dissociation of the autoinhibitory domain from the kinase domain and a consequent activation of MAPKKK . Mammalian MTK1/MEKK4 is a stress responsive MAPKKK that is structurally highly similar to the yeast Ssk2/Ssk22 and locates upstream in the p38 pathway [34 , 35] . Previous studies have identified the GADD45 family proteins ( GADD45α , GADD45β and GADD45γ ) as MTK1/MEKK4 activators [36] . GADD45 binds to the autoinhibitory domain of MTK1/MEKK4 and relieve autoinhibition . Furthermore , each GADD45 exhibits distinct tissue expression patterns and is induced by a certain subset of environmental stresses , showing functional distinction among the GADD45 isoforms in p38 activation [36] . Our data presented here suggest that ER stress activates the Hog1 MAPK cascade by induction of Ssk1 , in a manner similar to activation of p38 MAPK cascade though stress-mediated induction of GADD45 . To date , there is little understanding of the mechanism that controls the expression level of Ssk1 . In this study , we show that Snf1 acts as a negative regulator of SSK1 expression in ER stress response . We also demonstrate that the SSK1 promoter is important for Snf1 to negatively regulate the mRNA level of SSK1 . Snf1 phosphorylates a large number of transcription factors , and influences the transcription of hundreds of genes , including those involved in the utilization of alternate carbon sources and the metabolism of amino acids [5 , 6] . Therefore , Snf1 is the most likely to inhibit the promoter activity of the SSK1 gene through phosphorylation of the transcription factor . We also found that the expression level of Ssk1 protein was slightly different from that of SSK1 mRNA . Ssk1 protein is more abundant in the snf1 mutants than wild-type cells , although there was little difference in the expression level of SSK1 mRNA between wild-type and the snf1 mutant cells . This suggests that Snf1 inhibits Ssk1 expression at the translational or posttranslational levels . Intriguingly , it has been showed that the protein level of Ssk1 is negatively regulated by Ubc7 [37] . Ubc7 is an endoplasmic reticulum-associated ubiquitin-conjugating enzyme responsible for ER-associated degradation ( ERAD ) . Snf1 may modulate Ssk1 degradation mediated by the ubiquitin-proteasome system involving Ubc7 . Thus , identification of components downstream of Snf1 , for example , which is responsible for induction of Ssk1 in response to ER stress , will provide valuable insights into the evolutionally conserved mechanism for regulation of the p38/Hog1 MAPK cascade .
Strains used in this study are listed in S1 Table . Yeast strains harboring the complete gene deletions and carboxyl-terminally Myc or GFP-tagged genes were generated by a PCR-based method as described previously [38] . All strains constructed by a PCR-based method were verified by PCR to confirm that replacement had occurred at the expected locus . Standard procedures were followed for yeast manipulations [39] . Plasmids used in this study are described in S2 Table . In-Fusion cloning kits ( Takara ) was used to construct plasmids . The PSSK1-GFP and PMCM2-GFP plasmids were constructed as follows . The DNA fragment encoding GFP followed by the ADH1 terminator ( TADH1 ) was obtained by PCR using the pFA6a-GFP vector [38] as a template . The GFP-TADH1 DNA fragment was fused to 999-bp and 762-bp genomic fragments containing 5' upstream sequences of the SSK1 and MCM2 genes , yielding the PSSK1-GFP and PMCM2-GFP plasmids , respectively . Schemes detailing construction of plasmids and primer sequences are available on request . Protein extracts were prepared essentially as described previously [40] . Briefly , cells grown to exponential phase were incubated with YPD or SD medium containing 2 μg/ml tunicamycin , 4 mM DTT or 0 . 4 M sodium chloride , for the indicated times . Cells were transferred into test tubes , mixed 1:1 with boiled medium , submerged in the boiling water for 3 min , and harvested by centrifugation . Cells were then subjected to a mild alkali treatment-based protein extraction method [41] . Western blot analysis was performed using the immunoreaction enhancer solution Can Get Signal ( Toyobo ) according to the manufacturer's protocol . Anti-HA monoclonal antibody 16B12 ( Covance ) , anti-Myc monoclonal antibody 9E10 ( Santa Cruz ) , anti-GFP monoclonal antibody JL-8 ( Clontech ) , anti-phospho-p38 MAPK monoclonal antibody 28B10 ( Cell Signaling ) , anti-phospho-AMPKα monoclonal antibody 40H9 ( Cell Signaling ) , anti-Hog1 polyclonal antibody y-215 ( Santa Cruz ) , anti-Snf1 polyclonal antibody yk-16 ( Santa Cruz ) , and anti-Mcm2 polyclonal antibody N-19 ( Santa Cruz ) were used . Detection was carried out by using a LAS-4000 ( Fuji Film ) with Immobilon Westren ( Merck Millipore ) . Signal intensities were quantified by ImageQuant ( GE Healthcare ) , and statistical analysis was performed with Excel ( Microsoft ) . Cells grown to exponential phase were incubated with YPD medium containing 2 μg/ml tunicamycin or 4 mM DTT , and harvested at the indicated times . Total RNA was then prepared using ISOGEN reagent ( Nippon Gene ) and the RNeasy Mini kit ( Qiagen ) . First strands of cDNA were generated using the PrimeScript RT reagent Kit ( Takara ) . The HAC1 cDNA was amplified from first strands of cDNA with Blend Taq ( TOYOBO ) , and then analyzed by agarose gel electrophoresis . Detection , quantification , and statistical analysis was carried out by using a LAS-4000 ( Fuji Film ) , ImageQuant ( GE Healthcare ) , and Excel ( Microsoft ) , respectively . The cDNAs of ERO1 , KAR2 , and SSK1 , were quantitated by a quantitative real-time RT-PCR ( qRT-PCR ) method using a 7500 fast real-time RT-PCR system ( Applied Biosystems ) with SYBR Premix Ex Taq ( Takara ) . A standard curve was generated from diluted RNA derived from wild-type cells , and levels of gene expression were normalized to ACT1 expression . HAC1 primers ( CTGGCTGACCACGAAGACGC and TTGTCTTCATGAAGTGATGA ) were used to monitor splicing of HAC1 mRNA . ERO1 primers ( TAACAGCAAATCCGGAACG and ACCAAATTTGACCAGCTTGC ) , KAR2 primers ( AGACTAAGCGCTGGCAAGCT and ACCACGAAAAGGGCGTACAG ) , SSK1 primers ( AGCTGGAAGCAGGGAGAAAG and TGAGTGAGGGTTGGAAGGTG ) , and ACT1 primers ( TGCCGAAAGAATGCAAAAGG and TCTGGAGGAGCAATGATCTTGA ) were used to analyze the mRNA level of ERO1 , KAR2 , and SSK1 . Assays for tunicamycin toxicity were carried out as follows . Cells were grown to exponential phase , and cultures were adjusted to an optical density of 0 . 5 . Cell cultures were then serially diluted 5-fold , spotted onto normal plates or plates containing the indicated concentrations of tunicamycin , followed by incubation at 25°C for 3 days ( for plates lacking or containing 0 . 1 μg/ml tunicamycin ) , 5 days ( for plates containing 0 . 5 μg/ml tunicamycin ) and 7 days ( for plates containing above 1 μg/ml tunicamycin ) . Assays for DTT toxicity were carried out as follows . Cells were grown to exponential phase , and cultures adjusted to an optical density of 0 . 05 were incubated with YPD medium or YPD medium containing 4 mM DTT for 12 h at 25°C . The sensitivity to DTT was estimated by dividing absorbance units in the presence of DTT by absorbance units in the absence of DTT , and then the ratios of DTT sensitivities of the mutants/wild-type were calculated as the DTT sensitivity index . To visualize GFP-tagged Hog1 in living cells , cells grown to exponential phase were incubated with SD medium containing 2 μg/ml tunicamycin or 4 mM DTT . Cells were then harvested at the indicated times , suspended in SD medium , and observed immediately using a Keyence BZ-X700 microscope ( Keyence Corporation , Japan ) . Fluorescence intensities were quantified using Hybrid Cell Count BZ-H2C software ( Keyence Corporation , Japan ) . To confirm nuclear localization of Hog1-GFP , cells were fixed for 10 min at 25°C by direct addition of 37% formaldehyde to a final concentration of 3 . 7% . Cells were then washed with PBS , stained with 4' , 6-diamidino-2-phenylindole ( DAPI ) and subjected to microscope observation . Images of Hog1-GFP in fixed cells were similar to those observed in living cells . | All organisms are always exposed to several environmental stresses , including ultraviolet , heat , and chemical compounds . Therefore , every cell possesses defense mechanisms to maintain their survival under stressed conditions . Numerous studies have shown that a family of protein kinases plays a principal role in adaptive response to environmental stresses and perturbation of their regulation is implicated in a variety of human pathologies , such as cancer and neurodegenerative diseases . Elucidation of molecular mechanisms controlling their activities is still important not only for understanding how the organism acquires stress tolerance , but also for development of therapies for various diseases . In Saccharomyces cerevisiae , the Hog1 stress-responsive MAP kinase is activated by ER stress and coordinates a pleiotropic response to ER stress . However , the mechanisms for regulating Hog1 activity during ER stress response remain poorly understood . In this paper , we demonstrate that a Saccharomyces cerevisiae ortholog of mammalian AMP–activated protein kinase ( AMPK ) , Snf1 , negatively regulates Hog1 in ER stress response . ER stress induces expression of Ssk1 , a specific activator of the Hog1 MAPK cascade . Snf1 lowers the expression level of Ssk1 , thereby downregulating the signaling from upstream components to the Hog1 MAPK cascade . The activity of Snf1 is also enhanced by ER stress . Thus , our data suggest that Snf1 plays an important role in regulation of ER stress response signal mediated by Hog1 . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
]
| []
| 2015 | The Saccharomyces cerevisiae AMPK, Snf1, Negatively Regulates the Hog1 MAPK Pathway in ER Stress Response |
Low copy number plasmids in bacteria require segregation for stable inheritance through cell division . This is often achieved by a parABC locus , comprising an ATPase ParA , DNA-binding protein ParB and a parC region , encoding ParB-binding sites . These minimal components space plasmids equally over the nucleoid , yet the underlying mechanism is not understood . Here we investigate a model where ParA-ATP can dynamically associate to the nucleoid and is hydrolyzed by plasmid-associated ParB , thereby creating nucleoid-bound , self-organizing ParA concentration gradients . We show mathematically that differences between competing ParA concentrations on either side of a plasmid can specify regular plasmid positioning . Such positioning can be achieved regardless of the exact mechanism of plasmid movement , including plasmid diffusion with ParA-mediated immobilization or directed plasmid motion induced by ParB/parC-stimulated ParA structure disassembly . However , we find experimentally that parABC from Escherichia coli plasmid pB171 increases plasmid mobility , inconsistent with diffusion/immobilization . Instead our observations favor directed plasmid motion . Our model predicts less oscillatory ParA dynamics than previously believed , a prediction we verify experimentally . We also show that ParA localization and plasmid positioning depend on the underlying nucleoid morphology , indicating that the chromosomal architecture constrains ParA structure formation . Our directed motion model unifies previously contradictory models for plasmid segregation and provides a robust mechanistic basis for self-organized plasmid spacing that may be widely applicable .
parABC loci generate equally spaced positioning of many bacterial low copy number plasmids , thereby ensuring stable plasmid inheritance [1] . However , the underlying mechanism of action is not satisfactorily understood . In contrast , plasmid segregation mediated by actin homolog ParM is increasingly well explained and involves filaments that push plasmids apart in a mitotic-like process [2] . Understanding of the parABC mechanism is important , as it belongs to the most common class of DNA segregation systems in prokaryotes , used by chromosomes and antibiotic-resistance-carrying plasmids [1] , [3]–[5] . Moreover , it is used in other conceptually similar processes , such as chemotactic cluster positioning and partitioning of carbon-fixing carboxysomes [6] , [7] . The parABC locus present in Escherichia coli plasmids such as pB171 and P1 encodes two proteins: ParA , a P-loop ATPase that binds DNA non-specifically in its dimeric ATP-bound form ( ParA-ATP for short ) [8] , [9] , and the DNA-binding protein ParB that binds site-specifically to the parC region [10] , [11] . Fluorescence microscopy has provided evidence for ParA movement over the nucleoid with spatiotemporal oscillations in helix-like structures [12]–[14] . ParB and parC are required for these dynamics [12] , with ParB promoting the conversion of ParA-ATP to dimeric ParA-ADP ( ParA-ADP for short ) , causing ParA to unbind from the nucleoid [8] , [9] . The time period required for nucleoid-disassociated ParA to regain the ability to bind the nucleoid is sufficiently long in vitro to ensure that the relative locations of ParA-ADP unbinding and later ParA-ATP rebinding would be uncorrelated due to cytoplasmic ParA diffusion [8] . However , once nucleoid-bound , whether ParA-ATP then polymerizes to form long filaments in vivo is currently controversial . Furthermore , the means by which plasmids move under the influence of ParA , and whether ParA polymerization is important for this movement , are also unclear . Nevertheless , the outcome of these ParA dynamics in E . coli is equally spaced positioning of plasmid foci over the nucleoid [9] , [13]–[15] . This state is achieved regardless of the plasmid focus number np or cell length , with plasmid foci repositioned in the wake of retracting ParA structures [9] . Several mechanisms have been proposed to explain ParA-mediated plasmid movement . One hypothesis proposes that ParA-ATP polymerizes on the nucleoid to form long filaments and that plasmid translocation is achieved by ParB-stimulated retraction of the polymers , generating effective plasmid-pulling [3] , . Other proposals are based on ParA-ATP forming a gradient-like distribution on the nucleoid , without a necessity for polymerization [8] , [16]–[21] . It is currently unclear whether any of these mechanisms can explain equal plasmid spacing given the known physiological and biochemical constraints . Here , we therefore investigate which aspects of the polymer and gradient mechanisms are required and sufficient to explain the observed plasmid translocation and equal spacing over the nucleoid . We begin by showing mathematically that competition between dynamic ParA concentrations on either side of a plasmid can lead to equal plasmid spacing . This mechanism relies on an ability of a plasmid to move towards higher ParA concentrations , but the exact means of such movement is not important . We then investigate theoretically specific means of plasmid movement and examine whether predictions from such models are borne out experimentally . We define a computational diffusion/immobilization model where nucleoid-bound ParA-ATP can anchor diffusing plasmids . We show that diffusion/immobilization can in principle space mobile plasmids equally over the nucleoid . However , experiments measuring increased plasmid mobility in the presence of the pB171 parABC locus ( par2 ) , lead us to disfavor this model . Instead we favour a directed motion mechanism in which ParA structure formation provides directionality to plasmid motion thereby speeding up plasmid movement . The directed motion model produces robust equal plasmid spacing with , on average , relatively symmetric ParA distributions , a prediction we verify experimentally . Furthermore , we show experimentally that ParA organization is dependent on the underlying nucleoid structure , with nucleoid disruption resulting in perturbed plasmid positioning . Our combination of modeling and experiments has for the first time uncovered a robust mechanism for plasmid spacing that unifies previous proposals .
To study par2-mediated plasmid segregation , we investigated ParB-GFP localization , expressed from a par2-carrying mini-R1 test plasmid . The par2 locus containing the parB::sfGFP fusion is fully functional as judged by loss-frequency assays ( S1A Fig . ) . As previously described , usage of ParA-GFP and the tetO-TetR-mCherry labeling system also does not affect plasmid stability , indicating full functionality [9] , [12] . ParB-GFP forms foci that are regularly positioned along the long cell axis in vivo ( Fig . 1A ) , consistent with ParB-binding to plasmid-encoded parC regions [10] , [11] . Since plasmid dynamics occur primarily over the nucleoid , we reasoned that plasmid positioning with respect to the nucleoid rather than cell length is most informative . Therefore we measured ParB-GFP foci localization , together with Hoechst ( DNA ) stain to determine the nucleoid boundaries . As expected ParB-GFP foci colocalized exclusively with the Hoechst stain , and were equally spaced over the nucleoid ( Fig . 1B , C , D for np = 1 , 2 , S2A , B Fig . for np = 3 , 4 ) . Several studies have proposed that plasmid positioning is controlled by a concentration gradient of ParA over the nucleoid [8] , [16]–[20] . Intuitively in this mechanism , ParB bound to plasmid parC ( ParB-parC complex ) interacts with nucleoid associated ParA-ATP , which effectively anchors the plasmid to the nucleoid . At the same time , the ParB-parC complex stimulates ParA-ATP hydrolysis causing a local ParA-ATP depletion . These processes could then generate a ParA-ATP gradient which a plasmid is able to follow . Reorganization of ParA gradients under the influence of multiple ParB-parC complexes might then lead to equal plasmid spacing . To rigorously understand if , and with what requirements , equal spacing can be achieved we develop here a minimal mathematical model based on the above principles . We model the nucleoid as a 1d system of length ( along the long axis of the cell ) on which ParA-ATP and plasmids can interact . Let denote the nucleoid-associated ParA-ATP concentration at position relative to one nucleoid edge at time . Let be the positions of the np plasmids . ParA can bind to the nucleoid with flux . Once bound , ParA-ATP can diffuse along the nucleoid with diffusion constant . For simplicity , we first assume that the ParA-ATP concentration at each plasmid is zero due to a high ParA-ATP hydrolysis rate . Later on we will relax this assumption . This system can be described by the deterministic reaction-diffusion equations: We now use separation of time scales to obtain the steady-state solution for : we assume that plasmid motion is much slower than the time for individual ParA-ATP molecules to diffuse over the nucleoid and generate a concentration profile . In this way , the plasmid positions are effectively time-independent and a priori unknown . The equation for then simplifies to: ( 1 ) This equation can be solved by integrating twice using the boundary conditions . The solution is given by: ( 2 ) Next we use these equations to compute the diffusive fluxes of ParA-ATP , , at a plasmid location , where the + and – superscripts below refer to the flux from the right ( + ) and left ( - ) respectively . We find: Clearly , a symmetric ParA concentration profile , where fluxes from either side balance , is only possible for . The plasmids are then equally distributed with . We note that the predicted inter-plasmid spacing arising from this analysis is consistent with our experimental findings ( Fig . 1D , S2B ) . Importantly , the above analysis provides insight into the equal spacing mechanism . The key is that the above fluxes depend on the distances either between the plasmid and nucleoid end , or between neighboring plasmids . This feature is a consequence of ParA binding to the nucleoid anywhere , but with ParA release only occurring at a plasmid . In order for these on and off fluxes to balance at steady-state , the off-flux at a plasmid must scale with the inter-plasmid or plasmid-nucleoid-end distance . In this way , non-local information about lengths is converted into local spacing information encoded in the slope of ParA-ATP concentration . For non-equal plasmid spacing , the competing ParA concentrations on either side of a plasmid will be unequal , with one gradient steeper than the other . The steeper gradient corresponds to the side with the greater available space for ParA binding . If a plasmid can preferentially move ( on the appropriate slow time scale ) towards the side with the locally steepest ParA-ATP concentration , the plasmids are then progressively restored towards equal spacing . As this process occurs , the ParA-ATP concentrations will dynamically reorganize such that a symmetric configuration around a plasmid is reached only when the plasmids are equally spaced . In this state , where the competing ParA-ATP concentrations are symmetric , plasmid movement would no longer have a directional preference and would thus remain , on average , stationary . So far , we have assumed that the ParA-ATP concentration vanishes at a plasmid , corresponding to very fast ParA-ATP hydrolysis . However , our results also hold true when we only assume that this hydrolysis occurs with a finite rate , leading to a non-zero concentration of ParA-ATP at a plasmid . This ParA-ATP can then anchor a plasmid to the nucleoid before being hydrolysed . This more general and realistic case is presented in the S1 Text , but our overall conclusions reached above remain unchanged . From the above analysis , we see that the following conditions are required for equal plasmid spacing: ( 1 ) movement of a plasmid towards higher ParA-ATP concentrations . ( 2 ) diffusion of ( at least a fraction of ) ParA-ATP over the nucleoid to ensure formation of competitive concentration gradients . Single molecule tracking experiments in vitro support this assumption [17] , [18] . ( 3 ) ParA-ATP hydrolysis must occur ( predominantly ) by plasmid-associated ParB-parC complexes , again to ensure gradient formation . ( 4 ) ParA-ATP must adopt a 1d-like configuration , as previously claimed [9] , [13] , [14] . If ParA were not organized in this fashion , it would be possible for ParA to diffuse around the sides of a plasmid without encountering the hydrolyzing effect of the ParB-parC complex . This would equalize the ParA concentrations on both sides even in the case of asymmetrically placed plasmids , leading to failure of the equal spacing mechanism . This assumption is in line with our subsequent experiments ( see below ) . Due to this proposed 1d-like nature , we will from now on refer to the ParA distributions away from a plasmid as ParA structures . ( 5 ) There must be a separation of time scales between plasmid movement and ParA concentration reorganization , as discussed above . Importantly , this overall mechanism is not reliant on a specific type of plasmid translocation . Any process that would allow a plasmid to move into regions of higher ParA concentration will suffice . In the following sections we therefore analyze different means of plasmid movement and compare them with our experimental data to determine which is used in our par2 segregation system . In the previous section the mechanistic details of plasmid movement towards a higher ParA concentration were not specified . We now examine a specific implementation involving a diffusion-immobilization mechanism . Using a minimal modelling approach , we assume that nucleoid-associated ParA-ATP can immobilize freely diffusing plasmids through its interaction with the ParB-parC complex and that ParA-ATP does not polymerize ( Fig . 2A ) . Since the plasmid will tend to become immobilized in regions of higher ParA-ATP concentration , this process allows for effective plasmid translocation up a ParA-ATP concentration gradient . We also incorporate ParB-parC-stimulated ParA-ATP hydrolysis at a plasmid , in accordance with prior experimental data . To further investigate this mechanism , given the known physiological and biochemical constraints , we developed stochastic simulations using a Gillespie algorithm [22] . Here we use standard diffusion for the plasmid movement; below we discuss the potential impact of subdiffusive motion . In our simulation , a one dimensional lattice with sites of size dx = 5 nm represents the nucleoid . ParA-ATP and plasmids can diffuse on the lattice with diffusion coefficient DA and DP respectively . Up to 35 ParA-ATP can bind to a plasmid at the same site with reaction parameter kAB reflecting the binding interaction of ParA-ATP and the ParB-parC complex [11] . More than one ParA-ATP bound to a plasmid reduces the plasmid diffusion constant to zero . Plasmid-bound ParA-ATP can be hydrolysed with reaction parameter kB . Whenever a ParA-ATP hydrolysis event occurs , ParA unbinds from the nucleoid and becomes a cytoplasmic ParA-ADP . ParA-ADP can then be converted into a cytoplasmic ParA-ATP that is competent in DNA binding ( cytoplasmic ParA-ATP for short ) with a slow reaction parameter kW [8] . Cytoplasmic ParA-ATP can then bind anywhere along the nucleoid with parameter kon ( see Materials and Methods and Tables 1 , 2 for details ) . Prior work has demonstrated plasmid displacement along the long cell axis of up to 3–4 µm within 10 min [9] , [15] . With a diffusion/immobilization mechanism all plasmid movement in between immobilization events is generated by ( unbiased ) free diffusion , for which we have ( in 1d ) a mean square displacement ( MSD ) of . By inserting the above length and time scales into this equation , we conclude that a plasmid diffusivity of at least DP∼10−2 µm2s−1 would be required to generate sufficiently rapid diffusive movement in accordance with previous experiments . We therefore chose DP = 10−1 µm2s−1 . In order to physically justify that ParA can immobilise the plasmids , we chose the nucleoid bound ParA-ATP diffusivity to be lower than DP , with DA = 10−2 µm2s−1 ( Table 2 ) . We experimentally constrained the overall copy number of ParA for pB171 par2 by semi-quantitative Western blots , which revealed that there were approximately 8×103 ParA monomers per cell ( S1B Fig . ) . This diffusion/immobilization model could produce equal plasmid spacing on simulated growing nucleoids with varying numbers of plasmids ( Fig . 2B , C , S3A ) . This result demonstrates that using a sufficiently high ( low ) plasmid ( ParA ) diffusivity , respectively , the equal plasmid spacing seen in our experiments ( Fig . 1B , C , D , S2A , B Fig . ) and previously [9] , could in principle be achieved using a diffusion/immobilization mechanism . To test whether the requirement of a relatively high free plasmid mobility is met in vivo , we compared the movement of test-plasmids with and without par2 . We analyzed trajectories of labeled plasmid foci using the tetO-TetR-mCherry labeling system , measuring the positions over time ( Fig . 3A ) and MSDs for each time lag τ . Plasmid motion will be biased by a functional par2+ partitioning system , in contrast to the random motion of par- . Nevertheless comparing MSDs can still be informative in comparing relative overall mobilities . On time scales up to a minute we found that the par2+ MSD is higher than in par- ( Fig . 3B ) , showing that , on average , par2+ plasmids are more mobile than their par- counterparts . Note that the number of data points for the short time lags far exceeds the number of trajectories ( npar- = 747 , npar2+ = 763 ) , since every trajectory contains multiple short time lags . Consequently our estimates for the mean are relatively precise for short time lags . It is true that the error on the mean does not reflect inaccuracy due to experimental limitations in determining the actual plasmid position , for instance due to a finite pixel size . However , that error is the same for both par2+ and par- . Moreover , since the error is also time lag independent , it is taken into account in our fitting procedure as a time lag independent term ( for more details see below and Materials and Methods ) . Overall , these results are hard to reconcile with a diffusion/immobilization mechanism where the par2 system can only immobilize plasmids , and thus lower their MSD . These MSD values could in principle be limited due to cellular confinement . However , we found that MSD saturation only starts to occur at much larger length scales at times of up to 10 min ( Fig . 3C ) . In the presence of par2 , plasmids generally reside within the nucleoid region , while in its absence they tend to become somewhat more polar localized , although they can still sample the entire cell volume on long enough timescales [23] . Consistently we still find many par- plasmids located within the nucleoid region ( S3B Fig . ) . Restricting the mobility analysis to par- plasmids within the nucleoid region did not alter the resulting MSD curves significantly ( S3B Fig . ) . We conclude that the presence of par2 can increase plasmid mobility in the nucleoid region , which is inconsistent with a diffusion/immobilization mechanism . We emphasize that this conclusion can be made irrespective of the underlying ( par- ) plasmid transport processes , which we now describe in more detail . It has been reported that chromosomal loci and RNA-protein particles exhibit subdiffusive , rather than diffusive , behavior in the cytoplasm [24] , [25] . Therefore it is possible that plasmids without a segregation mechanism could also exhibit subdiffusive motion . Further analysis is required to fully distinguish subdiffusion from the additional effects of cellular confinement or glass-like properties of the bacterial cytoplasm [23] , [25] . Nevertheless such additional analysis is not required for the conclusions on par- plasmid mobility relevant to this study , as we now explain . Subdiffusion results in an expected MSD displacement of the form , with α<1 and D the apparent diffusion constant ( in units of µm2s-α ) . We find that our MSD displacements on both short and long timescales are well described by subdiffusion with α = 0 . 7–0 . 8 and an apparent diffusion constant D = 5–10×10−4 µm2s−α ( Fig . 3C and Materials and Methods for details ) . This is consistent with other recent reports on par- plasmid mobility [23] , [26] . Importantly the experimental MSD is lower on all observed timescales than a hypothetical particle that would perform free diffusion inside a cell with a diffusion constant Df = 10×10−4 µm2s−1 . This upper limit is already much lower than that needed to be consistent with the previously reported plasmid displacement data discussed above . We will further exploit this upper limit in our analysis below . To further investigate the effect of par2 on plasmid positioning , we also studied rapid plasmid segregation events . We defined these as cases where two plasmid foci whose separation is initially ≤0 . 3 µm , move within 20 s at least another 0 . 8 µm apart ( Fig . 3A , S3C ) . We also allowed for the two foci to be initially merged . Using these criteria , despite equally large data sets , we found 13 such events in par2+ and only one such case in par- . Furthermore , we only retrieved 2 further par2+ segregation events when we relaxed the criterion to separation within 60 s instead of 20 s . This analysis shows that most segregation events occur rapidly . When we investigated the 26 plasmid trajectories involved they showed larger maximal MSDs compared to sets of 26 trajectories that were repeatedly randomly sampled from the whole par2+ dataset ( p<10−6 ) . This finding indicates that the par2 system can particularly enhance the mobility of plasmids when they are in close proximity . We then simulated 300 plasmid duplication events with our diffusion/immobilization model to determine the magnitude of diffusion constant required to generate the experimentally observed segregation . Note that we used diffusion rather than subdiffusion here because we have already determined that par- plasmid movement is slower on all observed timescales than free diffusion with a diffusion constant Df = 10×10−4 µm2s−1 . Hence , if the required diffusion constant is larger than Df then we have also ruled out a subdiffusion/immobilization model . We required that 5% ( 15 out of 300 ) of segregated distances within 20 s were at least 0 . 8 µm ( a very conservative requirement , since the criterion was satisfied by 13 of our 15 experimental segregation events ) . This requirement necessitated a free plasmid diffusion constant on the order of 10−1 µm2s−1 , about two orders of magnitude higher than our experimentally observed upper bound Df on the experimental par- plasmid mobility . Hence , we conclude that the plasmids are generally too immobile for a diffusion/immobilization ( or subdiffusion/immobilization ) mechanism to explain these segregation events . Also the qualitative behaviour of segregation events in the diffusion/immobilization model appears different , since experimental segregation events ( Fig . 3A , S3C ) show more directionally biased motion , while the diffusion/immobilization model generates more sustained random , diffusive motion during segregation , prior to immobilization at equally spaced positions ( Fig . 2B ) . Nevertheless , these segregation events were sufficiently rare not to significantly alter the overall MSD behaviour of the entire dataset shown in Fig . 3B . Thus the increased average mobility in the presence of par2+ cannot only be ascribed to these segregation events . It is possible that the tetO-TetR-mCherry labeling system caused reduced plasmid mobility as compared to unlabelled plasmids . However , as we used the same labeling method for both par2+ and par- cases , our above conclusions on relative mobility are unaffected . Moreover , our tetO-TetR-mCherry labeled plasmids still exhibited rapid segregation events ( such as in Fig . 3A ) , underscoring the ability of par2 to overcome low plasmid mobility . Overall , we find that diffusion/immobilization cannot explain our data on par2+ versus par- plasmid mobility , as well as on rapid par2+ plasmid segregation . Given the shortcomings of the diffusion/immobilization model , we next tested models based on directed motion , allowing more rapid directed rather than unbiased diffusive plasmid movement . More specifically , we tested models based on the formation of competing ParA polymers , with ParB-parC-stimulated ParA-ATP hydrolysis directing plasmid movement . By modulating the length of these polymers , we thereby tested the robustness of directed motion models to generate equal plasmid positioning . We again used a Gillespie algorithm to simulate ParA dynamics on the nucleoid ( see Fig . 4A , Materials and Methods and Tables 3 , 4 for details ) . The nucleoid was represented as a rectangular lattice ( dx = 5 nm in both dimensions ) , with a much shorter width ( 30 nm ) than length ( several µm ) . Similar reactions as in the diffusion/immobilization model described the cytoplasmic dynamics of ParA-ADP and ParA-ATP . Nucleoid-associated ParA-ATP could also still diffuse across the nucleoid in a mobile state in all four directions to neighbouring sites with diffusion constant DA . However , two of these molecules at sites neighboring each other along the long nucleoid axis could interact to form a ParA polymer of two subunits , with reaction parameter kp . Further ParA-ATP polymerization could occur by attachment of mobile ParA-ATP , located at a site immediately next to the tip of an existing ParA polymer , but only along the long axis . ParA-ATP polymers were assumed to be immobile . A ParA-ATP polymeric subunit could depolymerize spontaneously with reaction parameter kdp , i . e . be converted into a mobile ParA-ATP at the same site . Given that its size is similar to the width of the lattice , we only took into account the plasmid position along the long axis and we assumed that it occupied all sites along the short axis simultaneously . The plasmid could diffuse with our experimentally estimated diffusion coefficient DP along the long axis when polymeric ParA-ATP was not present either at any of the sites that the plasmid occupied or sites neighbouring the plasmid . In the presence of polymeric ParA-ATP , the plasmid was assumed to be tethered to such a polymer ( via a ParB-parC complex ) , which prevented plasmid diffusion . At sites with a plasmid present , polymeric ParA-ATP could be converted into cytoplasmic ParA-ADP with reaction parameter kB . Reflecting directed motion , at sites neighbouring a plasmid occupied by polymeric ParA-ATP , a plasmid could with reaction parameter kdm move to the coordinate along the long axis of that ParA-ATP subunit , coinciding with conversion of that ParA-ATP into cytoplasmic ParA-ADP . For wild-type simulations , any plasmid in the system formed a hard wall to mobile ParA-ATP diffusion so that diffusing ParA-ATP molecules could not diffuse past a plasmid . We first adjusted the ParA-ATP polymerization rate to generate short filaments , of approximately 10 subunits in length ( Table 4 for parameters ) . Simulations again faithfully reproduced the equal spacing of plasmids along simulated growing nucleoids with varying numbers np of plasmids ( Fig . 4B , np = 1 , 2 in Fig . 4C , np = 3 , 4 in S4A Fig . ) in good agreement with our experiments ( Fig . 1 , S2 ) . By adjusting the ParA-ATP polymerization rate ( Table 4 ) , long continuous ParA polymer bundles could also be generated . In that case equal spacing could also be achieved ( S4B , C Fig . ) . Intuitively , in both short and long filament cases , this occurs because in an irregularly spaced plasmid configuration , the unequal ParA concentrations on either side of a plasmid result in an unequal degree of ParA polymerization . This in turn results in an unequal amount of competitive directed motion events to each side , resulting in effective directed translocation over longer length scales back towards an equally positioned state . Plasmid separation occurs when two nearby plasmids encounter two ParA-ATP structures extending in opposite directions away from the plasmids . The two ParA-ATP structures will then necessarily mediate a segregation event . The effect of directed movement in this model is clearest in the case of plasmid segregation events ( Fig . 4B , S4B ) , where we see rapid segregation consistent with the fast segregation events observed experimentally ( see Fig . 3A ) . Intriguingly , simulations of the directed motion model did not generally produce sustained spatiotemporal oscillations of ParA across the nucleoid ( short polymers: Fig . 4B , long polymers: S4B Fig . ) . A lack of sustained oscillations would therefore appear to be a common feature of models where competitive ParA structures generate equal plasmid spacing . This absence was unexpected , as prior experimental work had emphasized the oscillatory aspect of the ParA dynamics [12]–[14] . To experimentally test this key model prediction in an unbiased fashion , we experimentally measured the degree of ParA asymmetry in the par2 system in a large dataset ( n = 134 ) of snapshots of ParA-GFP across the nucleoid . We examined only cases with a single plasmid tetO-TetR-mCherry focus , where sustained oscillations should be easiest to infer . The ParA-GFP fluorescence signal from pole to plasmid position was summed and divided by the respective pole-to-plasmid distance . This generated two ParA-GFP fluorescence densities IL and IR for either side extending to the two cell poles . This allowed us to compute the normalized asymmetry measure |IL-IR|/|IL+IR| [27] for ParA ( see Materials and Methods for details ) . Asymmetric ParA-GFP distributions , arising for example from oscillations , where for example IL≈0 , IR≈1 , will give asymmetry values closer to one , whereas symmetric ParA-GFP distributions , where IL≈IR , will give values closer to zero . Note that the ParA-GFP exposure time used here was 1 . 5 s; clearly , we cannot measure asymmetries that occur on a timescale faster than this exposure time . However , the timescales of the plasmid and ParA-GFP dynamics are on the order of tens of seconds or longer and it is therefore unlikely that any significant asymmetry is being missed by our measurements . When we examined our whole distribution of cells exhibiting single plasmid tetO-TetR-mCherry foci , we found that the degree of ParA-GFP asymmetry ( Fig . 5A , B ) was low in comparison with the well-established MinD spatiotemporal oscillator [27] . Furthermore , the ParA-GFP asymmetry did not correlate with cell length ( S5A Fig . , R2 = 0 . 08 ) , unlike the case of MinD-YFP [27] . We also compared the ParA-GFP asymmetry to the Hoechst signal . This DNA stain labels the nucleoid itself , which is relatively uniform along the long cell axis [28]–[30] . Here , any asymmetry is not expected to depend on the plasmid foci positions . The Hoechst asymmetry distribution was indeed concentrated around relatively small values , but was apparently measurable within our approach ( Fig . 5B , S5B ) . Importantly , we found that the ParA-GFP asymmetry measure had a similarly low value as for the Hoechst case ( Fig . 5B , S5B , no significant difference , Kolmogorov-Smirnov test ) , and that for both the asymmetry is uncorrelated to the plasmid focus position ( S5C Fig . ) . We therefore conclude that for a single plasmid focus , ParA-GFP typically resides on both sides of a plasmid , with relatively little asymmetry or oscillation , as predicted by the directed motion model , irrespective of a weak ( Fig . 5B ) or strong ( S5B Fig . ) degree of polymerization . Previous analyses had focused on plasmids migrating in the wake of retracting ParA-GFP structures [9] . Such events can transiently give rise to relatively high ParA-GFP asymmetries ( see , for example , Fig . 3A , 5C ) . Accordingly , we conclude that ParA asymmetry or oscillations are not continuously required for par2 mediated plasmid positioning . Transient asymmetry , including oscillations , instead likely arises from the dynamics needed to bring about equal plasmid spacing following a spatial perturbation or plasmid duplication event ( Fig . 5C ) . Once the ParA distribution has returned to being relatively symmetric , this coincides with an equally spaced plasmid configuration ( Fig . 5C ) . Such dynamics can be seen in our model simulations ( Figs . 4B , S4B ) : asymmetric during plasmid segregation events , but relatively symmetric otherwise . This analysis can therefore accommodate both our findings of a relatively symmetric ParA distribution with previous reports emphasizing asymmetry and oscillations . Overall , our finding of predominantly symmetric , non-oscillatory ParA dynamics may help to reconcile similar findings for ParA in other plasmid partitioning systems , such as for plasmid P1 [15] , [16] . One required feature to achieve equal plasmid spacing is that the ParA-ATP should be organized in a 1d-like structure along the nucleoid as concluded above . However , it is unclear why ParA-ATP on either side of a plasmid would align in a coherent 1d-like structure with their ends coinciding with a plasmid . One potential explanation for this 1d-like behavior is that the ParA-ATP structures are sensitive to the overall nucleoid architecture . To test these features , we examined the localization of ParA-GFP and Hoechst signal simultaneously using optical sectioning in WT cells ( n = 678 ) without par2-carrying plasmids to prevent dynamic ParA-GFP structure disassembly . ParA-GFP intensity correlated well with the DNA stain ( Fig . 6A , B , S6A , Pearson's correlation coefficient rP = 0 . 81 ) , indicating that ParA-GFP localization was indeed dependent on the underlying nucleoid . Importantly , ParA-GFP overlaid more with Hoechst than the reverse ( Fig . 6C ) , indicating that ParA forms structures within the nucleoid region rather than uniformly covering the nucleoid . Although the resolution of our techniques does not allow identification of potential individual ParA polymers , in many cases we did observe extended 1d-like ParA-GFP structures on the nucleoid ( Fig . 6B , S6A ) . Care must be taken in interpreting fluorescent localization studies due to potential artifacts , for example GFP-induced polymerization [31] . However , wild-type plasmid loss rates and plasmid foci positioning in cells expressing ParA-GFP argue against localization or polymerization artifacts in our case [9] , [12] . We reasoned that if ParA structures are reliant on the nucleoid morphology for their organization , then mutants/treatments that perturb the overall nucleoid structure should also exhibit alterations in ParA localization and therefore plasmid focus positioning ( Fig . 7A ) . We measured plasmid focus positioning in mukE , mukF and matP mutant strains , as well as in cells treated with the DNA gyrase inhibitor nalidixic acid ( Nal ) , all of which exhibit defects in nucleoid organization [32]–[34] . Nucleoid length distributions were altered in all of these cases ( S7A Fig . ) and , consistent with our hypothesis , there was in each case a similar deterioration in the fidelity of plasmid focus positioning ( np = 1 , 2 in Fig . 7B , S7B , C , np = 3 , 4 in S7B , C , D Fig . ) towards a random distribution ( S7E Fig . ) . This deterioration may not have been large enough to detect in stability assays [35] , [36] . Similarly , in E . coli mukB mutants , perturbed plasmid positioning without compromising plasmid stability has also been observed , although for the segregation mechanism mediated by ParM [37] . The deteriorations in plasmid positioning could have resulted from other effects , such as an induction of the SOS response in Nal-treated cells . However , the similarity of the altered plasmid positioning in all four cases instead suggests a common positioning defect based on nucleoid perturbation . This deterioration could also be due to an altered plasmid structure . However , at least for the case of matP we do not favor this hypothesis , due to the absence of MatP target sites ( matS ) on our test-plasmid . To provide evidence that the above deterioration in plasmid positioning arose from an altered ParA distribution , we systematically examined localization of ParA-GFP and Hoechst stain simultaneously in Nal-treated cells ( n = 862 ) , which had the largest visible perturbations . We were able to quantify ( Fig . 7C , S6B , p<10−149 , see Materials and Methods ) perturbations in nucleoid structure that were detectable by eye ( Fig . 6A , S6A ) . Moreover , visual inspection showed that the ParA-GFP distribution followed the nucleoid structure less closely than in the WT ( Fig 6A , S6A ) . This finding was quantitatively confirmed by a correlation coefficient of rP = 0 . 68 , decreased from its WT value of 0 . 81 ( p<10−34 ) , and also by a decrease in the ParA-GFP overlap coefficient ( Fig . 7C ) . Altogether , these findings support our hypothesis that the nucleoid provides a template for 1d-like ParA-ATP structure formation , which is partially compromised when the nucleoid structure is perturbed . To reproduce this behavior in the directed motion model , we assumed that mobile DNA-bound ParA-ATP could now diffuse past a plasmid ( see Materials and Methods for details ) . This could be due to the disordered nucleoid structure resulting in a deteriorated ParA-ATP structure organization , thereby allowing ParA-ATP to spatially bypass ParB-parC complexes and compromise the ParA concentration differences between either side of a plasmid . The directed motion model with a weak ( Fig . 7D , S7F Fig . ) or strong ( S7F Fig . ) degree of polymerization could then reproduce the observed plasmid focus distributions ( Fig . 7B ) .
Stable DNA inheritance is important for the viability of essentially all organisms . In bacteria , the parABC genes have a major role in this process for plasmid DNA [1] . In this study , we have investigated how E . coli utilizes the par2 partitioning system from plasmid pB171 . We have for the first time provided a robust mechanistic explanation for how plasmids are equally spaced over the nucleoid , a process vital for the fidelity of low copy number plasmid inheritance . We propose that competing ParA structures function to direct plasmid movement over the nucleoid to equally spaced positions . This mechanism is likely relevant to other parABC systems that move and position sub-cellular objects . It has previously been proposed that plasmid positioning is controlled by concentration gradients of ParA-ATP over the nucleoid , caused by plasmid-associated ParB-parC complexes mediating ParA-ATP hydrolysis [8] , [16]–[20] . In this so-called diffusion-ratchet mechanism [8] , [17] , [18] , [20] , it has remained unclear whether such a mechanism could actually mediate equal plasmid spacing , and if so , which specific properties of the system were key . In particular , it was left unclear how ParA actually influenced plasmid movement [8] , [17] , [18] , e . g . through immobilizing plasmids or actively directing their motion through a chemophoresis force [19] , [20] . Furthermore , although the diffusion-ratchet mechanism did not strictly preclude some degree of ParA polymerization , its gradient-aspect was emphasized as opposed to polymerization [8] , [16]–[18] , [20] , leaving open the potential importance of polymerization . To provide elucidation of these key issues , we have therefore performed a mathematical analysis , which has led to predictions that we have experimentally verified . We found that ParA-ATP nucleoid-binding , followed by diffusion over the nucleoid , and subsequent ParB-parC-stimulated ParA unbinding in a 1d model , is sufficient to generate dynamic ParA-ATP concentration gradients on either side of a plasmid . We have further shown that these ParA concentrations on either side of a plasmid are only symmetric in the case of equally spaced plasmids; unequally-spaced plasmid configurations will cause the ParA gradient to be steeper on one side rather than the other . Fundamentally , this asymmetry arises from two key properties: ( i ) a greater space for binding of ParA on one side as opposed to the other in unequally-spaced configurations , and ( ii ) ParA only being returned to the cytoplasm at discrete plasmid positions occupied by ParB-parC . The combination of these two features leads to the ParA density being increased in larger versus smaller inter-plasmid regions and hence to asymmetric ParA concentrations in unequally spaced plasmid configurations . According to our analysis , all that is then required for equal plasmid spacing is that the plasmids have a means to preferentially move up the locally steepest ParA concentration gradient and thus locate the equally spaced configuration with symmetric , competitive ParA concentrations around each plasmid . The exact means of plasmid translocation is therefore not critical; all that is important is that such movement can occur . With this general framework established , we then investigated which specific means of plasmid movement up a concentration gradient were possible , and which was implemented for the par2 segregation system . We first developed a diffusion/immobilization model and found that such a model could indeed lead to plasmid movement up a ParA gradient , as the plasmid tends to become trapped in regions of higher ParA concentration . However , when we tested this model experimentally , its predictions did not verify: in particular , plasmid mobility was higher in the presence rather than the absence of par2 , and overall free plasmid mobility was too low to allow the experimentally-observed rapid plasmid segregation following duplication events . This intrinsically low mobility agrees with earlier measurements [23] , [26] , [38] and is likely a general feature for relatively large intracellular components , given the glass-like properties of the cytoplasm [23] . We then considered active means of ParA-mediated plasmid movement . In particular , we assumed that ParA-ATP could form polymeric filaments , which could subsequently depolymerize through the action of plasmid-associated ParB-parC . In this case , ParA-ATP could bind to the nucleoid , diffuse and then subsequently polymerize to form gradients of ParA polymers , with the degree of polymerization influenced by the overall ParA concentration at a particular location . We found that ParA polymer models could naturally explain enhanced plasmid mobility in the presence of par2 , as well as rapid plasmid segregation events , much more satisfactorily than the diffusion-immobilization model , regardless of whether long or short ParA polymers were formed . This finding in particular shows that our directed motion model is sufficiently general to explain equal plasmid spacing as found in various parABC systems with different extents of ParA polymerization [8] , [9] , [18] . In addition , we note that this mechanism does not critically depend on ParA-ATP binding to the nucleoid as a dimer . A scenario where ParA polymerizes to a certain extent cytoplasmically , and subsequently binds and diffuses on the nucleoid before polymerizing further into immobile filaments , could also suffice . A key aspect of our models is competition between ParA structures on either side of a plasmid to direct plasmid movement . Therefore our model predicts a comparatively symmetric ParA distribution on average , a prediction which we experimentally verified . We note here that such competition makes the system dynamics robust to alterations in ParA expression levels , since it is only the relative rather than absolute ParA levels on either side of a plasmid that are critical . This analysis potentially explains why cells with variable amounts of ParA-GFP ( S1C Fig . ) , still possess functional segregation systems with low plasmid loss rates [9] . In the above polymer models , the movement of a plasmid is assumed to be directed by retracting ParA structures . The precise nature of this short-ranged directed motion is not specified by our analysis , and could include locally biased plasmid diffusion along a retracting polymer in a “burnt-bridge” mechanism [4] or even direct pulling [39] . This arbitrariness is a special case of our more general result that the mechanism by which a plasmid is able to move up a ParA concentration gradient is not important , only that such movement is possible . Other mechanisms of directed motion are also plausible . One possibility is that ParA-ATP does not polymerize at all , but nevertheless forms dense structures on the nucleoid with many ParA-ATP contacting a plasmid at any given time . In this variant , biased diffusion through an analog of a “burnt-bridge” mechanism is still possible . Another possibility is a DNA-relay , where directed motion is generated by the elastic dynamics of the nucleoid DNA to which ParA-ATP dimers are bound [21] . Moreover , plasmid diffusion seems not always required for directed plasmid movement . Brownian dynamics simulations based on ParB-parC-mediated disassembling ParA polymer bundles can both tether and pull plasmids simultaneously without the need for plasmid diffusion [39] . We propose that distinct underlying translocation mechanisms , as exemplified above , could be responsible for directed motion in different parABC systems and yet still attain similar equal plasmid spacing . For our models to generate equal plasmid spacing , ParA should be organized into a 1d-like configuration along the nucleoid . If ParA were not organized in this way , it would be possible for ParA to diffuse around the sides of a plasmid without encountering the hydrolyzing effect of the ParB-parC complex . This would equalize the ParA concentrations on both sides even in the case of asymmetrically placed plasmids , leading to failure of the equal spacing mechanism . Potentially such ParA structures could consist of long ParA polymer bundles , or an extended region containing short ParA polymers or dimers . Importantly , in this work , we have provided experimental evidence for such ParA structure formation within the nucleoid region . Interestingly , it has been reported that the E . coli chromosome adopts a helical shape [28] , [30] . Potentially the ParA structures could be preferentially located within a "valley" in this configuration , thereby naturally generating a 1d-like appearance , even for dimers or short polymers . Consistent with these concepts , we found experimentally that plasmid positioning is compromised in nucleoid perturbed strains . ParA structures could also provide a high enough ParA concentration to ensure plasmid tethering and directed plasmid motion , whilst preventing plasmids from diffusing away from the nucleoid , a process which would compromise regular positioning . Further investigation of the exact involvement of the nucleoid in intracellular cargo positioning is therefore an important future goal .
On the one dimensional lattice with sites of size dx = 5 nm , sites are numbered 0… ( L-1 ) . Reactants are : ParA-ATP at site i with number ( ≥0 ) , : plasmids with j ParA-ATP bound to it at site i with number ( ≥0 ) , : cytoplasmic ParA-ADP with number AADP ( ≥0 ) , : cytoplasmic ParA-ATP with number ACYTO ( ≥0 ) The reactions and corresponding propensities are described in Table 1 . Parameter values used are listed in Table 2 . We varied the exact number of ParA-ATP molecules forming a complex that are required to completely immobilize the plasmid and this variation does not alter the qualitative behavior of the system . Introduction of a low spontaneous ParA-ATP hydrolysis parameter koff also does not alter the behaviour of the system . We do not keep track of the spatial positions of ParA-ADP and ParA-ATP in the cytoplasm . Instead we merely keep track of their number . The ParA concentration is assumed to be constant throughout the cell cycle , consistent with the total ParA-GFP fluorescence as a function of cell volume when expressed from an inducible promoter ( S1C Fig . ) . In accordance with estimates for average ParA copy numbers obtained by semi-quantitative Western blots ( S1B Fig . ) , the ParA concentration is assumed to be 2400 ParA ( dimers ) per µm of nucleoid . Simulations start at time t = 0 and run until time t , updated according to the Gillespie algorithm , exceeds a predefined time T . To simulate nucleoid growth during the cell cycle the nucleoid lattice is extended by two sites of size dx ( not containing any ParA or plasmids ) , at one randomly chosen position along the nucleoid length . Such a growth event occurs at regular time intervals . Reaction propensities are then updated in accordance with the new state . In Fig . 2B the nucleoid grows from 1 . 5 µm to 3 µm in T = 40 min , reflecting one cell cycle . Initially a quarter of the total ParA in the system is in the cytoplasmic ParA-ADP form , 11 ParA-ATP are bound to each plasmid to ensure initial anchoring , and the rest are bound randomly to the nucleoid . In Fig . 2B the plasmid is initially located at site 0 . In the simulations used to generate the histograms shown in Fig . 2C , S3A , all plasmids are initially distributed randomly across the nucleoid . At regular time intervals of 5 s the simulation state is output along with the plasmid positions to generate a time-averaged probability distribution for the plasmid positions along the long axis of the cell . In cases where the total number of plasmids ( np ) is more than one , the plasmids are ordered and labeled 1…np according to their positions ( by increasing site number ) along the nucleoid . Their position is then used to generate distributions for every plasmid label 1…np for that particular overall number of plasmids np . In the event of plasmid duplication at a particular site where an existing plasmid is located , a new plasmid without any bound ParA is added to the same site and the reaction propensities are updated accordingly . In case of two or more existing plasmids , one is chosen randomly for duplication . Plasmid duplication events in Fig . 2B occur at regular time intervals T/3 , although the model behaves equally well with duplication at any time as it dynamically segregates the plasmids to equally spaced positions . The nucleoid was represented as a rectangular lattice divided into square sites of sides dx = 5 nm . The long axis could grow from 1 . 5 µm to 3 µm in length , while the short axis of the nucleoid lattice remained fixed . For wild-type directed motion model simulations the short axis length was 30 nm ( directed motion model with short polymers ) and 25 nm ( directed motion model with long polymers ) . Thus every site had a coordinate along the long axis ( labelled as 0… L-1 ) as well as a coordinate along the short axis ( labelled 0… S-1 ) . Reactants are: : mobile ParA-ATP at site ( i , j ) with number ( ≥0 ) , : polymeric ParA-ATP at site ( i , j ) with number ( 0 or 1 ) ; : plasmids at site i with number ( ≥0 ) ; : cytoplasmic ParA-ADP with number ( ≥0 ) and : cytoplasmic ParA-ATP with number ( ≥0 ) . The reactions and corresponding propensities are listed in Table 3 . Parameter values used are listed in Table 4 . In the perturbed-nucleoid simulations , mobile ParA-ATP can diffuse past a plasmid with 10% ( short ) or 100% ( long ) of the normal diffusion rate and the short axis length of the nucleoid is altered to 10 nm in the long polymer model . Lastly , to allow for mobile ParA-ATP to move past the plasmid without being hydrolyzed , kmB is reduced 10-fold compared to its standard value . As for the diffusion/immobilization model , the total ParA concentration was constrained to be 2400 ParA ( dimers ) per µm of nucleoid ( long axis ) and the total length of simulated time was T = 40 min . Initially a quarter of the total number of ParA in the system was in the cytoplasmic ParA-ADP form , with the rest distributed randomly on the nucleoid in the mobile ParA-ATP form . Initial plasmid positioning , state output , plasmid position distribution generation and plasmid duplication rules were also as described previously . Nucleoid growth was implemented as described previously , with one generalization: a position along the long axis of the nucleoid was first chosen randomly . Then two nucleoid slices of 1 site ( along the long axis ) by S sites ( along the short axis ) were inserted . The ParA-GFP fusion and tetO-TetR-mCherry plasmid labeling system were described previously [9] , [12] . To obtain the functional ParB-GFP fusion , the parB gene in the par2 locus was replaced by parB::sfGFP and inserted in a mini-R1 test-plasmid . See S2 Text for more details on the strains and plasmids construction , semi-quantitative ParA western blotting and supplemental figure data analysis . E . coli strains carrying plasmids of interest ( see Table S1 , S2 in S2 Text for details on strains and plasmids ) were grown to stationary phase while being shaken at 37°C in LB medium supplemented with appropriate antibiotics ( 30 or 50 µg/ml ampicillin , 25 or 50 µg/ml kanamycin , 15 µg/ml chloramphenicol ) , with the exception of the muk strains , which were grown at 24°C . Cultures were diluted to an OD450 of 0 . 025 in antibiotic-free M9 minimal medium containing supplements ( 0 . 2% casamino acids , 0 . 2% glycerol , 1 µg/ml thiamine , 1 mM MgSO4 , 0 . 1 mM CaCl2 ) . Inoculated cultures were incubated until an OD450 of ≈0 . 2 was reached , typically taking 3 h . When nalidixic acid was used to condense the nucleoids , the antibiotic was added to a growing culture at a final concentration of 50 µg/ml two hours before imaging . Where appropriate , culture samples were mixed with Hoechst 33342 ( Invitrogen ) at a final concentration of 50 µg/ml for DNA staining immediately before microscopy . For imaging , cells were immobilized on 1 . 5% agarose-M9 pads mounted on microscopy slides using Gene Frames ( Thermo Scientific ) . All microscopy experiments , unless specified otherwise ( see below ) , were carried out using an Olympus IX71 inverted microscope with a CoolSNAP HQ EMCCD digital camera ( Photometrics , pixel size = 0 . 066 µm ) . A temperature-controlled incubation chamber ( Applied Scientific ) fitted to a Weather Station ( Precision Control ) kept samples at a constant 30°C . Images were acquired using SoftWoRx version 5 . 5 . 0 with a Zeiss Plan-Neofluar 100X/1 . 30 NA oil objective and Olympus Mercury 100 W burner ( U-LH100HG ) fluorescent light source . Filter set specifics are given in Table S3 in S2 Text . Expression of ParA-GFP from plasmid pGE230 ( mini-R1 , par- , Plac::parA::eGFP ) in E . coli strain KG22 or FS1 ( KG22ΔmatP ) was induced by adding 10 µM IPTG to the culture medium two hours before microscopy . A 31 image Z-stack with 0 . 1 µm section widths was taken for all projections ( exposure times Phase Contrast ( GFP channel ) : 0 . 05 s , ParA-GFP: 1 . 5 s , Phase Contrast ( Hoechst channel ) : 0 . 1 s , Hoechst: 2 s ) . Image stacks were subsequently deconvolved using SoftWoRx v . 5 . 5 . 0 with the following parameters: 10 iterations , medium noise reduction , conservative method . Expression of ParA-GFP from plasmid pSR233 ( mini-R1 , par2+ , Plac::parA::eGFP , tetO120 ) in E . coli KG22 cells harboring pSR124 ( PBAD::tetR::mCherry ) was induced by adding 10 µM IPTG to the culture medium one hour before microscopy . Samples were treated with Hoechst stain and imaged immediately thereafter . Expression of TetR-mCherry was not induced , as baseline activity of PBAD produced sufficient amounts of TetR-mCherry to detect foci in a single image at mid Z-height . Similarly , a single Hoechst stain image was acquired . For ParA-GFP , a 21 image Z-stack with 0 . 2 µm section widths was taken ( exposure times Phase Contrast: 0 . 1 s , TetR-mCherry 1 . 5 s , ParA-GFP: 1 . 5 s , Hoechst 0 . 15 s ) . Images were acquired using a Zeiss Axiovert 200 M inverted epifluorescence microscope with a Zeiss Plan-Neofluar 100X/1 . 3 NA oil objective in a temperature-controlled room at 22°C . The microscope was controlled using MetaMorph software version 7 . 7 . 5 . 0 ( Molecular Devices , Inc . ) . Cells were illuminated using a Lambda LS xenon-arc lamp and images acquired using a CoolSnap HQ2 EMCCD digital camera ( Photometrics , pixel size = 0 . 0625 µm ) . Filter set specifics are given in Table S3 in S2 Text . Plasmid foci of the par- mini-R1 plasmids pMH82tetO120 ( par- , tetO120+ ) or pSR236 ( parC1+ , ΔparA , parB+ , parC2+ , Plac::parA::eGFP , tetO120+ ) in E . coli strain SR1 ( KG22ΔpcnB ) were visualised by labelling tetO arrays on the plasmid in trans with TetR-mCherry provided from the pSR124 vector ( see [40] for the original method ) . TetR-mCherry expression was induced by adding L-arabinose to a final concentration of 0 . 02% to growing cultures for 15 minutes , followed by catabolite repression with 1% glucose for 10 minutes . In strains harbouring pSR236 , expression of ParA-GFP was induced by the addition of 10 µM IPTG inducer 2 h before microscopy . Time-lapse image series were acquired for different total durations/time intervals: 1 min/4 s or 15 min/30 s for pMH82tetO120 ( exposure times phase contrast: 0 . 1 s; TetR-mCherry: 1 . 5 s ) and 1 min/5 s or 15 min/20 s for pSR236 respectively ( exposure times phase contrast: 0 . 1 s; TetR-mCherry: 1 . 5 s; ParA-GFP: 1 s ) . The maximum rate of image acquisition possible with our imaging system was every 4 s and 5 s ( without and with ParA-GFP channel ) for pMH82tetO120 and pSR236 respectively . Sample focus was maintained in the mid-cell plane throughout the experiment using the UltimateFocus system ( Applied Precision ) sampling and refreshing before the acquisition of each individual frame . E . coli strains KG22 , FS1 ( KG22ΔmatP ) , FS2 ( KG22: mukE::kan ) or FS3 ( KG22: mukF::kan ) harbouring pFS21 ( mini-R1 , parC1+ , parA+ , parB::sfGFP , parC2+ ) were grown to an OD450 of 0 . 3 . Samples were treated with Hoechst stain and imaged immediately in the mid-cell plane ( exposure times ParB-GFP: 1 s , Hoechst: 0 . 5 s ) . Images of muk strains were acquired using a Zeiss Axiovert 200 M inverted epifluorescence microscope with a Zeiss Plan-Neofluar 100X/1 . 3 NA oil objective in a temperature-controlled room at 22°C . The microscope was controlled using the MetaMorph software version 7 . 7 . 5 . 0 ( Molecular Devices , Inc . ) . Cells were illuminated using a Lambda LS xenon-arc lamp and images acquired using a CoolSnap HQ2 EMCCD digital camera ( Photometrics , pixel size = 0 . 0625 µm ) . Filter set specifics are given in Table S3 . Other strains were imaged using both the Olympus IX71 and Zeiss Axiovert 200 M systems described above . Using the MATLAB-based software suite MicrobeTracker ( MT ) [27] , we determined E . coli cell outlines from phase contrast ( PC ) images , as well as the distribution of tetO-TetR-mCherry-labeled plasmids along the long axis of cells . The cell outlines were used together with the MATLAB tools spotFinderZ and spotFinderM [27] to determine tetO-TetR-mCherry foci positions in par- time-lapses of 1 min ( short ) or 15 min ( long ) in duration with images taken at intervals of 4 s or 30 s respectively . The linear tetO-TetR-mCherry distribution was used to control the peak detection method for false positives/negatives . For the short time-lapses we analysed cells with one or more foci , although all our results were unchanged if analysis was restricted to one focus cells to prevent potential foci labelling errors . For the long time-lapses , we only analysed cells exhibiting one focus . This was due to difficulties in distinguishing between multiple foci due to merging/splitting events , out of focus plane movement and photobleaching when acquiring images using a time interval of 30 s . These effects could have resulted in biases in the analysis due to labelling errors . We were unable to lower the time interval and simultaneously image for long time periods due to TetR-mCherry photobleaching . At every time point the two-dimensional squared foci displacements r2 ( τ ) after time lag τ were determined . All measured displacements for the same time lag were then averaged together to obtain Mean Square Displacements ( MSD ) with time lags from 4 s to 15 min ( Fig . 3B , C , S3B ) . The measured plasmid displacement rp ( τ ) can report the true plasmid displacement rp ( τ ) at a resolution no greater than our measurement error , which can be up to 0 . 1 µm due to microscope drift . Our measurements are also limited by a finite pixel size of 0 . 066 µm . We therefore have: , where ε is the error due to both of the above effects . Squaring and averaging over many plasmid trajectories results in an MSD: . The last term vanishes due to averaging , but the second term remains and generates a small time independent value for τ>0 . Even at short timescales of up to a minute , the MSD has a nonlinear shape , as has been reported before [26] . This is fully consistent with subdiffusive motion on these timescales . We thus expect the experimentally observed planar MSD for free particle subdiffusion in three dimensions to have the form: . We carefully measured the par- MSD up to 1 min with short time intervals between measurements ( Fig . 3B ) . We performed a nonlinear least squares fit ( weighted by the standard error of the mean ( SEM ) : 1/SEM ( τ ) ) for resulting in the values α = 0 . 78±0 . 04 , D = 6 . 8±1 . 2×10−4 µm2s−α , β = 6±1×10−3 µm2 ( R2 = 0 . 99 , p-values: 4×10−10 , 1×10−4 and 8×10−4 respectively ) . On longer timescales up to 15 min ( Fig . 3C ) , plasmid mobility also showed subdiffusive behaviour with a similar analysis giving α = 0 . 78±0 . 05 , D = 6 . 2±2 . 1×10−4 µm2s−α , β = 4±1×10−2 µm2 ( R2 = 0 . 99 , p-values: 8×10−15 , 8×10−3 and 2×10−3 respectively ) . Analysing the two datasets combined ( Fig . 3C ) also generated consistent results , although the constant β was not significantly different from zero in this case: α = 0 . 73±0 . 02 , D = 9 . 7±1 . 3×10−4 µm2s−α , β = 1 . 6±2 . 4×10−3 µm2 , ( R2 = 0 . 99 , p-values: 8×10−15 , 8×10−3 and 0 . 50 respectively , fit shown in Fig . 3C ) . Fitting instead to this combined data set did not alter our estimates for α and D significantly . On all observable timescales ( i . e . 4 s and longer ) the experimentally found par- MSD is bounded from above by the function , with Df = 10×10−4 µm2s−1 . Moreover , free diffusion with diffusion constant Df inside a box of cellular dimensions still exceeds the experimental subdiffusive mobility . Cell outlines and linear projections of ParB-GFP and Hoechst signal distributions along their long axis were determined as described above using MicrobeTracker ( MT ) [27] . ParB-GFP foci detection of snapshots was also performed using the methods described above . The positions of the half-maxima of the linear Hoechst signal distribution in every cell were then determined . We defined the nucleoid length as the length between the two half-maxima of the Hoechst stain . This analysis allowed us to determine the positions of plasmid foci with respect to the nucleoid . Here , we summed 6 planes that are in focus from a Z-stack of ParA-GFP fluorescence signal images ( dz = 0 . 2 µm ) , although the results are not different when using the ParA-GFP signal obtained from single confocal planes focused at mid-cell . Cell outlines , linear projections of ParA-GFP , tetO-TetR-mCherry and Hoechst stain fluorescence signal distributions , and tetO-TetR-mCherry foci positions were determined as described above . We confirmed that positioning of the tetO-TetR-mCherry foci from this dataset was similar to that measured previously [9] . In cells containing one plasmid focus ( n = 134 ) , the ParA-GFP fluorescence signal from pole to plasmid position was summed and divided by the respective pole-to-plasmid distance . This generates two ParA-GFP fluorescence densities IL and IR for either side extending to the two cell poles . This allows us to compute the normalized ParA asymmetry measure |IL-IR|/|IL+IR| . Irrespective of the plasmid position , a completely uniform fluorescence distribution would give an asymmetry value of zero . On the other hand , if all the ParA-GFP was located on one side of the plasmid the asymmetry measure would be one . Using a single confocal plane focused at mid-cell , we also computed the Hoechst asymmetry measure with respect to the plasmid position in the same manner . As shown in [27] by using the same MT software package for analysis , the MinD-YFP asymmetry measure with respect to mid-cell follows an approximate sinusoidal oscillation over time , with a cell-length-dependent oscillation amplitude . In large cells the MinD-YFP oscillations are clearest with an amplitude of around 0 . 6 . To generate an asymmetry measure appropriate for the MinD-YFP oscillations , we sampled 103 time points t uniformly in [0 , 2π] ( which constitutes one period ) . We then computed for every time point . The resulting asymmetry distribution ( Fig . 5B ) therefore reflects the experimental MinD-YFP asymmetry with respect to mid-cell in large cells [27] . In this way , we can directly compare the asymmetry present in the ParA-GFP and Hoechst signal distributions with that induced by the spatiotemporal oscillations of MinD-YFP . We also generated asymmetry measures using our directed motion model . In simulation outcomes shown in Fig . 4B ( directed motion model with short polymers ) and S4B ( directed motion model with long polymers ) , the plasmid position , cytoplasmic ParA-ADP , cytoplasmic ParA-ATP , nucleoid-bound mobile ParA-ATP and polymeric ParA-ATP levels on either side of the plasmid were output at regular time intervals of dt = 5 s during a time period prior to plasmid duplication ( first 2 min and 1 . 5 min of simulated time for directed motion model with short and long polymers respectively ) . Cytoplasmic ParA was assumed to be uniformly distributed throughout the cell ( independently of the plasmid position ) , thus effectively only contributing to the denominator |IL + IR| . With this information we computed the ParA asymmetry using the same method as described for the experimental data . Results are shown in Fig . 5B ( short polymers ) and S5B Fig . ( long polymers ) . It should be noted that according to both models , the ParA asymmetry remains very low once a plasmid is stably positioned at mid-cell , pushing the asymmetry distribution further towards zero over time . This is consistent with time lapses where stable equally spaced plasmid foci positioning correlates with ParA-GFP on either side of a plasmid focus ( Fig . 5C and [9] ) . To compare the extent of overlay and 3D structure of Hoechst ( nucleoid DNA ) stain and ParA-GFP , we first had to align the Z-stack pairs in an unbiased manner . To achieve this , one phase contrast ( PC ) image ( at mid z height ) of the Hoechst signal sections was aligned with one GFP section PC image ( at the same z position ) using the TurboReg ImageJ plugin ( option: translation ) [41] , after cropping both PC images to match the output size of the deconvolved Z-stacks . Using the same translation as for the Hoechst PC image , Hoechst Z-stacks were then translated in ImageJ to align them with the ParA-GFP Z-stacks . We determined cell outlines in MT as described above using the PC image acquired with the GFP channel and excluded cells that did not show visible ParA-GFP and Hoechst stain simultaneously . We then computed the linear distributions ( for every z height ) along the long cell axis for the deconvolved Hoechst and ParA-GFP Z-stacks . We next determined for the ParA-GFP and Hoechst signals separately in every cell the maximal intensity value in the whole cell ( Imaxcell ) and the maximal values at every z height ( Imax ( z ) ) . To find the 9 z planes from the Z-stacks ( dz = 0 . 1 µm ) that are in-focus for each cell in an automated fashion , we summed Imax ( z ) over 9 consecutive z positions including a given starting plane and determined the starting plane that gave the largest associated summed value . This starting plane and its 8 consecutive planes formed the in focus plane set . We verified that this method generated the right focus planes by inspecting the chosen planes visually for several cells . This method circumvents the problem of different focus planes for cells on the same image stack as well as alignment inaccuracies in the z direction between ParA-GFP and Hoechst signals which are difficult to control for manually . Visual comparison of the nucleoid shape between WT and nalidixic acid ( Nal ) treated cells ( Fig . 6A , B , S6A ) revealed clear differences . In Nal-treated cells , the nucleoid signals , where present inside a cell , were more uniform along the long cell axis than in the WT ( S6A , B Fig . ) . Shape differences were also visible in the raw Z-stacks suggesting they were not artefacts of the deconvolution method . To quantify these shape differences in an unbiased and systematic manner , we performed the following analysis ( S6B Fig . ) . We reasoned that a more uniform pattern would result in a profile along the long axis that resembled a first harmonic ( first non-constant term of a Fourier expansion ) between the nucleoid edges . Such a harmonic would not fit so well to a more spatially oscillating pattern that would arise , for example , from helical structures . Using the Hoechst stain Imaxcell and the Imax ( z ) arising from the 9 relevant focus planes we determined the half-maximum intensity locations along the long cell axis closest to the cell poles xL and xR at every z height . At every focus plane z height we could now define the ‘first harmonic’ function defined for xL≤x≤xR: . For every ( x , z ) we calculated the squared error SE ( x , z ) between the actual intensity value I ( x , z ) and H ( x , z ) : . Lastly we summed over the SEs at every ( x , z ) and divided by the number of position points ( x , z ) to obtain a single measure of deviation SEcell in a cell that is independent of the number of data points ( and thus nucleoid size ) and expression level variation between cells ( because of normalization to Imaxcell ) . We then performed a Wilcoxon rank sum test on the set of SEcell comparing a population of WT cells with nucleoid-perturbed cells ( nWT = 678 and nNal = 862 ) . Nucleoid shapes in Nal-treated cells were indeed altered ( p<10-149 ) . Note that this method did not detect a notable shape change in matP cells ( nmatP = 579 ) , potentially due to our techniques not being sufficiently sensitive . To quantitate the colocalization of ParA-GFP and Hoechst signal in each cell , we also calculated , for every cell , the Pearson's correlation coefficient rP using all the intensity values IParA-GFP ( x , z ) and IHoechst ( x , z ) [42] . To determine the fraction of ParA-GFP intensity signal that overlaps with Hoechst signal and vice versa we computed Manders overlap coefficients [42] . This method requires a choice of threshold TManders to distinguish between positions ( x , z ) that are considered to contain or lack sufficient intensity signal . We therefore performed our analyses for the complete range of threshold values to show that our qualitative conclusions are insensitive to the choice of a particular TManders ( Fig . 6C , 7C ) . Manders overlap coefficients of ParA-GFP and Hoechst were calculated as follows:with Likewise the Manders overlap coefficient of Hoechst onto ParA-GFP is defined as:with Note that taking TManders = 0 , will generate an overlap coefficient of one by construction . The normalization to Imaxcell in determining the colocalizing positions allows the overlap coefficients to be comparable between cells . In a small fraction of cells the alignment procedure described above did not result in proper alignment . This is clearly reflected in the rP values being considerably lower for these cases than for the cell population mean rP value . However , without excluding these few , possibly false negative , cases the population mean rP value is still high ( 0 . 81 and 0 . 68 for WT and Nal-treated cells respectively ) , indicating that ParA-GFP and Hoechst signals generally correlate strongly at a population level . Poor alignment affects Manders overlap coefficients for the ParA-GFP and Hoechst signals on average equally and is not biased towards a particular strain/treatment . Therefore the observed misalignment of a small fraction of cells does not affect the qualitative conclusions that we state in this study . Note that in matP cells , we did not observe any significant alteration in intensity correlation ( rP = 0 . 80 for matP ) , nor ParA-GFP overlap coefficient , as compared to the WT . This result was expected given that we could not detect any significant nucleoid structure alteration , as described above . | How DNA is stably inherited through cell division is a fundamental question in cell biology . The most common system that mediates plasmid DNA inheritance in bacteria is through a parABC locus , encoding proteins ParA and ParB , and DNA sequence parC . These components can position plasmids at equally spaced positions throughout a cell to ensure plasmids are present in both daughter cells when the cell divides into two . Here we study the mechanism by which ParA structures achieve this precise positioning . We show that ParA can direct relatively immobile plasmids over the bacterial chromosome using self-organizing , competitive ParA structures , whose disassembly is induced by plasmid parC-bound ParB . More generally these findings will help us to understand transport and regular positioning of intracellular cargo . | [
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| 2014 | Competing ParA Structures Space Bacterial Plasmids Equally over the Nucleoid |
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