Magnaporthe) infected conditions to demonstrate how it helps to understand the CRE-mediated combinatorial gene regulation. Our analysis includes a wide spectrum of biologically important results. The CRE pairs having a strong tendency to co-occur often exhibit very similar joint distribution patterns at the promoters of rice. We couple the network approach with experimental results of plant gene regulation and defense mechanisms and find evidences of auto and cross regulation among TF families, cross-talk among multiple hormone signaling pathways, similarities and dissimilarities in regulatory combinatorics between different tissues, etc. Our analyses have pointed a highly distributed nature of the combinatorial gene regulation facilitating an efficient alteration in response to fungal attack. All together, our proposed methodology could be an important approach in understanding the combinatorial gene regulation. It can be further applied to unravel the tissue and/or condition specific combinatorial gene regulation in other eukaryotic systems with the availability of annotated genomic sequences and suitable experimental data.Combinations of cis-regulatory elements (CREs) present at the promoters facilitate the binding of several transcription factors (TFs), thereby altering the consequent gene expressions. Due to the eminent complexity of the regulatory mechanism, the combinatorics of CRE-mediated transcriptional regulation has been elusive. In this work, we have developed a new methodology that quantifies the co-occurrence tendencies of CREs present in a set of promoter sequences; these co-occurrence scores are filtered in three consecutive steps to test their statistical significance; and the significantly co-occurring CRE pairs are presented as networks. These networks of co-occurring CREs are further transformed to derive higher order of regulatory combinatorics. We have further applied this methodology on the differentially up-regulated gene-sets of rice tissues under fungal ( Gene regulation is an essential cellular process that controls the expression level of a gene and thereby controls the phenotypic attributes of an organism. Transcriptional control plays a key role in gene regulation –3. The mApriori algorithm to identify the co-occurrence of cis-regulatory elements. In recent years, Vandenbon et al.  was identified in the co-occurrence network of “Defense Responses” genes  differenIn the co-occurrence networks of “G-protein coupled receptor protein signaling pathway” genes GCCCORE co-occurred with BIHD1OS (in a clique of leaf) and GT1CONSENSUS (in a clique of root). The GCCCORE (GCC box), reported in the promoters of several PR genes, shows JA dependent defense responses interacting with Ethylene response factors(ERF) . The BIHThese CRE combinations indicated a possible scenario in which SA and JA mediated defense signaling may be operated synergistically or antagonistically depending on the requirements of plant immuno-system. The differentially up-regulated genes of these hormone  biosynthetic processes in infected tissues also suggested their involvement in plant defense response under fungal attack. Finally, this study illustrated the SA and JA mediated transcriptional regulation among defense response genes by multiple TF families and thus, depicting the cross-talk of different hormonal signaling pathways in defense response mechanism.Previously, Marcel et al.  establisWe found a number of cliques those exhibit partial differences in between the two data sets. For example, a clique  occurred in the WRKY promoters in leaf and another clique  was identified in the WRKY promoters in root . While tThe differences were found not only in the cliques but also in the frequecies of the CRE-involvement in the cliques. In the CRE co-occurrence network of “Response to stress”, several MYC recognition sites  were found in different cliques of leaf whereas they were absent in the cliques of root. Significant differences in MYB binding sites and their frequencies of involvement in between the cliques of leaf and root were also observed. We found 9 MYB binding sites  in the cliques of leaf, while only 2 of them  were present in the cliques of root with a significantly lower frequency. In addition, 4 WRKY binding sites  were involved in different cliques of leaf but only one (WBOXATNPR1) appeared in a single clique of root. This observation showed a clear difference of the co-occurrence tendencies of some TF binding site families  between these two data sets.Our analysis revealed that in spite of their presence (as nodes) in the co-occurrence networks of both leaf and root, several CREs formed cliques in any one of the tissues. For example, in both (leaf and root) co-occurrence networks of “Defense response” genes, 5 CREs  were present as common nodes. But the existing edges (co-occurrence relationship) among these CREs were not common in both the networks. In leaf network, 3 CREs  formed a clique whereas, in root network 4 CREs  were combined in a clique . The cliThe CRE co-occurrence networks of up-regulated gene-sets of leaf and root shared some common as well as some unique CREs. These results clearly showed that there exists some similarities as well as some dissimilarities in regulatory combinatorics in between the leaf and root data sets. These dissimilarities might be indicative of the following possibilities: (i) the different biological modes of infection in leaf and root, which induce different defense response patterns, or (ii) the difference of time points (4 DPI in leaf and 2 DPI in root) between the two data sets, or (iii) both.k ≥ 3) and the associations among them. We computed two topological parameters of the CRE co-occurrence networks and the clique-clique networks to obtain their inherent properties.Co-occurrence of CREs at the promoters of a gene implies that TFs are likely acting in a co-associative fashion, involving different TF families. Therefore, a CRE co-occurrence network represents transcriptional co-regulation of a set of genes. On the other hand, a clique (co-occurrence combination) of CREs represents one of the possible higher order combinations of TFs those can regulate the expression of that gene. Thus, a clique-clique network illustrates the different possible higher order regulatory combinatorics (r) and the network clustering coefficients (r = −0.131), while its clique-clique network was assortative (r = 0.536). In addition, its network clustering coefficient also elevated from 0.118 to 0.725  . CRE co-to 0.725 . Balaji to 0.725 , supportIn cellular system, an efficient reprogramming of transcriptional regulation is necessary for quick response against external perturbation like pathogen attack. A clique-clique network represents the higher order combinatorics of transcription regulation of a set of genes, the ability of the transcriptional reprogramming should be reflected in its topology. Here, we observed that they simultaneously exhibit strong assortative mixing of nodes as well as higher clustering coefficients. These features illustrate that the CRE combinations, central to the regulation, tend to associate with several other CRE combinations those are also central to the regulation. This might be an indication of the ability of efficient alteration in the combinatorial logic of CREs so that a quick change of transcriptional regulation may occur at the time of blast infection.Here, we proposed a methodology that first quantified the co-occurrence tendencies of CREs in a gene-set of interest, filtered these scores to obtain significantly co-occurring CRE pairs, transformed these pairs into networks and analyzed these networks to fetch higher order CRE combinatorics. This allowed us to perform system level analysis of regulatory phenomena. To demonstrate the potential applicability of our methodology, we applied it to the blast infected condition of rice tissues.We observed that strong co-occurrence of some CRE pairs are associated with a specific positional distribution pattern of their joint occurrences at the promoter regions. In a few cases, such highly similar positional distribution and simultaneous strong co-occurrence tendency of CREs were associated with the respective TF cooperativity.Transcriptional control of gene expression involves auto/cross regulation among various TF families. The co-occurring CREs found at the promoters of TF genes indicated auto-regulation and cross regulation. In this study, preparing CRE co-occurrence networks, we showed that the essence of cross-talk among multiple hormone signaling pathways can be captured in the form of cliques of CREs. For example, during blast infection, we found that CREs associated with SA and JA mediated signaling pathways made up several cliques in an intermingled fashion. CRE co-occurrence networks of leaf and root data sets revealed a number of differences as well as similarities of CRE combinatorics in between them.Social networks exhibit assortative mixing topology which is generally lacking in the non-social networks  , 77, witin vivo transcription regulation remains elusive. In this study, we conveyed a potential methodology to compute co-occurrence tendencies among cis-regulatory elements and its application to reveal the regulatory cross-talk among multiple defense signaling pathways. One can apply this computational approach to explore the combinatorial gene regulation in other eukaryotes, under any cellular condition.Transcription regulation and its involvement in altering cellular physiology under various stressed conditions has been proven difficult to resolve due to the inherent complexity of the process. As a result, even after decades of research, a systematic description of S1 TextCOR value under different possible cases are illustrated. In addition, the significance of COR value cutoff 1.5 is shown here.The calculation of (PDF)Click here for additional data file.S1 Fig(A) Differentially up-regulated TFs in infected leaf and root of rice. The X axis represents individual TF-family and the Y axis represents the numbers of differentially up-regulated TF genes from each family. (B), (C) The pie charts representing the GO biological processes which mapped to ≥ 10 differentially up-regulated loci of infected rice leaf and root, respectively. In leaf 882 (out of 1201) loci and in root 502 (out of 677) loci are shown here. (D) The intersect region of each Venn diagram indicates the number of common up-regulated loci of respective gene-sets in between leaf (4 DPI) and root (2 DPI).(PNG)Click here for additional data file.S1 Table(XLS)Click here for additional data file.


Bacillus cereus strains possess the genetic fittings to produce two different types of toxins, the heat-stable cereulide or different heat-labile proteins with enterotoxigenic potential. Unlike the diarrheal toxins, cereulide is (pre-)formed in food and can cause foodborne intoxications shortly after ingestion of contaminated food. Based on the widely self-limiting character of cereulide intoxications and rarely performed differential diagnostic in routine laboratories, the real incidence is largely unknown. Therefore, during a 7-year period about 4.300 food samples linked to foodborne illness with a preliminary report of vomiting as well as food analysed in the context of monitoring programs were investigated to determine the prevalence of emetic B. cereus in food environments. In addition, a lux-based real-time monitoring system was employed to assess the significance of the detection of emetic strains in different food matrices and to determine the actual risk of cereulide toxin production in different types of food. This comprehensive study showed that emetic strains are much more volatile than previously thought. Our survey highlights the importance and need of novel strategies to move from the currently taxonomic-driven diagnostic to more risk orientated diagnostics to improve food and consumer safety.Several  B. cereus strains called “emetic B. cereus,” is a small heat-stable cyclic peptide ) and an IVIS camera system as described earlier )n e.g., , 8) but  but  B. n . It is therefore assumed that B. cereus was indeed the etiological agent of the reported outbreaks. The detection of emetic B. cereus in low levels in samples from suspected foodborne illness could be an indication that the bacteria themselves were reduced by the food production and processing procedure, but the preformed heat- and acid-stable toxin cereulide was not eliminated or inactivated. In addition, it is known that the capability of toxin formation varies significantly among emetic B. cereus strains and the actual toxin production depends on external parameters , was quantified in situ after incubation of the samples for 24 hours at 24°C . Food samples were grouped into three main classes regarding their toxin formation capability via a software-assisted region-of-interest (ROI) analysis. The bioassay revealed that 44% of the foods could be categorized as high-risk foods, while the remaining 20% and 36% were categorized as risk or low-risk foods, respectively. Derived mean ROI values of each risk category and the corresponding determined threshold values are listed in Table S1.Click here for additional data file.


Cutaneous leishmaniasis (CL) is a vector-borne disease of increasing importance in northeastern Brazil. It is known that sandflies, which spread the causative parasites, have weather-dependent population dynamics. Routinely-gathered weather data may be useful for anticipating disease risk and planning interventions.We fit time series models using meteorological covariates to predict CL cases in a rural region of Bahía, Brazil from 1994 to 2004. We used the models to forecast CL cases for the period 2005 to 2008. Models accounting for meteorological predictors reduced mean squared error in one, two, and three month-ahead forecasts by up to 16% relative to forecasts from a null model accounting only for temporal autocorrelation.These outcomes suggest CL risk in northeastern Brazil might be partially dependent on weather. Responses to forecasted CL epidemics may include bolstering clinical capacity and disease surveillance in at-risk areas. Ecological mechanisms by which weather influences CL risk merit future research attention as public health intervention targets. Leishmania parasites, which humans may acquire when bitten by an infected sandfly. From a public health standpoint, it is important to identify cases early and monitor patients' clinical outcomes because unsuccessfully-treated patients are at risk for severe complications. Since weather conditions affect survival and reproduction of sandflies that transmit Leishmania, routinely-gathered weather and climate data may be useful for anticipating CL outbreaks, bolstering clinical capacity for high-risk periods, and initiating interventions such as active case-finding during these periods to limit disease burden. Here we assessed whether the number of CL cases occurring per month in a rural region of Bahía, Brazil was associated temperature, humidity, precipitation, and El Niño sea surface temperature oscillation patterns observed during preceding seasons. We formulated models that improved accuracy of one, two, and three month-ahead CL predictions by accounting for weather. Forecasts of this nature can contribute to reducing CL burden by informing resource allocation and intervention planning in preparation for epidemics.Cutaneous leishmaniasis (CL) is a disease resulting from infection by the  Leishmania parasites, including cutaneous leishmaniasis (CL), are important in tropical and subtropical areas worldwide, causing over one million cases per year Leishmania (Viannia) braziliensis is endemic, have seen increasing CL case notifications in recent decades cerrado uplands toward coastal Atlantic forests Diseases caused by the L. (V.) braziliensis are significant public health concerns. While CL does not cause death in the absence of complications, the disease causes debilitating and stigmatizing lesions and may progress to dangerous manifestations including mucosal or disseminated infection if treatment is not initiated early in the clinical course L. (V.) braziliensis are more likely than other CL victims to experience such complications, which have been observed with increasing frequency in northeastern Brazil over the last three decades L. (V). braziliensis infections are resistant to first-line antimonial treatment The increase in CL incidence and geographic range expansion by L. (V.) braziliensis has historically hindered efforts to anticipate CL risk in Brazil L. (V.) braziliensis transmission.Forecasting CL epidemics could aid the allocation of public health resources in advance of high-risk periods L. amazonensis, only L. (V.) braziliensis has been isolated from CL patients in the past two decades The Corte de Pedra health post in Presidente Tancredo Neves, Bahía, Brazil maintained paper records for leishmaniasis cases treated from 1988 onward. The health post treats over 90% of CL cases from surrounding municipalities; although the area has historically supported http://www.inmet.gov.br/portal/). Daily meteorological data were available from 11 weather stations in and adjacent to Bahía as listed in the supplement (Table S1). Data from the weather stations were sparse prior to 1992. To allow consideration of lags up to 24 months in length between weather exposures and disease outcomes, we considered only cases presenting for treatment from 1994 onward. To monitor ENSO variations, we used the monthly Multivariate ENSO Index (MEI) We obtained daily ground-surface meteorological observations from all weather stations within a 500 km radius of the health post, as reported through the historical databank of the Instituto Nacional de Meterologia . Using these interpolated time series, we calculated the expected mean noontime temperature (°C), relative humidity (%), days with rainfall (%), and total daily rainfall (cm) within each municipality in the study area for each month over the period 1992–2008. To aggregate values at the regional level, we took the mean interpolated value for each month across all municipalities.Because the location of the weather stations does not necessarily match the study area, we interpolated We normalized the time series of monthly CL cases by taking the square root. We identified an autoregressive integrated moving average (ARIMA) or seasonal ARIMA (SARIMA) specification for a null model describing temporal dependence in the transformed case series. Formal descriptions of the ARIMA and SARIMA frameworks, and procedures for model identification, are presented elsewhere iX) on the basis of the variable's autocorrelation and partial autocorrelation functions, reducing the residuals of the iX input to white noise. We used the fitted models for the predictors to filter the transformed case series (Y). We computed the cross-correlation function (CCF) between the residuals of the Y and iX series and tested for significance at the 95% confidence level . We considered as covariates all lags of the iX variables where the absolute value of the CCF between the filtered series exceeded the cut-off.We used a common pre-whitening approach to select lags of the predictors to be used as covariates in forecasting models We partitioned the data into an initial “training” period comprising observations for the interval ending in 2004 (132 months), and a “validation” period for the remaining 48 months from 2005 to 2008. The data from the training period served as a basis for estimating the initial autocorrelation and partial autocorrelations to be used for time-series modeling and lag filtering. We parameterized models to fit the training data and used the fitted models to forecast the number of cases in future time periods. The model fit was updated iteratively with the next most recent month, and new forecasts were generated based on the updated models. We generated forecasts at predictive horizons ranging from one month to the maximum number of months ahead that would be possible to predict from incoming data; the shortest significant lag in the CCFs thus specified the maximum forecast horizon (3 months). We centered and scaled all covariates prior to modeling by subtracting their means and dividing by their standard deviations; this allows parameters to be interpreted in terms of covariate standard deviation units to facilitate comparison of effect sizes We considered several potential forecasting models for CL. First, we generated a null (S)ARIMA model predicting the transformed case series on the basis of its temporal dependence patterns alone. We additionally generated regression models considering all possible combinations of covariates, and fit each model with the null (S)ARIMA error specification determined from the ACF and PACF of the transformed case series. Last, we used Bayesian model averaging We evaluated models' predictive accuracy on the basis of MSE in predictions; we computed this value by comparing model forecasts to the square root-transformed cases observed during the validation period. We compared predictive accuracy for models with covariates relative to the null model to ascertain improvements in forecasting.The dataset included 1,209 leishmaniasis cases treated at the Corte de Pedra health post between 1988 and 2008. We identified 853 cases without disseminated or mucosal infection presenting for care between 1994 and 2008. Of these, 586 occurred in the initial training period (1994–2004) and 267 occurred in the validation period (2005–2008). The most notable epidemic appeared in 1999–2000 Figure 1The transformed case series had a stationary mean indicating differencing was not required. The autocorrelation function showed significant dependence extending to a four-month lag, while significant partial autocorrelation cut off after a two-month lag Figure 1We identified significant cross-correlations between the case series and all predictors except temperature Figure 26 = 64 models in total. The best-fitting model according to BIC weights accounted only for a negative association between cases and five month-lagged relative humidity. The best-fitting model by AIC and AICc included a negative association with rainfall frequency at the five-month lag and total rainfall at 10- and 21-month lags. Averaging across all models did not reveal noteworthy differences in variables' contributions to model fit, as evidenced by similarity in PPP values among covariates under each averaging scheme. Parameter estimates averaged according to AIC and AICc weights differed by less than 10−4 and are consequently presented together as a single averaged model. Under the BIC and AIC/AICc weighting strategies, the models with the greatest posterior probabilities accounted for relative humidity and rainfall frequency at five-month lags, and total rainfall at 10- and 21-month lags. Meteorological parameter estimates differed across models, leading to averaged 95% confidence intervals including zero in all cases. The BIC-averaged model offered more conservative estimates and narrower confidence intervals for all meteorological parameters than the AIC/AICc-averaged model. Using model weights computed from the training period only rather than monthly-updated model weights did not lead to numerical changes in parameter estimates or PPPs greater than 10−4. Variable selection for the best-fitting models by AIC/AICc and BIC did not change as we updated the models.For the multivariate models, each covariate had the binary option of being included or not included. Since we identified six significant cross correlations, we fit 2, Figure S2). This model provided the most accurate forecasts at all prediction lengths; two month-ahead forecasts were the most accurate in terms of minimizing MSE. The averaged model constructed according to BIC offered smaller marginal reductions in MSE than the averaged model constructed according to AIC/AICc weights for all but the one month-ahead predictive horizon. Marginal reductions in prediction MSE were poorest from the best-fitting model selected according to AIC/AICc weights.We compared out-of-fit prediction accuracy for the null model with the best-fit models and the averaged models, which accounted for meteorological covariates Table 2.Figure S1, Figure S2). Adjusting models to include a seasonal autoregressive for the twelve-month period term did not improve forecasting of the May epidemic peaks, which became a regular feature in the data only from 2005 onward. Residuals from models incorporating covariates did not show significant temporal dependence via the Ljung-Box and test or in their autocorrelation and partial autocorrelation functions.The best-fitting model by BIC offered one-, two-, and three month-ahead predictions with on average 6.0%, 7.3%, and 8.0% lower variance than the null model, respectively. These improvements in precision did not incur penalties to forecast accuracy. Observed cases exceeded the upper limits of the 95% confidence envelopes from all models in May of 2006 and May of 2008, at the peaks of epidemics during those years. One- and three month-ahead predictions from all models additionally under-estimated a secondary peak in July of 2008 (L. (V.) braziliensis transmission in Northeast Brazil. Notably, dry conditions with respect to relative humidity and precipitation were significantly associated with CL case notifications three to five months later. Our results are consistent with the view that CL is sensitive to meteorological and climatic forcing In this study we found that accounting for meteorological and climatic factors improved accuracy and precision of CL forecasts in a region of endemic Differences in out-of-fit predictive accuracy among models likely indicate where models may be overfit to within-sample data. The model with the best predictive accuracy at all horizons was selected by BIC and accounted only for five month-lagged relative humidity as a meteorological covariate. AIC and AICc have a lower penalty than BIC for potential overfitting Lutzomyia sandflies in CL-endemic areas of Brazil and South America to be inversely correlated with relative humidity and rainfall in recent months Lu. whitmani and Lu. migonei) are particularly abundant during the warm, dry season L. (V.) braziliensis host species than by sandflies, whose life cycles span only one to several months. For instance, the positive cross-correlations with rainfall and MEI at 21- and 22-month lags most probably relate to longer-term effects of moisture surpluses on biological productivity necessary to sustain large populations of mammalian reservoirs Although our analysis was not suited for identification of causal effects, numerous biological mechanisms may support associations between weather and CL epidemics. Inverse correlations between precipitation and humidity variables at lags between three and five months in particular demonstrate excess cases closely follow dry periods. Ecological sampling studies have indicated population densities of Our analyses have several limitations. Having fit models to a 10% subset of total reported cases treated at the health post, our estimates are sensitive to small month-to-month variations that may be less pronounced in a dataset providing complete case records. While forecasts succeeded in predicting overall epidemic patterns exhibited from 2005 to 2008, a notable weakness was the poor prediction of the size and duration of the 2008 epidemic. The low number of cases appearing in June of that year, mid-way through the epidemic, may be an artifact of the reduced dataset and likely contributed to this shortcoming. The passive surveillance system at the Corte de Pedra health post additionally provided an incomplete sample of total CL cases within the area, and may in particular under-represent persons unlikely to seek care. For instance, other analyses of the data presented here showed that many agricultural workers delay pursuit of therapy until onset of complications including mucosal or disseminated infection L. (V.) braziliensis in domestic, peridomestic, and sylvatic environments While the Corte de Pedra health post remained the primary center for CL diagnosis and treatment throughout the study period L. (V.) braziliensis is unknown. Ecological sampling studies in the state of Pernambuco, near the study region, suggest several species of mice and rats may contribute to transmission Leishmania spp. transmission cycles can be anticipated to vary spatially according to species compositions, contact rates and competence among local vectors and hosts, and ecological sensitivity to weather and other environmental stressors; additionally, individually- and regionally-varying social factors influence human exposure to primarily-enzootic transmission cycles, and vulnerability to weather-related health risks L. (V.) braziliensis and its vectors differ across Brazil, where predominantly sylvatic, peridomestic, or domestic transmission pathways in endemic foci reflect divergent underpinnings of CL eco-epidemiology Although the identified associations aided forecasting, inferential gains are limited by poor understanding of CL eco-epidemiology, including the fact that the local animal reservoir for L. (V.) braziliensis strains resistant to conventional treatments continue to emerge As CL continues to expand in parts of Brazil, developing capacity to forecast epidemics will facilitate public health responses. Using model-based predictions to anticipate disease risk and expanding clinical capacity to address excess CL cases may constitute an important operational strategy for alleviating burden of disease. For example, understanding the timing of epidemics will enable implementation of enhanced case detection in advance of and during high-risk periods, limiting lesion size at the time patients are identified and reducing patients' risk for treatment failure and metastatic complications. This can be accomplished in part by ensuring adequate clinical and laboratory personnel and diagnostic reagents or microscopy resources are available to identify CL patients during high-demand periods. Furthermore, since procurement and delivery of first-line pentavalent antimonial agents to endemic regions requires significant lead-time, acquiring and distributing these drugs preemptively in response to model-based predictions may ensure that treatment centers are adequately stocked for epidemics. This is also critical with respect to maintaining supplies of difficult-to-procure alternative therapies such as liposomal amphotericin B One key question with respect to the application of model-based forecasting to improve responses to CL is the definition of epidemic thresholds. The limited capacities of local and national leishmaniasis control programs in resource-poor settings contribute to difficulty identifying alert and response priorities for early warning systems Figure S1Two month-ahead forecasts. (A) Null model; (B) Best-fitting model according to BIC; (C) Averaged model according to AIC/AICc. Black lines plot the square root-transformed cases; orange lines plot model fit to data during the training period; red lines plot model forecasts, with the grey area representing the 95% confidence region.(TIFF)Click here for additional data file.Figure S2Three month-ahead forecasts. (A) Null model; (B) Best-fitting model according to BIC; (C) Averaged model according to AIC/AICc. Black lines plot the square root-transformed cases; orange lines plot model fit to data during the training period; red lines plot model forecasts, with the grey area representing the 95% confidence region.(TIFF)Click here for additional data file.Table S1Municipality locations.(DOCX)Click here for additional data file.Text S1Weather interpolation.(PDF)Click here for additional data file.Data file S1Case series. The file contains the data used for the study .(CSV)Click here for additional data file.


Middle East respiratory syndrome coronavirus (MERS-CoV) causes severe cases of human respiratory disease. Since 2012, the victims have mainly come from the Middle East countries or sporadically from some other geographical regions seeded by the travelers who visited the Middle East. Such an introduction through travelling led to the emergence of a MERS-CoV outbreak in Korea in May 2015, which caused more than 140 confirmed human cases in less than a month. Using 70 complete genome sequences of MERS-CoV isolates, including the most recent sequences for the Korean and Chinese isolates, we reconstructed the phylogenetic relationships of the complete genome and the individual protein coding regions. The Korean MERS-CoV strain clustered in the previously established Hafr-Al-Batin-1_2013 clade together with two Saudi Arabian and one Chinese strain sampled in 2015. Although these four strains remained monophyletic in the entire protein-coding region, this clade showed different phylogenetic relationships across the genome, indicating a shared unique recombination pattern that is different from previously reported putative recombination strains. Our findings suggest that the recent ancestor of the Korean and its related MERS-CoV strains is characterized by unique mosaic genome pattern that is different from other putative recombinants. MERS was first described in 2012 in fatal human cases that were caused by a single-stranded RNA coronavirus1258913The first case of traveler-associated MERS-CoV outbreak in Korea occurred in May 201517We first analyzed the phylogenetic relationships between the KOR002 strain and all available MERS-CoV sequences. The complete genome and individual open reading frame (ORF) sequences  were analyzed separately using time-resolved Bayesian phylogenetic inference method implemented in BEAST (v1.8.2)Recombination has been described previously in other coronavirus genomes2122Based on the phylogenetic clustering patterns and the recombination imprints we detected , one of 2125Outside the Arabian Peninsula, Korea experienced the biggest outbreak of MERS. Through seeding by only a single patient, MERS-CoV resulted in more than 160 confirmed patients in less than a month and thousands of people were confined under close monitoring. The CFR of MERS outbreak in Korea may appear to be relatively low (approximately 11.7%), compared with the previous outbreaks in the Middle East and no signs of community transmission have been reported. In addition, an announcement regarding the situation assessment of MERS outbreak in Korea issued by the WHO Global Alert and Response program stated that significant virological change was not seen so far and the transmission patterns are unlikely to be different from those previously reported in the Middle EastIn conclusion, we suggest that the MERS-CoV outbreak in Korea appears to be caused by a strain that is closely related to three 2015 strains from the Hafr-Al-Batin-1 clade and that the relatively recent ancestor of these viruses exhibits a unique recombination pattern that is different from other putative recombinants.http://www.ncbi.nlm.nih.gov/genomes/VirusVariation/Database/nph-select2.cgi). Detailed information of isolation and genomic sequencing of a Korean MERS-CoV strain was published previouslyIn this study, we investigated a total of 70 complete genome sequences of human MERS-CoV strains that were downloaded from the database of National Center for Biotechnology Information , and the time (year) of the most recent common ancestor (tMRCA) were estimated using a time-framed Bayesian evolution analysis approach via a Markov Chain Monte Carlo (MCMC) inference method, implemented in the BEAST package (v1.8.2)To detect putative recombinant regions in the MERS-CoV genome, we used the RDP4 program (v.4.39)How to cite this article: Kim, J. I. et al. The recent ancestry of Middle East respiratory syndrome coronavirus in Korea has been shaped by recombination. Sci. Rep.6, 18825; doi: 10.1038/srep18825 (2016).


Varroa destructor is a vector for viral pathogens of the Western honeybee (Apis mellifera), in particular the Iflavirus Deformed Wing Virus (DWV). In the absence of Varroa low levels DWV occur, generally causing asymptomatic infections. Conversely, Varroa-infested colonies show markedly elevated virus levels, increased overwintering colony losses, with impairment of pupal development and symptomatic workers. To determine whether changes in the virus population were due Varroa amplifying and introducing virulent virus strains and/or suppressing the host immune responses, we exposed Varroa-naïve larvae to oral and Varroa-transmitted DWV. We monitored virus levels and diversity in developing pupae and associated Varroa, the resulting RNAi response and transcriptome changes in the host. Exposed pupae were stratified by Varroa association (presence/absence) and virus levels (low/high) into three groups. Varroa-free pupae all exhibited low levels of a highly diverse DWV population, with those exposed per os (group NV) exhibiting changes in the population composition. Varroa-associated pupae exhibited either low levels of a diverse DWV population (group VL) or high levels of a near-clonal virulent variant of DWV (group VH). These groups and unexposed controls (C) could be also discriminated by principal component analysis of the transcriptome changes observed, which included several genes involved in development and the immune response. All Varroa tested contained a diverse replicating DWV population implying the virulent variant present in group VH, and predominating in RNA-seq analysis of temporally and geographically separate Varroa-infested colonies, was selected upon transmission from Varroa, a conclusion supported by direct injection of pupae in vitro with mixed virus populations. Identification of a virulent variant of DWV, the role of Varroa in its transmission and the resulting host transcriptome changes furthers our understanding of this important viral pathogen of honeybees.The globally distributed ectoparasite  Varroa, has spread worldwide, with significant impacts on honeybee colony health as a consequence of its transmission of a cocktail of viruses while feeding on honeybee ‘blood’. The most important virus for colony health is deformed wing virus (DWV), high levels of which cause developmental deformities and premature ageing resulting in high overwintering colony losses. In experiments on individual Varroa-exposed pupae we demonstrate that a single type of virulent DWV is amplified 1,000–10,000 times in the recipient pupae, despite the mite containing a high diversity of replicating DWV strains. We could recapitulate this by direct injection of pupae with mixed virus populations, showing the virulent strain is advantaged by the route of transmission. In parallel, we detected changes in the immune response and developmental gene expression of the honeybee and propose that these contribute to the characteristic pathogenesis of DWV. Identification of a virulent strain of DWV has implications for therapeutic or prophylactic interventions to improve honeybee colony health, as well as contributing to our understanding of the biology of this important honeybee viral pathogen.Honeybees are the most important managed pollinating insect, contributing billions of dollars to annual global agricultural production. Over the last century a parasitic mite,  Host-pathogen interactions can be broadly divided into asymptomatic or symptomatic infections Apis mellifera) is the predominant managed pollinating insect and delivers economically important pollination services for agriculture which are estimated to add ∼$40bn globally to crop value/annum A. mellifera are caused by a range of viruses many of which are vectored by the ectoparasitic mite Varroa destructor when feeding on honeybee haemolymph. Varroa is believed to have expanded its host range from Apis cerana to A. mellifera during the first half of the 20th century and subsequently spread to all beekeeping regions of the world with the exception of Australia The European honeybee , a picorna-like single-stranded, positive-sense, RNA virus Varroa on the immune responses of honeybees. Initial reports indicated that Varroa-exposed honeybees were immuno-compromised Drosophila Toll, Imd and Jak-Stat signalling pathways in controlling RNA virus infection DrosophilaVarroa-exposed honeybees could be the result of a suppression of these antivirus responses and so warranted further analysis.Previous studies using functional or gene expression analysis have produced contradictory conclusions on the impact of Varroa infestation is associated with the accumulation in mite-exposed pupae of a particular subset of DWV-like viruses Varroa and honeybees, resulting in their amplification to the markedly elevated levels observed in Varroa-parasitized pupae (about 1000 times higher than in unexposed pupae). In recent complementary studies, changes in the composition of the DWV population over a large temporal and spatial scale following Varroa infestation were reported for honeybees colonies following accidental introduction of Varroa into the Hawaiian islands Varroa was associated with a marked restriction in DWV diversity measured in the pooled honeybee samples collected from the Varroa-infested colonies, although the precise identity of the dominant virus was not determined We have previously demonstrated that Varroa in the development of high-level DWV infection in the honeybee, namely that the mite (i) amplifies and transmits virulent genotypes of DWV, and (ii) suppresses antiviral responses, including immune signalling pathways and RNA interference. The experimental procedure included exposure of Varroa-naïve honeybees to mites and their associated DWV payload together with the per os in-hive horizontal transmission. The use of Varroa-naïve honeybees from a Varroa-free region allowed us to monitor changes in DWV diversity and loads, as well as potential antivirus responses in the honeybee responses, following exposure to the viral genotypes associated with Varroa infestation. Importantly, we analysed immune responses and viral load/diversity in individual mite-exposed and –unexposed pupae, rather than in pooled samples. This allowed us to stratify individual responses into four distinct experimental groups, characterised by Varroa exposure and viral load, that clearly correlated with characteristic changes in the transcriptome and virus population diversity. In addition, we recapitulated the exposure of Varroa-free honeybees to DWV by direct injection and analysed virus diversity in bees of a colony with long-established Varroa infestation.In the present study we devised a novel experimental system to specifically test two hypotheses on the role of Varroa or experimental injection. This results in massive reduction of DWV diversity in bees with high virus levels, both in the Varroa-exposed pupae and newly emerged bees with symptomatic deformed wing disease. Significantly, the same virulent recombinant form of DWV reached the highest levels in mite-exposed pupae and in adult bees exhibiting characteristic deformed wing symptoms. Although exposure to Varroa resulted in changes in expression of a number of immune-related genes, the roles of which should be further explored, we demonstrate that it is the route of virus acquisition that is responsible for the amplification of this virulent form of DWV in a Varroa-infested colony.Our results indicate that a virulent recombinant form of DWV, while transmissible orally, only replicates to high levels when directly inoculated into honeybee haemolymph – by Varroa-free colony (sourced from a region with no historic contacts with or presence of Varroa) were moved in a frame transfer experiment to a Varroa-infested colony. The larvae were subsequently exposed through feeding to DWV strains circulating in the infested colony from day 4 until the cells were capped at day 9  sampled pupae maintained in the Varroa-free colony , in all 19 sampled pupae transferred to the Varroa-infested colony that were not capped with a Varroa mite and therefore subjected only to Treatment 1 , and in 10 of 33 pupae upon which Varroa mites had fed, Treatment 2 . In contrast, high levels of DWV-like viruses were detected in the remaining 23 of 33 Varroa-associated pupae, which experienced both Treatment 1 and Treatment 2 . The Ct ranges for the VH group lay entirely below the VL range, indicating significantly higher virus levels in VH (p = 0.5261). We have previously reported similar proportions of Varroa-associated pupae with low and high levels of DWV in an independent  study Varroa exposure does not inevitably lead to high, presumed pathogenic, DWV levels, as reported previously Varroa mites per cell (data not shown). These two distinct classes of Varroa-exposed pupae, and their associated mites, were included as separate groups in subsequent analyses to investigate host or parasite determinants that influenced the outcome of exposure.We assessed the total levels of DWV viruses in 80 individual pupae by qRT-PCR using a primer pair for a conserved polymerase-coding region, designed to detect all known DWV strains, including DWV, VDV-1 and KV . The reals in VH  whereas We sampled eight honeybee pupae selected at random from each of the four groups  or at mite feeding (VL). Similarly the C to VL contrast can be split at NV. These decompositions exhibit high orthogonality . Distinct clustering by experimental group was observed when two independent sets of DE genes with the lowest p-values were analysed i.e. those from the DE genes pooled from all contrasts . The C to VH and C to VL contrasts exhibited the highest number of DE immune-related genes , whereas oral exposure (C to NV) resulted in 18 DE immune-related genes  analysis, focusing on the GO Biological Process (BP) ed genes . Indepene.g. Argonaute, Dicer) Although no significant changes in expression of genes associated with the RNAi response  in honeybee pupae exhibiting high viral loads Varroa mites we used generic NS qRT-PCR primers or primer pairs specific for DWV or VDV-1 CP or NS coding regions  because significant amplification of viruses bearing VDV-1 CP- (by 27-fold [P = 0.0217]) and DWV NS-regions (10-fold [P = 0.0314]) also occurred in the NV group relative to the control group C (P = 0.0151) of the DWV-type CP and 26-fold decrease (P = 0.0477) of the VDV-1 NS compared with the NV group. The levels of VDV-1 CP and DWV NS coding regions showed strong positive correlation (r = 0.9691) suggesting that this particular recombinant was preferentially acquired or amplified in the VH group pupae. The Varroa-exposed VL group also potentially acquired DWV from the mite as well as during larval feeding. It was therefore interesting to note that, when compared to the NV group, there was a statistically significant 54-fold decrease of DWV CP- (P = 0.0057) and a 36-fold decrease of VDV-1 NS-regions (P = 0.0151) in the VL group. Since both the VL and VH group pupae were mite-exposed but contained distinct levels and populations of DWV-like viruses we also characterised the viruses, and evidence of their replication, in the associated mites to determine if there was a correlation between high levels of virus in the honeybee and replication in the mite, as previously reported As already indicated , the qRTt groups . In comp group C . The drar = 0.567). Notably, we found that correlation between the levels of VDV-1 CP- and DWV NS-regions  in the ectoparasite. Negative strand RNA was generally low but detectable in 10/15 mites analysed, with no significant difference between the DWV or VDV-1 CP levels  and four specific (inner) primer pairs , and consequently all clustered within a single clade  and by Varroa is that the latter involves direct inoculation of virus to the haemolymph in pupae we investigated the recapitulation of this process by direct injection of pupae in vitro.One interpretation of the near-clonality of viral sequences in the VH group was that these were the only ones carried, and hence transmitted, by the mite. However, with the exception of the non-recombinant DWV cluster, which was not detected in the mite, the 32 viral sequences obtained from e RF4 in  are amplin vitro (as described in We directly injected white eye pupae (day 12–13 of development) maintained Varroa-infested honeybee colonies, and additionally indicate that this advantage is manifest after transmission of the virus by direct inoculation and is not dependent upon Varroa per se.We additionally conducted next generation sequencing (Illumina paired-end reads) to comprehensively characterise the group C inocula and the viruses present in pupae injected with the group C virus. The composition of the inoculum, as determined by qRT-PCR and subsequent MosaicSolver Varroa-exposed pupae  virus purified from pupae from the same colony a month earlier. Analysis was conducted on individual pupae using a high-throughput RNA-seq approach The sequence analysis of DWV in ed pupae  in the fVarroa-naïve control pupae, for pupae inoculated with buffer alone and for asymptomatic nurse bees from the Varroa-infested colony , no differences were observed at the 5% level (Fisher LSD test) for the NS region. Indeed, combined low virus level and high virus level groups showed significant differences in Shannon's diversity index values for the CP and NS regions even at the 0.1% level . Remarkably, while the diversity index of all samples with low DWV levels  were well above this threshold, diversity values of samples with high DWV levels (virus-injected pupae and symptomatic nurse honeybees) were either very close or below this clonality threshold. Similar results were obtained when diversity was estimated using multiple sampling as described in Varroa-infested colony indicating that the diversity in these honeybee samples was close to the limit of detection using NGS analysis. This reinforces the near-clonal nature of the virus population in Varroa-exposed symptomatic nurse bees and is in good agreement with the sequence analysis of VH group pupae following PCR amplification of the central region of the virus genome . The central 1330 nt. region of this clone was identical to that previously characterised from VH group pupae  pupae  and for pupae injected with buffer alone ; The NGS reads were aligned to reference DWV and VDV-1 sequences (GenBank Accession numbers GU109335 and AY251269 respectively), and the pileup profiles were analysed. The proportions of DWV and VDV-1 reads in the libraries (each containing about 10 million reads) showed a bimodal distribution and were either very high  has had significant impact on the health and survival of infested colonies Varroa are predominantly attributed to the RNA virus payload vectored by the parasite and transmitted when the mite feeds on honeybee haemolymph Varroa were reported to vector at least 5 RNA viruses, the picorna-like Iflavirus deformed wing virus (DWV) is of particular interest and importance; deformed wing disease is associated with mite infestation Varroa to Hawaii Varroa at the regional scale (e.g. Hawaii), which is associated with a dramatic reduction in DWV variation and the emergence of dominant strains over a scale of months or years Varroa-free and Varroa-infested colonies. By using different molecular approaches including RNA-seq, cloned cDNA sequencing and qRT-PCR, we demonstrated that, contrary to the recently reported figure of 36% DWV presence in the UK honeybees n = 250 tested honeybees, including those from a Varroa-free region (n = 47)  , 7. , 7.VarrVarroa and DWV that account for the observed emergence of presumed pathogenic, near-clonal strains of the virus we analysed events at the level of individual honeybee pupae. We reasoned that the large scale global, temporal and population changes observed reflect the cumulative outcome of interactions that occur at the level of individual pupae within the colony. We further reasoned that analysis at this level would allow two hypotheses accounting for the high levels of overt deformed wing disease beekeepers associate with heavy Varroa infestation to be tested. These hypotheses, themselves not mutually exclusive, are that; a) Varroa suppresses the antiviral honeybee defences so allowing unrestricted DWV replication and b) the presence of Varroa results in the selection and transmission of particular pathogenic variants of DWV, resulting in serial amplification within a mite infested colony. We tested these hypotheses by transferring honeybee larvae from a Varroa-free to an experimental Varroa-infested environment, stratified pupae of a standardised age in terms of mite-exposure and viral load, and investigated transcriptome and RNAi responses of the host and the virus population in individual mites and associated pupae.To better understand the interactions of the honeybee host, Varroa-naïve honeybee larvae (group C) transferred into a Varroa-infested colony  can, after incubation for 6 days, be stratified into three distinct pupal groups by Varroa exposure (presence or absence) and DWV level (high or low). High virus levels were not observed in the absence of Varroa. Group NV comprised pupae from capped cells free of Varroa. As Varroa enters the cell immediately prior to capping per os during larval feeding. The VH and VL groups contained Varroa within the capped cell with evidence of Varroa feeding on the pupae – including nymphal forms present and signs of abdominal piercing. These groups of pupae harbour strikingly different DWV populations: the VL group having low viral levels and high diversity that are not significantly different from the C and NV groups, whereas the VH group carries 1,000–10,000 times the viral loads of a single phylogenetic type  and 951 (∼9%) significantly differentially expressed (DE) genes when compared with the control group C  and those triggered by the high viral load , many of which were also altered in the C to VL (220) and C to VH (385) contrasts , was largely different from the DE genes in contrast C to NV with only one gene shared. At the same time, the NV to VL set showed high commonality with the DE genes in the contrasts C to VH, C to VL, and NV to VH , exposure to mite feeding (NV to VL), and high DWV (VL to VH)  .per os exposure (C to NV contrast) and to Varroa and/or Varroa-injected virus (NV to VL contrast) (Gene Ontology (GO) analysis gave additional insights into the transcriptional responses in honeybees following experimental treatments . It has ontrast) . Such coontrast) , which wontrast) . This suDrosophila homologs Varroa-exposed groups (VL and VH) the NV group was the only group in which there were more up- than down-regulated immune-related genes when compared with the control , implying that Varroa exposure may exert a dominant immunosuppressive influence over any up-regulation observed following oral exposure alone. Significantly enhanced expression of the honeybee orthologue of Vago  when compared with group C.We analysed changes in expression of known and presumed immune-related genes  defined  control . The majGB10896; , a secreVarroa exposure (VL or VH groups) resulted in down-regulation of several putative components of the honeybee Toll signalling pathway Varroa per se. Toll signaling pathways are implicated in antiviral resistance to the RNA virus Drosophila virus X pannier (GB19895), a GATA transcription factor required for differentiation of plasmatocytes , a membrane ligand for the Notch receptor implicated in differentiation of haemocyte-related crystal cell precursors which function in pathogen defence via melanisation Varroa mites damage moth caterpillar haemocytes Varroa-mediated depletion of haemocytes, a key component of the immune response of insects ush) ortholog (GB16457), in the C to VL and NV to VL contrasts. Drosophila ush is reported to antagonise crystal cell development ush down-regulation.Varroa and virus-exposure, also provides direct insights into possible pathogenic mechanisms. In the contrast C to VH we observed differential expression of orthologs of five Drosophila homeobox genes . To distinguish between these possibilities we sequenced qRT-PCR amplified viral RNA from mites co-isolated from capped cells containing group VL and VH pupae. We also investigated the consequences of Varroa-independent mechanical virus transmission by direct injection of mixed virus preparations to Varroa-naïve pupae and subsequent monitoring of virus levels and diversity.We reasoned that there were two possibilities that might account for the marked amplification of DWVVarroa mites contained a diversity of DWV-like sequences that were well distributed throughout the phylogenetic tree of virus sequences from pupae , in part imposed by experimental limitations of working with late-stage larvae and early-stage pupae which are vulnerable to handling damage. Despite these limitations, these results clearly demonstrate that direct inoculation of a mixed virus preparation, recapitulating virus inoculation by the mite, results in a marked reduction in virus diversity. We additionally demonstrated, by RNA-seq analysis of temporally and geographically independent symptomatic nurse bees and similarly independent pupae directly injected with virus preparations, that essentially the same near-clonal virus (DWVV) was present as previously identified in the VH group pupae. In parallel, control asymptomatic nurse bees or mock-injected pupae exhibited high diversity and low levels of virus  host genes using RNAi-based strategies We propose that the strikingly elevated levels and associated restricted diversity of DWVevidence  that DWVVarroa has a devastating effect on honeybee colony viability and consequent honey production and pollination services. We show here that the markedly elevated levels of DWV-like viruses in Varroa-exposed honeybee pupae are likely attributable to the direct inoculation of a specific virus, DWVV, by Varroa to haemolymph. Repeated cycles of Varroa-replication within an infested colony would preferentially amplify DWVV, potentially resulting in it becoming the predominant virus present, transferred both by Varroa and per os. Further studies will be required to determine whether such a virus, if sufficient were ingested, would also cause symptomatic infection. Oral susceptibility to a virulent form of DWV may also explain reported cases of deformed wing disease symptoms seen in Varroa-free colonies in Hawaii Without proper management Varroa-exposed pupae (the VL group) do not exhibit elevated levels of the near-clonal DWVV recombinant  colony with a naturally-mated one-year-old queen was imported from Colonsay, Scotland, an island with no historic reports of Varroa incidence and no imports of honeybees from Varroa-infested areas. This allowed us to exclude the presence of DWV strains associated with Varroa mite infestation. As a source of Varroa mites and the mite-associated DWV strains, we selected a Warwickshire honeybee colony, heavily infested with Varroa and having high DWV levels in honeybees and mites. The Varroa-free and Varroa-infested colonies were contained in separate mesh flight cages  and maintained on an artificial diet of sugar syrup and pollen. The pollen was imported from Varroa-free Australia to exclude possible contamination with Varroa-associated viruses through foraged food and was pre-screened by PCR before use for DWV-like viruses. In order to minimise possible effects on honeybee gene expression due to the differences in nutrition, both the control Varroa-free and the Varroa-infested colonies were maintained in flight cages in the same apiary  and were fed on the artificial diet for two months before the start of the frame transfer experiments. Neither colony was treated with miticides.This study was based around an experiment in which a brood frame containing newly hatched larvae from a th–15th August 2011. As stated previously, it involved the transfer of a brood frame, which contained newly hatched honeybee worker larvae (on day 4 of development), from the Varroa-free to the Varroa-infested colony. As a result, the transferred larvae were exposed to Varroa-selected DWV-like viruses in brood food delivered by the nurse honeybees of the Varroa-infested colony for five days before brood cell capping on day nine of development  according to the manufacturer's instructions. Total RNA extraction from Varroa mites was carried out using RNeasy spin columns (Qiagen RNeasy Plant Mini kit).The pupae and the Virus purification from honeybee material and extraction of the viral genomic RNA from virus particles were carried out as described previously Apis mellifera Official Gene Set 1, OGS1 Apis mellifera transcriptome (OGS1) and Apis mellifera fungal and viral pathogens , are available in the ArrayExpress database (www.ebi.ac.uk/arrayexpress) under accession number A-MEXP-2251. Following hybridisation, the microarrays were scanned using Agilent Technologies G2565CA Scanner and the fluorescence intensity data were processed using feature extraction software (Agilent Technologies). Cy3 and Cy5 fluorescence intensities for each spot were measured as values of green and red pixels respectively. The details of the array experiment design, sample description, and microarray data are available in the ArrayExpress database (www.ebi.ac.uk/arrayexpress) For genome-wide analysis of the honeybee transcriptome total RNA preparations from eight individual honeybees from each of the four experimental groups  were purified further using RNeasy Plant Mini kit spin columns (Qiagen). RNA concentration, purity and integrity were assessed using a 2100 Bioanalyzer and an RNA 6000 LabChip (Agilent Technologies). The probe preparation, hybridization and scanning were carried out according to the Agilent instructions, essentially as in Paenibacillus larvae ssp and Melissococcus plutonius , corresponding to genes in A. mellifera OGS1 were found using protein blast. GO terms associated with Amel_4.0 genes were then obtained using Blast2GO For Gene Ontology (GO) analysis a three-stage process was used. Genes in the latest p-value. The 60 with the lowest adjusted p-value were selected, all of which appeared in the contrast C to VH; the other contrasts' contributions were 35 (C to VL), 21 (NV to VH), 19 (C to NV), 4 (NV to VL) and 11 (VL to VH) genes. Principal components of the expression profiles across the 62 microarray channels were found and (the first two) plotted using the princomp and biplot functions in R For Principal Component Analysis (PCA), the significant DE genes in all six contrasts were pooled and ranked by their adjusted www.ebi.ac.uk/arrayexpress) For high throughput sequencing of small RNA, we pooled equal amounts of the Trizol-extracted total RNA from individual honeybees and isolated the 15 to 40 nt RNA fraction, which was separated using denaturing polyacrylamide gel. The RNA pools were ligated to the oligonucleotide adapters, reverse-transcribed and amplified using the TruSeq Small RNA Sample Prep Kit . The libraries were sequenced using the Illumina HighSeq 2000 platform, producing 15–25 million reads per libraries . The small RNA NGS sequencing data are available in the ArrayExpress database  and the tagged primers designed to anneal to the negative strands RNA of DWV or VDV-1, primers 389 and 391 respectively . The qPCAmplification of the cDNA fragments corresponding to the central region of DWV genomic RNA was carried out by nested PCR using GoTaq PCR mix (Promega) and primers 155 and 156  using thwww.ebi.ac.uk/arrayexpress) For the next generation sequencing of RNA, a series of overlapping cDNA fragments were produced using viral RNA or total RNA preparations using the set of primers designed to the sequences of the genomic RNA conserved among DWV, VDV-1 and KV . The fraet al., unpublished data) and (ii) multiple sampling. For the latter we produced five samples of 3285 reads , which were aligned using Bowtie to the reference DWV and VDV-1 sequences, and the NGS nucleotide pileups were then generated for each nucleotide position of the reference sequences using samtools. Shannon's diversity index of the aligned nucleotides was calculated for each position in the reference sequence. Then, the average Shannon's index values were calculated for the selected regions in the reference genomes for each sample. The averages values of and standard deviation of five samples were used in the statistical analysis.The next generation sequencing of the poly(A) RNA fraction (RNA-seq) of the total RNA preparations isolated from the honeybees was carried out using Illumina HiSeq 2000 (GATC-Biotech) protocol, with about 10 million 101 nucleotide-long reads generated for each sample. The RNA-seq sequencing data are available in the EBI Sequence Read Archive Figure S1Bimodal distribution of DWV accumulation in the experimental honeybee pupae. (A) Dotplot of Ct values by experimental group, determined by qRT-PCR, showing means and 95% confidence intervals for the means. The means for C, NV and VL are not significantly different. The difference between the mean of VH and the pooled C, NV and VL is significant with p-value <10−16. (B) A histogram shows bimodality of Ct values.(PDF)Click here for additional data file.Figure S2Orthogonality of the differential gene expression pattern. A geometrical visualization of the three-stage experimental process. The first stage is “frame transfer” which includes exposure to Varroa-selected viruses through feeding at larval stage (contrast C to NV), the second stage is exposure to the Varroa mite feeding on the pupae haemolyph (contrast NV to VL) and the third stage is development of high viral load (contrast VL to VH). (A) Numbers of significantly differentially expressed genes in each of the three stages are shown alongside the directional vectors, together with numbers of differentially expressed genes in the composite stages . (B) The three stages involve distinct sets of differentially expressed genes, depicted in the graphic as orthogonality of the associated vectors; the very small number of genes common to pairs of contrasts are shown. (C) The large number of differentially expressed genes common to the pairs of non-orthogonal contrasts are shown.(PDF)Click here for additional data file.Figure S3Principal component analysis (PCA) produced with 30 genes selected from the top genes from each contrast ranked by adjusted p-value. The genes were selected as follows: 7 top genes were selected from each of the 6 contrasts, and the 30 with the lowest adjusted p-values used in subsequent analysis. The scatterplot of the first two principal components for all honeybee samples (average for Cy3 and Cy5 replicates) is shown.(PDF)Click here for additional data file.Figure S4Summary of numbers of differentially expressed immune-related genes. The number of up- and down-regulated genes in each contrast are marked, respectively, as ↑ and ↓. An up-regulated gene level is higher at the head arrow showing the contrast; commonality is shown in brackets.(PDF)Click here for additional data file.Figure S5Correlation between the virus levels in honeybee pupae and the corresponding mites. Two-dimensional plots showing the results of the qRT-PCR quantification of viral RNA in the honeybee pupae  and the corresponding Varroa mites  from experiment groups VL and VH. Panels shows results of (A) total DWV-like virus quantified with the primers recognising the NS region of DWV, VDV-1 and KV, then specific quantification of (B) VDV-1 CP, (C) VDV-1 NS, (D) DWV CP and (E) DWV NS regions.(PDF)Click here for additional data file.Figure S6Quantification of negative strands of DWV RNA in the Varroa mites of the groups VH and VL. The graph shows average copy number per mite of DWV- and VDV-1-like CP-coding sequence as determined by negative-strand specific qRT-PCR using primers listed in (PDF)Click here for additional data file.Figure S7Genetic diversity of DWV in the honeybee groups. Average Shannon's diversity index values. (A) the CP region positions 1751 to 4595, (B) the NS region, positions 5008 to 9826, and the central region, positions 5250 to 6250. Positions are given for the reference DWV genome, GenBank Accession number AJ489744. Average Shannon's index was calculated for five random 3285-read samples from the viral reads for each NGS library of individual bees. Bars indicate SD. Letters above the bars represent statistically significant groupings according to Fisher's Least Significant Difference (LSD) test as 5% and 0.1% levels, marked with * and *** respectively. The dashed lines indicate the average Shannon's diversity index values for the NGS sequencing error, ± standard deviation (SD). In panel (C) the dotted line at 0.0417 marks the Shannon's diversity index for Group NV of the frame transfer experiment.(PDF)Click here for additional data file.Table S1Oligonucleotides used in this study. Primer descriptions are given as follows: target (position in DWV or VDV-1 nucleotide sequence), polarity . GenBank accession numbers used to express primer positions are AJ489744 (DWV), NC_006494 (VDV-1), and AB242568 (Varroa destructor β-actin mRNA).(PDF)Click here for additional data file.Table S2Honeybee genes differentially expressed in the experiment. Shown are, the honeybee OGS1 gene ID, adjusted P-value, average expression levels in the groups , and the contrasts where the given gene is differentially expressed (NV-C (1), VL-C (2), VH-C (3), VL-NV (4), VH-NV (5), VH-VL (6)).(PDF)Click here for additional data file.Table S3Summary of gene expression commonality between the contrasts. Number of differentially expressed genes common to pairs of contrasts. In each cell (corresponding to the pair of contrasts) the three figures show the commonality for all differentially expressed genes in the pair, the up-regulated genes in the pair, and the down-regulated genes in the pair respectively.(PDF)Click here for additional data file.Table S4Significantly overrepresented GO terms  in the contrasts .(PDF)Click here for additional data file.Table S5Summary of the small RNA sequencing in the experimental groups. The single read libraries were aligned using Bowtie Apis mellifera miRNA (PDF)Click here for additional data file.Table S6Correlation of virus-specific siRNA coverage between experimental groups. Pearson correlations, P<0.001 are shown. The small RNA libraries determined by high-throughput sequencing were aligned to the DWV or VDV-1 sequences (GenBank Accession numbers GU109335 and AY251269 respectively) using bowtie (PDF)Click here for additional data file.Table S7Summary of the NGS libraries and consensus viral sequences from individual honeybees from Varroa-infested colony. RNA-seq libraries were produced using poly(A) RNA extracts. The reads were aligned to the reference full-length DWV and VDV-1 sequences, GenBank Accession numbers GU109335 and AY251269 respectively, using the “ —very-sensitive-local” option which allowed the highest number of mismatches. The aligned reads were used to generate consensus nucleotide sequences. The assembled viral sequences showed highest identity with the DWV-VDV-1 recombinant clone identified in the sampled colony (GenBank Accession number KJ437447) and the group VH sequences (e.g. GenBank Accession number JX661656).(PDF)Click here for additional data file.Table S8Differential expression of putative homeobox genes in the contrasts.(PDF)Click here for additional data file.Text S1Next generation sequencing: error correction and diversity testing outline.(PDF)Click here for additional data file.


Immunogenic lipids may play key roles in host defenses against infection and in generating autoimmune inflammation and organ-specific damage. In multiple sclerosis (MS) there are unequivocal autoimmune features and vulnerability to aggravation or induction by microbial or viral infection. We have found glycolipid-driven anergy of circulating lymphocytes in MS indicating that this immune response is affected in MS and the robust effects of iNKT activation with potent cellular and cytokine activities emphasizes its potential importance. Diverse glycolipids including the endogenous myelin acetylated-galactosylceramides  can drive activation that could be critical to the inflammatory demyelination in the central nervous system and clinical consequences. The iNKT cells and their invariant or iTCR (Vα24Jα18Vβ11) receptor an innate defense–a discrete immune arm that is separate from peptide-driven acquired immune responses. This offers new possibilities for insight including a likelihood that the pattern recognition of exogenous microbial and myelin immunogens can overlap and cross-react especially in an inflammatory milieu. A relatively unexplored frontier in multiple sclerosis (MS) research is the role of immunogenic lipids and particularly complex glycolipids (GLs) and sphingolipids in disease mechanisms. Unlike peptides that reside internally, complex lipophilic molecules are on the microbial surface and first encounter host immune defenses in the course of infection. Hence, they qualify as possible “original antigens” igniting immune responses that can lead in the susceptible host to breaking tolerance and enabling autoimmune reactivity, inflammation, demyelination and symptomatic MS. The detailed mechanism of the development of intolerance is problematic but molecular over-lapping implicit in such suggestions as molecular mimicry  or polysThe target organ of MS is mainly myelin though other CNS cells including neurons and their axons, astrocytes, microglia, endothelial cells or pericytes may also be affected. Our interest in the lipids of myelin and their established and potential roles as immunogens and bioactive mediators in the mechanisms of the inflammatory demyelination of MS has been described –6 and wi+ or other T cells with a role for B cells too. The demyelination is largely inflammatory in nature and the pathogenic events are believed to follow a sequence for T cells of initial priming, activation in the periphery by cytokines or other means, migration across the endothelial cell and blood-brain barrier, and re-activation during CNS invasion. There have been many reports describing changes in lipids in MS plaque lesions and in normal-appearing white matter (NAWM). The main or most compelling ones will be briefly reviewed in the following with particular attention to gangliosides, sulfatides, phospholipids, and AcGalCer. In the past, AcGalCers have also been operationally called fast-migrating cerebrosides (FMCs) reflecting the TLC behavior that enabled resolution from gluco- and galactosylceramides and purification for structural characterization  and (c.) a cranial nerve variant of GBS, Miller Fisher syndrome (MFS) [C. jejuni isolates from patients with pure motor or axonal GBS frequently express a GM1-like and GD1a-like glyco-epitopes on liposaccharides (LPS), whereas those isolated from patients with ophthalmoplegia or MFS usually express a GD3-like, GT1a-like, or GD1c-like LPS. Antibodies in these patients are usually cross-reactive, and recognize LPS as well as gangliosides or ganglioside complexes strongly suggesting that molecular mimicry of PNS myelin gangliosides and microbial LPS is a mechanism of the autoimmune neuropathy [Molecular overlap and consequent cross-reactivity also occurs in several other immune-mediated inflammatory neurological disorders involving peripheral nervous system (PNS). In these disorders there is relatively strong experimental support for a role of GL and particularly the glycose moiety as immunogenic. Immune mechanisms play a crucial part in Guillain-Barré syndrome (GBS) pathogenesis. About two-thirds of GBS patients have symptoms of an infection in the 3 weeks before the onset of weakness. The most frequently identified infection is me (MFS) . Serum ame (MFS) . In addime (MFS)  and multuropathy .Identifying the antigens that play roles in the pathogenesis of maladaptive MS autoimmunity is crucial to understanding MS. Within a context of genetic and environmental factors that are complex and not fully understood but with key roles for both determinants, microbial triggering of autoimmunity occurs when the usual immune defense against pathogenic microbes with concurrent tolerance for myelin antigens loses non-self/self discrimination and allows myelin antigens to react with host immune elements and produce myelin-specific inflammation and damage. The conformational plasticity of complex lipids and especially GLs suffices for the range of an innate pattern recognition receptor the iTCR, Vα24Jα18Vβ11, and allows overlapping molecular diversity with consequent loss of tolerance leading to maladaptive organ-specific inflammation affecting CNS and particularly myelin. Our studies of the GL-reactive separate immune arm mediated by iNKT cells that revealed anergy of circulating lymphocytes in MS to the potent exogenous stimulant α-GalCer now extend to endogenous myelin lipid antigens; the acetyl-β-GalCer series. The indication that utilization of the iNKT pathway is a feature of the infection-MS transition linking infection and autoimmune disorder not only offers novel insight into MS pathogenesis but may also have more fundamental importance indicating a mechanism for organ GLs to initiate induction of numerous other organ-targeted autoimmune disorders such as myasthenia gravis and rheumatoid arthritis. To us the prospect of gaining molecular insight into the mechanism of the infectious trigger adds welcome light to the darkness enveloping the enigmatic and disabling scourge MS.


Nostoc sp. PCC 7120. Here we combine this dataset with a prediction of Rho-independent transcriptional terminators and an analysis of phylogenetic conservation of potential sRNAs among 89 available cyanobacterial genomes. In contrast to predictive genome-wide approaches, the use of an experimental dataset comprising all active transcriptional start sites  facilitates the identification of bona fide sRNAs. The output of our approach is a dataset of predicted potential sRNAs in Nostoc sp. PCC 7120, with different degrees of phylogenetic conservation across the 89 cyanobacterial genomes analyzed. Previously described sRNAs appear among the predicted sRNAs, demonstrating the performance of the algorithm. In addition, new predicted sRNAs are now identified that can be involved in regulation of different aspects of cyanobacterial physiology, including adaptation to nitrogen stress, the condition that triggers differentiation of heterocysts . Transcription of several predicted sRNAs that appear exclusively in the genomes of heterocystous cyanobacteria is experimentally verified by Northern blot. Cell-specific transcription of one of these sRNAs, NsiR8 (nitrogen stress-induced RNA 8), in developing heterocysts is also demonstrated.Small RNAs (sRNAs) are a growing class of non-protein-coding transcripts that participate in the regulation of virtually every aspect of bacterial physiology. Heterocystous cyanobacteria are a group of photosynthetic organisms that exhibit multicellular behavior and developmental alternatives involving specific transcriptomes exclusive of a given physiological condition or even a cell type. In the context of our ongoing effort to understand developmental decisions in these organisms we have undertaken an approach to the global identification of sRNAs. Using differential RNA-Seq we have previously identified transcriptional start sites for the model heterocystous cyanobacterium  Small RNAs (sRNAs) are involved in virtually every aspect of bacterial physiology and especially in adaptation to variable nutrient availability and biotic or abiotic stresses  sp. PCC 7120, a model heterocyst-forming cyanobacteria, resulting in a genome-wide map of all the positions in which active promoters lead to transcription of different types of RNAs, including antisense and putative sRNAs , we compare all sequences possibly encoding sRNAs in the reference genome with all other genomes available for cyanobacteria with the exception of the fast-evolving clade of Prochlorococcus and marine Synechococcus. Experimental validation confirms transcription of several previously unknown sRNAs predicted by the algorithm implemented, including NsiR8 (nitrogen stress induced RNA 8), a new HetR-dependent heterocyst-specific sRNA.In this work we have undertaken a predictive approach to the identification of sequences transcribed as sRNAs in cyanobacteria, using Nostoc sp. PCC 7120, hetR mutant 216  and grown photoautotrophically at 30°C in BG110C medium methyl-2-aminoethanesulfonic acid-NaOH buffer (pH 7.5) until exponential phase. To induce nitrogen deficiency, cells grown in the presence of ammonium were collected by filtration using nitrocellulose membranes (0.45 μm pore diameter), washed with and resuspended in nitrogen-free BG110C medium. RNA samples were isolated from cells taken at the time points indicated after removing combined nitrogen from the media. For the experiment shown in Figure 4D, the cells were grown at 30°C in BG11C medium containing NaNO3 . 32P-labeled oligonucleotides (labeled with polynucleotide kinase and γ-32P-dATP) were used for primer extension analysis and Northern blot of the RNA transcribed from position 4547237r. Oligonucleotides used for primer extension and to amplify PCR products used as templates to prepare 32P-labeled probes are shown in Table Total RNA was isolated using hot phenol as described  were downloaded from RefSeq (NCBI Reference Sequence Database). Rho-independent transcriptional terminators were predicted on each replicon using TransTermHP 2.08 with default parameters  of the reference genome used  rendering a total of 3364 possible terminators.We have carried out a prediction of genomic sequences that could encode sRNAs using a TSS dataset from a dRNA-Seq-based transcriptomic analysis of We implemented a pipeline  and the two nutritional conditions analyzed (growth in the presence of ammonia or in the absence of combined nitrogen for 8 h) is also included.Upon inspection of the selected sequences we noticed that for some of the predicted sRNAs there were several nearly identical copies in the reference genome that appeared among the sequences selected by the algorithm, indicating that more than one copy was associated to a nTSS defined by RNA-Seq (and therefore being actively transcribed under the growth conditions used). Thus, we pooled all predicted sequences resulting from the algorithm and compared them by BLASTn, so that identical sequences were further grouped into families. Each family is represented by only one member in the output table , therefore leading to a final number of 327 entries. The complete table containing all results for the seven replicons is presented in Table Nostoc sp. PCC 7120. Previously described NsiR1  or because their TSS are not annotated as nTSS in the RNA-Seq dataset (ssaA encoding 6S RNA and ffs encoding 4.5S RNA). The latter is also the case for NsiR4  and other 88 completed or draft genomes from phylogenetically diverse cyanobacteria  appear in every cyanobacterial genome analyzed Figure . Yfr1, iNostoc sp. PCC 7120 and a weblogo obtained for the putatively translated peptide in 83 strains are shown in Figure Careful inspection of some of the alignments for sRNAs with many homologs allows the identification of conserved features. For instance, conservation of the sequence 5′CACUCCUCACACC3′ in all the homologs of Yfr1 Figure . SimilarThe approach we described here is based on a dataset of transcriptional start sites defined by dRNA-Seq. The number of reads associated to each TSS by RNA-Seq is influenced by several factors, including size of the transcript  or transcription under specific physiological conditions different from the standard growth conditions . Also, sRNAs that are processed in the 5′ end are under-represented because the dRNA-Seq procedure counter-selects processed, non-primary 5′ ends. In this context we hypothesized that the analysis of phylogenetic conservation of possibly transcribed sequences could facilitate the identification of true sRNAs with small numbers of reads. As an example, among the phylogenetically conserved predicted sRNAs (with homologs in more than 10 heterocystous strains), we have experimentally verified transcription of one of the predicted sRNAs with the lowest number of reads Figure .According to our predictive approach, transcription from position 4547237r, for which only eight reads are mapped in dRNA-Seq, would produce an sRNA slightly above 50 nucleotides in size, therefore in the lower size limit for the fractionated RNA sample used in RNA-Seq. 12 out of the 27 genomes for heterocystous strains included in the homology search contain sequences that the algorithm identifies as homologs with the parameters used Figure . The con2. The TSS dataset for the model heterocystous cyanobacterium Nostoc sp. PCC 7120 contains combined information for the transcriptome of the wild-type strain and a hetR mutant (heterocyst-defective) both in the presence of combined nitrogen and after 8 h of nitrogen deficiency  and differentiation of heterocysts (HetR). Therefore, we expect these two sRNAs to be involved in the response to other stresses as well.Table hetR mutant, therefore suggesting the last two sRNAs could be related to heterocysts differentiation or function. An schematic representation of the genomic context of the sRNAs included in Figure In addition to previously described NsiR3 , the algorithm identifies a transcribed sRNA.Differential RNA-Seq-based datasets containing genome-wide maps of active promoters provide unprecedented details on transcription in bacteria. Using a previously available dataset for the model organism Nostoc sp. PCC 7120, whereas some of the sRNAs predicted by the algorithm appear widely distributed among relatively distant cyanobacterial strains , homologs to a few predicted sRNAs are exclusively found in the genomes of heterocystous strains . For some of the predicted sRNAs, in particular those that are specifically transcribed in heterocysts  a possible function related to heterocyst differentiation or function can be suspected. Finally, phylogenetic conservation of certain sequences in the predicted sRNAs can suggest translation of a peptide  or possible interactions with other RNA or protein molecules . The predicted sRNAs described in this work, several of them experimentally validated here, constitute a set of potentially regulatory elements to be analyzed in further detail in heterocystous cyanobacteria.Phylogenetic conservation of sequences predicted to be transcribed as sRNAs in related (or even distant) genomes provides additional information concerning a possible functional role. In the case of gfp fusions and performed confocal fluorescence microscopy analysis. All authors were involved in analyzing the results and writing the manuscript.AM and MB designed the computational approach. MB wrote scripts and implemented the sRNAs prediction. MB and AV performed all other computational tasks. MB, EO, and AM performed experimental validation of sRNAs. AM prepared This work was supported by grant BFU2013-48282-C2-1  to AM. MB is the recipient of a fellowship from Agencia Estatal CSIC, Spain (Jae Intro) and a contract from Ministerio de Educación Cultura y Deporte, Spain (FPU014/05123).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The aim of the present meta-analysis was to evaluate the effect of disease-modifying drugs (DMD) on brain atrophy in patients with relapsing-remitting multiple sclerosis (RRMS) using available randomized-controlled trial (RCT) data.We conducted a systematic review and meta-analysis according to PRISMA guidelines of all available RCTs of patients with RRMS that reported data on brain volume measurements during the study period.2 = 30%, p = 0.19) nor publication bias in the Funnel plots. Sensitivity analyses stratifying studies according to brain atrophy neuroimaging protocol disclosed no evidence of heterogeneity (p = 0.16). In meta-regression analyses, the percentage change in brain volume was found to be inversely related with duration of observation period in both DMD  and placebo subgroups . However, the rate of percentage brain volume loss over time was greater in placebo than in DMD subgroup .We identified 4 eligible studies, including a total of 1819 RRMS patients . The mean percentage change in brain volume was found to be significantly lower in DMD versus placebo subgroup . We detected no evidence of heterogeneity between estimates (IDMD appear to be effective in attenuating brain atrophy in comparison to placebo and their benefit in delaying the rate of brain volume loss increases linearly with longer treatment duration. Longitudinal studies have shown that brain atrophy is a significant predictor of subsequent long-term neurologic deterioration, impaired life quality and sustained disability in patients with multiple sclerosis (MS) . Brain aBrain atrophy may occur in early disease stages, progresses more rapidly than in healthy individuals and appears to advance relentlessly throughout the course of MS, independent of the underlying disease subtype when adjusted for baseline volume ,5. A recTo the best of our knowledge the efficacy of DMD in attenuating brain atrophy in MS patients has not been investigated systematically using a meta-analytical approach. In view of the former considerations we conducted a systematic review and meta-analysis to evaluate the effect of available DMD on brain atrophy in patients with RRMS using available RCT data.This meta-analysis has adopted the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for systematic reviews and meta-analyses . EligiblAll retrieved studies were scanned independently by two reviewers (GT & AHK) to include only placebo-control RCTs of RRMS patients that reported changes in brain volume during the study period. We excluded from the final analysis: 1. Observational studies, 2. case series, 3. case reports, 4. RCTs without placebo subgroups, 5. studies reporting the use of drugs that are still not officially approved , and 6. studies reporting brain volume data with median values or not providing measures of dispersion in the form of standard deviation (SD). We considered that including trials reporting brain volume loss both in medians  and in means .Unadjusted mean differences of percentage changes in brain volumes between treatment and placebo subgroups were pooled as standardized mean differences (SMDs). SMD estimates were calculated as the mean differences divided by the corresponding pooled standard deviations and were subsequently interpreted using a general rule of thumb reported by Cohen, in which an SMD of 0.2 represents a small effect, an SMD of 0.5 represents a medium effect, and an SMD of 0.8 or larger represents a large effect . A rando2 statistics. For the qualitative interpretation of heterogeneity, I2 values of at least 50% were considered to represent substantial heterogeneity, while values of at least 75% indicated considerable heterogeneity, as per the Cochrane Handbook [Heterogeneity between studies was assessed with the Cochran Q and IHandbook . PublicaHandbook .We subsequently conducted subgroup analyses according (i) to the MRI protocol that was used for the measurement of brain volume changes as well as the assessment of brain atrophy and (ii) the year of the treatment on the studies that provided data on brain volume changes for both time-periods . The mixed-effects model was used to calculate both the pooled point estimate in each subgroup and the overall estimates. According to the mixed-effects model, we used a random effects model (DerSimonian Laird) to combine studies within each subgroup and a fixed effect model (Mantel—Haenszel method) to combine subgroups and estimate the overall effect. We assumed the study-to-study variance (tau-squared) to be the same for all subgroups. Tau-squared was first computed within subgroups and then pooled across subgroups.Finally, we performed post-hoc meta-regression analyses to evaluate both time alone (in placebo subgroups) and time-on-treatment (in treatment subgroups) as possible moderators of the percentage brain volume change. Univariate meta-regression analyses were performed using the random-effects model (Method of Moments). The regression lines derived from the aforementioned univariate meta-regression analyses were compared using the analysis of covariance (ANCOVA) to determine the effect of treatment on percentage brain volume change over time.Statistical analyses were conducted using Review Manager (RevMan) Version 5.2 software  and Comprehensive Meta-analysis Version 2 software .Systematic search of MEDLINE and SCOPUS databases yielded 60 and 57 results respectively. Subsequent search in the CENTRAL Register of Controlled Trials retrieved no additional RCTs. After removing duplicates, the titles and abstracts from the remaining 75 studies were screened and 17 potentially eligible studies for the meta-analysis were retained. After retrieving the full-text version of the aforementioned 17 studies, 4 studies were excluded because they did not include a placebo subgroup and 2 studies because they were not RCT (cohort studies), 6 studies because they reported the brain volume changes in median values and 1 study because it did not report the SDs or other measures of dispersion . In the sentitivity analysis stratifying patients according to MRI protocol that was used for the measurement of the brain volume change, there was no evidence of heterogeneity in the estimates between the studies using the SIENA or the BPF method  and second (12–24 months) treatment year ,10, the Publication bias was evaluated using inspection of funnel plot asymmetry in view of the small number (n = 4) of included studies. The slight asymmetry observed on the funnel plot inspection can be attributed to the existing differences among studies with regard to neuroimaging protocols  and linearly increases with longer treatment duration.In untreated MS patients receiving placebo, the rate of atrophy is about 1%-1.5% of brain volume per year . Given tOur sensitivity analyses indicated that the pooled beneficial effect of DMD on brain atrophy doubled during the second (SMD: -30) in comparison to the first year of treatment (SMD: -14). This observation may be attributed to an increase in non-tissue-related brain volume loss during the first 6–9 months of disease modifying therapies (termed pseudoatrophy), thought to be due to the resolution of inflammation and brain edema ,26. InteOur meta-regression analyses underline that the protective effect of DMD on brain volume loss is accentuated with longer treatment duration. This observation raises the clinical hypothesis that early initiation of DMD may extend the therapeutic time window leading to greater cumulative benefit in terms of brain atrophy prevention. This hypothesis is in agreement with pathology data indicating that acute axonal damage occurs early during disease course and consequently early treatment with disease-modifying therapies may prevent axonal and brain volume loss leading to disability progression . FurtherCertain limitations of this report need to be acknowledged. First, 8 study protocols were excluded from the quantitative analysis, as they reported the mean brain volume percentage changes in median values or did not provide the corresponding measures of dispersion in SDs. As a result the number of included studies was relatively small. Despite evaluating independent data from the different subgroups and/or time-points of each study in the overall analysis and meta-regression models, the findings of the present meta-analysis need to be reproduced in future using a larger number of RCTs. Second, even though quality control of the included studies suggests an overall low risk of bias, we cannot exclude bias related to funding source, as all study protocols had financial and/or other support from pharmaceutical industries with a clear conflict of interest on the study outcomes. Third, variable DMD , in different dosages (Fingolimod 1.25mg/0.5 mg) and in different routes of administration  were investigated in different RCTs. Thus, it is possible that the aforementioned differences in the treatment subgroups could be-at least partially- responsible for the identified correlations, even though no evidence of heterogeneity was detected among trials. Fourth, the imaging methods that were used for the measurement of brain volume change were not identical in the included studies and this may have confounded the reported associations. However, it should be kept in mind that no significant heterogeneity was detected between trials using different neuroimaging protocols in our sensitivity analyses. Finally, this meta-analysis evaluated only the effect of DMD on brain volume loss and our findings are not applicable to other treatment options currently evaluated in MS such as immunosuppressive or chemotherapy agents that have been shown to induce excessive and sustained brain volume reductions in MS patients ,27,31.S1 FileTable A. List of the full-text excluded articles with the reasons for exclusion. Supplemental references in S1 File. Fig. A. Risk of bias summary: review authors’ judgments about each risk of bias item for each included study. Fig. B. Risk of bias graph: review authors’ judgments about each risk of bias item presented as percentages across all included studies. Fig. C. Subgroup analysis according to the imaging protocol that was used to measure the changes in brain volume. Figs. D&E. Funnel plots of the included studies.(RTF)Click here for additional data file.