Foodborne norovirus outbreak data in Japan from 2005–2006, involving virological surveillance of all symptomatic and asymptomatic individuals, were reanalyzed to estimate the asymptomatic ratio of norovirus infection along with the risk of infection and the probability of virus shedding.Employing a statistical model that is considered to capture the data-generating process of the outbreak and virus surveillance, maximum likelihood estimation of the asymptomatic ratio was implemented.n = 55) were the result of random sampling from an identical distribution and ignoring genogroup and genotype specificities, the asymptomatic ratio was estimated at 32.1% . Although not significant, separate estimation of the asymptomatic ratio of the GII.4 genotype appeared to be greater than other genotypes and was estimated at 40.7% .Assuming that all norovirus outbreaks (The present study offered the first explicit empirical estimates of the asymptomatic ratio of norovirus infection in natural infection settings. The estimate of about 30% was consistent with those derived from volunteer challenge studies. Practical difficulty in controlling GII.4 outbreaks was supported by the data, considering that a large estimate of the asymptomatic ratio was obtained for the GII.4 genotype. Caliciviridae family, and are responsible for a substantial part of acute viral gastroenteritis in humans.1,2 Norovirus outbreaks have happened both in developing and industrialized nations.3 Once infected, symptoms are characterized by non-bloody diarrhea, vomiting, nausea, and abdominal cramps, and there has been no specific treatment to accelerate the cure of patients infected with the virus.4 Vaccines have yet to be fully developed and brought into practice,5 and the presence of various genotypes prevents infected human from acquiring sufficient specific immunity.6Noroviruses are an evolutionarily diverse group of single-stranded positive-sense RNA viruses without the envelope, belonging to the 7 while such asymptomatically infected individuals are known to excrete substantial volume of viruses.8,9 Vomit from infectious individuals contains over 107 copies/gram of noroviruses,8,9 and infectious vomitus is known to be sometimes aerosolized,10,11 making it difficult to prevent secondary transmissions. The virus spreads through fecal-oral routes, such as via contaminated water, food, and people’s contaminated hands.12,13 Published studies indicate that infections associated with environmental contaminants are mainly caused by contaminated water or foods ,11 accounting for seasonal outbreaks that are frequently observed in winter.14 Given these features, workers who are directly engaged in cooking and handling of foods, such as cooks, food servers, and food factory workers, are considered as one of the key subjects for prevention of secondary transmissions.15–17Published studies have reported that around 30% of norovirus infection remains asymptomatic,18 As of 2017, the most prevalent genotype in humans is GII.4,19,20 which accounts for as many as 80% of all reported norovirus infections with virus isolation.21 Some pieces of evidence indicate that this genotype evolves rapidly, escaping from selection pressure.22,23 In Japan, the majority of outbreaks are caused by genogroup II, and a large proportion is caused by GII.4.24 GII.4-associated epidemics in Japan were first observed in 2006, and the genotype has been continuously observed every year since then.25One of the notable virological features of noroviruses is genotype variation, which is divided into five genogroups and many genotypes in each genogroup.5,26; moreover, it is unclear if the natural history of experimentally infected individuals are similar to those based on natural infection. A small number of mathematical modelling studies estimated a part of the abovementioned values,27–30 but explicit model-based estimates of the asymptomatic ratio have yet to be offered. The present study aims to estimate the asymptomatic ratio of norovirus infection, reanalyzing foodborne outbreak data with laboratory testing in Japan, along with other parameters, including virus shedding frequency and the risk of infection. In addition to estimating the asymptomatic ratio for all noroviruses, the present study also compares the estimate across different genogroups and genotypes.To decipher the most effective preventive measures against this virus, it is vital to quantitatively clarify the natural history characteristics, including asymptomatic ratio, the risk of infection per exposure, and the probability of virus shedding. Nevertheless, explicit estimates are very scarce, except for rigorous challenge studies that helped quantify the natural historyn = 55 outbreaks).31 In that study, a total of 2,229 involved individuals, including food-handlers, other workers, and potentially exposed customers, were examined for the presence of symptoms (ie defined as either non-bloody diarrhea or vomiting) and also virus shedding from their feces, conducting a survey shortly after each outbreak (within 1 week). Asymptomatic individuals were defined as persons without diarrhea and vomiting, and the asymptomatic ratio was defined as the proportion of asymptomatically infected individuals among the total of infected individuals. Through stool specimen tests using highly sensitive real-time reverse transcription polymerase chain reaction (RT-PCR), which has the highest detection limit among all existing genome testing methods32 the virus and its genogroup/genotype were identified. Due to its highly sensitive nature, the real-time RT-PCR was shown to detect positive norovirus specimens if >10 copies were included in a well.32 Since asymptomatic healthy individuals as well as symptomatic cases were tested, the results in each outbreak are classified into four categories, ie, (i) symptomatic and virus positive, (ii) symptomatic but virus negative, (iii) asymptomatic and virus positive, and (iv) asymptomatic and virus negative. In addition to the number of people in each category for each outbreak, genotyping results were obtained. Figure 31 has been very rigorous, in that all individuals involved in each food-borne outbreak were fully surveyed, which has been extremely rare and not routinely attainable. In addition, such a study must employ the highly sensitive real-time RT-PCR, a condition that was also satisfied by the 2005–2006 study.31 Moreover, all events were observed in the same year in Japan within food handling facilities of comparable sizes.We reanalyzed a published dataset that tested both symptomatic and asymptomatic individuals during norovirus outbreaks at food catering settings in Japan from November 10, 2005 to December 9, 2006 (p be the risk (or the probability) of norovirus infection given an exposure at food handling center, and similarly, let s be the asymptomatic ratio of norovirus infection, representing the probability that infected individuals escape from symptomatic illness.33 Moreover, let q be the probability of virus detection from an infected individual. While these parameters are allocated, all these processes are not directly observed in the original survey. The epidemiological information we have an access includes (i) the total number of people involved  in each outbreak, (ii) the number of symptomatic cases, and (iii) the numbers of symptomatic virus shedding cases and asymptomatic virus shedding cases. From these pieces of information, we estimate p, q and s jointly.Here, we describe a statistical model with which the asymptomatic ratio and other parameters were jointly estimated using the abovementioned foodborne outbreak data. First, the data generating process is schematically illustrated in Figure i be an identity of an outbreak . All three parameters were dealt with as deterministic parameters; we did not consider demographic stochasticity in the data generating process. Since observed processes are the single and independent success/failure process, the resulting process is a Bernoulli trial, and thus, we assume that each observed event was a result of binomial sampling . The probability that ni cases experience symptomatic illness among a total of ni symptomatic and mi asymptomatic individuals is described byyi cases are asymptomatic but positive among a total of mi asymptomatic individuals  isxi cases shed the virus among a total of ni symptomatic cases isq was assumed as independent of symptoms because the RT-PCR testing employed was highly sensitive and the cut-off value must have allowed q to be comparable between symptomatic and asymptomatic infected individuals.8,26 Sensitivity analysis was conducted to address potential difference of q between symptomatic and asymptomatic infection (see below). It should also be noted that the parameter p should have ideally reflected the demographic stochasticity of the data generating process, but p is a mixture of food-borne and human-to-human transmissions and we do not have a sufficient dataset to characterize the distribution. Thus, the abovementioned process in principle adopted a deterministic modeling approach and measured the uncertainty of p based on sampling error alone.Let p, q and s. These parameters were estimated assuming several different interpretation scenarios of empirical data. First, a single set of p, q, and s was estimated, assuming that each outbreak was a random sampling result from the abovementioned mechanisms (1)–(3). Second, parameters were assumed to be different between genogroup 1 and 2, so there were six unknown parameters. Third, parameters were assumed to be different by genotype. When estimating parameters by genotype, it should be noted that the available number of outbreaks differed by genotype , and we excluded outbreaks in which multiple genotypes were identified. To identify any improved fit despite increased number of parameters, Akaike Information Criterion (AIC) was computed for each scenario.A maximum likelihood method was employed to estimate parameter values, L for each single interpretation scenario, computed asp, q, and s were obtained by minimizing the negative logarithm of (4), and the 95% confidence intervals (CI) were computed using the profile likelihood.Using (1)–(3), we derive the total likelihood q0 is the probability of virus shedding from an asymptomatic case and q1 is the probability from a symptomatic case. We set the baseline value of α as 1.00, which indicates that asymptomatic and symptomatic cases have the same probability of virus shedding , which is supported by empirical observation.31,32 However, as part of sensitivity analysis, the value of α was changed from 0.50 to 1.00, anticipating that virus shedding frequency from asymptomatic cases might potentially be smaller than that from symptomatic cases.To analyze the sensitivity of parameter estimates to differential frequency of virus shedding between symptomatic and asymptomatic infections, we used the following α, ie,p, s, and q were estimated as 25.4% , 32.1%  and 73.2% , respectively. s estimated by genogroups and by genotypes did not reveal any significant difference from combined result, with estimated values ranging from 22.2–66.7% .Calculating the AIC for each interpretation scenario, the AIC of the genogroup-combined model  yielded the value of 1,227.9, while that of the genogroup-separated model  was 1,177.4. The smaller value of AIC for a genogroup-separated model indicated that the model with genogroup information was better fitted to outbreak data, so there might potentially be a difference between GI and GII. Figure p) and the asymptomatic ratio (s) were estimated to be greater. The probability of virus shedding among symptomatic cases remained stable , which could lead us to feel that the overall results may be highly influenced by those two outbreaks. Removing the two outbreaks and analyzing the remaining 53 altogether, parameters p, s, and q were estimated at 44.0% , 25.9%  and 73.1% , respectively. The risk of infection (p) was significantly greater than the estimate in which all 55 outbreaks were analyzed together, but two other parameters, including asymptomatic ratio (s) and the risk of virus shedding (q), were not significantly deviated.Of the total of 55 outbreaks, there were two outbreaks involving large number of subjects (p) and the asymptomatic ratio (s) were estimated to be 25.4% and 32.1%, respectively. The probability of virus shedding (q) was estimated at 73.2%. Possible differences by genogroup were implicated by penalized likelihoods. The risk of infection was small for GII.4, while the asymptomatic ratio was estimated to be high for this common genotype.The present study reanalyzed foodborne norovirus outbreaks in Japan from 2005–2006 that involved laboratory testing of all individuals including healthy people. To statistically infer asymptomatic ratio in joint with the risk of infection and the probability of virus shedding, a statistical model was developed to describe the data generating process. Assuming that all 55 outbreaks occurred due to repeated random samplings, the risk of infection ; moreover, evolutionary changes in GII.4  might have later varied the virus characteristics, including the infectiousness of GII.4. Evolutionary epidemiological study of GII.4 to be adapted to human populations is a subject of our ongoing study.There was no statistically significant difference between GI and GII, while the AIC value was indicative that there might be a difference between GI and GII. This was caused by limited sample size, and slight non-significant differences  were identified, implying that GI might be more controllable than GII. To our knowledge, the present study is the first to epidemiologically endorse different patterns of outbreaks by genogroups in natural settings, though differences have been indicated in experimental studies.8 Second, the model involved a few simplifications. The simplified points include the ignorance of the dependent happening in the risk of infection. Since the majority of outbreaks were considered as purely foodborne, a single parameter p was allocated, but if human-to-human transmission was involved, it might have been better to account for that information, which was not attainable in the present study using the same dataset. Another simplification was the use of binomial sampling process for each likelihood. As a consequence, the uncertainty bound of p in the present study should be regarded as an underestimate. One of the drawbacks has been seen in significantly greater p that we obtained when two big outbreaks were removed from analysis; in other words, the estimate might not have been significantly different if the uncertainty also reflected demographic stochasticity.Two limitations must be discussed. First, the survey data contained epidemiologically limited information. That is, to define symptomatic cases, diarrhea or vomiting was employed, but they were qualitative definitions that did not explicitly involve the frequency, such as the number of defecations/vomiting per day. Such qualitative definition is favored for clearly defining what a symptomatic case is, and an important advantage of the present study is that an identical research group has conducted the entire survey and minimized the measurement of error that could potentially arise from researchers. Nevertheless, when it comes to additionally measuring the severity of illness, which is out of the scope of the present study, it might have been unavoidable to include human perception error. Such misclassification could have potentially affected the asymptomatic ratio . Moreover, the exact timing of stool sampling was unavailable; however, it is likely that all sampling took place in a matter of 1 week from the onset of outbreaks, during which the virus shedding level of both symptomatic and asymptomatic cases should be substantial.Despite the abovementioned limitations, our study has successfully demonstrated that the asymptomatic ratio can be estimated from outbreak data in which all involved individuals undertook laboratory testing and also that the asymptomatic ratio was about 30% in natural outbreak settings. Practical difficulty in controlling GII.4 outbreaks was supported by its large estimate of the asymptomatic ratio. Collecting similar datasets from further outbreaks, we will be able to gain further insights into the natural history of norovirus infection, including genotype-specific differences. Understanding the natural history better, similar explicit estimates based on natural infection in future could help clarify better control strategies of norovirus outbreaks, even by genotype.


Macaca fascicularis model. Further, because the effect of virus passage in cell culture may be more pronounced when lower exposure doses are used, viruses that possessed either the characteristics of wild type viruses (possessing predominantly 7-uridine (7U) genotype and a high particle-to-plaque forming unit (PFU) ratio) or cell culture-passaged viruses (predominantly 8-uridine (8U) genotype, a lower particle-to-PFU ratio) were used. The time to death after a low dose exposure was delayed in comparison to higher exposure doses. These data demonstrated that an extremely low dose of EBOV or SUDV is sufficient to cause lethal disease. A low dose exposure model can help inform studies on pathogenesis, transmission, and optimization of prevention strategies.The filoviruses Ebola virus (EBOV) and Sudan virus (SUDV) can cause severe diseases, and there are currently no licensed countermeasures available for use against them. Transmission occurs frequently via contact with bodily fluids from infected individuals. However, it can be difficult to determine when or how someone became infected, or the quantity of infectious virus to which they were exposed. Evidence suggests the infectious dose is low, but the majority of published studies use high exposure doses. This study characterized the outcome of exposure to a low dose of EBOV or SUDV, using a  The filoviruses Ebola virus (EBOV) and Sudan virus (SUDV) are enveloped, negative-sense, single-stranded, RNA viruses that can cause disease with high case fatality rates , and theIn many of the initial outbreaks, contaminated needles were implicated in transmission . Now, diGP) gene via RNA editing. At the GP RNA editing site, the wild type virus genome contains seven uridines (7U) and without editing, messenger RNA transcripts contain seven adenosines and produce soluble GP, a truncated form of full length GP. When editing occurs, an eighth adenosine is incorporated into the transcript and full length GP is produced [During animal exposure studies, the effect of using virus that has been passaged in cell culture may be more pronounced when lower exposure doses are used. For example, EBOV and SUDV can generate multiple products from the glycoprotein  in compliance with the Animal Welfare Act and other federal statutes and regulations relating to animals and experiments involving animals. The facility where this research was conducted is accredited by the Association for Assessment and Accreditation of Laboratory Animal Care International and adheres to principles stated in the 8th Edition of the Guide for the Care and Use of Laboratory Animals, National Research Council [Nonhuman primates were observed at least twice daily for up to 21 days post-exposure, at which time any survivors were euthanized. Clinical scores were reported to the responsible veterinarian, and euthanasia was approved when scores indicated that an NHP was terminally ill. A brief description of the clinical scoring system (described previously in ) is as f2 in normal growth media that consisted of minimum Essential Media  containing 2 mM l-glutamine  and 1 mM sodium pyruvate  with 10% heat-inactivated fetal calf serum . Starting virus material consisted of Zaire ebolavirus isolate Ebola virus/H. sapiens-tc/COD/1995/Kikwit-199510621 and Sudan ebolavirus isolate Sudan virus/H. sapiens-tc/SDN/2000/Gulu-200011676, both passage number 2 (P2) on Vero E6 cells, acquired from Dr. T. Ksiazek, University of Texas Medical Branch. The 7U virus material used for NHP exposures was generated by amplifying P2 virus once in Vero E6 cells at a multiplicity of infection (MOI) of 0.001. The 7U EBOV stock was generated in September 2012 (lot number 201209171), and the abundance of genomes with 7U at the editing site locus was approximately 94%. The 7U SUDV stock was generated in June 2012 (lot number 201206211), and the abundance of genomes with 7U at the editing site locus was approximately 75%. The 8U virus used for SUDV exposure was generated (April 2013) by amplifying the aforementioned P3 virus 10 additional times [BEI-sourced Vero E6 cells  were grown at 37 °C with five percent CO April 20 by amplial times ; the abuTransmission electron microscopy, using a JEOL 100CX microscope , was used to determine the particle concentration of each virus stock, as previously described ,15. The Viral titers were determined by plaque assay using either a methylcellulose and crystal violet assay, or an agarose and neutral red assay (previously described in ). The meRNA was extracted from serum collected at time of death from all NHPs, and subjected to deep sequencing, as described before ,22. BrieMacaca fascicularis were intramuscularly (IM) injected in the deltoid muscle with a target dose of 0.01 PFU of a 7U virus stock (n = 4) or 8U virus stock (n = 4) of SUDV, or a 7U virus stock (n = 4) or 8U virus stock (n = 4) of EBOV . NonhumBiochemical analysis was performed using the mammalian liver enzyme profile rotor on a Vet Scan analyzer . Complete blood counts were performed using a Vet HM2 machine . Coagulation times were determined using the IDEXX Coag Dx Analyzer . Clinical Pathology at Texas Biomed has determined normal ranges for cynomolgus macaques housed under laboratory conditions at Texas Biomed facilities.The log-rank Mantel–Cox test was used to analyze the survival curves.M. fascicularis were exposed intramuscularly (IM) with a target dose of 0.01 PFU of either EBOV (n = 8) or SUDV (n = 8). Two different virus stocks were used for both EBOV and SUDV exposures: a 7U virus stock , or an 8U virus stock . Within each virus exposure group, four animals were exposed to the 7U virus stock, and the other four were exposed to the 8U virus stock . After exposure to 0.01 PFU of EBOV, three out of four NHPs exposed to 7U virus stock and zero out of four NHPs exposed to the 8U virus stock survived. The median time to death was undefined for the 7U group, and 10.5 days for the 8U group; there was no significant difference .The time to death for all animals is summarized in Animals were observed at least twice daily for up to 21 days post-exposure, at which time, any survivors were euthanized. Clinical scores over the course of the study for all animals are displayed in n = 4), three had titers greater than 1 × 107 PFU/mL at the time of death, and animal 956, which survived its exposure to 7U virus stock, had a serum titer of 3.4 × 105 PFU/mL on day 10 post-exposure. All NHPs that survived exposure to the 8U stock of SUDV (n = 3) exhibited no detectable levels of infectious virus throughout the study. For EBOV-exposed NHPs, in the 7U exposure group, only the NHP that did not survive exhibited evidence of viremia. All NHPs exposed to the 8U virus stock of EBOV had high serum titers on or near the day of death [On each scheduled sedation day , and when possible at the time of death, blood was collected from all NHPs for determination of viremia . For theof death .Most NHPs that survived exposure  exhibited no detectable levels of infectious virus throughout the study. Thus, it was possible the low infective dose did not result in infection. Deep sequencing was used in an attempt to determine the likelihood of infection. RNA was extracted from the serum collected at the time of death , and subjected to deep sequencing. Sequence reads were mapped to EBOV or SUDV genomes to determine whether there was evidence of viral RNA; the number of total sequence reads and median depth across the genome were calculated.Values for all EBOV-exposed animals were generally high. On average, animals that did not survive exposure to EBOV virus exhibited 1,059,602 sequence reads mapped to the genome , while animals that did survive exposure to EBOV virus exhibited an average of 19,131 sequence reads mapped to the genome . Overall,  and when possible, at the time of death. For SUDV-exposed animals, the 7U group exhibited higher temperatures than the 8U group. The 7U stock of SUDV-exposed animals exhibited febrility between days 7 and 14 post-exposure, temperatures of 39.7 to 40.2 °C (1.3 to 2 °C increases when compared to day 0 post-exposure). Only one NHP in the 8U SUDV group  exhibited febrility, a temperature of 39.6 °C (a 1.7 °C increase when compared to day 0) on day 10 post exposure. For EBOV-exposed animals, the 7U group did not exhibit substantial increases and the 8U group exhibited rectal temperatures of 39.4 to 39.9 °C (increases of 1 to 2.6 °C when compared to day 0), shortly before death (as described previously ).During scheduled sedations, blood samples were taken for the analysis of blood chemistry, coagulation times, and hematology. For SUDV-exposed NHPs, hematological analysis showed that over the duration of the study, in all animals, the percentage of granulocytes increased, while the percentage of lymphocytes decreased , consistFor EBOV-exposed animals, the NHPs that did not survive exhibited increased granulocytes and decreased lymphocytes after days 3 to 5 post-exposure . Two outThere were no clear differences in anatomical pathology between the 7U and 8U exposure groups for both SUDV and EBOV, and findings were similar to what would be expected from exposure with a higher dose. Animals that did not survive exposure exhibited similar macroscopic and microscopic findings, consistent with acute filovirus infection . Common M. fascicularis model of infection. M. fascicularis were exposed to 0.01 PFU of either SUDV or EBOV. The virus used for exposure was either predominantly the 7U genotype (characteristics of wild type virus) or predominantly the 8U genotype (characteristics of cell culture adapted virus). We found that exposure to a target dose of 0.01 PFU of the 7U stock of SUDV was lethal in 75% of NHPs, while the 8U stock caused lethality in only 25% of NHPs. Viremia and clinical pathology data indicated that 100% of the NHPs exposed to the 7U stock of SUDV became infected. One animal succumbed to exposure to the 8U stock of SUDV, while the three NHPs that survived exposure showed some evidence of possible infection via deep sequencing, which yielded reads mapped to the SUDV genome. Conversely, exposure to a target dose of 0.01 PFU of the 7U stock of EBOV caused lethality in only 25% of NHPs, while previously published data show that 0.01 PFU of an 8U stock is 100% lethal [Herein, we characterized the outcome of exposure to a low dose of SUDV and EBOV, in the % lethal . SequencIn previous studies, subtle differences were found between EBOV stock of viruses with predominately 7U and 8U genotypes when tested in animals   but the It is important to note though, the difference in survival between the 7U SUDV and 8U SUDV groups could also be related to the different particle to PFU ratios of the two SUDV stocks used. Filovirus particle to PFU ratios have been shown to decrease during cell culture adaptation ,16, so tPotential differences between the 7U and 8U SUDV exposure groups are not surprising, as the virus possessing characteristics of the wild type virus was lethal in the majority of exposed animals, while the virus possessing the characteristics of cell culture-adapted virus resulted in a lower lethality. One would expect that the cell culture passaged virus may be attenuated in vivo. However, any differences in the EBOV exposure groups are not necessarily expected, as the virus possessing characteristics of cell culture-adapted virus is the stock that appeared to be more lethal than the virus possessing the characteristics of the wild type virus. This could be related to the methods that were used to generate the stocks; the predominantly 8U genotype SUDV, possessing the characteristics of a cell culture-adapted virus, was generated through serial passage in cell culture, while the predominantly 8U genotype EBOV, possessing the characteristics of a cell culture-adapted virus, was passaged in cell culture but not to the same extent as the SUDV virus. This may also be a product of the low dose system used herein. However, due to the small group numbers, it is difficult to draw conclusions.The time to death after low dose exposures to both SUDV and EBOV was delayed in comparison to that observed by the authors during studies performed at the same time, but using higher exposure doses ,15. The However, this timeframe may more closely resemble the time to death observed during human infections ,27. In oThese data also demonstrate that a low number of particles is sufficient to cause death, and they are consistent with previous work showing that some particles do not yield plaques in cell culture, but they can nonetheless cause lethal disease. Previous studies have used a range of doses, and they have demonstrated a lethal dose as low as approximately 2 PFU ,28. Our More information about low dose exposures is needed to better understand transmission and persistence. Virus has been found in a variety of bodily fluids from infected individuals, including blood, semen, saliva, and sweat  but it iFinally, studies to develop and test public health countermeasures may benefit from the use of different exposure doses. The higher doses that are frequently used may be too stringent for suitable medical countermeasure testing. For example, studies with influenza have shown that high exposure doses can have exaggerated effects that increase pathogenicity and that mask the activity of antivirals, and thus, lower doses are often used when testing such countermeasures .In summary, we found that a low number of Ebola virus or Sudan virus particles is sufficient to cause death, and modeling the consequences of low dose exposure is important for gaining a better understanding of pathogenesis, the prevention of transmission, and countermeasure developments.


Prevalence of A(H9) virus in poultry and level of environmental contamination were also higher in LBMs with >1 wholesaler. We found a high level of circulation of both avian influenza viruses in surveyed LBMs. Prevalence was influenced by type of poultry, environmental site, and trading.We conducted a cross-sectional study in live bird markets (LBMs) in Dhaka and Chittagong, Bangladesh, to estimate the prevalence of avian influenza A(H5) and A(H9) viruses in different types of poultry and environmental areas by using Bayesian hierarchical logistic regression models. We detected these viruses in nearly all LBMs. Prevalence of A(H5) virus was higher in waterfowl than in chickens, whereas prevalence of A(H9) virus was higher in chickens than in waterfowl and, among chicken types, in industrial broilers than in cross-breeds and indigenous breeds. LBMs with  Birds of different types and from different geographic areas are introduced daily into LBMs and might be caged together, promoting local transmission of multiple virus subtypes and generating opportunities for reassortment , therefore being exposed to different pathogens and pathogen loads  and A(H9) viruses in marketed poultry and the LBM environment. We also accounted for the clustering effect at LBM level by using Bayesian hierarchical logistic regression models. Second, we assessed the effect of type of poultry and environmental site, and the position of LBMs in the poultry value chain on AIV prevalence.>2 poultry stalls sell live poultry at least once a week, and only those selling >400 poultry/day were considered eligible for this study. We aimed to sample 40 LBMs, and from each of these LBMs, 60 birds and 50 environmental sites  and Chittagong (n = 14) was proportional to the number of LBMs eligible in each city (n = 80 for Dhaka and n = 36 for Chittagong). In each city, we further stratified LBMs by size: 50% of the selected LBMs were large, trading the highest number of poultry (13 largest LBMs in Dhaka and 7 largest LBMs in Chittagong); 50% were small, randomly selected from the bottom 50% of eligible LBMs in terms of number of poultry traded.At the second sampling stage, we randomly selected stalls and environmental sites in each LBM independently for each type of poultry and environmental site. We created a list of stalls selling each poultry type for each LBM. We then selected stalls from these lists by using a random number generator. Likewise, for each type of environmental site, we selected sites from a list of sites identified in each LBM by using a random number generator. We collected 1 swab specimen from each environmental site. We pooled 5 swab specimens collected from the same LBM and site type.At the third sampling stage, for each poultry type, we randomly selected 5 birds from each of the stalls selected for that type and collected cloacal and oropharyngeal swab specimens from each of the selected birds. We pooled 5 swab specimens collected from the same stall and poultry type separately for cloacal and oropharyngeal swab specimens. We transported samples collected in Chittagong on the day of sampling to the Chittagong Veterinary and Animal Sciences University (Chittagong) and samples collected in Dhaka on the day of sampling to the Bangladesh Livestock Research Institute (Dhaka). Samples were stored at −80°C until diagnostic laboratory processing.,t) <40 for the AIV matrix gene for the H5 and H9 genes. Results were considered positive for the H5 subtype if Ct <38 and positive for the H9 subtype if Ct<40  and specific primers and probes  as mixed. Each LBM-level risk factor  was assessed separately because we could not include them simultaneously in any given model due to the small number of sampled LBMs. In models for poultry samples, we differentiated birds into 1) chicken and waterfowl or 2) broiler, Desi, Sonali , and waterfowl. Waterfowl were not differentiated further because of the small number of pools collected from ducks and geese. In models for environmental samples, we differentiated environmental sites into stall and slaughter areas or classified as environmental area without differentiation. We ran models  and higher-level (LBMs) risk factors. We developed separate models for poultry and environmental samples to avoid parameters related to different sampling units interfering with each other , and waterfowl in 25 LBMs (0–4 pairs/LBM). Broilers accounted for most samples (32.1%), followed by Desi (26.6%) and Sonali (25.6%). Ducks accounted for 76% of 75 pool pairs collected from waterfowl and geese accounted for 24%. We collected 10 environmental pools in each LBM .>1 A(H5) virus–positive poultry or environmental pool, 97.5% had >1 A(H9) virus–positive poultry or environmental pool. We determined the prevalence of pools that were positive for A(H5) and A(H9) viruses according to sample and LBM type (Of 47.4% (416/877) pools considered positive for AIV, 6.5% pools were negative for the AIV matrix gene but positive for any of the H9 and H5 subtypes. The H9 subtype (63.2% positive pools) was detected more frequently than the H5 subtype (21.6%), and 12.3% of pools were positive for both subtypes and 27.4% of pools were negative for both subtypes. Although 80.0% of the LBMs had LBM type .Approximately 33.3% of pools collected from waterfowl were positive for A(H5) virus, whereas only 5.5% of those collected from chickens were positive. In contrast, the prevalence of A(H9) virus–positive pools was higher in chickens (36.3%) than in waterfowl (18.7%). Among waterfowl, ducks (19.3%) and geese (16.7%) had a similar prevalence of A(H9) virus–positive pools, but the prevalence of A(H5) virus–positive pools was higher in ducks (36.8%) than in geese (22.2%). For both H5 and H9 subtypes, the prevalence of positive pools was higher for oropharyngeal samples (8.6% for H5 and 31.9% for H9) than for cloacal samples (3.6% and 9.9%) in all surveyed poultry types  Table 1.Approximately 25% of environmental pools were positive for A(H9) virus, and the prevalence of positive pools was higher in slaughter areas (31.5%), especially knives and boards used for slaughter and processing, than stall areas (20.2%). The prevalence of A(H5) virus–positive environmental pools was lower (10.8%) and did not vary between slaughter and stall areas  Table 2.Convergence was achieved for all models; the Gelman and Rubin statistic was <1.001 and the effective sample size was >10,000 for all parameters. For each AIV subtype, the best models reasonably predicted the number of positive pools  Figure 1LBM-level A(H5) virus prevalence was lower in retail LBMs than in mixed LBMs, and the posterior median estimate was ≈100% for mixed LBMs. However, among contaminated LBMs, levels of virus detection in birds and environmental areas did not vary between LBM types, but A(H5) virus prevalence in waterfowl was ≈6 times higher than in chickens . The preIn contrast to that for A(H5) virus, we found that the posterior median estimate of the LBM-level A(H9) virus prevalence was ≈100% for retail and mixed LBM groups, but the level of virus detection in birds and environmental areas was higher for mixed LBMs than for retail LBMs. A(H9) virus prevalence was highest in broilers and lowest in waterfowl. The prevalence in broilers was 3.8 times as high as that in waterfowl and 1.6 times as high as that in Desi and Sonali . The envWe detected A(H5) and A(H9) viruses in marketed poultry and environmental sites in nearly all LBMs sampled in Chittagong and Dhaka. The prevalence of A(H5) virus was higher in waterfowl than in chickens, whereas the prevalence of A(H9) virus was higher in chickens than waterfowl and also varied among chicken types, being more prevalent in broilers than in Desi and Sonali local breeds. Slaughter areas were more frequently contaminated by A(H9) virus than stall areas. Whereas mixed LBMs were more frequently contaminated by A(H5) virus than were retail LBMs, prevalence of A(H9) virus was higher in mixed LBMs than in retail LBMs for birds and environmental areas.>1 environmental sample pool contaminated by A(H5) virus and 12.5% of LBMs had >1 environmental sample pool contaminated by A(H9) virus. This difference might have been caused by different sampling schemes, in our study, we collected a larger number of pools per LBM.AIVs were ubiquitous in surveyed LBMs. The LBM-level prevalence of A(H5) virus in Bangladesh was higher than in other AIV-endemic countries, including Egypt  virus in broilers than in Sonali and Desi might result from differences in the structure of their respective value chains (–Co-circulation of A(H5) and A(H9) viruses arouses concerns over evolution of novel reassortant variants . However, pools that were negative for the AIV matrix gene but positive for any of the H5 and H9 subtypes indicate that accounting for actual test sensitivity and specificity would enable more robust prevalence estimation. Virus isolation might be attempted for RT-PCR–positive pools to assess the viability of virus material. However, this testing was not attempted in our study. Each pool consisted of swab specimens from different birds or environmental sites. Thus, multiple AIV subtypes and virus species, including Newcastle disease viruses, could be present in the same pool and interfere with growth of each virus in chicken eggs  and A(H9) viruses. The level of virus detection was associated with the type of poultry and environmental area and the presence of wholesalers in LBMs. These findings need to be included in the design of risk-based surveillance and control interventions aimed at reducing AIV prevalence, human exposure, and the risk for emergence of novel virus reassortant variants.Additional information  on prevalence of avian influenza A H5 and H9 viruses in live bird markets, Bangladesh.Additional information  on prevalence of avian influenza A H5 and H9 viruses in live bird markets, Bangladesh.


Microbes drive critical ecosystem functions and affect global nutrient cycling along with human health and disease. They do so under strong constraints exerted by viruses, which shape microbial communities’ structure and shift host cell metabolism during infection. Microbes drive critical ecosystem functions and affect global nutrient cycling along with human health and disease. They do so under strong constraints exerted by viruses, which shape microbial communities’ structure and shift host cell metabolism during infection. While the majority of viruses and their associated impacts remain poorly characterized, a number of mechanisms by which viruses alter microbial cells and ecosystems have already been revealed. Here I outline how a comprehensive host-resolved mapping of viral sequence space will enable a thorough characterization of virus-encoded mechanisms for microbial manipulation. With soon-to-be millions of virus genomes obtained from metagenomes, one of the major challenges resides in the development of methods for high-throughput and high-resolution virus-host pairing, before multi-omics approaches can be leveraged to fully decipher virus-host dynamics in nature. Beyond novel fundamental biological knowledge, these studies will likely provide new molecular tools enabling a precise engineering of microbial cells and communities. Microbes are now recognized to play key roles in all of Earth’s ecosystems, driving nutrient and energy transfers, and directly influencing human health and disease . These mVibrio cholerae, Clostridium botulinum, or Escherichia coli  , 4. Prophia coli . Meanwhihia coli . Beyond hia coli , but othBecause exploring environmental viral communities has remained a challenging task until very recently, our collective understanding of the molecular mechanisms by which viruses can influence microbial cells is still largely partial, and known examples are mostly derived from a few well-studied cultivated model systems. Interrogating the vast diversity of uncultivated viruses holds tremendous potential for the discovery of entirely novel avenues for microbial manipulation, including strategies tailored to specific hosts and environmental conditions. Fortunately, this type of approach might become viable in a not-too-distant future considering the current pace of progress and improvements in the field of viral ecogenomics. Here I outline the major steps and remaining challenges toward a comprehensive characterization of viral diversity and virus-host interactions in nature and the potential use of this knowledge for manipulation of microbial communities.de novo genome assembly from shotgun metagenomes  of viral diversity must first be achieved. This is the topic that saw the most remarkable improvement in the last few years, especially compared to the exploration of microbial diversity, mainly due to a shift in the methods used. The vast diversity of uncultivated microbes was initially revealed through 16S and 18S amplicon sequencing (agenomes . In contagenomes .https://img.jgi.doe.gov/cgi-bin/vr/main.cgi) is composed of >700,000 uncultivated genome fragments, the overwhelming majority collected in the last 2 years alone  similarity between host and virus genomes . While tOne of the major challenges faced by the field of viral ecogenomics is thus to develop experimental approaches providing high-throughput, high-resolution host linkage for uncultivated viruses. These approaches will most likely rely on high-throughput sequencing of single cells and/or targeted host (sub)populations, sorted and selected via a combination of, e.g., microfluidic separation, microdroplet encapsulation, and/or flow cytometry cell sorting. The codetection of virus and host at the individual cell level should then provide unique insights into strain-resolved host range and infection rates for individual virus genotypes in nature, without cultivation of either the microbial host or the virus. Given that pilot experiments are already ongoing, it seems reasonable to hope for some of these methods to be established and broadly available at some point in the next decade. The data obtained by applying these methods across a large range of biomes and ecological conditions will then enable the construction of a global host-resolved mapping of the virus sequence space.in situ, will be especially relevant in this context. Beyond a treasure trove of fundamental biological discoveries about ecological and evolutionary drivers of virus-host dynamics, these studies would also benefit every approach and biotechnological application leveraging microbes and microbial processes. Specifically, a comprehensive understanding of the broad range of virus-host interactions ongoing in nature would provide a fantastic resource that could be mined for new ways to precisely and predictably engineer microbial cells, including for targeted modification of specific populations within a complex microbial community.Finally, a global host-contextualized mapping of the virosphere would serve as a foundation for multi-omics studies aiming at deciphering virus-host interactions and dynamics in nature. Sample sets such as time series, patient cohorts, and incubations under controlled conditions, either in the laboratory or Inoviridae family, i.e., filamentous phages, are particularly interesting in this framework because they have a small genome which can be expressed in a nonhost cell and relatively simple virions whose properties could likely be modified by mutating only one or a couple of genes.Already, results from isolated viruses can provide a glimpse at the type of mechanisms and solutions for microbial manipulations that could be inspired by or derived from viruses. The first and most obvious application of viruses would be to kill specific microbial strains such as contaminants, either through active viruses, i.e., “phage therapy,” or by using only specific components of virus particles, e.g., tailocins. Viruses can also contain many key genes able to redirect entire metabolic pathways or to fundamentally alter host behavior and fitness, including variants of key cellular enzymes displaying unique properties as well as highly efficient transcriptional and translational regulators. Hence, a global and host-contextualized virus genome database could be used to identify a candidate gene(s) for modulating specific metabolisms or expressing artificial constructs in any target microbe. Finally, some viruses are particularly amenable to genome engineering and could thus be repurposed as custom-targeted delivery mechanisms. Members of the Right now is an exciting time to be part of (or join) the young and highly dynamic field of virus ecogenomics. A lot of the infrastructure and conceptual framework is being built , and dat


Medicago lupulina) plant collected in Austria. The virus was mechanically transmitted to Nicotiana benthamiana, M. lupulina, M. sativa, Pisum sativum and Vicia faba. The complete genome was determined by high throughput sequencing. The genome of JKI 29327 consists of eight RNA segments closely related to those of melon chlorotic spot virus (MeCSV) isolate E11–018 from France. Since segments RNA 7 and 8 of JKI 29327 are shorter, its genome is slightly smaller (by 247 nts) than that of E11–018. Pairwise comparisons between the predicted virus proteins of JKI 29327 and their homologues in E11–018 showed aa identities ranging from 80.6 to 97.2%. Plants infected with E11–081 gave intermediate DAS-ELISA reactions with polyclonal antibodies to JKI 29327. Since JKI 29327 and E11–018 appear to be closely related both serologically and genetically, we propose to regard JKI 29327 as the black medic strain of MeCSV. To our knowledge, JKI 29327 represents the second tenuivirus identified from a dicotyledonous plant. Serological and molecular diagnostic methods were developed for future detection.A tenuivirus, referred to here as JKI 29327, was isolated from a black medic ( Tenuivirus, family Phenuiviridae, are plant viruses that possess non-enveloped filamentous particles and a genome consisting of four to eight single-stranded RNA segments with negative or ambisense polarity. The thin filamentous particles consist of ribonucleoprotein (RNP) complexes, measuring 3–10 nm in diameter and with lengths proportional to the sizes of the RNAs they contain. Based on the RNA sizes, the particles may appear as small, large or even branched circles [Tenuivirus: Echinochloa hoja blanca virus (EHBV), Iranian wheat stripe virus (IWSV), Maize stripe virus (MSpV), Rice grassy stunt virus (RGSV), Rice hoja blanca virus (RHBV), Rice stripe virus (RSV) and Urochloa hoja blanca virus (UHBV). In addition, three more species have been proposed and are pending recognition by ICTV: melon chlorotic spot virus (MeCSV), Ramu stunt virus (RmSV) and wheat yellow head virus (WYHV) [Poaceae family causing yield losses in important food crops such as rice (Oryza sativa L.) and maize (Zea mays L.) [Cucumis melo) in France represents the first report of a tenuivirus naturally infecting a dicotyledonous plant [Members of the genus  circles . Tenuivis (WYHV) –4. The nmays L.) . The recus plant .Medicago lupulina L.) plant showing virus-like symptoms was collected in Stadl-Paura, Austria, but the symptoms were not recorded at the time. The sample was sent to Julius Kuehn Institute for analysis. Electron microscopy revealed the presence of RNP that appeared to resemble disassembled rhabdovirus particles [Nicotiana benthamiana, M. lupulina, M. sativa, Pisum sativum and Vicia faba, and was referred to as JKI 29327. Three weeks post inoculation, N. benthamiana plants showed systemic mottling, slight vein clearing and top leaf curling, whilst M. lupulina and M. sativa plants showed systemic vein clearing. P. sativum plants showed systemic vein clearing and severe yellowing and Vicia faba showed systemic mottling, yellowing and leaf rolling  followed by ribosomal RNA depletion using the RiboMinus Plant kit (Invitrogen). The ribo-depleted RNA was used for high throughput sequencing (HTS) on a MiSeq (v3) platform (2 × 301) as described before [N. benthamiana. The amplicons were gel-purified using Zymoclean Gel DNA Recovery Kit (Zymo Research) and Sanger sequenced; sequence analyses of these amplicons showed that they were 100% identical to the corresponding segment sequences obtained by the HTS analysis and thus confirmed the presence of each individual viral segment.For determination of the complete genome sequence of JKI 29327, total RNA was extracted from infected rm 2 × 30 as descrd before  on freshBunyavirus RNA-dependent RNA polymerase (accession no.: cl20265) and RNA4-ORF1 with Tenuivirus/Phlebovirus nucleocapsid protein (accession no.: cl05345) (Table To predict the open reading frames’ (ORF) functions, the translation of each ORF was used to search for conserved domains on NCBI’s conserved domain database CDD v 3.16) . Only tw6 . Only 5) Table . AdditioFor additional confirmation, purified RNP preparations of JKI 29327 were again examined by electron microscopy and shown to contain tenuivirus-like circular filamentous particles representing the individual genome segments  were tested in DAS-ELISA using the JKI 1608 antibodies to JKI 29327. The latter gave strong  reactions with JKI 29327 (in four different plant spp.) and intermediate reactions (A405 nm: 1.0 to 2.0) with E11–018, indicating that the serological relationship between these two isolates is close. Additionally, JKI 29327 was mechanically inoculated to melon cv. Védrantais (kindly provided by Dr. C. Desbiez). The plants showed chlorotic spots only on inoculated leaves and tested positive in DAS-ELISA with the JKI 1608 antibodies. Whilst JKI 29327 could be detected in inoculated leaves, no systemic infection was observed (data not shown).To assess the serological relationship between JKI 29327 and E11–018, Tenuivirus suggest that a new species should be considered when (i) the aa sequence identities between any corresponding gene products is below 85%; (ii) the nt sequence identities between corresponding IRs is below 60%; (iii) there are different sizes and/or numbers of genomic components; (iv) there are differences in host range; (v) the vectors are different [The species demarcation criteria of ICTV for the genus ifferent . For cerifferent . For exaFabaceae, the Cucurbitaceae and the Solanaceae families under experimental conditions. Moreover, these two isolates appear to be serologically closely related when tested with the JKI 1608 antibodies. Small differences in size, particularly in the intergenic regions, are common and can be observed between isolates of RSV [The black medic tenuivirus isolate JKI 29327 fulfils three out of these five criteria. Firstly, its ORF2 of RNA7 shares 80.6% aa identity with its homologue in the E11–081 genome. Secondly, the IRs of both RNA7 and RNA8 share < 60% nt identities with those of E11–081. Thirdly, the overall genome size of JKI 29327 is 247 nt shorter than that of E11–081. Based on these three criteria, the black medic virus should be considered a new species. However, although the host range was not studied in full detail, both JKI 29327 and E11–081 infected members of the s of RSV , 13. Als


DCLs of three cotton species and analyzed their expression profiles during biotic stress.Dicer-like proteins (DCLs) are essential players in RNA-silencing mechanisms, acting in gene regulation via miRNAs and in antiviral protection in plants and have also been associated to other biotic and abiotic stresses. To the best of our knowledge, despite being identified in some crops, cotton DCLs haven’t been characterized until now. In this work, we characterized the DCLs in the allotetraploid cotton Gossypium hirsutum, 7 and 6 in the diploid G. arboreum and G. raimondii, were identified, respectively. Among some DCLs duplications observed in these genomes, the presence of an extra DCL3 in the three cotton species were detected, which haven’t been found in others eudicots. All the DCL types identified by in silico analysis in the allotetraploid cotton genome were able to generate transcripts, as observed by gene expression analysis in distinct tissues. Based on the importance of DCLs for plant defense against virus, responses of cotton DCLs to virus infection and/or herbivore attack using two commercial cotton cultivars (cv.), one susceptible (FM966) and another resistant (DO) to polerovirus CLRDV infection, were analyzed. Both cvs. Responded differently to virus infection. At the inoculation site, the resistant cv. showed strong induction of DCL2a and b, while the susceptible cv. showed a down-regulation of these genes, wherever DCL4 expression was highly induced. A time course of DCL expression in aerial parts far from inoculation site along infection showed that DCL2b and DCL4 were repressed 24 h after infection in the susceptible cotton. As CLRDV is aphid-transmitted, herbivore attack was also checked. Opposite expression pattern of DCL2a and b and DCL4 was observed for R and S cottons, showing that aphid feeding alone may induce DCL modulation.As main results, 11 G. hirsutum cotton were found in their relative diploids. Duplications of DCL2 and DCL3 were found in the three species. All four classes of DCL responded to aphid attack and virus infection in G. hirsutum. DCLs initial responses against the virus itself and/or herbivore attack may be contributing towards virus resistance.Almost all the DCLs of the allotetraploide  The long non-translated RNA (lsiRNA) and the viral small RNA (vsiRNA) were generated by DICER cutting of intermediate viral genome structures during viruses infection. Once produced, small RNAs are incorporated into ARGONAUTE (AGO)-containing RNA-induced silencing complexes (RISCs) to confer sequence specificity in the silencing of RNA information [DICER proteins represent an ubiquitous class of RNAse III enzymes that are vital for the establishment of RNA interference (RNAi). DCLs recognize and cut a long ds-RNA substrate to release 21–24-nt small RNA duplexes, which have 2-nt 3′ overhangs at each end . Plant sormation , 7, 8. Iormation , 10.DICERs, or DICER-like (DCLs), as these proteins are called in plants, present six domains: DEAD, Helicase-C and DUF283 domains at the N-terminus, a Piwi/Argonaute/Zwille (PAZ) domain in the middle and a dual RNAse III domain followed by one or two dsRNA-binding domains in the C-terminal half , 12. In DCL1–4) encoding DCL proteins have been found [loci and transposons, as well as retrotransposon insertion of DNA regions. AtDCL4 responds to the biogenesis of primary vsiRNAs that has 21-nt and are generated by dicing of viral genomes replicative intermediates. This DCL is also responsible for the production of phased siRNAs, ta-siRNAs and virus-activated siRNAS  [In Arabidopsis, 4 genes  –33. DCL4fection) , 35. So,DCLs have been identified in the tomato Solanum lycopersicum [Brassica napus [Archis duranensis, and four in chickpea, pigeonpea, A. ipaensis [G. raimondii genome [In contrast to the number of reports about DCL characterization and functions in Arabidopsis, only a few reports have characterized them in other plant species. In general, these reports show the presence of 4 classes of DCLs, with more than one member for each class. Seven persicum , eight ipersicum , five inpersicum , five inpersicum , 39, foupersicum , eight ica napus , three iipaensis  and fouripaensis . Thus fai genome , but theGossypium genus is marked by two important events. First, a divergence occurred 5–10 million years ago (MYA), when A and D diploid genomes form two separated branches. Subsequently, around 1–2 MYA, allopolyploidization events occurred by interspecific hybridization between A and B-genome ancestors resembling G. arboreum and G. raimondii, respectively, generating new Gossypium species, as the cultivated G. hirsutum (AADD) between others [G. arboreum and G. raimondii [Cotton crop is important to the economies of more than 30 countries and represent the principal source of natural fiber in the textile industry worldly. Besides cotton fiber, cotton oil is widely used for human consumption. The evolution of the n others , 45. As aimondii .G. hirsutum and two diploids, G. arboreum and G. raimondii, cotton species. As observed earlier in G. raimondii [DCL3 is also present in G. arboreum and G. hirsutum, where it is currently expressed. Due to the importance of DCL in viral defense, we studied the expression patterns of G. hirsutum DCLs in two commercial cotton cultivars (cv.) during biotic stress induced by CLRDV (Cotton leafroll dwarf virus) infection and/or herbivore attack by Aphis gossypii. Comparison of a CLRDV-resistant and susceptible cotton accession or cv. during infection showed that the modulation of DCL expression, especially DCL2a, 2b, and DCL4, might explain the contrasting viral responses exhibited by each accession. Interestingly, both cotton DCL3 were also differentially modulated during viral infection.Here, we identify and characterize the DCL family of the allotetraploid aimondii , an extrArabidopsis and rice DCLs as query sequences, we identified the Dicer-like genes of two diploid cottons, G. raimondii and G. arboreum, and of the allotetraploid G. hirsutum acc. TM-1 were identified by searching the cotton genome database [G. raimondii, 7 in G. arboreum and 11 in the allotetraploid G. hirsutum, respectively  of 132.99 to 220.99 kDa, respectively. A very small DCL putative polyprotein was identified in G. arboreum for DCL2a (Cotton_A_34031), which were 770 amino acids in length (84.7 KDa). It represents an extra DCL2a, that is present only in the G. arboreum genome.Detailed information for these genes is listed in Table DCL genes is shown in Fig. GhDCL genes were distributed on 10 G. hirsutum chromosomes. All the chromosomes  contained a single representative of GhDCL, with the exception of chromosome D05, which contained GhDCL2a and GhDCL4. Curiously, almost all the GhDCLs were correlated with chromosomes inherited from each parental-related species. G. raimondii presented 6 DCL genes distributed in 5 chromosomes, and G. arboreum 7 had DCLs distributed in 5 chromosomes and in a scaffold region (scaffold3086), that had not yet been incorporated into the physical map of chromosomes. G. arboreum presented two DCL2a located in very close proximity on chromosome 10, separated by approximately 9 kb. These two DCL2as (named herein as GaDCL2a31 and 32) were highly similar, sharing 93.4% identity at the amino acid level. The predicted GaDCL2a32 was shorter than its orthologs and seemed to have lost nucleotides/amino acids at the 5′ extremity/N-terminus.The physical location of the three cotton species DCL3a and b of G. raimondii. DCL4 of all three distinct species showed a very similar intron/exon distribution. The same could be observed for DCL3a of G. arboreum, G. raimondii, and G. hirsutum from subgenome D. GhDCL3a from subgenome A, however, showed a distinct pattern at the 5′ end of the gene. It seemed to have lost part of the nucleotides from this region. A very similar distribution of introns and exons was also observed for DCL2b of G. raimondii and G. hirsutum subgenomes A and D. GaDCL2b, on the other hand, showed a very different distribution of I/E.The intron/exon (I/E) distribution as well as intron numbers, are shown in Table G. raimondii, G. arboreum, and G. hirsutum) were used to construct a neighbor-joining (N-J) phylogenetic tree, using MEGA 7.0 software  and Delta Opal (DO), at 60 days post germination (60 dpg) and analyzed gene expression by quantitative real-time PCR (RT-qPCR). These two cultivars were especially selected because they show contrasting phenotypes against an important worldwide distributed cotton virus disease, the cotton blue disease (CBD). Fibermax is susceptible to CBD, while Delta Opal is resistant.To evaluate if the in silico-identified DCLs types were identified in G. hirsutum plants. DCL1 was expressed in all analyzed tissues from both cv. FM and DO, as well as DCL2a, DCL3a, and DCL4. DCL2b was not detected in leaves from the DO cv. and flowers of cv. FM, while DCL3b was almost undetectable in stems from FM and leaves from both cultivars. DCL4, which is essential for intracellular antiviral silencing, was expressed at the same levels in FM and DO plants, as well as DCL2a. However, DCL2b showed slightly contrasting basal expression levels between them. The extra DCL3 identified in cotton seemed to be important, especially in flower and root tissues from healthy plants.As shown in Fig. DCL expression is modulated during RNA viral infection using virus-resistant and virus-susceptible contrasting cotton cvs.: Delta Opal and FM966, respectively. Cotton leafroll dwarf virus (CLRDV) , which is transmitted only by an aphid vector (Aphis gossypii), is the causal agent of cotton blue disease [Plant DCLs initiate the RNAi innate defense system against invading viruses because they recognize and process incoming viral and transposon nucleic acids into small siRNAs of 21, 22, and 24 nts. Thus, we were interested in shedding some light on how cotton  disease . CLRDV i disease .Aphis gossypii and/or CLRDV infection.As CLRDV is only transmitted by its aphid vector, another important point is to understand the aphid vector component in the DCL modulation. Consequently, we evaluated cotton DCL expression patterns during aphid herbivore attack, mediated by To analyze the influence of herbivore attack on DCL expression, 30 dag cotton plants were inoculated in the greenhouse with virus-free aphids. Aphid were restricted to one basal leave (inoculated leave) per plant and 24 h after inoculation, the aphids were eliminated by insecticide application. The expression levels of all cotton DCLs were evaluated in young systemic leaves (3–4 leaves above the inoculated leaves) at 24 hpi, 5, 15, and 25 dpi after contact with the aphids  showed strong repression during the first 24 hpi to 5 dpi in both cvs.DCLs showed a distinct expression pattern in systemic leaves, compared with those inoculated with virus-free aphids, showing that the presence of virus also modulate DCL expression , with the exception of DCL2a. Five days later, however, the levels of all FM DCLs markedly increased, showing almost 60, 10, 47, 17, 28 and 28-fold change variations for DCL1, DCL2a, DCL2b, DCL3a, DCL3b, and DCL4, respectively. In contrast, the DCL transcript levels from DO plants at all time points were lightly induced or repressed, maintaining levels that were very similar to uninfected mock plants. Even with the strong DCL modulation observed in the susceptible plant FM, CLRDV could be easily detected in systemic leaves from 24 hpi, showing that even with such extensive efforts of the antiviral machinery, the virus was replicating and spreading throughout the plant  in the resistant cv. at 24 hpi , while only DCL3b was induced in DO cv. (approximately 100-fold greater expression than in the mock). These results showed that DCL3b seemed to be important during the initial virus defense activation.An induction of FM Taken together, our results suggested that the contrasting CLRDV susceptibility phenotype demonstrated by FM and DO plants might be related to their distinct DCL modulation mediated both by aphid feed and virus infection.DCL2 in the polerovirus infection, a profile of the vsiRNAs produced by DCL dicing was obtained for CLRDV and another polerovirus infected cotton plants by deep sequencing. As observed in Fig. Corroborating the importance of cotton G. hirsutum DCL genes were obtained from the cotton genome project to attempt to understand how cotton DCLs are modulated by both virus infection and herbivore attack. Transcriptional responsive cis-elements of DCL gene promoters were analyzed using PlantCare. Analysis of the promoter region of all 11 upland cotton DCL genes revealed the presence of various biotic and abiotic stress-responsive cis-acting regulatory elements, including the TCA-element, ERE and ABRE. Light stress-responsive elements were relatively the most abundant in the promoters of the upland cotton DCL genes, specifically Box 1, Box 4 and GT1-motif , salicylic acid (TCA) and other biotic stresses , revealing possible mechanisms mediated by almost all DCLs in drought tolerance and biotic stress responses in the upland cotton G. hirsutum. Surprisingly, GhDCL4A did not show typical biotic stress or ethylene-responsive cis elements, but they were present in GhDCL4D. In general, there were significant differences in the average proportions of the promoter elements detected within the different DCL gene families, as well as between the same DCLs originating from a distinct parental diploid cotton  at the genomic level. Our results showed that cotton carried extra copies of DCL2 and DCL3. The extra DCL2 has been previously reported in poplar, rice and maize between other plants. Our phylogenic analysis corroborated other findings, showing that DCL2 duplication seemed to have evolved independently in mono- and eudicots, after their genetic separation. The DCL3 duplication shown here is a unique event in eudicots [DCL3, denoted herein as DCL3b, showed a complete DCL3 structure in the allotetraploid G. hirsutum and in the diploids G. arboreum and G. raimondii. Furthermore, quantitative RT-PCR showed that it was expressed in distinct cotton tissues/organs such as flowers and roots. The importance of this extra DCL3 for cotton must be studied, but we can hypothesize that it may be associated with the large number of transposons detected in the cotton genome. G. raimondii, G. arboreum and G. hirsutum genome account approximately to 53, 67 and 62% of retrotransposons, respectively [DCL3b, which probably derived from its G. raimondii-like parental species. DCL3b from the parental G. arboreum seemed to have been missed during evolution.In the present study, we first characterized the eudicots . The extectively . CuriousAnalyzing expression of these cotton extra DCL transcripts during virus infection and herbivore attack, we could observe that they seem to be functional and are modulated in both stresses. However, a further characterization of all the cotton DCL2s and DCL3s in heterologous system will be necessary to understand the paper of these DCLs extra copies in RNA silencing machinery.G. hirsutum impacts small RNA accumulation both quantitatively and qualitatively [G. hirsutum cv [gypsy- and copia-like retrotransposon transcripts [Previous studies from our group have shown that CLRDV infection in tatively , 55. Oursutum cv . All thenscripts . In contnscripts . Thus, cDCLs in two contrasting virus-susceptible commercial cotton cvs., during two types of biotic stress, revealed that almost all DCLs were modulated during these stresses. The CLRDV-susceptible cv. FM966 showed a strong downregulation of DCL2a, 2b, 3a, 3b, and 4 in the aerial part of the plant during the initial hours after aphid contact. This downregulation was maintained until 25 dpi for DCL2b, DCL2b, and DCL4 at the majority of the analyzed time points. For the CLRDV-resistant cv. Delta Opal, this downregulation was not observed, except for DCL3b, which was repressed almost 15-fold compared with the mock-infected control plants at 24 hpi  infection of N. benthamiana, for example, DCL4 has been observed to restrict the systemic movement of the virus [DCL expression during virus infection by CLRDV showed that the virus induced strong systemic repression of DCL1, 2b, 3b, and 4 in the susceptible cv. FM966 during the initial hours after infection. This drastic repression was not observed in the resistant cv., in which the expression of all these DCLs remained almost similar to that of mock infected plants. Investigation of DCL expression modulation at infection sites showed that even FM DCL4 was expressed 6x higher than DO DCL4, and this high induction of DCL4 was insufficient for virus restriction to the initially infected cells. In contrast, both FM DCL2a and b were almost not induced in the inoculated leaves. Others authors have reported previously that DCL2 and DCL4 may have different antiviral activities in inoculated and systemic leaves [DCL4 can downregulate DCL2 expression [DCL4 overexpression may impair DCL2a and b expression at the inoculation site in the susceptible cotton cv., allowing the cell-to-cell spread of the virus and establishment of systemic infection due to the noneffective intercellular antiviral activity of DCL2 at these sites. In DO-resistant plants, the high levels of DCL2a and b induced by the presence of virus may efficiently activate intercellular antiviral silencing, resulting in restriction of the virus to the first cell-autonomous or inoculated cells during the initial hours after infection and dramatically reducing virus replication and aborting infection spread completely.Antiviral activity of DCLs was already intensively reported  vsiRNA accumulation was analyzed in single-infected N. benthamiana plants, and the 22-nt class represented the dominant class of BrYV vsiRNAs, followed by the 21-nt class [Donaire et al.  analyzednt class . These rnt class , 54 and A. thaliana, DCL3 are also responsible for the directed cleavage of positive-sense RNA viruses, including Cucumber mosaic virus (a cucumovirus), Oilseed rape mosaic virus (a tobamovirus) and Turnip crinkle virus (a carmovirus) [GhDCL3a and b were induced on infected leaves (with high levels of expression), as well as systemic leaves, suggesting a possible contribution of these DCLs in viral defense.In addition to classical DCL4 and DCL2 antiviral functions, several authors have reported the importance of DCL3 in virus defense via the production of high levels of 24-nt siRNAs from invading viruses , 69, 70,movirus) , 71. ThuIt should take in account, however, that ours results to not exclude that other CLRDV-resistance mechanisms not directly related to DCL2/4 modulation should be active in DO plants. These resistance mechanisms may turn down virus replication consequentially decreasing virus imposed stress levels in these plants. One way to deeply check the importance of the DCL modulation observed here by itself in the CLRDV resistance in DO plants would be induce the silencing of each cotton DCL genes individually and/or in combinations in DO cotton plants by VIGS and CLRDV-infect plants the plants further.G. raimondii, G. arboreum and G. hirsutum cotton DCLs and found duplication of DCL2 and DCL3. Additionally, we found that all G. hirsutum DCLs are involved in polerovirus infection responses, especially the DCL2 and DCL3 paralogs and DCL4, and that modulation of the expression of these DCLs by viruses may be crucial for virus susceptibility and/or resistance response.We identified G. arboreum (BGI-CGB v2.0 assembly genome), G. raimondii (JGI assembly v2.0 data) and G. hirsutum acc. TM-1 (NAU-NBI v1.1 assembly genome), were downloaded from the Phytozone (http://www.phytozone.net/) and CottonGen (https://www.cottongen.org/) databases. Dicer-like protein sequence data were obtained for A. thaliana and O. sativa from the General Feature Format (GFF) le Arabidopsis Information Resource  and from the Rice Genome Annotation Project Database . Gene names and IDs are listed in Additional file The genome sequences of three cotton species, http://au.expasy.org/tools/pi_tool.html; Bjellqvistetal Bjellqvistetal, 1993).The physico-chemical properties of cotton DCL proteins were predicted using the ExPASy Compute pI/Mw tool (http://www.softsea.com/review/MapInspect.html) using start and end position of each open read frame obtained from the genome database. .The locations of the DCLs in chromosomes were assessed using Mapinspect software (https://phytozome.jgi.doe.gov/pz) and National Center for Biotechnology Information database - NCBI (https://www.ncbi.nlm.nih.gov) was further performed to obtain the DCL genes in different plant organisms.A phylogenetic tree was constructed using MUSCLE (Multiple Sequence Comparison by Log-Expectation) alignment and the neighbor-Joining (NJ) method in MEGA 7.0 software , with thhttp://gsds.cbi.pku.edu.cn/index.php) was used to analyze the intron–exon structure by comparing the CDS of cotton DCL genes with their corresponding genomic sequences [http://smart.embl-heidelberg.de).The Gene Structure Display Server   and acc. Delta Opal (DO) . Seeds were kindly provided by IMA, Instituto Matogrossense do Algodão, Primavera do Leste, Mato Grosso state, Brazil. Plantlets were grown under greenhouse conditions at 28 +/− 2 °C as previously described [Two upland cotton escribed .DCL expression, samples of leaves, shoots, flowers, and roots from independent plants at 30 days after germination (dag) were collected from each cotton acc. FM and DO. Samples were immediately frozen in liquid N2 and stored at − 80 °C until RNA extraction.For cotton organ Gossypium hirsutum) plants of cultivars acc. FM966 (susceptible to Cotton blue disease) and acc. DO (resistant to Cotton blue disease) grown in the greenhouse and at the 30 dag stage were infected with Cotton leafroll dwarf virus  by viruliferous aphids (Aphis gossypii Glover) as described previously [Cotton  and 5, 15 and 25 days post-infection (dpi) with CLRDV aphids or non-viruliferous aphids. Leaves from the same position of uninfected plants were used as uninoculated controls. Leaves from 3 to 5 independent inoculated plants composed each biological replicate. Samples were stored at − 80 °C until RNA isolation and expression analysis.All the samples recovered from aphid-free inoculated, CLRDV infected and uninoculated were assayed for CLRDV detection by a Nested RT-PCR assay that amplifies viral coat protein sequence following . CLRDV sG. hirsutum DCL reverse and forward primers were designed using NCBI primer-BLAST  were used as reference genes for the analysis of DCL expression in different organs, the results were obtained by △CT method and the 2−△△CT method was used to analyze DCL expression during viral infection and/or aphid inoculation [GhmiR390 and GhPP2A (PROTEIN PHOSPHATASE 2), which were previously identified as the best reference genes for CLRDV infected cotton studies [2O, and 2 μL of each primer to make a final concentration of 10 μM. Three biological replicates were performed, and three technical replicates were assayed per cDNA sample.Total RNA was isolated using the Invisorb Spin Plant RNA Kit . The quality and concentration of each RNA samples was determined by agarose gel electrophoresis and using a NanoDrop 2000 (Thermo Fisher Scientific Co.) spectrophotometer. Only RNA samples with a 260/280 nm ratio between 1.8–2.1 and 260/230 ratio ≥ 2.0 were used. The c-DNA first-strand reverse transcription was conducted with the Revertaid First Strand cDNA, Synthesis Kit (Thermo Fisher Scientific Co.) in accordance with the manufacturer’s instructions. One microgram of total RNA from each sample was treated with DNAse I (Fermentas Co.), and cDNAs were synthetized by adding 100 μM of oligo dT24V primer. culation , 52. Cot studies , were usDCLs expression pattern was established by complete clustering method analysis using Euclidean distance and calculated in the R software environment. The DCL Cts results are shown at Additional file In order to show DCL expression in each organ, a heat-map of the Leaves of cotton infected with Cotton anthocyanosis virus (CAV) obtained in Mato Grosso state, Brazil, were used for total RNA extraction and small RNA purification and sequencing by Illumina plataform following procedures described by .DCL relative expression levels determined for the different samples under herbivore attack and/or virus infection were compared with these controls: untreated plants (control) x aphid inoculated plants for herbivore expression analysis, and mock (virus-free aphids inoculated plants) x CLRDV-aphid inoculated plants for viral infection using the parametric one-way ANOVA test at P ≤ 0.05 and P ≤ 0.01. For checking if the different relative expression levels of DCLs, from FM and DO, are statistical significant T-student test were used.G. hirsutum DCLs, upstream sequences of 1500 nucleotides from ATG star codon were downloaded from the CottonGen website (https://www.cottongen.org) and cis elements predicted with PlantCARE software (http://bioinformatics.psb.ugent.be/webtools/plantcare/html/).For the identification of cis-regulatory elements present in Additional file 1. Cotton DCL c-DNA and protein sequences.Additional file 2: Table S1. Gene name and gene ID of DCLs in Arabidopsis, Medicago, rice, Populus, Physcomitrella and grapevine. Table S2. List of primers and amplicon characteristics of DCL genes. Table S3. DCL expression in organs. Figure S1. DCL amplification melting curves. Figure S2. Expression pattern of G. hirsutum acc. FM DCL genes based on transcriptome sequencing data.Additional file 3: Figure S3.Cis-elements distribution in DCL promoters with enhancer elements. Figure S4. Promoter sequences of Gossypium hirsutum DCLs.Additional file 4. Positions of all cis elements found in G. hirustum DCLs promoters.


Pteropus (Greek pteron [“wing”] + pous [“foot”]). In 1999, scientists investigated reports of febrile encephalitis and respiratory illness among workers exposed to pigs in Malaysia and Singapore. In 1994, a newly described virus, initially called equine morbillivirus, killed 13 horses and a trainer in Hendra, a suburb of Brisbane, Australia. The reservoir was subsequently identified as flying foxes , bats ofHenipavirus, in the family Paramyxoviridae. Three additional species of Henipavirus—Cedar virus, Ghanaian bat virus, and Mojiang virus—have since been described, but none is known to cause human disease. Outbreaks of Nipah virus occur almost annually in India and Bangladesh, but Pteropus bats can be found throughout the tropics and subtropics, and henipaviruses have been isolated from them in Central and South America, Asia, Oceania, and East Africa.The causative agent was determined to be closely related to Hendra virus and was later named for the Malaysian village of Kampung Sungai Nipah. The 2 viruses were combined into the genus


Lassa virus is the causative agent of Lassa fever, a viral hemorrhagic fever with a case fatality rate of approximately 30% in Africa. Previous studies disclosed a geographical pattern in the distribution of Lassa virus strains and a westward movement of the virus across West Africa during evolution. Our study provides a deeper understanding of the geography of genetic lineages and sublineages of the virus in Nigeria. In addition, we modeled how the virus spread in the country. This knowledge allows us to predict into which geographical areas the virus might spread in the future and prioritize areas for Lassa fever surveillance. Our study not only aimed to generate Lassa virus sequences from across Nigeria but also to isolate and conserve the respective viruses for future research. Both isolates and sequences are important for the development and evaluation of medical countermeasures to treat and prevent Lassa fever, such as diagnostics, therapeutics, and vaccines. Hylomyscus pamfi. Within lineages II and III, seven and five sublineages, respectively, were distinguishable. Phylogeographic analysis suggests an origin of lineage II in the southeastern part of the country around Ebonyi State and a main vector of dispersal toward the west across the Niger River, through Anambra, Kogi, Delta, and Edo into Ondo State. The frontline of virus dispersal appears to be in Ondo. Minor vectors are directed northeast toward Taraba and Adamawa and south toward Imo and Rivers. Lineage III might have spread from northern Plateau State into Kaduna, Nasarawa, Federal Capital Territory, and Bauchi. One sublineage moved south and crossed the Benue River into Benue State. This study provides a geographic mapping of lineages and phylogenetic clusters in Nigeria at a higher resolution. In addition, we estimated the direction and time frame of virus dispersal in the country.Lassa virus is genetically diverse with several lineages circulating in West Africa. This study aimed at describing the sequence variability of Lassa virus across Nigeria and inferring its spatiotemporal evolution. We sequenced and isolated 77 Lassa virus strains from 16 Nigerian states. The final data set, including previous works, comprised metadata and sequences of 219 unique strains sampled between 1969 and 2018 in 22 states. Most of this data originated from Lassa fever patients diagnosed at Irrua Specialist Teaching Hospital, Edo State, Nigeria. The majority of sequences clustered with the main Nigerian lineages II and III, while a few sequences formed a new cluster related to Lassa virus strains from IMPORTANCE Lassa virus is the causative agent of Lassa fever, a viral hemorrhagic fever with a case fatality rate of approximately 30% in Africa. Previous studies disclosed a geographical pattern in the distribution of Lassa virus strains and a westward movement of the virus across West Africa during evolution. Our study provides a deeper understanding of the geography of genetic lineages and sublineages of the virus in Nigeria. In addition, we modeled how the virus spread in the country. This knowledge allows us to predict into which geographical areas the virus might spread in the future and prioritize areas for Lassa fever surveillance. Our study not only aimed to generate Lassa virus sequences from across Nigeria but also to isolate and conserve the respective viruses for future research. Both isolates and sequences are important for the development and evaluation of medical countermeasures to treat and prevent Lassa fever, such as diagnostics, therapeutics, and vaccines. Lassa fever is a febrile illness in West Africa. Severe infections are associated with bleeding, central nervous system manifestations, and renal failure . The cas4–Mastomys natalensis, although Mastomys erythroleucus has recently been described as an alternative host for lineages III and IV , Irrua, in Edo State, Nigeria (Previous studies have provided a comprehensive overview of the sequence variability of the viruses circulating in the catchment area of ISTH . CompletPRJNA482054), our 2018 sequencing project at ISTH (NCBI BioProject accession number PRJNA482058)  , and GenR2 = 0.41 for the L segment (P < 0.001) and 0.21 for the S segment (P < 0.001). These results are in line with previous analyses us pamfi  (clades n = 8), Kaduna (n = 4), Bauchi, Federal Capital Territory, and Benue ; 3b in Benue (n = 4) and Nasarawa; 3c in Nasarawa (n = 12), Federal Capital Territory (n = 3), and Plateau; 3d in Taraba; and 3e in Bauchi (n = 19) and Plateau (n = 3). Within lineage II, seven sublineages were distinguishable (2a to 2g) (n = 3) and Abia; 2b in Imo (n = 3), Rivers (n = 3), Enugu, and Akwa Ibom; 2c in Ebonyi (n = 20) and Imo; 2d in Taraba (n = 5), Anambra, and Adamawa; 2e in Delta; 2f in Kogi (n = 3) and Anambra; and 2G in Edo (n = 46), Ondo (n = 41), Kogi (n = 3), Delta, Anambra, and Ekiti. Owing to the large number of sequences in sublineage 2g, it has even been possible to distinguish minor phylogenetic clusters in specific localities : 2g1 along the Niger River in Delta and Anambra; 2g2 around Uromi town in Edo; 2g3 around Owo and Ifon towns in Ondo; and 2g4 along the Niger River in Kogi. However, the matching of phylogenetic clusters with specific locations has to be interpreted with caution, as most viruses stem from patients and the precise location where the infection was acquired is not known. Indeed, the phylogenetic origin of a few sequences  was implausible in view of the recorded origin of the patient. These sequences were excluded from the above mapping.Previous sequencing studies revealed that lineage III strains circulate in the north of Nigeria and lineage II strains are prevalent in the south , 14. Whia to 2g)  and 2. S2 per year for both lineage II and III  and time to most common recent ancestor of currently circulating strains in Nigeria of about 800 years are in agreement with previous studies  and Edo (n = 2), lineage I has been observed only once during a nosocomial outbreak in 1969 in the town of Lassa in the north of the country and never again. It is conceivable that the natural hosts of these rare strains or lineages are not commensal rodents living close to humans, such as M. natalensis, but wild rodents, which usually do not have contact to humans, such as H. pamfi. If host switching were involved in the evolution of Lassa virus lineages, this would increase the complexity and timelines of the spatiotemporal evolution of the virus.Besides the known lineages I, II, and III, we found a well-supported monophyletic cluster of sequences that does not fall within these lineages. This clade comprises the strains from a  Nigeria , as well Nigeria . Unfortulineages –11, 13. http://virological.org/t/2019-lassa-virus-sequencing-in-nigeria-final-field-report-75-samples/291).This project not only aimed at generating Lassa virus sequences from across Nigeria but also at isolating and conserving the respective viruses for future research. This work has been performed by a Nigerian fellow (D. U. Ehichioya) in a biosafety level 4 facility in Hamburg. Besides their use in studies on virus evolution, the sequences and isolates are relevant to public health research. For example, they have been important for the development and evaluation of the RealStar Lassa virus reverse transcriptase PCR (RT-PCR) kits , which are now a diagnostic reference assay in Nigeria. In addition, they served as background information for recent sequencing studies in Nigeria, demonstrating that the high incidences of Lassa fever cases in 2018 and 2019 are not associated with the circulation of newly emerging strains or lineages or increased human-to-human transmission  .http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Reads were filtered and trimmed for adapters and contaminants using the BBDuk tools (available at https://jgi.doe.gov/data-and-tools/bbtools/) and then compared against the NCBI nonredundant database with blastx. Genome finishing, sequence assembly, and analysis were performed using Geneious v9.1.5. . If Lassa virus isolation or Illumina sequencing failed, Lassa virus genome fragments were amplified from clinical samples of patients by RT-PCR using primers targeting the S segment  to remove cellular debris and bacterial-cell-sized particles. Filtrates were treated with a cocktail of nucleases, namely, Turbo DNase (Ambion), Benzonase (Novagen), Baseline-Zero DNase (Epicentre), and RNase A (ThermoFisher Scientific), to eliminate contamination from non-particle-protected nucleic acids, bacteria, and eukaryotic cells , 25. RNA segment , 27 and PRJNA482058) (n = 201) were gathered from various sources . If only the local governmental area of a patient’s residence was known (n = 11), the centroid point of the polygon representing that area in the GADM database (https://gadm.org/) was taken as a sampling point. If only the patient’s state of residence was known (n = 7), the median coordinates of all of the other strains sampled within the same state were used. For strains from rodents, coordinates of the reported trapping sites were taken as the sampling sites. The final data set for phylogeographic inference included sequences (n = 216 for S segment and n = 157 for L segment) and metadata for 219 unique Lassa virus strains . Metadata, accession numbers, and references for all Lassa virus strains/isolates used in this study are listed in Table S1.Metadata for phylogeographic inference included the date of sampling  and the place of residence of the patient , which was taken as the sampling site of the virus. Minimum information for inclusion of a strain in the analysis was year of sampling and the patient’s state of residence. If the hometown or village of a patient was not known, the hospital, which was attended by the patient or where the diagnostic specimen had been taken, was used as a proxy. Metadata for the Lassa virus strains sequenced in this study were retrieved from the laboratory request forms that accompanied the specimens. In addition, information was gathered through interaction with the submitter of the specimen. To enlarge the sample size, we also included Lassa virus sequences in the analysis, which we sequenced in 2018 from clinical specimens processed in the diagnostic laboratory at ISTH (NCBI BioProject accession number A482058) . To faciR2) of the linear regression were estimated with the program TempEst  generated in this study have been deposited with NCBI under BioProject accession number PRJNA482054.Lassa virus S and L segment sequences (