A hypervirulent fowl adenovirus serotype 4 (FAdV-4) has caused hepatitis-hydropericardium syndrome (HHS) with mortalities that range from 30 to 80% in outbreaks across China since 2015. The FAdV-4 strain was characterized as a novel genotype based on the specific genome characteristics. However, our understanding of the dynamic distribution, tissue tropism, and pathogenesis of the novel FAdV-4 is incomplete. In this study, a new, sensitive and FAdV-4-specific real-time PCR was developed and applied to detect the dynamic distribution of the duck origin, novel FAdV-4 strain HLJDAd15 in experimentally infected special-pathogen free (SPF) chickens and ducks. Notably, the pathogenicity and replication pattern of HLJDAd15 were completely different between chickens and ducks. Severe hydropericardium and 10% mortality were induced in chickens, whereas no clinical signs were observed in any duck. The virus replicated was detected throughout the study in both chickens and ducks. However, only one replication peak with a high virus concentration appeared in chickens at 5 days post infection (dpi), whereas two peaks with relatively low virus titres appeared in ducks at 7 and 21 dpi. Thus, ducks could be a natural reservoir of the novel FAdV-4 absent of clinical signs, and a new transmission route from ducks shedding FAdV-4 continually to chickens was revealed, which might aggravate the outbreak of HHS in chickens. This study provides the first accurate quantitative data for the replication kinetics of the novel FAdV-4 in different hosts. The different pathogenicity, dynamic distribution and replication pattern in chickens and ducks provide a foundation for further clarification of the pathogenesis of the novel FAdV-4. Adenoviridae, are non-enveloped and double-stranded DNA viruses  based largely on molecular criteria and restriction enzyme digest pattern or twelve serotypes (designated FAdV-1 to 8a and -8b to 11) based on the results of serum cross-neutralization tests  and performed in accordance with the ‘Guidelines for Experimental Animals’ of the Ministry of Science and Technology . All SPF chickens and ducks were cared for in accordance with humane procedures.Duck origin FAdV-4 (HLJDAd15) was isolated from the field in 2015 in Heilongjiang Province, China , and is 3.0 ELD50 doses of the homogenates of HLJDAd15 in 0.2 ml of phosphate-buffered saline (PBS) at 35 days old, whereas the 10 other SPF chickens and ducks served as negative controls. The challenged and control groups were separately housed in different negative-pressure isolators and monitored daily for 30 days.Forty of each 1-day-old specific pathogen free (SPF) chickens and ducks were purchased from the Experimental Animal Center of Harbin Veterinary Research Institute (HVRI) of the Chinese Academy of Agricultural Sciences (CAAS), China. Thirty SPF chickens and ducks were orally inoculated with 10At 1, 3, 5, 7, 10, 14, 21, 28, 35, and 42 days post infection (dpi), three infected birds and one control bird were sacrificed by intraperitoneal injection of sodium pentobarbital. Tissue samples of heart, liver, spleen, lung, kidney, thymus, bursa, caecal tonsi, proventriculus, and rectum were collected. The homogenates of 10 to 100 mg of tissue samples in 200 μl of PBS were subjected to DNA extraction or stored at -80°C. Three serum samples were also collected at each dpi and stored at -80°C until used for antibody assays.The viral DNA of tissue samples was extracted from the tissue homogenates using a DNeasy Tissue kit  according to the manufacturer’s instructions, whereas the positive DNA or proviral DNA of FAdV-1, Marek’s disease virus (MDV) , chickenThe primers used in this study to detect the genotype of FAdV were designed based on the L1 region of the Hexon gene (FAd-F: 5′-AACTTCGACCCCATGTCGCGTCAGG-3′ and FAd-R: 5′-TGGCGAAAGGCGTACGGAAGTAAGC-3′). The PCR products were subjected to electrophoresis in a 1% (w/v) agarose gel. The PCR procedure consisted of an initial incubation for 5 min at 95°C; 35 cycles of 30 s at 95°C, annealing for 60 s at 55°C, and extension for 60 s at 72°C; and a final extension for 5 min at 72°C.1 to 1 × 1011 copies/μl were used to produce a standard curve.Real-time PCR was performed with a LightCycler 480 real-time thermocycler . The primers were designed based on the FAdV-4 specific L1 region of the Hexon gene  as folloTissue samples were fixed in 10% formalin for 48 h at room temperature (RT), and then routinely processed, embedded in paraffin wax, and cut into 5-μm sections. The sections were stained with hematoxylin and eosin (HE) and examined using light microscopy.Antisera against the viral isolate HLJFAd15 were collected from 5-week-old SPF chickens at 1, 3, 5, 7, 10, 14, 21, 28, 35, and 42 days post infection. Serum samples were tested for FAdV-specific antibodies using a commercial enzyme-linked immunosorbent assay (ELISA) kit .1 to 1 × 1011 copies/μl, and the serial-diluted plasmids were detected by the real-time PCR assay. Threshold cycle values (CT) were plotted against the known copy numbers of the standard controls. The good correlation  between CT values and copy numbers is shown in Figure 1A.To generate a standard curve for the real-time PCR, the standard recombinant plasmid pAD341 was serially diluted from 1 × 10Figure 1B).Viral DNA from 11 chicken origin and 1 duck origin FAdV-4 isolates was subjected to the established real-time PCR and was well detected, whereas fluorescent signals from viral DNA of FAdV-1, MDV, CAV, and AGV2 and proviral DNA of REV, ALV-A, ALV-B, and ALV-J were not detected, indicating the specificity of the real-time PCR for FAdV-4 , whereas the detection limit of routine PCR was more than 102 copies/μl , demonstrating that the sensitivity of our real-time PCR was at least 10-fold higher than that of routine PCR.The sensitivity of the real-time PCR was compared with that of routine PCR. The recombinant plasmid pAD341 was used as a template. The results showed that the detection limit of the real-time PCR established in this study was fewer than 10Figure 2A). The virus DNA was detectable throughout the experiment, and the peak of virus replication appeared at 5 dpi in all tissues except in caecal tonsi with the peak at 3 dpi. After 14 dpi, the concentration of virus in all tissues decreased and remained at a low level without another replication peak until 42 dpi at the end of the experiment. The highest number of virus copies in different tissues was compared among the heart, liver, spleen, lung, kidney, thymus, bursa, proventriculus, and rectum at 5 dpi and in the caecal tonsi at 3 dpi . The virus loads in all tissues were higher than 105 copies mg-1, with the concentration in livers significantly higher (p < 0.01) than that in any other tissue. All chickens in the control group were alive and healthy at the end of the experiment, and viral DNA was not detected by either routine PCR or the real-time PCR.For chickens in the experimentally infected group, 1 chicken and 2 chickens died on 3 and 5 dpi, respectively. The dead chickens showed severe hydropericardium and IBH. The dynamic distributions of the duck origin FAdV-4 in different tissues of SPF chickens were detected by real-time PCR . Generally, there was no significant histopathology damages presented in chickens of control group, while massive pathological damages were observed in various tissues of chickens in infection group: degeneration, vacuolar necrosis and basophilic inclusion bodies presented in liver cells ; large numbers of vacuolar necrosis showed in kidney cells ; large numbers of macrophages proliferation and cortical lymphocyte depletion appeared in thymus ; severe reduction and necrosis of lymphocytes showed in spleen  and bursa .Tissues of the heart, liver, spleen, kidney, thymus, and bursa of chickens in different groups were collected 5 days post challenge and fixed, cut into sections, and stained with HE . The virus DNA was detectable throughout the experiment, and two peaks of virus replication appeared at 7 and 21 dpi (28 dpi for spleen). The virus copy peaks at both 7 dpi  and 21 dpi  in the heart, liver, spleen, lung, kidney, thymus, bursa, proventriculus, rectum and caecal tonsi were analyzed. The results showed that the virus concentrations in all tissues were between 102 and 105 copies mg-1 tissue, with the exceptions of concentrations higher than 105 in livers at 7 dpi and lower than 102 in spleens from 5 dpi. Furthermore, the trends between the two virus replication peaks in the different tissues were similar.All ducks in both experimentally infected and control groups remained alive and did not show visible clinical signs by 42 dpi at the end of the experiment. The dynamic changes of HLJDAd15 in different tissues of SPF ducks were detected by real-time PCR , 7 dpi , and 21 dpi , respectively. The results showed that the virus copies in different tissues of chickens were significantly higher (p < 0.01) than ducks at 5 dpi, when the unique replication peak showed in chickens. However, the virus concentrations in chickens rapidly decreased after 5 dpi and lower than ducks at 21 dpi, when the second replication peak appeared in ducks. Moreover, the peak concentrations in different tissues of different hosts were also analyzed . The data showed that the virus peak concentrations in all tissues of chickens were significantly higher (p < 0.01) than those at both peaks in ducks, and particularly in the liver, heart, spleen, lung, thymus, bursa, and caecal tonsi, the virus copies were 100-fold higher in chickens than in ducks. Notably, the highest concentration of more than 108 copies mg-1 in livers was significantly higher (p < 0.01) than that in any other tissues in chickens and ducks at any other times, and the concentration of virus copies in the spleen of ducks was significantly lower (p < 0.01) than that in other tissues of ducks.Based on the dynamic distributions of the duck origin FAdV-4, the virus replications in different tissues of SPF chickens and ducks were compared at 5 dpi  post infection. The FAdV-specific antibody response was negative in all control birds at all times. From 7 dpi, the antibody titres of chickens continued to increase until reaching a peak at 35 dpi and then began to decrease to 42 dpi. The antibody titres of ducks were completely different from those of chickens and increased from 7 dpi, with the first peak 3 weeks earlier than that of chickens at 14 dpi and the second one at 35 dpi. The peak antibody titre was almost equal between chickens and ducks.An antibody response of SPF chickens and ducks against FAdV was detected at 10 different times (in vivo, which is critical for enclosing the pathogenesis of the emerged novel FAdV-4. As previous reported, Several routine PCR methods have been reported based on hexon gene (The HHS associated with FAdV-4 infection has emerged since 2015 and is reported in different provinces of China, inducing huge economic losses for poultry production . The FAdxon gene  and a SYxon gene . Howeverin vivo and might be a key index for evaluation of vaccine development; however, the detailed functions of the antibody in virus infection require further investigation.Chickens were usually considered to the most susceptible natural hosts, including broilers, layers, breeder broilers, but some other hosts were also the potential infection hosts for FAdVs, such as falcons have been reported as the natural host and primary reservoir for FAdV, although the FAdV was characterized as a novel species . The novHepatitis-hydropericardium syndrome has been caused by outbreaks of a novel genotype FAdV-4 across China since June 2015, but little is known about this novel FAdV-4. In this study, a new, sensitive and FAdV-4-specific real-time PCR was developed and applied to detect the dynamic distribution of a duck origin FAdV-4 in chickens and ducks. The novel virus showed completely different replication patterns and antibody response in different hosts, which provide a foundation for further clarification of the pathogenesis and the epidemic transmission mechanism of the novel FAdV-4.XW and HC conceived and designed the experiments. QP, YY, ZS, and LL performed the experiments. QP and YY analyzed the data. CL and YZ contributed reagents/materials/analysis tools. QP wrote the paper. XQ and YG were involved in the interpretation of the results and critically read the manuscript.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.


A recent innovation in support of the final segment of the immunization supply chain is licensing certain vaccines for use in a controlled temperature chain (CTC), which allows excursions into ambient temperatures up to 40°C for a specific number of days immediately prior to administration. However, limited evidence exists on CTC economics to inform investments for labeling other eligible vaccines for CTC use. Using data collected during a MenAfriVac™ campaign in Togo, we estimated economic costs for vaccine logistics when using the CTC approach compared to full cold chain logistics (CCL) approach.We conducted the study in Togo’s Central Region, where two districts were using the CTC approach and two relied on a fullCCL approach during the MenAfriVac™ campaign. Data to estimate vaccine logistics costs were obtained from primary data collected using costing questionnaires and from financial cost data from campaign microplans. Costs are presented in 2014 US dollars.Average logistics costs per dose were estimated at $0.026±0.032 for facilities using a CTC and $0.029±0.054 for facilities using the fullCCL approach, but the two estimates were not statistically different. However, if the facilities without refrigerators had not used a CTC but had received daily deliveries of vaccines, the average cost per dose would have increased to $0.063 (range $0.007 to $0.33), with larger logistics cost increases occurring for facilities that were far from the district.Using the CTC approach can reduce logistics costs for remote facilities without cold chain infrastructure, which is where CTC is designed to reduce logistical challenges of vaccine distribution. Several vaccines commercialized for use in low- and middle-income countries, such as vaccines against meningitis A (MenA), hepatitis B, and cholera, can withstand higher temperatures without losing potency , 2. DespDistributing vaccines in a CTC simplifies the logistics of handling vaccines starting at the location where the CTC begins, removing the need for additional freezers to prepare icepacks, saving health worker time for logistics, and potentially increasing the geographical reach of vaccines and hence coverage . A modelWe compared the estimated economic costs for vaccine storage and distribution when using the CTC approach to the fullCCL approach. We also modeled what the logistics costs would have been for facilities that did not have cold chain equipment if they had not used the CTC approach. In addition, we compared the closed-vial vaccine wastage rates for facilities using the CTC approach and those using the fullCCL approach.We conducted a costing study in one of the four regions performing a MenAfriVac™ introduction campaign in Togo in 2014. The Central Region was selected as the study setting on the recommendation of the Ministry of Health, whom had been requested to select a region for the study that was representative of Togo’s supply chain challenges and would reflect the benefits of CTC use. Of the Central Region’s four districts, two (Tchaoudjo and Tchamba) used the fullCCL approach, and the other two (Sotouboua and Blitta) implemented a CTC approach. The decision on which districts used CTC and which did not was made by the Ministry of Health based on their assessment of the cold chain and logistics realities of each district and whether CTC would be beneficial. We used purposive sampling to select four health centers in each of the four districts to ensure variation of two criteria: size (the target population for the campaign) and access . The target populations for the campaign for the health centers in our sample ranged from 1,200 to 12,000, and one-way distances ranged from 0 km to approximately 200 km between the district vaccine store and the facilities. In addition, the national level and the regional level were included in the sampling as they are part of the vaccine supply chain. A total of 22 sites were included in the data collection: national level (n=1), regional level (n=1), districts (n=4), and health centers (n=16).A review of the microplans revealed that of the 94 health centers in the Central Region, approximately 30% did not have refrigerators for vaccine storage. Analyzing these data by district indicated that between 17% and 61% of the health centers in each district did not have refrigerators. In addition, 19% of the health centers had refrigerators that were reported as broken or out of use, and this ranged from 9% to 29% of the refrigerators in each district. Despite the lack of refrigerators in nearly half of the health centers in the region, none reported that refrigerators had been provided specifically for use during the campaign, even for the facilities that were using the full CCL approach. Instead of augmenting the cold chain equipment, the system dealt with the limited availability of cold chain equipment by having districts make daily deliveries of vaccines to the health centers without refrigerators.Quantitative data for the costing analysis were collected using structured campaign costing questionnaires adapted to the specific context of Togo. These questionnaires were used to interview health workers . The questionnaires used an ingredients-based costing methodology  to gathePrimary data were collected during two field visits. The first visit was held in October 2014 to collect data on vaccine storage and distribution for the campaign from the national vaccine warehouse and Central Region. A second field visit was conducted in December 2014 during the campaign to collect district- and service-level data.We analyzed the data for the costing analysis in a Microsoft Excel spreadsheet. We collected all costs in West African CFA, then converted them to 2014 US dollars using an exchange rate of US$1 to 470 CFA, the rate used in the microplans. The main outcome metric used in this analysis was the average logistics cost per dose (CPD) delivered at each level: national, district, and service delivery. At this latter level, the cost metric was calculated for CTC and the fullCCL approach.A combination of financial and economic costs were collected and analyzed. The financial costs captured direct expenditures incurred for the campaign , whereas the economic costs expanded the scope of the analysis by including the opportunity costs of using existing resources available from the routine immunization program that were “borrowed” for the campaign . These rThe estimated costs were grouped into three main categories: (a) cold chain costs; (b) transportation costs for logistics; and (c) human resources costs for logistics, including per diems. The cold chain costs included the cost of the equipment used to store the MenA vaccine at all levels of the vaccine supply chain. This included costs for the cold rooms and refrigerators for storing vaccines, freezer rooms and freezers to make frozen icepacks, and cold boxes and vaccine carriers for storing vaccines during transport. For facilities that had cold chain equipment, the annual depreciation and energy costs were allocated to the ten-day campaign, assuming that the equipment was exclusively used for storing the MenAfriVac™ vaccines. All cold chain equipment costs (including cold boxes and vaccine carriers) were annualized using the straight-line depreciation method and assuming a five-year useful life .During the campaign, health centers relied on a transportation system organized at the district level around planned delivery circuits to continually resupply vaccination posts with vaccines and frozen icepacks. The analysis included the financial costs of transportation for vaccines, which were calculated based on fuel and distances traveled, and of renting vehicles for logistics. The analysis also included the depreciation cost of using existing available vehicles as part of the national immunization program but mobilized for use during the campaign, and the cost of using private or personal vehicles, particularly at health centers. At times, additional trips were required outside of the scheduled distribution, and the costs of these trips were factored into the analysis.Using information on local monthly salaries by staff type, the economic cost of human resources for campaign logistics was estimated for the existing staff employed to work on the national immunization program, but mobilized to work on the planning and implementation of the MenA campaign. The estimation of staff time was based on information collected via direct interviews and field observations during the campaign. Only the human resources costs for health workers and drivers with logistics duties were included in our reported estimates since our focus was on logistics costs: this included human resources time spent on pre-campaign activities associated with cold chain and logistics and the time that was spent on organizing cold boxes and vaccine carriers, conditioning icepacks, and traveling to and from vaccination sites for logistics during the campaign. The financial costs for the human resources category were based on official per diems provided to health workers for logistics activities related to the campaign.We conducted an analysis to estimate the transport costs for vaccine delivery if sampled health centers without refrigerators had not used the CTC approach but had received daily deliveries. We compared this estimate with the estimated costs when they used the CTC approach, enabling vaccines to be stored in vaccine carriers or cold boxes at the health centers for up to four days. For the CTC approach, we assumed three deliveries would be made to each health center during the ten-day campaign. These costs show the savings from using the CTC approach in facilities that did not have refrigerators.We estimated the logistics costs at the national store at $49,210 for the campaign; the largest share of the total logistics costs were for cold chain (43%) and transportation of vaccines to the districts (34%). For the cold chain costs, maintenance of equipment was the largest share of costs (90%), including maintenance of a private-sector cold room, which was rented to store routine vaccines to make space for the MenA vaccine at the national store. The estimated logistics CPD at the national store was $0.017. At the district vaccine stores, the two largest components of logistics costs for the campaign were for human resources and transport of vaccines to the health centers . The traWe modeled the transport costs for the health centers without refrigerators that were using the CTC approach to show what the costs would have been had they received daily deliveries of vaccines from the districts rather than three deliveries during the campaign. The average CPD for all the facilities using a CTC would have increased to $0.063 (range $0.003 to $0.33) per dose had they not used a CTC. The upper range is for one health facility that was located 200 km from the district and did not have a refrigerator  inflated the overall cost of transport in CTC sites. Additional studies are needed to increase the evidence on the economics of the CTC approach.We found that using the CTC approach will reduce the costs of conducting a campaign in remote locations, without increasing closed-vial vaccine wastage. These findings have potential beyond reducing the logistics cost of conducting a campaign. A CTC approach allows vaccinators to extend their reach to remote areas that would otherwise be hard to reach based on the cold chain infrastructure, mitigating the risk of vaccine damage caused by inadvertent freezing as well as reducing the burden on health workers associated with the preparation of conditioned icepacks.Several vaccines commercialized for use in low- and middle-income countries, such as vaccines against meningitis A (Men A), hepatitis B, and cholera, can withstand higher temperatures without losing potency;The cold chain infrastructure in some low- and middle-income countries is weak, posing logistical challenges for vaccine storage and transport and so any innovations that reduce the strain on the cold chain would be beneficial;Using the controlled temperature chain approach can simplify vaccine logistics during campaigns and facilitate outreach at locations beyond the cold chain, allowing for improved coverage and equity.To our knowledge this is the first study that provides evidence on the economic costs and savings of using the CTC approach compared to the full cold chain logistics approach where the evidence was generated using data collected during a campaign implementing the CTC approach;The study findings demonstrate that using the CTC approach can reduce the costs of conducting campaigns and these savings are higher for remote locations;The study also finds that closed-vial vaccine wastage is not higher when using the CTC approach compared to the full CCL approach.The authors declare no competing interests.


A plethora of viruses can be transmitted by the food- and waterborne route. However, their recognition is challenging because of the variety of viruses, heterogeneity of symptoms, the lack of awareness of clinicians, and limited surveillance efforts. Classical food- and waterborne viral disease outbreaks are mainly caused by caliciviruses, but the source of the virus is often not known and the foodborne mode of transmission is difficult to discriminate from human-to-human transmission. Atypical food- and waterborne viral disease can be caused by viruses such as hepatitis A and hepatitis E. In addition, a source of novel emerging viruses with a potential to spread via the food- and waterborne route is the repeated interaction of humans with wildlife. Wildlife-to-human adaptation may give rise to self- limiting outbreaks in some cases, but when fully adjusted to the human host can be devastating. Metagenomic sequencing has been investigated as a promising solution for surveillance purposes as it detects all viruses in a single protocol, delivers additional genomic information for outbreak tracing, and detects novel unknown viruses. Nevertheless, several issues must be addressed to apply metagenomic sequencing in surveillance. First, sample preparation is difficult since the genomic material of viruses is generally overshadowed by host- and bacterial genomes. Second, several data analysis issues hamper the efficient, robust, and automated processing of metagenomic data. Third, interpretation of metagenomic data is hard, because of the lack of general knowledge of the virome in the food chain and the environment. Further developments in virus-specific nucleic acid extraction methods, bioinformatic data processing applications, and unifying data visualization tools are needed to gain insightful surveillance knowledge from suspect food samples. Transmission via the food- and waterborne route is a common mode of spread of a wide range of viruses. Many commonly recognized food- and waterborne infections are caused by viruses that are transmitted by the fecal-oral route. Particularly caliciviruses  can cause diarrhea and vomiting and less commonly astroviruses, rotaviruses, and adenoviruses . Other vChallenges of detecting viruses transmitted by the food- and waterborne route are their diversity and the frequent secondary person-to-person transmissions, which may mask an initial food- or waterborne introduction. In addition, there is a lack of awareness among clinicians , as the In the past years, high-throughput sequencing technologies have increased the ability to measure genomic material from diverse samples tremendously. These methods will most likely continue to improve in the future . SpecifiWith the development of multiplex real-time polymerase chain reaction (RT-PCR) protocols came the realization that unraveling etiologies of main disease syndromes is more complex than previously recognized. This led to questions about the detection of viruses for which the role as causes of illness remains to be evaluated, the importance of co-infections and recognition of less common disease etiologies . SimilarFigure 1), and the full extent of disease can only be captured through epidemiological studies addressing incidence and etiology at community level coupled with severity of a range of enteric pathogens , which gives an overview of all these viruses and circumvents the mentioned sampling biases. The potential of such an approach for food-related purposes was exemplified by Polyomaviridae. Picornaviridae, and Papillomaviridae viral families.. Sentinel screening of imported foods, especially risk foods such as fresh fruits and vegetables, dried meats and seafood, could prevent foodborne viral outbreaks such as the international HAV outbreak in Europe from 2012 to 2013 . Using metagenomic sequencing, the effort of detecting and genotyping of a virus can be combined to trace an outbreak, regardless of prior knowledge of the virus, provided the data is analyzed in combination with relevant metadata. The use of this integrative approach has been demonstrated in an investigation of a hospital outbreak of human parainfluenza virus, which was investigated using high-throughput metagenomic sequencing  and public health application  (quencing . Both thquencing , have poquencing . Recent quencing . The devquencing . Howeverquencing . AspectsThe routine application of metagenomic sequencing for clinical diagnosis and surveillance is dependent costs versus performance criteria such as speed, reliability, and comparability of results with those of reference methods. Improvements are necessary in the standardization and speedup of sample preparation, sequencing and data analysis for clinical and public health application. A recent study has shown the potential of fast whole-genome sequence based epidemiological tracing in the recent Ebola outbreak . HoweverThe developments and different choices of sequencing technology make it difficult to decide how to standardize routine diagnostics and surveillance protocols. Studies that directly compare platforms help in this decision making process. Two studies compared the Illumina MiSeq, Roche-454 titanium, Ion Torrent PGM, and PacBio RS platforms . For virNovel approaches, such as the MinION nanopore sequencer, increase speed and depth of coverage at the cost of sequence error rate. A comparison of a metagenomics approach using the MinION nanopore or the Illumina MiSeq sequencer reports a sample-to-result time of 6 h for a MinION nanopore setup compared to 20 h using an Illumina MiSeq setup . DespiteTo increase the viral specific output of metagenomics sequencing approaches sample preparation methods can be used to reduce non-viral genomic material or specifically select for viruses. Approaches that are being investigated, range from different extraction protocols  to usingAside from lab-based technical difficulties, there are several challenges concerning data analysis of metagenomic sequencing experiments. First, due to the high and increasing read output of sequencing machines, data analysis of high throughput sequencing projects generally requires strong computational infrastructure, which, can require large investments and technological expertise . HoweverSecond, the assembly of millions of genomic fragments into 1000s of different individual genomes is a daunting task. Historically, short-read assemblers were developed and optimized to assemble a single genome out of a set of sequencing reads. These assemblers are therefore not suited for the reconstruction of metagenomes and are prone to creating synthetic chimeric genomes . VariousThird, assigning all assembled genomes to a reference genome is hampered by miss-annotations and incomplete reference databases. One example is “non-A, non-B hepatitis virus”, a sequence present in the NCBI GenBank, which was miss-annotated and the sequence was shown to belong to a bacteriophage . The volYersinia pestis in the New York subway system. Further inspection showed that the reads mapping to Yersinia pestis could have mapped with similar likelihood to other bacterial species (Sequence homology of multiple reference genomes can lead to the spurious assignment of sequencing reads to one of these genomes. An example of the impact of spurious read annotation was the alleged detection of genomic material of  species . Such miThe final challenge of metagenomic sequencing based surveillance is the interpretation of the annotated sequences. There is still little knowledge of the presence and dynamics of viruses in the environment and the food chain, which is of influence on the interpretation of food- and waterborne viral surveillance samples. Various factors are expected to influence the virome, and without knowledge of the typical viral content of a sample, the relevance of the detection of a virus is hard to determine. An example of this is a study showing a large discrepancy between the levels of HAV genotypes detected in sewage samples compared to the genotype infecting patients in the clinic in the same time . A potenin vitro (Detection of a virus by molecular methods relies on intact genomic material of a virus being present in the sample. However, the relationship between the infectivity of a detected virus and the detection of a fragment of its genome is not unambiguous. Apart from intrinsic virus characteristics, infectivity and detection of a virus depends on its stability in the sample matrix  and duriin vitro . High gein vitro .5, should make the interpretation of metagenomics experiments easier in the future.It is becoming increasingly clear that integration of different data sources and experimental results is crucial for the interpretation of metagenomic sequencing experiments. Therefore, browsing of these data and visualization of relationships between genome datasets and metadata should be facilitated. In the recent years, interactive web-based data browsing and visualization tools have increased in popularity to facilitate the interaction with and the browsing through highly complicated data in a user-friendly manner. Further development of tools that facilitate interaction with and visualization of metagenomic sequencing results, such as Kronatools  and TaxoIn our current society, there is much attention for the diseases that are causing occasional outbreaks. However, there are multiple strong signs that there are viruses hiding below the radar, due to a focus on viruses with direct clinical impact. As such, the disease burden of food- and waterborne viral infections is mainly recorded in outbreaks, signified by severe symptoms and hospitalization. However, it is estimated that the large abundance of viral infections causing mild symptoms, and thus not being recorded, carry a large portion of the global food- and waterborne disease burden. Moreover, this disease burden is expanded by the consequential infections and outbreaks of these viruses in susceptible populations. Global food trading, diversification of food sources and interactions with animals and other reservoirs of food- and waterborne disease related viruses complicate the capability of investigators to detect the original source and to determine the transmission pattern of viruses causing foodborne outbreaks. Therefore, surveillance efforts should look to metagenomic sequencing technologies, bioinformatics analysis tools and data sharing initiatives to get a more realistic insight in the global burden of food- and waterborne viral disease, and to make informed decisions on how to reduce this burden.DN and MK designed the focus of the review, DN did the literature search and drafted the manuscript, MK reviewed and revised.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.


Vespa crabro in Italy. Although the virus has never been isolated from this insect, the sequence of this virus shows a strong sequence homology with isolates obtained from Apis mellifera, which is considered its natural host.In this article, we document the first isolation of a replication-competent deformed wing virus from  Varroa destructor, which is the biological vector of DWV. Clinical symptoms in honeybees include early death of pupae and deformed wings, shortened abdomen, and cuticle discoloration in adult bees that result in an inability to fly (Deformed wing virus (DWV) is a viral pathogen of honeybees that is responsible for asymptomatic infection (covert) that can become clinically manifested (overt) in a stressful situation such as high infestation of the mite y to fly .Iflavirus genus belonging to the family Picornaviridae. Its genome consists of a 10-kb positive single-stranded RNA with a single open reading frame (ORF) flanked by a long 5′ untranslated region (5′ UTR) and a short, highly conserved 3′ UTR terminating with a 3′ poly(A) tail. DWV sequences available from various parts of the world share 98% to 99% identity, which is consistent with the suggested recent global spread of the virus and evolutionary divergence that is still limited  eluted in 30 µl as previously described  and sequenced by BMR Genomics . Sequences were then assembled using Bioedit software  in length, comprising 2 UTRs. The long UTR at the 5′ end spanning from nt 1 to 1136 contains the putative internal ribosome entry site (IRES). The ORF spans from nt 1137 to 9817 and encodes the viral polyprotein. A 317-nt-long UTR is present at the 3′ end, including a 31-nt poly(A) tail.V. crabro sequence scored 97.9% nucleotide identity with previously reported DWV isolated in Italy from Apis mellifera L.  (The DWV J489744) . SimplotJ489744) .V. crabro collected in Italy. This finding demonstrates for the first time the presence of DWV in a new species belonging to the family Vespidae. Sequence analysis showed a strong homology to DWV circulating viruses isolated from Apis mellifera, indicating the adaptation ability of the virus into a new host. This information is important to understand alternative transmission routes of the virus.We present the first whole-genome sequence of a DWV type A isolated from a symptomatic Vespa_crabro_DWV_PI_2016 has been deposited in the GenBank database under the accession number KY909333.The complete genome sequence of deformed wing virus isolate


The chromosome conformation capture (3C) technique is a method to study chromatin interactions at specific genomic loci. Initially established for yeast the 3C technique has been adapted to plants in recent years in order to study chromatin interactions and their role in transcriptional gene regulation. As the plant scientific community continues to implement this technology, a discussion on critical controls, validations steps and interpretation of 3C data is essential to fully benefit from 3C in plants.Arabidopsis. As a case study, we applied this methodology to the genomic locus of a floral integrator gene SUPPRESSOR OF OVEREXPRESSION OF CONSTANS1 (SOC1), and demonstrate the need of several controls and standard validation steps to allow a meaningful interpretation of 3C data. The intricacies of this promising but challenging technique are discussed in depth.Here we assess the reliability and robustness of the 3C technique for the detection of chromatin interactions in The 3C technique offers an interesting opportunity to study chromatin interactions at a resolution infeasible by microscopy. However, for interpretation of 3C interaction data and identification of true interactions, 3C technology demands a stringent experimental setup and extreme caution.The online version of this article (10.1186/s13007-017-0251-x) contains supplementary material, which is available to authorized users. Perception and response to internal and external stimuli is the fundamental nature of cellular life. The transcriptional regulatory system plays an integral role in fulfilling the needs of the cell and organism by ensuring proper gene activity. In comparison to bacteria, transcriptional control of an eukaryotic cell is far more complex, involving several layers of regulation inside the nucleus. It takes more than just the action and sufficient quantity of activator or repressor proteins to modulate gene expression.cis-elements) located in the core promoter region close to the transcription start site (TSS). For instance, the TATA box is one such evolutionarily conserved core promoter cis-acting element found upstream of most eukaryotic genes [cis-elements, they interact with other proteins and form complexes to recruit RNA polymerase II, thereby initiating transcription. Examples of these other proteins include specific TFs, which can bind to cis-elements more distantly located from the core promoter elements [cis-elements are involved in the specific activation of gene expression they are called transcriptional enhancers. These enhancers can be found upstream, downstream, or within introns of coding regions and are reported to be located as far as several hundred kilo bases (kb) from the TSS [Many modules, such as transcription factors (TFs), RNA polymerase, chromatin remodellers and associated proteins, and regulatory DNA sequences, are determinants of eukaryotic transcription , 2. All ic genes –5. Upon elements , 7. When the TSS , 9. Thes the TSS –12.Over the last two decades much attention has been paid to the role of chromatin and its conformation in the regulation of gene expression. Various processes, including the differential deposition of histone variants, histone modifications such as methylation and acetylation, DNA methylation, and the activity of other non-histone architectural proteins are known to regulate the structure of chromatin –15. EmpiChromosome Conformation Capture (3C) [Over the years, different imaging methods have been deployed to study chromosome conformation , 17. Howure (3C) .In 3C, chromatin in the intact nucleus is cross-linked by formaldehyde, followed by digestion with a restriction enzyme (RE) and intramolecular ligation . The 3D Zea mays) that describes the role of a distant enhancer sequence at the b1 locus. At the b1 Locus, a hepta-repeat around 100 kb upstream of the transcription start site (TSS) appeared to interact with the TSS region in a tissue and epiallele-specific manner [Arabidopsis thaliana.The first report exploring 3C in plants comes from a study in maize (c manner . Since tFLOWERING LOCUS C (FLC) locus upon vernalization [FLC, a potent floral repressor and a polycomb target gene, is under tight control of winter cold. Vernalization is a classical epigenetic process in which prolonged cold exposure quantitatively affects the time of flowering. A robust gene loop, due to an interaction between the 5′ and 3′ flanking sequences of the FLC locus, has been reported and this interaction is independent of the level of FLC transcript in different genetic backgrounds and genomic contexts. However, upon vernalization, within the first 2 weeks of cold exposure, the loop is disrupted and it has been proposed that this disruption is an early event in the transition of the FLC locus to an epigenetically silent stage. Subsequently, other DNA contacts in the FLC locus increase in frequency under control of the COLDAIR and COLDWRAP long non-coding RNAs (LncRNAs), giving rise to polycomb-dependent and stable repression of FLC expression [Crevillen et al. reported the presence and condition-dependent disruption of a chromatin loop at the lization . FLC, a pression .TERMINAL FLOWER1 (TFL1) locus that appears to be regulating TFL1 transcription [TFL1 locus results in TFL1 suppression.Another study by Liu et al. in 2013 reported the occurrence of a conformational change in chromosome looping at the cription . In thisFLOWERING LOCUS T (FT), that are associated with the photoperiod-dependent flowering response [FT, a floral integrator, can unite signals from multiple pathways to induce flowering. The first study reports the occurrence of multiple loops between a distal enhancer element (that contains CCAAT boxes) and core cis regulatory sites located in the promoter of FT (pFT) [CCAAT-boxes and these CCAAT-bound NF-Y complexes are hypothesised to come into close proximity with core pFT sites, enabling improved recruitment and stabilized binding of CONSTANS (CO), together initiating photoperiod-dependent flowering in Arabidopsis [FT locus into a three-dimensional structure, favouring interactions between two regulatory regions  with another region called Block ID, an intermittent promoter region between Block C and A [FT, i.e., an increase in the distance between Block C and Block ID, by a T-DNA insertion, abolished the C-ID interaction and resulted in reduced chromatin interactions of Block C with Block A. It is noteworthy to mention that the chromatin interactions identified in these two studies do not overlap.Likewise, two independent studies have identified gene loops at another flowering related gene, response , 29. FT,FT (pFT) . Nuclearbidopsis . A seconTogether, the 3C studies discussed above provide intriguing insights into the possible roles of chromatin interactions to regulate gene expression in plants, similar to the studies reported in yeast and other model organisms , 31.Arabidopsis. Based on this investigation, we provide detailed guidelines on necessary controls and how interaction data should be interpreted in a 3C experiment. Intricacies of this promising but challenging technique are further discussed.3C provides an interesting opportunity to study in vivo chromatin interactions at a high-resolution and thus has become a standard method for studying chromatin contacts at single gene loci , 24. HowArabidopsis, we used this methodology to investigate the chromatin conformation at the locus of the floral integrator gene SUPPRESSOR OF OVEREXPRESSION OF CONSTANS1 (SOC1) [SOC1 locus, including the ~ 3.8 kb promoter, the gene body, and ~ 1 kb downstream region, was divided into fragments using the four-cutter RE FspBI, as schematically shown in Fig. SOC1 locus, were tested for chromatin contacts. Fragment VII, which contains the transcriptional start site (TSS), was used as the bait  to generate a chromatin interaction profile  . Initialile Fig. ” sectionSOC1 locus the highest interaction frequencies were observed with fragments X, XI and XII, all downstream of the VII-bait  that show(s) the highest interaction frequency with the bait fragment is (are) considered as chromatin contact(s). For the TSS region in the SOC1 TSS as bait  experiment. In an r3C experiment, the fragment showing the highest interaction frequency with the 3C bait in the initial assay is used as a new 3C bait to generate an interaction profile. Thus we performed r3C experiments using one of the potential interacting fragments downstream of the SOC1 locus we identified in this way e.g., an interaction between bait VII and fragment X , is identical. Performing an r3C experiment is certainly of value, since a comprehensive profile of chromatin interaction of the locus will be obtained from yet another viewpoint. However, it is good to realize that the outcome of 3C experiments might be biased due to different characteristics of the used REs or technical constraints of PCR [The key difference between the 3C and r3C experiment is the fragment that is selected as bait and consequently, the combinations of qPCR primers used to detect the relative interaction frequency. For the t X Fig. . In any s of PCR . TherefoSOC1 locus using a different four-cutter enzyme (NlaIII). Now 16 distinct fragments of varying length spanning the entire SOC1 locus were tested for chromatin contacts . The next logical step was to perform an r3C experiment using NlaIII. For this purpose fragment N, located in the 3′-region of SOC1  collisions is observed , 38. Thu compact , hence sReviewing published literature we found only a limited number of 3C studies that have been performed in plants. Among these studies we observed considerable shortcomings within the 3C experimental set up , and differences in the way the 3C interaction data have been interpreted and represented. Furthermore, comparing 3C results across experiments and laboratories is complicated due to differences in the experimental set up, such as PCR method, normalization method, plant growth conditions, and the way the interaction frequency is determined. For example, in literature, one can find chromatin interactions determined using 3C by either semi-quantitative PCR or by qPCR-based approaches. Only the latter provide numeric peak interactions and is therefore a much more trusted and widely adopted method in recent years. A recent publication manually curated more than 3000 interactions from 5000 publications across 17 species into a database called 3C database (3CDB) . This 3CFLC locus [SOC1 is known to be actively expressed [SOC1 locus, when using the NlaIII RE . Hence, optimization of several parameters is essential in order to overcome technical biases in a PCR-based 3C experiment. Most of these technical aspects and their optimization procedures have been very-well addressed in literature , 41–43. LC locus . To exemxpressed . For thiArabidopsis genomes [Arabidopsis genome is densely packed with a gene density of one gene per 4.5 kb. However, most of the chromosome conformation capture technologies are best suited to study mid-range and long-range chromatin interaction and therefore, less suitable for Arabidopsis. Nevertheless, one Hi-C study did report contact maps of up to 2 kb resolution [Since the development of the 3C method, many variants of this technology have been rolled out  conditions . Two grams of seedling material (above ground tissues) per biological sample were collected during the afternoon hour of the day. In addition, material was sampled 7 days later and after transfer and growth of the plants for a week at long day photoperiod conditions .SOC1 locus using the previously described protocol with some adaptations for Arabidopsis [Arabidopsis above-ground seedling material was crosslinked with 2% paraformaldehyde PBS buffer under vacuum for 30 min (mins) on ice. The cross-linking reaction was stopped by addition of ice-cold 2 M glycine  under vacuum for 5 min on ice. The crosslinked tissue was ground and nuclei were isolated and purified using nuclei extraction (NE) buffer. Before digestion, the purified nuclei pellets were re-suspended in 1.2 × restriction buffer and treated with 0.2% SDS at 65 °C for 20 min. Later, SDS was sequestered by incubating with 2%Triton X-100 for 30 min. 3C analysis was performed on the SOC1 locus using two different REs (namely FspBI and NlaIII) independently. 400 U of RE was used for overnight digestion at 37 °C. Digestion was stopped by incubation at 65 °C for 20 min. Ligation was performed using 100 U of T4 DNA ligase, initially at 16 °C for 5 h, followed by room temperature for 45 min. Reverse cross-linking was done overnight with a treatment of proteinase K at 65 °C. After reversal of the crosslinks, phenol/chloroform extraction and ethanol precipitation was performed for recovery of the DNA.3C was performed on the bidopsis . Two graSOC1 locus and followed by re-ligation in small volumes to obtain all the possible random ligation events. Secondly, in order to control for the quality and quantity of each 3C sample, the 3C data needed to be further normalized to 3C values measured for an endogenous control locus  unrelated to the SOC1 locus. The chromatin state of such a reference gene is assumed to be stable across samples. Therefore, for each 3C sample, 3C values were also obtained for the reference gene TIP41-like. The reference gene primer amplification efficiencies were also corrected with a RL control obtained by digestion and re-ligation of a BAC clone containing the TIP41-like locus. The 3C data of SOC1 was normalized to the 3C values measured for the TIP41-like locus to obtain relative interaction frequencies. For more details on step-by-step data analysis of the qPCR-based 3C method see [All the primers used in this study are listed in Tables thod see . All fig


Dear Editor,. Ribeiro et al.concluded that "Although the neuroimaging findings in congenital Zika syndromeare not pathognomonic, many are quite suggestive of the diagnosis, and radiologistsshould be prepared to interpret such findings accordingly" and raise aquestion "what do we know so far?" We would like to share ideas and experience of thistopic. In our country, in tropical Southeast Asia, the Zika virus infection is usuallyasymptomatic and there isusually negative neurological finding. This means that there is not only nopathognomonic finding but also no finding. The use of neuroimaging investigation haslimited role in investigation of the case in our setting.We read the publication on "Congenital Zika syndrome and neuroimaging findings" with agreat interest , as well as foraffording us the opportunity to discuss the topic further. We believe that thisfacilitates the exchange of information and the expansion of knowledge about CZS. Acareful reading of the letter shows that Joob and Wiwanitkit are doubtful that thereare any neuroimaging findings characteristic of CZS, because they deny the existenceof this entity among the cases of Zika occurring in their region. However, it isimportant to mention that our review article was based not on our opinions but onevidence, as well as on an active search of recent articles on the subject publishedin major international journals, including the New England Journal ofMedicine-7, Lancet, Radiology, and others-16. Therefore, thedata in the current literature are sufficient to foster a belief in the teratogenicpotential of the Zika virus,6. Nevertheless, we carefullyconsidered whether or not there is a causal relationship between infection with theZika virus and malformations of the central nervous system, which led to thediscussion of the topic in the section "Causality between malformations andcongenital infection with the Zika virus" in our article.Initially, we would like to thank Joob and Wiwanitkit for their letter and theirinterest in our review article on neuroimaging findings in congenital Zika syndrome(CZS). Ultimately, webelieve that personal experiences are valuable and can make important contributionswithin the scientific community. However, at this moment, we need information basedon scientific evidence rather than personal opinions. Further studies should bringgreater clarity to this issue.Certainly, paradigm shifts require cautious reflection and are sometimes difficult toaccept, especially when they are related to a disease that was previously unknownworldwide. The situation is complicated by the fact that the last epidemic of birthdefects caused by an infectious pathogen (the rubella virus) occurred more than 50years ago


The costs of medical research are a concern. Clinical Trials Units (CTUs) need to better understand variations in the costs of their activities.κ.Representatives of ten CTUs and two grant-awarding bodies pooled their experiences in discussions over 1.5 years. Five of the CTUs provided estimates of, and written justification for, costs associated with CTU activities required to implement an identical protocol. The protocol described a 5.5-year, nonpharmacological randomized controlled trial (RCT) conducted at 20 centres. Direct and indirect costs, the number of full time equivalents (FTEs) and the FTEs attracting overheads were compared and qualitative methods  were used to interpret the results. Four members of the group (funding-body representatives or award panel members) reviewed the justification statements for transparency and information content. Separately, 163 activities common to trials were assigned to roles used by nine CTUs; the consistency of role delineation was assessed by Cohen’s κ = 0.21–0.40) for statisticians/data managers and poor for other roles (κ < 0.20).Median full economic cost of CTU activities was £769,637 . Indirect costs varied considerably, accounting for between 15% and 59% (median 35%) of the full economic cost of the grant. Excluding one CTU, which used external statisticians, the total number of FTEs ranged from 2.0 to 3.0; total FTEs attracting overheads ranged from 0.3 to 2.0. Variation in directly incurred staff costs depended on whether CTUs: supported particular roles from core funding rather than grants; opted not to cost certain activities into the grant; assigned clerical or data management tasks to research or administrative staff; employed extensive on-site monitoring strategies . Funders preferred written justifications of costs that described both FTEs and indicative tasks for funded roles, with itemised non-staff costs. Consistency in role delineation was fair  contains supplementary material, which is available to authorized users. The increasing costs of clinical trials, of great concern a decade ago , 2, contClinical trials units (CTUs) are specialised research entities that may assist with the design and central coordination of trials . The casBetween March 2013 and June 2014 the authors pooled their experiences at four 2-h teleconferences, two full-day workshops and intermittent e-mail contact. Three more formal pieces of research were also designed, to help the group to pool their experiences and expertise. One investigated variation in costs of CTU-based activities, a second involved written justifications of the costs, and a third considered differences in role delineation across CTUs.CTUs based in higher education institutions (HEIs) volunteered to cost CTU activities for the same protocol given in vignette form . Summary costs were tabulated using the Transparent Approach to Costing (TRAC) categories . In the Telephone interviews with CTU representatives who performed the costings were conducted by the lead author to elicit data on local practices and reasons why they might produce costs that differed from those of other CTUs. We had few preconceived opinions about reasons for varying costs and we used unstructured interviews to collect data, with no theoretical framework for its analysis , 42. TheCTUs that submitted costs were also asked to submit justification of their costs within a 2000-character limit typical of Grant Application Forms. These statements were anonymised and given to four members of the group not involved in the costings exercise, but who represented funding bodies or sat on funding panels. Informal feedback was elicited about the content and style that was most helpful for allowing funders to judge the appropriateness of the resources being requested.The objective of this exercise was to describe variation between CTUs in the allocation of particular CTU activities to staff roles. We assessed variation across CTUs represented by members of the group on the involvement of each role in each of 163 activities which would be necessary in a clinical trial of an investigational medicinal product. This list of activities had been drawn up by the UK Trial Managers Network in 2009  job titles differ from unit to unit, (2) where units have similar job titles, it is unclear whether the roles are in fact the same and (3) not all units employ, for example, people called ‘programmers’ or ‘statisticians’ . Therefore, we allowed units to specify their own job titles/roles and, when they generally allocated activities in this way, to complete the form by allocating tasks to job families rather than discrete job categories per se – a shortcoming discussed below. By job families, we mean groupings of CTU workers, whose task sets are subjectively thought to be closely related in functional terms. For instance, one unit considered their trial manager, trial coordinator and senior trial manager, all to be working in ‘trial management’ . The most common job family terms across units were ‘statistics’, ‘study/trial management’, ‘data management’, ‘quality assurance’ (QA) and ‘clerical/admin’, with other job families, such as programming, not adopted by all units.κ (kappa), a chance-corrected statistic in which a value of 1 corresponds to perfect consistency, 0 to agreement no better than chance alone, and negative values to agreement worse than chance (STATA v13) [For each role, we used STATA v13 software to calculate Cohen’s ATA v13) . We usedATA v13) . A firstSummary results of the cost-comparison exercise are displayed in Table The greatest variation between CTU costs was observed in the level of indirect costs , which accounted for between 15% and 59% of the full economic cost (median 35%). CTU B, whilst having median staff costs and the lowest non-staff costs, had the highest overall costs due to the relatively high level of indirect costs levied by its host institution (59% of the full economic cost). Excluding the CTU which did not provide statistical services at the time, the total number of FTEs ranged from 2.0 to 3.0; the number of FTEs attracting overheads ranged from 0.3 to 2.0.A related issue was variation in the designation of project staff. When TRAC methods are applied (see above), HEI staff designated as researchers attract indirect costs whilst other HEI staff and NHS-based staff do not. The only roles which were consistently designated research staff were the senior trial methodology advisory roles  and statisticians, although it was rare for study managers and research assistants to be on technical contracts. Data managers and programmers were typically, but not always, on technical contracts, with QA officers equally likely to be designated as researchers or not.The second largest source of variation was the differences in staffing – although there were considerable commonalities in the way some roles were costed. Several over-arching issues emerged from interviews and from discussions at teleconferences and meetings. These are described first, followed by variation in staffing by job family; in this section, annual salary costs are described.Estimating the direct costs of a trial for a grant application is an inexact exercise that tries to anticipate the resources required based on specific trial factors .The resources required for a trial are not constant over time but are difficult to schedule since trials may not run according to plan. Therefore, CTUs are typically project-managing a specific trial (and budget) in the context of a larger envelope of resources (and budget) accruing over a portfolio of trials. This justifies budgeting for an average amount of resource for a job family of activity for a trial and managing fluctuations in workload across multiple trials. CTUs with larger portfolios may be able to achieve this flexibility more easily.CTUs have markedly different histories and infrastructure affecting staff-mix, established ways of working, portfolio size and activities they offer . These factors are largely fixed but inevitably impact on the ways they manage and budget for trials.CTUs varied in their access to NIHR CTU Support Funding (partly related to size and history), to charitable ‘infrastructure’ funding and to ‘core’ investment . NIHR CTU Support Funding is intended to be used to facilitate the submission of more, higher-quality grant applications to the NIHR but this and other resources, not derived from grants, allow CTUs/HEIs flexibility in costing grant applications. Core investment (e.g. research fellowships) may cover staff time, providing flexibility for a person to carry out research within a broad designated health area .Study management oversight by senior trials unit staff was mainly at 5% throughout – with one CTU dropping provision to 2% for the final 6 months and one CTU opting for 10% throughout; the base salary for this role, however, varied from £45,053  to a professorial level, although salaries in the range £47,787 to £53,765 (‘senior research fellow’) were most common. These individuals generally played a role in proposal development and costing in the pre-award phase, and, post award, supervision of study staff and essential documentation, attendance at Trial Management Groups, Trial Steering Committees and Data Monitoring and Ethics Committees and interpretation and writing of trial reports. Some had substantial core funding from a variety of sources and some were wholly funded from grants.‘For a lot of units it is very difficult to have that fluctuation in work load within a project… it is about the infrastructure and the set-up that you’ve got as a unit’.Project management was much more complicated and heterogeneous with a wide variety of job titles and role boundaries making direct comparison difficult . The project manager would typically be full time and have a salary in the range of £28,972 to £36,661, with some CTUs capping salaries for this work at just over £34,000. One CTU made considerable savings over the course of the project by variable costing of study managers, dropping the post from 100% to 40% during the recruitment period once all centres were open and, further, to 20% during the period in which patients were only being followed up. The representative of this CTU acknowledged that their unit benefitted from dependable core/infrastructure funding and economies of scale not available to many smaller units. These circumstances meant that, in the event of unexpected problems with governance or attrition which can take up time in the later phases of a trial, there were always staff available to deal with these problems effectively without including a costing to cover this risk for individual trials:Project management staff were not limited to the project manager. One CTU budgeted for 25% of a more senior project manager  in addition to the project manager themselves. It was more common to see a proportion of a more junior researcher, sometimes called a ‘research assistant’, to support the project manager.Reassuringly, the level of funding for statistics varied little between units. Aside from unit D (see above) statistical support (excluding programming) was commonly at 30–33% throughout, with one unit preferring variable costing  of a junior statistician, usually salaried between £30,728 and £36,661. The junior statistician was typically supported by at least one senior statistician, typically at 5% and with a salary in the range £42,476 to £53,765, although occasionally the cost of this resource implied that senior statistical oversight would be carried out by someone on a much higher, professorial salary. Smoothing of junior statistician salaries across grants was near ubiquitous, despite more or less predictable peaks and troughs in activity .‘What CTUs are trying to do is to run a portfolio. Within that, you recognise that, despite your best intentions, trials have a life of their own. They very rarely go according to the timeline that you set, they very rarely go according to the budget that you set, and nearly all of the funding is fixed. So, you have to have 30% of a junior statistician. The point about burnout of the junior statisticians is incredibly important. If you keep loading them with four to six studies and expect them to deliver excellence at a relatively early part of their career, there can be psychological harms and professional costs’.The following rationale was given by one CTU representative and accepted as legitimate by funding-body representatives:‘A lot of our trial managers spend a lot of time being very expensive administrators and secretaries so the only way we seem to be able to address that is to significantly increase the amount of assistant trial manager time’.Two CTUs did not budget for clerical officers and, in each case, allocated what other CTUs saw as clerical tasks  to research staff on higher salaries. For one unit this was a choice; for the other it was imposed on them by a host institution because of perceived imbalances in the departmental staffing profile (see above regarding designation of staff as researchers or not and the impact on indirect costs):When included, clerical salaries ranged from £15,456 to £21,597 and such staff were never designated as researchers . Those at the bottom end were typically associated with data entry tasks and those at the top end were associated with tasks allocated to research staff at other institutions .Data managers, typically on nonresearch salaries of between £28,132 and £36,661, were budgeted at an average of 30% throughout most of a study, with some units varying the amount of time for this role across the duration of the trial. In some CTUs the boundaries between data management, data entry  and programming were often ‘fuzzy’. Some units assumed that research nurses would input data on site , whilst others arranged for paper Case Report Forms (CRFs) to be returned to the CTU for data entry. One unit outsourced its programming for data management, showing this activity in the budget as a consultancy fee instead of a staff cost. At other units, there was great variation in how programmers were costed in terms of their salary ranges and the duration of their input on the trial – with many costing this role only for 3 to 6 months in the first year and some smoothing the costs throughout. There was also some overlap in duties between database programmers and statistical programmers at some units, all of which made comparison difficult.Quality assurance was an activity which was budgeted for by three of the five CTUs. One CTU funded this activity wholly from core/infrastructure funding; another had never been able to fund a formal QA role from either core or grant funding. A number of CTUs reported distributing key QA tasks, such as risk assessment and the authorship/update of standard operating procedures, across their staff, because they were unable to fund a QA officer adequately. Where present, QA officer salaries were in the range of £21,597 to £34,575, with higher whole time equivalents (WTEs) requested for junior (e.g. 20%) compared to senior staff (e.g. 5%). QA officers were sometimes designated as having academic and sometimes nonacademic contracts.‘… there’s always some rogue sites… and you can’t underestimate the value of contact… for keeping the study in the forefront of people’s minds.’A breakdown of non-staff costs can be found in Table For the three units using proprietary, fee-based, electronic data capture systems , the fees were £5,500 , £7,500 (CTU D: not including programming undertaken as part of the work of 0.3 WTE data managers) and £45,640 . CTU E’s costs included a database licence at £10,000 and £35,640  for site access to the electronic CRF (eCRF). The use of an eCRF with this cost structure is not something for which the other CTUs had to cost. CTU A used paper CRFs at the centres, returned by post, resulting in low database costs and above average consumable costs. For commercial database systems with a flat fee workplace licence, units with larger portfolios were able to achieve greater economies of scale. Two units used programmers to adapt or modify locally developed systems: CTU B at £59,252 and CTU C at £23,800 – although, as with CTU D, this figure masked a certain amount of additional programming undertaken by data managers.Representatives of four CTUs submitted anonymised statements justifying the costs they had submitted in the exercise described above  for senior statisticians and data managers and poor for other roles (κ < 0.20)  Table . When we0) Table . The rolThis study confirms the subjective impression of grant award panel members that CTU costs on grant applications vary in ways that cannot be attributed wholly to study design or complexity . Levels of indirect costs levied by HEIs, level of staffing requested , number of WTEs attracting overheads and level of site-based monitoring activities were the main drivers of variation in CTU costs.The diverse perspectives of contributors to this study  is an important strength. CTU members were chosen (from a larger number of nominees) for the group on the basis of their stated expertise with respect to costing research. Representatives of participating units found no difficulties in costing the protocol and, whilst noting differences in definition and attribution of costs for the set-up period of a trial, confirmed that other timescales were within what was considered normal locally. The conduct of this exercise over 12–18 months, with several opportunities for debate and feedback, is another strength.The main limitation of this exercise is the limited sample size. The findings of this paper reflect the pooled experience of members from only ten of the 45 CTUs registered with the UK Clinical Research Collaboration in 2013. Although this sample of CTUs is diverse in terms of funding, portfolios and organisation, it is unlikely that they are fully representative of all UK units. In particular, the comparative costs exercise is based on detailed feedback from a self-selected sample of just five CTUs that were able to prioritise an unfunded contribution to this exercise, fewer than considered satisfactory for thematic saturation . Our remA second limitation is the extent to which costing of the vignette by a CTU followed processes that would have been used when costing an actual application for funding for a trial. The formatting of the costs that were reported by CTUs and details provided in subsequent interviews demonstrated that the costs had been generated by staff familiar with CTUs’ usual costing processes and experienced in applying them. However, the exercise could not reproduce all of the formal steps for an actual grant submission and it is possible that further checks, required as part of approving a grant for actual submission, might have led to some revisions.Our findings confirm that, for some trial activities – for instance statistical support – there is remarkable similarity in approaches to costing between CTUs. Also, despite the differences in headline CTU costs, even the lowest CTU costs exceeded £0.5 m. We mention this figure because some funding opportunities invite applications for nonpilot trials and, nevertheless, specify this, or a lower figure, as a funding ceiling; specifying a ceiling in this way tends to lead to trial budgets just under the ceiling but which are not credible.Similarity may arise either because the underlying costs of an activity are, indeed, similar across CTUs or just because CTUs have adopted a similar model for costing – potentially, communicated from one CTU to another because ‘that’s what the funder will accept’. The extent to which CTUs cost planned research at the level of the task was discussed, and was observed to vary; some did this  and others relied on standard costing templates in which many elements can vary only at the margins from project to project. Several members argued that task-based costing is difficult and time-consuming to do, and not necessarily predictive of the actual work required on a project.This study has begun the process of documenting real differences in the activities and associated costs which CTUs feel are necessary to deliver a particular trial, and how they express those requirements. The differences in role nomenclature . CTUs should strive to provide as much information and detail on costs as possible to ensure that funders have a transparent view.The largest variations in costs are outside the control of CTUs, arising from factors that are features of, or are imposed by, host organisations. These include staff profile, levels of indirect costs and access to core funding. Levels of indirect costs have also attracted attention in the USA where, between 2003 to 2012, indirect costs of National Institute for Health-funded studies increased by 16.9% compared with 11.7% for direct costs . CalculaLocal idiosyncrasies of host institutions , as well as the diverse portfolios of CTUs , make the adoption of a common, standardised costing template impossible. In practice, CTU-based researchers costing grant applications expect the budget to reflect the needs of specific studies . Workload models for organisations have principally been published by teams working within the US National Institutes of Health, National Cancer Institute and the National Cancer Institute of Canada; these are primarily designed to estimate site costs , not CTU activities. This literature supports the view that trials are increasing in complexity but this conclusion may be influenced by the predominant focus on cancer trials. Workload modes are criticised for over-simplicity and are implicated in staff burnout and poor quality standards , 36.These criticisms resonate with researchers in CTUs with responsibility for estimating trial management workload. Many trials commissioned by the UK NIHR involve difficult-to-reach study populations and complex interventions, making trial management workload unpredictable . CTU repProjecting a timetable and recruitment for a trial are critical to estimating a trial budget and are often uncertain. There was scepticism among our group members that national targets to bring down approval times were uniformly successful , or robuDespite growing evidence that an increase in central monitoring and reduced site monitoring can safely reduce costs , 60–63, Some variation in costs is due to factors outside the control of CTUs such as access to core funding and levels of indirect costs imposed by host institutions. CTUs need to undertake a clear and transparent costing that fully explains the resources required when submitting a grant application to enable funders to make informed decisions. Research is needed on barriers to implementing evidence-based strategies which minimise costs, especially, risk-based monitoring strategies.Additional file 1:Example protocol for costing exercise. (DOCX 13 kb)Additional file 2:Generic clinical trial tasks (UKCRC TMN). (DOCX 23 kb)Additional file 3:Tasks by role – all CTUs. (XLSX 129 kb)Additional file 4:Justification of costs statements. (DOCX 18 kb)Additional file 5:Summary of tasks by role by CTU. (XLSX 20 kb)


Vigna unguiculata L. ) is an annual tropical grain legume. Often referred to as “poor man’s meat”, cowpea is one of the most important subsistence legumes cultivated in West Africa due to the high protein content of its seeds. However, African cowpea production can be seriously constrained by viral diseases that reduce yields. While twelve cowpea-infecting viruses have been reported from Africa, only three of these have so-far been reported from Burkina Faso. Here we use a virion-associated nucleic acids (VANA)-based metagenomics method to screen for the presence of cowpea viruses from plants collected from the three agro-climatic zones of Burkina Faso. Besides the three cowpea-infecting virus species which have previously been reported from Burkina Faso  five additional viruses were identified: Southern cowpea mosaic virus (Sobemovirus genus), two previously uncharacterised polerovirus-like species (Family Luteoviridae), a previously uncharacterised tombusvirus-like species (Family Tombusviridae) and a previously uncharacterised mycotymovirus-like species (Family Tymoviridae). Overall, potyviruses were the most prevalent cowpea viruses (detected in 65.5% of samples) and the Southern Sudan zone of Burkina Faso was found to harbour the greatest degrees of viral diversity and viral prevalence. Partial genome sequences of the two novel polerovirus-like and tombusvirus-like species were determined and RT-PCR primers were designed for use in Burkina Faso to routinely detect all of these cowpea-associated viruses.Cowpea, ( Vigna unguiculata L. ), which is one of the most important subsistence legumes cultivated in West Africa [Cowpea,  and metagenomics-based approaches to study viromes at scales ranging from individual organisms to entire communities, have enabled the discovery of increasing numbers of viruses in both wild ecosystems and agro-ecosystems –21. MetaHere, we used a virion-associated nucleic acids (VANA) based metagenomics approach –27 to scThree hundred and twelve leaf samples were randomly collected  in 2013 . 104 plaEight cowpea cultivars  obtained from Burkina Agricultural institute  and one unknown cultivar from Togo were grown at Montpellier, France within an insect-proof plant growth-chamber. Eighty-one seeds of each Burkina accession and twenty seeds of the Togo cultivar were sown in single use plastic pots containing sterilized peat and compost. Germinated seeds were examined daily during two weeks for the presence of symptoms on the primary and trifoliate leaves .5’-CCT TCG GAT CCT CCN NNN NNN NNN NN-3’) at 85°C for 2 min. The mixture was immediately placed on ice. Subsequently, 10 mM dithiothreitol, 1 mM of each deoxynucloside triphosphate (dNTP), 4 μl of 5X Superscript buffer, and 5 U of SuperScript III  were added to the mixture , which was then incubated at 25°C for 10 min, followed by 42°C incubation for 60 min and 70°C incubation for 5 min before being placed on ice for 2 min. cDNAs were purified using the QiaQuick PCR cleanup kit (Qiagen). Priming and extension was then performed using Large (Klenow) Fragment DNA polymerase (Promega). First, 20 μl of cDNA in the presence of 2 μM of primer DoDec were heated to 95°C for 2 min and then cooled to 4°C. 2.5 U of Klenow Fragment, 10X Klenow reaction buffer and 0.4 mM of each dNTP  were added. The mixture was incubated at 37°C for 60 min followed by 75°C for 10 min. PCR amplification was carried out using 5 μl of the reaction described above in a 20 μl reaction containing 2 μM of one of the 96 primers listed in De novo assemblies of cleaned reads were performed using CAP3 [http://www.ncbi.nlm.nih.gov/gorf/gorf.html). Primary sequence outputs have been deposited in the sequence read archive of GenBank (accession number: SRP083221).The VANA-based 454 pyrosequencing approach  was useding CAP3 . Contigsing CAP3 . Open reThe prevalence of a particular group of viruses was defined as the proportion of the 307 field sampled cowpea plants containing at least one VANA-read with a high degree of similarity  to that group of viruses. Five samples were considered to have failed because no VANA-reads were produced.A subset of fifty-two cowpea plants  that wer2 dried cowpea leaves with the Qiagen® RNeasy Plant Mini Kit  as described by the manufacturer. The detection of potyviruses was carried out using the primer pair Oligo1N/Oligo2N [www.uku.fi/~kuulasma/OligoSoftware) with customized settings .Total RNA was extracted from 35–40 mg of CaCl/Oligo2N . For the/Oligo2N  and prim® OneStep RT-PCR Kit. The 25 μL RT-PCR reaction mix consisted of 1 μL of eluted RNA (concentration range of 12–350 ng/μL), 14 μL of RNAse-free water, 5 μL of RT-PCR buffer (5X), 1 μL of dNTP mix (10 mM), 1.5 μL of each primer (10 μM) and 1 μL of RT-PCR enzyme mix. The RT-PCR program was as follows with the annealing temperature (Ta) and extension time (Ext) for each targeted virus listed in RT-PCR reactions were performed using the QiagenSpecific nested or semi-nested-PCR assays were also designed to improve the detection of Cowpea mottle virus (CPMoV), Southern cowpea mosaic virus (SCPMV), tombusvirus-like viruses and mycotymovirus. RT-PCRs were performed as described above using the following primers: CPMoV1138F/CPMoV1686R for CPMoV; SCPMVNB2698F /SCPMVNB3419R for SCPMV, Tombus2NB237F/Tombus4NB79R for Cowpea tombusvirid 1 and TymoNB120F/TymoNB415R for Cowpea associated mycotymovirid 1 . PCR ampTwenty specific primers  were des®-T vector as recommended by the manufacturer (Promega) and sequenced by the Sanger method  using the universal primers, T7 and SP6.VANA-contigs potentially coding RdRp and coat proteins of a novel virus hereafter referred to as Cowpea tombusvirid 1 were used to design primers for amplifying the genomic region encompassing these two positive sense single stranded RNA virus genes , partial genome of Cowpea polerovirus 2 (KX599164), partial RdRp gene of Cowpea mottle virus (KX599165-KX599169), partial genome of Southern cowpea mosaic virus (KX599170), partial RdRp gene of Southern cowpea mosaic virus (KX599171-KX599173), partial genome of Cowpea tombusvirid 1 (KX599174), partial RdRp gene of Cowpea tombusvirid 1 (KX599175-KX599177), partial RdRp gene of Cowpea tombusvirid 2 (KX599183), partial RdRp gene of Cowpea tombusvirid 3 (KX599184), partial RP gene of Cowpea associated mycotymovirid 1 (KX599178-KX599182).Partial genome of Cowpea polerovirus 1 (KX599154), partial Tombusviridae) nucleotidic substitution models (selected as best fit by MEGA) and 1000 bootstrap replicates as a test for the support of branches.Sanger sequences were assembled using DNAMAN and were used as queries to perform BlastN and BlastX searches . SequencPotyviridae, Luteoviridae, Tombusviridae and Tymoviridae families and the unassigned Sobemovirus genus , sobemovirus genes , and carmovirus genes  of the evaluated plants .Reads related to sequences of viruses in the family d plants . Eleven Tombusviridae, were also identified from 3/203 plants  sometimes occurred in mixed infections , it is likely that this virus represents a new Polerovirus species  of the luteovirus-like isolate infecting plant BE167 was obtained using specific primers designed from the luteovirus-like related VANA-contigs recovered from this plant. This consensus sequence corresponds to >83% of a typical polerovirus genome length. A BlastN search of GenBank returned Chickpea chlorotic stunt virus  as the closest match . Six ORFs were identified from this contig, including ORF0 (Beet mild yellowing virus (BMYV), accession number ACA61672, highest percent identity = 27%, e-value = 0.049), ORF1 (Phasey bean mild yellows virus (PBMYV), accession number ALR87184, highest percent identity = 33%, e-value = 9e-86), ORF2 (Brassica yellows virus (BrYV), accession number ADW41603, highest percent identity = 69%, e-value = 0.0), ORF3 (Groundnut rosette assistor virus (GRAV), accession number AAG29926, highest percent identity = 85%, e-value = 1e-94), ORF4  and ORF3-ORF5 (Beet western yellows virus (BWYV), accession number ADR74374, highest percent identity = 81%, e-value = 2e-117). The partial genome that was obtained had an organization typical of poleroviruses in that it was comprised of six ORFs, including ORF0, which is absent in viruses of the genus Luteovirus, and ORF4, which is absent in Pea enation mosaic virus-1; which is presently the only member of the genus Enamovirus [Luteoviridae. It is also noteworthy that ORF2 shares >80% nucleotide sequence identity with that of PBMYV, a novel polerovirus also isolated from phasey bean (Macroptilium lathyroides), a legume of the Fabaceae family [A second partial luteovirus-like genome fragment (3164 nt in length) was obtained by RT-PCR from plant BE179. A BlastN search revealed that this partial genome shares ~81% nucleotidic identity with PBMYV . Three complete ORFs identified in this sequence are most similar to the ORF2 of PBMYV , the ORF3 of PBMYV  and the ORF4 of Suakwa aphid-borne yellows virus . In addition, two partial ORFs were also found which were most similar to the ORF1 of PBMYV  and the ORF5 of CpCSV . The only canonical polerovirus ORF that was completely missing from this 3184 nt long fragment was ORF0. It is nevertheless likely that this luteovirus-like sequence is from a virus that should be classified as belonging to the Polerovirus genus of the e family . Howevercp gene sequences of these ten isolates, possible evolutionary relationships with other poleroviruses were investigated using phylogenetic analyses. While nine isolates tightly cluster around the isolate from plant BE167, from which we recovered the 5012 nt long partial genome of Cowpea polerovirus 1, the isolate BE179 branches from a different part of the tree, suggesting that this virus (Cowpea polerovirus 2) is probably a new species of cowpea-infecting polerovirus . One ORF was identified within this consensus sequence, encoding a tombusvirus-like RdRp protein that is most similar to that of Saguaro cactus virus . While this result suggests that this virus, hereafter referred to as Cowpea tombusvirid 1 , should sviridae , Cowpea RdRp gene fragment revealed that the four Cowpea tombusvirid 1 isolates from Burkina Faso cluster together on a branch that is not closely associated with any sequences classified within any of the established Tombusviridae species, suggesting that Cowpea tombusvirid 1 genome fragment is likely derived from a previously unknown tombusvirus species  is potentially a second member of the new mycotymovirus lineage of the family Tymoviridae [RT-PCR detection was most successful using the TymoNB120F/TymoNB415R primer pair , yieldinoviridae .Field-sampled plants displayed a large range of symptom types , includiOverall, RT-PCR assay sensitivities were found to be slightly higher than that of the VANA-based metagenomic assay . While nAmong the various groups of viruses identified using the VANA-based metagenomic approach, the potyvirus CABMV is the most prevalent within cowpea grown in Burkina Faso. Whereas CABMV was found in 195/201 (97.0%) of the plants testing positive for potyviruses, BCMV-BlCM was found in only 6/201 (2.99%) of these plants.The prevalence of viruses from other families were low: 10/307 (3.26%) for the poleroviruses, 3/307 (0.98%) for the carmoviruses, and 3/307 (0.98%) for the sobemoviruses. Nevertheless, the prevalence of all cowpea viruses (other than perhaps the potyviruses) was probably slightly under-estimated because the detection rate of the VANA-based approach may have been reduced due to the high frequency of viral co-infections as evidenced by the comparison of RT-PCR/VANA cowpea virus detection approaches.Potyviridae, sobemovirus, Luteoviridae, Tombusviridae and Tymoviridae) and four in the Sudan-Sahel zone , only two taxonomic groups are present in the Sahel zone  although one isolate was found in the Sudan zone . Tombusvirus-like viruses were identified from the Sahel zone  and the Sudan-Sahel zone . Finally, the Cowpea associated mycotymovirid 1 was identified in the Sudan-Sahel zone firstly with VANA-based approach in one sample , whereas in RT-PCR method other positive samples were detected in both the Sudan and Sudan-Sahel zones.The occurrence of potyviruses and poleroviruses in mixed infection can be related to the fact that these viruses are both aphid transmitted , 52, whiPlant virus-associated VANA-reads were found from 16.66% (12/72) of the cowpea seedlings grown in an insect-proof growth chamber in France . While eight cowpea-infecting viruses  are reported to be potentially seed-transmissible (reviewed in ), only pOverall, these results highlight the fact that potyvirus seed-transmission rates are likely high in Togo and Burkina Faso: a fact that could certainly have a major impact on the recurrence of diseases associated with potyviruses in this African region and can partly account for the very high prevalence of potyvirus infections in cowpeas grown throughout Burkina Faso. Minimizing or removing this primary source of viral inoculum would probably be a first step towards better control of potyvirus diseases of cowpea within this country.Overall, a combination of VANA-based metagenomics and classical RT-PCR- based molecular detection approaches have strengthened our knowledge about the diversity of viruses infecting cowpea in Burkina Faso; which is a first step towards minimizing the economic burden of these viral diseases on the smallholder farmers whose are the principal producers of legumes both in this country, and the rest of west Africa. The cowpea viruses identified in this study should be further studied and taken into account in future efforts to control diseases in this important crop.S1 FigPotyviridae. CABMV, Cowpea aphid-borne mosaic virus; BCMV, Blackeye cowpea mosaic strain of Bean common mosaic virus; PPST, Passiflora virus; ABMV, Azuki bean mosaic virus; HMV, Hardenbergia mosaic virus. B) Maximum-likelihood phylogenetic trees of partial RdRp genes from five isolates of Cowpea tombusvirus-like virus and representative species from the family Tombusviridae. OCSV, Oat chlorotic stunt virus; TBTV, Tobacco bushy top virus; SCNMV, Sweet clover necrotic mosaic virus; CRSV, Carnation ringspot virus; FNSV, Furcraea necrotic streak virus; GaMV, Galinsoga mosaic virus; OLV1, Olive latent virus 1; TNV A, Tobacco necrosis virus A; JCSMV, Johnsongrass chlorotic stripe mosaic virus; MWLMV, Maize white line mosaic virus; MNSV, Melon necrotic spot virus; PNSV, Pelargonium necrotic spot virus; CIRV, Carnation Italian ringspot virus; LWSV, Leek white stripe virus; BBSV, Beet black scorch virus; TurCV, Turnip crinkle virus; CarMV, Carnation mottle virus; MCMV, Maize chlorotic mottle virus; PMV, Panicum mosaic virus; CkMMV, Cocksfoot mild mosaic virus; PMV, Panicum mosaic panicovirus.A) Maximum-likelihood phylogenetic trees of partial nuclear inclusion genes from 21 isolates of Cowpea polerovirus and representative species from the family (EPS)Click here for additional data file.S2 Fig(A) RT-PCR for detection of potyviruses (B), RT-PCR for detection of Cowpea mottle virus (C), RT-PCR for detection of Southern cowpea mosaic virus (D), RT-PCR for detection of Cowpea polerovirus1 and Cowpea polerovirus2 (E) Nested RT-PCR for detection of Tombusvirid1 and (F) RT-PCR for detection of Cowpea associated mycotymovirid 1.(DOC)Click here for additional data file.S3 Fig(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOC)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.