Design Strategies for Efficient Arbovirus Surveillance.
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ABSTRACT:
SUBMITTER: Scarpino SV
PROVIDER: S-EPMC5367434 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Effective vector and arbovirus surveillance requires timely and accurate screening techniques that can be easily upscaled. Next-generation sequencing (NGS) is a high-throughput technology that has the potential to modernize vector surveillance. When combined with DNA barcoding, it is termed 'metabarcoding.' The aim of our study was to establish a metabarcoding protocol to characterize pools of mosquitoes and screen them for virus. Pools contained 100 morphologically identified individuals, including one Ross River virus (RRV) infected mosquito, with three species present at different proportions: 1, 5, 94%. Nucleic acid extracted from both crude homogenate and supernatant was used to amplify a 269-bp section of the mitochondrial cytochrome c oxidase subunit I (COI) locus. Additionally, a 67-bp region of the RRV E2 gene was amplified from synthesized cDNA to screen for RRV. Amplicon sequencing was performed using an Illumina MiSeq, and bioinformatic analysis was performed using a DNA barcode database of Victorian mosquitoes. Metabarcoding successfully detected all mosquito species and RRV in every positive sample tested. The limits of species detection were also examined by screening a pool of 1000 individuals, successfully identifying the species and RRV from a single mosquito. The primers used for amplification, number of PCR cycles and total number of individuals present all have effects on the quantification of species in mixed bulk samples. Based on the results, a number of recommendations for future metabarcoding studies are presented. Overall, metabarcoding shows great promise for providing a new alternative approach to screening large insect surveillance trap catches.
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Project description:BACKGROUND:Detecting and monitoring the transmission of arboviruses such as Zika virus (ZIKV), dengue virus, and chikungunya virus is critical for prevention and control activities. Previous work has compared the ability of different human-focused surveillance strategies to detect ZIKV transmission in U.S. counties where no known transmission had occurred, but whether virological surveillance in mosquitoes could represent an effective surveillance system is unclear. OBJECTIVES:We leveraged a unique set of data from human and virological surveillance in Ae. aegypti during the 2016 ZIKV epidemic in Caguas, Puerto Rico, to compare alternative strategies for detecting and monitoring ZIKV activity. METHODS:We developed a simulation model for mosquito and human surveillance strategies and simulated different transmission scenarios with varying infection rates and mosquito trap densities. We then calculated the expected weekly number of detected infections, the probability of detecting transmission, and the number of tests needed and compared the simulations with observed data from Caguas. RESULTS:In simulated high transmission scenarios (1 infection per 1,000 people per week), the models demonstrated that both approaches had estimated probabilities of detection of greater than 78%. In simulated low incidence scenarios, vector surveillance had higher sensitivity than human surveillance and sensitivity increased with more traps, more trapping effort, and testing. In contrast, the actual data from Caguas indicated that human virological surveillance was more sensitive than vector virological surveillance during periods of both high and low transmission. CONCLUSION:In scenarios where human surveillance is not possible or when transmission intensity is very low, virological surveillance in Ae. aegypti may be able to detect and monitor ZIKV epidemic activity. However, surveillance for humans seeking care for Zika-like symptoms likely provides an equivalent or more sensitive indicator of transmission intensity in most circumstances.
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Project description:The ability to identify all the viruses within a sample makes metatranscriptomic sequencing an attractive tool to screen mosquitoes for arboviruses. Practical application of this technique, however, requires a clear understanding of its analytical sensitivity and specificity. To assess this, five dilutions (1:1, 1:20, 1:400, 1:8,000 and 1:160,000) of Ross River virus (RRV) and Umatilla virus (UMAV) isolates were spiked into subsamples of a pool of 100 Culex australicus mosquitoes. The 1:1 dilution represented the viral load of one RRV-infected mosquito in a pool of 100 mosquitoes. The subsamples underwent nucleic acid extraction, mosquito-specific ribosomal RNA depletion, and Illumina HiSeq sequencing. The viral load of the subsamples was also measured using reverse transcription droplet digital PCR (RT-ddPCR) and quantitative PCR (RT-qPCR). Metatranscriptomic sequencing detected both RRV and UMAV in the 1:1, 1:20 and 1:400 subsamples. A high specificity was achieved, with 100% of RRV and 99.6% of UMAV assembled contigs correctly identified. Metatranscriptomic sequencing was not as sensitive as RT-qPCR or RT-ddPCR; however, it recovered whole genome information and detected 19 other viruses, including four first detections for Australia. These findings will assist arbovirus surveillance programs in utilising metatranscriptomics in routine surveillance activities to enhance arbovirus detection.
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Project description:We tested 700 serum samples collected throughout Panama from 2015 to 2016 for detecting antibodies and RNA of arboviruses. In convalescent specimens, microsphere immunoassay detected an antibody prevalence of 59.3% for dengue virus (DENV) and 30.3% for Zika virus (ZIKV), which included samples that were collected before the Panamanian surveillance system reported the first case of Zika in the country. For acute sera, the most common arbovirus was DENV with 18 positive samples (6%), followed by four (1.3%) of ZIKV and one (0.6%) of chikungunya virus (CHIKV). Our results indicate a change in the chronology of when ZIKV was first detected in Panama and stress the importance of integrating various approaches to enable improved surveillance of both endemic and emerging arboviruses.
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Project description:Surveillance programs are essential for the prevention and control of mosquito-borne arboviruses that cause serious human and animal diseases. Viral metatranscriptomic sequencing can enhance surveillance by enabling untargeted, high-throughput arbovirus detection. We used metatranscriptomic sequencing to screen field-collected mosquitoes for arboviruses to better understand how metatranscriptomics can be utilised in routine surveillance. Following a significant flood event in 2016, more than 56,000 mosquitoes were collected over seven weeks from field traps set up in Victoria, Australia. The traps were split into samples of 1000 mosquitoes or less and sequenced on the Illumina HiSeq. Five arboviruses relevant to public health (Ross River virus, Sindbis virus, Trubanaman virus, Umatilla virus, and Wongorr virus) were detected a total of 33 times in the metatranscriptomic data, with 94% confirmed using reverse transcription quantitative PCR (RT-qPCR). Analysis of metatranscriptomic cytochrome oxidase I (COI) sequences enabled the detection of 12 mosquito and two biting midge species. Screening of the same traps by an established public health arbovirus surveillance program corroborated the metatranscriptomic arbovirus and mosquito species detections. Assembly of genome sequences from the metatranscriptomic data also led to the detection of 51 insect-specific viruses, both known and previously undescribed, and allowed phylogenetic comparison to past strains. We have demonstrated how metatranscriptomics can enhance surveillance by enabling untargeted arbovirus detection, providing genomic epidemiological data, and simultaneously identifying vector species from large, unsorted mosquito traps.
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Project description:An understanding of the patterns of animal contact networks provides essential information for the design of risk-based animal disease surveillance and control strategies. This study characterises pig movements throughout England and Wales between 2009 and 2013 with a view to characterising spatial and temporal patterns, network topology and trade communities. Data were extracted from the Animal and Plant Health Agency (APHA)'s RADAR (Rapid Analysis and Detection of Animal-related Risks) database, and analysed using descriptive and network approaches. A total of 61,937,855 pigs were moved through 872,493 movements of batches in England and Wales during the 5-year study period. Results show that the network exhibited scale-free and small-world topologies, indicating the potential for diseases to quickly spread within the pig industry. The findings also provide suggestions for how risk-based surveillance strategies could be optimised in the country by taking account of highly connected holdings, geographical regions and time periods with the greatest number of movements and pigs moved, as these are likely to be at higher risk for disease introduction. This study is also the first attempt to identify trade communities in the country, information which could be used to facilitate the pig trade and maintain disease-free status across the country in the event of an outbreak.
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Project description:BACKGROUND: RNA sequencing (RNA-Seq) has emerged as a powerful approach for the detection of differential gene expression with both high-throughput and high resolution capabilities possible depending upon the experimental design chosen. Multiplex experimental designs are now readily available, these can be utilised to increase the numbers of samples or replicates profiled at the cost of decreased sequencing depth generated per sample. These strategies impact on the power of the approach to accurately identify differential expression. This study presents a detailed analysis of the power to detect differential expression in a range of scenarios including simulated null and differential expression distributions with varying numbers of biological or technical replicates, sequencing depths and analysis methods. RESULTS: Differential and non-differential expression datasets were simulated using a combination of negative binomial and exponential distributions derived from real RNA-Seq data. These datasets were used to evaluate the performance of three commonly used differential expression analysis algorithms and to quantify the changes in power with respect to true and false positive rates when simulating variations in sequencing depth, biological replication and multiplex experimental design choices. CONCLUSIONS: This work quantitatively explores comparisons between contemporary analysis tools and experimental design choices for the detection of differential expression using RNA-Seq. We found that the DESeq algorithm performs more conservatively than edgeR and NBPSeq. With regard to testing of various experimental designs, this work strongly suggests that greater power is gained through the use of biological replicates relative to library (technical) replicates and sequencing depth. Strikingly, sequencing depth could be reduced as low as 15% without substantial impacts on false positive or true positive rates.
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Project description:BACKGROUND:Increased frequency of arbovirus outbreaks in East Africa necessitated the determination of distribution of risk by entomologic arbovirus surveillance. A systematic vector surveillance programme spanning 5 years and covering 11 sites representing seven of the eight provinces in Kenya and located in diverse ecological zones was carried out. METHODS:Mosquitoes were sampled bi-annually during the wet seasons and screened for arboviruses. Mosquitoes were identified to species, pooled by species, collection date and site and screened for arboviruses by isolation in cell culture and/or RT-PCR screening and sequencing. RESULTS:Over 450,000 mosquitoes in 15,890 pools were screened with 83 viruses being detected/isolated that include members of the alphavirus, flavivirus and orthobunyavirus genera many of which are known to be of significant public health importance in the East African region. These include West Nile, Ndumu, Sindbis, Bunyamwera, Pongola and Usutu viruses detected from diverse sites. Ngari virus, which was associated with hemorrhagic fever in northern Kenya in 1997/98 was isolated from a pool of Anopheles funestus sampled from Tana-delta and from Aedes mcintoshi from Garissa. Insect only flaviviruses previously undescribed in Kenya were also isolated in the coastal site of Rabai. A flavivirus most closely related to the Chaoyang virus, a new virus recently identified in China and two isolates closely related to Quang Binh virus previously unreported in Kenya were also detected. CONCLUSION:Active transmission of arboviruses of public health significance continues in various parts of the country with possible undetermined human impact. Arbovirus activity was highest in the pastoralist dominated semi-arid to arid zones sites of the country where 49% of the viruses were isolated suggesting a role of animals as amplifiers and indicating the need for improved arbovirus disease diagnosis among pastoral communities.
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