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      Data-driven identification of potential Zika virus vectors

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          Abstract

          Zika is an emerging virus whose rapid spread is of great public health concern. Knowledge about transmission remains incomplete, especially concerning potential transmission in geographic areas in which it has not yet been introduced. To identify unknown vectors of Zika, we developed a data-driven model linking vector species and the Zika virus via vector-virus trait combinations that confer a propensity toward associations in an ecological network connecting flaviviruses and their mosquito vectors. Our model predicts that thirty-five species may be able to transmit the virus, seven of which are found in the continental United States, including Culex quinquefasciatus and Cx. pipiens. We suggest that empirical studies prioritize these species to confirm predictions of vector competence, enabling the correct identification of populations at risk for transmission within the United States.

          DOI: http://dx.doi.org/10.7554/eLife.22053.001

          eLife digest

          Mosquitoes carry several diseases that pose an emerging threat to society. Outbreaks of these diseases are often sudden and can spread to previously unaffected areas. For example, the Zika virus was discovered in 1947, but only received international attention when it spread to the Americas in 2014, where it caused over 100,000 cases in Brazil alone. While we now recognize the threat Zika can pose for public health, our knowledge about the ecology of the disease remains poor. Nine species of mosquitoes are known to be able to carry the Zika virus, but it cannot be ruled out that other mosquitoes may also be able to spread the disease.

          There are hundreds of species of mosquitoes, and testing all of them is difficult and costly. So far, only a small number of species have been tested to see if they transmit Zika. However, computational tools called decision trees could help by predicting which mosquitoes can transmit a virus based on common traits, such as a mosquito's geographic range, or the symptoms of a virus.

          Evans et al. used decision trees to create a model that predicts which species of mosquitoes are potential carriers of Zika virus and should therefore be prioritized for testing. The model took into account all known viruses that belong to the same family as Zika virus and the mosquitoes that carry them. Evans et al. predict that 35 species may be able to carry the Zika virus, seven of which are found in the United States. Two of these mosquito species are known to transmit West Nile Virus and are therefore prime examples of species that should be prioritized for testing. Together, the ranges of the seven American species encompass the whole United States, suggesting Zika virus could affect a much larger area than previously anticipated.

          The next step following on from this work will be to carry out experiments to test if the 35 mosquitoes identified by the model are actually able to transmit the Zika virus.

          DOI: http://dx.doi.org/10.7554/eLife.22053.002

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          Most cited references313

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          Experimental Infection of North American Birds with the New York 1999 Strain of West Nile Virus

          To evaluate transmission dynamics, we exposed 25 bird species to West Nile virus (WNV) by infectious mosquito bite. We monitored viremia titers, clinical outcome, WNV shedding (cloacal and oral), seroconversion, virus persistence in organs, and susceptibility to oral and contact transmission. Passeriform and charadriiform birds were more reservoir competent (a derivation of viremia data) than other species tested. The five most competent species were passerines: Blue Jay (Cyanocitta cristata), Common Grackle (Quiscalus quiscula), House Finch (Carpodacus mexicanus), American Crow (Corvus brachyrhynchos), and House Sparrow (Passer domesticus). Death occurred in eight species. Cloacal shedding of WNV was observed in 17 of 24 species, and oral shedding in 12 of 14 species. We observed contact transmission among four species and oral in five species. Persistent WNV infections were found in tissues of 16 surviving birds. Our observations shed light on transmission ecology of WNV and will benefit surveillance and control programs.
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            Differential Susceptibilities of Aedes aegypti and Aedes albopictus from the Americas to Zika Virus

            Background Since the major outbreak in 2007 in the Yap Island, Zika virus (ZIKV) causing dengue-like syndromes has affected multiple islands of the South Pacific region. In May 2015, the virus was detected in Brazil and then spread through South and Central America. In December 2015, ZIKV was detected in French Guiana and Martinique. The aim of the study was to evaluate the vector competence of the mosquito spp. Aedes aegypti and Aedes albopictus from the Caribbean (Martinique, Guadeloupe), North America (southern United States), South America (Brazil, French Guiana) for the currently circulating Asian genotype of ZIKV isolated from a patient in April 2014 in New Caledonia. Methodology/Principal Findings Mosquitoes were orally exposed to an Asian genotype of ZIKV (NC-2014-5132). Upon exposure, engorged mosquitoes were maintained at 28°±1°C, a 16h:8h light:dark cycle and 80% humidity. 25–30 mosquitoes were processed at 4, 7 and 14 days post-infection (dpi). Mosquito bodies (thorax and abdomen), heads and saliva were analyzed to measure infection, dissemination and transmission, respectively. High infection but lower disseminated infection and transmission rates were observed for both Ae. aegypti and Ae. albopictus. Ae. aegypti populations from Guadeloupe and French Guiana exhibited a higher dissemination of ZIKV than the other Ae. aegypti populations examined. Transmission of ZIKV was observed in both mosquito species at 14 dpi but at a low level. Conclusions/Significance This study suggests that although susceptible to infection, Ae. aegypti and Ae. albopictus were unexpectedly low competent vectors for ZIKV. This may suggest that other factors such as the large naïve population for ZIKV and the high densities of human-biting mosquitoes contribute to the rapid spread of ZIKV during the current outbreak.
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              Phylogeny of the genus Flavivirus.

              We undertook a comprehensive phylogenetic study to establish the genetic relationship among the viruses of the genus Flavivirus and to compare the classification based on molecular phylogeny with the existing serologic method. By using a combination of quantitative definitions (bootstrap support level and the pairwise nucleotide sequence identity), the viruses could be classified into clusters, clades, and species. Our phylogenetic study revealed for the first time that from the putative ancestor two branches, non-vector and vector-borne virus clusters, evolved and from the latter cluster emerged tick-borne and mosquito-borne virus clusters. Provided that the theory of arthropod association being an acquired trait was correct, pairwise nucleotide sequence identity among these three clusters provided supporting data for a possibility that the non-vector cluster evolved first, followed by the separation of tick-borne and mosquito-borne virus clusters in that order. Clades established in our study correlated significantly with existing antigenic complexes. We also resolved many of the past taxonomic problems by establishing phylogenetic relationships of the antigenically unclassified viruses with the well-established viruses and by identifying synonymous viruses.
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                Author and article information

                Contributors
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                28 February 2017
                2017
                : 6
                : e22053
                Affiliations
                [1 ]deptOdum School of Ecology , University of Georgia , Athens, United States
                [2 ]Center for the Ecology of Infectious Diseases, University of Georgia , Athens, United States
                [3 ]deptDepartment of Environmental Science and Policy , University of California-Davis , Davis, United States
                [4 ]Cary Institute of Ecosystem Studies , Millbrook, United States
                [5 ]deptDepartment of Infectious Disease , University of Georgia , Athens, United States
                [6 ]Center for Tropical Emerging Global Diseases, University of Georgia , Athens, United States
                [7 ]Center for Vaccines and Immunology, University of Georgia , Athens, United States
                [8 ]River Basin Center, University of Georgia , Athens, United States
                [9]London School of Hygiene and Tropical Medicine , United Kingdom
                [10]London School of Hygiene and Tropical Medicine , United Kingdom
                Author notes
                Author information
                http://orcid.org/0000-0002-5628-0502
                http://orcid.org/0000-0003-3328-9958
                http://orcid.org/0000-0002-9948-3078
                http://orcid.org/0000-0003-4646-1235
                Article
                22053
                10.7554/eLife.22053
                5342824
                28244371
                b6dce2f3-320f-45ed-b03a-b78cf738fffb
                © 2017, Evans et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 05 October 2016
                : 13 February 2017
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: DEB-1640780
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100007699, University of Georgia;
                Award ID: Presidential Fellowship
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: U01GM110744
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Computational and Systems Biology
                Ecology
                Custom metadata
                2.5
                Data-driven methods predict over 35 mosquitoes are potential vectors of Zika virus, suggesting a larger geographic area and a greater human population is at risk of infection.

                Life sciences
                mosquito-borne disease,machine learning,zika virus,virus
                Life sciences
                mosquito-borne disease, machine learning, zika virus, virus

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