Abstract
Mosquito transmitted viruses are responsible for an increasing burden of human disease. Despite this, little is known about the diversity and ecology of viruses within individual mosquito hosts. Here, using a meta-transcriptomic approach, we determined the viromes of 2,438 individual mosquitoes (81 species), spanning ~4,000 km along latitudes and longitudes in China. From these data we identified 393 viral species associated with mosquitoes, including 7 (putative) species of arthropod-borne viruses (that is, arboviruses). We identified potential mosquito species and geographic hotspots of viral diversity and arbovirus occurrence, and demonstrated that the composition of individual mosquito viromes was strongly associated with host phylogeny. Our data revealed a large number of viruses shared among mosquito species or genera, enhancing our understanding of the host specificity of insect-associated viruses. We also detected multiple virus species that were widespread throughout the country, perhaps reflecting long-distance mosquito dispersal. Together, these results greatly expand the known mosquito virome, linked viral diversity at the scale of individual insects to that at a country-wide scale, and offered unique insights into the biogeography and diversity of viruses in insect vectors.
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Data availability
The meta-transcriptomic sequencing reads (non-host, non-rRNA reads) generated in this study have been deposited in the CNSA (CNGB Sequence Archive) of CNGBdb (China National GeneBank database, https://db.cngb.org/cnsa/; project accession: CNP0004669). The assembled viral genome sequences have been deposited in the CNGBdb with the accession codes N_AAACQU010000000-N_AAADML010000000 (Supplementary Data 2). The sample metadata and other materials required to reproduce our computational and statistical results are provided in the GitHub repository along with code and scripts (https://github.com/Augustpan/MosquitoVirome).
Code availability
Code and scripts are provided in a GitHub repository (https://github.com/Augustpan/MosquitoVirome).
References
Kilpatrick, A. M. & Randolph, S. E. Drivers, dynamics, and control of emerging vector-borne zoonotic diseases. Lancet 380, 1946–1955 (2012).
Bolling, B. G., Weaver, S. C., Tesh, R. B. & Vasilakis, N. Insect-specific virus discovery: significance for the arbovirus community. Viruses 7, 4911–4928 (2015).
Vasilakis, N. & Tesh, R. B. Insect-specific viruses and their potential impact on arbovirus transmission. Curr. Opin. Virol. 15, 69–74 (2015).
Olmo, R. P. et al. Mosquito vector competence for dengue is modulated by insect-specific viruses. Nat. Microbiol. 8, 135–149 (2023).
Zhang, Y. Z., Shi, M. & Holmes, E. C. Using metagenomics to characterize an expanding virosphere. Cell 172, 1168–1172 (2018).
Wang, J. et al. Individual bat virome analysis reveals co-infection and spillover among bats and virus zoonotic potential. Nat. Commun. 14, 4079 (2023).
Ni, X.-B. et al. Metavirome of 31 tick species provides a compendium of 1,801 RNA virus genomes. Nat. Microbiol. 8, 162–173 (2023).
Shi, M. et al. Redefining the invertebrate RNA virosphere. Nature 540, 539–543 (2016).
Shi, M. et al. High-resolution metatranscriptomics reveals the ecological dynamics of mosquito-associated RNA viruses in Western Australia. J. Virol. 91, e00680–17 (2017).
Liu, Q. et al. Association of virome dynamics with mosquito species and environmental factors. Microbiome 11, 101 (2023).
Batson, J. et al. Single mosquito metatranscriptomics identifies vectors, emerging pathogens and reservoirs in one assay. eLife 10, e68353 (2021).
Shi, C. et al. Stable distinct core eukaryotic viromes in different mosquito species from Guadeloupe, using single mosquito viral metagenomics. Microbiome 7, 121 (2019).
Webster, J. P., Borlase, A. & Rudge, J. W. Who acquires infection from whom and how? Disentangling multi-host and multi-mode transmission dynamics in the ‘elimination’ era. Philos. Trans. R. Soc. B 372, 20160091 (2017).
Bigot, D. et al. Discovery of Culex pipiens associated tunisia virus: a new ssRNA(+) virus representing a new insect associated virus family. Virus Evol. 4, vex040 (2018).
Chandler, J. A., Liu, R. M. & Bennett, S. N. RNA shotgun metagenomic sequencing of northern California (USA) mosquitoes uncovers viruses, bacteria, and fungi. Front. Microbiol. 6, 185 (2015).
Seabloom, E. W. et al. The community ecology of pathogens: coinfection, coexistence and community composition. Ecol. Lett. 18, 401–415 (2015).
Mihaljevic, J. R. Linking metacommunity theory and symbiont evolutionary ecology. Trends Ecol. Evol. 27, 323–329 (2012).
Miller, E. T., Svanback, R. & Bohannan, B. J. M. Microbiomes as metacommunities: understanding host-associated microbes through metacommunity ecology. Trends Ecol. Evol. 33, 926–935 (2018).
Jones, K. E. et al. Global trends in emerging infectious diseases. Nature 451, 990–993 (2008).
Dzul-Manzanilla, F. et al. Identifying urban hotspots of dengue, chikungunya, and Zika transmission in Mexico to support risk stratification efforts: a spatial analysis. Lancet Planet. Health 5, e277–e285 (2021).
Murray, K. A. et al. Global biogeography of human infectious diseases. Proc. Natl Acad. Sci. USA 112, 12746–12751 (2015).
Engler, O. et al. European surveillance for West Nile Virus in mosquito populations. Int. J. Environ. Res. Public Health 10, 4869–4895 (2013).
Kilpatrick, A. M. & Pape, W. J. Predicting human West Nile virus infections with mosquito surveillance data. Am. J. Epidemiol. 178, 829–835 (2013).
Kuwata, R. et al. Surveillance of Japanese encephalitis virus infection in mosquitoes in Vietnam from 2006 to 2008. Am. Soc. Trop. Med. Hyg. 88, 681–688 (2013).
Weaver, S. C. & Barrett, A. D. T. Transmission cycles, host range, evolution and emergence of arboviral disease. Nat. Rev. Microbiol. 2, 789–801 (2004).
Zhou, J. & Ning, D. Stochastic community assembly: does it matter in microbial ecology? Microbiol Mol. Biol. Rev. 81, e00002–e00017 (2017).
Power, A. & Flecker, A. The Role Of Vector Diversity In Disease Dynamics (Princeton Univ. Press, 2008).
Streicker, D. G. et al. Host phylogeny constrains cross-species emergence and establishment of rabies virus in bats. Science 329, 676–679 (2010).
Gao, J. et al. Dispersal patterns and population genetic structure of Aedes albopictus (Diptera: Culicidae) in three different climatic regions of China. Parasites Vectors 14, 1–13 (2021).
Huestis, D. L. et al. Windborne long-distance migration of malaria mosquitoes in the Sahel. Nature 574, 404–408 (2019).
Tatem, A. J., Rogers, D. J. & Hay, S. I. In Advances in Parasitology Vol. 62 (eds Hay, S. I., Graham, A. & Rogers, D. J.) 293–343 (Academic Press, 2006).
Ratnasingham, S. & Hebert, P. D. BOLD: The Barcode of Life Data System. Mol. Ecol. Notes 7, 355–364 (2007).
Edgar, R. URMAP, an ultra-fast read mapper. PeerJ 8, e9338 (2020).
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).
Chen, Y. et al. SOAPnuke: a MapReduce acceleration-supported software for integrated quality control and preprocessing of high-throughput sequencing data. GigaScience 7, gix120 (2018).
Cantu, V. A., Sadural, J. & Edwards, R. PRINSEQ++, a multi-threaded tool for fast and efficient quality control and preprocessing of sequencing datasets. PeerJ Prepr. 7, e27553v27551 (2019).
Li, D., Liu, C.-M., Luo, R., Sadakane, K. & Lam, T.-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).
Shi, M. et al. Total infectome characterization of respiratory infections in pre-COVID-19 Wuhan, China. PLoS Pathog. 18, e1010259 (2022).
Katoh, K. & Standley, D. M. MAFFT Multiple Sequence Alignment Software Version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).
Capella-Gutiérrez, S., Silla-Martínez, J. M. & Gabaldón, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).
Posada, D. jModelTest: phylogenetic model averaging. Mol. Biol. Evol. 25, 1253–1256 (2008).
Guindon, S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst. Biol. 59, 307–321 (2010).
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
Lembrechts, J. J., Nijs, I. & Lenoir, J. Incorporating microclimate into species distribution models. Ecography 42, 1267–1279 (2019).
Bioclimatic variables. WorldClim https://www.worldclim.org/data/bioclim.html (2017).
Klein Goldewijk, K., Beusen, A., Doelman, J. & Stehfest, E. Anthropogenic land use estimates for the Holocene—HYDE 3.2. Earth Syst. Sci. Data 9, 927–953 (2017).
R: A Language and Environment for Statistical Computing (R Project, 2022).
Barton, K. MuMIn: multi-model inference. R-Forge http://r-forge.r-project.org/projects/mumin/ (2009).
Oksanen, J. et al. vegan: Community Ecology Package http://CRAN.R-project.org/package=vegan (2012).
Csárdi, G. & Nepusz, T. The igraph software package for complex network research. InterJournal Complex Systems, 1695 (2006).
GaryBikini/ChinaAdminDivisonSHP: ChinaAdminDivisonSHP v1.1. Zenodo https://doi.org/10.5281/zenodo.4167299 (2020).
Acknowledgements
This study was funded by grants from the National Key R&D Program of China (2021YFC2300900), National Natural Science Foundation of China (32270160), Shenzhen Science and Technology Program (JCYJ20210324124414040) and open project of BGI-Shenzhen Shenzhen 518000, China (BGIRSZ20210001). M.S. was supported by Shenzhen Science and Technology Program (KQTD20200820145822023), Guangdong Province ‘Pearl River Talent Plan’ Innovation, Entrepreneurship Team Project (2019ZT08Y464) and the Fund of Shenzhen Key Laboratory (ZDSYS20220606100803007). E.C.H. was supported by an NHMRC (Australia) Investigator Award (GNT2017197). We gratefully acknowledge colleagues at BGI-Shenzhen and China National Genebank (CNGB) for RNA extraction, library construction and sequencing.
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Contributions
Conceptualization: D. Wa., Junh. L., W.-C.W. and M.S.; methodology: Y.-F.P., H.Z., Q.-Y.G., D. Wa., Junh. L., W.-C.W. and M.S.; sample collection and processing: Q.-Y.G., G.-Y.L., G.-Y.X., S.-J.L., Ji. W., X. Ho., C.-H.Y., J.-X.C., Y.-Q.L., M.-W.P., S.-Q.M., J.-B.K., X.-X.C., X. Hu., Ju. W., C., Y.-H.W., J.-B.W., T.A. and W.-C.W.; data analysis: Y.-F.P., H.Z., Q.-Y.G., P.-B.S., D. Wa., Junh. L., W.-C.W. and M.S.; writing—original draft: Y.-F.P.; writing—review and editing: H.Z., Q.-Y.G., P.-B.S., J.-H.T., Y.F., K.L., W.-H.Y., D. Wu., G.T., B.Z., Z.R., S.P., G.-Y.L., S.-J.L., G.-Y.X., Ji. W., X. Ho., M.-W.P., J.-B.K., X.-X.C., C.-H.Y., S.-Q.M., Y.-Q.L., J.-X.C., Ju. W., C., Y.-H.W., J.-B.W., T.A., X. Hu., J.-S.E., Jun. L., D.G., G.L., X.J., E.C.H., B.L., D. Wa., Junh. L., W.-C.W. and M.S; funding acquisition: D. Wa., Junh. L., W.-C.W. and M.S.; resources (sampling): J.-H.T., Y.F., K.L., W.-H.Y., D. Wu., G.T., B.Z. and W.-C.W.; resources (computational): D. Wa., Junh. L. and M.S.; supervision: B.L., D. Wa., Junh. L., W.-C.W. and M.S.
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Extended data
Extended Data Fig. 1 Phylogenetic diversity of the 564 viruses determined in this study and the identification of mosquito-associated viruses.
The phylogenetic trees were estimated using hallmark proteins of respective viral taxa. Trees of RNA viruses were estimated at the ‘super clade’ level, while those of DNA virus were constructed at family level. The hallmark proteins utilized were the RNA-directed RNA replicase (RdRp) for all RNA viruses, Rep protein for the Circoviridae, NS1 protein for the Parvoviridae, and DNA polymerase for other DNA viruses. The number of mosquito-associated species and the total viral species is shown below the superclade names (number of mosquito-associated species / number of total species). Viral families were indicated with vertical lines and abbreviated family names (the suffixes ‘-viridae’ were omitted) on the right side of each tree.
Supplementary information
Supplementary Information
Supplementary Figs. 1–16 and Tables 1–14.
Supplementary Data 1 and 2
Supplementary Data 1. Sample and library metadata. Supplementary Data 2. Virus metadata.
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Pan, YF., Zhao, H., Gou, QY. et al. Metagenomic analysis of individual mosquito viromes reveals the geographical patterns and drivers of viral diversity. Nat Ecol Evol (2024). https://doi.org/10.1038/s41559-024-02365-0
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DOI: https://doi.org/10.1038/s41559-024-02365-0
