Single mosquito metatranscriptomics identifies vectors, emerging pathogens and reservoirs in one assay

  1. Joshua Batson
  2. Gytis Dudas
  3. Eric Haas-Stapleton
  4. Amy L Kistler  Is a corresponding author
  5. Lucy M Li
  6. Phoenix Logan
  7. Kalani Ratnasiri
  8. Hanna Retallack
  1. Chan Zuckerberg Biohub, United States
  2. Gothenburg Global Biodiversity Centre, Sweden
  3. Alameda County Mosquito Abatement District, United States
  4. Stanford University, United States
  5. University of California, San Francisco, United States

Abstract

Mosquitoes are major infectious disease-carrying vectors. Assessment of current and future risks associated with the mosquito population requires knowledge of the full repertoire of pathogens they carry, including novel viruses, as well as their blood meal sources. Unbiased metatranscriptomic sequencing of individual mosquitoes offers a straightforward, rapid and quantitative means to acquire this information. Here, we profile 148 diverse wild-caught mosquitoes collected in California and detect sequences from eukaryotes, prokaryotes, 24 known and 46 novel viral species. Importantly, sequencing individuals greatly enhanced the value of the biological information obtained. It allowed us to a) speciate host mosquito, b) compute the prevalence of each microbe and recognize a high frequency of viral co-infections, c) associate animal pathogens with specific blood meal sources, and d) apply simple co-occurrence methods to recover previously undetected components of highly prevalent segmented viruses. In the context of emerging diseases, where knowledge about vectors, pathogens, and reservoirs is lacking, the approaches described here can provide actionable information for public health surveillance and intervention decisions.

Data availability

Raw and assembled sequencing data are deposited in NCBI Bioproject PRJNA605178. Code is available on Github at https://github.com/czbiohub/california-mosquito-study. Derived data (including all contigs) and supplementary data are available on Figshare at dx.doi.org/10.6084/m9.figshare.11832999 .

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Joshua Batson

    Data Science, Chan Zuckerberg Biohub, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Gytis Dudas

    Gothenburg Global Biodiversity Centre, Gothenburg, Sweden
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0227-4158
  3. Eric Haas-Stapleton

    Mosquito surveillance and control, Alameda County Mosquito Abatement District, Hayward, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Amy L Kistler

    Infectious Disease Initiative, Chan Zuckerberg Biohub, San Francisco, United States
    For correspondence
    amy.kistler@czbiohub.org
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1112-719X
  5. Lucy M Li

    Data Science, Chan Zuckerberg Biohub, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6562-4004
  6. Phoenix Logan

    Data Sciences, Chan Zuckerberg Biohub, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Kalani Ratnasiri

    Program in Immunology, Stanford University, Davis, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5953-0004
  8. Hanna Retallack

    University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0533-9102

Funding

Chan Zuckerberg Initiative

  • Joshua Batson

UCSF Medical Science Training Program

  • Hanna Retallack

National Science Foundation

  • Kalani Ratnasiri

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2021, Batson et al.

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

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  1. Joshua Batson
  2. Gytis Dudas
  3. Eric Haas-Stapleton
  4. Amy L Kistler
  5. Lucy M Li
  6. Phoenix Logan
  7. Kalani Ratnasiri
  8. Hanna Retallack
(2021)
Single mosquito metatranscriptomics identifies vectors, emerging pathogens and reservoirs in one assay
eLife 10:e68353.
https://doi.org/10.7554/eLife.68353

Share this article

https://doi.org/10.7554/eLife.68353

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