1. Epidemiology and Global Health
  2. Genetics and Genomics

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
https://doi.org/10.7554/eLife.68353
Share this article
Cite this article
  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
  2. Download BibTeX
  3. Download .RIS

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.

Metrics

  • 6,020
    views
  • 794
    downloads
  • 88
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  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

Categories and tags

Research organism

Further reading

    1. Epidemiology and Global Health
    2. Genetics and Genomics

    Systematic evaluation of multifactorial causal associations for Alzheimer’s disease and an interactive platform MRAD developed based on Mendelian randomization analysis

    Tianyu Zhao, Hui Li ... Li Chen
    Research Article

    Alzheimer’s disease (AD) is a complex degenerative disease of the central nervous system, and elucidating its pathogenesis remains challenging. In this study, we used the inverse-variance weighted (IVW) model as the major analysis method to perform hypothesis-free Mendelian randomization (MR) analysis on the data from MRC IEU OpenGWAS (18,097 exposure traits and 16 AD outcome traits), and conducted sensitivity analysis with six models, to assess the robustness of the IVW results, to identify various classes of risk or protective factors for AD, early-onset AD, and late-onset AD. We generated 400,274 data entries in total, among which the major analysis method of the IVW model consists of 73,129 records with 4840 exposure traits, which fall into 10 categories: Disease, Medical laboratory science, Imaging, Anthropometric, Treatment, Molecular trait, Gut microbiota, Past history, Family history, and Lifestyle trait. More importantly, a freely accessed online platform called MRAD (https://gwasmrad.com/mrad/) has been developed using the Shiny package with MR analysis results. Additionally, novel potential AD therapeutic targets (CD33, TBCA, VPS29, GNAI3, PSME1) are identified, among which CD33 was positively associated with the main outcome traits of AD, as well as with both EOAD and LOAD. TBCA and VPS29 were negatively associated with the main outcome traits of AD, as well as with both EOAD and LOAD. GNAI3 and PSME1 were negatively associated with the main outcome traits of AD, as well as with LOAD, but had no significant causal association with EOAD. The findings of our research advance our understanding of the etiology of AD.

    1. Epidemiology and Global Health

    Noncaloric monosaccharides induce excessive sprouting angiogenesis in zebrafish via foxo1a-marcksl1a signal

    Xiaoning Wang, Jinxiang Zhao ... Dong Liu
    Research Article

    Artificially sweetened beverages containing noncaloric monosaccharides were suggested as healthier alternatives to sugar-sweetened beverages. Nevertheless, the potential detrimental effects of these noncaloric monosaccharides on blood vessel function remain inadequately understood. We have established a zebrafish model that exhibits significant excessive angiogenesis induced by high glucose, resembling the hyperangiogenic characteristics observed in proliferative diabetic retinopathy (PDR). Utilizing this model, we observed that glucose and noncaloric monosaccharides could induce excessive formation of blood vessels, especially intersegmental vessels (ISVs). The excessively branched vessels were observed to be formed by ectopic activation of quiescent endothelial cells (ECs) into tip cells. Single-cell transcriptomic sequencing analysis of the ECs in the embryos exposed to high glucose revealed an augmented ratio of capillary ECs, proliferating ECs, and a series of upregulated proangiogenic genes. Further analysis and experiments validated that reduced foxo1a mediated the excessive angiogenesis induced by monosaccharides via upregulating the expression of marcksl1a. This study has provided new evidence showing the negative effects of noncaloric monosaccharides on the vascular system and the underlying mechanisms.

    1. Epidemiology and Global Health
    2. Microbiology and Infectious Disease

    Antigenic drift and subtype interference shape A(H3N2) epidemic dynamics in the United States

    Amanda C Perofsky, John Huddleston ... Cécile Viboud
    Research Article

    Influenza viruses continually evolve new antigenic variants, through mutations in epitopes of their major surface proteins, hemagglutinin (HA) and neuraminidase (NA). Antigenic drift potentiates the reinfection of previously infected individuals, but the contribution of this process to variability in annual epidemics is not well understood. Here, we link influenza A(H3N2) virus evolution to regional epidemic dynamics in the United States during 1997—2019. We integrate phenotypic measures of HA antigenic drift and sequence-based measures of HA and NA fitness to infer antigenic and genetic distances between viruses circulating in successive seasons. We estimate the magnitude, severity, timing, transmission rate, age-specific patterns, and subtype dominance of each regional outbreak and find that genetic distance based on broad sets of epitope sites is the strongest evolutionary predictor of A(H3N2) virus epidemiology. Increased HA and NA epitope distance between seasons correlates with larger, more intense epidemics, higher transmission, greater A(H3N2) subtype dominance, and a greater proportion of cases in adults relative to children, consistent with increased population susceptibility. Based on random forest models, A(H1N1) incidence impacts A(H3N2) epidemics to a greater extent than viral evolution, suggesting that subtype interference is a major driver of influenza A virus infection ynamics, presumably via heterosubtypic cross-immunity.