Transmission of SARS-CoV-2 from humans to animals and potential host adaptation

  1. Cedric C. S. Tan
  2. Su Datt Lam
  3. Damien Richard
  4. Christopher J. Owen
  5. Dorothea Berchtold
  6. Christine Orengo
  7. Meera Surendran Nair
  8. Suresh V. Kuchipudi
  9. Vivek Kapur
  10. Lucy van Dorp
  11. François Balloux

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Abstract

SARS-CoV-2, the causative agent of the COVID-19 pandemic, can infect a wide range of mammals. Since its spread in humans, secondary host jumps of SARS-CoV-2 from humans to multiple domestic and wild populations of mammals have been documented. Understanding the extent of adaptation to these animal hosts is critical for assessing the threat that the spillback of animal-adapted SARS-CoV-2 into humans poses. We compare the genomic landscapes of SARS-CoV-2 isolated from animal species to that in humans, profiling the mutational biases indicative of potentially different selective pressures in animals. We focus on viral genomes isolated from mink ( Neovison vison ) and white-tailed deer ( Odocoileus virginianus ) for which multiple independent outbreaks driven by onward animal-to-animal transmission have been reported. We identify five candidate mutations for animal-specific adaptation in mink (NSP9_G37E, Spike_F486L, Spike_N501T, Spike_Y453F, ORF3a_L219V), and one in deer (NSP3a_L1035F), though they appear to confer a minimal advantage for human-to-human transmission. No considerable changes to the mutation rate or evolutionary trajectory of SARS-CoV-2 has resulted from circulation in mink and deer thus far. Our findings suggest that minimal adaptation was required for onward transmission in mink and deer following human-to-animal spillover, highlighting the ‘generalist’ nature of SARS-CoV-2 as a mammalian pathogen.

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  1. Version published to 10.1038/s41467-022-30698-6
  2. Version published to 10.21203/rs.3.rs-1305015/v1 on Research Square
  3. Version published to 10.1101/2020.11.16.384743v2 on bioRxiv
  4. ScreenIT

    SciScore for 10.1101/2020.11.16.384743: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    All genome assemblies were profile aligned to the SARS-CoV-2 reference genome Wuhan-Hu-1 (NC_045512.2) using MAFFT v7.20550.
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)
    We constructured a maximum likelihood phylogenetic tree over the 55,030 included genomes using IQ-TREE v2.1.0 Covid release54 specifying the fast mode.
    IQ-TREE
    suggested: (IQ-TREE, RRID:SCR_017254)
    Trees were queried and plotted using the R packages Ape v5.456 and ggtree v1.16.657 (see Figure 1).
    Ape
    suggested: (APE, RRID:SCR_009122)
    This was done by retrieving the amino acid changes corresponding to all SNPs at these positions using a custom Biopython (v1.76) script (https://github.com/cednotsed/mink_homoplasies/blob/main/dnds/snp_to_sav_parser.py) with annotations reported in Table S3.
    Biopython
    suggested: (Biopython, RRID:SCR_007173)
    Assemblies were also uploaded to CoVSurver (https://www.gisaid.org/epiflu-applications/covsurver-mutations-app/) to report the prevalence of mutations and indels relative to SARS-CoV-2 assemblies available on the GISAID database48,49.
    GISAID
    suggested: (GISAID, RRID:SCR_018279)
    CpG dinucleotide frequencies for both SARS-CoV-2 alignments of genomes isolated from human and mink were also calculated using a custom R script (https://github.com/cednotsed/mink_homoplasies/blob/main/CpG/plot_CpG.R) (Figure S6).
    SARS-CoV-2
    suggested: (Active Motif Cat# 91351, RRID:AB_2847848)
    Identification of recurrent mutations: We screened for the presence of recurrent mutations in the mink SARS-CoV-2 masked alignment using HomoplasyFinder v0.0.0.960, as described in our previous work61,62.
    HomoplasyFinder
    suggested: (HomoplasyFinder, RRID:SCR_017300)
    We visualised this structure using PyMOL v2.4.166.
    PyMOL
    suggested: (PyMOL, RRID:SCR_000305)
    We generated query–template alignments using HH-suite67 and predicted 3D models using MODELLER v.9.2468.
    MODELLER
    suggested: (MODELLER, RRID:SCR_008395)

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  5. ScreenIT

    SciScore for 10.1101/2020.11.16.384743: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    All genome assemblies were profile aligned to the SARSCoV-2 reference genome Wuhan-Hu-1 (NC_045512.2) using MAFFT v7.20550.
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)
    We constructured a maximum likelihood phylogenetic tree over the 55,030 included genomes using IQ-TREE v2.1.0 Covid release54 specifying the fast mode.
    IQ-TREE
    suggested: (IQ-TREE, RRID:SCR_017254)
    Trees were queried and plotted using the R packages Ape v5.456 and ggtree v1.16.657 (see Figure 1).
    Ape
    suggested: (APE, RRID:SCR_009122)
    This was done by retrieving the amino acid changes corresponding to all SNPs at these positions using a custom Biopython (v1.76) script (https://github.com/cednotsed/mink_homoplasies/blob/main/dnds/snp_to_sav_parser.py) with annotations reported in Table S3.
    Biopython
    suggested: (Biopython, RRID:SCR_007173)
    CpG dinucleotide frequencies for both SARS-CoV-2 alignments of genomes isolated from human and mink were also calculated using a custom R script (https://github.com/cednotsed/mink_homoplasies/blob/main/CpG/plot_CpG.R) (Figure S6).
    SARS-CoV-2
    suggested: (SARS-CoV-2-Sequences, RRID:SCR_018319)
    Identification of recurrent mutations We screened for the presence of recurrent mutations in the mink SARS-CoV-2 masked alignment using HomoplasyFinder v0.0.0.960, as described in our previous work61,62.
    HomoplasyFinder
    suggested: (HomoplasyFinder, RRID:SCR_017300)
    We generated query–template alignments using HH-suite67 and predicted 3D models using MODELLER v.9.2468.
    MODELLER
    suggested: (MODELLER, RRID:SCR_008395)
    This figure was rendered using PyMOL (v2.4.1).
    PyMOL
    suggested: (PyMOL, RRID:SCR_000305)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  6. Version published to 10.1101/2020.11.16.384743v1 on bioRxiv