Genomic and transcriptomic characterization of delta SARS-CoV-2 infection in free-ranging white-tailed deer (Odocoileus virginianus)

  1. Jonathon D. Kotwa
  2. Briallen Lobb
  3. Ariane Massé
  4. Marianne Gagnier
  5. Patryk Aftanas
  6. Arinjay Banerjee
  7. Andra Banete
  8. Juliette Blais-Savoie
  9. Jeff Bowman
  10. Tore Buchanan
  11. Hsien-Yao Chee
  12. Peter Kruczkiewicz
  13. Kuganya Nirmalarajah
  14. Catherine Soos
  15. Oksana Vernygora
  16. Lily Yip
  17. L. Robbin Lindsay
  18. Allison J. McGeer
  19. Finlay Maguire
  20. Oliver Lung
  21. Andrew C. Doxey
  22. Bradley Pickering
  23. Samira Mubareka

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Abstract

No abstract available

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  1. Version published to 10.1016/j.isci.2023.108319
  2. Version published to 10.1101/2022.01.20.476458v2 on bioRxiv
  3. ScreenIT

    SciScore for 10.1101/2022.01.20.476458: (What is this?)

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

    Table 1: Rigor

    EthicsField Sample Permit: Samples were collected at two big game registration stations in Dunham and Brownsburg (Figure 1); the Brownsburg station included collection of retropharyngeal lymph node (RPLN) tissues for Chronic Wasting Disease surveillance conducted by the Ministère des Forêts, de la Faune et des Parcs (MFFP) in free-ranging WTD Québec in early November 2021 17.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Percentage inhibition was calculated for each sample using the following equation:Samples with greater than or equal to 30% inhibition were considered positive for SARS-CoV-2 neutralizing antibodies.
    SARS-CoV-2 neutralizing antibodies .
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    Vero E6 cells were seeded at a concentration of 3×105 cells/well in a six well-plate.
    Vero E6
    suggested: None
    Software and Algorithms
    SentencesResources
    Reverse-transcription polymerase chain reaction (RT-PCR) was performed using the Luna Universal Probe One-Step RT-qPCR kit (New England BioLabs; https://www.neb.ca).
    New England BioLabs
    suggested: (New England Biolabs, RRID:SCR_013517)
    Quantstudio 3 software (Thermo Fisher Scientific; https://www.thermofisher.com) was used to determine cycle threshold (Ct).
    Thermo Fisher Scientific
    suggested: (Thermo Fisher Scientific, RRID:SCR_008452)
    Both reactions were combined and purified with 1X ratio Sample Purification Beads (Illumina; https://www.illumina.com).
    https://www.illumina.com
    suggested: (Illumina, RRID:SCR_010233)
    Libraries were constructed using Illumina DNA Prep (Illumina) and IDT for Illumina DNA/RNA UD Indexes (Illumina) and sequenced on Illumina MiniSeq using 2×149 paired-end reads.
    MiniSeq
    suggested: None
    Paired-end Illumina reads for samples 4055, 4204, 4205 and 4249 were analyzed using the nf-core/viralrecon Nextflow workflow (v2.2) 20–22, which performed the following analysis steps: FastQC (v0.11.9) read quality assessment (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/); fastp (v0.20.1) read quality filtering and trimming 23; read mapping to Wuhan-Hu-1 (MN908947.3) SARS-CoV-2 reference sequence with Bowtie2 (v2.4.2) 24; read mapping statistics calculation with Mosdepth (v0.3.1) 25 and Samtools (v1.12)
    FastQC
    suggested: (FastQC, RRID:SCR_014583)
    Bowtie2
    suggested: (Bowtie 2, RRID:SCR_016368)
    Samtools
    suggested: (SAMTOOLS, RRID:SCR_002105)
    ARTIC V3 primer trimming, variant calling and consensus sequence generation with iVar (v1.3.1) 28; variant effect analysis and summarization with SnpEff (v5.0) 29 and SnpSift (v4.3t) 30, respectively; SARS-CoV-2 lineage assignment with Pangolin (v3.1.17).
    SnpEff
    suggested: (SnpEff, RRID:SCR_005191)
    SnpSift
    suggested: (SnpSift, RRID:SCR_015624)
    The following analysis steps were performing as part of the CFIA-NCFAD/scovtree workflow: WTD consensus sequences from nf-core/viralrecon (v2.2) analysis were assigned to a Pangolin lineage using Pangolin (v3.1.17); GISAID sequences were filtered for up to 100,000 total sequences from Canada and the USA and belonging to the same Pangolin lineages as the WTD sequences; Nextalign CLI (v0.2.0) 34 multiple sequence alignment (MSA) of WTD and filtered GISAID sequences; down-sampling of Nextalign MSA to 20,000 sequences for phylogenetic tree inference preferentially filtering for MSA sequences with the least gaps and Ns and from Canada (due to the WTD samples being collected in Canada and to reduce the bias from a large proportion of GISAID sequences being from the USA); IQ-TREE (v2.1.4-beta) 35,36 phylogenetic analysis with the GTR model 37; pruning of the 20,000 taxa IQ-TREE phylogenetic tree with BioPython 38 down to 50 taxa (outgroup Wuhan-Hu-1 reference strain (MN908947.3), 4 WTD taxa and 45 GISAID taxa most closely related to WTD sequences) for visualization; Nextclade CLI (v0.14.4) 34 analysis to identify amino acid substitutions and deletions in WTD and GISAID sequences.
    BioPython
    suggested: (Biopython, RRID:SCR_007173)
    All statistical analyses were conducted using Stata/SE 15.1 (StataCorp, College Station, Texas, USA; http://www.stata.com).
    StataCorp
    suggested: (Stata, RRID:SCR_012763)
    http://www.stata.com
    suggested: (COLELMS: Stata module to calculate Coles LMS values for growth data, RRID:SCR_007244)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    There are several limitations for this study that should be considered. First, while leveraging the regular WTD hunting season resulted in a large number of samples, the present work was conducted over a short time period and a relatively small geographic region. Therefore, our study represents a snapshot in time and space. Future work should aim to obtain longitudinal data to investigate maintenance of SARS-CoV-2 in Québec WTD populations and assess for spatiotemporal patterns in pathogen ecology. Second, samples analyzed in this study were derived from harvested deer and were therefore collected post-mortem. Although 98% of samples were collected within 48 hours of harvest, it is possible that inhibitors or sample degradation occurred between harvest and sample collection. Lastly, our study only focuses on free-ranging WTD populations and we therefore cannot make inferences about SARS-CoV-2 in captive conspecifics. At the time of writing, surveillance is currently ongoing across Canada. Our findings underscore that further surveillance efforts in WTD in Québec and across Canada are warranted. Further work is needed to understand how the virus is transmitted from humans to deer, how efficiently and sustainably the virus is transmitted among deer in a natural setting, if WTD could ultimately serve as a reservoir for SARS-CoV-2 in Canada, how viral adaptations occur in WTD, and if and how frequently is deer-to-human transmission occurring. Ongoing coordinated and cross-discipl...

    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.
    • No funding statement was detected.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    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.

  4. Version published to 10.1101/2022.01.20.476458v1 on bioRxiv