Animal Model Prescreening: Pre-exposure to SARS-CoV-2 impacts responses in the NHP model

  1. Keersten M. Ricks
  2. Andrew S. Herbert
  3. Jeffrey W. Koehler
  4. Paul A. Kuehnert
  5. Tamara L. Clements
  6. Charles J. Shoemaker
  7. Ana I. Kuehne
  8. Cecilia M. O’Brien
  9. Susan R. Coyne
  10. Korey L. Delp
  11. Kristen S. Akers
  12. John M. Dye
  13. Jay W. Hooper
  14. Jeffrey M. Smith
  15. Jeffrey R. Kugelman
  16. Brett F. Beitzel
  17. Kathleen M. Gibson
  18. Sara C. Johnston
  19. Timothy D. Minogue

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

COVID-19 presents herculean challenges to research and scientific communities for producing diagnostic and treatment solutions. Any return to normalcy requires rapid development of countermeasures, with animal models serving as a critical tool in testing vaccines and therapeutics. Animal disease status and potential COVID-19 exposure prior to study execution may severely bias efficacy testing. We developed a toolbox of immunological and molecular tests to monitor countermeasure impact on disease outcome and evaluate pre-challenge COVID-19 status. Assay application showed critical necessity for animal pre-screening. Specifically, real-time PCR results documented pre-exposure of an African Green Monkey prior to SARS-CoV-2 challenge with sequence confirmation as a community-acquired exposure. Longitudinal monitoring of nasopharyngeal swabs and serum showed pre-exposure impacted both viral disease course and resulting immunological response. This study demonstrates utility in a comprehensive pre-screening strategy for animal models, which captured the first documented case of community-acquired, non-human primate infection.

One Sentence Summary

Pre-exposure to SARS-CoV-2 affects biomarker responses in animal models, highlighting a need for robust pre-screening protocols prior to medical countermeasure studies.

Article activity feed

  1. Version published to 10.1101/2020.07.06.189803v2 on bioRxiv
  2. ScreenIT

    SciScore for 10.1101/2020.07.06.189803: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    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: 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.

  3. Version published to 10.1101/2020.07.06.189803v1 on bioRxiv