Shared Antigen-specific CD8+ T cell Responses Against the SARS-COV-2 Spike Protein in HLA A*02:01 COVID-19 Participants

  1. William Chour
  2. Alex M Xu
  3. Alphonsus H.C. Ng
  4. Jongchan Choi
  5. Jingyi Xie
  6. Dan Yuan
  7. John K. Lee
  8. Diane C. Delucia
  9. Rick Edmark
  10. Lesley Jones
  11. Thomas M. Schmitt
  12. Mary E. Chaffee
  13. Venkata Duvvuri
  14. Philip D. Greenberg
  15. Kim Murray
  16. Julie Wallick
  17. Heather A. Algren
  18. William R. Berrington
  19. D. Shane O'Mahoney
  20. Jason D. Goldman
  21. James R Heath

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Abstract

We report here on antigens from the SARS-CoV-2 virus spike protein, that when presented by Class I MHC, can lead to cytotoxic CD8+ T cell anti-viral responses in COVID-19 patients. We present a method in which the SARS-CoV-2 spike protein is converted into a library of peptide antigen-Major Histocompatibility Complexes (pMHCs) as single chain trimers that contain the peptide antigen, the MHC HLA allele, and the β-2 microglobulin sub-unit. That library is used to detect the evolution of virus-specific T cell populations from two COVID-19 patients, at two time points over the course of infection. Both patients exhibit similar virus-specific T cell populations, but very different time-trajectories of those populations. These results can be used to track those virus-specific T cell populations over the course of an infection, thus providing deep insight into the variations in immune system trajectories observed in different COVID-19 patients.

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    SciScore for 10.1101/2020.05.04.20085779: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: Joseph’s Health IRB) and participant-written informed consent, in accordance with 45 CFR 46.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Band detection and quantification with Python scripts were utilized to compare relative SCT yields.
    Python
    suggested: (IPython, RRID:SCR_001658)

    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.

  2. Version published to 10.1101/2020.05.04.20085779 on medRxiv