Local SARS-CoV-2 Peptide-Specific Immune Responses in Lungs of Convalescent and Uninfected Human Subjects

  1. Kayla F. Goliwas
  2. Anthony M. Wood
  3. Christopher S. Simmons
  4. Rabisa Khan
  5. Saad A. Khan
  6. Yong Wang
  7. Joel L. Berry
  8. Mohammad Athar
  9. James A. Mobley
  10. Young-il Kim
  11. Victor J. Thannickal
  12. Kevin S. Harrod
  13. James M. Donahue
  14. Jessy S. Deshane

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Abstract

Multi-specific and long-lasting T cell immunity have been recognized as indicators for long term protection against pathogens including the novel coronavirus SARS-CoV-2, the causative agent of the COVID-19 pandemic. Functional significance of peripheral memory T cells in individuals recovering from COVID-19 (COVID-19 + ) are beginning to be appreciated; but little is known about lung resident memory T cells (lung TRM) in SARS-CoV-2 infection. Here, we utilize a perfused three dimensional (3D) human lung tissue model and identify pre-existing local T cell immunity against SARS-CoV-2 proteins in lung tissues. We report ex vivo maintenance of functional multi-specific IFN-γ secreting lung TRM in COVID-19 + and their induction in lung tissues of vaccinated COVID-19 + . Importantly, we identify SARS-CoV-2 peptide-responding B cells and IgA + plasma cells in lung tissues of COVID-19 + in ex vivo 3D-tissue models. Our study highlights the importance of balanced and local anti-viral immune response in the lung with persistent induction of TRM and IgA + plasma cells for future protection against SARS-CoV-2 infection. Further, our data suggest that inclusion of multiple viral antigens in vaccine approaches may broaden the functional profile of memory T cells to combat the severity of coronavirus infection.

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  1. Version published to 10.1101/2021.09.02.21263042v2 on medRxiv
  2. ScreenIT

    SciScore for 10.1101/2021.09.02.21263042: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: This study was approved by the University of Alabama at Birmingham Institutional Review Board (IRB-300003092 and IRB-300003384) and conducted following approved guidelines and regulations.
    Consent: Written informed consent was obtained from all participants.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Multiparametric Flow Cytometry: The following antibodies were used for multiparametric flow cytometry for T cell analysis: Anti-CD3-alexafluor 700 (Clone: UCHT1); anti-CD4-FITC (Clone: RPA-T4); anti-CD69-BV563 (Clone: FN50) from BD Biosciences (Germany).
    Anti-CD3-alexafluor
    suggested: None
    anti-CD4-FITC
    suggested: (Sigma-Aldrich Cat# SAB4700062, RRID:AB_10898908)
    anti-CD69-BV563
    suggested: None
    The following antibodies were used for multiparametric flow cytometry for analysis of resident immune and structural cells: Anti-CD64-PerCp-eFluor710 (Clone: 10.1); anti-CD11b-APC-Cy7 (Clone: ICRF44); anti-HLA-DR-FITC (Clone: LN3); anti-EpCAM(CD326)-Alexafluor 594 (Clone:9C4) from eBioscience (Thermo Fisher, Germany).
    Anti-CD64-PerCp-eFluor710
    suggested: None
    anti-CD11b-APC-Cy7
    suggested: None
    anti-HLA-DR-FITC (Clone: LN3); anti-EpCAM(CD326)-Alexafluor 594
    suggested: None
    The following antibodies were used for multiparametric flow cytometry for B cell analysis: anti-CD10-BV650 (Clone:HI10A); anti-CD19-eFluor450 (Clone:HIB19) from eBioscience (Thermo Fisher, Germany).
    anti-CD10-BV650
    suggested: None
    anti-CD19-eFluor450
    suggested: None
    Software and Algorithms
    SentencesResources
    Analyses were performed on FACSymphony A3 Cell Analyzer with FACSDiva software version 8.0.1 (BD Biosciences, Germany).
    FACSDiva
    suggested: (BD FACSDiva Software, RRID:SCR_001456)
    Data were analyzed with FlowJo 10.7.1 (Treestar, USA).
    FlowJo
    suggested: (FlowJo, RRID:SCR_008520)
    All other statistical analyses were performed using SAS 9.4 (SAS Institute, USA).
    SAS Institute
    suggested: (Statistical Analysis System, RRID:SCR_008567)

    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.

    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.

  3. Version published to 10.1101/2021.09.02.21263042v1 on medRxiv