SARS-CoV-2-specific T cells associate with inflammation and reduced lung function in pulmonary post-acute sequalae of SARS-CoV-2

  1. Katherine M. Littlefield
  2. Renée O. Watson
  3. Jennifer M. Schneider
  4. Charles P. Neff
  5. Eiko Yamada
  6. Min Zhang
  7. Thomas B. Campbell
  8. Michael T. Falta
  9. Sarah E. Jolley
  10. Andrew P. Fontenot
  11. Brent E. Palmer

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Abstract

As of January 2022, at least 60 million individuals are estimated to develop post-acute sequelae of SARS-CoV-2 (PASC) after infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). While elevated levels of SARS-CoV-2-specific T cells have been observed in non-specific PASC, little is known about their impact on pulmonary function which is compromised in the majority of these individuals. This study compares frequencies of SARS-CoV-2-specific T cells and inflammatory markers with lung function in participants with pulmonary PASC and resolved COVID-19 (RC). Compared to RC, participants with respiratory PASC had between 6- and 105-fold higher frequencies of IFN-γ- and TNF-α-producing SARS-CoV-2-specific CD4 + and CD8 + T cells in peripheral blood, and elevated levels of plasma CRP and IL-6. Importantly, in PASC participants the frequency of TNF-α-producing SARS-CoV-2-specific CD4 + and CD8 + T cells, which exhibited the highest levels of Ki67 indicating they were activity dividing, correlated positively with plasma IL-6 and negatively with measures of lung function, including forced expiratory volume in one second (FEV 1 ), while increased frequencies of IFN-γ-producing SARS-CoV-2-specific T cells associated with prolonged dyspnea. Statistical analyses stratified by age, number of comorbidities and hospitalization status demonstrated that none of these factors affect differences in the frequency of SARS-CoV-2 T cells and plasma IL-6 levels measured between PASC and RC cohorts. Taken together, these findings demonstrate elevated frequencies of SARS-CoV-2-specific T cells in individuals with pulmonary PASC are associated with increased systemic inflammation and decreased lung function, suggesting that SARS-CoV-2-specific T cells contribute to lingering pulmonary symptoms. These findings also provide mechanistic insight on the pathophysiology of PASC that can inform development of potential treatments to reduce symptom burden.

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  1. Version published to 10.1371/journal.ppat.1010359
  2. ScreenIT

    SciScore for 10.1101/2022.02.14.480317: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: Study approval: This study was approved by the Colorado Multiple Institutional Review Board (COMIRB# 20-1219) at the University of Colorado Anschutz Medical Campus.
    Consent: All participants provided written informed consent prior to any study procedures.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Flow cytometry: Cells were analyzed using a LSRII flow cytometer (BD Immunocytometry Systems).
    BD Immunocytometry Systems
    suggested: (South Florida University College of Medicine Fred Wright Jr Flow Cytometry Core Facility, RRID:SCR_017901)
    To ensure accuracy and precision of the measurements taken from day-to-day, quality control was performed on the LSRII daily using the Cytometer Setup & Tracking (CS&T) feature of the BD FACSDiva software.
    BD FACSDiva
    suggested: (BD FACSDiva Software, RRID:SCR_001456)
    A manual quality control (QC) using rainbow beads was also performed daily to verify the laser delay and area scaling determined by CS&T. Statistics: Statistical analyses were performed using GraphPad-Prism (Graphpad, San Diego, CA).
    Graphpad
    suggested: (GraphPad, RRID:SCR_000306)
    Both of these student T and permutation tests of the SPICE analysis were corrected for 21 concurrent comparisons(54).
    SPICE
    suggested: (SPICE, RRID:SCR_016603)

    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: Please consider improving the rainbow (“jet”) colormap(s) used on page 31. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

    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.

  3. Version published to 10.1101/2022.02.14.480317v1 on bioRxiv