Mucosal Associated Invariant T (MAIT) Cell Responses Differ by Sex in COVID-19

  1. Chen Yu
  2. Sejiro Littleton
  3. Nicholas Giroux
  4. Rose Mathew
  5. Shengli Ding
  6. Joan Kalnitsky
  7. Elizabeth W. Petzold
  8. Hong Chung
  9. Grecia Rivera Palomino
  10. Tomer Rotstein
  11. Rui Xi
  12. Emily R. Ko
  13. Ephraim L. Tsalik
  14. Gregory D. Sempowski
  15. Thomas N. Denny
  16. Thomas W. Burke
  17. Micah T. McClain
  18. Christopher W. Woods
  19. Xiling Shen
  20. Daniel R. Saban

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Abstract

Sexual dimorphisms in immune responses contribute to coronavirus disease 2019 (COVID-19) outcomes, yet the mechanisms governing this disparity remain incompletely understood. We carried out sex-balanced sampling of peripheral blood mononuclear cells from confirmed COVID-19 inpatients and outpatients, uninfected close contacts, and healthy controls for 36-color flow cytometry and single cell RNA-sequencing. Our results revealed a pronounced reduction of circulating mucosal associated invariant T (MAIT) cells in infected females. Integration of published COVID-19 airway tissue datasets implicate that this reduction represented a major wave of MAIT cell extravasation during early infection in females. Moreover, female MAIT cells possessed an immunologically active gene signature, whereas male counterparts were pro-apoptotic. Collectively, our findings uncover a female-specific protective MAIT profile, potentially shedding light on reduced COVID-19 susceptibility in females.

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  1. ScreenIT

    SciScore for 10.1101/2020.12.01.407148: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Ethics statement: This study and relevant protocols were approved by the Institutional Review Boards of Duke University Health System (DUHS) ?.
    Consent: Written informed consent was obtained from all subjects or legally authorized representatives.
    RandomizationPBMC scRNA-seq data were randomly downsampled to 50,000 cells and T and monocyte clusters were extracted based on the expression of their lineage markers.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Antibody titrations used in this study were previously established by Cytek Biosciences with slight modifications (see Table S2 for flow panel information).
    Cytek Biosciences
    suggested: None
    Raw data were unmixed and further analyzed using either FlowJo for manual gating or Omiq (https://www.omiq.ai) for clustering visualization and analysis.
    FlowJo
    suggested: (FlowJo, RRID:SCR_008520)
    High-dimensional data analysis of flow cytometry data: Uniform Manifold Approximation and Projection (UMAP) and FlowSOM clustering analyses were performed on Omiq (https://www.omiq.ai), using equal random sampling of 3000 live CD45+ singlets. from each FCS file.
    FlowSOM
    suggested: (FlowSOM, RRID:SCR_016899)
    FASTQ files were aligned with STAR aligner to the human genome reference GRCh38 from Ensemble database.
    STAR
    suggested: (STAR, RRID:SCR_015899)
    Inference of ligand-receptor interactions between T cells and monocytes: Ligand-receptor interactions between T cells and monocytes were inferred using CellPhoneDB (33).
    CellPhoneDB
    suggested: (CellPhoneDB, RRID:SCR_017054)
    Graphical data of quantifications presented throughout are expressed as the means ± SEMs and were plotted using Graphpad Prism 8.
    Graphpad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)
    Other graphs in this study were generated using either the corresponding analytic packages or R package ggplot2.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)

    Results from OddPub: Thank you for sharing your data.

    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 10.1101/2020.12.01.407148 on bioRxiv