Impairment of T cells' antiviral and anti-inflammation immunities may be critical to death from COVID-19

  1. Luhao Zhang
  2. Rong Li
  3. Gang Song
  4. Gregory D. Scholes
  5. Zhen-Su She

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Abstract

Clarifying dominant factors determining the immune heterogeneity from non-survivors to survivors is crucial for developing therapeutics and vaccines against COVID-19. The main difficulty is quantitatively analysing the multi-level clinical data, including viral dynamics, immune response and tissue damages. Here, we adopt a top-down modelling approach to quantify key functional aspects and their dynamical interplay in the battle between the virus and the immune system, yielding an accurate description of real-time clinical data involving hundreds of patients for the first time. The quantification of antiviral responses gives that, compared to antibodies, T cells play a more dominant role in virus clearance, especially for mild patients (96.5%). Moreover, the anti-inflammatory responses, namely the cytokine inhibition and tissue repair rates, also positively correlate with T cell number and are significantly suppressed in non-survivors. Simulations show that the lack of T cells can lead to more significant inflammation, proposing an explanation for the monotonic increase of COVID-19 mortality with age and higher mortality for males. We propose that T cells play a crucial role in the immunity against COVID-19, which provides a new direction–improvement of T cell number for advancing current prevention and treatment.

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  1. Version published to 10.1098/rsos.211606
  2. Version published to 10.1101/2021.04.26.21256093v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2021.04.26.21256093: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Antibodies
    SentencesResources
    The instructions are similar for T cells(T) and antibodies(A).
    antibodies(A
    suggested: None
    Software and Algorithms
    SentencesResources
    Extraction of data from published literature: A software tool WebPlotDigitizer (https://automeris.io/WebPlotDigitizer) was used to extract data from fig.
    WebPlotDigitizer
    suggested: (WebPlotDigitizer, RRID:SCR_013996)
    For methods and results of uncertainties of antiviral parameters, see SI. fmincon function of MATLAB (MathWorks, version 2012 and higher) with the implemented interior point optimization algorithm was used to perform the fits.
    MATLAB
    suggested: (MATLAB, RRID:SCR_001622)

    Results from OddPub: Thank you for sharing your code and 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.

    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.

  4. Version published to 10.1101/2021.04.26.21256093v1 on medRxiv