Interplay of Antibody and Cytokine Production Reveals CXCL13 as a Potential Novel Biomarker of Lethal SARS-CoV-2 Infection

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Abstract

The SARS-CoV-2 pandemic is continuing to impact the global population, and knowledge of the immune response to COVID-19 is still developing. This study assesses the interplay of different parts of the immune system during COVID-19 disease.

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  1. SciScore for 10.1101/2020.08.24.20180877: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Secondary antibody buffer (100μL of 1 % milk diluted in PBS-T containing 1:500 goat anti-human-IgG-HRP; Invitrogen Part #: 31410) was added immediately following the washing procedure.
    anti-human-IgG-HRP
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    SARS-CoV-2 RBD protein was produced by transient transfection of HEK293T cells cultured in 300 cm2 flasks.
    HEK293T
    suggested: None
    Software and Algorithms
    SentencesResources
    Serological results from EUA approved antibody testing (Abbott Architect) performed in the WVUH clinical laboratory and ABO blood type were documented when available.
    Abbott Architect
    suggested: (Abbott ARCHITECT i1000sr System, RRID:SCR_019328)
    Principal component and heatmap analysis: Serological data from patients tested for cytokine production and antibody production were pooled into Microsoft Excel and imported to ClustVis17.
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)
    Pearson correlation coefficients and p-values were calculated in GraphPad Prism using the “Correlation” analysis.
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)

    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.