1. Was this evaluation helpful?
  2. Reviewer #2 (Public Review):

    In this manuscript, Galbraith et al add to our understanding of COVID19 pathobiology by undertaking a cross-sectional survey of 73 hospitalized COVID19 patients with non-severe disease. They perform very broad multi-omics analysis, including plasma proteomics, cytokine profiling and mass cytometry. The authors propose that disease course can be classified by the titer of anti-CoV2 antibodies, which in turn is associated with distinct changes in circulating proteins, cytokines and immune subsets. Interesting correlations with complement and coagulation factors are noted. These findings suggest an alternative way to map disease progression in COVID19 and have implications for broader studies of COVID19 pathobiology. In particular, it will in interesting to extend this framework to analyze a broader spectrum of COVID19 patients, particularly those with poor outcome.

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  3. Reviewer #1 (Public Review):

    Galbraith et al., using systems immunology approach document in a very detailed manner, provide the textbook example of innate and adaptive immune responses over time following an infection. Here, their clinical assessment is linked to SARS-CoV2 infection. While novelty aspects are not immense, this study is nonetheless well executed, detailed and thorough.

    The authors perform association studies and propose that simple seroconversion test should be considered in determining the clinical treatment. While some would argue that is already practiced and perhaps expected, the authors have done an excellent job at detailed immune analyses which they coupled with statistically sound associations. Thus these findings are important to document, and should be considered as experimental ex vivo evidence of what clinical practice may have implicitly already considered.

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  4. Evaluation Summary:

    In this study, the authors use a systems immunology approach to document innate and adaptive immune responses during clincal SARS-CoV-2 infection. This general impact of this work is a better understanding of COVID19 pathobiology and more specifically, the identification of serum antibodies as a novel classification framework to understand COVID-19 disease course and associated changes.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 agreed to share their name with the authors.)

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  5. SciScore for 10.1101/2020.12.05.20244442: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Study design, participant recruitment, and clinical data capture: Research participants were recruited and consented for participation in the COVID Biobank of the University of Colorado Anschutz Medical Campus [Colorado Multiple Institutional Review Board (COMIRB) Protocol # 20-0685].
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    The Evosep One system (Evosep, Odense, Denmark) was used to separate peptides on a Pepsep column, (150 µm internal diameter, 15 cm) packed with ReproSil C18 1.9 µm, 120A resin.
    suggested: None
    Files were debarcoded using the Matlab DebarcoderTool (38).
    suggested: (MATLAB, RRID:SCR_001622)
    Biostatistics and bioinformatics analyses: Preprocessing, statistical analysis, and plot generation for all datasets was carried out using R (R 4.0.1 / Rstudio 1.3.959 / Bioconductor v 3.11) (40-42), as detailed below.
    suggested: (BWH Biostatistics Center, RRID:SCR_009680)
    suggested: (Bioconductor, RRID:SCR_006442)
    Plasma concentration values (pg/mL) for IgGs recognizing SARS-Co-V-2 and Flu A Hong Kong H3 epitopes were adjusted for Sex and Age using the removeBatchEffect function from the limma package (v 3.44.3) (43).
    suggested: (LIMMA, RRID:SCR_010943)

    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.

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