Proteomics Uncovers Immunosuppression in COVID-19 Patients with Long Disease Course

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Abstract

Little is known regarding why a subset of COVID-19 patients exhibited prolonged positivity of SARS-CoV-2 infection. Here, we studied the sera proteomic dynamics in 37 COVID-19 patients over nine weeks, quantifying 2700 proteins with high quality. Remarkably, we found that during the first three weeks since disease onset, while clinical symptoms and outcome were indistinguishable, patients with prolonged disease course displayed characteristic immunological responses including enhanced Natural Killer cell-mediated innate immunity and regulatory T cell-mediated immunosuppression. We further showed that it is possible to predict the length of disease course using machine learning based on blood protein levels during the first three weeks. Validation in an independent cohort achieved an accuracy of 82%. In summary, this study presents a rich serum proteomic resource to understand host responses in COVID-19 patients and identifies characteristic Treg-mediated immunosuppression in patients with prolonged disease course, nominating new therapeutic target and diagnosis strategy.

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  1. SciScore for 10.1101/2020.06.14.20131078: (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
    Flow cytometry analysis: Peripheral blood samples from EDTA anticoagulants were incubated with mixture antibodies including CD4-PE-Cy7 (UB105441,
    CD4-PE-Cy7
    suggested: None
    UB105441
    suggested: None
    Software and Algorithms
    SentencesResources
    The serum was firstly depleted of 14 high abundant serum proteins using a human affinity depletion kit (Thermo Fisher Scientific™, San Jose, USA).
    Thermo Fisher Scientific™
    suggested: (Thermo Fisher Scientific, RRID:SCR_008452)
    Database search and statistical analysis: MS data was performed using Proteome Discoverer (Version 2.4.1.15, Thermo Fisher) (Colaert et al., 2011) …