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

  1. Shaohua Tang
  2. Rui Sun
  3. Qi Xiao
  4. Tingting Mao
  5. Weigang Ge
  6. Chongquan Huang
  7. Meng Luo
  8. Liujia Qian
  9. Hao Chen
  10. Qiushi Zhang
  11. Sainan Li
  12. Wei Liu
  13. Shufei Li
  14. Xueqin Xu
  15. Huanzheng Li
  16. Lianpeng Wu
  17. Jianyi Dai
  18. Huanhuan Gao
  19. Lu Li
  20. Tian Lu
  21. Xiao Liang
  22. Xue Cai
  23. Guan Ruan
  24. Kexin Liu
  25. Fei Xu
  26. Yan Li
  27. Yi Zhu
  28. Jianping Huang
  29. Tiannan Guo

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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. ScreenIT

    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) search engine against the human protein database downloaded from UniProt (version 02/01/2020; 164,930 sequences), with a precursor ion mass tolerance of 10 ppm and fragment ion mass tolerance of 0.02 Da.
    Proteome Discoverer
    suggested: (Proteome Discoverer, RRID:SCR_014477)
    UniProt
    suggested: (UniProtKB, RRID:SCR_004426)
    Pathway analysis: For the pathway enrichment analysis, firstly, four databases including KEGG pathway, GO biological processes, Reactome gene sets and immunologic signatures were used for immune characterization analysis on the Metascape web-based platform (Zhou et al., 2019).
    KEGG
    suggested: (KEGG, RRID:SCR_012773)
    Metascape
    suggested: (Metascape, RRID:SCR_016620)
    Machine learning: The machine learning was performed using the R package randomForest (version 4.6.14) as described previously with some modifications (Shen, 2020) as described briefly in the following.
    randomForest
    suggested: (RandomForest Package in R, RRID:SCR_015718)

    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: Please consider improving the rainbow (“jet”) colormap(s) used on page 25. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

    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.

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  2. Version published to 10.1101/2020.06.14.20131078v2 on medRxiv
  3. Version published to 10.1101/2020.06.14.20131078v1 on medRxiv