Serum Protein Profiling Reveals a Landscape of Inflammation and Immune Signaling in Early-stage COVID-19 Infection

  1. Xin Hou
  2. Xiaomei Zhang
  3. Xian Wu
  4. Minya Lu
  5. Dan Wang
  6. Meng Xu
  7. Hongye Wang
  8. Te Liang
  9. Jiayu Dai
  10. Hu Duan
  11. Yingchun Xu
  12. Xiaobo Yu
  13. Yongzhe Li

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Abstract

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  1. Version published to 10.1074/mcp.rp120.002128
  2. ScreenIT

    SciScore for 10.1101/2020.05.08.20095836: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: Serum samples were collected with written informed consent under the approval of the intuitional review board (IRB) from the Peking Union Medical College Hospital (Ethical number: ZS-2303) and the Beijing Proteome Research Center (Beijing, China).
    IRB: Serum samples were collected with written informed consent under the approval of the intuitional review board (IRB) from the Peking Union Medical College Hospital (Ethical number: ZS-2303) and the Beijing Proteome Research Center (Beijing, China).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    In parallel with biotin labeling, antibody microarrays were blocked with 500 μL of PBS with 5% milk (w/v) for 1 h at room temperature.
    biotin labeling,
    suggested: None
    Software and Algorithms
    SentencesResources
    Bioinformatics analysis: Functional annotation of protein classes was performed using the PANTHER database (http://pantherdb.org/)(Mi et al., 2016).
    PANTHER
    suggested: (PANTHER, RRID:SCR_004869)
    The GO biological process analysis was performed using CluGO and visualized in Cytoscape (version 3.7.2) using two-sided hypergeometric test with a p-value less than 0.01 (Bindea et al., 2009; Franz et al., 2016).
    CluGO
    suggested: None
    Cytoscape
    suggested: (Cytoscape, RRID:SCR_003032)
    The analyses of signaling pathways, protein domains and cellular components were performed using the STRING database (Szklarczyk et al., 2015).
    STRING
    suggested: (STRING, RRID:SCR_005223)

    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: We detected the following sentences addressing limitations in the study:
    There are three limitations in this study. First, the number of serum samples was limited; thus, the biomarkers identified in our study should be validated in a large independent clinical cohort. Second, protein detection depends on the sensitivity and specificity of the capture antibodies immobilized on the microarray. Third, serological proteins of early-stage COVID-19 patients were only compared to early-stage influenza patients. In the future, protein profiling of COVID-19 patients should be examined over the entire course of infection. In addition, the profiles should be compared to healthy patients and patients infected with different viruses. Our study comprehensively profiled the serological proteins of early SARS-CoV-2, revealing a new understanding of the inflammation and immune signaling that occurs. Our data also identified potential biomarkers that could be used to diagnose COVID-19 patients and design effective treatment.

    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.

  3. Version published to 10.1101/2020.05.08.20095836 on medRxiv