An Autoantigen Profile of Human A549 Lung Cells Reveals Viral and Host Etiologic Molecular Attributes of Autoimmunity in COVID-19

  1. Julia Y. Wang
  2. Wei Zhang
  3. Michael W. Roehrl
  4. Victor B. Roehrl
  5. Michael H. Roehrl

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Abstract

We aim to establish a comprehensive COVID-19 autoantigen atlas in order to understand autoimmune diseases caused by SARS-CoV-2 infection. Based on the unique affinity between dermatan sulfate and autoantigens, we identified 348 proteins from human lung A549 cells, of which 198 are known targets of autoantibodies. Comparison with current COVID data identified 291 proteins that are altered at protein or transcript level in SARS-CoV-2 infection, with 191 being known autoantigens. These known and putative autoantigens are significantly associated with viral replication and trafficking processes, including gene expression, ribonucleoprotein biogenesis, mRNA metabolism, translation, vesicle and vesicle-mediated transport, and apoptosis. They are also associated with cytoskeleton, platelet degranulation, IL-12 signaling, and smooth muscle contraction. Host proteins that interact with and that are perturbed by viral proteins are a major source of autoantigens. Orf3 induces the largest number of protein alterations, Orf9 affects the mitochondrial ribosome, and they and E, M, N, and Nsp proteins affect protein localization to membrane, immune responses, and apoptosis. Phosphorylation and ubiquitination alterations by viral infection define major molecular changes in autoantigen origination. This study provides a large list of autoantigens as well as new targets for future investigation, e.g., UBA1, UCHL1, USP7, CDK11A, PRKDC, PLD3, PSAT1, RAB1A, SLC2A1, platelet activating factor acetylhydrolase, and mitochondrial ribosomal proteins. This study illustrates how viral infection can modify host cellular proteins extensively, yield diverse autoantigens, and trigger a myriad of autoimmune sequelae.

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  1. ScreenIT

    SciScore for 10.1101/2021.02.21.432171: (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

    Experimental Models: Cell Lines
    SentencesResources
    DS-affinity fractionation: The total proteins extracted from A549 cells were fractionated in a DS-Sepharose column with a BioLogic Duo-Flow system (Bio-Rad).
    A549
    suggested: NCI-DTP Cat# A549, RRID:CVCL_0023)
    Software and Algorithms
    SentencesResources
    Similarity searches were conducted between our data and the Coronascape database to identify DS-affinity proteins (or their corresponding genes) that are up- and/or down-regulated in the viral infection.
    Coronascape
    suggested: None
    Protein-protein interaction network analysis: Protein-protein interactions were analyzed by STRING (16).
    STRING
    suggested: (STRING, RRID:SCR_005223)
    Pathway and process enrichment analysis: Pathways and processes enrichment were analyzed with Metascape (17), which utilize various ontology sources such as KEGG Pathway, GO Biological Process, Reactome Gene Sets, Canonical Pathways, CORUM, TRRUST, and DiGenBase.
    Metascape
    suggested: (Metascape, RRID:SCR_016620)
    KEGG
    suggested: (KEGG, RRID:SCR_012773)
    GO Biological
    suggested: None
    Autoantigen confirmation literature text mining: Literature searches in Pubmed were performed for every DS-affinity protein identified in this study.
    Pubmed
    suggested: (PubMed, RRID:SCR_004846)
    Search keywords included the protein name, its gene symbol, alternative names and symbols, and the MeSH keyword “autoantibodies”.
    MeSH
    suggested: (MeSH, RRID:SCR_004750)

    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

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  2. Version published to 10.1101/2021.02.21.432171 on bioRxiv