An autoantigen profile from Jurkat T-Lymphoblasts provides a molecular guide for investigating autoimmune sequelae of 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

In order to understand autoimmune phenomena contributing to the pathophysiology of COVID-19 and post-COVID syndrome, we have been profiling autoantigens (autoAgs) from various cell types. Although cells share numerous autoAgs, each cell type gives rise to unique COVID-altered autoAg candidates, which may explain the wide range of symptoms experienced by patients with autoimmune sequelae of SARS-CoV-2 infection. Based on the unifying property of affinity between autoAgs and the glycosaminoglycan dermatan sulfate (DS), this paper reports 140 candidate autoAgs identified from proteome extracts of human Jurkat T-cells, of which at least 105 (75%) are known targets of autoantibodies. Comparison with currently available multi-omic COVID-19 data shows that 125 (89%) DS-affinity proteins are altered at protein and/or RNA levels in SARS-CoV-2-infected cells or patients, with at least 94 being known autoAgs in a wide spectrum of autoimmune diseases and cancer. Protein alterations by ubiquitination and phosphorylation during the viral infection are major contributors of autoAgs. The autoAg protein network is significantly associated with cellular response to stress, apoptosis, RNA metabolism, mRNA processing and translation, protein folding and processing, chromosome organization, cell cycle, and muscle contraction. The autoAgs include clusters of histones, CCT/TriC chaperonin, DNA replication licensing factors, proteasome and ribosome proteins, heat shock proteins, serine/arginine-rich splicing factors, 14-3-3 proteins, and cytoskeletal proteins. AutoAgs, such as LCP1 and NACA, that are altered in the T cells of COVID patients may provide insight into T-cell responses to viral infection and merit further study. The autoantigen-ome from this study contributes to a comprehensive molecular map for investigating acute, subacute, and chronic autoimmune disorders caused by SARS-CoV-2.

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  1. Version published to 10.1071/ch22268
  2. ScreenIT

    SciScore for 10.1101/2021.07.05.451199: (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
    In brief, Jurkat cells were lysed with 50 mM phosphate buffer (pH 7.4) containing the Roche Complete Mini protease inhibitor cocktail and then homogenized on ice with a microprobe sonicator until the turbid mixture turned nearly clear with no visible cells left.
    Jurkat
    suggested: None
    Software and Algorithms
    SentencesResources
    Protein network analysis: Protein-protein interactions were analyzed by STRING [26].
    STRING
    suggested: (STRING, RRID:SCR_005223)
    Pathways and processes enrichment were analyzed with Metascape [27], which utilizes various ontology sources such as KEGG Pathway, GO Biological Process, Reactome Gene Sets,
    Metascape
    suggested: (Metascape, RRID:SCR_016620)
    KEGG
    suggested: (KEGG, RRID:SCR_012773)
    GO Biological
    suggested: None
    Autoantigen literature text mining: Every DS-affinity protein identified in this study was searched for specific autoantibodies reported in the PubMed literature.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    Search keywords included the MeSH keyword “autoantibodies”, the protein name or its gene symbol, or alternative names and symbols.
    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.

    Results from scite Reference Check: We found no unreliable references.

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  3. Version published to 10.1101/2021.07.05.451199v1 on bioRxiv