A master autoantigen-ome links alternative splicing, female predilection, and COVID-19 to autoimmune diseases

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

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Abstract

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  1. Version published to 10.1016/j.jtauto.2022.100147
  2. ScreenIT

    SciScore for 10.1101/2021.07.30.454526: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    DS-affinity autoAg identification: Potential autoAgs were identified by DS-affinity from protein extracts from six human cell lines as previously described, including HFL1 fetal lung fibroblasts [1], A549 lung epithelial cells [2], HS-Sultan B-lymphoblasts [4], Wil2-NS B-lymphoblasts [7], Jurkat T-lymphoblasts [5], and HEp-2 fibroblasts [11].
    A549
    suggested: None
    Jurkat
    suggested: TKG Cat# TKG 0209, RRID:CVCL_0065)
    HEp-2
    suggested: CLS Cat# 300397/p694_Hep-2, RRID:CVCL_1906)
    Software and Algorithms
    SentencesResources
    Autoantigen literature text mining: Each DS-affinity protein was verified as to whether it is a target of autoantibodies by an extensive literature search on PubMed.
    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)
    Protein network analysis: Protein-protein interactions were analyzed with STRING [14].
    STRING
    suggested: (STRING, RRID:SCR_005223)
    Enrichment of pathways and processes were analyzed with Metascape [16], which utilize various ontological sources such as KEGG Pathway, GO Biological Process, Reactome Gene Sets, and Canonical Pathways.
    Metascape
    suggested: (Metascape, RRID:SCR_016620)
    KEGG
    suggested: (KEGG, RRID:SCR_012773)
    GO Biological
    suggested: None
    ShinyGO is based on a large annotation database derived from Ensembl and STRING-db.
    Ensembl
    suggested: (Ensembl, RRID:SCR_002344)

    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.

    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 10.1101/2021.07.30.454526v1 on bioRxiv