Proteome-wide autoantibody screening and holistic autoantigenomic analysis unveil COVID-19 signature of autoantibody landscape

Summary This study presents “aUToAntiBody Comprehensive Database (UT-ABCD)”, a comprehensive catalog of autoantibody profiles in 284 human individuals. The subjects include patients diagnosed with Coronavirus disease 2019 (COVID-19; n = 73), systemic sclerosis (SSc; n = 32), systemic lupus erythematosus (SLE; n = 60), anti-neutrophil cytoplasmic antibody-associated vasculitis (AAV; n = 29), atopic dermatitis (AD; n = 26), as well as healthy controls (HC; n = 64). Our investigation employs proteome-wide autoantibody screening (PWAbS) that utilizes 13,352 autoantigens displayed on wet protein arrays (WPAs). Our WPAs display human proteins synthesized in vitro utilizing a wheat germ cell-free system, maintained in a hydrated state. Our findings demonstrated significant elevation in the number of IgG autoantibody positivity in COVID-19, SSc, SLE, AAV, and AD patients compared to HCs. Employing machine learning, we distinguished COVID-19 cases with high accuracy based on autoantibody profiles, notably identifying antibodies against proteins encoded by BCORP1 and KAT2A as highly specific to COVID-19 (specificity: 87% and 97%, respectively). Our research highlights the effectiveness of integrating PWAbS and autoantigenomics in exploring immune responses in COVID-19 and other diseases. It provides a deeper understanding of the autoimmunity landscape in human disorders and introduces a new bioresource for further investigation. Competing Interest Statement K Yamaguchi, T Okumura, C Ono, Y Kobayashi, A Miya, A Sato, and N Goshima were employed by ProteoBridge Corporation. T Fukasawa and A Yoshizaki belong to the Social Cooperation Program, Department of Clinical Cannabinoid Research, The University of Tokyo Graduate School of Medicine, Tokyo, Japan, supported by Japan Cosmetic Association and Japan Federation of Medium and Small Enterprise Organizations. T Okamura belongs to the Social Cooperation Program, Department of Functional Genomics and Immunological Diseases, The University of Tokyo Graduate School of Medicine, Tokyo, Japan, supported by Chugai Pharmaceutical Corporation. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding Statement This study did not receive any funding. Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This study has been approved by The University of Tokyo Ethical Committee (Approval number 0695). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Footnotes Major Revisions: Clarified Methodology: Clearly defined input features for machine learning (ML), explained sample shuffling during cross-validation, and described minimal-feature models. Validation of Results: Conducted ELISA validation for anti-KAT2A and anti-BCOR antibodies, highlighting correlation for KAT2A but not BCOR. Statistical Analysis Improvement: Specified statistical tests used, recalculated significance thresholds, and included the Matthews correlation coefficient (MCC). Limitations Acknowledged: Explicitly discussed the limitations of sample size, age mismatch in controls, absence of acute non-COVID illness control group, and potential implications for findings. Performance Metrics Clarified: Corrected definitions, included detailed formulas, and recalculated key metrics including MCC. Clinical Relevance Enhanced: Highlighted minimal feature models (1-5 top features) achieving robust discrimination for COVID-19, specified sensitivity and specificity for anti-BCORP1 and anti-KAT2A antibodies. Minor Revisions: Terminology Adjustments: Replaced abbreviations (PWAS → PWAbS), retained UT-ABCD acronym after consideration. Abstract Clarified: Included patient numbers, defined "wet protein arrays," clarified comparison groups, and specified that only IgG autoantibodies were analyzed. Database Accessibility: Provided login credentials to reviewers for accessing UT-ABCD database. Figure Legends and Captions: Corrected and clarified captions, labels, and threshold definitions (e.g., Z-score cutoff corrected to mean +4SD). Data Availability Statement: Specified that full dataset is available upon reasonable request due to ethical constraints. Recruitment Criteria Expanded: Included clinical criteria, patient recruitment details, and confounding factors (e.g., disease duration, concomitant medications) in methods. Data availability statement The digest of the results is available as “aUToAntiBody Comprehensive Database (UT-ABCD)” at http://www.ut-abcd.org. The full dataset is available upon reasonable request to the corresponding author, in compliance with ethical guidelines and participant privacy considerations.