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

This study presents "aUToAntiBody Comprehensive Database (UT-ABCD)", a proteome-wide catalog of autoantibody profiles in 284 human individuals. The subjects included patients diagnosed with Coronavirus disease 2019 (COVID-19), systemic sclerosis (SSc), systemic lupus erythematosus (SLE), anti-neutrophil cytoplasmic antibody-associated vasculitis (AAV), atopic dermatitis (AD), as well as healthy controls (HC). Our investigation employed proteome-wide autoantibody screening (PWAS) that utilizes 13,350 autoantigens displayed on wet protein arrays, covering approximately 90% of the human transcriptome. Our findings demonstrated significant elevation of autoantibody levels in COVID-19, SSc, and SLE patients. Unique sets of disease-specific autoantibodies were identified, highlighting the role of autoantibodies against proteins associated with cytokine signaling in immune systems and viral infection pathways. 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. Longitudinal analysis revealed dynamic changes in autoantibody levels throughout the course of COVID-19, independent of disease severity. Our research highlights the effectiveness of integrating PWAS 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.