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

Abstract

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.

Deciphering autoantibody landscape of systemic sclerosis through systems-based approach: insights from a B-cell depletion clinical trial

“…18 As a result, our autoantibody measurement could encompass a wider range of antigens at an almost proteome-wide level, allowing for the application of omics-based bioinformatics approaches to interpret the data. 24 Meanwhile, one major limitation is the lack of functional assays both in vitro and in vivo. Further investigation is needed to determine whether the candidate autoantibodies we identified act as agonists or antagonists to their target proteins, and whether they influence the pathophysiology of SSc.…”

“…We identified the top 10 features in these six models (Extended Figure 2A), examined their inclusion relationships (Extended Figure 2B), and explored the prevalence of highlighted autoantibodies across a broader range of human disorders using the aUToAntiBody Comprehensive Database (UT-ABCD). 24 Most of these autoantibodies were found to be non-specifically elevated in various pathological conditions, except for well-known SSc-specific autoantibodies such as ATA and ARA (Extended Figure 2C). Attempts to distinguish between HRs and LRs based on pre-treatment autoantibody profiles did not yield satisfactory precision (Extended Table 1).…”