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

Matsuda, K. M.; Kawase, Y.; Iwadoh, K.; Kurano, M.; Yatomi, Y.; Okamoto, K.; Moriya, K.; Kotani, H.; Hisamoto, T.; Kuzumi, A.; Fukasawa, T.; Yoshizaki-Ogawa, A.; Kono, M.; Okamura, T.; Shoda, H.; Fujio, K.; Yamaguchi, K.; Okumura, T.; Ono, C.; Kobayashi, Y.; Sato, A.; Miya, A.; Goshima, N.; Uchino, R.; Murakami, Y.; Matsunaka, H.; Imai, H.; Sato, S.; Raymond, R.; Yoshizaki, A.

2024-06-08 allergy and immunology
10.1101/2024.06.07.24308592 medRxiv
Show abstract

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.

Published in BMC Immunology · not in our set (fewer than 10 published preprints to learn from) · training set

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