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| Item Type: | Article |
|---|---|
| Title: | Autoantibodies targeting GPCRs and RAS-related molecules associate with COVID-19 severity |
| Creators Name: | Cabral-Marques, O. and Halpert, G. and Schimke, L.F. and Ostrinski, Y. and Vojdani, A. and Baiocchi, G.C. and Freire, P.P. and Filgueiras, I.S. and Zyskind, I. and Lattin, M.T. and Tran, F. and Schreiber, S. and Marques, A.H.C. and Plaça, D.R. and Fonseca, D.L.M. and Humrich, J.Y. and Müller, A. and Giil, L.M. and Graßhoff, H. and Schumann, A. and Hackel, A. and Junker, J. and Meyer, C. and Ochs, H.D. and Lavi, Y.B. and Scheibenbogen, C. and Dechend, R. and Jurisica, I. and Schulze-Forster, K. and Silverberg, J.I. and Amital, H. and Zimmerman, J. and Heidecke, H. and Rosenberg, A.Z. and Riemekasten, G. and Shoenfeld, Y. |
| Abstract: | COVID-19 shares the feature of autoantibody production with systemic autoimmune diseases. In order to understand the role of these immune globulins in the pathogenesis of the disease, it is important to explore the autoantibody spectra. Here we show, by a cross-sectional study of 246 individuals, that autoantibodies targeting G protein-coupled receptors (GPCR) and RAS-related molecules associate with the clinical severity of COVID-19. Patients with moderate and severe disease are characterized by higher autoantibody levels than healthy controls and those with mild COVID-19 disease. Among the anti-GPCR autoantibodies, machine learning classification identifies the chemokine receptor CXCR3 and the RAS-related molecule AGTR1 as targets for antibodies with the strongest association to disease severity. Besides antibody levels, autoantibody network signatures are also changing in patients with intermediate or high disease severity. Although our current and previous studies identify anti-GPCR antibodies as natural components of human biology, their production is deregulated in COVID-19 and their level and pattern alterations might predict COVID-19 disease severity. |
| Keywords: | Antibodies, Autoimmunity, Molecular Medicine, Predictive Markers |
| Source: | Nature Communications |
| ISSN: | 2041-1723 |
| Publisher: | Nature Publishing Group |
| Volume: | 13 |
| Number: | 1 |
| Page Range: | 1220 |
| Date: | 9 March 2022 |
| Official Publication: | https://doi.org/10.1038/s41467-022-28905-5 |
| PubMed: | View item in PubMed |
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