Department of Immunology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, SP, Brazil.
Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, SP, Brazil.
Nat Commun. 2022 Mar 9;13(1):1220. doi: 10.1038/s41467-022-28905-5.
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.
COVID-19 与自身免疫性疾病具有自身抗体产生的特征。为了了解这些免疫球蛋白在疾病发病机制中的作用，探索自身抗体谱很重要。在这里，我们通过对 246 个人的横断面研究表明，靶向 G 蛋白偶联受体 (GPCR) 和 RAS 相关分子的自身抗体与 COVID-19 的临床严重程度相关。中度和重度疾病患者的自身抗体水平高于健康对照组和轻度 COVID-19 患者。在抗 GPCR 自身抗体中，机器学习分类将趋化因子受体 CXCR3 和 RAS 相关分子 AGTR1 鉴定为与疾病严重程度最强相关的抗体的靶标。除了抗体水平外，中度或高度疾病严重程度患者的自身抗体网络特征也在发生变化。尽管我们目前和以前的研究将抗 GPCR 抗体鉴定为人类生物学的天然成分，但它们在 COVID-19 中的产生失调，其水平和模式的改变可能预测 COVID-19 的疾病严重程度。
