Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Section Pediatric Infectious Diseases, Laboratory of Medical Immunology, Radboud Institute for Molecular Life Sciences, Nijmegen, the Netherlands; Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, the Netherlands; Center for Molecular and Biomolecular Informatics, Radboud University Medical Center, Nijmegen, the Netherlands.
Cell Rep Med. 2022 Jul 19;3(7):100680. doi: 10.1016/j.xcrm.2022.100680. Epub 2022 Jun 28.
The biological determinants underlying the range of coronavirus 2019 (COVID-19) clinical manifestations are not fully understood. Here, over 1,400 plasma proteins and 2,600 single-cell immune features comprising cell phenotype, endogenous signaling activity, and signaling responses to inflammatory ligands are cross-sectionally assessed in peripheral blood from 97 patients with mild, moderate, and severe COVID-19 and 40 uninfected patients. Using an integrated computational approach to analyze the combined plasma and single-cell proteomic data, we identify and independently validate a multi-variate model classifying COVID-19 severity (multi-class area under the curve [AUC] = 0.799, p = 4.2e-6; multi-class AUC = 0.773, p = 7.7e-6). Examination of informative model features reveals biological signatures of COVID-19 severity, including the dysregulation of JAK/STAT, MAPK/mTOR, and nuclear factor κB (NF-κB) immune signaling networks in addition to recapitulating known hallmarks of COVID-19. These results provide a set of early determinants of COVID-19 severity that may point to therapeutic targets for prevention and/or treatment of COVID-19 progression.
导致 2019 年冠状病毒（COVID-19）临床表现范围的生物学决定因素尚未完全阐明。在这里，我们对 97 名轻度、中度和重度 COVID-19 患者以及 40 名未感染患者的外周血进行了横断面评估，涉及超过 1400 种血浆蛋白和 2600 种单细胞免疫特征，包括细胞表型、内源性信号活性以及对炎症配体的信号反应。通过综合计算方法分析联合血浆和单细胞蛋白质组学数据，我们鉴定并独立验证了一种多变量模型，用于分类 COVID-19 严重程度（多类 AUC = 0.799，p = 4.2e-6；多类 AUC = 0.773，p = 7.7e-6）。对有意义的模型特征的检查揭示了 COVID-19 严重程度的生物学特征，包括 JAK/STAT、MAPK/mTOR 和核因子κB（NF-κB）免疫信号网络的失调，此外还重现了 COVID-19 的已知特征。这些结果提供了一组 COVID-19 严重程度的早期决定因素，这些因素可能指向 COVID-19 进展的预防和/或治疗的治疗靶点。
