Joint Laboratory of Infectious Diseases and Health, Wuhan Institute of Virology & Wuhan Jinyintan Hospital, Wuhan Jinyintan Hospital, Wuhan, Hubei 430023, China; State Key Laboratory of Virology, Wuhan Institute of Virology, Center for Biosafety Mega-Science, Chinese Academy of Sciences (CAS), Wuhan, Hubei 430071, China; Center for Translational Medicine, Wuhan Jinyintan Hospital, Wuhan, Hubei 430023, China.
MOE Key Laboratory of Molecular Biophysics, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology (HUST), Wuhan, Hubei 430074, China.
Immunity. 2020 Nov 17;53(5):1108-1122.e5. doi: 10.1016/j.immuni.2020.10.008. Epub 2020 Oct 20.
The coronavirus disease 2019 (COVID-19) pandemic is a global public health crisis. However, little is known about the pathogenesis and biomarkers of COVID-19. Here, we profiled host responses to COVID-19 by performing plasma proteomics of a cohort of COVID-19 patients, including non-survivors and survivors recovered from mild or severe symptoms, and uncovered numerous COVID-19-associated alterations of plasma proteins. We developed a machine-learning-based pipeline to identify 11 proteins as biomarkers and a set of biomarker combinations, which were validated by an independent cohort and accurately distinguished and predicted COVID-19 outcomes. Some of the biomarkers were further validated by enzyme-linked immunosorbent assay (ELISA) using a larger cohort. These markedly altered proteins, including the biomarkers, mediate pathophysiological pathways, such as immune or inflammatory responses, platelet degranulation and coagulation, and metabolism, that likely contribute to the pathogenesis. Our findings provide valuable knowledge about COVID-19 biomarkers and shed light on the pathogenesis and potential therapeutic targets of COVID-19.
新型冠状病毒病 2019(COVID-19)大流行是一场全球性的公共卫生危机。然而,人们对 COVID-19 的发病机制和生物标志物知之甚少。在这里,我们通过对 COVID-19 患者队列(包括非幸存者和从轻度或重度症状中康复的幸存者)进行血浆蛋白质组学分析,描绘了宿主对 COVID-19 的反应,并发现了许多与 COVID-19 相关的血浆蛋白改变。我们开发了一种基于机器学习的流水线来识别 11 种作为生物标志物的蛋白质和一组生物标志物组合,这些标志物通过独立的队列得到了验证,并能够准确地区分和预测 COVID-19 的结果。一些生物标志物通过使用更大的队列的酶联免疫吸附测定(ELISA)进一步得到了验证。这些明显改变的蛋白质,包括生物标志物,介导病理生理途径,如免疫或炎症反应、血小板脱颗粒和凝血以及代谢,这些可能有助于发病机制。我们的研究结果为 COVID-19 生物标志物提供了有价值的知识,并为 COVID-19 的发病机制和潜在治疗靶点提供了启示。