Plasma Proteomics Identify Biomarkers and Pathogenesis of COVID-19.
Plasma Proteomics Identify Biomarkers and Pathogenesis of COVID-19.
复制标题
血浆蛋白质组学识别 COVID-19 的生物标志物和发病机制。
DOI：
10.1016/j.immuni.2020.10.008
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发表时间：
2020-11-17
期刊：
影响因子：
32.4
通讯作者：
Zhou X
中科院分区：
文献类型：
--
作者：
Shu T;Ning W;Wu D;Xu J;Han Q;Huang M;Zou X;Yang Q;Yuan Y;Bie Y;Pan S;Mu J;Han Y;Yang X;Zhou H;Li R;Ren Y;Chen X;Yao S;Qiu Y;Zhang DY;Xue Y;Shang Y;Zhou X
2019冠状病毒病（COVID-19）大流行是一场全球公共卫生危机。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，我们通过对一组COVID-19患者（包括非幸存者和从轻度或重度症状中恢复的幸存者）进行血浆蛋白质组学分析，分析了宿主对COVID-19的反应，并发现了许多COVID-19相关的血浆蛋白质改变。我们开发了一个基于机器学习的管道，以识别11种蛋白质作为生物标志物和一组生物标志物组合，并通过独立队列验证，准确区分和预测COVID-19结果。一些生物标志物通过酶联免疫吸附测定（ELISA）使用更大的队列进一步验证。这些显著改变的蛋白质，包括生物标志物，介导病理生理学途径，如免疫或炎症反应，血小板脱粒和凝血，以及代谢，这可能有助于发病机制。我们的发现提供了关于COVID-19生物标志物的宝贵知识，并阐明了COVID-19的发病机制和潜在治疗靶点。我们分析了不同症状和时间点COVID-19病例的血浆蛋白质组学宿主血浆蛋白质的改变与COVID-19的发展有关基于机器学习的模型区分了不同严重程度的患者生物标志物组合显示出预测COVID-19临床结果的能力来自三个COVID-19队列的血浆样本的蛋白质组学定量和实验验证19名在不同时间点有不同症状的患者识别出与疾病严重程度相关的差异表达宿主蛋白，并优先考虑生物标志物组合，以准确预测COVID-19临床结果。
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. We profile plasma proteomics of COVID-19 cases at distinct symptoms and time points The alterations of host plasma proteins are linked with COVID-19 development Machine-learning-based models distinguish patients with different severity Biomarker combinations show the power to predict COVID-19 clinical outcomes Proteomic quantifications and experimental validation of plasma samples from three cohorts of COVID-19 patients with distinct symptoms at different time points identify differentially expressed host proteins that correlate with disease severity and prioritize biomarker combinations for accurately predicting COVID-19 clinical outcomes.
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影响因子：
--
作者：
Das J;Yu H
通讯作者：
Yu H
影响因子：
48
作者：
Li T;Wernersson R;Hansen RB;Horn H;Mercer J;Slodkowicz G;Workman CT;Rigina O;Rapacki K;Stærfeldt HH;Brunak S;Jensen TS;Lage K
通讯作者：
Lage K
影响因子：
14.9
作者：
The Gene Ontology Consortium
通讯作者：
The Gene Ontology Consortium
影响因子：
168.9
作者：
Huang, Chaolin;Wang, Yeming;Cao, Bin
通讯作者：
Cao, Bin
影响因子：
2.8
作者：
Lachmann, Peter J.
通讯作者：
Lachmann, Peter J.