A time-resolved proteomic and prognostic map of COVID-19.
A time-resolved proteomic and prognostic map of COVID-19.
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DOI：
10.1016/j.cels.2021.05.005
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发表时间：
2021-08-18
期刊：
影响因子：
9.3
通讯作者：
Kurth F
中科院分区：
文献类型：
--
作者：
Demichev V;Tober-Lau P;Lemke O;Nazarenko T;Thibeault C;Whitwell H;Röhl A;Freiwald A;Szyrwiel L;Ludwig D;Correia-Melo C;Aulakh SK;Helbig ET;Stubbemann P;Lippert LJ;Grüning NM;Blyuss O;Vernardis S;White M;Messner CB;Joannidis M;Sonnweber T;Klein SJ;Pizzini A;Wohlfarter Y;Sahanic S;Hilbe R;Schaefer B;Wagner S;Mittermaier M;Machleidt F;Garcia C;Ruwwe-Glösenkamp C;Lingscheid T;Bosquillon de Jarcy L;Stegemann MS;Pfeiffer M;Jürgens L;Denker S;Zickler D;Enghard P;Zelezniak A;Campbell A;Hayward C;Porteous DJ;Marioni RE;Uhrig A;Müller-Redetzky H;Zoller H;Löffler-Ragg J;Keller MA;Tancevski I;Timms JF;Zaikin A;Hippenstiel S;Ramharter M;Witzenrath M;Suttorp N;Lilley K;Mülleder M;Sander LE;PA-COVID-19 Study group;Ralser M;Kurth F
COVID-19的临床表现变化很大，从无症状感染到严重器官损伤和死亡。我们通过测量86个认可的诊断参数，如血细胞计数和酶活性，以及687个采样点的非靶向血浆蛋白质组学，表征了139例COVID-19住院患者疾病的时间依赖性进展。我们报告了系统性炎症反应的初始峰值，逐渐减轻，随后是指示组织修复，代谢重建和免疫调节的蛋白质特征。我们确定预后标记特征，以设计适应风险的治疗策略，并使用机器学习对治疗需求进行分类。我们表明，基于蛋白质组的机器学习模型可以转移到一个独立的队列中。我们的研究提出了一个地图链接常规使用的临床诊断参数血浆蛋白质组及其动态在传染病。血浆蛋白质组学结合临床参数表征COVID-19进展机器学习模型可以高度精确地预测疾病表型，早期分子宿主反应可预测COVID-19进展，对COVID-19的分子反应具有年龄特异性，Demichev， Tober-Lau等人提出了COVID-19的时间分辨分子图谱。在687个时间点测量COVID-19患者的血浆蛋白质组学以及广泛的临床诊断参数。它们描述了COVID-19分子特征的特异性和动态，以及预测和预后能力。
COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease. Plasma proteomes combined with clinical parameters characterize COVID-19 progression Machine learning models allow highly precise prediction of the disease phenotype The early molecular host response is predictive of COVID-19 progression The molecular response to COVID-19 is age specific Demichev, Tober-Lau et al., present a time-resolved molecular map of the COVID-19, measuring plasma proteomes of patients with COVID-19 along with an extensive panel of clinical diagnostic parameters at 687-time points. They describe the specificity and dynamics, as well as the predictive and prognostic power of the molecular signatures in COVID-19.
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影响因子：
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作者：
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通讯作者：
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影响因子：
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影响因子：
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DOI：
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发表时间：
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期刊：
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影响因子：
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作者：
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通讯作者：
Levine, Stewart J.
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
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通讯作者：
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