代谢组学
生物
蛋白质组学
2019年冠状病毒病(COVID-19)
代谢组
代谢物
队列
蛋白质组
免疫学
生物信息学
内科学
医学
疾病
生物化学
基因
传染病(医学专业)
作者
Bo Shen,Xiao Yi,Yaoting Sun,Xiaojie Bi,Juping Du,Chao Zhang,Sheng Quan,Fangfei Zhang,Rui Sun,Liujia Qian,Weigang Ge,Wei Liu,Shuang Liang,Hao Chen,Ying Zhang,Jun Li,Jiaqin Xu,Zebao He,Baofu Chen,Jing Wang
出处
期刊:Cell
[Cell Press]
日期:2020-05-28
卷期号:182 (1): 59-72.e15
被引量:1445
标识
DOI:10.1016/j.cell.2020.05.032
摘要
Early detection and effective treatment of severe COVID-19 patients remain major challenges. Here, we performed proteomic and metabolomic profiling of sera from 46 COVID-19 and 53 control individuals. We then trained a machine learning model using proteomic and metabolomic measurements from a training cohort of 18 non-severe and 13 severe patients. The model was validated using 10 independent patients, 7 of which were correctly classified. Targeted proteomics and metabolomics assays were employed to further validate this molecular classifier in a second test cohort of 19 COVID-19 patients, leading to 16 correct assignments. We identified molecular changes in the sera of COVID-19 patients compared to other groups implicating dysregulation of macrophage, platelet degranulation, complement system pathways, and massive metabolic suppression. This study revealed characteristic protein and metabolite changes in the sera of severe COVID-19 patients, which might be used in selection of potential blood biomarkers for severity evaluation.
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