Proteomic and Metabolomic Characterization of COVID-19 Patient Sera
By
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,
Haixi Yan,
Yufen Zheng,
Donglian Wang,
Jiansheng Zhu,
Ziqing Kong,
Zhouyang Kang,
Xiao Liang,
Xuan Ding,
Guan Ruan,
Nan Xiang,
Xue Cai,
Huanhuan Gao,
Lu Li,
Sainan Li,
Qi Xiao,
Tian Lu,
Yi Zhu,
Huafen Liu,
Haixiao Chen,
Tiannan Guo
Posted 07 Apr 2020
medRxiv DOI: 10.1101/2020.04.07.20054585
Severe COVID-19 patients account for most of the mortality of this disease. Early detection and effective treatment of severe 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 correctly classified severe patients with an accuracy of 93.5%, and was further validated using ten independent patients, seven of which were correctly classified. We identified molecular changes in the sera of COVID-19 patients implicating dysregulation of macrophage, platelet degranulation and complement system pathways, and massive metabolic suppression. This study shows that it is possible to predict progression to severe COVID-19 disease using serum protein and metabolite biomarkers. Our data also uncovered molecular pathophysiology of COVID-19 with potential for developing anti-viral therapies.
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