Proteomic and Metabolomic Characterization of COVID-19 Patient Sera
By B. Shen, Xiao Yi, Yaoting Sun, X. Bi, Juping Du, Chao Zhang, Sheng Quan, Fangfei Zhang, Rui Sun, L. Qian and 30 more. medRxiv, 2020
1,371 citations recorded by the source corpus.
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.
Subfields: Infectious Diseases, Molecular Biology, Neurology
View Paper: https://www.semanticscholar.org/paper/9afa5ef3fdce467246838e78ba845e57300bee14
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Citations in the corpus
- Wearable sensor data and self-reported symptoms for COVID-19 detection (2020, background): “As different individuals experience a wide range of symptomatic and biological responses to infection with SARS-CoV-2, it is likely that their measurable physiological changes will also vary 16–18 .”