학술논문
Integrative metabolomic and proteomic signatures define clinical outcomes in severe COVID-19
Document Type
article
Author
Mustafa Buyukozkan; Sergio Alvarez-Mulett; Alexandra C. Racanelli; Frank Schmidt; Richa Batra; Katherine L. Hoffman; Hina Sarwath; Rudolf Engelke; Luis Gomez-Escobar; Will Simmons; Elisa Benedetti; Kelsey Chetnik; Guoan Zhang; Edward Schenck; Karsten Suhre; Justin J. Choi; Zhen Zhao; Sabrina Racine-Brzostek; He S. Yang; Mary E. Choi; Augustine M.K. Choi; Soo Jung Cho; Jan Krumsiek
Source
iScience, Vol 25, Iss 7, Pp 104612- (2022)
Subject
Language
English
ISSN
2589-0042
Abstract
Summary: The coronavirus disease-19 (COVID-19) pandemic has ravaged global healthcare with previously unseen levels of morbidity and mortality. In this study, we performed large-scale integrative multi-omics analyses of serum obtained from COVID-19 patients with the goal of uncovering novel pathogenic complexities of this disease and identifying molecular signatures that predict clinical outcomes. We assembled a network of protein-metabolite interactions through targeted metabolomic and proteomic profiling in 330 COVID-19 patients compared to 97 non-COVID, hospitalized controls. Our network identified distinct protein-metabolite cross talk related to immune modulation, energy and nucleotide metabolism, vascular homeostasis, and collagen catabolism. Additionally, our data linked multiple proteins and metabolites to clinical indices associated with long-term mortality and morbidity. Finally, we developed a novel composite outcome measure for COVID-19 disease severity based on metabolomics data. The model predicts severe disease with a concordance index of around 0.69, and shows high predictive power of 0.83–0.93 in two independent datasets.