Integrative metabolomic and proteomic signatures define clinical outcomes in severe COVID-19
Mustafa Büyüközkan, Sergio Alvarez-Mulett, Alexandra C. Racanelli, Frank Schmidt, Richa Batra, Katherine L. Hoffman, Hina Sarwath, Rudolf Engelke, Luis G. Gómez-Escobar, Will Simmons, Elisa Benedetti, Kelsey Chetnik, Guoan Zhang, Edward J. 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
Abstract
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.