Integrative metabolomic and proteomic signatures define clinical outcomes in severe COVID-19

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. ChoiAugustine M.K. Choi, Soo Jung Cho, Jan Krumsiek

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

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.

Original languageEnglish (US)
Article number104612
JournaliScience
Volume25
Issue number7
DOIs
StatePublished - Jul 15 2022

Keywords

  • Biological sciences
  • Clinical finding
  • Human metabolism
  • Medicine
  • Physiology

ASJC Scopus subject areas

  • General

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