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

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.

Automated summary

In this study, large-scale integrative multi-omics analyses were conducted on serum samples from COVID-19 patients to uncover the pathogenic complexities and identify molecular signatures that predict clinical outcomes. A network of protein-metabolite interactions was assembled, revealing cross talk related to immune modulation, energy and nucleotide metabolism, vascular homeostasis, and collagen catabolism. A novel composite outcome measure for COVID-19 disease severity based on metabolomics data was developed and showed high predictive power.