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

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.

SUBMITTER: Buyukozkan M

PROVIDER: S-EPMC9212983 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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

Buyukozkan Mustafa M Alvarez-Mulett Sergio S Racanelli Alexandra C AC Schmidt Frank F Batra Richa R Hoffman Katherine L KL Sarwath Hina H Engelke Rudolf R Gomez-Escobar Luis L Simmons Will W Benedetti Elisa E Chetnik Kelsey K Zhang Guoan G Schenck Edward E Suhre Karsten K Choi Justin J JJ Zhao Zhen Z Racine-Brzostek Sabrina S Yang He S HS Choi Mary E ME Choi Augustine M K AMK Cho Soo Jung SJ Krumsiek Jan J

iScience 20220617 7

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 profili ...[more]

PMID: 35756895

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Project description:COVID-19 is a heterogenous disease. Biomarker-based approaches may identify patients at risk for severe disease, who may be more likely to benefit from specific therapies. Our objective was to identify and validate a plasma protein signature for severe COVID-19.DesignProspective observational cohort study.SettingTwo hospitals in the United States.PatientsOne hundred sixty-seven hospitalized adults with COVID-19.InterventionNone.Measurements and main resultsWe measured 713 plasma proteins in 167 hospitalized patients with COVID-19 using a high-throughput platform. We classified patients as nonsevere versus severe COVID-19, defined as the need for high-flow nasal cannula, mechanical ventilation, extracorporeal membrane oxygenation, or death, at study entry and in 7-day intervals thereafter. We compared proteins measured at baseline between these two groups by logistic regression adjusting for age, sex, symptom duration, and comorbidities. We used lead proteins from dysregulated pathways as inputs for elastic net logistic regression to identify a parsimonious signature of severe disease and validated this signature in an external COVID-19 dataset. We tested whether the association between corticosteroid use and mortality varied by protein signature. One hundred ninety-four proteins were associated with severe COVID-19 at the time of hospital admission. Pathway analysis identified multiple pathways associated with inflammatory response and tissue repair programs. Elastic net logistic regression yielded a 14-protein signature that discriminated 90-day mortality in an external cohort with an area under the receiver-operator characteristic curve of 0.92 (95% CI, 0.88-0.95). Classifying patients based on the predicted risk from the signature identified a heterogeneous response to treatment with corticosteroids (p = 0.006).ConclusionsInpatients with COVID-19 express heterogeneous patterns of plasma proteins. We propose a 14-protein signature of disease severity that may have value in developing precision medicine approaches for COVID-19 pneumonia.

Dataset Information

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

Publications

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

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