tetano
Editor, Senior Moderator
iScience
. 2022 Jun 17;104612.
doi: 10.1016/j.isci.2022.104612. Online ahead of print.
Integrative Metabolomic and Proteomic Signatures Define Clinical Outcomes in Severe COVID-19
Mustafa Buyukozkan[SUP] 1 2 [/SUP], Sergio Alvarez-Mulett[SUP] 3 [/SUP], Alexandra C Racanelli[SUP] 3 [/SUP], Frank Schmidt[SUP] 4 [/SUP], Richa Batra[SUP] 1 2 [/SUP], Katherine L Hoffman[SUP] 5 [/SUP], Hina Sarwath[SUP] 4 [/SUP], Rudolf Engelke[SUP] 4 [/SUP], Luis Gomez-Escobar[SUP] 3 [/SUP], Will Simmons[SUP] 5 [/SUP], Elisa Benedetti[SUP] 1 2 [/SUP], Kelsey Chetnik[SUP] 1 2 [/SUP], Guoan Zhang[SUP] 6 [/SUP], Edward Schenck[SUP] 3 [/SUP], Karsten Suhre[SUP] 7 [/SUP], Justin J Choi[SUP] 8 [/SUP], Zhen Zhao[SUP] 9 [/SUP], Sabrina Racine-Brzostek[SUP] 9 [/SUP], He S Yang[SUP] 9 [/SUP], Mary E Choi[SUP] 10 [/SUP], Augustine M K Choi[SUP] 3 [/SUP], Soo Jung Cho[SUP] 3 [/SUP], Jan Krumsiek[SUP] 1 2 [/SUP]
Affiliations
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.
. 2022 Jun 17;104612.
doi: 10.1016/j.isci.2022.104612. Online ahead of print.
Integrative Metabolomic and Proteomic Signatures Define Clinical Outcomes in Severe COVID-19
Mustafa Buyukozkan[SUP] 1 2 [/SUP], Sergio Alvarez-Mulett[SUP] 3 [/SUP], Alexandra C Racanelli[SUP] 3 [/SUP], Frank Schmidt[SUP] 4 [/SUP], Richa Batra[SUP] 1 2 [/SUP], Katherine L Hoffman[SUP] 5 [/SUP], Hina Sarwath[SUP] 4 [/SUP], Rudolf Engelke[SUP] 4 [/SUP], Luis Gomez-Escobar[SUP] 3 [/SUP], Will Simmons[SUP] 5 [/SUP], Elisa Benedetti[SUP] 1 2 [/SUP], Kelsey Chetnik[SUP] 1 2 [/SUP], Guoan Zhang[SUP] 6 [/SUP], Edward Schenck[SUP] 3 [/SUP], Karsten Suhre[SUP] 7 [/SUP], Justin J Choi[SUP] 8 [/SUP], Zhen Zhao[SUP] 9 [/SUP], Sabrina Racine-Brzostek[SUP] 9 [/SUP], He S Yang[SUP] 9 [/SUP], Mary E Choi[SUP] 10 [/SUP], Augustine M K Choi[SUP] 3 [/SUP], Soo Jung Cho[SUP] 3 [/SUP], Jan Krumsiek[SUP] 1 2 [/SUP]
Affiliations
- PMID: 35756895
- PMCID: PMC9212983
- DOI: 10.1016/j.isci.2022.104612
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.