Integrative metabolomic and proteomic signatures define clinical outcomes in severe COVID-19
Summary: 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 pathogen...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-07-01
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| Series: | iScience |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004222008847 |
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| author | 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. Choi Augustine M.K. Choi Soo Jung Cho Jan Krumsiek |
| author_facet | 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. Choi Augustine M.K. Choi Soo Jung Cho Jan Krumsiek |
| author_sort | Mustafa Buyukozkan |
| collection | DOAJ |
| description | Summary: 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. |
| first_indexed | 2024-12-12T13:47:48Z |
| format | Article |
| id | doaj.art-a62f77fb3df5497ebf2ec8150d80ce78 |
| institution | Directory Open Access Journal |
| issn | 2589-0042 |
| language | English |
| last_indexed | 2024-12-12T13:47:48Z |
| publishDate | 2022-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj.art-a62f77fb3df5497ebf2ec8150d80ce782022-12-22T00:22:38ZengElsevieriScience2589-00422022-07-01257104612Integrative metabolomic and proteomic signatures define clinical outcomes in severe COVID-19Mustafa Buyukozkan0Sergio Alvarez-Mulett1Alexandra C. Racanelli2Frank Schmidt3Richa Batra4Katherine L. Hoffman5Hina Sarwath6Rudolf Engelke7Luis Gomez-Escobar8Will Simmons9Elisa Benedetti10Kelsey Chetnik11Guoan Zhang12Edward Schenck13Karsten Suhre14Justin J. Choi15Zhen Zhao16Sabrina Racine-Brzostek17He S. Yang18Mary E. Choi19Augustine M.K. Choi20Soo Jung Cho21Jan Krumsiek22Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA; Meyer Cancer Center and Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USADepartment of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USADepartment of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USAProteomics Core, Weill Cornell Medicine – Qatar, Doha, QatarDepartment of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA; Meyer Cancer Center and Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USADepartment of Population Health Sciences, Division of Biostatistics, Weill Cornell Medicine, New York, NY, USAProteomics Core, Weill Cornell Medicine – Qatar, Doha, QatarProteomics Core, Weill Cornell Medicine – Qatar, Doha, QatarDepartment of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USADepartment of Population Health Sciences, Division of Biostatistics, Weill Cornell Medicine, New York, NY, USADepartment of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA; Meyer Cancer Center and Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USADepartment of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA; Meyer Cancer Center and Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USAProteomics and Metabolomics Core Facility, Weill Cornell Medicine, New York, NY, USADepartment of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USADepartment of Physiology and Biophysics, Weill Cornell Medicine – Qatar, Education City, Doha 24144, QatarDepartment of Medicine, Division of General Internal Medicine, Weill Cornell Medicine, New York, NY, USADepartment of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USADepartment of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USADepartment of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USADivision of Nephrology and Hypertension, Joan and Sanford I. Weill Department of Medicine, New York, NY, USADepartment of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USADepartment of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA; Corresponding authorDepartment of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA; Meyer Cancer Center and Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA; Corresponding authorSummary: 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.http://www.sciencedirect.com/science/article/pii/S2589004222008847Biological sciencesClinical findingHuman metabolismMedicinePhysiology |
| spellingShingle | 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. Choi Augustine M.K. Choi Soo Jung Cho Jan Krumsiek Integrative metabolomic and proteomic signatures define clinical outcomes in severe COVID-19 iScience Biological sciences Clinical finding Human metabolism Medicine Physiology |
| title | Integrative metabolomic and proteomic signatures define clinical outcomes in severe COVID-19 |
| title_full | Integrative metabolomic and proteomic signatures define clinical outcomes in severe COVID-19 |
| title_fullStr | Integrative metabolomic and proteomic signatures define clinical outcomes in severe COVID-19 |
| title_full_unstemmed | Integrative metabolomic and proteomic signatures define clinical outcomes in severe COVID-19 |
| title_short | Integrative metabolomic and proteomic signatures define clinical outcomes in severe COVID-19 |
| title_sort | integrative metabolomic and proteomic signatures define clinical outcomes in severe covid 19 |
| topic | Biological sciences Clinical finding Human metabolism Medicine Physiology |
| url | http://www.sciencedirect.com/science/article/pii/S2589004222008847 |
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