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Development of a multiomics model for identification of predictive biomarkers for COVID-19 severity: a retrospective cohort study

BACKGROUND: COVID-19 is a multi-system disorder with high variability in clinical outcomes among patients who are admitted to hospital. Although some cytokines such as interleukin (IL)-6 are believed to be associated with severity, there are no early biomarkers that can reliably predict patients who...

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Autores principales: Byeon, Seul Kee, Madugundu, Anil K, Garapati, Kishore, Ramarajan, Madan Gopal, Saraswat, Mayank, Kumar-M, Praveen, Hughes, Travis, Shah, Rameen, Patnaik, Mrinal M, Chia, Nicholas, Ashrafzadeh-Kian, Susan, Yao, Joseph D, Pritt, Bobbi S, Cattaneo, Roberto, Salama, Mohamed E, Zenka, Roman M, Kipp, Benjamin R, Grebe, Stefan K G, Singh, Ravinder J, Sadighi Akha, Amir A, Algeciras-Schimnich, Alicia, Dasari, Surendra, Olson, Janet E, Walsh, Jesse R, Venkatakrishnan, A J, Jenkinson, Garrett, O'Horo, John C, Badley, Andrew D, Pandey, Akhilesh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Author(s). Published by Elsevier Ltd. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9273185/
https://www.ncbi.nlm.nih.gov/pubmed/35835712
http://dx.doi.org/10.1016/S2589-7500(22)00112-1
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author Byeon, Seul Kee
Madugundu, Anil K
Garapati, Kishore
Ramarajan, Madan Gopal
Saraswat, Mayank
Kumar-M, Praveen
Hughes, Travis
Shah, Rameen
Patnaik, Mrinal M
Chia, Nicholas
Ashrafzadeh-Kian, Susan
Yao, Joseph D
Pritt, Bobbi S
Cattaneo, Roberto
Salama, Mohamed E
Zenka, Roman M
Kipp, Benjamin R
Grebe, Stefan K G
Singh, Ravinder J
Sadighi Akha, Amir A
Algeciras-Schimnich, Alicia
Dasari, Surendra
Olson, Janet E
Walsh, Jesse R
Venkatakrishnan, A J
Jenkinson, Garrett
O'Horo, John C
Badley, Andrew D
Pandey, Akhilesh
author_facet Byeon, Seul Kee
Madugundu, Anil K
Garapati, Kishore
Ramarajan, Madan Gopal
Saraswat, Mayank
Kumar-M, Praveen
Hughes, Travis
Shah, Rameen
Patnaik, Mrinal M
Chia, Nicholas
Ashrafzadeh-Kian, Susan
Yao, Joseph D
Pritt, Bobbi S
Cattaneo, Roberto
Salama, Mohamed E
Zenka, Roman M
Kipp, Benjamin R
Grebe, Stefan K G
Singh, Ravinder J
Sadighi Akha, Amir A
Algeciras-Schimnich, Alicia
Dasari, Surendra
Olson, Janet E
Walsh, Jesse R
Venkatakrishnan, A J
Jenkinson, Garrett
O'Horo, John C
Badley, Andrew D
Pandey, Akhilesh
author_sort Byeon, Seul Kee
collection PubMed
description BACKGROUND: COVID-19 is a multi-system disorder with high variability in clinical outcomes among patients who are admitted to hospital. Although some cytokines such as interleukin (IL)-6 are believed to be associated with severity, there are no early biomarkers that can reliably predict patients who are more likely to have adverse outcomes. Thus, it is crucial to discover predictive markers of serious complications. METHODS: In this retrospective cohort study, we analysed samples from 455 participants with COVID-19 who had had a positive SARS-CoV-2 RT-PCR result between April 14, 2020, and Dec 1, 2020 and who had visited one of three Mayo Clinic sites in the USA (Minnesota, Arizona, or Florida) in the same period. These participants were assigned to three subgroups depending on disease severity as defined by the WHO ordinal scale of clinical improvement (outpatient, severe, or critical). Our control cohort comprised of 182 anonymised age-matched and sex-matched plasma samples that were available from the Mayo Clinic Biorepository and banked before the COVID-19 pandemic. We did a deep profiling of circulatory cytokines and other proteins, lipids, and metabolites from both cohorts. Most patient samples were collected before, or around the time of, hospital admission, representing ideal samples for predictive biomarker discovery. We used proximity extension assays to quantify cytokines and circulatory proteins and tandem mass spectrometry to measure lipids and metabolites. Biomarker discovery was done by applying an AutoGluon-tabular classifier to a multiomics dataset, producing a stacked ensemble of cutting-edge machine learning algorithms. Global proteomics and glycoproteomics on a subset of patient samples with matched pre-COVID-19 plasma samples was also done. FINDINGS: We quantified 1463 cytokines and circulatory proteins, along with 902 lipids and 1018 metabolites. By developing a machine-learning-based prediction model, a set of 102 biomarkers, which predicted severe and clinical COVID-19 outcomes better than the traditional set of cytokines, were discovered. These predictive biomarkers included several novel cytokines and other proteins, lipids, and metabolites. For example, altered amounts of C-type lectin domain family 6 member A (CLEC6A), ether phosphatidylethanolamine (P-18:1/18:1), and 2-hydroxydecanoate, as reported here, have not previously been associated with severity in COVID-19. Patient samples with matched pre-COVID-19 plasma samples showed similar trends in muti-omics signatures along with differences in glycoproteomics profile. INTERPRETATION: A multiomic molecular signature in the plasma of patients with COVID-19 before being admitted to hospital can be exploited to predict a more severe course of disease. Machine learning approaches can be applied to highly complex and multidimensional profiling data to reveal novel signatures of clinical use. The absence of validation in an independent cohort remains a major limitation of the study. FUNDING: Eric and Wendy Schmidt.
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spelling pubmed-92731852022-07-11 Development of a multiomics model for identification of predictive biomarkers for COVID-19 severity: a retrospective cohort study Byeon, Seul Kee Madugundu, Anil K Garapati, Kishore Ramarajan, Madan Gopal Saraswat, Mayank Kumar-M, Praveen Hughes, Travis Shah, Rameen Patnaik, Mrinal M Chia, Nicholas Ashrafzadeh-Kian, Susan Yao, Joseph D Pritt, Bobbi S Cattaneo, Roberto Salama, Mohamed E Zenka, Roman M Kipp, Benjamin R Grebe, Stefan K G Singh, Ravinder J Sadighi Akha, Amir A Algeciras-Schimnich, Alicia Dasari, Surendra Olson, Janet E Walsh, Jesse R Venkatakrishnan, A J Jenkinson, Garrett O'Horo, John C Badley, Andrew D Pandey, Akhilesh Lancet Digit Health Articles BACKGROUND: COVID-19 is a multi-system disorder with high variability in clinical outcomes among patients who are admitted to hospital. Although some cytokines such as interleukin (IL)-6 are believed to be associated with severity, there are no early biomarkers that can reliably predict patients who are more likely to have adverse outcomes. Thus, it is crucial to discover predictive markers of serious complications. METHODS: In this retrospective cohort study, we analysed samples from 455 participants with COVID-19 who had had a positive SARS-CoV-2 RT-PCR result between April 14, 2020, and Dec 1, 2020 and who had visited one of three Mayo Clinic sites in the USA (Minnesota, Arizona, or Florida) in the same period. These participants were assigned to three subgroups depending on disease severity as defined by the WHO ordinal scale of clinical improvement (outpatient, severe, or critical). Our control cohort comprised of 182 anonymised age-matched and sex-matched plasma samples that were available from the Mayo Clinic Biorepository and banked before the COVID-19 pandemic. We did a deep profiling of circulatory cytokines and other proteins, lipids, and metabolites from both cohorts. Most patient samples were collected before, or around the time of, hospital admission, representing ideal samples for predictive biomarker discovery. We used proximity extension assays to quantify cytokines and circulatory proteins and tandem mass spectrometry to measure lipids and metabolites. Biomarker discovery was done by applying an AutoGluon-tabular classifier to a multiomics dataset, producing a stacked ensemble of cutting-edge machine learning algorithms. Global proteomics and glycoproteomics on a subset of patient samples with matched pre-COVID-19 plasma samples was also done. FINDINGS: We quantified 1463 cytokines and circulatory proteins, along with 902 lipids and 1018 metabolites. By developing a machine-learning-based prediction model, a set of 102 biomarkers, which predicted severe and clinical COVID-19 outcomes better than the traditional set of cytokines, were discovered. These predictive biomarkers included several novel cytokines and other proteins, lipids, and metabolites. For example, altered amounts of C-type lectin domain family 6 member A (CLEC6A), ether phosphatidylethanolamine (P-18:1/18:1), and 2-hydroxydecanoate, as reported here, have not previously been associated with severity in COVID-19. Patient samples with matched pre-COVID-19 plasma samples showed similar trends in muti-omics signatures along with differences in glycoproteomics profile. INTERPRETATION: A multiomic molecular signature in the plasma of patients with COVID-19 before being admitted to hospital can be exploited to predict a more severe course of disease. Machine learning approaches can be applied to highly complex and multidimensional profiling data to reveal novel signatures of clinical use. The absence of validation in an independent cohort remains a major limitation of the study. FUNDING: Eric and Wendy Schmidt. The Author(s). Published by Elsevier Ltd. 2022-09 2022-07-11 /pmc/articles/PMC9273185/ /pubmed/35835712 http://dx.doi.org/10.1016/S2589-7500(22)00112-1 Text en © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Articles
Byeon, Seul Kee
Madugundu, Anil K
Garapati, Kishore
Ramarajan, Madan Gopal
Saraswat, Mayank
Kumar-M, Praveen
Hughes, Travis
Shah, Rameen
Patnaik, Mrinal M
Chia, Nicholas
Ashrafzadeh-Kian, Susan
Yao, Joseph D
Pritt, Bobbi S
Cattaneo, Roberto
Salama, Mohamed E
Zenka, Roman M
Kipp, Benjamin R
Grebe, Stefan K G
Singh, Ravinder J
Sadighi Akha, Amir A
Algeciras-Schimnich, Alicia
Dasari, Surendra
Olson, Janet E
Walsh, Jesse R
Venkatakrishnan, A J
Jenkinson, Garrett
O'Horo, John C
Badley, Andrew D
Pandey, Akhilesh
Development of a multiomics model for identification of predictive biomarkers for COVID-19 severity: a retrospective cohort study
title Development of a multiomics model for identification of predictive biomarkers for COVID-19 severity: a retrospective cohort study
title_full Development of a multiomics model for identification of predictive biomarkers for COVID-19 severity: a retrospective cohort study
title_fullStr Development of a multiomics model for identification of predictive biomarkers for COVID-19 severity: a retrospective cohort study
title_full_unstemmed Development of a multiomics model for identification of predictive biomarkers for COVID-19 severity: a retrospective cohort study
title_short Development of a multiomics model for identification of predictive biomarkers for COVID-19 severity: a retrospective cohort study
title_sort development of a multiomics model for identification of predictive biomarkers for covid-19 severity: a retrospective cohort study
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9273185/
https://www.ncbi.nlm.nih.gov/pubmed/35835712
http://dx.doi.org/10.1016/S2589-7500(22)00112-1
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