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Organ and cell-specific biomarkers of Long-COVID identified with targeted proteomics and machine learning
BACKGROUND: Survivors of acute COVID-19 often suffer prolonged, diffuse symptoms post-infection, referred to as “Long-COVID”. A lack of Long-COVID biomarkers and pathophysiological mechanisms limits effective diagnosis, treatment and disease surveillance. We performed targeted proteomics and machine...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9942653/ https://www.ncbi.nlm.nih.gov/pubmed/36809921 http://dx.doi.org/10.1186/s10020-023-00610-z |
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author | Patel, Maitray A. Knauer, Michael J. Nicholson, Michael Daley, Mark Van Nynatten, Logan R. Cepinskas, Gediminas Fraser, Douglas D. |
author_facet | Patel, Maitray A. Knauer, Michael J. Nicholson, Michael Daley, Mark Van Nynatten, Logan R. Cepinskas, Gediminas Fraser, Douglas D. |
author_sort | Patel, Maitray A. |
collection | PubMed |
description | BACKGROUND: Survivors of acute COVID-19 often suffer prolonged, diffuse symptoms post-infection, referred to as “Long-COVID”. A lack of Long-COVID biomarkers and pathophysiological mechanisms limits effective diagnosis, treatment and disease surveillance. We performed targeted proteomics and machine learning analyses to identify novel blood biomarkers of Long-COVID. METHODS: A case–control study comparing the expression of 2925 unique blood proteins in Long-COVID outpatients versus COVID-19 inpatients and healthy control subjects. Targeted proteomics was accomplished with proximity extension assays, and machine learning was used to identify the most important proteins for identifying Long-COVID patients. Organ system and cell type expression patterns were identified with Natural Language Processing (NLP) of the UniProt Knowledgebase. RESULTS: Machine learning analysis identified 119 relevant proteins for differentiating Long-COVID outpatients (Bonferonni corrected P < 0.01). Protein combinations were narrowed down to two optimal models, with nine and five proteins each, and with both having excellent sensitivity and specificity for Long-COVID status (AUC = 1.00, F1 = 1.00). NLP expression analysis highlighted the diffuse organ system involvement in Long-COVID, as well as the involved cell types, including leukocytes and platelets, as key components associated with Long-COVID. CONCLUSIONS: Proteomic analysis of plasma from Long-COVID patients identified 119 highly relevant proteins and two optimal models with nine and five proteins, respectively. The identified proteins reflected widespread organ and cell type expression. Optimal protein models, as well as individual proteins, hold the potential for accurate diagnosis of Long-COVID and targeted therapeutics. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s10020-023-00610-z. |
format | Online Article Text |
id | pubmed-9942653 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-99426532023-02-22 Organ and cell-specific biomarkers of Long-COVID identified with targeted proteomics and machine learning Patel, Maitray A. Knauer, Michael J. Nicholson, Michael Daley, Mark Van Nynatten, Logan R. Cepinskas, Gediminas Fraser, Douglas D. Mol Med Research Article BACKGROUND: Survivors of acute COVID-19 often suffer prolonged, diffuse symptoms post-infection, referred to as “Long-COVID”. A lack of Long-COVID biomarkers and pathophysiological mechanisms limits effective diagnosis, treatment and disease surveillance. We performed targeted proteomics and machine learning analyses to identify novel blood biomarkers of Long-COVID. METHODS: A case–control study comparing the expression of 2925 unique blood proteins in Long-COVID outpatients versus COVID-19 inpatients and healthy control subjects. Targeted proteomics was accomplished with proximity extension assays, and machine learning was used to identify the most important proteins for identifying Long-COVID patients. Organ system and cell type expression patterns were identified with Natural Language Processing (NLP) of the UniProt Knowledgebase. RESULTS: Machine learning analysis identified 119 relevant proteins for differentiating Long-COVID outpatients (Bonferonni corrected P < 0.01). Protein combinations were narrowed down to two optimal models, with nine and five proteins each, and with both having excellent sensitivity and specificity for Long-COVID status (AUC = 1.00, F1 = 1.00). NLP expression analysis highlighted the diffuse organ system involvement in Long-COVID, as well as the involved cell types, including leukocytes and platelets, as key components associated with Long-COVID. CONCLUSIONS: Proteomic analysis of plasma from Long-COVID patients identified 119 highly relevant proteins and two optimal models with nine and five proteins, respectively. The identified proteins reflected widespread organ and cell type expression. Optimal protein models, as well as individual proteins, hold the potential for accurate diagnosis of Long-COVID and targeted therapeutics. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s10020-023-00610-z. BioMed Central 2023-02-21 /pmc/articles/PMC9942653/ /pubmed/36809921 http://dx.doi.org/10.1186/s10020-023-00610-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Article Patel, Maitray A. Knauer, Michael J. Nicholson, Michael Daley, Mark Van Nynatten, Logan R. Cepinskas, Gediminas Fraser, Douglas D. Organ and cell-specific biomarkers of Long-COVID identified with targeted proteomics and machine learning |
title | Organ and cell-specific biomarkers of Long-COVID identified with targeted proteomics and machine learning |
title_full | Organ and cell-specific biomarkers of Long-COVID identified with targeted proteomics and machine learning |
title_fullStr | Organ and cell-specific biomarkers of Long-COVID identified with targeted proteomics and machine learning |
title_full_unstemmed | Organ and cell-specific biomarkers of Long-COVID identified with targeted proteomics and machine learning |
title_short | Organ and cell-specific biomarkers of Long-COVID identified with targeted proteomics and machine learning |
title_sort | organ and cell-specific biomarkers of long-covid identified with targeted proteomics and machine learning |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9942653/ https://www.ncbi.nlm.nih.gov/pubmed/36809921 http://dx.doi.org/10.1186/s10020-023-00610-z |
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