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Untargeted saliva metabolomics by liquid chromatography—Mass spectrometry reveals markers of COVID-19 severity

BACKGROUND: The COVID-19 pandemic is likely to represent an ongoing global health issue given the potential for new variants, vaccine escape and the low likelihood of eliminating all reservoirs of the disease. Whilst diagnostic testing has progressed at a fast pace, the metabolic drivers of outcomes...

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Autores principales: Frampas, Cecile F., Longman, Katie, Spick, Matt, Lewis, Holly-May, Costa, Catia D. S., Stewart, Alex, Dunn-Walters, Deborah, Greener, Danni, Evetts, George, Skene, Debra J., Trivedi, Drupad, Pitt, Andy, Hollywood, Katherine, Barran, Perdita, Bailey, Melanie J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9498978/
https://www.ncbi.nlm.nih.gov/pubmed/36137157
http://dx.doi.org/10.1371/journal.pone.0274967
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author Frampas, Cecile F.
Longman, Katie
Spick, Matt
Lewis, Holly-May
Costa, Catia D. S.
Stewart, Alex
Dunn-Walters, Deborah
Greener, Danni
Evetts, George
Skene, Debra J.
Trivedi, Drupad
Pitt, Andy
Hollywood, Katherine
Barran, Perdita
Bailey, Melanie J.
author_facet Frampas, Cecile F.
Longman, Katie
Spick, Matt
Lewis, Holly-May
Costa, Catia D. S.
Stewart, Alex
Dunn-Walters, Deborah
Greener, Danni
Evetts, George
Skene, Debra J.
Trivedi, Drupad
Pitt, Andy
Hollywood, Katherine
Barran, Perdita
Bailey, Melanie J.
author_sort Frampas, Cecile F.
collection PubMed
description BACKGROUND: The COVID-19 pandemic is likely to represent an ongoing global health issue given the potential for new variants, vaccine escape and the low likelihood of eliminating all reservoirs of the disease. Whilst diagnostic testing has progressed at a fast pace, the metabolic drivers of outcomes–and whether markers can be found in different biofluids–are not well understood. Recent research has shown that serum metabolomics has potential for prognosis of disease progression. In a hospital setting, collection of saliva samples is more convenient for both staff and patients, and therefore offers an alternative sampling matrix to serum. METHODS: Saliva samples were collected from hospitalised patients with clinical suspicion of COVID-19, alongside clinical metadata. COVID-19 diagnosis was confirmed using RT-PCR testing, and COVID-19 severity was classified using clinical descriptors (respiratory rate, peripheral oxygen saturation score and C-reactive protein levels). Metabolites were extracted and analysed using high resolution liquid chromatography-mass spectrometry, and the resulting peak area matrix was analysed using multivariate techniques. RESULTS: Positive percent agreement of 1.00 between a partial least squares–discriminant analysis metabolomics model employing a panel of 6 features (5 of which were amino acids, one that could be identified by formula only) and the clinical diagnosis of COVID-19 severity was achieved. The negative percent agreement with the clinical severity diagnosis was also 1.00, leading to an area under receiver operating characteristics curve of 1.00 for the panel of features identified. CONCLUSIONS: In this exploratory work, we found that saliva metabolomics and in particular amino acids can be capable of separating high severity COVID-19 patients from low severity COVID-19 patients. This expands the atlas of COVID-19 metabolic dysregulation and could in future offer the basis of a quick and non-invasive means of sampling patients, intended to supplement existing clinical tests, with the goal of offering timely treatment to patients with potentially poor outcomes.
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spelling pubmed-94989782022-09-23 Untargeted saliva metabolomics by liquid chromatography—Mass spectrometry reveals markers of COVID-19 severity Frampas, Cecile F. Longman, Katie Spick, Matt Lewis, Holly-May Costa, Catia D. S. Stewart, Alex Dunn-Walters, Deborah Greener, Danni Evetts, George Skene, Debra J. Trivedi, Drupad Pitt, Andy Hollywood, Katherine Barran, Perdita Bailey, Melanie J. PLoS One Research Article BACKGROUND: The COVID-19 pandemic is likely to represent an ongoing global health issue given the potential for new variants, vaccine escape and the low likelihood of eliminating all reservoirs of the disease. Whilst diagnostic testing has progressed at a fast pace, the metabolic drivers of outcomes–and whether markers can be found in different biofluids–are not well understood. Recent research has shown that serum metabolomics has potential for prognosis of disease progression. In a hospital setting, collection of saliva samples is more convenient for both staff and patients, and therefore offers an alternative sampling matrix to serum. METHODS: Saliva samples were collected from hospitalised patients with clinical suspicion of COVID-19, alongside clinical metadata. COVID-19 diagnosis was confirmed using RT-PCR testing, and COVID-19 severity was classified using clinical descriptors (respiratory rate, peripheral oxygen saturation score and C-reactive protein levels). Metabolites were extracted and analysed using high resolution liquid chromatography-mass spectrometry, and the resulting peak area matrix was analysed using multivariate techniques. RESULTS: Positive percent agreement of 1.00 between a partial least squares–discriminant analysis metabolomics model employing a panel of 6 features (5 of which were amino acids, one that could be identified by formula only) and the clinical diagnosis of COVID-19 severity was achieved. The negative percent agreement with the clinical severity diagnosis was also 1.00, leading to an area under receiver operating characteristics curve of 1.00 for the panel of features identified. CONCLUSIONS: In this exploratory work, we found that saliva metabolomics and in particular amino acids can be capable of separating high severity COVID-19 patients from low severity COVID-19 patients. This expands the atlas of COVID-19 metabolic dysregulation and could in future offer the basis of a quick and non-invasive means of sampling patients, intended to supplement existing clinical tests, with the goal of offering timely treatment to patients with potentially poor outcomes. Public Library of Science 2022-09-22 /pmc/articles/PMC9498978/ /pubmed/36137157 http://dx.doi.org/10.1371/journal.pone.0274967 Text en © 2022 Frampas et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Frampas, Cecile F.
Longman, Katie
Spick, Matt
Lewis, Holly-May
Costa, Catia D. S.
Stewart, Alex
Dunn-Walters, Deborah
Greener, Danni
Evetts, George
Skene, Debra J.
Trivedi, Drupad
Pitt, Andy
Hollywood, Katherine
Barran, Perdita
Bailey, Melanie J.
Untargeted saliva metabolomics by liquid chromatography—Mass spectrometry reveals markers of COVID-19 severity
title Untargeted saliva metabolomics by liquid chromatography—Mass spectrometry reveals markers of COVID-19 severity
title_full Untargeted saliva metabolomics by liquid chromatography—Mass spectrometry reveals markers of COVID-19 severity
title_fullStr Untargeted saliva metabolomics by liquid chromatography—Mass spectrometry reveals markers of COVID-19 severity
title_full_unstemmed Untargeted saliva metabolomics by liquid chromatography—Mass spectrometry reveals markers of COVID-19 severity
title_short Untargeted saliva metabolomics by liquid chromatography—Mass spectrometry reveals markers of COVID-19 severity
title_sort untargeted saliva metabolomics by liquid chromatography—mass spectrometry reveals markers of covid-19 severity
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9498978/
https://www.ncbi.nlm.nih.gov/pubmed/36137157
http://dx.doi.org/10.1371/journal.pone.0274967
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