Untargeted saliva metabolomics by liquid chromatography—Mass spectrometry reveals markers of COVID-19 severity
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...
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creator | 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 |
description | 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. 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. 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. 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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S ; Stewart, Alex ; Dunn-Walters, Deborah ; Greener, Danni ; Evetts, George ; Skene, Debra J ; Trivedi, Drupad ; Pitt, Andy ; Hollywood, Katherine ; Barran, Perdita ; Bailey, Melanie J</creator><creatorcontrib>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</creatorcontrib><description>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. 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. 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. 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.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0274967</identifier><language>eng</language><publisher>San Francisco: Public Library of Science</publisher><subject>Agreements ; Amino acids ; Biological markers ; Biology and Life Sciences ; Biomarkers ; C-reactive protein ; Chromatography ; Consent ; Coronaviruses ; COVID-19 ; COVID-19 vaccines ; Diagnosis ; Discriminant analysis ; Global health ; Identification and classification ; Infections ; Liquid chromatography ; Markers ; Mass spectrometry ; Mass spectroscopy ; Medical research ; Medicine and Health Sciences ; Metabolism ; Metabolites ; Metabolomics ; Methods ; Oxygen ; Oxygen content ; Pandemics ; Patients ; Physical Sciences ; Polymerase chain reaction ; Public health ; Research and Analysis Methods ; Respiration ; Respiratory rate ; Saliva ; Salivary glands ; Sampling ; Scientific imaging ; secretions ; Severe acute respiratory syndrome coronavirus 2 ; Spectroscopy ; Testing ; Vaccines</subject><ispartof>PloS one, 2022-09, Vol.17 (9), p.e0274967-e0274967</ispartof><rights>COPYRIGHT 2022 Public Library of Science</rights><rights>2022 Frampas et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2022 Frampas et al 2022 Frampas et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c669t-6b7a9d4fcfc3054faf8f2b42e7318fc4663bdaf4e99656712e799780152c68153</citedby><cites>FETCH-LOGICAL-c669t-6b7a9d4fcfc3054faf8f2b42e7318fc4663bdaf4e99656712e799780152c68153</cites><orcidid>0000-0002-9417-6511 ; 0000-0001-8202-6180</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9498978/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9498978/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79343,79344</link.rule.ids></links><search><creatorcontrib>Frampas, Cecile F</creatorcontrib><creatorcontrib>Longman, Katie</creatorcontrib><creatorcontrib>Spick, Matt</creatorcontrib><creatorcontrib>Lewis, Holly-May</creatorcontrib><creatorcontrib>Costa, Catia D. S</creatorcontrib><creatorcontrib>Stewart, Alex</creatorcontrib><creatorcontrib>Dunn-Walters, Deborah</creatorcontrib><creatorcontrib>Greener, Danni</creatorcontrib><creatorcontrib>Evetts, George</creatorcontrib><creatorcontrib>Skene, Debra J</creatorcontrib><creatorcontrib>Trivedi, Drupad</creatorcontrib><creatorcontrib>Pitt, Andy</creatorcontrib><creatorcontrib>Hollywood, Katherine</creatorcontrib><creatorcontrib>Barran, Perdita</creatorcontrib><creatorcontrib>Bailey, Melanie J</creatorcontrib><title>Untargeted saliva metabolomics by liquid chromatography—Mass spectrometry reveals markers of COVID-19 severity</title><title>PloS one</title><description>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. 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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. 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.</description><subject>Agreements</subject><subject>Amino acids</subject><subject>Biological markers</subject><subject>Biology and Life Sciences</subject><subject>Biomarkers</subject><subject>C-reactive protein</subject><subject>Chromatography</subject><subject>Consent</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>COVID-19 vaccines</subject><subject>Diagnosis</subject><subject>Discriminant analysis</subject><subject>Global health</subject><subject>Identification and classification</subject><subject>Infections</subject><subject>Liquid chromatography</subject><subject>Markers</subject><subject>Mass spectrometry</subject><subject>Mass spectroscopy</subject><subject>Medical research</subject><subject>Medicine and Health Sciences</subject><subject>Metabolism</subject><subject>Metabolites</subject><subject>Metabolomics</subject><subject>Methods</subject><subject>Oxygen</subject><subject>Oxygen content</subject><subject>Pandemics</subject><subject>Patients</subject><subject>Physical Sciences</subject><subject>Polymerase chain reaction</subject><subject>Public health</subject><subject>Research and Analysis Methods</subject><subject>Respiration</subject><subject>Respiratory rate</subject><subject>Saliva</subject><subject>Salivary glands</subject><subject>Sampling</subject><subject>Scientific imaging</subject><subject>secretions</subject><subject>Severe acute respiratory syndrome coronavirus 2</subject><subject>Spectroscopy</subject><subject>Testing</subject><subject>Vaccines</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><sourceid>DOA</sourceid><recordid>eNqNk9-L1DAQx4soeJ7-B4IFQfRh1yZNk-ZFONZfCycL6t1rSNNJN2fb9JJ0cd_8I_wL_UvM3la5yj1IHhIyn_lmZjKTJE9RtkQ5Q6-v7Oh62S4H28Myw4xwyu4lJ4jneEFxlt-_dX6YPPL-KsuKvKT0JBku-iBdAwHq1MvW7GTaQZCVbW1nlE-rfdqa69HUqdo628lgGyeH7f7Xj5-fpPepH0CFaIDg9qmDHcjWp51038D51Op0tblcv10gnvpocybsHycPdGTgybSfJhfv331dfVycbz6sV2fnC0UpDwtaMclropVWeVYQLXWpcUUwsByVWhFK86qWmgDntKAMRQPnrMxQgRUtUZGfJs-OukNrvZgq5AVmiHJEspJHYn0kaiuvxOBMDHsvrDTi5sK6RkgXjGpBYIkRKXFFMXDCaiZLpCWUlS5wjTTPo9ab6bWx6qBW0Acn25no3NKbrWjsTnDCyxh3FHg5CTh7PYIPojNeQdvKHux4EzejZU7YIbPn_6B3ZzdRjYwJmF7b-K46iIozhngsVFSL1PIOKq4a4vfHdtIm3s8cXs0cIhPge2jk6L1Yf_n8_-zmcs6-uMVuYxuFrbftGIzt_RwkR1A5670D_bfIKBOHafhTDXGYBjFNQ_4b3rT-yQ</recordid><startdate>20220922</startdate><enddate>20220922</enddate><creator>Frampas, Cecile F</creator><creator>Longman, Katie</creator><creator>Spick, Matt</creator><creator>Lewis, Holly-May</creator><creator>Costa, Catia D. 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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. 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. 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. 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.</abstract><cop>San Francisco</cop><pub>Public Library of Science</pub><doi>10.1371/journal.pone.0274967</doi><tpages>e0274967</tpages><orcidid>https://orcid.org/0000-0002-9417-6511</orcidid><orcidid>https://orcid.org/0000-0001-8202-6180</orcidid><oa>free_for_read</oa></addata></record> |
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recordid | cdi_plos_journals_2716914089 |
source | Public Library of Science (PLoS) Journals Open Access; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central; Free Full-Text Journals in Chemistry |
subjects | Agreements Amino acids Biological markers Biology and Life Sciences Biomarkers C-reactive protein Chromatography Consent Coronaviruses COVID-19 COVID-19 vaccines Diagnosis Discriminant analysis Global health Identification and classification Infections Liquid chromatography Markers Mass spectrometry Mass spectroscopy Medical research Medicine and Health Sciences Metabolism Metabolites Metabolomics Methods Oxygen Oxygen content Pandemics Patients Physical Sciences Polymerase chain reaction Public health Research and Analysis Methods Respiration Respiratory rate Saliva Salivary glands Sampling Scientific imaging secretions Severe acute respiratory syndrome coronavirus 2 Spectroscopy Testing Vaccines |
title | Untargeted saliva metabolomics by liquid chromatography—Mass spectrometry reveals markers of COVID-19 severity |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-03T05%3A25%3A32IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Untargeted%20saliva%20metabolomics%20by%20liquid%20chromatography%E2%80%94Mass%20spectrometry%20reveals%20markers%20of%20COVID-19%20severity&rft.jtitle=PloS%20one&rft.au=Frampas,%20Cecile%20F&rft.date=2022-09-22&rft.volume=17&rft.issue=9&rft.spage=e0274967&rft.epage=e0274967&rft.pages=e0274967-e0274967&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0274967&rft_dat=%3Cgale_plos_%3EA719015834%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2716914089&rft_id=info:pmid/&rft_galeid=A719015834&rft_doaj_id=oai_doaj_org_article_2a21482b62e947d7a81fae8bf52d1f93&rfr_iscdi=true |