دورية أكاديمية
أعرض في EDS

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

التفاصيل البيبلوغرافية
العنوان: Untargeted saliva metabolomics by liquid chromatography-Mass spectrometry reveals markers of COVID-19 severity.
المؤلفون: Frampas CF; Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, United Kingdom., Longman K; Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, United Kingdom., Spick M; Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, United Kingdom., Lewis HM; Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, United Kingdom., Costa CDS; Surrey Ion Beam Centre, University of Surrey, Guildford, United Kingdom., Stewart A; Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom., Dunn-Walters D; Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom., Greener D; Frimley Park Hospital, Frimley Health NHS Trust, Camberley, United Kingdom., Evetts G; Frimley Park Hospital, Frimley Health NHS Trust, Camberley, United Kingdom., Skene DJ; Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom., Trivedi D; Manchester Institute of Biotechnology, University of Manchester, Manchester, United Kingdom., Pitt A; Manchester Institute of Biotechnology, University of Manchester, Manchester, United Kingdom., Hollywood K; Manchester Institute of Biotechnology, University of Manchester, Manchester, United Kingdom., Barran P; Manchester Institute of Biotechnology, University of Manchester, Manchester, United Kingdom., Bailey MJ; Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, United Kingdom.; Surrey Ion Beam Centre, University of Surrey, Guildford, United Kingdom.
المصدر: PloS one [PLoS One] 2022 Sep 22; Vol. 17 (9), pp. e0274967. Date of Electronic Publication: 2022 Sep 22 (Print Publication: 2022).
نوع المنشور: Journal Article; Research Support, Non-U.S. Gov't
اللغة: English
بيانات الدورية: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
أسماء مطبوعة: Original Publication: San Francisco, CA : Public Library of Science
مواضيع طبية MeSH: COVID-19*/diagnosis, Amino Acids/metabolism ; Biomarkers/metabolism ; C-Reactive Protein/metabolism ; COVID-19 Testing ; Chromatography, Liquid/methods ; Humans ; Mass Spectrometry/methods ; Metabolomics/methods ; Pandemics ; Saliva/metabolism
مستخلص: 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.
Competing Interests: The authors have declared that no competing interests exist.
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معلومات مُعتمدة: BB/T002212/1 United Kingdom BB_ Biotechnology and Biological Sciences Research Council
المشرفين على المادة: 0 (Amino Acids)
0 (Biomarkers)
9007-41-4 (C-Reactive Protein)
تواريخ الأحداث: Date Created: 20220922 Date Completed: 20220926 Latest Revision: 20221004
رمز التحديث: 20221213
مُعرف محوري في PubMed: PMC9498978
DOI: 10.1371/journal.pone.0274967
PMID: 36137157
قاعدة البيانات: MEDLINE
الوصف
تدمد:1932-6203
DOI:10.1371/journal.pone.0274967