TY - JOUR
T1 - Untargeted saliva metabolomics by liquid chromatography-Mass spectrometry reveals markers of COVID-19 severity
AU - Frampas, Cecile F.
AU - Longman, Katie
AU - Spick, Matt
AU - Lewis, Holly May
AU - Costa, Catia D.S.
AU - Stewart, Alex
AU - Dunn-Walters, Deborah
AU - Greener, Danni
AU - Evetts, George
AU - Skene, Debra J.
AU - Trivedi, Drupad
AU - Pitt, Andy
AU - Hollywood, Katherine
AU - Barran, Perdita
AU - Bailey, Melanie J.
N1 - Publisher Copyright:
© 2022 Frampas et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2022/9/22
Y1 - 2022/9/22
N2 - 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.
AB - 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.
KW - Amino Acids/metabolism
KW - Biomarkers/metabolism
KW - C-Reactive Protein/metabolism
KW - COVID-19/diagnosis
KW - COVID-19 Testing
KW - Chromatography, Liquid/methods
KW - Humans
KW - Mass Spectrometry/methods
KW - Metabolomics/methods
KW - Pandemics
KW - Saliva/metabolism
UR - http://www.scopus.com/inward/record.url?scp=85138354425&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0274967
DO - 10.1371/journal.pone.0274967
M3 - Article
C2 - 36137157
AN - SCOPUS:85138354425
SN - 1932-6203
VL - 17
JO - PLoS ONE
JF - PLoS ONE
IS - 9
M1 - e0274967
ER -