Imperial College London
Alternate

ProfessorElaineHolmes

Faculty of Medicine, Department of Metabolism, Digestion and Reproduction

Professor of Chemical Biology
 
 
 
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Contact

 

+44 (0)20 7594 3220elaine.holmes

 
 
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Location

 

661Sir Alexander Fleming BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Whiley:2024:10.1021/acs.jproteome.3c00797,
author = {Whiley, L and Lawler, NG and Zeng, AX and Lee, A and Chin, S-T and Bizkarguenaga, M and Bruzzone, C and Embade, N and Wist, J and Holmes, E and Millet, O and Nicholson, JK and Gray, N},
doi = {10.1021/acs.jproteome.3c00797},
journal = {J Proteome Res},
pages = {1313--1327},
title = {Cross-Validation of Metabolic Phenotypes in SARS-CoV-2 Infected Subpopulations Using Targeted Liquid Chromatography-Mass Spectrometry (LC-MS).},
url = {http://dx.doi.org/10.1021/acs.jproteome.3c00797},
volume = {23},
year = {2024}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - To ensure biological validity in metabolic phenotyping, findings must be replicated in independent sample sets. Targeted workflows have long been heralded as ideal platforms for such validation due to their robust quantitative capability. We evaluated the capability of liquid chromatography-mass spectrometry (LC-MS) assays targeting organic acids and bile acids to validate metabolic phenotypes of SARS-CoV-2 infection. Two independent sample sets were collected: (1) Australia: plasma, SARS-CoV-2 positive (n = 20), noninfected healthy controls (n = 22) and COVID-19 disease-like symptoms but negative for SARS-CoV-2 infection (n = 22). (2) Spain: serum, SARS-CoV-2 positive (n = 33) and noninfected healthy controls (n = 39). Multivariate modeling using orthogonal projections to latent structures discriminant analyses (OPLS-DA) classified healthy controls from SARS-CoV-2 positive (Australia; R2 = 0.17, ROC-AUC = 1; Spain R2 = 0.20, ROC-AUC = 1). Univariate analyses revealed 23 significantly different (p < 0.05) metabolites between healthy controls and SARS-CoV-2 positive individuals across both cohorts. Significant metabolites revealed consistent perturbations in cellular energy metabolism (pyruvic acid, and 2-oxoglutaric acid), oxidative stress (lactic acid, 2-hydroxybutyric acid), hypoxia (2-hydroxyglutaric acid, 5-aminolevulinic acid), liver activity (primary bile acids), and host-gut microbial cometabolism (hippuric acid, phenylpropionic acid, indole-3-propionic acid). These data support targeted LC-MS metabolic phenotyping workflows for biological validation in independent sample sets.
AU  - Whiley,L
AU  - Lawler,NG
AU  - Zeng,AX
AU  - Lee,A
AU  - Chin,S-T
AU  - Bizkarguenaga,M
AU  - Bruzzone,C
AU  - Embade,N
AU  - Wist,J
AU  - Holmes,E
AU  - Millet,O
AU  - Nicholson,JK
AU  - Gray,N
DO  - 10.1021/acs.jproteome.3c00797
EP  - 1327
PY  - 2024///
SP  - 1313
TI  - Cross-Validation of Metabolic Phenotypes in SARS-CoV-2 Infected Subpopulations Using Targeted Liquid Chromatography-Mass Spectrometry (LC-MS).
T2  - J Proteome Res
UR  - http://dx.doi.org/10.1021/acs.jproteome.3c00797
UR  - https://www.ncbi.nlm.nih.gov/pubmed/38484742
VL  - 23
ER  -
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