Cross-Validation of Metabolic Phenotypes in SARS-CoV-2 Infected Subpopulations Using Targeted Liquid Chromatography-Mass Spectrometry (LC-MS)

Luke Whiley; Nathan G Lawler; Annie Xu Zeng; Alex Lee; Sung-Tong Chin; Maider Bizkarguenaga; Chiara Bruzzone; Nieves Embade; Julien Wist; Elaine Holmes; Oscar Millet; Jeremy K Nicholson; Nicola Gray

doi:10.1021/acs.jproteome.3c00797

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Cross-Validation of Metabolic Phenotypes in SARS-CoV-2 Infected Subpopulations Using Targeted Liquid Chromatography-Mass Spectrometry (LC-MS)

Journal article

Open access

Peer reviewed

Cross-Validation of Metabolic Phenotypes in SARS-CoV-2 Infected Subpopulations Using Targeted Liquid Chromatography-Mass Spectrometry (LC-MS)

Luke Whiley, Nathan G Lawler, Annie Xu Zeng, Alex Lee, Sung-Tong Chin, Maider Bizkarguenaga, Chiara Bruzzone, Nieves Embade, Julien Wist, Elaine Holmes, …

Journal of proteome research, Vol.23(4), pp.1313-1327

2024

DOI: https://doi.org/10.1021/acs.jproteome.3c00797

PMID: 38484742

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Abstract

COVID-19

metabolic phenotyping array

hypoxia

LC-MS

SARS-CoV-2

bile acids

metabolic phenotyping

TCA cycle

organic acids

oxidative stress

validation

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 ( = 20), noninfected healthy controls ( = 22) and COVID-19 disease-like symptoms but negative for SARS-CoV-2 infection ( = 22). (2) Spain: serum, SARS-CoV-2 positive ( = 33) and noninfected healthy controls ( = 39). Multivariate modeling using orthogonal projections to latent structures discriminant analyses (OPLS-DA) classified healthy controls from SARS-CoV-2 positive (Australia; = 0.17, ROC-AUC = 1; Spain = 0.20, ROC-AUC = 1). Univariate analyses revealed 23 significantly different ( < 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.

Details

Title: Cross-Validation of Metabolic Phenotypes in SARS-CoV-2 Infected Subpopulations Using Targeted Liquid Chromatography-Mass Spectrometry (LC-MS)
Authors/Creators: Luke Whiley - Centre for Computational and Systems Medicine, Health Futures Institute Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, WA 6150, Australia
Nathan G Lawler - Centre for Computational and Systems Medicine, Health Futures Institute Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, WA 6150, Australia
Annie Xu Zeng - Australian National Phenome Centre, Health Futures Institute Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, WA 6150, Australia
Alex Lee - Australian National Phenome Centre, Health Futures Institute Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, WA 6150, Australia
Sung-Tong Chin - Murdoch University, School of Medical, Molecular and Forensic Sciences
Maider Bizkarguenaga - Euskadiko Parke Teknologikoa
Chiara Bruzzone - Euskadiko Parke Teknologikoa
Nieves Embade - Euskadiko Parke Teknologikoa
Julien Wist - Chemistry Department, Universidad del Valle, Cali 76001, Colombia
Elaine Holmes - Imperial College London
Oscar Millet - Euskadiko Parke Teknologikoa
Jeremy K Nicholson - Imperial College London
Nicola Gray - Centre for Computational and Systems Medicine, Health Futures Institute Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, WA 6150, Australia
Publication Details: Journal of proteome research, Vol.23(4), pp.1313-1327
Publisher: ACS Publications
Identifiers: 991005648965707891
Murdoch Affiliation: Health Futures Institute; Centre for Computational and Systems Medicine; School of Medical, Molecular and Forensic Sciences
Language: English
Resource Type: Journal article

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Collaboration types: Domestic collaboration; International collaboration
Citation topics: 2 Chemistry; 2.211 Mass Spectrometry; 2.211.990 Metabolomics
Web Of Science research areas: Biochemical Research Methods
ESI research areas: Biology & Biochemistry