Cross-Validation of Metabolic Phenotypes in SARS-CoV-2 Infected Subpopulations Using Targeted Liquid Chromatography-Mass Spectrometry (LC-MS)
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.
Medienart: |
E-Artikel |
---|
Erscheinungsjahr: |
2024 |
---|---|
Erschienen: |
2024 |
Enthalten in: |
Zur Gesamtaufnahme - volume:23 |
---|---|
Enthalten in: |
Journal of proteome research - 23(2024), 4 vom: 05. Apr., Seite 1313-1327 |
Sprache: |
Englisch |
---|
Beteiligte Personen: |
Whiley, Luke [VerfasserIn] |
---|
Links: |
---|
Themen: |
Bile Acids and Salts |
---|
Anmerkungen: |
Date Completed 08.04.2024 Date Revised 11.04.2024 published: Print-Electronic Citation Status MEDLINE |
---|
doi: |
10.1021/acs.jproteome.3c00797 |
---|
funding: |
|
---|---|
Förderinstitution / Projekttitel: |
|
PPN (Katalog-ID): |
NLM369744284 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | NLM369744284 | ||
003 | DE-627 | ||
005 | 20240411232449.0 | ||
007 | cr uuu---uuuuu | ||
008 | 240315s2024 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.1021/acs.jproteome.3c00797 |2 doi | |
028 | 5 | 2 | |a pubmed24n1372.xml |
035 | |a (DE-627)NLM369744284 | ||
035 | |a (NLM)38484742 | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
100 | 1 | |a Whiley, Luke |e verfasserin |4 aut | |
245 | 1 | 0 | |a Cross-Validation of Metabolic Phenotypes in SARS-CoV-2 Infected Subpopulations Using Targeted Liquid Chromatography-Mass Spectrometry (LC-MS) |
264 | 1 | |c 2024 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a ƒaComputermedien |b c |2 rdamedia | ||
338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
500 | |a Date Completed 08.04.2024 | ||
500 | |a Date Revised 11.04.2024 | ||
500 | |a published: Print-Electronic | ||
500 | |a Citation Status MEDLINE | ||
520 | |a 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 | ||
650 | 4 | |a Journal Article | |
650 | 4 | |a COVID-19 | |
650 | 4 | |a LC-MS | |
650 | 4 | |a SARS-CoV-2 | |
650 | 4 | |a TCA cycle | |
650 | 4 | |a bile acids | |
650 | 4 | |a hypoxia | |
650 | 4 | |a metabolic phenotyping | |
650 | 4 | |a metabolic phenotyping array | |
650 | 4 | |a organic acids | |
650 | 4 | |a oxidative stress | |
650 | 4 | |a validation | |
650 | 7 | |a Bile Acids and Salts |2 NLM | |
700 | 1 | |a Lawler, Nathan G |e verfasserin |4 aut | |
700 | 1 | |a Zeng, Annie Xu |e verfasserin |4 aut | |
700 | 1 | |a Lee, Alex |e verfasserin |4 aut | |
700 | 1 | |a Chin, Sung-Tong |e verfasserin |4 aut | |
700 | 1 | |a Bizkarguenaga, Maider |e verfasserin |4 aut | |
700 | 1 | |a Bruzzone, Chiara |e verfasserin |4 aut | |
700 | 1 | |a Embade, Nieves |e verfasserin |4 aut | |
700 | 1 | |a Wist, Julien |e verfasserin |4 aut | |
700 | 1 | |a Holmes, Elaine |e verfasserin |4 aut | |
700 | 1 | |a Millet, Oscar |e verfasserin |4 aut | |
700 | 1 | |a Nicholson, Jeremy K |e verfasserin |4 aut | |
700 | 1 | |a Gray, Nicola |e verfasserin |4 aut | |
773 | 0 | 8 | |i Enthalten in |t Journal of proteome research |d 2002 |g 23(2024), 4 vom: 05. Apr., Seite 1313-1327 |w (DE-627)NLM124173470 |x 1535-3907 |7 nnns |
773 | 1 | 8 | |g volume:23 |g year:2024 |g number:4 |g day:05 |g month:04 |g pages:1313-1327 |
856 | 4 | 0 | |u http://dx.doi.org/10.1021/acs.jproteome.3c00797 |3 Volltext |
912 | |a GBV_USEFLAG_A | ||
912 | |a GBV_NLM | ||
951 | |a AR | ||
952 | |d 23 |j 2024 |e 4 |b 05 |c 04 |h 1313-1327 |