Metabolomic alterations in the plasma of patients with various clinical manifestations of COVID-19.
Asymptomatic infection
COVID-19
Long-term nucleic acid test positive
Metabolomics
SARS-CoV-2
Journal
Virology journal
ISSN: 1743-422X
Titre abrégé: Virol J
Pays: England
ID NLM: 101231645
Informations de publication
Date de publication:
28 Oct 2024
28 Oct 2024
Historique:
received:
16
05
2024
accepted:
27
09
2024
medline:
29
10
2024
pubmed:
29
10
2024
entrez:
29
10
2024
Statut:
epublish
Résumé
The metabolomic profiles of individuals with different clinical manifestations of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection have not been clearly characterized. We performed metabolomics analysis of 166 individuals, including 62 healthy controls, 16 individuals with asymptomatic SARS-CoV-2 infection, and 88 patients with moderate (n = 42) and severe (n = 46) symptomatic 2019 coronavirus disease (COVID-19; 17 with short-term and 34 with long-term nucleic-acid test positivity). By examining differential expression, we identified candidate metabolites associated with different SARS-CoV-2 infection presentations. Functional and machine learning analyses were performed to explore the metabolites' functions and verify their candidacy as biomarkers. A total of 417 metabolites were detected. We discovered 70 differentially expressed metabolites that may help differentiate asymptomatic infections from healthy controls and COVID-19 patients with different disease severity. Cyclamic acid and N-Acetylneuraminic Acid were identified to distinguish symptomatic infected patients and asymptomatic infected patients. Shikimic Acid, Glycyrrhetinic acid and 3-Hydroxybutyrate can supply significant insights for distinguishing short-term and long-term nucleic-acid test positivity. Metabolomic profiling may highlight novel biomarkers for the identification of individuals with asymptomatic SARS-CoV-2 infection and further our understanding of the molecular pathogenesis of COVID-19.
Sections du résumé
BACKGROUND
BACKGROUND
The metabolomic profiles of individuals with different clinical manifestations of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection have not been clearly characterized.
METHODS
METHODS
We performed metabolomics analysis of 166 individuals, including 62 healthy controls, 16 individuals with asymptomatic SARS-CoV-2 infection, and 88 patients with moderate (n = 42) and severe (n = 46) symptomatic 2019 coronavirus disease (COVID-19; 17 with short-term and 34 with long-term nucleic-acid test positivity). By examining differential expression, we identified candidate metabolites associated with different SARS-CoV-2 infection presentations. Functional and machine learning analyses were performed to explore the metabolites' functions and verify their candidacy as biomarkers.
RESULTS
RESULTS
A total of 417 metabolites were detected. We discovered 70 differentially expressed metabolites that may help differentiate asymptomatic infections from healthy controls and COVID-19 patients with different disease severity. Cyclamic acid and N-Acetylneuraminic Acid were identified to distinguish symptomatic infected patients and asymptomatic infected patients. Shikimic Acid, Glycyrrhetinic acid and 3-Hydroxybutyrate can supply significant insights for distinguishing short-term and long-term nucleic-acid test positivity.
CONCLUSION
CONCLUSIONS
Metabolomic profiling may highlight novel biomarkers for the identification of individuals with asymptomatic SARS-CoV-2 infection and further our understanding of the molecular pathogenesis of COVID-19.
Identifiants
pubmed: 39468659
doi: 10.1186/s12985-024-02523-7
pii: 10.1186/s12985-024-02523-7
doi:
Substances chimiques
Biomarkers
0
N-Acetylneuraminic Acid
GZP2782OP0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
266Subventions
Organisme : the Department of Science and Technology of Shaanxi Province
ID : 2020ZDXM2-SF-02
Organisme : the Department of Science and Technology of Shaanxi Province
ID : 2020ZDXM2-SF-02
Informations de copyright
© 2024. The Author(s).
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