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PubMed Publications| Keyword search (4,164 papers available) |  |
"Metabolomics" Keyword-tagged Publications:
| Title | Authors | PubMed ID | |
|---|---|---|---|
| 1 | Integrated metabolomics and metagenomics analysis identifies a unique signature characterizing metabolic syndrome | Wannaiampikul S; Lee B; Chen J; Prentice KJ; Ayansola R; Xu A; Santosa S; Pantopoulos K; Sweeney G; | 41794383 HKAP |
| 2 | Metabolomics 2023 workshop report: moving toward consensus on best QA/QC practices in LC-MS-based untargeted metabolomics | Mosley JD; Dunn WB; Kuligowski J; Lewis MR; Monge ME; Ulmer Holland C; Vuckovic D; Zanetti KA; Schock TB; | 38980450 CHEMBIOCHEM |
| 3 | Establishing a framework for best practices for quality assurance and quality control in untargeted metabolomics | Mosley JD; Schock TB; Beecher CW; Dunn WB; Kuligowski J; Lewis MR; Theodoridis G; Ulmer Holland CZ; Vuckovic D; Wilson ID; Zanetti KA; | 38345679 CHEMBIOCHEM |
| 4 | Metabolomics 2022 workshop report: state of QA/QC best practices in LC-MS-based untargeted metabolomics, informed through mQACC community engagement initiatives | Dunn WB; Kuligowski J; Lewis M; Mosley JD; Schock T; Ulmer Holland C; Zanetti KA; Vuckovic D; | 37940740 CHEMBIOCHEM |
| 5 | New metabolic signature for Chagas disease reveals sex steroid perturbation in humans and mice | Golizeh M; Nam J; Chatelain E; Jackson Y; Ohlund LB; Rasoolizadeh A; Camargo FV; Mahrouche L; Furtos A; Sleno L; Ndao M; | 36590505 CHEMBIOCHEM |
| 6 | Assessment of solid phase microextraction as a sample preparation tool for untargeted analysis of brain tissue using liquid chromatography-mass spectrometry | Reyes-Garcés N; Boyaci E; Gómez-Ríos GA; Olkowicz M; Monnin C; Bojko B; Vuckovic D; Pawliszyn J; | 33433374 CHEMBIOCHEM |
| 7 | Dissemination and analysis of the quality assurance (QA) and quality control (QC) practices of LC-MS based untargeted metabolomics practitioners | Evans AM; O' Donovan C; Playdon M; Beecher C; Beger RD; Bowden JA; Broadhurst D; Clish CB; Dasari S; Dunn WB; Griffin JL; Hartung T; Hsu PC; Huan T; Jans J; Jones CM; Kachman M; Kleensang A; Lewis MR; Monge ME; Mosley JD; Taylor E; Tayyari F; Theodoridis G; Torta F; Ubhi BK; Vuckovic D; | 33044703 CONCORDIA |
| 8 | Functional Characterization of Clinical Isolates of the Opportunistic Fungal Pathogen Aspergillus nidulans. | Bastos RW, Valero C, Silva LP, Schoen T, Drott M, Brauer V, Silva-Rocha R, Lind A, Steenwyk JL, Rokas A, Rodrigues F, Resendiz-Sharpe A, Lagrou K, Marcet-Houben M, Gabaldón T, McDonnell E, Reid I, Tsang A, Oakley BR, Loures FV, Almeida F, Huttenlocher A, Keller NP, Ries LNA, Goldman GH | 32269156 CSFG |
| 9 | Dexamethasone-Induced Perturbations in Tissue Metabolomics Revealed by Chemical Isotope Labeling LC-MS analysis | Dahabiyeh LA; Malkawi AK; Wang X; Colak D; Mujamammi AH; Sabi EM; Li L; Dasouki M; Abdel Rahman AM; | 31973046 CHEMBIOCHEM |
| 10 | Comparison of underivatized silica and zwitterionic sulfobetaine hydrophilic interaction liquid chromatography stationary phases for global metabolomics of human plasma | Sonnenberg RA; Naz S; Cougnaud L; Vuckovic D; | 31439439 CHEMBIOCHEM |
| 11 | Introduction: Overview of Fungal Genomics. | de Vries RP, Grigoriev IV, Tsang A | 29876804 CSFG |
| Title: | Integrated metabolomics and metagenomics analysis identifies a unique signature characterizing metabolic syndrome | ||||
| Authors: | Wannaiampikul S, Lee B, Chen J, Prentice KJ, Ayansola R, Xu A, Santosa S, Pantopoulos K, Sweeney G | ||||
| Link: | https://pubmed.ncbi.nlm.nih.gov/41794383/ | ||||
| DOI: | 10.1016/j.jnutbio.2026.110327 | ||||
| Publication: | The Journal of nutritional biochemistry | ||||
| Keywords: | Host-Microbiome Interaction; Metabolic Syndrome; Metagenomics; Obesity; Serum Metabolomics; Thai Population; | ||||
| PMID: | 41794383 | Category: | Date Added: | 2026-03-08 | |
| Dept Affiliation: |
HKAP
1 Thai Canada Research Centre, Faculty of Medicine, Srinakharinwirot University, Ongkharak, Thailand.; Division of Internal Medicine, HRH Princess Maha Chakri Sirindhorn Medical Center, Srinakharinwirot University, Ongkharak, Thailand. 2 Division of Internal Medicine, HRH Princess Maha Chakri Sirindhorn Medical Center, Srinakharinwirot University, Ongkharak, Thailand. 3 State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong SAR, China; Department of Medicine, School of Clinical Medicine, The University of Hong Kong, Hong Kong SAR, China; Guangdong Provincial Key Laboratory of Food, Nutrition and Health, and Department of Nutrition, School of Public Health, Sun Yat-sen University, Guangzhou, P.R. China. 4 Department of Physiology, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Banting and Best Diabetes Centre, Toronto, Canada. 5 Department of Biology, York University, Toronto, Canada. 6 State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong SAR, China; Department of Medicine, School of Clinical Medicine, The University of Hong Kong, Hong Kong SAR, China; Guangdong-Hong Kong Joint Institute for Metabolic Medicine, The University of Hong Kong, Hong Kong SAR, China. 7 Department of Health, Kinesiology, and Applied Physiology, Concordia University, Montreal, Quebec, Canada. 8 Lady Davis Institute for Medical Research, Jewish General Hospital and Department of Medicine, McGill University, Montreal, Quebec, Canada. 9 Thai Canada Research Centre, Faculty of Medicine, Srinakharinwirot University, Ongkharak, Thailand.; Department of Biology, York University, Toronto, Canada.. Electronic address: gsweeney@yorku.ca. |
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Description: |
Background: Metabolic Syndrome (MetS) presents a global health challenge, characterized by obesity, hypertension, dyslipidemia, and insulin resistance. Despite recognition of the gut microbiome's role in metabolic health, there remains scope for defining association of unique microbes with clinical status. Unique genetic, dietary, and lifestyle factors may influence gut microbial composition and circulating metabolites, and consequently susceptibility to MetS. By identifying specific microbial and metabolomic signatures associated with MetS, we aim to uncover potential targets for reducing the disease burden. Methods: We correlate comprehensive clinical parameters with fecal metagenomics and untargeted serum metabolomics to delineate population-specific characteristics from 142 individuals with MetS (N=97) or control (CTRL; N=45). Results: Microbiome species-level alpha diversity was reduced in MetS compared to CTRL. After adjustment for sex, age, BMI, and intensity of statin usage, we identified 20 MetS-related species. A co-abundant network analysis revealed Eubacterium eligens, enriched in the CTRL population, with the highest node degree. Serum metabolomics identified 106 significantly differentially regulated metabolites. N-arachidonoyl dopamine (NADA), an endocannabinoid implicated in GABAergic signaling, was the most significantly altered, enriched in CTRL and correlated with E. Eligens. sPLS-DA modeling revealed that E. eligens and D. formicigenerans species cluster together with metabolites NADA and tetrahydrocorticosterone (THB), representing defining characteristics distinguishing MetS in this population. Conclusions: Our data reveal a distinct multi-omic signature of MetS, characterized by a significant reduction in E. eligens and D. formicigenerans abundance, and in circulating NADA and THB levels. |
