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High-Throughput UHPLC-MS to Screen Metabolites in Feces for Gut Metabolic Health

Authors :
Andressa de Zawadzki
Maja Thiele
Tommi Suvitaival
Asger Wretlind
Min Kim
Mina Ali
Annette F. Bjerre
Karin Stahr
Ismo Mattila
Torben Hansen
Aleksander Krag
Cristina Legido-Quigley
Source :
Metabolites, Vol 12, Iss 3, p 211 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

Feces are the product of our diets and have been linked to diseases of the gut, including Chron’s disease and metabolic diseases such as diabetes. For screening metabolites in heterogeneous samples such as feces, it is necessary to use fast and reproducible analytical methods that maximize metabolite detection. As sample preparation is crucial to obtain high quality data in MS-based clinical metabolomics, we developed a novel, efficient and robust method for preparing fecal samples for analysis with a focus in reducing aliquoting and detecting both polar and non-polar metabolites. Fecal samples (n = 475) from patients with alcohol-related liver disease and healthy controls were prepared according to the proposed method and analyzed in an UHPLC-QQQ targeted platform in order to obtain a quantitative profile of compounds that impact liver-gut axis metabolism. MS analyses of the prepared fecal samples have shown reproducibility and coverage of n = 28 metabolites, mostly comprising bile acids and amino acids. We report metabolite-wise relative standard deviation (RSD) in quality control samples, inter-day repeatability, LOD (limit of detection), LOQ (limit of quantification), range of linearity and method recovery. The average concentrations for 135 healthy participants are reported here for clinical applications. Our high-throughput method provides a novel tool for investigating gut-liver axis metabolism in liver-related diseases using a noninvasive collected sample.

Details

Language :
English
ISSN :
22181989
Volume :
12
Issue :
3
Database :
Directory of Open Access Journals
Journal :
Metabolites
Publication Type :
Academic Journal
Accession number :
edsdoj.216e48cb7884156878d17b3d73128b4
Document Type :
article
Full Text :
https://doi.org/10.3390/metabo12030211