Untargeted and Semi-Targeted Metabolomics Approach for Profiling Small Intestinal and Fecal Metabolome Using High-Resolution Mass Spectrometry
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The gut microbiome is a complex ecosystem varying along different gut sections, consisting of metabolites from food, host, and microbes. Microbially-derived metabolites like bile acids and short-chain fatty acids interact with host physiology. Current studies often use fecal samples, which don’t fully represent the upper gut due to stratification. To sample the proximal gut microbiome, endoscopic methods or new non-invasive devices are used. We developed an approach combining untargeted and semi-targeted metabolomics using a Q-Exactive Plus Orbitrap mass spectrometer to profile gut metabolites. We initially selected 49 key metabolites based on specific criteria, validated them through repeatability tests, and created a compound database with TraceFinder software. Our workflow enables molecule annotation in untargeted studies while validating 37 metabolites in semitargeted analyses. This method, applied to clinical trial samples ( NCT05477069 ), shows promise in discovering new gut metabolites.
Highlights
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Innovative Combined Metabolomics Workflow: The study introduces a combined approach that integrates semi-targeted and untargeted metabolomics analyses to characterized small intestinal and fecal metabolomes. This method allows for the relative quantification of carefully selected metabolites and the reanalysis of these metabolites using evolving curated databases, enhancing the understanding of the gut microbiome-health axis.
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Untargeted Metabolomics Strategy: The untargeted approach aims to determine the global metabolic profile of samples and discover new metabolites. This involves processing data through a detailed pipeline, statistical analysis, and feature annotation using tools like MZmine, MetaboAnalyst, and the GNPS platform.
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Proof of Concept on Clinical Samples: The combined approach was tested on clinical samples from a participant in a clinical trial, revealing distinct metabolomes between small intestinal content and fecal samples. This proof of concept demonstrated the method’s ability to identify and quantify metabolites, showing significant differences in metabolite abundance between the two sample types and highlighting the potential for discovering new bile acids through molecular networking.
