ArticleAnalytical and bioanalytical chemistry2025
Leveraging open cheminformatics tools for non-targeted metabolomics analysis of C. elegans: a workflow comparison and application to strains related to xenobiotic metabolism and neurodegeneration.
Article in Analytical and bioanalytical chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Leveraging open cheminformatics tools for non-targeted metabolomics analysis of C. elegans: a workflow comparison and application to strains related to xenobiotic metabolism and neurodegeneration.Analytical and bioanalytical chemistry · 2025Article
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4 authors.
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Abstract
Caenorhabditis elegans (C. elegans) is a well-established nematode model for studying metabolism and neurodegenerative disorders, such as Alzheimer's (AD) and Parkinson's disease (PD). Non-targeted metabolomics via liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) has proven useful for uncovering metabolic changes in biological systems. Here, we present workflows for C. elegans metabolomics, leveraging advanced open science tools. We compared two metabolite extraction methods: a monophasic extraction, which provided broader metabolite coverage in analyses conducted in hydrophilic interaction with positive polarity (HILIC POS), and a biphasic extraction, which yielded more features in reverse-phase C18 chromatography with negative polarity (RPLC NEG) analyses. Data were processed using patRoon, integrating IPO, XCMS, CAMERA, and MetFrag, which incorporated PubChemLite compounds and C. elegans-specific metabolites from an expanded WormJam database enhanced with PubChem and literature sources. MS-DIAL was also employed for data processing, allowing for expanded annotations with predicted spectra for the expanded WormJam metabolites calculated using CFM-ID. Significant metabolite differences were identified when comparing the Bristol (N2) wild-type strain with two knockout strains of xenobiotic-metabolizing enzymes and two transgenic strains related to neurodegenerative pathways. Pooled quality control (QC) samples for each strain ensured robust data quality and the detection of strain-related metabolites. Our study demonstrates the potential of non-targeted metabolomics for metabolite discovery employing open science tools in model organisms.
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