Untargeted Metabolome- and Transcriptome-Wide Association Study Suggests Causal Genes Modulating Metabolite Concentrations in Urine.
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5 novembre 2021
- doi: 10.1021/acs.jproteome.1c00585
- issn: 1535-3893
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info:eu-repo/semantics/altIdentifier/doi/10.1021/acs.jproteome.1c00585
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info:eu-repo/semantics/altIdentifier/eissn/1535-3907
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info:eu-repo/semantics/altIdentifier/urn/urn:nbn:ch:serval-BIB_83A8DE848FD30
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R. Sönmez Flitman et al., « Untargeted Metabolome- and Transcriptome-Wide Association Study Suggests Causal Genes Modulating Metabolite Concentrations in Urine. », Serveur académique Lausannois, ID : 10.1021/acs.jproteome.1c00585
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Résumé
Gene products can affect the concentrations of small molecules (aka "metabolites"), and conversely, some metabolites can modulate the concentrations of gene transcripts. While many specific instances of this interplay have been revealed, a global approach to systematically uncover human gene-metabolite interactions is still lacking. We performed a metabolome- and transcriptome-wide association study to identify genes influencing the human metabolome using untargeted metabolome features, extracted from 1 H nuclear magnetic resonance spectroscopy (NMR) of urine samples, and gene expression levels, quantified from RNA-Seq of lymphoblastoid cell lines (LCL) from 555 healthy individuals. We identified 20 study-wide significant associations corresponding to 15 genes, of which 5 associations (with 2 genes) were confirmed with follow-up NMR data. Using metabomatching, we identified the metabolites corresponding to metabolome features associated with the genes, namely, N-acetylated compounds with ALMS1 and trimethylamine (TMA) with HPS1. Finally, Mendelian randomization analysis supported a potential causal link between the expression of genes in both the ALMS1- and HPS1-loci and their associated metabolite concentrations. In the case of HPS1, we additionally observed that TMA concentration likely exhibits a reverse causal effect on HPS1 expression levels, indicating a negative feedback loop. Our study highlights how the integration of metabolomics, gene expression, and genetic data can pinpoint causal genes modulating metabolite concentrations.