Vincent Charron‐Lamoureux

The paper maps bacterial bile amidation by analyzing BSH transaminase activities and identifying BBAs in human samples.

The paper synthesizes 3-hydroxy N-acyl amides and uses reverse metabolomics to identify their presence in biological samples and link them to microbial production and metabolic disease.

The Everything Bagel feature finder automates metabolomics data processing, achieving comparable or better performance than existing methods with significantly reduced computational time.

The paper develops an agentic AI system using LLMs and domain tools to hypothesize and validate structures of unknown drug metabolites from mass spectrometry data.

This paper introduces molecular community networking to organize metabolite data, revealing hidden connections and discovering novel dipeptide-conjugated bile acids.

The paper identifies alkamines as a previously hidden class of metabolites formed via reductive amination, revealing significant gaps in metabolomics and drug metabolism understanding.

This study examines how maternal antibiotic use during breastfeeding affects the infant gut microbiome and metabolome, finding altered microbial profiles and increased BMI in some infants.

The paper identifies microbial bile acid conjugation as a drug-activating function for 5-ASA, revealing its therapeutic relevance through metabolomics analysis.

This study maps conjugated metabolites by analyzing MS/MS spectra from public repositories, identifying potential conjugation events and providing a resource for structural discovery.

The paper introduces microbiomeMASST, a metadata network graph linking metabolites across datasets, enabling cross-study analysis of microbial metabolites and their biological context.

The paper introduces a collaborative platform for curating microbial metabolite knowledge to enhance metabolomics data interpretation.

The paper uses synthetic multiplexing to annotate uncharacterized metabolites, identifying ibuprofen-carnitine as a metabolite linked to muscle toxicity.

The paper uses a colon simulator to study microbial lipid shifts in the gut and validates findings using human samples.

The paper examines how environmental and maternal factors influence infant gut metabolism through microbiome and metabolome profiling in Bangladeshi mother-infant pairs.

The paper presents a multi-organ metabolomics atlas in AD mouse models, identifying dysregulated metabolites and microbiome associations linked to oxidative stress and cognitive impairment.

This study identifies distinct microbial and metabolic signatures in VEO-IBD compared to healthy controls, linking microbiome changes to metabolic dysregulation.

The paper compares ethanol and methanol extraction methods for fecal metabolomics, showing ethanol is safer for off-site collection and preserves metabolic profiles.

The paper creates a spectral library of N-acyl lipids from metabolomics data, identifies novel compounds, and links them to health conditions like HIV and cognitive impairment.

The paper introduces ms1-id, a tool for structural annotation of MS1 data in LC-MS and MS imaging, enabling level 2/3 annotation and uncovering previously overlooked metabolites.

The paper develops a drug exposure resource from untargeted metabolomics data to identify medications without clinical records.

The paper curates LC-MS/MS data from plant extracts to create a taxonomically focused database for exploring plant-derived molecules.