Helena Mannochio-Russo

The paper expands a bile acid and steroidal MS/MS spectral library across three repositories, capturing diverse structures and providing a resource for classifying unknown spectra.

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.

Polyethylene glycol in human biofluids can mark drug exposure rather than contamination; it is oxidized to acid metabolites and appears in 2.3% of public metabolomics files.

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

The paper systematically extracts and catalogs acylcarnitine metabolites from multiple repositories, identifying previously unknown carnitines and providing a resource for further metabolic research.

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.

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

This paper introduces a mass spectrometry filtering tree to distinguish bile acid isomers in LC-MS/MS data and identifies a novel deoxycholyl-2-aminophenol amidate linked to whole grain consumption.

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 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.

The paper creates a suspect spectral library for untargeted metabolomics by propagating annotations from public MS/MS spectra to improve interpretation of unannotated spectra.

The paper introduces Ion Identity Molecular Networking to connect ion species of the same molecule, improving network connectivity and annotation.