A searchable metadata network graph for microbiome metabolomics.

Area of Science:

• Microbiome research
• Metabolomics
• Bioinformatics

Background:

• Identifying microbial metabolites and their biological roles is challenging.
• Existing data is fragmented across numerous studies and sample types.

Purpose of the Study:

• To develop a metadata-driven network graph (microbiomeMASST) for mapping microbial metabolites.
• To integrate diverse mass spectrometry datasets for cross-study analysis and biological contextualization.

Main Methods:

• Compiled 467 datasets with 144,424 mass spectrometry files from human, animal, and microbial sources.
• Integrated data from monocultures, synthetic communities, and host-associated samples.
• Developed a queryable network graph to trace metabolite occurrence across hosts, conditions, and interventions.

Main Results:

• Contextualized microbial-conjugated bile acids and investigated microbiome-mediated drug metabolism.
• Identified gut bacteria deprolyating the ACE inhibitor prodrug enalapril.
• Traced the metabolite across human, microbial, environmental, and gorilla samples, demonstrating loss of ACE inhibition.

Conclusions:

• MicrobiomeMASST effectively links mass spectrometry/mass spectrometry spectra to biological context.
• The framework enables the interpretation of isolated observations into a comprehensive microbiome map.
• Microbial deprolylation of enalapril inactivates its therapeutic effect, highlighting drug-metabolizing interactions.