A searchable metadata network graph for microbiome metabolomics

Establishing the biological context of microbial metabolites remains a major challenge. We present microbiomeMASST, a metadata-driven network graph that maps metabolites across 467 available datasets with 144,424 mass spectrometry files from humans, animals, and microbial culture systems. MicrobiomeMASST integrates monocultures, synthetic communities, and host-associated samples across multiple body sites and plants. MS/MS spectra can be queried to trace occurrence across hosts, experimental conditions, and interventions, enabling cross-study integration. We demonstrate this framework by contextualizing microbial-conjugated bile acids and interrogating microbiome-mediated drug metabolism. Screening gut bacteria revealed deprolylation of the angiotensin-converting enzyme (ACE) inhibitor prodrug enalapril. Using microbiomeMASST, we traced this metabolite across human cohorts, microbial isolates, environmental samples, and in <i>Gorilla gorilla</i> . Structural modeling and enzymatic assays showed that microbial deprolylation abolishes ACE inhibition, thereby inactivating its therapeutic effect. Together, microbiomeMASST links MS/MS spectra to biological context, converting isolated observations into an interpretable microbiome map for cross-study analysis.

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Publication Details

Journal
bioRxiv (Cold Spring Harbor Laboratory)
Published
2026-02-05
DOI
https://doi.org/10.64898/2026.02.04.703849
Citations
2
Primary Topic
Metabolomics and Mass Spectrometry Studies
Type
article
Field-Weighted Citation Impact
44.47

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article

A searchable metadata network graph for microbiome metabolomics

Gail Ackermann, Adrián Jinich, M. C. Wang, Anthony J. A. Molina et al.
2 citations
bioRxiv (Cold Spring Harbor Laboratory)
Metabolomics and Mass Spectrometry Studies
44.47
article

A searchable metadata network graph for microbiome metabolomics

Gail Ackermann, Adrián Jinich, M. C. Wang, Anthony J. A. Molina, Selene F. H. Shore, Lucas Patel, Santosh Lamichhane, April L. Lukowski, Manuela Raffatellu, Harsha Gouda, Julius Agongo, Sarkis K. Mazmanian, Grant J. Norton, Ricardo Almada-Monter, Anthony Martin, Shipei Xing, Rocío Paz-González, Kine Eide Kvitne, Loryn Chin, Erin R. Reilly, Jaclyn Bergstrom, Simone Renwick, Liang Liang, Yuhan Weng, Maria L. Marco, Prajit Rajkumar, V. Kalai Gandhi, Corinn Walker, N. Pérez, A. Norman, Victoria Deleray, Vincent Charron-Lamoureux, Nadia Suryawinata, Anna E. Skrip, Monica Guma, Andres Mauricio Caraballo Rodriguez, Karsten Zengler, Allegra Aron, Marta Sala-Climent, Thomas D. Horvath, Subhomitra Ghoshal, Haoqi Nina Zhao, Eric Kemen, Heejung Yang, Samira Dahesh, Sadie Giddings, Andrew D. Patterson, Antonio Gonzalez, Evi Stegmann, Jasmine Zemlin, Ipsita Mohanty, Paolo Stincone, Daniel McDonald, Abubaker Patan, Suzanne Devkota, Melinda A. Engevik, Monae Pinner, Victor Nizet, Pieter C. Dorrestein, Dionicio Siegel, Rob Knight, Zhewen Hu
article en
2 citations

Abstract

Establishing the biological context of microbial metabolites remains a major challenge. We present microbiomeMASST, a metadata-driven network graph that maps metabolites across 467 available datasets with 144,424 mass spectrometry files from humans, animals, and microbial culture systems. MicrobiomeMASST integrates monocultures, synthetic communities, and host-associated samples across multiple body sites and plants. MS/MS spectra can be queried to trace occurrence across hosts, experimental conditions, and interventions, enabling cross-study integration. We demonstrate this framework by contextualizing microbial-conjugated bile acids and interrogating microbiome-mediated drug metabolism. Screening gut bacteria revealed deprolylation of the angiotensin-converting enzyme (ACE) inhibitor prodrug enalapril. Using microbiomeMASST, we traced this metabolite across human cohorts, microbial isolates, environmental samples, and in <i>Gorilla gorilla</i> . Structural modeling and enzymatic assays showed that microbial deprolylation abolishes ACE inhibition, thereby inactivating its therapeutic effect. Together, microbiomeMASST links MS/MS spectra to biological context, converting isolated observations into an interpretable microbiome map for cross-study analysis.

bioRxiv (Cold Spring Harbor Laboratory)
University of California, Riverside (US), California Institute of Technology (US), Cedars-Sinai Medical Center (US), Pennsylvania State University (US), La Jolla Institute for Immunology (US), University of Denver (US), Scripps Institution of Oceanography (US), Medical University of South Carolina (US), University of San Diego (US), Kangwon National University (KR), Baylor College of Medicine (US), Medical College of Wisconsin (US), Turku University Hospital (FI), University of California San Diego (US), Therapeutics Clinical Research (US), German Center for Infection Research (DE), La Jolla Bioengineering Institute (US), Institute on Aging (US), Texas Children's Hospital (US), Instituto de Investigación Biomédica de A Coruña (ES), Mothers’ Milk Bank (US), University of Houston (US), University of Guelph (CA), University of Tübingen (DE), University of California, Davis (US)
National Science Foundation, Gordon and Betty Moore Foundation, Hong Kong University of Science and Technology, National Institutes of Health, University of California, San Diego, Natural Sciences and Engineering Research Council of Canada, Fonds de Recherche du Québec - Santé, Biotechnology and Biological Sciences Research Council, National Institute of General Medical Sciences, National Institute of Diabetes and Digestive and Kidney Diseases
Openalex Percentile: Top 0%
Metabolomics and Mass Spectrometry Studies
44.47
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