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Peer-reviewedCC BYmSystems

Gut microbiome and metabolite profiles are associated with subsequent histologic MASH in a longitudinal mouse model

  • Stephany Flores Ramos1,2,3
  • Kelly Fogelson1,2,3
  • Valentina B. Muti1,4
  • Allegra T. Aron4,5,6
  • Rodolfo A. Salido3,7
  • R. Alexander Richter1
  • Helena Mannochio-Russo4,5
  • Jingjing Hu8,9
  • Mojgan Hosseini8,9
  • Rohit Loomba1,10,11
  • Pieter C. Dorrestein4,5
  • Rob Knight3,7,12,13,14,15
1 more authors
  • Amir Zarrinpar1,7,9,12,16,17,18,19
19 affiliations
  1. 1Division of Gastroenterology, University of California San Diego, La Jolla, California, USA
  2. 2Biomedical Sciences Graduate Program, University of California San Diego, La Jolla, California, USA
  3. 3Department of Pediatrics, University of California San Diego, La Jolla, California, USA
  4. 4Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California, USA
  5. 5Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California, USA
  6. 6Department of Chemistry & Biochemistry, University of Denver, Denver, Colorado, USA
  7. 7Shu Chien-Gene Lay Department of Bioengineering, University of California San Diego, La Jolla, California, USA
  8. 8Department of Pathology, University of California San Diego, La Jolla, California, USA
  9. 9The Moores Cancer Center, University of California San Diego, La Jolla, California, USA
  10. 10NAFLD Research Center, Department of Medicine, University of California San Diego, La Jolla, California, USA
  11. 11Division of Epidemiology, Department of Family and Preventive Medicine, University of California San Diego, La Jolla, California, USA
  12. 12Center for Microbiome Innovation, University of California San Diego, La Jolla, California, USA
  13. 13Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA
  14. 14Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, California, USA
  15. 15Hong Kong University of Science and Technology Jockey Club Institute for Advanced Study, Hong Kong, China
  16. 16Synthetic Biology Institute, University of California San Diego, La Jolla, California, USA
  17. 17Division of Gastroenterology, Jennifer Moreno Department of Veterans Affairs Medical Center, La Jolla, California, USA
  18. 18Institute of Diabetes and Metabolic Health, University of California San Diego, La Jolla, California, USA
  19. 19Center for Circadian Biology and Medicine, University of California, San Diego, La Jolla, California, United States
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Abstract

Detecting metabolic dysfunction-associated steatohepatitis (MASH) before fibrosis develops remains a major challenge. We used a longitudinal streptozotocin/high-fat diet (STAM/HFD) mouse model to identify microbial and metabolic features that emerge before histological MASH onset. Integrative 16S rRNA and untargeted LC-MS/MS analyses revealed microbial and metabolite shifts weeks before disease development. Lachnospiraceae and Oscillospiraceae were enriched in non-MASH mice and were negatively correlated with fatty acids elevated under MASH conditions. Bile acid and fatty acid metabolites enriched under MASH and non-MASH states varied with collection time, showing that diurnal variation strongly shapes biomarker profiles. Analysis of a human MAFLD cohort confirmed higher abundances of Lachnospiraceae bacteria in healthy participants. Our results identify early microbial and metabolic features that are associated with subsequent histologic MASH onset and reveal how temporal factors influence biomarker detection. These time-sensitive features, together with cross-species concordance in a human MAFLD cohort, lay the groundwork for developing and validating microbiome-based diagnostics and circadian-informed interventions that target the gut-liver axis to prevent metabolic liver disease. IMPORTANCE: Metabolic dysfunction-associated steatohepatitis (MASH) often develops silently, limiting opportunities for early intervention before fibrosis occurs. In this study, we identify gut microbial taxa and stool metabolites that are associated with future histologic MASH development in a longitudinal mouse model. We show that members of the Lachnospiraceae and Oscillospiraceae families are enriched in mice that remain disease-free and that members of the Lachnospiraceae family exhibit similar patterns in a human MAFLD cohort. Importantly, we demonstrate that time of sample collection has a strong effect on metabolomic profiles, with disease-associated lipid and bile acid signals detectable only at specific sampling times. These findings highlight circadian timing as a critical variable in microbiome-based biomarker discovery and support the development of time-aware diagnostic strategies for early MASH risk assessment.

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Cite

BibTeXRIS
BibTeX
@article{ramos2026microbiome,
  title = {{Gut microbiome and metabolite profiles are associated with subsequent histologic MASH in a longitudinal mouse model}},
  author = {Stephany Flores Ramos and Kelly Fogelson and Valentina B. Muti and Allegra T. Aron and Rodolfo A. Salido and R. Alexander Richter and Helena Mannochio-Russo and Jingjing Hu and Mojgan Hosseini and Rohit Loomba and Pieter C. Dorrestein and Rob Knight and Amir Zarrinpar},
  journal = {mSystems},
  year = {2026},
  month = sep,
  doi = {10.1128/msystems.00720-26},
  url = {https://doi.org/10.1128/msystems.00720-26},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Gut microbiome and metabolite profiles are associated with subsequent histologic MASH in a longitudinal mouse model
AU  - Stephany Flores Ramos
AU  - Kelly Fogelson
AU  - Valentina B. Muti
AU  - Allegra T. Aron
AU  - Rodolfo A. Salido
AU  - R. Alexander Richter
AU  - Helena Mannochio-Russo
AU  - Jingjing Hu
AU  - Mojgan Hosseini
AU  - Rohit Loomba
AU  - Pieter C. Dorrestein
AU  - Rob Knight
AU  - Amir Zarrinpar
PY  - 2026
DA  - 2026/09/10
JO  - mSystems
DO  - 10.1128/msystems.00720-26
UR  - https://doi.org/10.1128/msystems.00720-26
AB  - Detecting metabolic dysfunction-associated steatohepatitis (MASH) before fibrosis develops remains a major challenge. We used a longitudinal streptozotocin/high-fat diet (STAM/HFD) mouse model to identify microbial and metabolic features that emerge before histological MASH onset. Integrative 16S rRNA and untargeted LC-MS/MS analyses revealed microbial and metabolite shifts weeks before disease development. Lachnospiraceae and Oscillospiraceae were enriched in non-MASH mice and were negatively correlated with fatty acids elevated under MASH conditions. Bile acid and fatty acid metabolites enriched under MASH and non-MASH states varied with collection time, showing that diurnal variation strongly shapes biomarker profiles. Analysis of a human MAFLD cohort confirmed higher abundances of Lachnospiraceae bacteria in healthy participants. Our results identify early microbial and metabolic features that are associated with subsequent histologic MASH onset and reveal how temporal factors influence biomarker detection. These time-sensitive features, together with cross-species concordance in a human MAFLD cohort, lay the groundwork for developing and validating microbiome-based diagnostics and circadian-informed interventions that target the gut-liver axis to prevent metabolic liver disease. IMPORTANCE: Metabolic dysfunction-associated steatohepatitis (MASH) often develops silently, limiting opportunities for early intervention before fibrosis occurs. In this study, we identify gut microbial taxa and stool metabolites that are associated with future histologic MASH development in a longitudinal mouse model. We show that members of the Lachnospiraceae and Oscillospiraceae families are enriched in mice that remain disease-free and that members of the Lachnospiraceae family exhibit similar patterns in a human MAFLD cohort. Importantly, we demonstrate that time of sample collection has a strong effect on metabolomic profiles, with disease-associated lipid and bile acid signals detectable only at specific sampling times. These findings highlight circadian timing as a critical variable in microbiome-based biomarker discovery and support the development of time-aware diagnostic strategies for early MASH risk assessment.
SN  - 2379-5077
ER  -