Abstract
Mammalian gut dysbiosis is recognized to influence host metabolism, yet key microbiota and mechanisms governing their effects remain poorly understood. Here we developed a genome-resolved ecology-aware, systems-level workflow to predict how gut microbial metabolism affects mammalian health, and we apply it to a “spinal cord–gut axis” dataset. By scaling and integrating temporally resolved network analytics and consensus statistical approaches, we identified 19 microbial species that best predict host physiology following neurological impairment. In silico validation through pathway-centric and comparative genomic analyses revealed that among these species, the biggest encoded microbial metabolic changes were in pathways linked to host nitrogen balance, varying by host sex and microbial ecotype/species. Further inference identified the specific bacteria (and their draft genomes) potentially driving urease-dependent versus amino acid-dependent nitrogen metabolism—findings that can explain previously mechanistically-ambiguous, but clinically relevant, ammonia-driven host nitrogen imbalance. This provides a generalizable data-driven hypothesis generation workflow for longitudinal microbiome data.
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Acknowledgements
We appreciate the help and support from all members of the Popovich and Sullivan laboratories, especially Natalie Solonenko, who participated in data sequencing, and Ami Fofana and Olivier ZABLOCKI for their immense help with visual representations. We’d also like to acknowledge the Center of Microbiome Science, Ohio Supercomputer Center, and the Riffomonas educational resource100.
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Mohssen, M., Zayed, A.A., Kigerl, K.A. et al. Precision Prediction of Microbial Ecosystem Impact on Host Metabolism Using Genome-Resolved Metagenomics.
npj Biofilms Microbiomes (2026). https://doi.org/10.1038/s41522-026-01107-3
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DOI: https://doi.org/10.1038/s41522-026-01107-3
Source: Ecology - nature.com
