Precision Prediction of Microbial Ecosystem Impact on Host Metabolism Using Genome-Resolved Metagenomics
Microorganisms often drive ecosystem function, yet precision disturbance response and ecosystem impact predictions remain challenging due to poorly captured ecological and metabolic interconnectedness and functional redundancy. For example, while mammalian gut dysbiosis is recognized to influence host metabolism, key microbiota and mechanisms governing their effects remain poorly understood. Here we developed a genome-resolved eco-systems biology workflow to predict how gut microbial metabolism affects mammalian health, and we applied it to a spinal cord-gut axis dataset. By scaling and integrating temporally resolved network analytics and consensus statistical approaches, we identified largely previously uncharacterized microbial species that best predict host physiology following neurological impairment. In silico validation through "complete" pathway-centric and comparative genomic analyses revealed that among these species, the major encoded microbial metabolic changes were in pathways directly linked to host nitrogen balance, and they varied by host sex and microbial ecotype/species. Moreover, we identified the exact bacterial species (and their draft genome sequences) driving urease-dependent versus amino acid-dependent nitrogen gut metabolism - findings that explain previously mechanistically-ambiguous, but clinically relevant, ammonia-driven host nitrogen imbalance. More broadly, these ecology- and community-aware approaches provide a framework to study dynamic, interconnected microbiomes that advances from enrichment-based single-taxon and single-gene correlations towards building microbe(s)-driven mechanistic insights that integrate community context and whole pathways.