Precision Prediction of Microbial Ecosystem Impact on Host Metabolism Using Genome-Resolved Metagenomics.

Microbes drive ecosystem function through their physical interactions and metabolic transformations. However, since microbiomes are ecologically and metabolically interconnected, it is challenging to predict emergent ecosystem responses once the microbiome is disturbed. While it is widely acknowledged that mammalian gut dysbiosis influences host metabolism, mechanistic links that predict these effects are understudied. This study employs a genome-resolved eco-systems biology approach, using a high-resolution 'spinal cord-gut axis' model system and dataset, to predict how dysbiotic gut metabolism impacts overall mammalian health. By scaling and combining temporally resolved network analytics and consensus statistical methods, key microbial species were identified that predict overall host physiology and presumably control the gut ecosystem. validation by pathway-centric functional analyses and comparative genomics revealed that key bacteria, sometimes exclusively, encode functions linking microbial and host metabolisms. Notably, spinal-mediated disturbances in the ecosystem shifted gut microbial nitrogen metabolism from urease-to amino acid-dependent pathways, with patterns that varied by host sex and bacterial species. Overall, this research challenges the traditional paradigm that only the host maintains whole-body nitrogen balance and instead invokes the microbiome as an environmentally-sensitive regulatory organ that can dictate health or disease by influencing mammalian whole-body elemental balance.