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

Area of Science:

• Microbial ecology
• Systems biology
• Mammalian physiology

Background:

• Microbes are crucial for ecosystem function, but predicting microbiome-induced changes in host metabolism is challenging.
• Gut dysbiosis is linked to host metabolic issues, yet the underlying mechanisms require further investigation.
• Understanding the interplay between the gut microbiome and host health is vital for predicting disease states.

Purpose of the Study:

• To predict how gut dysbiosis impacts mammalian health using a genome-resolved systems biology approach.
• To identify key microbial species and metabolic pathways that link gut microbiome alterations to host physiology.
• To challenge the traditional view of whole-body nitrogen balance regulation.

Main Methods:

• Utilized a high-resolution 'spinal cord-gut axis' model system and dataset.
• Employed temporally resolved network analytics and consensus statistical methods to identify key microbial species.
• Conducted in silico validation using pathway-centric functional analyses and comparative genomics.

Main Results:

• Identified key microbial species that predict overall host physiology and gut ecosystem control.
• Discovered that specific bacteria uniquely encode functions connecting microbial and host metabolisms.
• Observed shifts in gut microbial nitrogen metabolism pathways (urease-to amino acid-dependent) influenced by host sex and bacterial species.

Conclusions:

• The gut microbiome functions as an environmentally-sensitive regulatory organ influencing mammalian health.
• Microbial nitrogen metabolism plays a critical role in whole-body elemental balance, challenging host-centric paradigms.
• This research provides a framework for predicting health and disease outcomes based on microbiome-host metabolic interactions.