Function-based selection of synthetic communities enables mechanistic microbiome studies.

Area of Science:

• Microbiology
• Systems Biology
• Computational Biology

Background:

• Understanding microbial communities is crucial for various fields.
• Synthetic communities (SynComs) are valuable model systems.
• Existing methods for SynCom construction can be limiting.

Purpose of the Study:

• To develop a functionally directed approach for generating SynComs.
• To enable the rapid construction of SynComs tailored to specific ecosystems.
• To create SynComs that model complex host-microbe interactions, including disease states.

Main Methods:

• Selected microbial strains based on key functions identified in metagenomes.
• Utilized genome-scale metabolic models for in silico optimization of SynCom design.
• Designed host-specific SynComs for rumen, mouse, and human microbiomes.
• Weighted functions differentially enriched in diseased versus healthy individuals.

Main Results:

• Successfully constructed multiple host-specific SynComs.
• Designed an inflammatory bowel disease SynCom that induced colitis in mice.
• Demonstrated the potential of this method to model disease-associated microbiomes.
• Established a framework for designing functionally representative SynComs for any microbial ecosystem.

Conclusions:

• The developed functional approach enables rapid and tailored SynCom construction.
• This method facilitates the mechanistic study of complex host-microbe interactions.
• SynComs can be designed to model specific diseases and ecosystems.