MIGRENE: The Toolbox for Microbial and Individualized GEMs, Reactobiome and Community Network Modelling

Area of Science:

• Microbiome research
• Computational biology
• Metabolic modeling

Background:

• Understanding host-microbiome interactions is key to health and disease.
• Current genome-scale metabolic models (GEMs) struggle with uncharacterized microbes from metagenomic data.
• Personalized microbiome metabolism analysis is needed.

Purpose of the Study:

• To develop a computational framework for personalized microbiome metabolism.
• To generate microbial-level GEMs and individualized microbiome metabolisms.
• To investigate host-microbiome interactions in liver cirrhosis.

Main Methods:

• Developed a computational framework integrating microbiome composition, species profiles, and gene catalogs.
• Generated microbial GEMs and individualized microbiome metabolisms (reaction richness, abundance, reactobiome, iRSE, community models).
• Analyzed metabolic differences in individuals with liver cirrhosis.

Main Results:

• A significant reduction in reaction richness and GEM richness was observed in liver cirrhosis patients.
• Identified potential links between gut microbiota and liver cirrhosis, including increased LPS, ammonia production, and tyrosine metabolism.
• Highlighted the importance of microbiome factors in liver health.

Conclusions:

• The developed framework enables personalized microbiome metabolism analysis using metagenomic data.
• Gut microbiota alterations, particularly reduced metabolic richness, are associated with liver cirrhosis.
• Microbiome-related factors play a significant role in liver health and disease.