Modeling interaction networks between host, diet, and bacteria predicts obesogenesis in a mouse model.

Area of Science:

• Microbiology
• Computational Biology
• Host-Microbiome Interactions

Background:

• Host-microbiome interactions significantly impact human health.
• Human microbiome diversity complicates attributing specific features to host phenotypes.
• Animal models are crucial for studying host-microbiome interactions, but their relevance must be validated.

Purpose of the Study:

• To develop and validate computational models of host-microbiome-diet interactions.
• To assess the predictive power of network-based versus non-network-based models in host-microbiome studies.
• To gain insights into the molecular mechanisms underlying microbiome-associated obesogenesis.

Main Methods:

• Aggregated published host-microbiome mouse-model experiments.
• Integrated sequenced bacterial genomes and metametabolomic pathways.
• Developed three computational models: microbiome community structure prediction, metagenomic data prediction, and host obesogenesis prediction.
• Combined models into an integrated host-microbiome-diet interaction model.
• Replicated the Ridura et al. experiment *in silico*.

Main Results:

• Network-based computational models significantly outperformed non-network-based models in predictive accuracy.
• Models highlighted specific metabolites and metabolic pathways involved in microbiome-based obesogenesis.
• *In silico* replication of the Ridura experiment confirmed model efficacy.

Conclusions:

• Network-based modeling approaches offer a powerful tool for investigating host-microbiome interactions.
• The developed models provide mechanistic insights into diet-induced obesity mediated by the microbiome.
• While model specificity is a limitation, the approach is adaptable for diverse host-microbiome research.