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An In Vitro Batch-culture Model to Estimate the Effects of Interventional Regimens on Human Fecal Microbiota

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Predicting gut microbiota dynamics in obese individuals from cross-sectional data.

Ena Melvan^1,2, Andrew P Allen¹, Tea Vuckovic²

¹School of Natural Sciences, Macquarie University, Sydney, NSW, Australia.

Frontiers in Cellular and Infection Microbiology

|June 25, 2025

Summary

Obesity is linked to gut microbiome changes. Our study reveals that microbial interaction networks, not just abundance, are key in obesity, enabling personalized dietary interventions.

Keywords:

GLV method dietary interventions gut microbiota microbial interactions microbiome dynamics obesity personalized nutrition

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Area of Science:

Microbiome research
Systems biology
Obesity research

Background:

Obesity affects 39% of adults globally.
Gut microbiota is implicated in obesity, but research often overlooks microbial interactions.

Purpose of the Study:

To investigate the role of dynamic microbial interactions in obesity.
To develop a method for inferring microbiota dynamics from static data.

Main Methods:

Applied the BEEM-Static model (a Lotka-Volterra model) to 16S rRNA gut microbiome data.
Analyzed cross-sectional data from 2,435 lean and obese individuals across six public datasets.

Main Results:

Identified 57 significant microbial interactions in obese individuals (79% negative) vs. 37 in lean (92% negative).
Observed stronger inhibition of Firmicutes by Bacteroidetes in obese individuals.
Found higher carrying capacities for Firmicutes and Proteobacteria in obese populations.

Conclusions:

Microbial interaction networks are crucial in obesity-related dysbiosis, beyond taxonomic abundance.
The developed approach, termed Optibiomics, allows microbiota dynamics inference from single time points.
This opens avenues for tailored dietary interventions for obesity management.