Microbiomes Through The Looking Glass: Linking species interactions to dysbiosis through a disordered Lotka-Volterra framework

TL;DR

This paper introduces a novel theoretical framework combining disordered systems physics with metagenomic data to analyze microbiome stability and diversity, revealing distinct patterns in healthy versus diseased states.

Contribution

It develops a disordered Lotka-Volterra model linking species interactions, abundance, and stability, providing new insights into microbiome organization and disease-related variability.

Findings

Healthy microbiomes show consistent community structures with strong interactions.

Diseased microbiomes exhibit greater variability and less ecological stability.

The framework links ecological interactions to macroecological states and stability.

Abstract

The rapid advancement of environmental sequencing technologies, such as metagenomics, has significantly enhanced our ability to study microbial communities. The eubiotic composition of these communities is crucial for maintaining ecological functions and host health. Species diversity is only one facet of a healthy community organization; together with abundance distributions and interaction structures, it shapes reproducible macroecological states, i.e., joint statistical fingerprints that summarize whole-community behavior. Despite recent developments, a theoretical framework connecting empirical data with ecosystem modeling is still in its infancy, particularly in the context of disordered systems. Here, we present a novel framework that couples statistical physics tools for disordered systems with metagenomic data, explicitly linking diversity, interactions, and stability to define and compare these macroecological states. By employing the generalized Lotka-Volterra model with random interactions, we reveal two different emergent patterns of species interaction networks and species abundance distributions for healthy and diseased microbiomes. On the one hand, healthy microbiomes have similar community structures across individuals, characterized by strong species interactions and abundance diversity consistent with neutral stochastic fluctuations. On the other hand, diseased microbiomes show greater variability driven by deterministic factors, thus resulting in less ecologically stable and more divergent communities. Our findings suggest the potential of disordered system theory to characterize microbiomes and to capture the role of ecological interactions on stability and functioning.