Social interaction, noise and antibiotic-mediated switches in the intestinal microbiota

TL;DR

This paper presents a mathematical model explaining how antibiotic treatments can cause shifts in gut microbiota composition through social interactions between tolerant and sensitive bacteria, with implications for understanding microbiome resilience.

Contribution

It introduces a novel ecological framework based on statistical physics to model microbiota dynamics and validates the grouping of microbes as tolerant or sensitive using metagenomic data.

Findings

Identification of regions of dominance and multistability in microbiota composition

Calculation of the duration of microbiota states based on microbial interactions

Validation of microbial grouping using recent metagenomic data

Abstract

The intestinal microbiota plays important roles in digestion and resistance against entero-pathogens. As with other ecosystems, its species composition is resilient against small disturbances but strong perturbations such as antibiotics can affect the consortium dramatically. Antibiotic cessation does not necessarily restore pre-treatment conditions and disturbed microbiota are often susceptible to pathogen invasion. Here we propose a mathematical model to explain how antibiotic-mediated switches in the microbiota composition can result from simple social interactions between antibiotic-tolerant and antibiotic-sensitive bacterial groups. We build a two-species (e.g. two functional-groups) model and identify regions of domination by antibiotic-sensitive or antibiotic-tolerant bacteria, as well as a region of multistability where domination by either group is possible. Using a new framework that we derived from statistical physics, we calculate the duration of each microbiota composition state. This is shown to depend on the balance between random fluctuations in the bacterial densities and the strength of microbial interactions. The singular value decomposition of recent metagenomic data confirms our assumption of grouping microbes as antibiotic-tolerant or antibiotic-sensitive in response to a single antibiotic. Our methodology can be extended to multiple bacterial groups and thus it provides an ecological formalism to help interpret the present surge in microbiome data.