Dynamic mapping exposes hidden dynamics of gut microbiome

By Dr. Sanchari Sinha Dutta, Ph.D.Reviewed by Lauren HardakerJul 14 2025

By tracking every move and mutation of native gut bacteria and E. coli, scientists have revealed how community teamwork can make or break a bacterial takeover in the gut.

Study: Quantifying the intra- and inter-species community interactions in microbiomes by dynamic covariance mapping. Image credit: Kateryna Kon/Shutterstock.com

A study published in Nature Communications reports that complex inter- and intra-species interactions between E. coli and native gut bacterial communities shape the colonization of E. coli in the mouse gut.

Background

The composition, stability, and functioning of gut microbiota are closely associated with the host’s health and disease. These microbiota characteristics are determined by interactions between different species in a community (inter-species interactions). The gold standard method to measure community interactions is to perform pairwise co-culture competition experiments in animals or bacterial cultures.

Measuring these interactions is a useful strategy for predicting simple assembly rules of the community. However, microbes concurrently experience several species and face challenging conditions in their natural environment, which is difficult to mimic in bacterial cultures growing in laboratory settings. Some of these species are even challenging to isolate and culture.

Besides inter-species interactions, microbes belonging to a single species interact with each other, mainly due to their genetic variations that arise from mutations. However, this kind of intra-species interaction and its impact on community composition and stability have rarely been tested experimentally.

Given the significance of inter- and intra-species interactions in shaping the stability and dynamics of a microbiota, the researchers developed a general approach, called Dynamic Covariance Mapping (DCM), to estimate community interactions from high-resolution community abundance time-series data. They applied DCM during E. coli colonization of the mouse gut microbiome. Unlike traditional models, DCM does not assume that interaction strengths between species are fixed over time, allowing it to capture the temporal changes and evolutionary dynamics within the community.

The study

The researchers quantified inter- and intra-species interactions during E. coli colonization in the gut microbiome of three different groups of mice: germ-free mice, mice with reduced microbiome due to antibiotic pre-treatment, and mice with an innate microbiome. They used mice treated with antibiotics but not colonized by E. coli as experimental controls.

They introduced DNA-barcoded E. coli populations in experimental mice and collected fecal samples at various timepoints to capture the kinetics of E. coli transit through the gut. They extracted bacterial genomic DNA from fecal samples and conducted deep sequencing of the barcoded region of E. coli for high-resolution lineage tracking during gut colonization. They also simultaneously tracked the community dynamics of resident bacteria using 16S rRNA profiling.

They next combined this high-resolution community abundance time-series data with DCM to quantify inter- and intra-species interactions during colonization. To identify shifts in the dynamics, the researchers used principal component analysis (PCA) in the mathematical eigenvalues derived from DCM, allowing them to define and distinguish distinct temporal “phases” of colonization and community recovery.

The authors also performed technical simulations to ensure that experimental factors, such as PCR bias and barcode dropout, did not confound the high-resolution barcode lineage tracking, confirming the reliability of their data.

Key findings

The DCM analysis identified distinct temporal phases in susceptible communities during colonization. The introduction of E. coli in the mouse gut with reduced microbiome caused an initial reduction in the abundance of some resident bacterial communities, followed by a resurgence of the resident bacterial community and subsequent coexistence with E. coli.

Further analysis of co-clustering between E. coli clones and resident communities revealed that these temporal phases are shaped by intra- and inter-species interactions. Specific E. coli clonal lineages, distinguished by barcode, repeatedly interacted with and mirrored the abundance dynamics of specific bacterial families, such as Lachnospiraceae and Enterococcaceae.

Whole genome sequencing conducted on individually picked colonies from cultured fecal samples identified mutations following colonization that were common to both germ-free and reduced microbiota mice. These mutations, which were consistently identified across different mice and individual colonies, suggest their adaptive significance and may be considered genetic mechanisms causing intra-species variations.

Key mutations included large deletions in motility-related genes, such as the flhE-flhD region, changes in genes involved in sugar metabolism, like the maltose regulon and lactose operon repressor lacI, and even synonymous changes in core metabolic genes, such as isocitrate dehydrogenase. Many of these mutations have been previously linked to adaptation in the gut, as they can affect motility, biofilm production, and fundamental metabolic function of colonized E. coli.

Some of these genetic adaptations were unique to the type of microbiome environment (germ-free or antibiotic-reduced), while others appeared across both groups, highlighting both convergent and context-specific evolutionary pressures during colonization.

Study significance

The study provides a generalized approach to quantifying microbial community interactions and their consequences on the stability and dynamics of the microbiome, particularly following perturbation triggered by invading species.

The DCM approach developed in the study represents a model approach to analyze microbial colonization's stability and distinct temporal phases, starting simply from high-resolution time-series abundance data.

The working principle of DCM is similar to general mathematical frameworks, such as the Lotka-Volterra (gLV) model, which are used to explore the dynamics of interacting species in an ecosystem. However, the gLV model does not consider the presence of mutations, intra-species variations, and colonization; instead, it assumes a constant environment. This model, therefore, cannot capture the complexities of dynamic interactions that occur during gut microbiome colonization.

On the other hand, DCM links a species' growth rate to the abundance of other community members and does not assume that the interaction strength matrix within the community is constant. By incorporating time-dependent changes and high-resolution lineage data, DCM can reveal the interplay between ecological (community-level) and evolutionary (intra-species) dynamics that drive microbial community assembly and stability.

These properties make DCM a promising model for analyzing coupled ecological-evolutionary dynamics, where the gut microbiome serves as an ecological system and intra-species genetic variations serve as evolutionary dynamics.

One potential weakness of DCM is that the abundance sampling frequency needs to sufficiently capture the richness in community dynamics since this model solely depends on microbiome abundance time-series data. High-frequency and accurate sampling are critical to ensure that rapid or subtle changes in the microbiota are not missed.

The study also highlights the importance of “community resistance,” as mice with an innate (unperturbed) microbiome largely resist E. coli colonization and show variable responses across individuals. DCM analysis indicates few or no distinct temporal phases of invasion in these resistant mice. This underscores how the diversity and structure of the resident microbiota can buffer against invasion.

As the researchers stated, the DCM, with its future advancements, could provide a framework for predicting how microbiota responds to perturbations, especially during the invasion of pathogenic species and following fecal transplant to treat human disorders.

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