Phylogroup Homeostasis of Escherichia coli in the Human Gut Reflects the Physiological State of the Host

TL;DR

This study shows how different types of Escherichia coli in the gut change based on the health and treatments of the host, offering new insights into gut microbiome dynamics.

Contribution

The study introduces a novel application of k-mer barcoding to track E. coli phylogroups in gut microbiomes and links their abundance to host physiological states.

Findings

E. coli phylogroups show bidirectional abundance shifts in response to host physiological changes.

Phylogroup patterns can statistically distinguish between different host physiological states using machine learning.

Correlations with Prevotella and Bacteroides suggest E. coli phylogroups influence gut microbial networks.

Abstract

The advent of alignment-free k-mer barcoding has revolutionized taxonomic analysis, enabling bacterial identification at phylogroup resolution within natural communities. We applied this approach to characterize Escherichia coli intraspecific diversity in human gut microbiomes using publicly available datasets representing diverse human physiological states. By estimating the relative abundance of eight E. coli phylogroups defined by their 18-mer markers in 558 fecal samples, we compared their distribution between gut microbiomes of healthy individuals, patients with chronic bowel diseases and volunteers subjected to various external interventions. Across all datasets, phylogroups exhibited bidirectional abundance shifts in response to host physiological changes, indicating an inherent bimodality in their adaptive strategies. Correlation analysis of phylogroup persistence revealed positive intraspecific connectivity networks and dependence of their patterns on both acute interventions like antibiotic or probiotic treatment and chronic bowel disorders. Along with predominantly negative correlations with Bacteroides, we observed a transition from positive to negative associations with Prevotella in Prevotella-rich microbiomes. Several interspecific correlations individually established by E. coli phylogroups with dominant taxa suggest their potential role in shaping intraspecific networks. Machine learning techniques statistically confirmed an ability of phylogroup patterns to discriminate the physiological state of the host and virtual diagnostic assays opened a way to optimize intraspecific phylotyping for medical applications.

Figures