bioRxivEvolutionary BiologyPreprint
Intra- and inter-species interactions drive early phases of invasion in mice gut microbiota
M. Gencel, G. M. Cofino, C. Hui, Z. Sahaf, L. Gauthier, D. Tsang, D. Philpott, S. Ramanathan, A. Menendez, S. Bershtein, A. Serohijos
Abstract
The stability and dynamics of ecological communities are dictated by interaction networks typically quantified at the level of species.1-10 But how such networks are influenced by intra-species variation (ISV) is poorly understood.11-14 Here, we use ~500,000 chromosomal barcodes to track high-resolution intra-species clonal lineages of Escherichia coli invading mice gut with the increasing complexity of gut microbiome: germ-free, antibiotic-perturbed, and innate microbiota. By co-clustering the dynamics of intra-species clonal lineages and those of gut bacteria from 16S rRNA profiling, we show the emergence of complex time-dependent interactions between E. coli clones and resident gut bacteria. With a new approach, dynamic covariance mapping (DCM), we differentiate three phases of invasion in susceptible communities: 1) initial loss of community stability as E. coli enters; 2) recolonization of some gut bacteria; and 3) recovery of stability with E. coli coexisting with resident bacteria in a quasi-steady state. Comparison of the dynamics, stability and fitness from experimental replicates and different cohorts suggest that phase 1 is driven by mutations in E. coli before colonization, while phase 3 is by de novo mutations. Our results highlight the transient nature of interaction networks in microbiomes driven by the persistent coupling of ecological and evolutionary dynamics. One-Sentence SummaryHigh-resolution lineage tracking and dynamic covariance mapping (DCM) define three distinct phases during early gut microbiome invasion.
- organism: mouse
- assay: DNA methylation
- assay: metagenomics
- research focus: ecology & biodiversity
- research focus: cell type annotation
- research focus: clustering
- approach: experimental assay
- approach: statistical model
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