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<p>A microbiome’s composition, stability, and response to perturbations are governed by its community interaction matrix, typically quantified through pairwise competition. However, in natural environments, microbes encounter multispecies interactions, complex conditions, and unculturable members. Moreover, evolutionary and ecological processes occur on overlapping timescales, making intra-species clonal diversity a critical but poorly understood factor influencing community interactions. Here, we present Dynamic Covariance Mapping (DCM), a general approach to infer microbiome interaction matrices from abundance time-series data. By combining DCM with high-resolution chromosomal barcoding, we quantify inter- and intra-species interactions during <i>E. coli</i> colonization in the mouse gut under three contexts: germ-free, antibiotic-perturbed, and innate microbiota. We identify distinct temporal phases in susceptible communities: (1) destabilization upon <i>E. coli</i> invasion, (2) partial recolonization of native bacteria, and (3) a quasi-steady state where <i>E. coli</i> sub-lineages coexist with resident microbes. These phases are shaped by specific interactions between <i>E. coli</i> clones and community members, emphasizing the dynamic and lineage-specific nature of microbial networks. Our results reveal how ecological and evolutionary dynamics jointly shape microbiome structure over time. The DCM framework provides a scalable method to dissect complex community interactions and is broadly applicable to bacterial ecosystems both in vitro and in situ.</p>

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Quantifying the intra- and inter-species community interactions in microbiomes by dynamic covariance mapping

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月9日

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  • Melis Gencel,
  • Gisela Marrero Cofino,
  • Cang Hui,
  • Zahra Sahaf,
  • Louis Gauthier,
  • Chloé Matta,
  • David Gagné-Leroux,
  • Derek K. L. Tsang,
  • Dana P. Philpott,
  • Sheela Ramathan,
  • Alfredo Menendez,
  • Shimon Bershtein,
  • Adrian W. R. Serohijos

摘要

A microbiome’s composition, stability, and response to perturbations are governed by its community interaction matrix, typically quantified through pairwise competition. However, in natural environments, microbes encounter multispecies interactions, complex conditions, and unculturable members. Moreover, evolutionary and ecological processes occur on overlapping timescales, making intra-species clonal diversity a critical but poorly understood factor influencing community interactions. Here, we present Dynamic Covariance Mapping (DCM), a general approach to infer microbiome interaction matrices from abundance time-series data. By combining DCM with high-resolution chromosomal barcoding, we quantify inter- and intra-species interactions during E. coli colonization in the mouse gut under three contexts: germ-free, antibiotic-perturbed, and innate microbiota. We identify distinct temporal phases in susceptible communities: (1) destabilization upon E. coli invasion, (2) partial recolonization of native bacteria, and (3) a quasi-steady state where E. coli sub-lineages coexist with resident microbes. These phases are shaped by specific interactions between E. coli clones and community members, emphasizing the dynamic and lineage-specific nature of microbial networks. Our results reveal how ecological and evolutionary dynamics jointly shape microbiome structure over time. The DCM framework provides a scalable method to dissect complex community interactions and is broadly applicable to bacterial ecosystems both in vitro and in situ.