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Metagenomic Association Analysis of Gut Symbiont Limosilactobacillus reuteri Without Host-Specific Genome Isolation

Cited 1 time in Web of Science Cited 3 time in Scopus

Park, Sein; Steinegger, Martin; Cho, Ho-Seong; Chun, Jongsik

Issue Date
Frontiers Media S.A.
Frontiers in Microbiology, Vol.11, p. 585622
Limosilactobacillus reuteri is a model symbiont that colonizes the guts of vertebrates in studies on host adaptation of the gut symbiont. Previous studies have investigated host-specific phylogenetic and functional properties by isolating the genomic sequence. This dependency on genome isolation is a significant bottleneck. Here, we propose a method to study the association between L. reuteri and its hosts directly from metagenomic reads without strain isolation using pan-genomes. We characterized the host-specificity of L. reuteri in metagenomic samples, not only in previously studied organisms (mice and pigs) but also in dogs. For each sample, two types of profiles were generated: (1) genome-based strain type abundance profiles and (2) gene composition profiles. Our profiles showed host-association of L. reuteri in both phylogenetic and functional aspects without depending on host-specific genome isolation. We observed not only the presence of host-specific lineages, but also the dominant lineages associated with the different hosts. Furthermore, we showed that metagenome-assembled genomes provide detailed insights into the host-specificity of L. reuteri. We inferred evolutionary trajectories of host-associative L. reuteri strains in the metagenomic samples by placing the metagenome-assembled genomes into a phylogenetic tree and identified novel host-specific genes that were unannotated in existing pan-genome databases. Our pan-genomic approach reduces the need for time-consuming and expensive host-specific genome isolation, while producing consistent results with previous host-association findings in mice and pigs. Additionally, we predicted associations that have not yet been studied in dogs.
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  • College of Natural Sciences
  • School of Biological Sciences
Research Area Development of algorithms to search, cluster and assemble sequence data, Metagenomic analysis, Pathogen detection in sequencing data


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