Cataloging metagenome-assembled genomes and microbial genes from the athlete gut microbiome

Abstract

Aim: Exercise has been increasingly recognized as a potential influencer of the gut microbiome. Nevertheless, findings remain incongruous, particularly in relation to sport-specific patterns.

Methods: In this study, we harness all publicly available data from athlete gut microbiome shotgun studies to explore how exercise may influence the gut microbiota through metagenomic assembly supplemented with short read-based taxonomic profiling. Through this analysis, we provide insights into exercise-associated taxa and genes, including the identification and annotation of putative novel species from the analysis of approximately 2,000 metagenome-assembled genomes (MAGs), classified as high-quality (HQ) MAGs and assembled as part of this investigation.

Results: Our metagenomic analysis unveiled potential athlete-associated microbiome patterns at both the phylum and species levels, along with their associated microbial genes, across a diverse array of sports and individuals. Specifically, we identified 76 species linked to exercise, with a notable prevalence of the Firmicutes phylum. Furthermore, our analysis detected MAGs representing potential novel species across various phyla, including Bacteroidota, Candidatus Melainabacteria, Elusimicrobia, Firmicutes, Lentisphaerae, Proteobacteria, Tenericutes, and Verrucomicrobiota.

Conclusion: In summary, this catalog of MAGs and their corresponding genes stands as the most extensive collection yet compiled from athletes. Our analysis has discerned patterns in genes associated with exercise. This underscores the value of employing shotgun metagenomics, specifically a MAG recovery strategy, for pinpointing sport-associated microbiome signatures. Furthermore, the identification of novel MAGs holds promise for developing probiotics and deepening our comprehension of the intricate interplay between fitness and the microbiome.

Keywords

Athlete / microbiome / MAGs / genes / novel species / in silico

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