AUTHOR=Shi Yifan , Wang Peng , Zhou Da , Huang Longchang , Zhang Li , Gao Xuejin , Maitiabula Gulisudumu , Wang Siwen , Wang Xinying TITLE=Multi-Omics Analyses Characterize the Gut Microbiome and Metabolome Signatures of Soldiers Under Sustained Military Training JOURNAL=Frontiers in Microbiology VOLUME=13 YEAR=2022 URL=https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2022.827071 DOI=10.3389/fmicb.2022.827071 ISSN=1664-302X ABSTRACT=

Exercise can directly alter the gut microbiome at the compositional and functional metabolic levels, which in turn may beneficially influence physical performance. However, data how the gut microbiome and fecal metabolome change, and how they interact in soldiers who commonly undergo sustained military training are limited. To address this issue, we first performed 16S rRNA sequencing to assess the gut microbial community patterns in a cohort of 80 soldiers separated into elite soldiers (ES, n = 40) and non-elite soldiers (N-ES, n = 40). We observed that the α-diversities of the ES group were higher than those of the N-ES group. As for both taxonomical structure and phenotypic compositions, elite soldiers were mainly characterized by an increased abundance of bacteria producing short-chain fatty acids (SCFAs), including Ruminococcaceae_UCG-005, Prevotella_9, and Veillonella, as well as a higher proportion of oxidative stress tolerant microbiota. The taxonomical signatures of the gut microbiome were significantly correlated with soldier performance. To further investigate the metabolic activities of the gut microbiome, using an untargeted metabolomic method, we found that the ES and N-ES groups displayed significantly different metabolic profiles and differential metabolites were primarily involved in the metabolic network of carbohydrates, energy, and amino acids, which might contribute to an enhanced exercise phenotype. Furthermore, these differences in metabolites were strongly correlated with the altered abundance of specific microbes. Finally, by integrating multi-omics data, we identified a shortlist of bacteria-metabolites associated with physical performance, following which a random forest classifier was established based on the combinatorial biomarkers capable of distinguishing between elite and non-elite soldiers with high accuracy. Our findings suggest possible future modalities for improving physical performance through targeting specific bacteria associated with more energetically efficient metabolic patterns.