group.bmj.com on March 30, 2017 - Published by http://gut.bmj.com/ Downloaded from
Gut microbiota
interest. 24 – 29 Previously, using 16S rRNA amplicon sequencing, we demonstrated taxonomic differences in gut microbiota between an elite athlete cohort of international-level rugby players and a group of age-matched high (>28 kg/m 2 ) and low (<25kg/m 2 ) body mass index (BMI) controls. 26 This analysis illustrated a signi fi cantly greater intestinal microbial diversity among the athletes compared with both control groups. This taxonomic diversity signi fi cantly correlated with exercise and dietary protein consumption. However, the possibility existed that these differences did not equate to differences at a func- tional level. Here, we re-examine the microbiome in these parti- cipants by whole metagenome shotgun sequencing to provide deeper insight into taxonomic composition and functional potential and by complementary metabolic phenotyping ana- lyses of host-derived and microbial-derived (urine and faecal, respectively) metabolic pro fi les. This analysis shows that the dif- ferences in the gut microbiota between athletes and controls is even more pronounced at the functional metabolic level than at the compositional level as previously reported and provides further rationale for prospective controlled studies to unravel the relationship between diet, exercise and the gut microbiome. RESULTS The study groups comprise professional male athletes (n=40) and healthy controls (n=46). 26 To better represent the variabil- ity of BMI in the athletes, controls were classi fi ed as either low BMI (n=22, BMI ≤ 25.2) or high BMI (n=24, BMI ≥ 26.5). Participants made no report of GI distress or alterations of GI transit time throughout the course of the initial study. Functional structure of the enteric microbiome correlates with athletic state Functional metagenomic analysis of faecal samples allowed for the prediction of the operational potential of each individual ’ s microbiota. In total, 19 300 taxonomically linked metabolic pathways were identi fi ed in at least one individual. Comparison of phylogenetic constructions derived from the 16S rRNA amplicon data of our previous study and the functional data of
this present report reveals a greater level of identi fi cation at higher levels of taxonomy (eg, phylum) for 16S sequences, 26 while the metagenomic data had greater fi delity and superior resolution of lower levels of taxonomy (eg, species) ( fi gure 1). Consistent with previous results, the microbiota of the athletes were signi fi cantly more diverse than that of both the low-BMI and high-BMI control groups at the functional level ( fi gure 2A). Furthermore, our previous fi ndings of an enrichment of Akkermansia in athletes was corroborated by the presence of sig- ni fi cantly higher proportions of metabolic pathways associated with this genus in athletes when compared with high-BMI con- trols (p<0.001). Correlation analysis revealed that, of the total 19 300 pathways, 98 were signi fi cantly altered between the three cohorts (p<0.05) (see online supplementary table S1). Subsequently, large-scale functional dissimilarity between ath- letes and controls was determined and distinct patterns of pathway composition between groups were revealed (see online supplementary fi gure S1A). This functional distinction remained true whether applied to total pathway data or to the statistically signi fi cant subset of pathways (see online supplementary fi gure S1B). Correlation of pathways present in at least one member from both cohorts further exempli fi ed the uniformity of the ath- letes and the division between the athletes and control groups (see online supplementary fi gure S1C). Separation according to group membership was further illustrated through principal coordinate analysis (PCoA), with statistical support of the sig- ni fi cant separation between the athletes and both control groups (p<0.05) ( fi gure 2B). This was also the case for the statistically signi fi cant subset of pathways (see online supplementary fi gure S1D). Principal component analysis (PCA) supplemented with a correspondence analysis and k-nearest neighbour semisupervised learning approach cast further light (ie, visualisation of robustly de fi ned class associations of speci fi c individuals within the groups) on the clustering of participants within and between cohorts (see online supplementary fi gure S1E). Pathways exhibiting statistically signi fi cant variation between the athletes and both control groups were organised accord- ing to MetaCyc metabolic pathway hierarchy classi fi cation
Figure 1 Comparison of phylogenetic constructions from metagenomic and 16S rRNA gene sequencing sourced from all participants. Phylogenetic trees derived from (A) metagenomic sequencing and (B) 16S rRNA amplicon sequencing. Taxonomic levels are assigned from centre out with kingdom-level assignment in centre and strain-level assignment in outer most ring. Dark blue radial highlights correspond to poorly identi fi ed taxonomies (ie, ‘ unknown ’ and ‘ unassigned ’ database entries). Number of assignments at each level of phylogeny is displayed below the respective graph. Taxonomic trees derived from the two sequencing approaches illustrate an advantage of metagenomic sequencing in the number of predictions of lower taxonomic levels and the frequency of full identi fi cation of taxa, while 16S rRNA sequencing grants greater insight of high-level phylogenies within the population.
BartonW, et al . Gut 2017; 0 :1 – 9. doi:10.1136/gutjnl-2016-313627
2
Powered by FlippingBook