Complex Carbohydrate Utilization by the Healthy Human Microbiome

<div><p>The various ecological habitats in the human body provide microbes a wide array of nutrient sources and survival challenges. Advances in technology such as DNA sequencing have allowed a deeper perspective into the molecular function of the human microbiota than has been achievable in the past. Here we aimed to examine the enzymes that cleave complex carbohydrates (CAZymes) in the human microbiome in order to determine (i) whether the CAZyme profiles of bacterial genomes are more similar within body sites or bacterial families and (ii) the sugar degradation and utilization capabilities of microbial communities inhabiting various human habitats. Upon examination of 493 bacterial references genomes from 12 human habitats, we found that sugar degradation capabilities of taxa are more similar to others in the same bacterial family than to those inhabiting the same habitat. Yet, the analysis of 520 metagenomic samples from five major body sites show that even when the community composition varies the CAZyme profiles are very similar within a body site, suggesting that the observed functional profile and microbial habitation have adapted to the local carbohydrate composition. When broad sugar utilization was compared within the five major body sites, the gastrointestinal track contained the highest potential for total sugar degradation, while dextran and peptidoglycan degradation were highest in oral and vaginal sites respectively. Our analysis suggests that the carbohydrate composition of each body site has a profound influence and probably constitutes one of the major driving forces that shapes the community composition and therefore the CAZyme profile of the local microbial communities, which in turn reflects the microbiome fitness to a body site.</p> </div

Comparison of Prevalence of Healthy Human Microbiome Samples.

<p>(A) Heatmap of genes prevalence per body site using a heat color scheme (yellow to red), indicating low to high prevalence. (B) Gene repertoire distance as calculated by 1 - Sørensen’s similarity coefficient, between samples originating from the same body habitat. Higher distances indicate a lower number of proportionally shared genes between any two samples from the same body site.</p

Comparison of Relative Abundance of Healthy Human Microbiome Samples.

<p>(A) Bray-Curtis distances were calculated on relative gene abundances between samples and grouped by same body habitat (white boxes) or distinct habitat (grey box). (B) Total relative abundances are the results of adding the normalized relative abundances of each sample. (C) Average relative abundances are the total relative abundance divided by the total number of CAZy families in each sample.</p

Comparison of Reference Genomes.

<p>Bray-Curtis distances were calculated, using the relative abundance of gene families normalized by housekeeping genes GT28 and GT51, between the 493 human associated bacterial genomes using the ecodist library in R and compared within genomes in the same bacterial family (A) or the same general body site (B).</p

CAZymes statistically significant with differences in relative abundance between digestive site samples compared to non-digestive site samples.

<p>CAZymes statistically significant with differences in relative abundance between digestive site samples compared to non-digestive site samples.</p

pCO2 underway data from the Benguela Upwelling System in southeastern South Atlantic Ocean along METEOR cruise track M76/2

Supplementary Figure 1. (PDF 1453 kb

Additional file 3: of Feed in summer, rest in winter: microbial carbon utilization in forest topsoil

Supplementary Figure 2. (PDF 1423 kb

Summary of raw and assembled data obtained in the six 454 pyrosequencing runs.

<p>Summary of raw and assembled data obtained in the six 454 pyrosequencing runs.</p

Number of CAZyme hits on individual reads and assembled sequences.

<p>Number of CAZyme hits on individual reads and assembled sequences.</p

Functional analysis of metagenomes.

<p>Analysis was realized using reads and normalized to the total number of annotated genes in each meatgenome. In the upper panel representing primary functions, gene abundance is normalized to 10³ annotated genes. The lower panel presents secondary functions with gene numbers normalized to 10⁴ annotated genes.</p