Glycan degradation (GlyDeR) analysis predicts mammalian gut microbiota abundance and host diet-specific adaptations

Abstract

Glycans form the primary nutritional source for microbes in the human gut, and understanding their metabolism is a critical yet understudied aspect of microbiome research. Here, we present a novel computational pipeline for modeling glycan degradation (GlyDeR) which predicts the glycan degradation potency of 10,000 reference glycans based on either genomic or metagenomic data. We first validated GlyDeR by comparing degradation profiles for genomes in the Human Microbiome Project against KEGG reaction annotations. Next, we applied GlyDeR to the analysis of human and mammalian gut microbial communities, which revealed that the glycan degradation potential of a community is strongly linked to host diet and can be used to predict diet with higher accuracy than sequence data alone. Finally, we show that a microbe's glycan degradation potential is significantly correlated (R = 0.46) with its abundance, with even higher correlations for potential pathogens such as the class Clostridia (R = 0.76). GlyDeR therefore represents an important tool for advancing our understanding of bacterial metabolism in the gut and for the future development of more effective prebiotics for microbial community manipulation.

Importance: The increased availability of high-throughput sequencing data has positioned the gut microbiota as a major new focal point for biomedical research. However, despite the expenditure of huge efforts and resources, sequencing-based analysis of the microbiome has uncovered mostly associative relationships between human health and diet, rather than a causal, mechanistic one. In order to utilize the full potential of systems biology approaches, one must first characterize the metabolic requirements of gut bacteria, specifically, the degradation of glycans, which are their primary nutritional source. We developed a computational framework called GlyDeR for integrating expert knowledge along with high-throughput data to uncover important new relationships within glycan metabolism. GlyDeR analyzes particular bacterial (meta)genomes and predicts the potency by which they degrade a variety of different glycans. Based on GlyDeR, we found a clear connection between microbial glycan degradation and human diet, and we suggest a method for the rational design of novel prebiotics.

FIG 1

The GlyDeR platform. (a) A visual representation of the glycan degradation reaction performed for EC 3.2.1.115, breaking down Kojitriose into Kojibiose and glucose. (b) A schematic representation of the construction of the computational pipeline. Information is taken from multiple databases and analyzed as follows. Step 1 (lred arrow on left): by using CAZyme information and the GlyDeR algorithm, glycan degradation reactions are reconstructed. Step 2 (red arrow on right): a CAZyme table is constructed that represents the potency with which different CAZymes break different glycans. (c) GlyDeR score calculation. (Top) The organism has one enzyme (yellow PacMan) dedicated to the degradation of one glycan (purple); therefore, the GlyDeR score for the purple glycan equals 1. (Bottom) The organism has two enzymes capable of degrading 3 and 4 glycans, respectively, and therefore the GlyDeR score for the purple glycan equals 7/12. (d) GlyDeR utilization. (Meta)genomes are annotated for CAZymes by using CAZy, SEED, and KEGG databases, and with the CAZyme table a GlyDeR score can be calculated, reflecting the capacity of a (meta)genome to degrade a specific glycan.

FIG 2

Glycan degradation of gut microbiota reference genomes. (a) Distribution of species-specific GlyDeR scores (y axis) for all the glycans in KEGG. GlyDeR scores with corresponding reactions in KEGG appear on the left, while those with no KEGG reaction appear on the right (Student’s t = 6.14, P < 0.0001). (b) Bar plot comparing the animal-specific glycan degradation potential of different bacterial genera within the Bacteroidetes phylum. Each bar depicts the sum of GlyDeR scores of organisms belonging to their respected phylum. The height of the bar represents the mean, while the error bars reflect standard errors. (c) The log-log scatterplot shows the average abundance of 48 HMP strains within 325 human fecal samples (y axis) and the linear regression-predicted abundance of each individual strain (x axis) (linear regression correlation coefficient = 0.46, P = 0.0016). (d) Bar chart denoting the correlation value (height of the bar) between actual and predicted abundance from linear regression models built for each class of bacteria (x axis) based on the taxon’s GlyDeR features. The color of the bar reflects the number of species in the class. The feature extraction is explained in Materials and Methods.

FIG 3

The connection between glycan degradation and diet. (a) GlyDeR profiling analysis of the Muegge data set. Bars showing the average sum of plant-specific and animal-specific GlyDeR scores of the samples grouped according to their host diet and normalized by the number of CAZymes in each sample. A fourth group was created to segregate humans from all other omnivores. The plant- and animal-specific GlyDeR scores of herbivores and carnivores are significantly different (P = 0.04 and P = 0.0001, respectively). (b) The Yatsunenko data set. A scatterplot showing the sample projections on the first principal coordinate and colored according to the country of origin. Samples from individuals younger than 2 years old were omitted (see text).