A Machine Learning Approach to Study Glycosidase Activities from Bifidobacterium

Abstract

This study aimed to recover metagenome-assembled genomes (MAGs) from human fecal samples to characterize the glycosidase profiles of Bifidobacterium species exposed to different prebiotic oligosaccharides (galacto-oligosaccharides, fructo-oligosaccharides and human milk oligosaccharides, HMOs) as well as high-fiber diets. A total of 1806 MAGs were recovered from 487 infant and adult metagenomes. Unsupervised and supervised classification of glycosidases codified in MAGs using machine-learning algorithms allowed establishing characteristic hydrolytic profiles for B. adolescentis, B. bifidum, B. breve, B. longum and B. pseudocatenulatum, yielding classification rates above 90%. Glycosidase families GH5 44, GH32, and GH110 were characteristic of B. bifidum. The presence or absence of GH1, GH2, GH5 and GH20 was characteristic of B. adolescentis, B. breve and B. pseudocatenulatum, while families GH1 and GH30 were relevant in MAGs from B. longum. These characteristic profiles allowed discriminating bifidobacteria regardless of prebiotic exposure. Correlation analysis of glycosidase activities suggests strong associations between glycosidase families comprising HMOs-degrading enzymes, which are often found in MAGs from the same species. Mathematical models here proposed may contribute to a better understanding of the carbohydrate metabolism of some common bifidobacteria species and could be extrapolated to other microorganisms of interest in future studies.

Arabinoxylan and Pectin Metabolism in Crohn’s Disease Microbiota: An In Silico Study

“…MAGs were recovered according to the method described by Sabater et al [9], using validated workflows for metagenome assembly and taxonomic classification [5]. These computational pipelines were used to process metagenomes from patients with CD and healthy individuals to further compare their metabolic activities (Figure 1).…”

“…Genome annotation of MAGs can be used to study specific enzymatic activities such as those involved in carbohydrate metabolism [9]. Furthermore, MAGs could be used to build advanced genome-scale metabolic networks.…”

Cross-feeding of bifidobacteria promotes intestinal homeostasis: a lifelong perspective on the host health

“…Compared with other bifidobacterial species, B. bifidum has a complete set of extracellular GHs, including α-sialidase, α-fucosidase, N-Acetylβ-hexosaminidase, β-galactosidase, and LNBase, to assimilate complex HMOs and leave degradation products, such as lactose, fucose, and sialic acid, outside the cell 45,[64][65][66] . Nishiyama et al 67 identified a sialidase (SiaBb2) involved in the degradation of HMOs and mucin and may promote the adhesion and colonization of B. bifidum on the surface of the intestinal mucosa.…”

Functional Characterisation of Bile Metagenome: Study of Metagenomic Dark Matter

“…Once protein functional domains were annotated, those having Pfam entries that may correspond to bacterial MDRs described in the Transporter Classification Database (TCDB) database [19], were selected for comparative analysis. Furthermore, the distribution of these MDR functional domains in bile samples was compared to the one observed in 40 intestinal metagenomes sequenced by Kovatcheva-Datchary et al [20], which were pre-assembled in a previous work [21], and were introduced as inputs in this pipeline.…”

“…One two-domain protein structure was also determined although its specific function could not be fully elucidated by remote homology. The distribution of these functional domains in bile samples was compared to the one observed in 40 faecal metagenomes from healthy individuals [20], which were pre-assembled in a previous study [21]. As it can be observed in Table 1, most metagenomic dark matter functional domains were identified in sequence from the H-04 sample and the fosmid library, while no functional domains could be determined in H-06 probably due in part to the low number of filtered reads obtained from this sample and the higher percentage of previously assigned sequences [1].…”