Related Experiment Video
Updated: Nov 3, 2025
Structural Biology and Analytical Chemistry Approaches for Characterizing C-Glycoside Metabolic Enzymes in Human Gut Microbiota
Published on: May 23, 2025
A Machine Learning Approach to Study Glycosidase Activities from Bifidobacterium
Carlos Sabater1,2, Lorena Ruiz1,2, Abelardo Margolles1,2
1Department of Microbiology and Biochemistry of Dairy Products, Instituto de Productos Lácteos de Asturias (IPLA), Consejo Superior de Investigaciones Científicas (CSIC), Paseo Río Linares S/N, 33300 Villaviciosa, Asturias, Spain.
This study characterized Bifidobacterium glycosidase profiles using metagenome-assembled genomes (MAGs), revealing distinct hydrolytic patterns for different species. These profiles accurately identified Bifidobacterium, even with varied prebiotic exposures.
Area of Science:
- Microbiome research
- Genomics
- Enzymology
Background:
- Bifidobacterium species play crucial roles in gut health.
- Understanding their carbohydrate metabolism is key to harnessing their benefits.
- Prebiotic oligosaccharides influence gut microbiota composition and function.
Purpose of the Study:
- To characterize glycosidase profiles of Bifidobacterium species using metagenome-assembled genomes (MAGs).
- To investigate how prebiotic oligosaccharides (galacto-oligosaccharides, fructo-oligosaccharides, human milk oligosaccharides) and high-fiber diets affect these profiles.
- To develop computational models for predicting Bifidobacterium species based on their glycosidase activity.
Main Methods:
- Recovery of 1806 MAGs from 487 human fecal metagenomes.
- Application of unsupervised and supervised machine learning algorithms for glycosidase classification.
- Correlation analysis to identify associations between glycosidase families and carbohydrate metabolism.
Main Results:
- Established characteristic glycosidase profiles for five Bifidobacterium species (B. adolescentis, B. bifidum, B. breve, B. longum, B. pseudocatenulatum) with >90% classification accuracy.
- Identified specific glycosidase families (e.g., GH5_44, GH32, GH110 for B. bifidum) characteristic of each species.
- Demonstrated that these profiles are robust and can discriminate species irrespective of prebiotic exposure.
- Found strong associations between glycosidase families involved in human milk oligosaccharide (HMO) degradation within MAGs of the same species.
Conclusions:
- Characteristic glycosidase profiles can reliably identify Bifidobacterium species.
- These profiles are largely independent of dietary prebiotic interventions.
- The findings provide a foundation for understanding Bifidobacterium carbohydrate metabolism and can be extended to other gut microbes.
