CAZyme Gene Cluster Diversity in Human Gut Microbiome

Authors

Yi Xing, University of Nebraska-LincolnFollow

First Advisor

Yanbin Yin

Committee Members

Etsuko Moriyama, Edward Deehan

Date of this Version

7-2025

Document Type

Thesis

Citation

A thesis presented to the faculty of the Graduate College at the University of Nebraska in partial fulfillment of requirements for the degree of Master of Science

Major: Food Science and Technology

Under the supervision of Professor Yanbin Yin

Lincoln, Nebraska, July 2025

Comments

Copyright 2025, Yi Xing. Used by permission

Abstract

In gut microbiome research, carbohydrate-active enzyme gene clusters (CGCs) have emerged as key functional units for understanding microbial glycan degradation. Unlike taxonomic or broad pathway annotations, CGCs offer gene-cluster-level resolution and capture substrate-specific microbial functions. However, their diversity and distribution in relation to host metabolic phenotypes, such as obesity, remain poorly characterized. This study tests the hypothesis that the composition and abundance of fiber-targeting CGCs vary between obese and healthy human gut microbiomes, reflecting distinct microbial carbohydrate utilization strategies. To examine this, we constructed a high-quality reference CGC dataset comprising 94,019 clusters from the Unified Human Gastrointestinal Genome and profiled shotgun gut metagenomes from 40 individuals (20 obese, 20 healthy). We observed notable differences in both the abundance and substrate preferences of CGCs between the two groups. Specifically, 11 CGCs were significantly enriched in healthy individuals, while 21 were significantly enriched in obese individuals. Obese-associated CGCs displayed elevated abundance of glycoside hydrolase families such as GH13, GH57, and GH3, suggesting increased ability to metabolize readily digestible polysaccharides, including starch and β-glucan. In contrast, healthy-associated CGCs showed enrichment in enzymes targeting host-derived and mucosal glycans, including GH2, GH92, and GT2, and exhibited significantly higher abundance of arabinogalactan-associated clusters. These patterns suggest differing microbial strategies for carbohydrate metabolism, potentially contributing to host metabolic phenotypes. By establishing a scalable and reproducible workflow for CGC profiling, this study offers new insights into the functional dynamics of the gut microbiome and highlights the potential of CGC-based analyses for understanding diet-microbe-host interactions in metabolic health. Our findings lay the groundwork for future research into microbiome-mediated metabolic modulation and the development of precision dietary or microbial interventions.

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