Genome-wide multi-omics analysis reveals the nutrient-dependent metabolic features of mucin-degrading gut bacteria

Gut Microbes. 2023 Jan-Dec;15(1):2221811. doi: 10.1080/19490976.2023.2221811.

Abstract

The prevalence and occurrence of mucin-degrading (MD) bacteria, such as Akkermansia muciniphila and Ruminococcus gnavus, is highly associated with human health and disease states. However, MD bacterial physiology and metabolism remain elusive. Here, we assessed functional modules of mucin catabolism, through a comprehensive bioinformatics-aided functional annotation, to identify 54 A. muciniphila genes and 296 R. gnavus genes. The reconstructed core metabolic pathways coincided with the growth kinetics and fermentation profiles of A. muciniphila and R. gnavus grown in the presence of mucin and its constituents. Genome-wide multi-omics analyses validated the nutrient-dependent fermentation profiles of the MD bacteria and identified their distinct mucolytic enzymes. The distinct metabolic features of the two MD bacteria induced differences in the metabolite receptor levels and inflammatory signals of the host immune cells. In addition, in vivo experiments and community-scale metabolic modeling demonstrated that different dietary intakes influenced the abundance of MD bacteria, their metabolic fluxes, and gut barrier integrity. Thus, this study provides insights into how diet-induced metabolic differences in MD bacteria determine their distinct physiological roles in the host immune response and the gut ecosystem.

Keywords: Mucin-degrading bacteria; genome annotation; host immune response; nutrient-dependent fermentation; the gut ecosystem.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bacteria / genetics
  • Ecosystem
  • Gastrointestinal Microbiome*
  • Humans
  • Mucins*
  • Multiomics

Substances

  • Mucins

Grants and funding

This work was partly supported by the Bio & Medical Technology Development Program of the National Research Foundation (NRF) of Korea grant (2021M3A9I4021431 to DWL, 2021M3A9I4027993 to YKL, 2021M3A9I4023974 to BSK, and 2018M3A93056901 to MHN), funded by the Ministry of Science and ICT (MSIT), Republic of Korea. This work also partly supported by Bioindustrial Technology Development Program of Korea grant (200118770), funded by the Ministry of Trade, Industry and Energy (MOTIE, Korea).