期刊
GUT MICROBES
卷 13, 期 1, 页码 -出版社
TAYLOR & FRANCIS INC
DOI: 10.1080/19490976.2021.1915673
关键词
Metabolic modeling; microbiome; fusiobacteria; metagenomic data; flux balance analysis
资金
- H2020 European Research Council [757922]
- National Institute on Aging [1U19AG063744-01, 1RF1AG058942-01]
- European Research Council (ERC) [757922] Funding Source: European Research Council (ERC)
This study combines statistical association studies with in silico modeling to explore the metabolic functions of the gut microbiome, highlighting the importance of Fusobacterium spp. in colorectal cancer and providing evidence for the accuracy of model predictions.
Characterizing the metabolic functions of the gut microbiome in health and disease is pivotal for translating alterations in microbial composition into clinical insights. Two major analysis paradigms have been used to explore the metabolic functions of the microbiome but not systematically integrated with each other: statistical screening approaches, such as metabolome-microbiome association studies, and computational approaches, such as constraint-based metabolic modeling. To combine the strengths of the two analysis paradigms, we herein introduce a set of theoretical concepts allowing for the population statistical treatment of constraint-based microbial community models. To demonstrate the utility of the theoretical framework, we applied it to a public metagenomic dataset consisting of 365 colorectal cancer (CRC) cases and 251 healthy controls, shining a light on the metabolic role of Fusobacterium spp. in CRC. We found that (1) glutarate production capability was significantly enriched in CRC microbiomes and mechanistically linked to lysine fermentation in Fusobacterium spp., (2) acetate and butyrate production potentials were lowered in CRC, and (3) Fusobacterium spp. presence had large negative ecological effects on community butyrate production in CRC cases and healthy controls. Validating the model predictions against fecal metabolomics, the in silico frameworks correctly predicted in vivo species metabolite correlations with high accuracy. In conclusion, highlighting the value of combining statistical association studies with in silico modeling, this study provides insights into the metabolic role of Fusobacterium spp. in the gut, while providing a proof of concept for the validity of constraint-based microbial community modeling.
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