Multi-omics insights into the interplay between gut microbiota and colorectal cancer in the "microworld" age

Colorectal cancer (CRC) is a multifactorial heterogeneous disease largely due to both genetic predisposition and environmental factors including the gut microbiota, a dynamic microbial ecosystem inhabiting the gastrointestinal tract. Elucidation of the molecular mechanisms by which the gut microbiota interacts with the host may contribute to the pathogenesis, diagnosis, and promotion of CRC. However, deciphering the influence of genetic variants and interactions with the gut microbial ecosystem is rather challenging. Despite recent advancements in single omics analysis, the application of multi-omics approaches to integrate multiple layers of information in the microbiome and host to introduce effective prevention, diagnosis, and treatment strategies is still in its infancy. Here, we integrate host- and microbe-based multi-omics studies, respectively, to provide a strategy to explore potential causal relationships between gut microbiota and colorectal cancer. Specifically, we summarize the recent multi-omics studies such as metagenomics combined with metabolomics and metagenomics combined with genomics. Meanwhile, the sample size and sample types commonly used in multi-omics research, as well as the methods of data analysis, were also generalized. We highlight multiple layers of information from multi-omics that need to be verified by different types of models. Together, this review provides new insights into the clinical diagnosis and treatment of colorectal cancer patients.

Mucin degradation niche as a driver of microbiome composition and Akkermansia muciniphila abundance in a dynamic gut model is donor independent

Akkermansia muciniphila, an abundant mucin degrading intestinal bacterium, has been correlated with human health in various studies. The in vitro SHIME model was used to reach a mechanistic understanding of A. muciniphila's colonization preferences and its response to environmental parameters such as colon pH and mucins. These insight can help to identify the optimal conditions for successful in vivo application. After a period of mucin deprivation, we found that mucin supplementation resulted in significantly different microbial communities, with more Akkermansia, Bacteroides and Ruminococcus. Mucin treatment accounted for 26% of the observed variation in the microbial community at OTU level (P = 0.001), whereas the donor effect was limited (8%) (P = 0.035), indicating mucins to constitute an important ecological niche shaping the microbiota composition. The effect of colonic pH had a less profound impact on the microbiome with both pH and donor origin explaining around 10% of the variability in the dataset. Yet, higher simulated colonic pH had a positive impact on Akkermansia abundance while short chain fatty acid analysis displayed a preference for propionate production with higher colonic pH. Our results show that mucins as nutritional resource are a more important modulator of the gut microbiome than colon pH as environmental factor.