Gut Microbiota Enterotypes Mediate the Effects of Dietary Patterns on Colorectal Neoplasm Risk in a Chinese Population

Species-level identification of enterotype-specific microbial markers for colorectal cancer and adenoma

Enterotypes have been shown to be an important factor for population stratification based on gut microbiota composition, leading to a better understanding of human health and disease states. Classifications based on compositional patterns will have implications for personalized microbiota-based solutions. There have been limited enterotype based studies on colorectal adenoma and cancer. Here, an enterotype-based meta-analysis of fecal shotgun metagenomic studies was performed, including 1579 samples of healthy controls (CTR), colorectal adenoma (ADN) and colorectal cancer (CRC) in total. Gut microbiota of healthy people were clustered into three enterotypes (Ruminococcus-, Bacteroides- and Prevotella-dominated enterotypes). Reference-based enterotype assignments were performed for CRC and ADN samples, using the supervised machine learning algorithm, K-nearest neighbors. Differential abundance analyses and random forest classification were conducted on each enterotype between healthy controls and CRC-ADN groups, revealing novel enterotype-specific microbial markers for non-invasive CRC screening strategies. Furthermore, we identified microbial species unique to each enterotype that play a role in the production of secondary bile acids and short-chain fatty acids, unveiling the correlation between cancer-associated gut microbes and dietary patterns. The enterotype-based approach in this study is promising in elucidating the mechanisms of differential gut microbiome profiles, thereby improving the efficacy of personalized microbiota-based solutions.

Gut microbiota characteristics of colorectal cancer patients in Hubei, China, and differences with cohorts from other Chinese regions

The research on the correlation or causality between gut microbiota and the occurrence, development, and treatment of colorectal cancer (CRC) is receiving increasing emphasis. At the same time, the incidence and mortality of colorectal cancer vary among individuals and regions, as does the gut microbiota. In order to gain a better understanding of the characteristics of the gut microbiota in CRC patients and the differences between different regions, we initially compared the gut microbiota of 25 CRC patients and 26 healthy controls in the central region of China (Hubei Province) using 16S rRNA high-throughput sequencing technology. The results showed that Corynebacterium, Enterococcus, Lactobacillus, and Escherichia-Shigella were significantly enriched in CRC patients. In addition, we also compared the potential differences in functional pathways between the CRC group and the healthy control group using PICRUSt's functional prediction analysis. We then analyzed and compared it with five cohort studies from various regions of China, including Central, East, and Northeast China. We found that geographical factors may affect the composition of intestinal microbiota in CRC patients. The composition of intestinal microbiota is crucial information that influences colorectal cancer screening, early detection, and the prediction of CRC treatment outcomes. This emphasizes the importance of conducting research on CRC-related gut microbiota in various regions of China.

Abundance of Prevotella copri in gut microbiota is inversely related to a healthy diet in patients with type 2 diabetes

While the gut microbiota is known to be influenced by habitual food intake, this relationship is seldom explored in type 2 diabetes patients. This study aims to investigate the relationship between dietary patterns and gut microbial species abundance in 113 type 2 diabetes patients (mean age, 58 years; body mass index, 29.1; glycohemoglobin [HbA1c], 8.1%). We analyzed the gut microbiota using 16S amplicon sequencing, and all patients were categorized into either the Bacteroides enterotype (57.5%, n = 65) or the Prevotella enterotype (42.5%, n = 48) using the partitioning around medoids clustering algorithm, based on the most representative genera. Patients with the Bacteroides enterotype showed better glycemic control with a 2.71 odds of HbA1c ≤ 7.0% compared to the Prevotella enterotype (95% confidence interval, 1.02-7.87; P, 0.034). Dietary habits and the nutrient composition of all patients were assessed using a validated food frequency questionnaire. It was observed that the amounts of dietary fiber consumed were suboptimal, with an average intake of 16 g per day. Additionally, we extracted four dietary patterns through factor analysis: eating-out, high-sugar foods, fish-vegetable, and fermented foods patterns. Patients with the Bacteroides enterotype had higher scores for the fish-vegetable pattern compared to the Prevotella enterotype (0.17 ± 0.13 versus -0.23 ± 0.09; P, 0.010). We further investigated the relationship between the microbiota and the four dietary patterns and found that only the fish-vegetable dietary pattern scores were correlated with principal coordinate values. A lower pattern score was associated with the accumulated abundance of the 31 significant microbial features. Among these features, Prevotella copri was identified as the most significant by using a random forest model, with an area under the receiver operating characteristic of 0.93 (95% confidence interval, 0.88-0.98). To validate these results, we conducted a custom quantitative polymerase chain reaction assay. This assay confirmed the presence of P. copri (sensitivity, 0.96; specificity, 0.97) in our cohort, with a prevalence of 47.8%, and a mean relative abundance of 21.0% in subjects harboring P. copri. In summary, type 2 diabetes patients with the Prevotella enterotype demonstrated poorer glycemic control and deviations from a healthy dietary pattern. The abundance of P. copri, as a major contributing microbial feature, was associated with the severity in the deficiency in dietary fish and vegetables. Emphasis should be placed on promoting a healthy dietary pattern and understanding the microbial correlations.

WSGMB: weight signed graph neural network for microbial biomarker identification

The stability of the gut microenvironment is inextricably linked to human health, with the onset of many diseases accompanied by dysbiosis of the gut microbiota. It has been reported that there are differences in the microbial community composition between patients and healthy individuals, and many microbes are considered potential biomarkers. Accurately identifying these biomarkers can lead to more precise and reliable clinical decision-making. To improve the accuracy of microbial biomarker identification, this study introduces WSGMB, a computational framework that uses the relative abundance of microbial taxa and health status as inputs. This method has two main contributions: (1) viewing the microbial co-occurrence network as a weighted signed graph and applying graph convolutional neural network techniques for graph classification; (2) designing a new architecture to compute the role transitions of each microbial taxon between health and disease networks, thereby identifying disease-related microbial biomarkers. The weighted signed graph neural network enhances the quality of graph embeddings; quantifying the importance of microbes in different co-occurrence networks better identifies those microbes critical to health. Microbes are ranked according to their importance change scores, and when this score exceeds a set threshold, the microbe is considered a biomarker. This framework's identification performance is validated by comparing the biomarkers identified by WSGMB with actual microbial biomarkers associated with specific diseases from public literature databases. The study tests the proposed computational framework using actual microbial community data from colorectal cancer and Crohn's disease samples. It compares it with the most advanced microbial biomarker identification methods. The results show that the WSGMB method outperforms similar approaches in the accuracy of microbial biomarker identification.