Increasing correlation between oral and gastric microbiota during gastric carcinogenesis.

Background/Aims: Recent research has increasingly focused on the role of the gastric microbiome in the development of gastric cancer. We aimed to investigate the changes in the microbiome during gastric carcinogenesis in structural and functional aspects, with a specific focus on the association between oral and gastric microbiomes. Methods: We collected saliva, gastric juice, and gastric tissue samples from 141 patients at different stages of gastric carcinogenesis and processed them for microbiome analysis using 16S rRNA gene profiling. The alpha and beta diversities were analyzed, and the differences in microbiome composition and function profiles were analyzed among the groups, as well as the correlation between changes in the oral and gastric microbiomes during carcinogenesis. Results: We observed significant differences in microbial diversity and composition between the disease and control groups, primarily in the gastric juice. Specific bacterial strains, including Schaalia odontolytica, Streptococcus cristatus, and Peptostreptococcus stomatis, showed a significant increase in abundance in the gastric juice in the low-grade dysplasia and gastric cancer groups. Notably, the correlation between the oral and gastric microbiota compositions, increased as the disease progressed. Predictive analysis of the metagenomic functional profiles revealed changes in functional pathways that may be associated with carcinogenesis (ABC transport and two-component systems). Conclusions: During gastric carcinogenesis, the abundance of oral commensals associated with cancer increased in the stomach. The similarity in microbial composition between the stomach and oral cavity also increased, implying a potential role of oral-gastric bacterial interactions in gastric cancer development.

Dietary Efficacy Evaluation by Applying a Prediction Model Using Clinical Fecal Microbiome Data of Colorectal Disease to a Controlled Animal Model from an Obesity Perspective.

Obesity associated with a Western diet such as a high-fat diet (HFD) is a known risk factor for inflammatory bowel disease (IBD) and colorectal cancer (CRC). In this study, we aimed to develop fecal microbiome data-based deep learning algorithms for the risk assessment of colorectal diseases. The effects of a HFD and a candidate food (Nypa fruticans, NF) on IBD and CRC risk reduction were also evaluated. Fecal microbiome data were obtained from 109 IBD patients, 111 CRC patients, and 395 healthy control (HC) subjects by 16S rDNA amplicon sequencing. IBD and CRC risk assessment prediction models were then constructed by deep learning algorithms. Dietary effects were evaluated based on fecal microbiome data from rats fed on a regular chow diet (RCD), HFD, and HFD plus ethanol extracts or water extracts of NF. There were significant differences in taxa when IBD and CRC were compared with HC. The diagnostic performance (area under curve, AUC) of the deep learning algorithm was 0.84 for IBD and 0.80 for CRC prediction. Based on the rat fecal microbiome data, IBD and CRC risks were increased in HFD-fed rats versus RCD-fed rats. Interestingly, in the HFD-induced obesity model, the IBD and CRC risk scores were significantly lowered by the administration of ethanol extracts of NF, but not by the administration of water extracts of NF. In conclusion, changes in the fecal microbiome of obesity by Western diet could be important risk factors for the development of IBD and CRC. The risk prediction model developed in this study could be used to evaluate dietary efficacy.

Microbial changes in stool, saliva, serum, and urine before and after anti-TNF-α therapy in patients with inflammatory bowel diseases.

Inflammatory bowel diseases (IBD), including Crohn's disease and ulcerative colitis, are chronic immune-mediated intestinal inflammatory disorders associated with microbial dysbiosis at multiple sites, particularly the gut. Anti-tumor necrosis factor-α (TNF-α) agents are important treatments for IBD. We investigated whether microbiome changes at multiple sites can predict the effectiveness of such treatment in IBD. Stool, saliva, serum, and urine biosamples were collected from 19 IBD patients before (V1) and 3 months after (V2) anti-TNF-α treatment, and 19 healthy subjects (control). Microbiota analysis was performed using extracellular vesicles (EVs; all four sample types) and next-generation sequencing (NGS; stool and saliva). The stool, using NGS analysis, was the only sample type in which α-diversity differed significantly between the IBD and control groups at V1 and V2. Relative to non-responders, responders to anti-TNF-α treatment had significantly higher levels of Firmicutes (phylum), Clostridia (class), and Ruminococcaceae (family) in V1 stool, and Prevotella in V1 saliva. Non-responders had significantly higher V2 serum and urine levels of Lachnospiraceae than responders. Finally, Acidovorax caeni was detected in all V1 sample types in responders, but was not detected in non-responders. Microbiome changes at multiple sites may predict the effectiveness of anti-TNF-α treatment in IBD, warranting further research.

Dysbiotic change in gastric microbiome and its functional implication in gastric carcinogenesis.

Although there is a growing interest in the role of gastric microbiome on the development of gastric cancer, the exact mechanism is largely unknown. We aimed to investigate the changes of gastric microbiome during gastric carcinogenesis, and to predict the functional potentials of the microbiome involved in the cancer development. The gastric microbiome was analyzed using gastric juice samples from 88 prospectively enrolled patients, who were classified into gastritis, gastric adenoma, or early/advanced gastric cancer group. Differences in microbial diversity and composition were analyzed with 16S rRNA gene profiling, using next-generation sequencing method. Metagenomic biomarkers were selected using logistic regression models, based on relative abundances at genus level. We used Tax4Fun to predict possible functional pathways of gastric microbiome involved in the carcinogenesis. The microbial diversity continuously decreased in its sequential process of gastric carcinogenesis, from gastritis to gastric cancer. The microbial composition was significantly different among the four groups of each disease status, as well as between the cancer group and non-cancer group. Gastritis group was differently enriched with genera Akkermansia and Lachnospiraceae NK4A136 Group, whereas the cancer group was enriched with Lactobacillus and Veillonella. Predictive analysis of the functional capacity of the microbiome suggested enrichment or depletion of several functional pathways related to carcinogenesis in the cancer group. There are significant changes in the diversity and composition of gastric microbiome during the gastric carcinogenesis process. Gastric cancer was characterized with microbial dysbiosis, along with functional changes potentially favoring carcinogenesis.

Bacteria-Derived Extracellular Vesicles in Urine as a Novel Biomarker for Gastric Cancer: Integration of Liquid Biopsy and Metagenome Analysis.

Early detection is crucial for improving the prognosis of gastric cancer, but there are no non-invasive markers for the early diagnosis of gastric cancer in real clinical settings. Recently, bacteria-derived extracellular vesicles (EVs) emerged as new biomarker resources. We aimed to evaluate the microbial composition in gastric cancer using bacteria-derived EVs and to build a diagnostic prediction model for gastric cancer with the metagenome data. Stool, urine, and serum samples were prospectively collected from 453 subjects (gastric cancer, 181; control, 272). EV portions were extracted from the samples for metagenome analysis. Differences in microbial diversity and composition were analyzed with 16S rRNA gene profiling, using the next-generation sequencing method. Biomarkers were selected using logistic regression models based on relative abundances at the genus level. The microbial composition of healthy groups and gastric cancer patient groups was significantly different in all sample types. The compositional differences of various bacteria, based on relative abundances, were identified at the genus level. Among the diagnostic prediction models for gastric cancer, the urine-based model showed the highest performance when compared to that of stool or serum. We suggest that bacteria-derived EVs in urine can be used as novel metagenomic markers for the non-invasive diagnosis of gastric cancer by integrating the liquid biopsy method and metagenome analysis.

Fecal microbiota changes with fermented kimchi intake regulated either formation or advancement of colon adenoma.

Gut bacteria might contribute in early stage of colorectal cancer through the development and advancement of colon adenoma, by which exploring either beneficial bacteria, which are decreased in formation or advancement of colon adenoma and harmful bacteria, which are increased in advancement of colon adenoma may result in implementation of dietary interventions or probiotic therapies to functional means for prevention. Korean fermented kimchi is one of representative probiotic food providing beneficiary microbiota and exerting significant inhibitory outcomes in both APC/Min+ polyposis model and colitis-associated cancer. Based on these backgrounds, we performed clinical trial to document the changes of fecal microbiota in 32 volunteers with normal colon, simple adenoma, and advanced colon adenoma with 10 weeks of fermented kimchi intake. Each amplicon is sequenced on MiSeq of Illumina and the sequence reads were clustered into Operational Taxonomic Units using VSEARCH and the Chao Indices, an estimator of richness of taxa per individual, were estimated to measure the diversity of each sample. Though significant difference in α or ß diversity was not seen between three groups, kimchi intake significantly led to significant diversity of fecal microbiome. After genus analysis, Acinobacteria, Cyanobacteria, Clostridium sensu, Turicibacter, Gastronaeophillales, H. pittma were proven to be increased in patients with advanced colon adenoma, whereas Enterococcua Roseburia, Coryobacteriaceau, Bifidobacterium spp., and Akkermansia were proven to be significantly decreased in feces from patients with advanced colon adenoma after kimchi intake. Conclusively, fermented kimchi plentiful of beneficiary microbiota can afford significant inhibition of either formation or advancement of colon adenoma.

Lung Disease Diagnostic Model Through IgG Sensitization to Microbial Extracellular Vesicles.

PURPOSE: Recently, there has been a rise in the interest to understand the composition of indoor dust due to its association with lung diseases such as asthma, chronic obstructive pulmonary disease (COPD) and lung cancer. Furthermore, it has been found that bacterial extracellular vesicles (EVs) within indoor dust particles can induce pulmonary inflammation, suggesting that these might play a role in lung disease. METHODS: We performed microbiome analysis of indoor dust EVs isolated from mattresses in apartments and hospitals. We developed diagnostic models based on the bacterial EVs antibodies detected in serum samples via enzyme-linked immunosorbent assay (ELISA) in this analysis. RESULTS: Proteobacteria was the most abundant bacterial EV taxa observed at the phylum level while Pseudomonas, Enterobacteriaceae (f) and Acinetobacter were the most prominent organisms at the genus level, followed by Staphylococcus. Based on the microbiome analysis, serum anti-bacterial EV immunoglobulin G (IgG), IgG1 and IgG4 were analyzed using ELISA with EV antibodies that targeted Staphylococcus aureus, Acinetobacter baumannii, Enterobacter cloacae and Pseudomonas aeruginosa. The levels of anti-bacterial EV antibodies were found to be significantly higher in patients with asthma, COPD and lung cancer compared to the healthy control group. We then developed a diagnostic model through logistic regression of antibodies that showed significant differences between groups with smoking history as a covariate. Four different variable selection methods were compared to construct an optimal diagnostic model with area under the curves ranging from 0.72 to 0.81. CONCLUSIONS: The results of this study suggest that ELISA-based analysis of anti-bacterial EV antibodies titers can be used as a diagnostic tool for lung disease. The present findings provide insights into the pathogenesis of lung disease as well as a foundation for developing a novel diagnostic methodology that synergizes microbial EV metagenomics and immune assays.

Colorectal cancer diagnostic model utilizing metagenomic and metabolomic data of stool microbial extracellular vesicles.

Colorectal cancer (CRC) is the most common type cancers in the world. CRC occurs sporadically in the majority of cases, indicating the predominant cause of the disease are environmental factors. Diet-induced changes in gut-microbiome are recently supposed to contribute on epidemics of CRC. This study was aimed to investigate the association of metagenomics and metabolomics in gut extracellular vesicles (EVs) of CRC and healthy subjects. A total of 40 healthy volunteers and 32 patients with CRC were enrolled in this study. Metagenomic profiling by sequencing 16 S rDNA was performed for assessing microbial codiversity. We explored the small molecule metabolites using gas chromatography-time-of-flight mass spectrometry. In total, stool EVs were prepared from 40 healthy volunteers and 32 patients with CRC. Metagenomic profiling demonstrated that bacterial phyla, particularly of Firmicutes and Proteobacteria, were significantly altered in patients with colorectal cancer. Through metabolomics profiling, we determined seven amino acids, four carboxylic acids, and four fatty acids; including short-chain to long chain fatty acids that altered in the disease group. Binary logistic regression was further tested to evaluate the diagnostic performance. In summary, the present findings suggest that gut flora dysbiosis may result in alternation of amino acid metabolism, which may be correlated with the pathogenesis of CRC.

Development of a colorectal cancer diagnostic model and dietary risk assessment through gut microbiome analysis.

Colorectal cancer (CRC) is the third most common form of cancer and poses a critical public health threat due to the global spread of westernized diets high in meat, cholesterol, and fat. Although the link between diet and colorectal cancer has been well established, the mediating role of the gut microbiota remains elusive. In this study, we sought to elucidate the connection between the gut microbiota, diet, and CRC through metagenomic analysis of bacteria isolated from the stool of CRC (n = 89) and healthy (n = 161) subjects. This analysis yielded a dozen genera that were significantly altered in CRC patients, including increased Bacteroides, Fusobacterium, Dorea, and Porphyromonas prevalence and diminished Pseudomonas, Prevotella, Acinetobacter, and Catenibacterium carriage. Based on these altered genera, we developed two novel CRC diagnostic models through stepwise selection and a simplified model using two increased and two decreased genera. As both models yielded strong AUC values above 0.8, the simplified model was applied to assess diet-based CRC risk in mice. Mice fed a westernized high-fat diet (HFD) showed greater CRC risk than mice fed a regular chow diet. Furthermore, we found that nonglutinous rice, glutinous rice, and sorghum consumption reduced CRC risk in HFD-fed mice. Collectively, these findings support the critical mediating role of the gut microbiota in diet-induced CRC risk as well as the potential of dietary grain intake to reduce microbiota-associated CRC risk. Further study is required to validate the diagnostic prediction models developed in this study as well as the preventive potential of grain consumption to reduce CRC risk.