GUT MICROBIOTA, PREBIOTICS, PROBIOTICS, AND SYNBIOTICS IN GASTROINTESTINAL AND LIVER DISEASES: PROCEEDINGS OF A JOINT MEETING OF THE BRAZILIAN SOCIETY OF HEPATOLOGY (SBH), BRAZILIAN NUCLEUS FOR THE STUDY OF HELICOBACTER PYLORI AND MICROBIOTA (NBEHPM), AND BRAZILIAN FEDERATION OF GASTROENTEROLOGY (FBG)

Explore and Analyze the Composition and Characteristics of Intestinal Microbiota between Gastric Cancer Patients and Healthy People

Gastric cancer is one of the most common malignant tumors in the world. As the intestine is downstream of the digestive tract, the occurrence of gastric cancer may have a certain significant impact on it. Therefore, it is particularly important to find out the intestinal bacteria closely related to gastric cancer, to identify the specific flora related to gastric cancer, and to maintain the stability of the core structure of intestinal microecology in patients with gastric cancer. Based on this, the fecal samples of gastric cancer patients and healthy people were collected, and the diversity and composition of intestinal flora in patients with gastric cancer were analyzed by 16S rRNA sequencing technology. We found that there was no significant difference in the diversity and abundance of intestinal flora between gastric cancer patients and healthy people. The relative abundance of Faecalibacterium, Bifidobacterium, and Subdoligranulum in the intestinal tract of patients with gastric cancer was significantly lower than that in healthy people, while the relative abundance of Enterococcus, Streptococcus, and Bacteroides was increased. This study found that there were six kinds of intestinal microflora closely related to the occurrence of gastric cancer, which provided a theoretical basis for further exploring the pathogenesis of gastric cancer.

The Apparent Asymmetrical Relationship Between Small Bowel Bacterial Overgrowth, Endotoxemia, and Liver Steatosis and Fibrosis in Cirrhotic and Non-Cirrhotic Patients: A Single-Center Pilot Study

Introduction

Gut microbiota are a complex ecosystem harboring our intestine. They maintain human body equilibrium, while their derangement, namely, “dysbiosis“, has been associated with several gastrointestinal diseases, such as liver steatosis (NAFLD) and liver cirrhosis. Small intestinal bacterial overgrowth (SIBO) is an example of dysbiosis of the upper gastrointestinal (GI) tract.

Aim

The aim of this study is to evaluate the relationship between SIBO and levels of endotoxemia and grade of liver steatosis (LS) and liver fibrosis (LF) in hepatologic patients.

Materials and Methods

Consecutive outpatients referred to our hepatology clinic were tested for SIBO by the lactulose breath test (LBT) and peripheral blood levels of endotoxemia; LS grading and LF were assessed by abdominal ultrasound and transient elastography, respectively.

Results

Fifty-two consecutive patients (17 with alcohol abuse (4.5 ± 0.8 alcohol units per day), 4 with HCV and 2 with HBV infection, 24 of metabolic origin, 2 of autoimmune origin, and 3 with cholangiopathies; mean age 54.7 ± 8.3 years, 31 F, BMI 24.1 ± 1.1 Kg/m²) and 14 healthy volunteers (HV) (mean age 50.1 ± 4.3 years, 9 F, BMI 23.3 ± 1.1 Kg/m²) were enrolled. SIBO prevalence was significantly higher in cirrhotic (LC) vs. non-cirrhotic (LNC) patients and vs. HV (all, p < 0.05), with a significant positive trend according to Child-Pugh status (all, p < 0.05). SIBO prevalence was not correlated with LS stages (all, p = NS). Consensually, endotoxin levels were significantly higher in LC vs. LNC and vs. HV (all, p < 0.05) and significantly correlated with LF in patients with LC, according to Child-Pugh status (all, p < 0.05).

Conclusion

This study shows that SIBO prevalence and relative endotoxin blood levels seem to be significantly associated with the grade of LF vs. LS in LC. SIBO is also present under pre-cirrhotic conditions, but its prevalence seems to correlate with liver disease irreversible derangement.

Inflammatory bowel disease biomarkers of human gut microbiota selected via different feature selection methods

The tremendous boost in next generation sequencing and in the "omics" technologies makes it possible to characterize the human gut microbiome-the collective genomes of the microbial community that reside in our gastrointestinal tract. Although some of these microorganisms are considered to be essential regulators of our immune system, the alteration of the complexity and eubiotic state of microbiota might promote autoimmune and inflammatory disorders such as diabetes, rheumatoid arthritis, Inflammatory bowel diseases (IBD), obesity, and carcinogenesis. IBD, comprising Crohn's disease and ulcerative colitis, is a gut-related, multifactorial disease with an unknown etiology. IBD presents defects in the detection and control of the gut microbiota, associated with unbalanced immune reactions, genetic mutations that confer susceptibility to the disease, and complex environmental conditions such as westernized lifestyle. Although some existing studies attempt to unveil the composition and functional capacity of the gut microbiome in relation to IBD diseases, a comprehensive picture of the gut microbiome in IBD patients is far from being complete. Due to the complexity of metagenomic studies, the applications of the state-of-the-art machine learning techniques became popular to address a wide range of questions in the field of metagenomic data analysis. In this regard, using IBD associated metagenomics dataset, this study utilizes both supervised and unsupervised machine learning algorithms, (i) to generate a classification model that aids IBD diagnosis, (ii) to discover IBD-associated biomarkers, (iii) to discover subgroups of IBD patients using k-means and hierarchical clustering approaches. To deal with the high dimensionality of features, we applied robust feature selection algorithms such as Conditional Mutual Information Maximization (CMIM), Fast Correlation Based Filter (FCBF), min redundancy max relevance (mRMR), Select K Best (SKB), Information Gain (IG) and Extreme Gradient Boosting (XGBoost). In our experiments with 100-fold Monte Carlo cross-validation (MCCV), XGBoost, IG, and SKB methods showed a considerable effect in terms of minimizing the microbiota used for the diagnosis of IBD and thus reducing the cost and time. We observed that compared to Decision Tree, Support Vector Machine, Logitboost, Adaboost, and stacking ensemble classifiers, our Random Forest classifier resulted in better performance measures for the classification of IBD. Our findings revealed potential microbiome-mediated mechanisms of IBD and these findings might be useful for the development of microbiome-based diagnostics.