Gastric cancer (GC) is a common cancer in Asian, which arises due to a combination of genetic and environmental factors. Chronic inflammation with Helicobacter pylori is a major risk factor for GC. However, only about 3% of H. pylori-infected people develop GC. Changes in gastric microbial composition are associated with GC, but the role of bacteria other than H. pylori is yet to be established. We seek to characterize the microbial changes associated with histologic stages of gastric tumorigenesis. We performed 16S rRNA gene analysis of gastric mucosal samples from 81 cases including superficial gastritis (SG), atrophic gastritis (AG), intestinal metaplasia (IM) and gastric cancer (GC), to determine mucosal microbiome dysbiosis across stages of GC. We validated the results in mucosal samples of 126 cases from Inner Mongolia, China. Moreover, we identified differences in bacterial interactions across stages of gastric carcinogenesis in addition to microbial compositional changes. The significant enrichments and network centralities suggest potentially important roles of P. stomatis, D. pneumosintes, S. exigua, P. micra and S. anginosus in gastric cancer progression (Gut 2018). To identify non-H. pylori gastric microbes that are associated with inflammation, GA and IM post-treatment of H. pylori infection, we evaluated a total of 295 cases received one-week course omeprazole, amoxicillin and clarithromycin (OAC) while 292 cases received placebo. Subjects underwent endoscopy with biopsy at baseline and after one year. We demonstrated that Oral bacterial genera Peptostreptococcus, Streptococcus, Parvimonas, Prevotella and Porphyromonas are associated with the progression of gastric inflammation, GA and IM following anti-H. pylori therapy. Treatment targeting these bacteria may be prescribed to patients to reduce the risk of developing gastric cancer (unpublished data). The findings may help to provide new insights for the molecular pathogenesis of gastric carcinogenesis, the potential diagnostic bacteria markers for GC and the control of this major malignance.

The Center for Translational Microbiome Research (CTMR) started in January 2016 as a collaboration between Karolinska Institutet, Science for Life Laboratory and Ferring Pharmaceuticals. Since then, a broad technical, biological, clinical and epidemiological platform for studying complex microbiological communities in well-defined human materials has been established. CTMR aims to better understand the contribution of the human microbiome to physiology and pathophysiology with the goal to open opportunities for development of novel therapies in the area of gastroenterology and reproductive health. The talk will present details on CTMR´s efforts to define what is healthy in the human gut and vaginal microbiome and approaches to develop therapies or lifestyle interventions to change a dysbiotic profile back to normal again.

Gut mirobiome is associated with many chronic metabolic diseases, therefore gut microbiome check has a potential for disease diagnosis and risk assessment. Nevertheless, gut microbial composition is highly complex and could be affected by many host factors and become highly variable among individuals. Previous studies have reported inconsistent dysbiosis patterns for the same disease but the underlying reason remained unclear and this phenomenon hindered the application of gut microbiome in diseases diagnosis. We conducted the to-date largest gut microbiome and chronic disease survey (GGMP, Guangdong Gut Microbiome Project). Our analysis showed that geography has a much larger effect in correlating with gut microbiome variations than age, disease and lifestyle do. Gut microbiota biomarkers and machine learning models for chronic diseases were significantly limited by geographical effect. We further proposed a general framework to understand dysbiosis patterns for various diseases that the disease effect shall be compared with geographical effect to determine if the disease-related microbiome biomarkers can be generally applied or specific to a certain region. We then analyzed the dysbiosis pattern for metabolic syndrome using this population-based dataset and found the pattern is phylogenetically consistent which also showed additive effect with sedentary lifestyles in associating with metabolic syndrome epidemic. Our data indicated an association between gut microbiome and disease epidemiology. On the other hand, our study also warns commercial services of gut check that is not based on population-level databases. We emphasize that large-scale population-level dataset and sophisticated analysis are essential to support future applications of gut microbiome.