ObjectiveGastric carcinoma development is triggered by Helicobacter pylori. Chronic H. pylori infection leads to reduced acid secretion, which may allow the growth of a different gastric bacterial community. This change in the microbiome may increase aggression to the gastric mucosa and contribute to malignancy. Our aim was to evaluate the composition of the gastric microbiota in chronic gastritis and in gastric carcinoma.DesignThe gastric microbiota was retrospectively investigated in 54 patients with gastric carcinoma and 81 patients with chronic gastritis by 16S rRNA gene profiling, using next-generation sequencing. Differences in microbial composition of the two patient groups were assessed using linear discriminant analysis effect size. Associations between the most relevant taxa and clinical diagnosis were validated by real-time quantitative PCR. Predictive functional profiling of microbial communities was obtained with PICRUSt.ResultsThe gastric carcinoma microbiota was characterised by reduced microbial diversity, by decreased abundance of Helicobacter and by the enrichment of other bacterial genera, mostly represented by intestinal commensals. The combination of these taxa into a microbial dysbiosis index revealed that dysbiosis has excellent capacity to discriminate between gastritis and gastric carcinoma. Analysis of the functional features of the microbiota was compatible with the presence of a nitrosating microbial community in carcinoma. The major observations were confirmed in validation cohorts from different geographic origins.ConclusionsDetailed analysis of the gastric microbiota revealed for the first time that patients with gastric carcinoma exhibit a dysbiotic microbial community with genotoxic potential, which is distinct from that of patients with chronic gastritis.

The amount of host DNA poses a major challenge to metagenome analysis. However, there is no guidance on the levels of host DNA, nor on the depth of sequencing needed to acquire meaningful information from whole metagenome sequencing (WMS). Here, we evaluated the impact of a wide range of amounts of host DNA and sequencing depths on microbiome taxonomic profiling using WMS. Synthetic samples with increasing levels of host DNA were created by spiking DNA of a mock bacterial community, with DNA from a mouse-derived cell line. Taxonomic analysis revealed that increasing proportions of host DNA led to decreased sensitivity in detecting very low and low abundant species. Reduction of sequencing depth had major impact on the sensitivity of WMS for profiling samples with 90% host DNA, increasing the number of undetected species. Finally, analysis of simulated datasets with fixed depth of 10 million reads confirmed that microbiome profiling becomes more inaccurate as the level of host DNA increases in a sample. In conclusion, samples with high amounts of host DNA coupled with reduced sequencing depths, decrease WMS coverage for characterization of the microbiome. This study highlights the importance of carefully considering these aspects in the design of WMS experiments to maximize microbiome analyses.

We suggest that H. pylori alters the E-cadherin-catenin complex, leading to formation of a multiproteic complex composed of CagA, c-Met, E-cadherin, and p120-catenin. This complex abrogates c-Met and p120-catenin tyrosine phosphorylation and suppresses the cell-invasive phenotype induced by H. pylori.