Objective: Gastric 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. Design: The 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. Results: The 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. Conclusions: Detailed 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.

Cathepsins are proteolytic enzymes that function in the endocytic pathway, especially in lysosomes, where they contribute directly to pathogen killing or indirectly, by their involvement in the antigen presentation pathways. Mycobacterium tuberculosis (MTB) is a facultative intracellular pathogen that survives inside the macrophage phagosomes by inhibiting their maturation to phagolysosomes and thus avoiding a low pH and protease-rich environment. We previously showed that mycobacterial inhibition of the proinflammatory transcription factor NF-κB results in impaired delivery of lysosomal enzymes to phagosomes and reduced pathogen killing. Here, we elucidate how MTB also controls cathepsins and their inhibitors, cystatins, at the level of gene expression and proteolytic activity. MTB induced a general down-regulation of cathepsin expression in infected cells, and inhibited IFNγ-mediated increase of cathepsin mRNA. We further show that a decrease in cathepsins B, S and L favours bacterial survival within human primary macrophages. A siRNA knockdown screen of a large set of cathepsins revealed that almost half of these enzymes have a role in pathogen killing, while only cathepsin F coincided with MTB resilience. Overall, we show that cathepsins are important for the control of MTB infection, and as a response, it manipulates their expression and activity to favour its intracellular survival.

The high background of host RNA poses a major challenge to metatranscriptome analysis of human samples. Hence, metatranscriptomics has been mainly applied to microbe-rich samples, while its application in human tissues with low ratio of microbial to host cells has yet to be explored. Since there is no computational workflow specifically designed for the taxonomic and functional analysis of this type of samples, we propose an effective metatranscriptomics strategy to accurately characterize the microbiome in human tissues with a low ratio of microbial to host content. We experimentally generated synthetic samples with well-characterized bacterial and host cell compositions, and mimicking human samples with high and low microbial loads. These synthetic samples were used for optimizing and establishing the workflow in a controlled setting. Our results show that the integration of the taxonomic analysis of optimized Kraken 2/Bracken with the functional analysis of HUMAnN 3 in samples with low microbial content, enables the accurate identification of a large number of microbial species with a low false-positive rate, while improving the detection of microbial functions. The effectiveness of our metatranscriptomics workflow was demonstrated in synthetic samples, simulated datasets, and most importantly, human gastric tissue specimens, thus providing a proof of concept for its applicability on mucosal tissues of the gastrointestinal tract. The use of an accurate and reliable metatranscriptomics approach for human tissues with low microbial content will expand our understanding of the functional activity of the mucosal microbiome, uncovering critical interactions between the microbiome and the host in health and disease.