ReactomeGSA: new features to simplify public data reuse.

MOTIVATION: ReactomeGSA is part of the Reactome knowledgebase and one of the leading multi-omics pathway analysis platforms. ReactomeGSA provides access to quantitative pathway analysis methods supporting different 'omics data types. Additionally, ReactomeGSA can process different datasets simultaneously, leading to a comparative pathway analysis that can also be performed across different species. RESULTS: We present a major update to the ReactomeGSA analysis platforms that greatly simplifies the reuse and direct integration of public data. In order to increase the number of available datasets, we developed the new grein_loader Python application that can directly fetch experiments from the GREIN resource. This enabled us to support both EMBL-EBI's Expression Atlas and GEO RNA-seq Experiments Interactive Navigator within ReactomeGSA. To further increase the visibility and simplify the reuse of public datasets, we integrated a novel search function into ReactomeGSA that enables users to search for public datasets across both supported resources. Finally, we completely re-developed ReactomeGSA's web-frontend and R/Bioconductor package to support the new search and loading features, and greatly simplify the use of ReactomeGSA. AVAILABILITY AND IMPLEMENTATION: The new ReactomeGSA web frontend is available at https://www.reactome.org/gsa with an built-in, interactive tutorial. The ReactomeGSA R package (https://bioconductor.org/packages/release/bioc/html/ReactomeGSA.html) is available through Bioconductor and shipped with detailed documentation and vignettes. The grein_loader Python application is available through the Python Package Index (pypi). The complete source code for all applications is available on GitHub at https://github.com/grisslab/grein_loader and https://github.com/reactome.

Human gut metatranscriptome changes induced by a fermented milk product are associated with improved tolerance to a flatulogenic diet.

Healthy plant-based diets rich in fermentable residues may induce gas-related symptoms, possibly mediated by the gut microbiota. We previously showed that consumption of a fermented milk product (FMP) containing Bifidobacterium animalis subsp. lactis CNCM I-2494 and lactic acid bacteria improved gastrointestinal (GI) comfort in response to a flatulogenic dietary challenge in healthy individuals. To study the effects of the FMP on gut microbiota activity from those participants, we conducted a metatranscriptomic analysis of fecal samples (n = 262), which were collected during the ingestion of a habitual diet and two series of a 3-day high-residue challenge diet, before and following 28-days of FMP consumption. Most of the FMP species were detected or found enriched upon consumption of the product. FMP mitigated the effect of a flatulogenic diet on gas-related symptoms in several ways. First, FMP consumption was associated with the depletion of gas-producing bacteria and increased hydrogen to methane conversion. It also led to the upregulation of activities such as replication and downregulation of functions related to motility and chemotaxis. Furthermore, upon FMP intake, metabolic activities such as carbohydrate metabolism, attributed to B. animalis and S. thermophilus, were enriched; these activities were coincidentally found to be negatively associated with several GI symptoms. Finally, a more connected microbial ecosystem or mutualistic relationship among microbes was found in responders to the FMP intervention. Taken together, these findings suggest that consumption of the FMP improved the tolerance of a flatulogenic diet through active interactions with the resident gut microbiota.

Integrating Dietary Data into Microbiome Studies: A Step Forward for Nutri-Metaomics.

Diet is recognised as the main driver of changes in gut microbiota. However, linking habitual dietary intake to microbiome composition and activity remains a challenge, leaving most microbiome studies with little or no dietary information. To fill this knowledge gap, we conducted two consecutive studies (n = 84: a first pilot study (n = 40) to build a web-based, semi-quantitative simplified FFQ (sFFQ) based on three 24-h dietary recalls (24HRs); a second study (n = 44) served to validate the newly developed sFFQ using three 24HRs as reference method and to relate gut microbiome profiling (16S rRNA gene) with the extracted dietary and lifestyle data. Relative validation analysis provided acceptable classification and agreement for 13 out of 24 (54%) food groups and 20 out of 29 nutrients (69%) based on intraclass correlation coefficient, cross-classification, Spearman's correlation, Wilcoxon test, and Bland-Altman. Microbiome analysis showed that higher diversity was positively associated with age, vaginal birth, and intake of fruit. In contrast, microbial diversity was negatively associated with BMI, processed meats, ready-to-eat meals, sodium, and saturated fat. Our analysis also revealed a correlation between food groups or nutrients and microbial composition. Overall, we provide the first dietary assessment tool to be validated and correlated with microbiome data for population studies.

Dysbiosis and relapse-related microbiome in inflammatory bowel disease: A shotgun metagenomic approach.

Crohn's disease (CD) and ulcerative colitis (UC), the two main forms of inflammatory bowel disease (IBD), affect several million people worldwide. CD and UC are characterized by periods of clinical remission and relapse. Although IBD patients present chronic alterations of the gut microbiome, called dysbiosis, little attention has been devoted to the relapse-related microbiome. To address this gap, we generated shotgun metagenomic data from the stools of two European cohorts-134 Spanish (followed up for one year) and 49 Belgian (followed up for 6 months) subjects-to characterize the microbial taxonomic and metabolic profiles present. To assess the predictive value of microbiome data, we added the taxonomic profiles generated from a previous study of 130 Americans. Our results revealed that CD was more dysbiotic than UC compared to healthy controls (HC) and that strategies for energy extraction and propionate production were different in CD compared to UC and HC. Remarkably, CD and UC relapses were not associated with alpha- or beta-diversity, or with a dysbiotic score. However, CD relapse was linked to alterations at the species and metabolic pathway levels, including those involved in propionate production. The random forest method using taxonomic profiles allowed the prediction of CD vs. non-CD with an AUC = 0.938, UC vs. HC with an AUC = 0.646, and CD relapse vs. remission with an AUC = 0.769. Our study validates previous taxonomic findings, points to different relapse-related growth and defence mechanisms in CD compared to UC and HC and provides biomarkers to discriminate IBD subtypes and predict disease activity.