Biology subjects

Tap, J.

Publications and source records attributed to Tap, J..

4 recordsLinked to original sources

bioRxiv2025

Food Microbiome Metabolic Modules (F3M), a tool suite for functional profiling of food microbiomes

The study of microbial metabolic interactions within food microbiomes represents a key scientific approach for improving the quality and health benefits of food. In such studies, methods based on gene expression levels (metatranscriptomic) analysis are promising. However specific tools are required to overcome the challenges posed by food microbiomes, in particular the high variability of microbiomes between samples and the difficulty of automatically inferring the annotation of metabolic functions across taxa. To adress this gap, we present the Food Microbiome Metabolic Modules (F3M) tool suite, which comprises (1) a curated database containing about 1,985 functional genes representing key fermentative metabolic reactions in food microbiomes, (2) a F3M Builder for generating F3M-annotated gene catalogs and mapping of metatranscriptomic reads, and then (3) an F3M R package to parse and aggregate gene expression data by taxonomic and functional categories for downstream analysis. The F3M taxonomy is organized according to the Genome Taxonomy Database (GTDB) nomenclature, whereas the F3M functional repertoire is structured hierarchically into 183 metabolic modules, which enable multi-scale analysis of inter-organism metabolic interactions and meaningful fermentative outputs (e.g., primary alcohols, acetate). Notably, a dedicated redox module captures oxido-reduction mechanisms and NADH-dependent pathways central to fermentation, while an uptake module complements the metabolic pathways to trace potential metabolite exchanges across taxa. Together, the F3M suite provides a robust framework for uncovering functional dynamics within food microbiomes. The F3M tool suite is available as open-source.

microbiology↗

bioRxiv2024

A three-country analysis of the gut microbiome indicates taxon associations with diet vary by location and strain

Emerging research suggests that diet plays a vital role in shaping the composition and function of the gut microbiota. While significant efforts have been made to identify general patterns linking diet to the gut microbiome, much of this research lacks representation from low- and middle-income countries such as Mexico. Additionally, both diet and the gut microbiome have highly complex and individualized configurations, and there is growing evidence that tailoring diets to individual gut microbiota profiles may optimize the path toward improving or maintaining health and preventing disease. Using fecal metagenomic data from 1,291 individuals across three countries, we examine two bacterial genera prevalent in the human gut, Prevotella and Faecalibacterium, which have gained significant attention due to their potential roles in human health. We find that they show significant associations with many aspects of diet, but that these associations vary in scale and direction, depending on the level of metagenomic resolution and the contextual population. These results highlight the growing importance of assembling metagenomic datasets that are standardized, comprehensive, and representative of diverse populations to increase our ability to tease apart the complex relationship between diet and the microbiome.

microbiology↗

bioRxiv2020

Gut microbiome, diet and symptom interactions in irritable bowel syndrome

While several studies have documented associations between dietary habits and microbiota composition and function in healthy subjects, no study explored these associations in patients with irritable bowel syndrome (IBS), and especially in relation to symptoms. Here, we used a novel approach that combined data from 4-day food diary, integrated into a food tree, together with gut microbiota (shotgun metagenomic) for IBS patients (N=149) and healthy subjects (N=52). Paired microbiota and food-based trees allowed to detect new association between subspecies and diet. Combining co-inertia analysis and linear regression models, exhaled gas levels and symptom severity could be predicted from metagenomic and dietary data. IBS patients with severe symptoms had a diet enriched in food items of poorer quality, a high abundance of gut microbial enzymes involved in hydrogen metabolism in correlation with animal carbohydrate (mucin/meat-derived) metabolism. Our study provides unprecedented resolution of diet-microbiota-symptom interactions and ultimately paves the way for personalized nutritional recommendations.

microbiology↗

bioRxiv2017

Prediction of the intestinal resistome by a novel 3D-based method

The intestinal microbiota is considered to be a major reservoir of antibiotic resistance determinants (ARDs) that could potentially be transferred to bacterial pathogens. Yet, this question remains hypothetical because of the difficulty to identify ARDs from intestinal bacteria. Here, we developed and validated a new annotation method (called pairwise comparative modelling, PCM) based on homology modelling in order to characterize the Human resistome. We were able to predict 6,095 ARDs in a 3.9 million protein catalogue from the Human intestinal microbiota. We found that predicted ARDs (pdARDs) were distantly related to known ARDs (mean amino-acid identity 29.8%). Among 3,651 pdARDs that were identified in metagenomic species, 3,489 (95.6%) were assumed to be located on the bacterial chromosome. Furthermore, genes associated with mobility were found in the neighbourhood of only 7.9% (482/6,095) of pdARDs. According to the composition of their resistome, we were able to cluster subjects from the MetaHIT cohort (n=663) into 6 \"resistotypes\". Eventually, we found that the relative abundance of pdARDs was positively associated with gene richness, but not when subjects were exposed to antibiotics. Altogether, our results support that most ARDs in the intestinal microbiota should be considered as intrinsic genes of commensal microbiota with a low risk of transfer to bacterial pathogens.

microbiology↗