Mathématiques et Informatique Appliquées
du Génome à l'Environnement


Workshop Articulate

En raison de l'épidémie de Covid-19, le workshop est reporté à une date ultérieure.


Une matinée de présentations scientifiques sur l'articulation de données omiques, de modèles et d'expérimentations en écologie microbienne aura lieu le

17 mars 2020  Reporté

de 9h25 - 12h40, salle de séminaire, bâtiment 210, INRAE Jouy en Josas

Programme :

- 9h25-9h30 : présentation du workshop.

- 9h30 - 10h10 : Connor Tiffany (UC Davis, USA)

Titre : The metabolic footprint of Clostridia reveals their role in depleting sugar alcohols and acidic sugars in the large bowel.

Résumé : The gut microbiota contributes to health by producing metabolites important for nutrition, immune education and niche protection against pathogens. However, the nutrients consumed by common taxa within the microbiota remain incompletely understood. Here we employed an un-targeted metabolomics approach to determine whether depletion of small metabolites in the feces of mice was attributable to the presence of specific bacterial taxa. We noted that a depletion of Clostridia from the gut microbiota triggered by antibiotic treatment was associated with an increase in the fecal concentration of sugar acids and sugar alcohols, metabolites that are commonly classified as poorly absorbed fermentable dietary oligosaccharides, disaccharides, monosaccharides, and polyols (FODMAPs). Notably, when we inoculated germ-free mice with a community of 17 human commensal Clostridia isolates, we observed the inverse, with a marked decrease in the concentration of sugar alcohols and sugar acids in feces. We conclude that the metabolic footprint of Clostridia identifies this taxon as a main consumer of sugar alcohols and sugar acids in the intestine

- 10h10 - 10h50 : Rafael Munoz-Tamayo (INRAE, MoSAR)

Titre : Hydrogenotrophic methanogens of the mammalian gut: Functionally similar, thermodynamically different—A modelling approach

Résumé : Methanogenic archaea occupy a functionally important niche in the gut microbial ecosystem of mammals. Our purpose was to quantitatively characterize the dynamics of methanogenesis by integrating microbiology, thermodynamics and mathematical modelling. For that, in vitro growth experiments were performed with pure cultures of key methanogens from the human and ruminant gut, namely Methanobrevibacter smithii, Methanobrevibacter ruminantium and Methanobacterium formicium. Microcalorimetric experiments were performed to quantify the methanogenesis heat flux. We constructed an energetic-based mathematical model of methanogenesis. Our model captured efficiently the dynamics of methanogenesis with average concordance correlation coefficients of 0.95 for CO2, 0.98 for H2 and 0.97 for CH4. Together, experimental data and model enabled us to quantify metabolism kinetics and energetic patterns that were specific and distinct for each species despite their use of analogous methane-producing pathways. Then, we tested in silico the interactions between these methanogens under an in vivo simulation scenario using a theoretical modelling exercise. In silico simulations suggest that the classical competitive exclusion principle is inapplicable to gut ecosystems and that kinetic information alone cannot explain gut ecological aspects such as microbial coexistence. We suggest that ecological models of gut ecosystems require the integration of microbial kinetics with nonlinear behaviours related to spatial and temporal variations taking place in mammalian guts. Our work provides novel information on the thermodynamics and dynamics of methanogens. This understanding will be useful to construct new gut models with enhanced prediction capabilities and could have practical applications for promoting gut health in mammals and mitigating ruminant methane emissions.

- 11h00 - 12h00 : Stefanie Widder (U.Vienna, Autriche)

Titre : Linking lung microbiome dynamics and dysbiosis with pulmonary exacerbations in Cystic Fibrosis

People with cystic fibrosis (CF) suffer from persistent, poly-microbial infections in the lung. This lung microbiome is a dynamical, evolving ecosystem that displays classic features of a complex system. It shows temporal changes in composition, is spatially stratified in the alveolar microenvironment and microbial interactions generate an ecological dependency structure in the community. The emergent disease dynamics are characterized by abrupt inflammatory aggravation (pulmonary exacerbations) that cause irreversible lung damage and drive patient mortality. Dr. Widder and others hypothesize that the ecological state of the lung microbiome, in particular dysbiosis and the loss of interactions is directly related to resulting disease dynamics. They use 16S and metagenomic sequencing data derived from large sputum collections and apply complex systems theory, network science and modeling techniques to dig deep into the community structure of the airway microbiome. Comparing the airway microbiome during clinical baseline symptoms or acute exacerbation, reveals rearrangements in taxa dominance, interactions and metabolic activity of the community. Moreover, also the analysis of microbiome time series suggests that in exacerbation the microbiome shifts from self-organization to neutral and competitive dynamics driving dysbiosis. In summary, these results indicate that microbial community properties and their dynamics, rather than individual pathogens strongly contribute to CF lung disease dynamics. The vision is to build new treatment strategies building on the underlying ecological dependencies in the CF lung microbiome with the goal to avoid exacerbation and prolong patient’s lives.

- 12:00 - 12h40 : Andrea Rau (INRAE, Gabi)

Titre: Integrative multivariate methods for matched multi-omic data

Matched multi-omics data across individuals have become increasingly available in recent years, e.g. in large-scale studies on human health and precision breeding in livestock, allowing for a detailed interrogation of the complex interdependencies within and between the transcriptome, epigenome, proteome, metabolome, and genome. Integrating these complex sources of molecular variability into a unified framework represents a significant challenge, due in part to their high dimensionality, the limited available a priori information on existing relationships among different levels of omics, and the difficulty in posing well-defined questions of the multi-omic data themselves. In this talk, I will discuss some of the approaches that we have recently developed and deployed for gene- and pathway-centric integrative multi-omic approaches, with a focus on providing insight into multi-level gene regulation in cancer. I will also discuss some promising future avenues of methodological research for the integration of multi-omic data.

Contact : simon.labarthe(at)

This workshop is supported by the France-Berkely Fund ( ), and the MICA and NUMM INRAE departements.