Metabolic networks are graph-based objects that comprise biochemical reactions which transform substrates into products. Genome-scale metabolic network (GSMN) gather all reactions that an organism could catalyze according to the contents of its genome. They permit the modelling of the phenotype of an organism according to its genotype . While obtaining a GSMN for a species was a long run project a few years ago, it is now possible to systematically build good quality drafts thanks to automation methods. The reconstructed GSMNs are then the basis of mathematical models aiming at representing the behavior of an organism’s metabolism in given conditions.
A microbiota consists in microorganisms sharing an environment. The gut microbiota is extensively studied as a result of its association with many traits and diseases, together with its high potential for targeted interventions . Modelling the metabolism of a microbiota is important in order to deciphering interactions occurring within communities of microbes [3,4]. To do so, one can model the metabolism of a set of microbes, and apply methods to identify key member among them, or complementarity among the functions they harbor.
In a recent paper , we introduced a software program dedicated to providing answers to the above questions. In our study, we systematically reconstructed metabolic networks for collections of genomes associated to cultivable members of the human gut microbiome. We were able to identify, for groups of metabolites, key species and set of bacteria sharing a common function for ensuring the producibility of these metabolites. We showed that minimal communities associated to a metabolic objective were composed of essential symbionts, and alternative symbionts selected among groups of equivalent microbes. The first part of the internship is to continue this analysis by developing a method dedicated to the identification of the functions carried by the groups of equivalent bacteria. To do so, the intern will use logic programming6 to mine the functions present in some metabolic networks that would explain the observed equivalence groups.
The second part of the internship focuses no longer on collections of genomes/metabolic networks but on real metagenomic samples from cohorts of individuals. We recently published a study performed on a large collection of metagenomic samples . More than 2000 Metagenome-assembled genomes (MAGs) were reconstructed from the 5,230 samples, and for each of them, a metabolic network was automatically inferred. As a sample is composed of a subset of the MAGs, it is possible to build a community for each sample and analyze it at the metabolic scale. Primary runs of Metage2Metabo , our tool for microbial community analysis, were performed. The role of the intern is to analyze the results and put them in perspective with the phenotypic metadata available for the individuals. The objective is to assess how much of the underlying microbial community biology, systematic analysis of metabolic functions can explain.
Python development skills are required, as well as basic knowledge in statistics. ASP and logic programming skills are a bonus but can also be acquired during the internship.
1. Gu, C., Kim, G. B., Kim, W. J., Kim, H. U. & Lee, S. Y. Current status and applications of genome-scale metabolic models. Genome Biol 20, 121 (2019).
2. Fan, Y. & Pedersen, O. Gut microbiota in human metabolic health and disease. Nat Rev Microbiol 1–17 (2020) doi:10.1038/s41579-020-0433-9.
3. Colarusso, A. V., Goodchild-Michelman, I., Rayle, M. & Zomorrodi, A. R. Computational modeling of metabolism in microbial communities on a genome-scale. Curr Opin Syst Biology (2021) doi:10.1016/j.coisb.2021.04.001.
4. Frioux, C., Singh, D., Korcsmaros, T. & Hildebrand, F. From bag-of-genes to bag-of-genomes: metabolic modelling of communities in the era of metagenome-assembled genomes. Comput Struct Biotechnology J 18, 1722–1734 (2020).
5. Belcour, A. et al. Metage2Metabo, microbiota-scale metabolic complementarity for the identification of key species. Elife 9, e61968 (2020).
6. Gebser, M., König, A., Schaub, T., Thiele, S. & Veber, P. The BioASP Library: ASP Solutions for Systems Biology. 2010 22nd Ieee Int Conf Tools Artif Intell 1, 383–389 (2010).
7. Hildebrand, F. et al. Dispersal strategies shape persistence and evolution of human gut bacteria. Cell Host Microbe (2021) doi:10.1016/j.chom.2021.05.008.