Thesis of Gilles Vieira

5 décembre 2011 -

Metabolic diversity in Escherichia coli species
Using metabolic networks and models at genome scale

Gilles Vieira (mail) will support his thesis on December the 5th in Évry. His research was carried out in the “Laboratory of Bioinformatic Analysis for Genomic and Metabolism ( LABGeM )” team led by Claudine Medigue (mail) .

Metabolic differences in microorganisms can be investigated in many ways. The project can be focused on specific elements (i.e. presence or absence of a metabolic molecule) or it can be focused on the variations in metabolic capacities. These two types of study involve two levels of analysis: the first one is local and based on a small number of genes and enzymatic activities, which may establish causality between them. The second one is at genome scale, and provides a global view of the organism’s growth capacities under various conditions. However, complexity and interdependency of metabolic processes, and above all the lack of highly detailed reconstructed metabolic networks, make the links between phenotypic properties and genotypes hard to establish when one is working at cell scale. New technologies and in particular the sequencing of the whole genome of an organism, the increasing amount of knowledge referenced in biological databases, and finally methodological progress now permit the reconstruction of metabolic networks and models at global scale. Unfortunately, to provide highly quality networks, we still need to use manual curation, which make the process long and tedious. In this project, we propose a new strategy to reconstruct metabolic networks and models at genome scale. Our strategy can be applied to any number of organisms as long as they are members of the same species (or at least close from a phylogenetic point of view) and a curated metabolic network for one of the strains of this species is already reconstructed. The keystone of the strategy is the automatic utilization and propagation of both specific knowledge of the species and general knowledge in metabolic databases. This strategy was applied, to study the metabolic network of 23 strains of Escherichia coli and 6 of Shigella. Those strains cover all the E. coliphylogroups, including pathogenic strains. The reconstructed metabolic networks of these strains present a real improvement compared to the same strains’ networks reconstructed by the usual process. While analyzing these networks, we found a strong link between evolutionary aspects of metabolism and the strains’ evolutionary history. Nevertheless, all the processes are not impacted in the same way. We observe that some of them are highly conserved whereas others are under less selective pressure. Next, we have converted these networks into metabolic models, ‘constraint-based models’ precisely, to explore the metabolic capacities of these organisms. We compared the growth predictions of the models to experimental growth observations and also to the E. coli K-12 reference model. In the best case, the reconstructed models have better prediction numbers than the reference model and in the worst case we are still close to the reference model. This finding is the result of new functional reactions and pathways added in the reconstructed models by our strategy (i.e. according to the experimental observations). We prepared our models in order to integrate heterogeneous biological data concerning enzyme concentrations and flux from results from a kinetic model of central metabolism of E. coli. Finally, the studies carried out for this thesis consist of a new strategy, which allows the reconstruction of metabolic networks and models at cell scale. The models give us the opportunity to study the link between evolution, genomes and metabolic capacities of these organisms