Escherichia coli exhibits a wide range of lifestyles encompassing commensalism and various pathogenic behaviors which its highly dynamic genome contributes to develop. How environmental and host factors shape the genetic structure of E. coli strains remains, however, largely unknown. Following a previous study of E. coli genomic diversity, we investigated its diversity at the metabolic level by building and analyzing the genomescale metabolic networks of 29 E. coli strains (8 commensal and 21 pathogenic strains, including 6 Shigella strains). Using a tailor-made reconstruction strategy, we significantly improved the completeness and accuracy of the metabolic networks over default automatic reconstruction processes. Among the 1,545 reactions forming E. coli panmetabolism, 885 reactions were common to all strains. This high proportion of core reactions (57%) was found to be in sharp contrast to the low proportion (13%) of core genes in the E. coli pangenome, suggesting less diversity of metabolic functions compared to that of all gene functions. Core reactions were significantly overrepresented among biosynthetic reactions compared to the more variable degradation processes. Differences between metabolic networks were found to follow E. coli phylogeny rather than pathogenic phenotypes, except for Shigella networks, which were significantly more distant from the others. This suggests that most metabolic changes in non-Shigella strains were not driven by their pathogenic phenotypes. Using a supervised method, we were yet able to identify small sets of reactions related to pathogenicity or commensalism. The quality of our reconstructed networks also makes them reliable bases for building metabolic models.Escherichia coli is a versatile species encompassing commensal organisms, as well as intraintestinal E. coli (InPEc) and extraintestinal E. coli (ExPEc) pathogens (27, 49). This variety of lifestyles has been seen as a consequence of the huge E. coli genome plasticity (51). However, linking genomic elements to phenotypic behaviors is not trivial because several layers of biological processes separate genes from their phenotypic effects, and in extreme cases, the evolutionary path can lead either to the functional convergence of distinct sets of genes or to the functional divergence of an initially common set of genes. Consequently, in order to establish links between genomes and phenotypes, one needs an integrative layer. A recent study on a set of 20 E. coli strains (51) has shown that a large fraction of the shared genomic elements with known function is related to metabolism. Because it is now feasible to reconstruct metabolic networks at the genome scale (7,13,16,26), these metabolic networks can, in principle, be used as functional bridges between genomic diversity and phenotypic differences. Currently, such reconstructions are performed automatically from the annotation of input genomes, using algorithms that match these annotations with the contents of reference metabolic databases (13,16).In this work, we stu...