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...

To assess the extent of intra-species diversity and the links between phylogeny, lifestyle (habitat and pathogenicity) and phenotype, we assayed the growth yield on 95 carbon sources of 168 Escherichia strains. We also correlated the growth capacities of 14 E. coli strains with the presence/absence of enzyme-coding genes. Globally, we found that the genetic distance, based on multilocus sequence typing data, was a weak indicator of the metabolic phenotypic distance. Besides, lifestyle and phylogroup had almost no impact on the growth yield of non-Shigella E. coli strains. In these strains, the presence/absence of the metabolic pathways, which was linked to the phylogeny, explained most of the growth capacities. However, few discrepancies blurred the link between metabolic phenotypic distance and metabolic pathway distance. This study shows that a prokaryotic species structured into well-defined genetic and lifestyle groups can yet exhibit continuous phenotypic diversity, possibly caused by gene regulatory effects.

To gain insights into the adaptation of the Escherichia coli species to different environments, we monitored protein abundances using quantitative proteomics and measurements of enzymatic activities of central metabolism in a set of five representative strains grown in four contrasted culture media including human urine. Two hundred and thirty seven proteins representative of the genome-scale metabolic network were identified and classified into pathway categories. We found that nutrient resources shape the general orientation of metabolism through coordinated changes in the average abundances of proteins and in enzymatic activities that all belong to the same pathway category. For example, each culture medium induces a specific oxidative response whatever the strain. On the contrary, differences between strains concern isolated proteins and enzymes within pathway categories in single environments. Our study confirms the predominance of genotype by environment interactions at the proteomic and enzyme activity levels. The buffering of genetic variation when considering life-history traits suggests a multiplicity of evolutionary strategies. For instance, the uropathogenic isolate CFT073 shows a deregulation of iron demand and increased oxidative stress response.

Lignocellulosic materials from municipal solid waste emerge as attractive resources for anaerobic digestion biorefinery. To increase the knowledge required for establishing efficient bioprocesses, dynamics of batch fermentation by the cellulolytic bacterium Ruminiclostridium cellulolyticum were compared using three cellulosic materials, paper handkerchief, cotton discs and Whatman filter paper. Fermentation of paper handkerchief occurred the fastest and resulted in a specific metabolic profile: it resulted in the lowest acetate-to-lactate and acetate-to-ethanol ratios. By shotgun proteomic analyses of paper handkerchief and Whatman paper incubations, 151 proteins with significantly different levels were detected, including 20 of the 65 cellulosomal components, 8 non-cellulosomal CAZymes and 44 distinct extracytoplasmic proteins. Consistent with the specific metabolic profile observed, many enzymes from the central carbon catabolic pathways had higher levels in paper handkerchief incubations. Among the quantified CAZymes and cellulosomal components, 10 endoglucanases mainly from the GH9 families and 7 other cellulosomal subunits had lower levels in paper handkerchief incubations. An in-depth characterization of the materials used showed that the lower levels of endoglucanases in paper handkerchief incubations could hypothetically result from its lower crystallinity index (50%) and degree of polymerization (970). By contrast, the higher hemicellulose rate in paper handkerchief (13.87%) did not result in the enhanced expression of enzyme with xylanase as primary activity, including enzymes from the “xyl-doc” cluster. It suggests the absence, in this material, of molecular structures that specifically lead to xylanase induction. The integrated approach developed in this work shows that subtle differences among cellulosic materials regarding chemical and structural characteristics have significant effects on expressed bacterial functions, in particular the cellulolysis machinery, resulting in different metabolic patterns and degradation dynamics.

Parkinson’s disease (PD) is the second most common neurodegenerative disease clinically characterized by classical motor symptoms and a range of associated non-motor symptoms. Due to the heterogeneity of symptoms and variability in patient prognosis, the discovery of blood biomarkers is of utmost importance to identify the biological mechanisms underlying the different clinical manifestations of PD, monitor its progression and develop personalized treatment strategies. Whereas studies often rely on motor symptoms alone or composite scores, our study focused on finding relevant molecular markers associated with three clinical models describing either motor, cognitive or emotional symptoms. An integrative multiblock approach was performed using regularized generalized canonical correlation analysis to determine specific associations between lipidomics, transcriptomics and clinical data in 48 PD patients. We identified omics signatures confirming that clinical manifestations of PD in our cohort could be classified according to motor, cognition or emotion models. We found that immune-related genes and triglycerides were well-correlated with motor variables, while cognitive variables were linked to triglycerides as well as genes involved in neuronal growth, synaptic plasticity and mitochondrial fatty acid oxidation. Furthermore, emotion variables were associated with phosphatidylcholines, cholesteryl esters and genes related to endoplasmic reticulum stress and cell regulation.