Interactions between genotype and environment drive the metabolic phenotype within <scp><i>E</i></scp><i>scherichia coli</i> isolates

Sabarly, Victor; Aubron, Cécile; Glodt, Jérémy; Balliau, Thierry; Langella, Olivier; Chevret, Didier; Rigal, Odile; Bourgais, Aurélie; Picard, Bertrand; Vienne, Dominique de; Denamur, Erick; Bouvet, Odile; Dillmann, Christine

doi:10.1111/1462-2920.12855

Cited by 22 publications

(20 citation statements)

References 63 publications

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“…In another long‐term continuous culture of E. coli, where glucose was provided as sole carbon source, two subpopulations evolved: one, which utilized glucose and produced acetate as a metabolic by‐product and a second subpopulation, which specialized to utilize the exogenously available acetate . Consequently, the utilization of different carbon sources can cause significant differences in the distribution of metabolic fluxes and, as shown in this study, different biosynthetic costs. Interestingly, the amino acid biosynthetic cost differences between the two specialized subpopulations in the two above‐mentioned examples are highly reciprocal, because glucose is a glycolytic carbon source, whereas citrate or acetate, respectively, are gluconeogenic substrates (Fig.…”

Section: Discussionmentioning

confidence: 65%

Metabolic network architecture and carbon source determine metabolite production costs

et al. 2016

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Metabolism is essential to organismal life, because it provides energy and building block metabolites. Even though it is known that the biosynthesis of metabolites consumes a significant proportion of the resources available to a cell, the factors that determine their production costs remain less well understood. In this context, it is especially unclear how the nutritional environment affects the costs of metabolite production. Here, we use the amino acid metabolism of Escherichia coli as a model to show that the point at which a carbon source enters central metabolic pathways is a major determinant of individual metabolite production costs. Growth rates of auxotrophic genotypes, which in the presence of the required amino acid save biosynthetic costs, were compared to the growth rates that prototrophic cells achieved under the same conditions. The experimental results showed a strong concordance with computationally estimated biosynthetic costs, which allowed us, for the first time, to systematically quantify carbon source-dependent metabolite production costs. Thus, we demonstrate that the nutritional environment in combination with network architecture is an important but hitherto underestimated factor influencing biosynthetic costs and thus microbial growth. Our observations are highly relevant for the optimization of biotechnological processes as well as for understanding the ecology of microorganisms in their natural environments.

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Section: Discussionmentioning

confidence: 65%

Metabolic network architecture and carbon source determine metabolite production costs

et al. 2016

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“…Lactose allows the growth of the vast majority of E. coli strains [31]. It has been shown that the lag time and generation time of E. coli strains, which depend on metabolic efficiency and are crucial for gut colonization and persistence [32,33], are influenced by the type and abundance of available nutrients in the habitat [34]. Other components of milk replacers may have also influenced the E. coli population dynamics, such as vitamin D, for which the absence of the vitamin D receptor in the intestinal epithelium of mice has been associated with increased E. coli loads [35].…”

Section: Discussionmentioning

confidence: 99%

Temporal dynamics of the fecal microbiota in veal calves in a 6-month field trial

et al. 2020

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Background Little is known about maturation of calves’ gut microbiome in veal farms, in which animals are confined under intensive-farming conditions and the administration of collective antibiotic treatment in feed is common. We conducted a field study on 45 calves starting seven days after their arrival in three veal farms. We collected monthly fecal samples over six months and performed 16S rRNA gene sequencing and quantitative PCR of Escherichia coli to follow the dynamics of their microbiota, including that of their commensal E. coli populations. We used mixed-effect models to characterize the dynamics of α-diversity indices and numbers of E. coli, and searched for an effect of collective antibiotic treatments on the estimated parameters. On two farms, we also searched for associations between recommended daily doses of milk powder and bacterial abundance. Results There was high heterogeneity between calves’ microbiota upon their arrival at the farms, followed by an increase in similarity, starting at the first month. From the second month, 16 genera were detected at each sampling in all calves, representing 67.5% (± 9.9) of their microbiota. Shannon diversity index showed a two-phase increase, an inflection occurring at the end of the first month. Calves receiving antibiotics had a lower intercept estimate for Shannon index (− 0.17 CI95%[-0.27; − -0.06], p = 0.003) and a smaller number of E. coli/ gram of feces during the treatment and in the 15 days following it (− 0.37 log10 (E. coli/g) CI95%[− 0.66; − 0.08], p = 0.01) than unexposed calves. There were moderate to strong positive associations between the dose of milk powder and the relative abundances of the genera Megasphaera, Enterococcus, Dialister and Mitsuokella, and the number of E. coli (rs ≥ 0.40; Bonferroni corrected p < 0.05). Conclusions This observational study shows early convergence of the developing microbiota between veal calves and associations between the dose of milk powder and members of their microbiota. It suggests that administration of collective antibiotic treatment results in a reduction of microbial diversity and size of the E. coli population and highlights the need for additional work to fully understand the impact of antibiotic treatment in the veal industry.

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“…6B). It has been shown that the point, at which a carbon source enters the central metabolic network, strongly affects the distribution of metabolic uxes 169,170 and, in this way, also the production costs of individual amino acids. 164 Hence, a consequence of diverse carbon source preferences is that one species of the bacterial community can have a comparative advantage in the biosynthetic cost efficiency of a specic set of metabolites over another species, which in turn has a comparative advantage in the production of another set of metabolites ( Fig.…”

Section: Metabolic Factorsmentioning

confidence: 99%

Ecology and evolution of metabolic cross-feeding interactions in bacteria

et al. 2018

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Literature covered: early 2000s to late 2017Bacteria frequently exchange metabolites with other micro- and macro-organisms. In these often obligate cross-feeding interactions, primary metabolites such as vitamins, amino acids, nucleotides, or growth factors are exchanged. The widespread distribution of this type of metabolic interactions, however, is at odds with evolutionary theory: why should an organism invest costly resources to benefit other individuals rather than using these metabolites to maximize its own fitness? Recent empirical work has shown that bacterial genotypes can significantly benefit from trading metabolites with other bacteria relative to cells not engaging in such interactions. Here, we will provide a comprehensive overview over the ecological factors and evolutionary mechanisms that have been identified to explain the evolution and maintenance of metabolic mutualisms among microorganisms. Furthermore, we will highlight general principles that underlie the adaptive evolution of interconnected microbial metabolic networks as well as the evolutionary consequences that result for cells living in such communities.

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Interactions between genotype and environment drive the metabolic phenotype within Escherichia coli isolates

Cited by 22 publications

References 63 publications

Metabolic network architecture and carbon source determine metabolite production costs

Metabolic network architecture and carbon source determine metabolite production costs

Temporal dynamics of the fecal microbiota in veal calves in a 6-month field trial

Ecology and evolution of metabolic cross-feeding interactions in bacteria

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