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Model-driven discovery of underground metabolic functions in Escherichia coli.

ABSTRACT: Enzyme promiscuity toward substrates has been discussed in evolutionary terms as providing the flexibility to adapt to novel environments. In the present work, we describe an approach toward exploring such enzyme promiscuity in the space of a metabolic network. This approach leverages genome-scale models, which have been widely used for predicting growth phenotypes in various environments or following a genetic perturbation; however, these predictions occasionally fail. Failed predictions of gene essentiality offer an opportunity for targeting biological discovery, suggesting the presence of unknown underground pathways stemming from enzymatic cross-reactivity. We demonstrate a workflow that couples constraint-based modeling and bioinformatic tools with KO strain analysis and adaptive laboratory evolution for the purpose of predicting promiscuity at the genome scale. Three cases of genes that are incorrectly predicted as essential in Escherichia coli--aspC, argD, and gltA--are examined, and isozyme functions are uncovered for each to a different extent. Seven isozyme functions based on genetic and transcriptional evidence are suggested between the genes aspC and tyrB, argD and astC, gabT and puuE, and gltA and prpC. This study demonstrates how a targeted model-driven approach to discovery can systematically fill knowledge gaps, characterize underground metabolism, and elucidate regulatory mechanisms of adaptation in response to gene KO perturbations.

SUBMITTER: Guzman GI

PROVIDER: S-EPMC4311852 | biostudies-other | 2015 Jan

REPOSITORIES: biostudies-other

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Model-driven discovery of underground metabolic functions in Escherichia coli.

Guzmán Gabriela I GI Utrilla José J Nurk Sergey S Brunk Elizabeth E Monk Jonathan M JM Ebrahim Ali A Palsson Bernhard O BO Feist Adam M AM

Proceedings of the National Academy of Sciences of the United States of America 20150106 3

Enzyme promiscuity toward substrates has been discussed in evolutionary terms as providing the flexibility to adapt to novel environments. In the present work, we describe an approach toward exploring such enzyme promiscuity in the space of a metabolic network. This approach leverages genome-scale models, which have been widely used for predicting growth phenotypes in various environments or following a genetic perturbation; however, these predictions occasionally fail. Failed predictions of gen ...[more]

PMID: 25564669

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Project description:The Escherichia coli B strain BL21(DE3) has had a profound impact on biotechnology through its use in the production of recombinant proteins. Little is understood, however, regarding the physiology of this important E. coli strain. We show here that BL21(DE3) totally lacks activity of the four [NiFe]-hydrogenases, the three molybdenum- and selenium-containing formate dehydrogenases and molybdenum-dependent nitrate reductase. Nevertheless, all of the structural genes necessary for the synthesis of the respective anaerobic metalloenzymes are present in the genome. However, the genes encoding the high-affinity molybdate transport system and the molybdenum-responsive transcriptional regulator ModE are absent from the genome. Moreover, BL21(DE3) has a nonsense mutation in the gene encoding the global oxygen-responsive transcriptional regulator FNR. The activities of the two hydrogen-oxidizing hydrogenases, therefore, could be restored to BL21(DE3) by supplementing the growth medium with high concentrations of Ni²? (Ni²?-transport is FNR-dependent) or by introducing a wild-type copy of the fnr gene. Only combined addition of plasmid-encoded fnr and high concentrations of MoO?²? ions could restore hydrogen production to BL21(DE3); however, to only 25-30% of a K-12 wildtype. We could show that limited hydrogen production from the enzyme complex responsible for formate-dependent hydrogen evolution was due solely to reduced activity of the formate dehydrogenase (FDH-H), not the hydrogenase component. The activity of the FNR-dependent formate dehydrogenase, FDH-N, could not be restored, even when the fnr gene and MoO?²? were supplied; however, nitrate reductase activity could be recovered by combined addition of MoO?²? and the fnr gene. This suggested that a further component specific for biosynthesis or activity of formate dehydrogenases H and N was missing. Re-introduction of the gene encoding ModE could only partially restore the activities of both enzymes. Taken together these results demonstrate that BL21(DE3) has major defects in anaerobic metabolism, metal ion transport and metalloprotein biosynthesis.

Project description:Over the last decades, predictive microbiology has made significant advances in the mathematical description of microbial spoiler and pathogen dynamics in or on food products. Recently, the focus of predictive microbiology has shifted from a (semi-)empirical population-level approach towards mechanistic models including information about the intracellular metabolism in order to increase model accuracy and genericness. However, incorporation of this subpopulation-level information increases model complexity and, consequently, the required run time to simulate microbial cell and population dynamics. In this paper, results of metabolic flux balance analyses (FBA) with a genome-scale model are used to calibrate a low-complexity linear model describing the microbial growth and metabolite secretion rates of Escherichia coli as a function of the nutrient and oxygen uptake rate. Hence, the required information about the cellular metabolism (i.e., biomass growth and secretion of cell products) is selected and included in the linear model without incorporating the complete intracellular reaction network. However, the applied FBAs are only representative for microbial dynamics under specific extracellular conditions, viz., a neutral medium without weak acids at a temperature of 37?. Deviations from these reference conditions lead to metabolic shifts and adjustments of the cellular nutrient uptake or maintenance requirements. This metabolic dependency on extracellular conditions has been taken into account in our low-complex metabolic model. In this way, a novel approach is developed to take the synergistic effects of temperature, pH, and undissociated acids on the cell metabolism into account. Consequently, the developed model is deployable as a tool to describe, predict and control E. coli dynamics in and on food products under various combinations of environmental conditions. To emphasize this point,three specific scenarios are elaborated: (i) aerobic respiration without production of weak acid extracellular metabolites, (ii) anaerobic fermentation with secretion of mixed acid fermentation products into the food environment, and (iii) respiro-fermentative metabolic regimes in between the behaviors at aerobic and anaerobic conditions.

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Model-driven discovery of underground metabolic functions in Escherichia coli.

Publications

Model-driven discovery of underground metabolic functions in Escherichia coli.

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