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A genome-scale Escherichia coli kinetic metabolic model k-ecoli457 satisfying flux data for multiple mutant strains.

ABSTRACT: Kinetic models of metabolism at a genome scale that faithfully recapitulate the effect of multiple genetic interventions would be transformative in our ability to reliably design novel overproducing microbial strains. Here, we introduce k-ecoli457, a genome-scale kinetic model of Escherichia coli metabolism that satisfies fluxomic data for wild-type and 25 mutant strains under different substrates and growth conditions. The k-ecoli457 model contains 457 model reactions, 337 metabolites and 295 substrate-level regulatory interactions. Parameterization is carried out using a genetic algorithm by simultaneously imposing all available fluxomic data (about 30 measured fluxes per mutant). The Pearson correlation coefficient between experimental data and predicted product yields for 320 engineered strains spanning 24 product metabolites is 0.84. This is substantially higher than that using flux balance analysis, minimization of metabolic adjustment or maximization of product yield exhibiting systematic errors with correlation coefficients of, respectively, 0.18, 0.37 and 0.47 (k-ecoli457 is available for download at http://www.maranasgroup.com).

SUBMITTER: Khodayari A

PROVIDER: S-EPMC5187423 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

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A genome-scale Escherichia coli kinetic metabolic model k-ecoli457 satisfying flux data for multiple mutant strains.

Khodayari Ali A Maranas Costas D CD

Nature communications 20161220

Kinetic models of metabolism at a genome scale that faithfully recapitulate the effect of multiple genetic interventions would be transformative in our ability to reliably design novel overproducing microbial strains. Here, we introduce k-ecoli457, a genome-scale kinetic model of Escherichia coli metabolism that satisfies fluxomic data for wild-type and 25 mutant strains under different substrates and growth conditions. The k-ecoli457 model contains 457 model reactions, 337 metabolites and 295 s ...[more]

PMID: 27996047

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Project description:BackgroundConstraint-based models of Escherichia coli metabolic flux have played a key role in computational studies of cellular metabolism at the genome scale. We sought to develop a next-generation constraint-based E. coli model that achieved improved phenotypic prediction accuracy while being frequently updated and easy to use. We also sought to compare model predictions with experimental data to highlight open questions in E. coli biology.ResultsWe present EcoCyc-18.0-GEM, a genome-scale model of the E. coli K-12 MG1655 metabolic network. The model is automatically generated from the current state of EcoCyc using the MetaFlux software, enabling the release of multiple model updates per year. EcoCyc-18.0-GEM encompasses 1445 genes, 2286 unique metabolic reactions, and 1453 unique metabolites. We demonstrate a three-part validation of the model that breaks new ground in breadth and accuracy: (i) Comparison of simulated growth in aerobic and anaerobic glucose culture with experimental results from chemostat culture and simulation results from the E. coli modeling literature. (ii) Essentiality prediction for the 1445 genes represented in the model, in which EcoCyc-18.0-GEM achieves an improved accuracy of 95.2% in predicting the growth phenotype of experimental gene knockouts. (iii) Nutrient utilization predictions under 431 different media conditions, for which the model achieves an overall accuracy of 80.7%. The model's derivation from EcoCyc enables query and visualization via the EcoCyc website, facilitating model reuse and validation by inspection. We present an extensive investigation of disagreements between EcoCyc-18.0-GEM predictions and experimental data to highlight areas of interest to E. coli modelers and experimentalists, including 70 incorrect predictions of gene essentiality on glucose, 80 incorrect predictions of gene essentiality on glycerol, and 83 incorrect predictions of nutrient utilization.ConclusionSignificant advantages can be derived from the combination of model organism databases and flux balance modeling represented by MetaFlux. Interpretation of the EcoCyc database as a flux balance model results in a highly accurate metabolic model and provides a rigorous consistency check for information stored in the database.

Project description:Kinetic models of metabolic networks offer the promise of quantitative phenotype prediction. The mechanistic characterization of enzyme catalyzed reactions allows for tracing the effect of perturbations in metabolite concentrations and reaction fluxes in response to genetic and environmental perturbation that are beyond the scope of stoichiometric models. In this study, we develop a two-step computational pipeline for the rapid parameterization of kinetic models of metabolic networks using a curated metabolic model and available 13C-labeling distributions under multiple genetic and environmental perturbations. The first step involves the elucidation of all intracellular fluxes in a core model of E. coli containing 74 reactions and 61 metabolites using 13C-Metabolic Flux Analysis (13C-MFA). Here, fluxes corresponding to the mid-exponential growth phase are elucidated for seven single gene deletion mutants from upper glycolysis, pentose phosphate pathway and the Entner-Doudoroff pathway. The computed flux ranges are then used to parameterize the same (i.e., k-ecoli74) core kinetic model for E. coli with 55 substrate-level regulations using the newly developed K-FIT parameterization algorithm. The K-FIT algorithm employs a combination of equation decomposition and iterative solution techniques to evaluate steady-state fluxes in response to genetic perturbations. k-ecoli74 predicted 86% of flux values for strains used during fitting within a single standard deviation of 13C-MFA estimated values. By performing both tasks using the same network, errors associated with lack of congruity between the two networks are avoided, allowing for seamless integration of data with model building. Product yield predictions and comparison with previously developed kinetic models indicate shifts in flux ranges and the presence or absence of mutant strains delivering flux towards pathways of interest from training data significantly impact predictive capabilities. Using this workflow, the impact of completeness of fluxomic datasets and the importance of specific genetic perturbations on uncertainties in kinetic parameter estimation are evaluated.

Project description:Transcription of the Escherichia coli biotin (bio) operon is directly regulated by the biotin protein ligase BirA, the enzyme that covalently attaches biotin to its cognate acceptor proteins. Binding of BirA to the bio operator requires dimerization of the protein, which is triggered by BirA-catalyzed synthesis of biotinoyl-adenylate (biotinoyl-5'-AMP), the obligatory intermediate of the ligation reaction. Although several aspects of this regulatory system are well understood, no BirA superrepressor mutant strains had been isolated. Such superrepressor BirA proteins would repress the biotin operon transcription in vivo at biotin concentrations well below those needed for repression by wild-type BirA. We isolated mutant strains having this phenotype by a combined selection-screening approach and resolved multiple mutations to give several birA superrepressor alleles, each having a single mutation, all of which showed repression dominant over that of the wild-type allele. All of these mutant strains repressed bio operon transcription in vivo at biotin concentrations that gave derepression of the wild-type strain and retained sufficient ligation activity for growth when overexpressed. All of the strains except that encoding G154D BirA showed derepression of bio operon transcription upon overproduction of a biotin-accepting protein. In BirA, G154D was a lethal mutation in single copy, and the purified protein was unable to transfer biotin from enzyme-bound biotinoyl-adenylate either to the natural acceptor protein or to a biotin-accepting peptide sequence. Consistent with the transcriptional repression data, each of the purified mutant proteins showed increased affinity for the biotin operator DNA in electrophoretic mobility shift assays. Surprisingly, although most of the mutations were located in the catalytic domain, all of those tested, except G154D BirA, had normal ligase activity. Most of the mutations that gave superrepressor phenotypes altered residues located close to the dimerization interface of BirA. However, two mutations were located at sites well removed from the interface. The properties of the superrepressor mutants strengthen and extend other data indicating that BirA function entails extensive interactions among the three domains of the protein and show that normal ligase activity does not ensure normal DNA binding.

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A genome-scale Escherichia coli kinetic metabolic model k-ecoli457 satisfying flux data for multiple mutant strains.

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A genome-scale Escherichia coli kinetic metabolic model k-ecoli457 satisfying flux data for multiple mutant strains.

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