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Genome-scale reconstruction and analysis of the Pseudomonas putida KT2440 metabolic network facilitates applications in biotechnology.

Puchałka J, Oberhardt MA, Godinho M, Bielecka A, Regenhardt D, Timmis KN, Papin JA, Martins dos Santos VA - PLoS Comput. Biol. (2008)

Bottom Line: Auxotrophy was correctly predicted in 75% of the cases.These systematic analyses revealed that the metabolic network structure is the main factor determining the accuracy of predictions, whereas biomass composition has negligible influence.Finally, we drew on the model to devise metabolic engineering strategies to improve production of polyhydroxyalkanoates, a class of biotechnologically useful compounds whose synthesis is not coupled to cell survival.

View Article: PubMed Central - PubMed

Affiliation: Synthetic and Systems Biology Group, Helmholtz Center for Infection Research (HZI), Braunschweig, Germany.

ABSTRACT
A cornerstone of biotechnology is the use of microorganisms for the efficient production of chemicals and the elimination of harmful waste. Pseudomonas putida is an archetype of such microbes due to its metabolic versatility, stress resistance, amenability to genetic modifications, and vast potential for environmental and industrial applications. To address both the elucidation of the metabolic wiring in P. putida and its uses in biocatalysis, in particular for the production of non-growth-related biochemicals, we developed and present here a genome-scale constraint-based model of the metabolism of P. putida KT2440. Network reconstruction and flux balance analysis (FBA) enabled definition of the structure of the metabolic network, identification of knowledge gaps, and pin-pointing of essential metabolic functions, facilitating thereby the refinement of gene annotations. FBA and flux variability analysis were used to analyze the properties, potential, and limits of the model. These analyses allowed identification, under various conditions, of key features of metabolism such as growth yield, resource distribution, network robustness, and gene essentiality. The model was validated with data from continuous cell cultures, high-throughput phenotyping data, (13)C-measurement of internal flux distributions, and specifically generated knock-out mutants. Auxotrophy was correctly predicted in 75% of the cases. These systematic analyses revealed that the metabolic network structure is the main factor determining the accuracy of predictions, whereas biomass composition has negligible influence. Finally, we drew on the model to devise metabolic engineering strategies to improve production of polyhydroxyalkanoates, a class of biotechnologically useful compounds whose synthesis is not coupled to cell survival. The solidly validated model yields valuable insights into genotype-phenotype relationships and provides a sound framework to explore this versatile bacterium and to capitalize on its vast biotechnological potential.

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Interdependency between the metabolic network, the minimal set                                and the set of essential reactions.The set sizes are given for glucose growth conditions.
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pcbi-1000210-g005: Interdependency between the metabolic network, the minimal set and the set of essential reactions.The set sizes are given for glucose growth conditions.

Mentions: To assess the robustness of the metabolic network to genetic perturbations (e.g., knock-out mutations), we carried out an in-silico analysis of the essentiality of single genes and reactions, which enabled us to identify the most fragile nodes of the iJP815 network. Reaction essentiality simulations were performed by systematically removing each reaction from the network and by assessing the ability of the model to produce biomass in silico via FBA in minimal medium with a sole carbon source (glucose and acetate). Gene essentiality was assessed by: (i) identifying for each gene the operability of the reaction(s) dependent on this gene, (ii) removing from the network the reactions rendered inoperative by the deletion of that particular gene, and (iii) determining the ability of the model to produce biomass in the same manner as for the reaction essentiality tests. Additionally, we estimated for both carbon sources the smallest possible set of reactions able to sustain in silico growth, in order to estimate the number of reactions necessary for biomass synthesis in minimal medium (minimal set). This set encompasses both all reactions that are essential (including those essential regardless of the medium and those ‘conditionally essential’) and the minimal number of non-essential reactions that, together, are able to provide in silico growth (see Figure 5). These conditionally essential reactions can be used as a reference for identifying sections of metabolism for which alternative pathways exist. For both glucose and acetate, the minimal sets encompassed approximately 315 reactions (Table S5). This estimate is consistent with values obtained for other bacteria [54].


Genome-scale reconstruction and analysis of the Pseudomonas putida KT2440 metabolic network facilitates applications in biotechnology.

Puchałka J, Oberhardt MA, Godinho M, Bielecka A, Regenhardt D, Timmis KN, Papin JA, Martins dos Santos VA - PLoS Comput. Biol. (2008)

Interdependency between the metabolic network, the minimal set                                and the set of essential reactions.The set sizes are given for glucose growth conditions.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2563689&req=5

pcbi-1000210-g005: Interdependency between the metabolic network, the minimal set and the set of essential reactions.The set sizes are given for glucose growth conditions.
Mentions: To assess the robustness of the metabolic network to genetic perturbations (e.g., knock-out mutations), we carried out an in-silico analysis of the essentiality of single genes and reactions, which enabled us to identify the most fragile nodes of the iJP815 network. Reaction essentiality simulations were performed by systematically removing each reaction from the network and by assessing the ability of the model to produce biomass in silico via FBA in minimal medium with a sole carbon source (glucose and acetate). Gene essentiality was assessed by: (i) identifying for each gene the operability of the reaction(s) dependent on this gene, (ii) removing from the network the reactions rendered inoperative by the deletion of that particular gene, and (iii) determining the ability of the model to produce biomass in the same manner as for the reaction essentiality tests. Additionally, we estimated for both carbon sources the smallest possible set of reactions able to sustain in silico growth, in order to estimate the number of reactions necessary for biomass synthesis in minimal medium (minimal set). This set encompasses both all reactions that are essential (including those essential regardless of the medium and those ‘conditionally essential’) and the minimal number of non-essential reactions that, together, are able to provide in silico growth (see Figure 5). These conditionally essential reactions can be used as a reference for identifying sections of metabolism for which alternative pathways exist. For both glucose and acetate, the minimal sets encompassed approximately 315 reactions (Table S5). This estimate is consistent with values obtained for other bacteria [54].

Bottom Line: Auxotrophy was correctly predicted in 75% of the cases.These systematic analyses revealed that the metabolic network structure is the main factor determining the accuracy of predictions, whereas biomass composition has negligible influence.Finally, we drew on the model to devise metabolic engineering strategies to improve production of polyhydroxyalkanoates, a class of biotechnologically useful compounds whose synthesis is not coupled to cell survival.

View Article: PubMed Central - PubMed

Affiliation: Synthetic and Systems Biology Group, Helmholtz Center for Infection Research (HZI), Braunschweig, Germany.

ABSTRACT
A cornerstone of biotechnology is the use of microorganisms for the efficient production of chemicals and the elimination of harmful waste. Pseudomonas putida is an archetype of such microbes due to its metabolic versatility, stress resistance, amenability to genetic modifications, and vast potential for environmental and industrial applications. To address both the elucidation of the metabolic wiring in P. putida and its uses in biocatalysis, in particular for the production of non-growth-related biochemicals, we developed and present here a genome-scale constraint-based model of the metabolism of P. putida KT2440. Network reconstruction and flux balance analysis (FBA) enabled definition of the structure of the metabolic network, identification of knowledge gaps, and pin-pointing of essential metabolic functions, facilitating thereby the refinement of gene annotations. FBA and flux variability analysis were used to analyze the properties, potential, and limits of the model. These analyses allowed identification, under various conditions, of key features of metabolism such as growth yield, resource distribution, network robustness, and gene essentiality. The model was validated with data from continuous cell cultures, high-throughput phenotyping data, (13)C-measurement of internal flux distributions, and specifically generated knock-out mutants. Auxotrophy was correctly predicted in 75% of the cases. These systematic analyses revealed that the metabolic network structure is the main factor determining the accuracy of predictions, whereas biomass composition has negligible influence. Finally, we drew on the model to devise metabolic engineering strategies to improve production of polyhydroxyalkanoates, a class of biotechnologically useful compounds whose synthesis is not coupled to cell survival. The solidly validated model yields valuable insights into genotype-phenotype relationships and provides a sound framework to explore this versatile bacterium and to capitalize on its vast biotechnological potential.

Show MeSH