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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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Comparison of FVA calculations with 13C experimental flux                            data.The explanation of color codes is given in the figure.                            “0*” means that the reaction is not included                            in the particular metabolic network; double-headed arrows depict                            reversible reactions, the bigger head shows direction of the positive                            flux.
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pcbi-1000210-g004: Comparison of FVA calculations with 13C experimental flux data.The explanation of color codes is given in the figure. “0*” means that the reaction is not included in the particular metabolic network; double-headed arrows depict reversible reactions, the bigger head shows direction of the positive flux.

Mentions: Genome-scale metabolic networks are, in general, algebraically underdetermined [41]. As a consequence, the optimal growth rate can often be attained through flux distributions different than the single optimal solution predicted by FBA simulations. Therefore we used flux variability analysis (FVA) to explore the network, as this method provides the intervals inside which the flux can vary without influencing the value of the growth yield (if the flux of the reaction cannot vary then the range is limited to a single value) [41]. The results of the simulations are given in Figure 4. As isotopic (13C) measurements are not able to distinguish which glucose uptake route is being used by P. putida, all the fluxes in the 13C experiment and in the FVA simulations were computed assuming that glucose is taken up directly into the cell. For the precise description of the network models used in this comparison (i.e., FBA/FVA vs. 13C-Flux analysis) see Text S1 and Text S2 (sections “Comparison of FVA analyses with 13C flux measurement data”).


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)

Comparison of FVA calculations with 13C experimental flux                            data.The explanation of color codes is given in the figure.                            “0*” means that the reaction is not included                            in the particular metabolic network; double-headed arrows depict                            reversible reactions, the bigger head shows direction of the positive                            flux.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1000210-g004: Comparison of FVA calculations with 13C experimental flux data.The explanation of color codes is given in the figure. “0*” means that the reaction is not included in the particular metabolic network; double-headed arrows depict reversible reactions, the bigger head shows direction of the positive flux.
Mentions: Genome-scale metabolic networks are, in general, algebraically underdetermined [41]. As a consequence, the optimal growth rate can often be attained through flux distributions different than the single optimal solution predicted by FBA simulations. Therefore we used flux variability analysis (FVA) to explore the network, as this method provides the intervals inside which the flux can vary without influencing the value of the growth yield (if the flux of the reaction cannot vary then the range is limited to a single value) [41]. The results of the simulations are given in Figure 4. As isotopic (13C) measurements are not able to distinguish which glucose uptake route is being used by P. putida, all the fluxes in the 13C experiment and in the FVA simulations were computed assuming that glucose is taken up directly into the cell. For the precise description of the network models used in this comparison (i.e., FBA/FVA vs. 13C-Flux analysis) see Text S1 and Text S2 (sections “Comparison of FVA analyses with 13C flux measurement data”).

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