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Abstract
BackgroundPseudomonas putida is a promising candidate for the industrial production of biofuels and biochemicals because of its high tolerance to toxic compounds and its ability to grow on a wide variety of substrates. Engineering this organism for improved performances and predicting metabolic responses upon genetic perturbations requires reliable descriptions of its metabolism in the form of stoichiometric and kinetic models.ResultsIn this work, we developed kinetic models of P. putida to predict the metabolic phenotypes and design metabolic engineering interventions for the production of biochemicals. The developed kinetic models contain 775 reactions and 245 metabolites. Furthermore, we introduce here a novel set of constraints within thermodynamics-based flux analysis that allow for considering concentrations of metabolites that exist in several compartments as separate entities. We started by a gap-filling and thermodynamic curation of iJN1411, the genome-scale model of P. putida KT2440. We then systematically reduced the curated iJN1411 model, and we created three core stoichiometric models of different complexity that describe the central carbon metabolism of P. putida. Using the medium complexity core model as a scaffold, we generated populations of large-scale kinetic models for two studies. In the first study, the developed kinetic models successfully captured the experimentally observed metabolic responses to several single-gene knockouts of a wild-type strain of P. putida KT2440 growing on glucose. In the second study, we used the developed models to propose metabolic engineering interventions for improved robustness of this organism to the stress condition of increased ATP demand.ConclusionsThe study demonstrates the potential and predictive capabilities of the kinetic models that allow for rational design and optimization of recombinant P. putida strains for improved production of biofuels and biochemicals. The curated genome-scale model of P. putida together with the developed large-scale stoichiometric and kinetic models represents a significant resource for researchers in industry and academia.
Highlights
Pseudomonas putida is a promising candidate for the industrial production of biofuels and biochemicals because of its high tolerance to toxic compounds and its ability to grow on a wide variety of substrates
Curated genome‐scale model of P. putida Integration of thermodynamics data Methods that use thermodynamic data such as the thermodynamics-based flux analysis Thermodynamics-based Flux Balance Analysis (TFA) [35,36,37,38,39] allow to: (i) integrate the metabolomics and fluxomics data into models, and compute values of metabolic fluxes and metabolite concentrations whose experimental measurements are not available; (ii) eliminate in silico designed biosynthetic pathways not obeying the second law of thermodynamics [51, 52]; (iii) eliminate infeasible thermodynamic cycles [53,54,55]; and (iv) identify how far reactions operate from thermodynamic equilibrium [46, 56]
A reason for this mismatch could lie in the fact that the H+/ATP stoichiometry in the electron transport chain (ETC) of P. putida might be inaccurately determined in iJN1411 which would lead to large discrepancies in ATP yield on glucose [3, 66]
Summary
Pseudomonas putida is a promising candidate for the industrial production of biofuels and biochemicals because of its high tolerance to toxic compounds and its ability to grow on a wide variety of substrates. Engineering this organism for improved performances and predicting metabolic responses upon genetic perturbations requires reliable descriptions of its metabolism in the form of stoichiometric and kinetic models. The first reconstructed Genome-Scale Model (GEM) of P. putida KT2440, iJN746, was published in 2008 and it comprised 911 metabolites, 950 reactions, and 746 genes [10] It was rapidly followed by the publication of iJP815 [11] and other reconstructions [12, 13].
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