Large-scale kinetic metabolic models ofPseudomonas putidafor a consistent design of metabolic engineering strategies

Abstract

A high tolerance of Pseudomonas putida to toxic compounds and its ability to grow on a wide variety of substrates makes it a promising candidate for the industrial production of biofuels and biochemicals. 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. In this work, we developed large-scale 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.We started by a gap-filling and thermodynamic curation of iJN1411, the genome-scale model of P. putida KT2440. We then applied the redGEM and lumpGEM algorithms to reduce the curated iJN1411 model systematically, 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 employed the ORACLE framework to generate 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. Overall, we demonstrated the potential and predictive capabilities of developed kinetic models that allow for rational design and optimization of recombinant P. putida strains for improved production of biofuels and biochemicals.

Bacterial Valorization of Lignin: Strains, Enzymes, Conversion Pathways, Biosensors, and Perspectives

“…Combining with computational studies would be more helpful for engineering, where metabolic flux could be possible to predict according to the design. Recently, the metabolic models of P. putida have been developed with single gene knockout by computational tool for the biochemical production (Tokic et al, 2019).…”