Genome-Scale Reconstruction and Analysis of the Pseudomonas putida KT2440 Metabolic Network Facilitates Applications in Biotechnology

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, 13C-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.

A standardized workflow for surveying recombinases expands bacterial genome‐editing capabilities

“…P. putida derives this sturdiness from its EDEMP cycle, a series of metabolic pathways that facilitate NAD(P)H production via carbon recycling processes (Nikel et al ., 2015). An adaptability to a myriad of harsh conditions has made P. putida a popular focus of biosynthetic studies aimed at industrial biotransformations as well as soil and water bioremediation (Garmendia et al ., 2008; Puchałka et al ., 2008; Poblete‐Castro et al ., 2012; Loeschcke and Thies, 2015). Unfortunately, the collection of genetic tools available in Pseudomonads is limited in scope and, when used in conjunction, is subject to bottlenecks that hinder the generation of a biotechnological chassis (Martínez‐García and de Lorenzo, 2011; Martínez‐García et al ., 2011, 2014).…”

Prediction of metabolic fluxes by incorporating genomic context and flux-converging pattern analyses

“…However, this approach still works with a flux solution space that is too large compared with the biologically feasible flux solution space of a real organism (15)(16)(17). Thus, additional information and procedures, such as providing more constraints, are needed to narrow down the set of candidate solutions.…”

Genome-scale reconstruction and in silico analysis of the Ralstonia eutropha H16 for polyhydroxyalkanoate synthesis, lithoautotrophic growth, and 2-methyl citric acid production

“…These flux values were compared with the control flux solution space to examine the changes of flux solution space for each reaction under genetic and environmental perturbations [26,28,29]. In addition, in order to identify the gene knockout targets for the enhanced production of 2-methylcitric acid, the method of minimization of metabolic adjustment (MOMA) using quadratic programming (QP) was employed [30].…”