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, (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.

Figure 1. Schematic diagram of the metabolic reconstruction and analysis processes.

Solid lines indicate consecutive steps of the reconstruction. Dashed lines represent information transfer. Dotted lines specify planned tasks.

Figure 2. Schematic representation of various reaction classes and their interdependency.

The areas of the squares correspond to the sizes of the subsets.

Figure 3. Assignment of the reactions to the particular pathways.

Figure 4. 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.

Figure 5. Interdependency between the metabolic network, the minimal set and the set of essential reactions.

The set sizes are given for glucose growth conditions.

Figure 6. Mutational strategies for increased PHA production.

This figure highlights 6 strategies suggested by the modified optknock approach for increased production of AcCoA, a precursor for polyhydroxyalkanoates. (A) AcCoA production ranges vs. growth yield of in silico strains developed using the ‘AcCoA production’ strategy. (B) AcCoA pooling versus growth yield of in silico strains developed using the ‘AcCoA pooling’ strategy.