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PMID: 18974823 Published · ppublish English Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.

Genome-scale reconstruction and analysis of the Pseudomonas putida KT2440 metabolic network facilitates applications in biotechnology.

PLoS computational biology ·Vol. 4 ·No. 10 ·2008-10-00 ·Pages e1000210

Puchałka J, Oberhardt MA, Godinho M, Bielecka A, Regenhardt D, Timmis KN, Papin JA, Martins dos Santos VA

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.

MeSH Terms
Biocatalysis Biomass Biotechnology/methods Databases, Genetic Genome, Bacterial/physiology Genomics/methods Metabolic Networks and Pathways/physiology Models, Biological Mutagenesis, Site-Directed Polyhydroxyalkanoates/biosynthesis Pseudomonas putida/genetics,growth & development,metabolism Systems Biology/methods
Chemicals
Polyhydroxyalkanoates
Authors & Affiliations
8 authors, click to expand affiliations / ORCID
Puchałka Jacek
Synthetic and Systems Biology Group, Helmholtz Center for Infection Research (HZI), Braunschweig, Germany.
Oberhardt Matthew A
Godinho Miguel
Bielecka Agata
Regenhardt Daniela
Timmis Kenneth N
Papin Jason A
Martins dos Santos Vítor A P
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Article Info
Journal
PLoS computational biology
Abbr.
PLoS Comput Biol
ISSN
1553-7358
DOI
10.1371/journal.pcbi.1000210
Published
2008-10-00
Epub
2008-00-31
Pages
e1000210
Language
English
Region
United States
NLM ID
101238922
PMCID
PMC2563689
Subset
IM
External Links
PubMed source DOI: 10.1371/journal.pcbi.1000210 PMC Full Text View journal details
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