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

被引:193
作者
Puchalka, Jacek [1 ]
Oberhardt, Matthew A. [2 ]
Godinho, Miguel [1 ]
Bielecka, Agata [1 ]
Regenhardt, Daniela [3 ]
Timmis, Kenneth N. [3 ]
Papin, Jason A. [2 ]
dos Santos, Vitor A. P. Martins [1 ]
机构
[1] Helmholtz Ctr Infect Res HZI, Synthet & Syst Biol Grp, Braunschweig, Germany
[2] Univ Virginia, Dept Biomed Engn, Charlottesville, VA USA
[3] Helmholtz Ctr Infect Res HZI, Environm Microbiol Grp, Braunschweig, Germany
基金
美国国家科学基金会; 美国国家卫生研究院;
关键词
D O I
10.1371/journal.pcbi.1000210
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
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, C-13-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.
引用
收藏
页数:18
相关论文
共 89 条
[1]   Kinetics model for growth of Pseudomonas putida F1 during benzene, toluene and phenol biodegradation [J].
Abuhamed, T ;
Bayraktar, E ;
Mehmetoglu, T ;
Mehmetoglu, Ü .
PROCESS BIOCHEMISTRY, 2004, 39 (08) :983-988
[2]   Phenotype MicroArrays for high-throughput phenotypic testing and assay of gene function [J].
Bochner, BR ;
Gadzinski, P ;
Panomitros, E .
GENOME RESEARCH, 2001, 11 (07) :1246-1255
[3]   Flux analysis of underdetermined metabolic networks: The quest for the missing constraints [J].
Bonarius, HPJ ;
Schmid, G ;
Tramper, J .
TRENDS IN BIOTECHNOLOGY, 1997, 15 (08) :308-314
[4]   BACTERIAL BIOVOLUME AND BIOMASS ESTIMATIONS [J].
BRATBAK, G .
APPLIED AND ENVIRONMENTAL MICROBIOLOGY, 1985, 49 (06) :1488-1493
[5]   OptKnock: A bilevel programming framework for identifying gene knockout strategies for microbial strain optimization [J].
Burgard, AP ;
Pharkya, P ;
Maranas, CD .
BIOTECHNOLOGY AND BIOENGINEERING, 2003, 84 (06) :647-657
[6]   Flux coupling analysis of genome-scale metabolic network reconstructions [J].
Burgard, AP ;
Nikolaev, EV ;
Schilling, CH ;
Maranas, CD .
GENOME RESEARCH, 2004, 14 (02) :301-312
[7]  
CAETANOANOLLES G, 1993, PCR METH APPL, V3, P85
[8]   Enhanced production of cis,cis-muconate in a cell-recycle bioreactor [J].
Choi, WJ ;
Lee, EY ;
Cho, MH ;
Choi, CY .
JOURNAL OF FERMENTATION AND BIOENGINEERING, 1997, 84 (01) :70-76
[9]   Integrating high-throughput and computational data elucidates bacterial networks [J].
Covert, MW ;
Knight, EM ;
Reed, JL ;
Herrgard, MJ ;
Palsson, BO .
NATURE, 2004, 429 (6987) :92-96
[10]   Regulation of acetate metabolism by protein phosphorylation in enteric bacteria [J].
Cozzone, AJ .
ANNUAL REVIEW OF MICROBIOLOGY, 1998, 52 :127-164