Dataset Information

Sohn2010 - Genome-scale metabolic network of Pseudomonas putida (PpuMBEL1071)

ABSTRACT: Sohn2010 - Genome-scale metabolic network of Pseudomonas putida (PpuMBEL1071) This model is described in the article: In silico genome-scale metabolic analysis of Pseudomonas putida KT2440 for polyhydroxyalkanoate synthesis, degradation of aromatics and anaerobic survival. Sohn SB, Kim TY, Park JM, Lee SY. Biotechnol J 2010 Jul; 5(7): 739-750 Abstract: Genome-scale metabolic models have been appearing with increasing frequency and have been employed in a wide range of biotechnological applications as well as in biological studies. With the metabolic model as a platform, engineering strategies have become more systematic and focused, unlike the random shotgun approach used in the past. Here we present the genome-scale metabolic model of the versatile Gram-negative bacterium Pseudomonas putida, which has gained widespread interest for various biotechnological applications. With the construction of the genome-scale metabolic model of P. putida KT2440, PpuMBEL1071, we investigated various characteristics of P. putida, such as its capacity for synthesizing polyhydroxyalkanoates (PHA) and degrading aromatics. Although P. putida has been characterized as a strict aerobic bacterium, the physiological characteristics required to achieve anaerobic survival were investigated. Through analysis of PpuMBEL1071, extended survival of P. putida under anaerobic stress was achieved by introducing the ackA gene from Pseudomonas aeruginosa and Escherichia coli. This model is hosted on BioModels Database and identified by: MODEL1507180043. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

SUBMITTER: Nicolas Le Novère

PROVIDER: MODEL1507180043 | BioModels | 2015-07-30

REPOSITORIES: BioModels

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In silico genome-scale metabolic analysis of Pseudomonas putida KT2440 for polyhydroxyalkanoate synthesis, degradation of aromatics and anaerobic survival.

Sohn Seung Bum SB Kim Tae Yong TY Park Jong Myoung JM Lee Sang Yup SY

Biotechnology journal 20100701 7

Genome-scale metabolic models have been appearing with increasing frequency and have been employed in a wide range of biotechnological applications as well as in biological studies. With the metabolic model as a platform, engineering strategies have become more systematic and focused, unlike the random shotgun approach used in the past. Here we present the genome-scale metabolic model of the versatile Gram-negative bacterium Pseudomonas putida, which has gained widespread interest for various bi ...[more]

PMID: 20540110

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Project description:Puchalka2008 - Genome-scale metabolic network of Pseudomonas putida (iJP815) This model is described in the article: Genome-scale reconstruction and analysis of the Pseudomonas putida KT2440 metabolic network facilitates applications in biotechnology. Puchałka J, Oberhardt MA, Godinho M, Bielecka A, Regenhardt D, Timmis KN, Papin JA, Martins dos Santos VA. PLoS Comput. Biol. 2008 Oct; 4(10): e1000210 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. This model is hosted on BioModels Database and identified by: MODEL1507180044. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Project description:Lee2010 - Genome-scale metabolic network of Zymomonas mobilis (iZmobMBEL601) This model is described in the article: The genome-scale metabolic network analysis of Zymomonas mobilis ZM4 explains physiological features and suggests ethanol and succinic acid production strategies. Lee KY, Park JM, Kim TY, Yun H, Lee SY. Microb. Cell Fact. 2010; 9: 94 Abstract: BACKGROUND: Zymomonas mobilis ZM4 is a Gram-negative bacterium that can efficiently produce ethanol from various carbon substrates, including glucose, fructose, and sucrose, via the Entner-Doudoroff pathway. However, systems metabolic engineering is required to further enhance its metabolic performance for industrial application. As an important step towards this goal, the genome-scale metabolic model of Z. mobilis is required to systematically analyze in silico the metabolic characteristics of this bacterium under a wide range of genotypic and environmental conditions. RESULTS: The genome-scale metabolic model of Z. mobilis ZM4, ZmoMBEL601, was reconstructed based on its annotated genes, literature, physiological and biochemical databases. The metabolic model comprises 579 metabolites and 601 metabolic reactions (571 biochemical conversion and 30 transport reactions), built upon extensive search of existing knowledge. Physiological features of Z. mobilis were then examined using constraints-based flux analysis in detail as follows. First, the physiological changes of Z. mobilis as it shifts from anaerobic to aerobic environments (i.e. aerobic shift) were investigated. Then the intensities of flux-sum, which is the cluster of either all ingoing or outgoing fluxes through a metabolite, and the maximum in silico yields of ethanol for Z. mobilis and Escherichia coli were compared and analyzed. Furthermore, the substrate utilization range of Z. mobilis was expanded to include pentose sugar metabolism by introducing metabolic pathways to allow Z. mobilis to utilize pentose sugars. Finally, double gene knock-out simulations were performed to design a strategy for efficiently producing succinic acid as another example of application of the genome-scale metabolic model of Z. mobilis. CONCLUSION: The genome-scale metabolic model reconstructed in this study was able to successfully represent the metabolic characteristics of Z. mobilis under various conditions as validated by experiments and literature information. This reconstructed metabolic model will allow better understanding of Z. mobilis metabolism and consequently designing metabolic engineering strategies for various biotechnological applications. This model is hosted on BioModels Database and identified by: MODEL1507180031. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Project description:Oberhardt2008 - Genome-scale metabolic network of Pseudomonas aeruginosa (iMO1056) This model is described in the article: Genome-scale metabolic network analysis of the opportunistic pathogen Pseudomonas aeruginosa PAO1. Oberhardt MA, Puchałka J, Fryer KE, Martins dos Santos VA, Papin JA. J. Bacteriol. 2008 Apr; 190(8): 2790-2803 Abstract: Pseudomonas aeruginosa is a major life-threatening opportunistic pathogen that commonly infects immunocompromised patients. This bacterium owes its success as a pathogen largely to its metabolic versatility and flexibility. A thorough understanding of P. aeruginosa's metabolism is thus pivotal for the design of effective intervention strategies. Here we aim to provide, through systems analysis, a basis for the characterization of the genome-scale properties of this pathogen's versatile metabolic network. To this end, we reconstructed a genome-scale metabolic network of Pseudomonas aeruginosa PAO1. This reconstruction accounts for 1,056 genes (19% of the genome), 1,030 proteins, and 883 reactions. Flux balance analysis was used to identify key features of P. aeruginosa metabolism, such as growth yield, under defined conditions and with defined knowledge gaps within the network. BIOLOG substrate oxidation data were used in model expansion, and a genome-scale transposon knockout set was compared against in silico knockout predictions to validate the model. Ultimately, this genome-scale model provides a basic modeling framework with which to explore the metabolism of P. aeruginosa in the context of its environmental and genetic constraints, thereby contributing to a more thorough understanding of the genotype-phenotype relationships in this resourceful and dangerous pathogen. This model is hosted on BioModels Database and identified by: MODEL1507180020. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Project description:The ability of certain Pseudomonas (P.) species to grow or persist in anoxic habitats by either denitrification, acetate fermentation or arginine fermentation has been described in several studies as a special property. Previously, we had isolated strains belonging to the species P. lundensis, P. weihenstephanensis and P. fragi from anoxic MAP minced beef and further proved their anaerobic growth in vitro on agar plates. This follow-up study investigated the anaerobic growth of two strains per respective species in situ on inoculated chicken breast fillet under 100% N2 modified atmosphere. We were able to prove anaerobic growth of all six strains on chicken breast fillet with cell division rates of 0.2-0.8 /day. Furthermore, we characterized the anaerobic metabolic lifestyle of these Pseudomonas strains by comparative proteomics, upon their cultivation in meat simulation media, which were constantly gassed with either air or 100% N2 atmospheres. From these proteomic predictions, and respective complementation by physiological experiments, we conclude that the Pseudomonas strains P. fragi, P. weihenstephanensis, P. lundensis exhibit a similar anaerobic lifestyle and employ arginine fermentation via the arginine deiminase (ADI) pathway to grow anaerobically also on MAP meats. Furthermore, glucose fermentation to ethanol via the ED-pathway is predicted to enable long term survival but no true growth, while respiratory growth with nitrate as alternative electron acceptor or glucose fermentation to acetate could be excluded due to absence of essential genes. The citric acid cycle is partially bypassed by the glyoxylate shunt, functioning as the gluconeogenetic route without production of NADH2 under carbon limiting conditions as e.g. in packaged meats. Triggered by an altered redox balance, we also detected upregulation of enzymes involved in protein folding as well as disulphide bonds isomerization under anoxic conditions as a counteracting mechanism to reduce protein misfolding. Hence, this study reveals the mechanisms enabling anaerobic grow and persistence of common meat-spoiling Pseudomonas species, and further complements the hitherto limited knowledge of the anaerobic lifestyle of Pseudomonas species in general.