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Functional characterization of TetR-like transcriptional regulator PA3973 from Pseudomonas aeruginosa [RNA-seq]

ABSTRACT: Pseudomonas aeruginosa, a human opportunistic pathogen, is a common cause of nosocomial infections. Its ability to survive under different conditions relies on a complex regulatory network engaging transcriptional regulators controlling metabolic pathways and capabilities to efficiently use the available resources. P. aeruginosa PA3973 encodes a putative TetR family transcriptional regulator, with a helix-turn-helix motif involved in DNA binding. We applied transcriptome profiling (RNA-seq), and genome-wide identification of binding sites using ChIP-seq to unravel the biological role of PA3973.

ORGANISM(S): Pseudomonas aeruginosa

PROVIDER: GSE211771 | GEO | 2022/11/22

REPOSITORIES: GEO

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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.

Dataset Information

Functional characterization of TetR-like transcriptional regulator PA3973 from Pseudomonas aeruginosa [RNA-seq]

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