Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Definition of the AmrZ regulon under a twitching-promoting condition

ABSTRACT: A novel approach using cellophane sheets was designed to promote Pseudomonas aeruginosa twitching motility, as well as to provide convenience for cell harvest. The AmrZ regulon was defined under this specific growth condition by microarray analysis. WT P. aeruginosa and amrZ mutants were cultivated under this novel condition, and their transcriptional profiles were defined. WT P. aeruginosa strains include PAO1 and WFPA510 (complemented strain), while amrZ mutants include WFPA205 (ΔamrZ) and WFPA513 (AmrZV20A, DNA binding deficient). Each strain has 3 biological replicates.

ORGANISM(S): Pseudomonas aeruginosa

SUBMITTER: Binjie Xu

PROVIDER: E-GEOD-68066 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Publications

Development of a Novel Method for Analyzing Pseudomonas aeruginosa Twitching Motility and Its Application to Define the AmrZ Regulon.

Xu Binjie B Wozniak Daniel J DJ

PloS one 20150826 8

Twitching motility is an important migration mechanism for the Gram-negative bacterium Pseudomonas aeruginosa. In the commonly used subsurface twitching assay, the sub-population of P. aeruginosa with active twitching motility is difficult to harvest for high-throughput studies. Here we describe the development of a novel method that allows efficient isolation of bacterial sub-populations conducting highly active twitching motility. The transcription factor AmrZ regulates multiple P. aeruginosa ...[more]

PMID: 26309248

Similar Datasets

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

Definition of the AmrZ regulon under a twitching-promoting condition

Publications

Development of a Novel Method for Analyzing Pseudomonas aeruginosa Twitching Motility and Its Application to Define the AmrZ Regulon.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets