Project description:Purpose: To determine effects of arsenic on gene expression in polarized primary human bronchial epithelial (HBE) cells and impact on transcriptional response to Pseudomonas aeruginosa infection Methods: mRNA profiles of HBE cells from 6 donors exposed to 0, 5, 10 or 50 ug/L total arsenic +/- Pseudomonas aeruginosa (48 samples) were generated using Illumina sequencing, aligned in CLC Genomics workbench and analyzed for DE in EdgeR Findings: 20-30 million reads were mapped per sample and transcripts were identifed that were significantly differentially expressed in response to arsenic and Pseudomonas aeruginosa
Project description:Analysis of Pseudomonas aeruginosa PAO1 treated with 200 µM sphingomyelin. Results provide insight into the response to sphingomyelin in P. aeruginosa.
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
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and identified by:
MODEL1507180020.
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Project description:In the present study, we employed Affymetrix Pseudomonas aeruginosa GeneChip arrays to investigate global gene expression profiles during the cellular response of Pseudomonas aeruginosa to sodium hypochlorite Keywords: Antimicrobial response
Project description:Genome-scale modeling of Pseudomonas aeruginosa PA14 unveils its broad metabolic capabilities and role of metabolism in drug potentiation
