Unknown,Transcriptomics,Genomics,Proteomics

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Transcription profiling of Pseudomonas aeruginosa PAO1 wild type and PA2384 strains at different culture densities to investigate their response to iron limitation

ABSTRACT: The gene PA2384 in Pseudomonas aeruginosa PAO1, annotated originally as unknown function, has been previously shown to be dramatically responsive to iron limitation. In the present study, PA2384 is shown by bioinformatics analysis to have a weak similarity to the N-termini DNA binding domain of fur, a well-known ferric uptake regulator. To experimentally investigate the function of PA2384 P. aeruginosa PAO1 recombinant (pUCP20::PA2384) overexpressing PA2384 and PA2384 knock-out mutant PAO1-23844 are constructed and studied. Physiological characterization in a carefully controlled cultivation system shows that the knockout strain needed a longer lag phase to grow and exhibited a significantly reduced production speed of siderophore (pyoverdine and pyochelin) in the stationary phase. Genome-scale transcriptional profiles at different growth stages are compared between the wild type and âPA2384 mutant grown under iron-limiting conditions. The expression of more than 350 genes is affected. Among them, seventy-one genes involved in iron uptake are significantly induced by PA2384. 102 quorum sensing (QS) dependent genes exhibited differential transcription. They include genes related to the biosynthesis of some important virulence factors such as pyocyanin, rhamnolipids and hydrogen cyanide. Transcription of genes responsible for the synthesis of Pseudomonas Quinolone Signal (PQS) is greatly advanced by the knockout of PA2384. We postulate that PA2384 affects QS via PQS. Furthermore, the knockout of PA2384 also results in altered expression of genes involved in electron transfer, central metabolism, phosphorus starvation and translation. It implies that PA2384 may affect more basic physiology than only respond to iron uptake and is a versatile global regulator in P. aeruginosa under iron starvation. Experiment Overall Design: mutant and wilde type biological replicates were analyzed at 3 timepoints

ORGANISM(S): Pseudomonas aeruginosa

SUBMITTER: Robert Geffers

PROVIDER: E-GEOD-2885 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:Virulence factor production and the development of biofilms in Pseudomonas aeruginosa have been shown to be regulated by two hierarchically organised quorum sensing (QS) systems via small acyl-homoserine lactone (AHL) signal molecules. Recently, a third bacterial signal molecule, the Pseudomonas quinolone signal (PQS), has been identified, which positively regulates a subset of genes dependent on the QS systems. To further dissect the various independent regulation levels for many QS induced genes and to evaluate the impact of PQS on the QS circuitry, we performed a transcriptome analysis of PAO1 cultures supplemented with PQS. The global transcriptional profile in response to PQS revealed a marked up-regulation of genes belonging to the tightly interdependent functional groups of iron acquisition and oxidative stress response. Remarkably, not only most of the differentially regulated genes but also the induction of a lacZ transcriptional fusion of rhlR could be traced back to a iron chelating effect of PQS. Nevertheless, although iron deficiency per se induced rhlR, there seems to be PQS specific effects that are independent of the PQS effect on P. aeruginosa iron homeostasis Experiment Overall Design: For RNA extraction bacteria were harvested at early logarithmic, late logarithmic and stationary phase of growth. Three independent cultures of PQS-treated and untreated PAO1 each were pooled and the RNA was immediately stabilized with RNAprotect Bacteria Reagent (Qiagen, Valencia, CA). RNA was isolated using the RNeasy kit (Qiagen), treated with DNaseI (Roche) for 30 min at 37M-BM-0C and re-purified with the RNeasy spin column. The subsequent steps of cDNA generation and Biotin-ddUTP terminal labelling were performed as described in the manufacturerM-bM-^@M-^Ys instructions for the P. aeruginosa GeneChipM-CM-^R. Fragmented and labelled cDNA was hybridised to a GeneChip at 50 M-BM-0C for 16 h. The washing steps, staining and scanning of the microarrays were performed using the Affymetrix GeneChip system. Data analysis was performed using the Affymetrix Microarray Suite Software 5.0 with Affymetrix default parameters. As expression analysis was performed in duplicate, a total of two GeneChips per culture condition was scanned at 570 nm, 3 M-BM-5m resolution in an Affymetrix GeneChip scanner. The signals were multiplied by a scaling factor to make the average signal for all the arrays equivalent. The data were imported into a Microsoft Access database capable of searching for genes, which were found in all four pairings defined by the Affymetrix Microarray Suite Software as having significant changes in their signal intensities. Data were combined with the latest annotation from the website of the P. aeruginosa PAO1 sequence and the community annotation project provided at www.pseudomonas.com.

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.

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Transcription profiling of Pseudomonas aeruginosa PAO1 wild type and PA2384 strains at different culture densities to investigate their response to iron limitation

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