Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1075801 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR27948423.fastq.gz | Fastqsanger.gz | |||
| SRR27948424.fastq.gz | Fastqsanger.gz | |||
| SRR27948425.fastq.gz | Fastqsanger.gz | |||
| SRR27948426.fastq.gz | Fastqsanger.gz | |||
| SRR27948427.fastq.gz | Fastqsanger.gz |
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Project description:Pseudomonas aeruginosa strains PAHM4 and PAO1 were grown at 37C on LB and RNA was hybridized on the Affymetrix P. aeruginosa chip to compare transcript differences from a BQ isolate to a well characterized wound isolate.
2012-10-01 | GSE40461 | GEO
Project description:Genomic DNA from Pseudomonas aeruginosa strains PAO1 and PA14
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Project description:Pseudomonas aeruginosa Environmental Isolate Sequencing
| PRJNA393338 | ENA
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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2015-07-30 | MODEL1507180020 | BioModels
Project description:Pseudomonas aeruginosa strains PAHM4 and PAO1 were grown at 37C on LB and RNA was hybridized on the Affymetrix P. aeruginosa chip to compare transcript differences from a BQ isolate to a well characterized wound isolate. Strains PAHM4 and PAO1 were grown in triplicate. Total RNA from each sample was pooled and amplified then assayed in triplicate resulting in 6 total samples. PAO1 is the control strain while PAHM4 is the experimental strain.
2012-10-01 | E-GEOD-40461 | biostudies-arrayexpress
Project description:ErfA is a transcription factor of Pseudomonas aeruginosa. We here define the genome-wide binding sites of ErfA by DAP-seq in Pseudomonas aeruginosa PAO1 and IHMA87, Pseudomonas chlororaphis PA23, Pseudomonas protegens CHA0 and Pseudomonas putida KT2440.
2019-12-20 | GSE137648 | GEO
Project description:We performed transcriptomic analysis of prrA antitoxin mutant and WT strain of the clinical isolate 39016 of Pseudomonas aeruginosa. The purpose: understanding the gene expression pattern in the mutant with and without prophage induction- in compaarison to the WT strain with and without induction.
2022-05-01 | GSE179116 | GEO
Project description:Regulatory networks including virulence-related transcriptional factors (TFs) determine bacterial pathogenicity in response to different environmental cues. Pseudomonas aeruginosa, a Gram-negative opportunistic pathogen of humans, recruits numerous TFs in quorum sensing (QS) system, type III secretion system (T3SS) and Type VI secretion system (T6SS) to mediate the pathogenicity. Although many virulence-related TFs have been illustrated individually, very little is known about their crosstalks and regulatory network. Here, based on chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-seq) and transcriptome profiling (RNA-seq), we primarily focused on understanding the crosstalks of 20 virulence-related TFs, which led to construction of a virulence regulatory network named PAGnet (Pseudomonas aeruginosa Genomic integrated regulatory network), including 82 crosstalk targets. The PAGnet uncovered the intricate mechanism of virulence regulation and revealed master regulators in QS, T3SS and T6SS pathways. In particular, GacA and ExsA showed novel functions in QS and nitrogen metabolism. In addition, an online PAGnet platform was provided to analyze these TFs and more virulence factors. Taken together, the present study revealed the function-specific crosstalks of virulence regulatory network, which might provide new strategies for treating infections in P. aeruginosa in the future.
2019-07-16 | GSE128430 | GEO
Project description:To use whole genome microarrays to compare the differences in genome contents of 5 B. pseudomallei isolated from clinical specimens and environmental sample with B. pseudomallei K96243 reference strain and reveals variable patterns of Genomic Islands (GIs) Keywords: Comparative genomic hybridization DNA microarrays were used to compare genome of clinical and environmental B. pseudomallei isolates with B. pseudomallei K96243 reference strain (B. pseudomallei K96243 vs. B. pseudmallei isolates). Each hybridization was used for comparison between B. pseudomallei K96243 as a reference strain with environmental isolate BP45s, environmental isolate BP28L, clinical isolate H307, clinical isolate P54, clinical isolate P82. Two replicate per array. Multiple hits with 90-99.99 % identity correspond to other locus are replicate of their genes were averaged and analyzed.
2012-07-30 | E-GEOD-19558 | biostudies-arrayexpress