Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1066478 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR27638268_1.fastq.gz | Fastqsanger.gz | |||
| SRR27638268_2.fastq.gz | Fastqsanger.gz | |||
| SRR27638269_1.fastq.gz | Fastqsanger.gz | |||
| SRR27638269_2.fastq.gz | Fastqsanger.gz | |||
| SRR27638270_1.fastq.gz | Fastqsanger.gz |
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Project description:Characterization of the sRNA content of OMVs harvested from Pseudomonas aeruginosa strain PA14 LB cultue with and without tobramycin (1ug/mL)
2021-09-12 | GSE183895 | GEO
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.
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2015-07-30 | MODEL1507180020 | BioModels
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.
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Project description:To further determine the origin of the increased virulence of Pseudomonas aeruginosa PA14 compared to Pseudomonas aeruginosa PAO1, we report a transcriptomic approach through RNA sequencing. Next-generation sequencing (NGS) has revolutioned sistems-based analsis of transcriptomic pathways. The goals of this study are to compare the transcriptomic profile of all 5263 orthologous genes of these nearly two strains of Pseudomonas aeruginosa.
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Project description:Pseudomonas aeruginosa PA14 metabolic model
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Project description:Analysis of a SigX knockout mutant of Pseudomonas aeruginosa H103 strain in minimal medium with glucose as carbon source (M9G). SigX, one of the 19 extra-cytoplasmic function sigma factors of P. aeruginosa, was only known to be involved in transcription of the gene encoding the major outer membrane protein OprF in Pseudomonas aeruginosa. Deletion of the ECF sigma factor sigX gene provide insights into the SigX role in several virulence and biofilm- related phenotypes in Pseudomonas aeruginosa.
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Project description:Genomic DNA from Pseudomonas aeruginosa strains PAO1 and PA14
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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
2007-12-04 | GSE7402 | GEO
Project description:Among multiple interconnected pathways for L-Lysine (L-Lys) catabolism in pseudomonads, Pseudomonas aeruginosa PAO1 employed the decarboxylase and the transaminase pathways. However, up till now several genes involved in the operation and regulation of these pathways were still missing. Transcriptome analyses coupled with promoter activity measurements and mutant growth phenotype analysis lead us to identify several new members of the L-Lys and D-Lys catabolic pathways and their regulatory elements, including argR to trigger lysine decarboxylation into cadaverine, PauR for the γ-glutamylation pathway of polyamine catabolism into 5-aminovalerate, gcdR-gcdHG for glutarate utilization, dpkA, amaR-amaAB and PA2035 for D-Lys catabolism, lysR-lysXE for L-Lys efflux, and lysP for L-Lys uptake. Gene expression microarray, including probe preparation, hybridization, fluidics run and chip scan, was performed by Georgia State University DNA/Protein Core Facility. P. aeruginosa PAO1 was grown aerobically in minimal medium P with 350 rpm shaking at 37C, in the presence of 10mM L-glutamate supplemented with 10 mM L-lysine, cadaverine, 5-amino valerate, glutaric acid, D-lysine or 5mM L-pipecolate. Cells were harvested when the optical density at 600 nm reached 0.5~0.6 by centrifugation for 5 minutes at 4C. Total RNA samples were isolated by RNeasy purification kit following instructions of the manufacturer (Qiagen). Reverse transcription for cDNA synthesis, fragmentation by DNase I treatment, cDNA probe labeling and hybridization were performed according to the instructions of GeneChip manufacturer (Affymetrix). Data were processed by Microarray Suite 5.0 software normalizing the absolute expression signal values of all chips to a target intensity of 500. GeneSpring software (Silicon Genetics) was used for expression pattern analysis and comparison. Data collection was carried out using GCOS 1.4 software (Affymetrix). Probe intensity values were normalized to a target value of 500 with normalization factor equal to 1. Data analysis was performed using GeneSpring GX 11 Software (Aglient, Palo Alto,CA). Related Research Papers: 1. Indurthi SM, Chou HT, Lu CD. Molecular Characterization of lysR-lysXE, gcdR-gcdHG, and amaR-amaAB Operons for Lysine Export and Catabolism: A Comprehensive Lysine Catabolic Network in Pseudomonas aeruginosa PAO1. Microbiology. 2015 Submitted [EMID:04803de65c782] 2. Chou HT, Li J, and Lu CD. Functional Characterization of the agtABCD and agtSR Operons for γ-Aminobutyrate and δ-Aminovalerate Uptake and Regulation in Pseudomonas aeruginosa PAO1. Curr. Microbiology. 2014 Jan;68(1):59-63. 3. Chou HT, Li JY, Peng YC, Lu CD. Molecular characterization of PauR and its role in control of putrescine and cadaverine catabolism through the γ-glutamylation pathway in Pseudomonas aeruginosa PAO1. J Bacteriol. 2013 Sep; 195(17):3906-13. 4. Chou HT, Hegazy M, Lu CD. L-lysine Catabolism is Controlled by Arginine/ArgR in Pseudomonas aeruginosa PAO1. J Bacteriol. 2010 Nov;192(22):5874-80
2015-12-09 | E-GEOD-75502 | biostudies-arrayexpress