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ABSTRACT:
PROVIDER: PRJNA392234 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Genomic DNA from Pseudomonas aeruginosa strains PAO1 and PA14
2015-11-15 | GSE67038 | 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.
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2015-07-30 | MODEL1507180020 | BioModels
Project description:Pseudomonas aeruginosa is an opportunistic pathogen which causes acute and chronic infections that are difficult to treat. Comparative genomic analysis has showed a great genome diversity among P. aeruginosa clinical strains and revealed important regulatory traits during chronic adaptation. While current investigation of epigenetics of P. aeruginosa is still lacking, understanding the epigenetic regulation may provide biomarkers for diagnosis and reveal important regulatory mechanisms. The present study focused on characterization of DNA methyltransferases (MTases) in a chronically adapted P. aeruginosa clinical strain TBCF10839. Single-molecule real-time sequencing (SMRT-seq) was used to characterize the methylome of TBCF. RCCANNNNNNNTGAR and TRGANNNNNNTGC were identified as target motifs of DNA MTases, M.PaeTBCFI and M.PaeTBCFII, respectively.
2023-07-22 | GSE209530 | GEO
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:Pseudomonas aeruginosa Genomic Structure and Diversity
| PRJNA771935 | ENA
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:The clinical importance of microbiomes to the chronicity of wounds is widely appreciated, yet little is understood about patient-specific processes shaping wound microbiome composition. Here, a two-cohort microbiome-genome wide association study is presented through which patient genomic loci associated with chronic wound microbiome diversity were identified. Further investigation revealed that alternative TLN2 and ZNF521 genotypes explained significant inter-patient variation in relative abundance of two key pathogens, Pseudomonas aeruginosa and Staphylococcus epidermidis. Wound diversity was lowest in Pseudomonas aeruginosa infected wounds, and decreasing wound diversity had a significant negative linear relationship with healing rate. In addition to microbiome characteristics, age, diabetic status, and genetic ancestry all significantly influenced healing. Using structural equation modeling to identify common variance among SNPs, six loci were sufficient to explain 53% of variation in wound microbiome diversity, which was a 10% increase over traditional multiple regression. Focusing on TLN2, genotype at rs8031916 explained expression differences of alternative transcripts that differ in inclusion of important focal adhesion binding domains. Such differences are hypothesized to relate to wound microbiomes and healing through effects on bacterial exploitation of focal adhesions and/or cellular migration. Related, other associated loci were functionally enriched, often with roles in cytoskeletal dynamics. This study, being the first to identify patient genetic determinants for wound microbiomes and healing, implicates genetic variation determining cellular adhesion phenotypes as important drivers of infection type. The identification of predictive biomarkers for chronic wound microbiomes may serve as risk factors and guide treatment by informing patient-specific tendencies of infection.
2020-04-25 | GSE149314 | GEO
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.
2019-09-23 | GSE125646 | GEO
Project description:Pseudomonas aeruginosa PA14 metabolic model
2022-05-09 | MODEL2205060001 | BioModels