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.
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.
Project description:Chromosome segregation in Pseudomonas aeruginosa is assisted by the tripartite ParAB-parS system, composed of an ATPase (ParA), a DNA-binding protein (ParB), and its target parS sequence(s). ParB forms a nucleoprotein complex around four parSs (parS1-parS4), which is positioned within the cell by ParA. Remarkably, ParB of P. aeruginosa binds to multiple heptanucleotides (half-parSs) scattered in the genome. In this work we analysed the transcriptome of P. aeruginosa with mutated 25 half-parSs forming the strongest ParB ChIP-seq peaks. Inactivation of ParB binding to even a small fraction of these sites modulated the gene expression, however this effect is most likely indirect. Overall this work suggests complex relation between ParB binding to genome and P. aeruginosa transcriptome.
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:We report RNA sequencing data for mRNA transcripts obtained from tobramycin exposed phoenix colonies, VBNCs, and various controls (untreated lawn, edge of the zone of clearance of tobramycin, treated outer background lawn). Extracted mRNA was sequenced using an Illumina HiSeq 4000, mapped to a Pseudomonas aeruginosa PAO1 reference genome, and processed to obtain counts for all gene transcripts for each sample. This is the first sequencing data generated for Pseudomonas aeruginosa phoenix colonies and VBNCs.
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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