Ontology highlight
ABSTRACT:
PROVIDER: PRJNA835589 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Nosocomial KPC-2-producing Pseudomonas aeruginosa genome sequencing
| PRJNA291357 | ENA
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:Whole-genome sequencing is an important way to understand the genetic information, gene function, biological characteristics, and living mechanisms of organisms. There is no difficulty to have mega-level genomes sequenced at present. However, we encountered a hard-to-sequence genome of Pseudomonas aeruginosa phage PaP1. The shotgun sequencing method failed to dissect this genome. After insisting for 10 years and going over 3 generations of sequencing techniques, we successfully dissected the PaP1 genome with 91,715 bp in length. Single-molecule sequencing revealed that this genome contains lots of modified bases, including 51 N6-methyladenines (m6A) and 152 N4-methylcytosines (m4C). At the same time, further investigations revealed a novel immune mechanism of bacteria, by which the host bacteria can recognize and repel the modified bases containing inserts in large scale, and this led to the failure of the shotgun method in PaP1 genome sequencing. Strategy of resolving this problem is use of non-library dependent sequencing techniques or use of the nfi- mutant of E. coli DH5M-NM-1 as the host bacteria to construct the shotgun library. In conclusion, we unlock the mystery of phage PaP1 genome hard to be sequenced, and discover a new mechanism of bacterial immunity in present study. Methylation profiling of Pseudomonas aeruginosa phage PaP1 using kinetic data generated by single-molecule, real-time (SMRT) sequencing on the PacBio RS.
2013-08-22 | E-GEOD-50100 | biostudies-arrayexpress
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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MODEL1507180020.
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2015-07-30 | MODEL1507180020 | BioModels
Project description:To further understand the gene expression characteristics of originating biocontrol strain Pseudomonas aeruginosa M18, we have applied whole genome microarray expression profiling as a discovery platform to to specify the PCA-dependent expression of M18 genome. We constructed a series of PCA-producing mutant strains (high PCA: M18MSU1; low PCA: M18MS; and no PCA: M18MSP1P2). The comparison analysis of the M18 mutants genome expressional profiles indicated that the expression of PCA in both M18MSU1 and M18MS alters the expression of a total of 545 different genes; however, the higher level of PCA in M18MSU1 altered more genes (489) as compared to M18MS (129).
2014-07-02 | GSE58981 | 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: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.
2022-03-27 | GSE199323 | GEO
Project description:Primary objectives: The primary objective is to investigate circulating tumor DNA (ctDNA) via deep sequencing for mutation detection and by whole genome sequencing for copy number analyses before start (baseline) with regorafenib and at defined time points during administration of regorafenib for treatment efficacy in colorectal cancer patients in terms of overall survival (OS).
Primary endpoints: circulating tumor DNA (ctDNA) via deep sequencing for mutation detection and by whole genome sequencing for copy number analyses before start (baseline) with regorafenib and at defined time points during administration of regorafenib for treatment efficacy in colorectal cancer patients in terms of overall survival (OS).
| 2533916 | ecrin-mdr-crc
Project description:Genome-scale modeling of Pseudomonas aeruginosa PA14 unveils its broad metabolic capabilities and role of metabolism in drug potentiation
2024-04-18 | MODEL2205090001 | BioModels