Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1091195 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR28442019_1.fastq.gz | Fastqsanger.gz | |||
| SRR28442019_2.fastq.gz | Fastqsanger.gz | |||
| SRR28442020_1.fastq.gz | Fastqsanger.gz | |||
| SRR28442020_2.fastq.gz | Fastqsanger.gz | |||
| SRR28442021_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 66 |
Similar Datasets
Project description:This data was collected on lung tissue associated with cystic fibrosis from 3 patients
2015-12-01 | MSV000079398 | GNPS
Project description:The complex milieu of inflammatory mediators associated with many diseases is often too dilute to directly measure in the periphery, necessitating development of more sensitive measurements suitable for mechanistic studies, earlier diagnosis, guiding selection of therapy, and monitoring interventions. Previously, we determined that plasma of recent-onset (RO) Type 1 diabetes (T1D) patients induce a proinflammatory transcriptional signature in fresh peripheral blood mononuclear cells (PBMC) relative to that of unrelated healthy controls (HC). Here, using an optimized cryopreserved PBMC-based protocol, we compared the signature found between unrelated healthy controls and non-diabetic cystic fibrosis patients possessing Pseudomonas aeruginosa pulmonary tract infection. UPN727 cells were stimulated with autologous plasma (n=5), unrelated healthy control plasma (n=24), or plasma from patients with cystic fibrosis possessing Psuedomonas aeruginosa pulmonary tract infection (n=20). Gene expression analysis was perfromed in order to evaluate the transcriptional signature associated with cystic fibrosis.
2012-09-01 | E-GEOD-35711 | biostudies-arrayexpress
Project description:Pseudomonas aeruginosa airway infection is the primary cause of death in Cystic Fibrosis (CF). During early infection P. aeruginosa produces multiple virulence factors, which cause acute pulmonary disease and are largely regulated by quorum sensing (QS) intercellular signalling networks. Longitudinal clinical studies have observed the loss, through adaptive mutation, of QS and QS-related virulence in late chronic infection. Although the mechanisms are not understood, infection with QS mutants has been linked to a worse outcome for CF patients. By comparing QS-active and QS-inactive P. aeruginosa CF isolates, we have identified novel virulence factors and pathways associated with QS disruption. In particular, we noted factors implicating increased intra-phagocyte survival. Our data present novel targets as candidates for future CF therapies. Some of these targets are already the subject of drug development programmes for the treatment of other bacterial pathogens and may provide cross-over benefit to the CF population. Refer to individual Series. This SuperSeries is composed of the following subset Series: GSE25128: Gene expression data from Pseudomonas aeruginosa strains isolated from cystic fibrosis lung infections GSE25129: Comparative genomic hybridisation data from Pseudomonas aeruginosa strains isolated from cystic fibrosis lung infections
2010-11-04 | E-GEOD-25130 | biostudies-arrayexpress
Project description:A collection of sputum and mouth rinse samples from cystic fibrosis patients collected cross sectionally. GC-MS data!!
2017-06-09 | MSV000081150 | GNPS
Project description:Backtround:Cystic fibrosis (CF) is an inherited genetic disorder caused by the cystic fibrosis transmembrane conductance regulator(CFTR) gene mutation, producing sticky and thick mucosal fluids. This leads to an environment that facilitates the colonization ofopportunistic microorganisms, causing progressive acute and chronic lung infections.Rothia mucilaginosa, an oral commensal, isrelatively abundant in the lungs of CF patients. Recent studies have unveiled the anti-inflammatory effects ofR. mucilaginosausinginvitro3D lung epithelial cell culture andin vivomouse models. Apart from its probiotic effect,R. mucilaginosacan be pathogenic,resulting in severe infections. This dual nature highlights the bacterium’s complexity and diverse impact on health and disease.However, its metabolic capabilities and genotype-phenotype relationships remain largely unknown.Results:To gain insights intoR. mucilaginosa’s cellular metabolism and genetic alterations, we developed the first manually curatedgenome-scale metabolic model,iRM23NL. Through growth kinetic experiments and high-throughput phenotypic microarray testings,we defined its complete catabolic phenome. Subsequently, we assessed the model’s effectiveness in accurately predicting growthbehaviors and utilizing multiple substrates. We used constraint-based modeling techniques to formulate novel hypotheses that couldexpedite the development of antimicrobial strategies. More specifically, we detected putative essential genes and assessed their effecton metabolism under varying nutritional conditions. These predictions could offer novel potential antimicrobial targets without laboriouslarge-scale screening of knock-outs and mutant transposon libraries. Finally, we examined the production levels of enterobactin undervarying nutritional conditions and suggested compounds that could alter its production.Conclusion:Overall,iRM23NL demonstrates a solid capability to predict cellular phenotypes and holds immense potential as avaluable resource for accurate predictions in advancing antimicrobial therapies. Moreover, it can guide the metabolic engineering totailorR. mucilaginosa’s metabolism for desired performance.Availability and implementation:Source code and model are freely accessible from GitHub and BioModels.
2024-04-10 | MODEL2310240001 | BioModels
Project description:To identify genes relevant for cystic fibrosis pathophysiology, we profiled blood samples in CF patients and healthy controls using RNA-seq. Weighted Gene Co-expression Network Analysis of a transcriptomic dataset allowed us to identify 28 co-expressed modules that correlated with clinical traits of interest in cystic fibrosis.
2020-03-26 | GSE136371 | GEO