Ontology highlight
ABSTRACT:
PROVIDER: PRJNA544655 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR9118160_1.fastq.gz | Fastqsanger.gz | |||
| SRR9118160_2.fastq.gz | Fastqsanger.gz | |||
| SRR9118170_1.fastq.gz | Fastqsanger.gz | |||
| SRR9118170_2.fastq.gz | Fastqsanger.gz | |||
| SRR9118180_1.fastq.gz | Fastqsanger.gz |
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Project description:We have compared gene expression in human nasal brushing cells from 19 cystic fibrosis (CF) patients and 19 healthy controls using a 5.2K cDNA microarray. Our aim is to identify new disease biomarkers for the Cystic Fibrosis Gene Therapy Consortium. These markers will be used to report more effectively on the response to the administration of gene therapy in vivo. Cystic Fibrosis is a recessive genetic disease caused by mutations in the cystic fibrosis conductance regulator (CFTR) gene which encodes a chloride ion channel. The most common mutation is the ∆F508 mutation, present on 70% of CF chromosomes in Caucasian populations. The disease affects many organs in the body such as the pancreas, liver, sweat glands, small intestine and reproductive tracts but is most commonly associated with progressive, inflammatory lung disease. The current average life expectancy of CF patients is 35 years. Gene therapy is being developed as a treatment for CF airway disease, however, means of measuring the efficiency and efficacy of gene therapy in vivo are lacking. This is mainly due to the difficulty in measuring the chloride conductance of CFTR in cells and tissues. Furthermore, clinical assays for measuring improvements in lung function are insensitive. Surrogate markers of inflammation and CFTR function will therefore be important for the effective assessment of gene therapy in vivo. We have analysed gene expression in human nasal epithelium as this is considered an accessible surrogate for the conducting airways where disease manifests in the majority of patients. Additionally, this tissue will be sampled in clinical trials.
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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.
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Project description:This data was collected on lung tissue associated with cystic fibrosis from 3 patients
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Project description:The gene expression of the opportunictic cystic fibrosis lung pathogen Burkholderia multivorans ATCC 17616 was investigated under different growth conditions relevant for growth in the cystic fibrosis lung.
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Project description:The gene expression of the opportunictic cystic fibrosis lung pathogen Burkholderia multivorans ATCC 17616 was investigated under different growth conditions relevant for growth in the cystic fibrosis lung.
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Project description:The gene expression of the opportunictic cystic fibrosis lung pathogen Burkholderia multivorans ATCC 17616 was investigated under different growth conditions relevant for growth in the cystic fibrosis lung.
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