Ontology highlight
ABSTRACT:
PROVIDER: PRJNA503799 | ENA |
REPOSITORIES: ENA
Similar Datasets
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.
2012-05-11 | E-MEXP-3210 | biostudies-arrayexpress
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.
2012-05-10 | E-MEXP-3208 | biostudies-arrayexpress
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.
2012-05-11 | E-MEXP-3209 | biostudies-arrayexpress
Project description:Pseudomonas isolated from Human lung afflicted with cystic Fibrosis
2014-03-19 | MSV000078563 | MassIVE
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