Comparing Insertion Libraries in Two Pseudomonas aeruginosa Strains to Assess Gene Essentiality

2008 ◽  
pp. 153-169 ◽  
Author(s):  
Nicole T. Liberati ◽  
Jonathan M. Urbach ◽  
Tara K. Thurber ◽  
Gang Wu ◽  
Frederick M. Ausubel
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Gene Essentiality

scholarly journals Genome-scale modeling of Pseudomonas aeruginosa PA14 unveils its broad metabolic capabilities and role of metabolism in drug potentiation

2021 ◽  
Author(s):  
Sanjeev Dahal ◽  
Laurence Yang
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Substrate Utilization ◽  
Scale Model ◽  
Extensive Literature ◽  
Gene Essentiality ◽  
Carnitine Metabolism ◽  
Utilization Data ◽  
Opportunistic Human Pathogen ◽  
Specific Reactions ◽  
Genome Scale

AbstractIn this study, we developed an updated genome-scale model (GEM) of Pseudomonas aeruginosa PA14 and utilized it to showcase the broad capabilities of the GEM. P. aeruginosa is an opportunistic human pathogen that is one of the leading causes of nosocomial infections in hospital settings. We used both automated and manual approaches to reconstruct and curate the model, and then added strain-specific reactions (e.g., phenazine transport and redox metabolism, cofactor metabolism, carnitine metabolism, oxalate production, etc.) after extensive literature review. We validated and improved the model using a set of gene essentiality and substrate utilization data. This effort led to a highly curated, three-compartment and mass-and-charge balanced BiGG model of PA14 that contains 1511 genes and 2036 reactions. Even with considerable increase in model contents (genes, reactions, and metabolites), compared to the previous model (mPA14) of the same strain, this model (iSD1511) has similar prediction accuracy for gene essentiality and higher accuracy for substrate utilization assay. We assessed iSD1511 using another set of gene essentiality and substrate utilization data and computed the prediction accuracies as high as 92.7% and 93.5%, respectively. The model can simulate growth in both aerobic and anaerobic conditions. Finally, we utilized the model to recapitulate the results of multiple case studies including drug potentiation by citric acid cycle intermediates. Overall, we have built a highly curated computational model of the P. aeruginosa to decipher the metabolic mechanisms of drug resistance, and to help in the development of effective intervention strategies.

scholarly journals Reconciling high-throughput gene essentiality data with metabolic network reconstructions

2018 ◽  
Author(s):  
Anna S. Blazier ◽  
Jason A. Papin
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Metabolic Network ◽  
High Throughput ◽  
Large Scale ◽  
Essential Genes ◽  
Growth And Survival ◽  
Gene Essentiality ◽  
Model Driven ◽  
Functional Explanations ◽  
Genome Scale

AbstractThe identification of genes essential for bacterial growth and survival represents a promising strategy for the discovery of antimicrobial targets. Essential genes can be identified on a genome-scale using transposon mutagenesis approaches; however, variability between screens and challenges with interpretation of essentiality data hinder the identification of both condition-independent and condition-dependent essential genes. To illustrate the scope of these challenges, we perform a large-scale comparison of multiple published Pseudomonas aeruginosa gene essentiality datasets, revealing substantial differences between the screens. We then contextualize essentiality using genome-scale metabolic network reconstructions and demonstrate the utility of this approach in providing functional explanations for essentiality and reconciling differences between screens. Genome-scale metabolic network reconstructions also enable a high-throughput, quantitative analysis to assess the impact of media conditions on the identification of condition-independent essential genes. Our computational model-driven analysis provides mechanistic insight into essentiality and contributes novel insights for design of future gene essentiality screens and the identification of core metabolic processes.Author SummaryWith the rise of antibiotic resistance, there is a growing need to discover new therapeutic targets to treat bacterial infections. One attractive strategy is to target genes that are essential for growth and survival. Essential genes can be identified with transposon mutagenesis approaches; however, variability between screens and challenges with interpretation of essentiality data hinder the identification and analysis of essential genes. We performed a large-scale comparison of multiple gene essentiality screens of the microbial pathogen Pseudomonas aeruginosa. We implemented a computational model-driven approach to provide functional explanations for essentiality and reconcile differences between screens. The integration of computational modeling with high-throughput experimental screens may enable the identification of drug targets with high-confidence and provide greater understanding for the development of novel therapeutic strategies.

306: Relationships between Biofilm-Forming Capacities of Pseudomonas Aeruginosa Isolates and Clinical Background / Antimicrobial Resistence in Urinary Tract Infections

2007 ◽  
Vol 177 (4S) ◽  
pp. 102-103
Author(s):  
Shinya Uehara ◽  
Koichi Monden ◽  
Koichiro Wada ◽  
Ayano Ishii ◽  
Reiko Kariyama ◽  
...  
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Urinary Tract ◽  
Urinary Tract Infections ◽  
Clinical Background ◽  
Tract Infections

Green striped nails: chromonychia due to Pseudomonas aeruginosa

1968 ◽  
Vol 97 (2) ◽  
pp. 149-153 ◽  
Author(s):  
W. V. Shellow
Keyword(s):  
Pseudomonas Aeruginosa

Effekt von Acetylcystein auf die Pseudomonas aeruginosa-induzierte Mukusexpression

Pneumologie ◽  
2010 ◽  
Vol 64 (01) ◽  
Author(s):  
L Sprenger ◽  
T Goldmann ◽  
E Vollmer ◽  
B Wollenberg ◽  
P Zabel ◽  
...  
Keyword(s):  
Pseudomonas Aeruginosa

Effekt von Dexamethason auf die Pseudomonas aeruginosa-induzierte Mukusexpression in humaner Atemwegsmukosa

Pneumologie ◽  
2010 ◽  
Vol 64 (S 03) ◽  
Author(s):  
L Spenger ◽  
T Goldmann ◽  
E Vollmer ◽  
B Wollenberg ◽  
HP Hauber ◽  
...  
Keyword(s):  
Pseudomonas Aeruginosa
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