Communications BiologySystems BiologyPeer-reviewed journal article
Genome-scale modeling of Pseudomonas aeruginosa PA14 unveils its broad metabolic capabilities and role of metabolism in drug potentiation
Sanjeev DahaliD, Alina RenziD, Andreas DrägeriD, Laurence YangiD
Abstract
P. aeruginosa is an opportunistic human pathogen that is one of the leading causes of hospital-acquired infections. We have developed an updated genome-scale model (GEM) of Pseudomonas aeruginosa PA14 for systems-study of the pathogen. We used both automated and semi-manual approaches to reconstruct and curate the model. After an extensive literature research, we added organism-specific reactions (e.g., phenazine transport and redox metabolism, cofactor metabolism, carnitine metabolism, oxalate production, etc.) to the model. This effort led to a highly curated, three-compartment, and mass-and-charge balanced BiGG model of PA14 that contains 1509 genes, 1779 metabolic reactions and 1151 unique metabolites. The model (iSD1509) has the largest genome coverage of P. aeruginosa PA14 to date with 424 more genes than the previous model (iPau1129). It is also the most accurate with prediction accuracies as high as 92.4% (for gene essentiality) and 93.5% (for substrate utilization). The model simulates growth in both aerobic and anaerobic conditions. It predicts the biosynthesis of the virulence factor phenazine as a process for the pathogen to grow in low-oxygen environment. Further, a mechanism for the overproduction of a drug susceptibility biomarker (gluconate) can be elucidated by the principles of optimal growth. Finally, the model also simulates drug activity potentiation and protection by fumarate and glyoxylate, respectively, and provides mechanistic explanations for these processes. Overall, iSD1509 can be utilized to decipher the metabolic mechanisms associated with virulence and antibiotic susceptibility of P. aeruginosa PA14 to aid in the development of effective intervention strategies.
- organism: bacteria (other)
- assay: whole-genome sequencing
- research focus: variant calling
- approach: statistical model
- Citations
- 18
- FWCI
- 2.31
- Field percentile
- top 10%
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- An updated genome-scale metabolic network reconstruction of Pseudomonas aeruginosa PA14 to characterize mucin-driven shifts in bacterial metabolism
Dawson Payne, Alina Renz, … Jason A. Papin·npj Systems Biology and ApplicationsPeer-reviewed·Oct 8, 2021
- Coarse-graining genome-scale metabolic models for dynamic, multi-scale simulations of Pseudomonas aeruginosa in gel environments
Morgan S Kim, Jason A Papin, Roseanne M. Ford·bioRxiv·Nov 6, 2026
- Pseudomonas aeruginosa core metabolism exerts a widespread growth-independent control on virulence
S. Panayidou, K. Georgiades, … Y. Apidianakis·bioRxiv·Nov 20, 2019
- Network analysis reveals differential metabolic functionality in antibiotic-resistant Pseudomonas aeruginosa
Laura J. Dunphy, Phillip Yen, Jason A. Papin·bioRxiv·Apr 18, 2018
- Global stress response in Pseudomonas aeruginosa upon malonate utilization
Karishma Bisht, Moamen M. Elmassry, … Catherine A. Wakeman·bioRxiv·Mar 26, 2024
- Pseudomonas aeruginosa reverse diauxie is an optimized, resource utilization strategy
S. Lee McGill, Yeni P. Yung, … Ross P. Carlson·Scientific ReportsPeer-reviewed·Jan 14, 2021