Genome-scale metabolic modeling of responses to polymyxins in Pseudomonas aeruginosa

Yan Zhu, Tobias Czauderna, Jinxin Zhao, Matthias Klapperstueck, Mohd Hafidz Bin Mahamad Maifiah, Mei-Ling Han, Jing Lu, Bjorn Sommer, Tony Velkov, Trevor Lithgow, Jiangning Song, Falk Schreiber, Jian Li

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Background: Pseudomonas aeruginosa often causes multidrug-resistant infections in immunocompromised patients, and polymyxins are often used as the last-line therapy. Alarmingly, resistance to polymyxins has been increasingly reported worldwide recently. To rescue this last-resort class of antibiotics, it is necessary to systematically understand how P. aeruginosa alters its metabolism in response to polymyxin treatment, thereby facilitating the development of effective therapies. To this end, a genome-scale metabolic model (GSMM) was used to analyze bacterial metabolic changes at the systems level. Findings: A high-quality GSMM iPAO1 was constructed for P. aeruginosa PAO1 for antimicrobial pharmacological research. Model iPAO1 encompasses an additional periplasmic compartment and contains 3022 metabolites, 4265 reactions, and 1458 genes in total. Growth prediction on 190 carbon and 95 nitrogen sources achieved an accuracy of 89.1%, outperforming all reported P. aeruginosa models. Notably, prediction of the essential genes for growth achieved a high accuracy of 87.9%. Metabolic simulation showed that lipid A modifications associated with polymyxin resistance exert a limited impact on bacterial growth and metabolism but remarkably change the physiochemical properties of the outer membrane. Modeling with transcriptomics constraints revealed a broad range of metabolic responses to polymyxin treatment, including reduced biomass synthesis, upregulated amino acid catabolism, induced flux through the tricarboxylic acid cycle, and increased redox turnover. Conclusions: Overall, iPAO1 represents the most comprehensive GSMM constructed to date for Pseudomonas. It provides a powerful systems pharmacology platform for the elucidation of complex killing mechanisms of antibiotics.

LanguageEnglish
Article numbergiy021
Number of pages18
JournalGigaScience
Volume7
Issue number4
DOIs
Publication statusPublished - 1 Apr 2018

Keywords

  • genome-scale metabolic model
  • pseudomonas aeruginosa
  • polymyxin
  • lipid A modification
  • outer membrane

Cite this

Zhu, Yan ; Czauderna, Tobias ; Zhao, Jinxin ; Klapperstueck, Matthias ; Mahamad Maifiah, Mohd Hafidz Bin ; Han, Mei-Ling ; Lu, Jing ; Sommer, Bjorn ; Velkov, Tony ; Lithgow, Trevor ; Song, Jiangning ; Schreiber, Falk ; Li, Jian. / Genome-scale metabolic modeling of responses to polymyxins in Pseudomonas aeruginosa. In: GigaScience. 2018 ; Vol. 7, No. 4.
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abstract = "Background: Pseudomonas aeruginosa often causes multidrug-resistant infections in immunocompromised patients, and polymyxins are often used as the last-line therapy. Alarmingly, resistance to polymyxins has been increasingly reported worldwide recently. To rescue this last-resort class of antibiotics, it is necessary to systematically understand how P. aeruginosa alters its metabolism in response to polymyxin treatment, thereby facilitating the development of effective therapies. To this end, a genome-scale metabolic model (GSMM) was used to analyze bacterial metabolic changes at the systems level. Findings: A high-quality GSMM iPAO1 was constructed for P. aeruginosa PAO1 for antimicrobial pharmacological research. Model iPAO1 encompasses an additional periplasmic compartment and contains 3022 metabolites, 4265 reactions, and 1458 genes in total. Growth prediction on 190 carbon and 95 nitrogen sources achieved an accuracy of 89.1{\%}, outperforming all reported P. aeruginosa models. Notably, prediction of the essential genes for growth achieved a high accuracy of 87.9{\%}. Metabolic simulation showed that lipid A modifications associated with polymyxin resistance exert a limited impact on bacterial growth and metabolism but remarkably change the physiochemical properties of the outer membrane. Modeling with transcriptomics constraints revealed a broad range of metabolic responses to polymyxin treatment, including reduced biomass synthesis, upregulated amino acid catabolism, induced flux through the tricarboxylic acid cycle, and increased redox turnover. Conclusions: Overall, iPAO1 represents the most comprehensive GSMM constructed to date for Pseudomonas. It provides a powerful systems pharmacology platform for the elucidation of complex killing mechanisms of antibiotics.",
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Genome-scale metabolic modeling of responses to polymyxins in Pseudomonas aeruginosa. / Zhu, Yan; Czauderna, Tobias; Zhao, Jinxin; Klapperstueck, Matthias; Mahamad Maifiah, Mohd Hafidz Bin; Han, Mei-Ling; Lu, Jing; Sommer, Bjorn; Velkov, Tony; Lithgow, Trevor; Song, Jiangning; Schreiber, Falk; Li, Jian.

In: GigaScience, Vol. 7, No. 4, giy021, 01.04.2018.

Research output: Contribution to journalArticleResearchpeer-review

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T1 - Genome-scale metabolic modeling of responses to polymyxins in Pseudomonas aeruginosa

AU - Zhu, Yan

AU - Czauderna, Tobias

AU - Zhao, Jinxin

AU - Klapperstueck, Matthias

AU - Mahamad Maifiah, Mohd Hafidz Bin

AU - Han, Mei-Ling

AU - Lu, Jing

AU - Sommer, Bjorn

AU - Velkov, Tony

AU - Lithgow, Trevor

AU - Song, Jiangning

AU - Schreiber, Falk

AU - Li, Jian

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