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

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

Research output: Contribution to journalArticle

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 employed to analyse 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 3,022 metabolites, 4,265 reactions and 1,458 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. Modelling with transcriptomics constraints revealed a broad range of metabolic responses to polymyxin treatment, including reduced biomass synthesis, upregulated amino acids 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
StatePublished - 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 ; Han, Mei-Ling ; Lu, Jing ; Sommer, Bjorn ; Velkov, Tony ; Lithgow, Trevor ; Song, Jiangning ; Schreiber, Falk ; Li, Jian. / Genome-scale metabolic modelling of responses to polymyxins in Pseudomonas aeruginosa. In: GigaScience. 2018 ; Vol. 7, No. 4.
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abstract = "BackgroundPseudomonas 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 employed to analyse bacterial metabolic changes at the systems level.FindingsA high-quality GSMM iPAO1 was constructed for P. aeruginosa PAO1 for antimicrobial pharmacological research. Model iPAO1 encompasses an additional periplasmic compartment and contains 3,022 metabolites, 4,265 reactions and 1,458 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. Modelling with transcriptomics constraints revealed a broad range of metabolic responses to polymyxin treatment, including reduced biomass synthesis, upregulated amino acids catabolism, induced flux through the tricarboxylic acid cycle, and increased redox turnover.ConclusionsOverall, 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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author = "Yan Zhu and Tobias Czauderna and Jinxin Zhao and Matthias Klapperstueck and {Mahamad Maifiah}, {Mohd Hafidz} and Mei-Ling Han and Jing Lu and Bjorn Sommer and Tony Velkov and Trevor Lithgow and Jiangning Song and Falk Schreiber and Jian Li",
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Genome-scale metabolic modelling of responses to polymyxins in Pseudomonas aeruginosa. / Zhu, Yan; Czauderna, Tobias; Zhao, Jinxin; Klapperstueck, Matthias; Mahamad Maifiah, Mohd Hafidz; Han, Mei-Ling; Lu, Jing; Sommer, Bjorn; Velkov, Tony; Lithgow, Trevor; Song, Jiangning; Schreiber, Falk; Li, Jian.

In: GigaScience, Vol. 7, No. 4, giy021, 04.2018.

Research output: Contribution to journalArticle

TY - JOUR

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

AU - Zhu,Yan

AU - Czauderna,Tobias

AU - Zhao,Jinxin

AU - Klapperstueck,Matthias

AU - Mahamad Maifiah,Mohd Hafidz

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

PY - 2018/4

Y1 - 2018/4

N2 - BackgroundPseudomonas 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 employed to analyse bacterial metabolic changes at the systems level.FindingsA high-quality GSMM iPAO1 was constructed for P. aeruginosa PAO1 for antimicrobial pharmacological research. Model iPAO1 encompasses an additional periplasmic compartment and contains 3,022 metabolites, 4,265 reactions and 1,458 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. Modelling with transcriptomics constraints revealed a broad range of metabolic responses to polymyxin treatment, including reduced biomass synthesis, upregulated amino acids catabolism, induced flux through the tricarboxylic acid cycle, and increased redox turnover.ConclusionsOverall, 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.

AB - BackgroundPseudomonas 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 employed to analyse bacterial metabolic changes at the systems level.FindingsA high-quality GSMM iPAO1 was constructed for P. aeruginosa PAO1 for antimicrobial pharmacological research. Model iPAO1 encompasses an additional periplasmic compartment and contains 3,022 metabolites, 4,265 reactions and 1,458 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. Modelling with transcriptomics constraints revealed a broad range of metabolic responses to polymyxin treatment, including reduced biomass synthesis, upregulated amino acids catabolism, induced flux through the tricarboxylic acid cycle, and increased redox turnover.ConclusionsOverall, 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.

KW - genome-scale metabolic model

KW - pseudomonas aeruginosa

KW - polymyxin

KW - lipid A modification

KW - outer membrane

U2 - 10.1093/gigascience/giy021

DO - 10.1093/gigascience/giy021

M3 - Article

VL - 7

JO - GigaScience

T2 - GigaScience

JF - GigaScience

SN - 2047-217X

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Zhu Y, Czauderna T, Zhao J, Klapperstueck M, Mahamad Maifiah MH, Han M-L et al. Genome-scale metabolic modelling of responses to polymyxins in Pseudomonas aeruginosa. GigaScience. 2018 Apr;7(4). giy021. Available from, DOI: 10.1093/gigascience/giy021