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

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 antibiotic...

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Autores principales: Zhu, Yan, Czauderna, Tobias, Zhao, Jinxin, Klapperstueck, Matthias, Maifiah, Mohd Hafidz Mahamad, Han, Mei-Ling, Lu, Jing, Sommer, Björn, Velkov, Tony, Lithgow, Trevor, Song, Jiangning, Schreiber, Falk, Li, Jian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2018
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Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6333913/
https://www.ncbi.nlm.nih.gov/pubmed/29688451
http://dx.doi.org/10.1093/gigascience/giy021
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author Zhu, Yan
Czauderna, Tobias
Zhao, Jinxin
Klapperstueck, Matthias
Maifiah, Mohd Hafidz Mahamad
Han, Mei-Ling
Lu, Jing
Sommer, Björn
Velkov, Tony
Lithgow, Trevor
Song, Jiangning
Schreiber, Falk
Li, Jian
author_facet Zhu, Yan
Czauderna, Tobias
Zhao, Jinxin
Klapperstueck, Matthias
Maifiah, Mohd Hafidz Mahamad
Han, Mei-Ling
Lu, Jing
Sommer, Björn
Velkov, Tony
Lithgow, Trevor
Song, Jiangning
Schreiber, Falk
Li, Jian
author_sort Zhu, Yan
collection PubMed
description 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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spelling pubmed-63339132019-01-24 Genome-scale metabolic modeling of responses to polymyxins in Pseudomonas aeruginosa Zhu, Yan Czauderna, Tobias Zhao, Jinxin Klapperstueck, Matthias Maifiah, Mohd Hafidz Mahamad Han, Mei-Ling Lu, Jing Sommer, Björn Velkov, Tony Lithgow, Trevor Song, Jiangning Schreiber, Falk Li, Jian Gigascience Research 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. Oxford University Press 2018-03-13 /pmc/articles/PMC6333913/ /pubmed/29688451 http://dx.doi.org/10.1093/gigascience/giy021 Text en © The Author(s) 2018. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Zhu, Yan
Czauderna, Tobias
Zhao, Jinxin
Klapperstueck, Matthias
Maifiah, Mohd Hafidz Mahamad
Han, Mei-Ling
Lu, Jing
Sommer, Björn
Velkov, Tony
Lithgow, Trevor
Song, Jiangning
Schreiber, Falk
Li, Jian
Genome-scale metabolic modeling of responses to polymyxins in Pseudomonas aeruginosa
title Genome-scale metabolic modeling of responses to polymyxins in Pseudomonas aeruginosa
title_full Genome-scale metabolic modeling of responses to polymyxins in Pseudomonas aeruginosa
title_fullStr Genome-scale metabolic modeling of responses to polymyxins in Pseudomonas aeruginosa
title_full_unstemmed Genome-scale metabolic modeling of responses to polymyxins in Pseudomonas aeruginosa
title_short Genome-scale metabolic modeling of responses to polymyxins in Pseudomonas aeruginosa
title_sort genome-scale metabolic modeling of responses to polymyxins in pseudomonas aeruginosa
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6333913/
https://www.ncbi.nlm.nih.gov/pubmed/29688451
http://dx.doi.org/10.1093/gigascience/giy021
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