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 antibioti...

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Main Authors:	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
Format:	Article
Language:	English
Published:	Oxford Academic 2018
Subjects:	QR Microbiology RM Therapeutics. Pharmacology RM300 Drugs and their action
Online Access:	http://irep.iium.edu.my/65380/ http://irep.iium.edu.my/65380/ http://irep.iium.edu.my/65380/ http://irep.iium.edu.my/65380/1/2018_giy021.pdf

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spelling	iium-653802018-08-27T08:18:24Z http://irep.iium.edu.my/65380/ 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 QR Microbiology RM Therapeutics. Pharmacology RM300 Drugs and their action 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 Academic 2018-04 Article PeerReviewed application/pdf en http://irep.iium.edu.my/65380/1/2018_giy021.pdf Zhu, Yan and Czauderna, Tobias and Zhao, Jinxin and Klapperstueck, Matthias and Maifiah, Mohd Hafidz Mahamad and Han, Mei-Ling and Lu, Jing and Sommer, Björn and Velkov, Tony and Lithgow, Trevor and Song, Jiangning and Schreiber, Falk and Li, Jian (2018) Genome-scale metabolic modeling of responses to polymyxins in Pseudomonas aeruginosa. GigaScience, 7 (4). pp. 1-18. ISSN 2047-217X https://academic.oup.com/gigascience/article/7/4/giy021/4931736?searchresult=1 10.1093/gigascience/giy021
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	QR Microbiology RM Therapeutics. Pharmacology RM300 Drugs and their action
spellingShingle	QR Microbiology RM Therapeutics. Pharmacology RM300 Drugs and their action 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
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.
format	Article
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
title	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_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_sort	genome-scale metabolic modeling of responses to polymyxins in pseudomonas aeruginosa
publisher	Oxford Academic
publishDate	2018
url	http://irep.iium.edu.my/65380/ http://irep.iium.edu.my/65380/ http://irep.iium.edu.my/65380/ http://irep.iium.edu.my/65380/1/2018_giy021.pdf
first_indexed	2023-09-18T21:32:46Z
last_indexed	2023-09-18T21:32:46Z
_version_	1777412621141540864

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

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