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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...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2018
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Materias: | |
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. |
format | Online Article Text |
id | pubmed-6333913 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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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