Supporting data for "Genome-scale metabolic modelling of responses to polymyxins in Pseudomonas aeruginosa."

Dataset type: Genomic, Transcriptomic
Data released on February 22, 2018

Zhu Y; Czauderna T; Zhao J; Klapperstueck M; Maifiah MHM; Han ML; Lu J; Sommer B; Velkov T; Lithgow T; Song J; Schreiber F; Li J (2018): Supporting data for "Genome-scale metabolic modelling of responses to polymyxins in Pseudomonas aeruginosa." GigaScience Database. http://dx.doi.org/10.5524/100414

DOI10.5524/100414

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.
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 exerted a limited impact on bacterial growth and metabolism, but remarkably changed the physiochemical properties of the outer membrane. Modelling with transcriptomic 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.
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.

Additional details

Read the peer-reviewed publication(s):

Zhu, Y., Czauderna, T., Zhao, J., Klapperstueck, M., Maifiah, M. H. M., Han, M.-L., … Li, J. (2018). Genome-scale metabolic modeling of responses to polymyxins in Pseudomonas aeruginosa. GigaScience, 7(4). doi:10.1093/gigascience/giy021
Gioiosa, S., Bolis, M., Flati, T., Massini, A., Garattini, E., Chillemi, G., … Castrignanò, T. (2018). Massive NGS Data Analysis Reveals Hundreds Of Potential Novel Gene Fusions in Human Cell Lines. GigaScience. doi:10.1093/gigascience/giy062

Accessions (data included in GigaDB):

PROJECT: PRJNA414673
MTBLS: MTBLS630





Sample IDTaxonomic IDCommon NameGenbank NameScientific NameSample Attributes
SAMN07807133208964  Pseudomonas aeruginosa PAO1 Strain:PAO1
Description:RNA extract from Pseudomonas aeruginos...
Analyte type:RNA
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Displaying 1-1 of 1 Sample(s).




File NameSample IDData TypeFile FormatSizeRelease Date 
MD5sumTEXT0.07 KB2018-02-20
Mass Spectrometry dataTAR212.84 MB2018-02-20
Mass Spectrometry dataTAR256.19 MB2018-02-20
Mass Spectrometry dataTAR641.14 MB2018-02-20
ReadmeTEXT2.52 KB2018-02-20
annotationTAR1.37 GB2018-02-20
scriptUNKNOWN2.38 KB2018-02-20
Displaying 1-7 of 7 File(s).
Funding body Awardee Award ID Comments
Mosash University Jian Li
National Health and Medical Research Council Jian Li APP1127948
National Institute of Allergy and Infectious Diseases Jian Li R01 AI111965
National Health and Medical Research Council Tony Velko APP1086825
National Health and Medical Research Council Jian Li APP1063069
Australian Research Councill Trevor Lithgow FL130100038
Date Action
February 22, 2018 Dataset publish
April 20, 2018 File readme.txt updated
July 3, 2018 Manuscript Link added : 10.1093/gigascience/giy021
July 9, 2018 Manuscript Link added : 10.1093/gigascience/giy062