Growth-dependent bacterial susceptibility to ribosome-targeting antibiotics
Growth-dependent bacterial susceptibility to ribosome-targeting antibiotics
Bacterial growth environment strongly influences the efficacy of antibiotic treatment, with slow growth often being associated with decreased susceptibility. Yet in many cases, the connection between antibiotic susceptibility and pathogen physiology remains unclear. We show that for ribosome-targeting antibiotics acting on Escherichia coli, a complex interplay exists between physiology and antibiotic action; for some antibiotics within this class, faster growth indeed increases susceptibility, but for other antibiotics, the opposite is true. Remarkably, these observations can be explained by a simple mathematical model that combines drug transport and binding with physiological constraints. Our model reveals that growth-dependent susceptibility is controlled by a single parameter characterizing the ‘reversibility’ of ribosome-targeting antibiotic transport and binding. This parameter provides a spectrum classification of antibiotic growth-dependent efficacy that appears to correspond at its extremes to existing binary classification schemes. In these limits, the model predicts universal, parameter-free limiting forms for growth inhibition curves. The model also leads to non- trivial predictions for the drug susceptibility of a translation mutant strain of E. coli, which we verify experimentally. Drug action and bacterial metabolism are mechanistically complex; nevertheless, this study illustrates how coarse-grained models can be used to integrate pathogen physiology into drug design and treatment strategies
1-11
Greulich, Philip
65da32ad-a73a-435a-86e0-e171437430a9
Scott, Matthew
0ae7680b-58e8-4077-95aa-c5559ab1eb15
Evans, Martin R.
4c3b60de-f3fc-4b0e-a8ea-842b1156aea6
Allen, Rosalind J.
853bb04d-9136-4235-8ec6-0ea995465264
19 March 2015
Greulich, Philip
65da32ad-a73a-435a-86e0-e171437430a9
Scott, Matthew
0ae7680b-58e8-4077-95aa-c5559ab1eb15
Evans, Martin R.
4c3b60de-f3fc-4b0e-a8ea-842b1156aea6
Allen, Rosalind J.
853bb04d-9136-4235-8ec6-0ea995465264
Greulich, Philip, Scott, Matthew, Evans, Martin R. and Allen, Rosalind J.
(2015)
Growth-dependent bacterial susceptibility to ribosome-targeting antibiotics.
Molecular Systems Biology, 11 (796), .
(doi:10.15252/msb.20145949).
Abstract
Bacterial growth environment strongly influences the efficacy of antibiotic treatment, with slow growth often being associated with decreased susceptibility. Yet in many cases, the connection between antibiotic susceptibility and pathogen physiology remains unclear. We show that for ribosome-targeting antibiotics acting on Escherichia coli, a complex interplay exists between physiology and antibiotic action; for some antibiotics within this class, faster growth indeed increases susceptibility, but for other antibiotics, the opposite is true. Remarkably, these observations can be explained by a simple mathematical model that combines drug transport and binding with physiological constraints. Our model reveals that growth-dependent susceptibility is controlled by a single parameter characterizing the ‘reversibility’ of ribosome-targeting antibiotic transport and binding. This parameter provides a spectrum classification of antibiotic growth-dependent efficacy that appears to correspond at its extremes to existing binary classification schemes. In these limits, the model predicts universal, parameter-free limiting forms for growth inhibition curves. The model also leads to non- trivial predictions for the drug susceptibility of a translation mutant strain of E. coli, which we verify experimentally. Drug action and bacterial metabolism are mechanistically complex; nevertheless, this study illustrates how coarse-grained models can be used to integrate pathogen physiology into drug design and treatment strategies
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Accepted/In Press date: 19 February 2015
Published date: 19 March 2015
Organisations:
Applied Mathematics
Identifiers
Local EPrints ID: 408688
URI: http://eprints.soton.ac.uk/id/eprint/408688
ISSN: 1744-4292
PURE UUID: c3c95651-617b-44dc-8867-c5e5565c0cdb
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Date deposited: 26 May 2017 04:02
Last modified: 16 Mar 2024 04:17
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Author:
Matthew Scott
Author:
Martin R. Evans
Author:
Rosalind J. Allen
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