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Predicting the dynamics of bacterial growth inhibition by ribosome-targeting antibiotics

Predicting the dynamics of bacterial growth inhibition by ribosome-targeting antibiotics
Predicting the dynamics of bacterial growth inhibition by ribosome-targeting antibiotics
Understanding how antibiotics inhibit bacteria can help to reduce antibiotic use and hence avoid antimicrobial resistance – yet few theoretical models exist for bacterial growth inhibition by a clinically relevant antibiotic treatment regimen. In particular, in the clinic, antibiotic treatment is time-dependent. Here, we use a theoretical model, previously applied to steady-state bacterial growth, to predict the dynamical response of a bacterial cell to a time-dependent dose of ribosome-targeting antibiotic. Our results depend strongly on whether the antibiotic shows reversible transport and/or low-affinity ribosome binding (“low-affinity antibiotic”) or, in contrast, irreversible transport and/or high affinity ribosome binding (“high-affinity antibiotic”). For low-affinity antibiotics, our model predicts that growth inhibition depends on the duration of the antibiotic pulse, and can show a transient period of very fast growth following removal of the antibiotic. For high-affinity antibiotics, growth inhibition depends on peak dosage rather than dose duration, and the model predicts a pronounced post-antibiotic effect, due to hysteresis, in which growth can be suppressed for long times after the antibiotic dose has ended. These predictions are experimentally testable and may be of clinical significance.
1478-3967
Greulich, Philip
65da32ad-a73a-435a-86e0-e171437430a9
Dolezal, Jakub
f691dd3f-2366-4bd7-ab3e-fe25753d804e
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
65da32ad-a73a-435a-86e0-e171437430a9
Dolezal, Jakub
f691dd3f-2366-4bd7-ab3e-fe25753d804e
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, Dolezal, Jakub, Scott, Matthew, Evans, Martin R. and Allen, Rosalind J. (2017) Predicting the dynamics of bacterial growth inhibition by ribosome-targeting antibiotics. Physical Biology, [PB-100693.R2]. (doi:10.1088/1478-3975/aa8001).

Record type: Article

Abstract

Understanding how antibiotics inhibit bacteria can help to reduce antibiotic use and hence avoid antimicrobial resistance – yet few theoretical models exist for bacterial growth inhibition by a clinically relevant antibiotic treatment regimen. In particular, in the clinic, antibiotic treatment is time-dependent. Here, we use a theoretical model, previously applied to steady-state bacterial growth, to predict the dynamical response of a bacterial cell to a time-dependent dose of ribosome-targeting antibiotic. Our results depend strongly on whether the antibiotic shows reversible transport and/or low-affinity ribosome binding (“low-affinity antibiotic”) or, in contrast, irreversible transport and/or high affinity ribosome binding (“high-affinity antibiotic”). For low-affinity antibiotics, our model predicts that growth inhibition depends on the duration of the antibiotic pulse, and can show a transient period of very fast growth following removal of the antibiotic. For high-affinity antibiotics, growth inhibition depends on peak dosage rather than dose duration, and the model predicts a pronounced post-antibiotic effect, due to hysteresis, in which growth can be suppressed for long times after the antibiotic dose has ended. These predictions are experimentally testable and may be of clinical significance.

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Accepted/In Press date: 17 July 2017
e-pub ahead of print date: 16 November 2017
Published date: 16 November 2017

Identifiers

Local EPrints ID: 412839
URI: http://eprints.soton.ac.uk/id/eprint/412839
ISSN: 1478-3967
PURE UUID: 8c968c15-4815-4909-b0bb-0213b6da60b7
ORCID for Philip Greulich: ORCID iD orcid.org/0000-0001-5247-6738

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Date deposited: 02 Aug 2017 16:30
Last modified: 16 Mar 2024 05:34

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Contributors

Author: Philip Greulich ORCID iD
Author: Jakub Dolezal
Author: Matthew Scott
Author: Martin R. Evans
Author: Rosalind J. Allen

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