4.6 Article

Growth-dependent bacterial susceptibility to ribosome-targeting antibiotics

期刊

MOLECULAR SYSTEMS BIOLOGY
卷 11, 期 3, 页码 -

出版社

WILEY
DOI: 10.15252/msb.20145949

关键词

antibiotic pharmacodynamics; bacterial physiology; phenomenological growth laws; ribosome binding antibiotics

资金

  1. EPSRC [EP/J007404/1]
  2. DAAD postdoc fellowship
  3. DFG research fellowship
  4. Royal Society University Research Fellowship
  5. Natural Sciences and Engineering Research Council of Canada
  6. Engineering and Physical Sciences Research Council [EP/J007404/1] Funding Source: researchfish
  7. EPSRC [EP/J007404/1] Funding Source: UKRI

向作者/读者索取更多资源

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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