Growth-dependent bacterial susceptibility to ribosome-targeting antibiotics

TL;DR

This study reveals that the effectiveness of ribosome-targeting antibiotics on E. coli depends on bacterial growth rate, with susceptibility varying based on a simple parameter related to drug transport and binding, supported by experimental validation.

Contribution

The paper introduces a mathematical model linking bacterial physiology and antibiotic susceptibility, providing a unified framework for understanding growth-dependent drug efficacy.

Findings

Growth rate influences antibiotic susceptibility differently across drugs.

A single parameter explains the reversibility of drug transport and binding.

Model predictions validated experimentally on mutant strains.

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 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.