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Growth-dependent bacterial susceptibility to ribosome-targeting antibiotics

View Article: PubMed Central - PubMed

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.

No MeSH data available.


Related in: MedlinePlus

Growth inhibition curves for our bactericidal and bacteriostatic drugs collapse onto two qualitatively different limiting forms as predicted by the modelData for the bactericidal antibiotics streptomycin (closed symbols) and kanamycin (open symbols) collapse onto  (black line)Data for the bacteriostatic antibiotics tetracycline (closed symbols) and chloramphenicol (open symbols) collapse onto λ/λ0 = 1/[1+aex/IC50] (black line).
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fig05: Growth inhibition curves for our bactericidal and bacteriostatic drugs collapse onto two qualitatively different limiting forms as predicted by the modelData for the bactericidal antibiotics streptomycin (closed symbols) and kanamycin (open symbols) collapse onto (black line)Data for the bacteriostatic antibiotics tetracycline (closed symbols) and chloramphenicol (open symbols) collapse onto λ/λ0 = 1/[1+aex/IC50] (black line).

Mentions: The fitted parameters are in good agreement with biochemical parameter values available from literature data (Supplementary Table S4) and are consistent with the fact that aminoglycosides are believed to bind and be transported irreversibly (small )  (Davis, 1987), whereas for tetracycline and chloramphenicol, both transport and binding processes are reversible (large ) (Harvey & Koch, 1980; Berens, 2001). For kanamycin and streptomycin, the model does not provide very good quantitative agreement with the growth inhibition curves; nevertheless, it does correctly predict the sigmoidal form of these curves and the fact that susceptibility to these antibiotics decreases with increasing growth rate (see also Figs4 and 5).


Growth-dependent bacterial susceptibility to ribosome-targeting antibiotics
Growth inhibition curves for our bactericidal and bacteriostatic drugs collapse onto two qualitatively different limiting forms as predicted by the modelData for the bactericidal antibiotics streptomycin (closed symbols) and kanamycin (open symbols) collapse onto  (black line)Data for the bacteriostatic antibiotics tetracycline (closed symbols) and chloramphenicol (open symbols) collapse onto λ/λ0 = 1/[1+aex/IC50] (black line).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4380930&req=5

fig05: Growth inhibition curves for our bactericidal and bacteriostatic drugs collapse onto two qualitatively different limiting forms as predicted by the modelData for the bactericidal antibiotics streptomycin (closed symbols) and kanamycin (open symbols) collapse onto (black line)Data for the bacteriostatic antibiotics tetracycline (closed symbols) and chloramphenicol (open symbols) collapse onto λ/λ0 = 1/[1+aex/IC50] (black line).
Mentions: The fitted parameters are in good agreement with biochemical parameter values available from literature data (Supplementary Table S4) and are consistent with the fact that aminoglycosides are believed to bind and be transported irreversibly (small )  (Davis, 1987), whereas for tetracycline and chloramphenicol, both transport and binding processes are reversible (large ) (Harvey & Koch, 1980; Berens, 2001). For kanamycin and streptomycin, the model does not provide very good quantitative agreement with the growth inhibition curves; nevertheless, it does correctly predict the sigmoidal form of these curves and the fact that susceptibility to these antibiotics decreases with increasing growth rate (see also Figs4 and 5).

View Article: PubMed Central - PubMed

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.

No MeSH data available.


Related in: MedlinePlus