Limits...
Growth-dependent bacterial susceptibility to ribosome-targeting antibiotics.

Greulich P, Scott M, Evans MR, Allen RJ - Mol. Syst. Biol. (2015)

Bottom Line: Bacterial growth environment strongly influences the efficacy of antibiotic treatment, with slow growth often being associated with decreased susceptibility.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.

View Article: PubMed Central - PubMed

Affiliation: Cavendish Laboratory, University of Cambridge, Cambridge, UK SUPA School of Physics and Astronomy University of Edinburgh, Edinburgh, UK.

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).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
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).


Growth-dependent bacterial susceptibility to ribosome-targeting antibiotics.

Greulich P, Scott M, Evans MR, Allen RJ - Mol. Syst. Biol. (2015)

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).

Bottom Line: Bacterial growth environment strongly influences the efficacy of antibiotic treatment, with slow growth often being associated with decreased susceptibility.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.

View Article: PubMed Central - PubMed

Affiliation: Cavendish Laboratory, University of Cambridge, Cambridge, UK SUPA School of Physics and Astronomy University of Edinburgh, Edinburgh, UK.

No MeSH data available.


Related in: MedlinePlus