The inoculum effect and band-pass bacterial response to periodic antibiotic treatment.
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The inoculum effect (IE) refers to the decreasing efficacy of an antibiotic with increasing bacterial density. It represents a unique strategy of antibiotic tolerance and it can complicate design of effective antibiotic treatment of bacterial infections. To gain insight into this phenomenon, we have analyzed responses of a lab strain of Escherichia coli to antibiotics that target the ribosome. We show that the IE can be explained by bistable inhibition of bacterial growth. A critical requirement for this bistability is sufficiently fast degradation of ribosomes, which can result from antibiotic-induced heat-shock response. Furthermore, antibiotics that elicit the IE can lead to 'band-pass' response of bacterial growth to periodic antibiotic treatment: the treatment efficacy drastically diminishes at intermediate frequencies of treatment. Our proposed mechanism for the IE may be generally applicable to other bacterial species treated with antibiotics targeting the ribosomes.
Colony Count, Microbial
Microbial Sensitivity Tests
Published Version (Please cite this version)10.1038/msb.2012.49
Publication InfoTan, Cheemeng; Smith, Robert Phillip; Srimani, Jaydeep K; Riccione, Katherine A; Prasada, Sameer; Kuehn, Meta; & You, Lingchong (2012). The inoculum effect and band-pass bacterial response to periodic antibiotic treatment. Mol Syst Biol, 8. pp. 617. 10.1038/msb.2012.49. Retrieved from https://hdl.handle.net/10161/10659.
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Associate Professor of Biochemistry
Enterotoxigenic E. coli (ETEC) causes traveler's diarrhea and infant mortality in underdeveloped countries, and Pseudomonas aeruginosa is an opportunistic pathogen for immunocompromised patients. Like all gram negative bacteria studied to date, ETEC and P. aeruginosa produce small outer membrane vesicles that can serve as delivery "bombs" to host tissues. Vesicles contain a subset of outer membrane and soluble periplasmic proteins and lipids. In tissues and sera of infected hosts,
Professor of Biomedical Engineering
The You lab uses a combination of mathematical modeling, machine learning, and quantitative experiments to elucidate principles underlying the dynamics of microbial communities in time and space and to control these dynamics for applications in computation, engineering, and medicine.
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