This research addresses fundamental aspects of interactions between antibiotics and antibiotic resistance expressed by bacteria. Due to a recently discovered innate growth-rate dependence of bacterial gene expression, the expressions of even unregulated genes are affected by sub-lethal doses of antibiotics. If the expressed gene product confers some antibiotic resistance, then a previously unappreciated feedback loop is realized. This feedback effect can drastically affect the response of bacteria to an applied antibiotic, leading to phenomenon such as growth bistability with abrupt transitions between growth and no-growth states. The long-term goal of this research program is to characterize different types of feedback corresponding to different modes of growth inhibition, and to quantify the consequences of these feedback effects on resistance and cell growth. Experiments will initially focus on the effect of chloramphenicol (Cm), a translation-inhibiting drug, on the growth of E. coli cells expressing chloramphenicol acetyltransferase (CAT), which modifies Cm to render them inactive. The Cm-CAT system is chosen because it is well characterized molecularly, so that efforts can be focused on isolating the global feedback effects between the components. The experiments will be carried out by a combination of biochemical assays on bulk culture and single-cell analysis using time-lapse microcopy aided by microfluidic chemostat chambers. Quantitative, predictive models of the growth dynamics will be developed by correlating CAT expression and the instantaneous rate of cell growth at a cell-by-cell level.
The specific aims are to establish the predicted growth bistability effect, quantify parameters that determine its onset, and characterize the dynamics of the transition between the growth and no-growth states. In addition, other mechanisms of Cm-resistance will be explored, as will the qualitative effects of a number of other clinically relevant drugs, in order to test the generality of the models developed.

Public Health Relevance

Quantitative, predictive models of drug-bacteria interactions will allow better characterization of the response and adaptation of bacteria to various antibiotics, and shed light on forces driving the long-term evolution of antibiotic resistance. New knowledge and insights will guide the development of antibacterial strategies that are more effective and more difficult for bacteria to overcome, thereby addressing the ever-increasing medical threat presented by multi-drug resistant bacteria.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
3R01GM095903-03S1
Application #
8704514
Study Section
Program Officer
Reddy, Michael K
Project Start
2011-08-01
Project End
2015-05-31
Budget Start
2013-06-01
Budget End
2014-05-31
Support Year
3
Fiscal Year
2013
Total Cost
$10,000
Indirect Cost
Name
University of California San Diego
Department
Physics
Type
Schools of Arts and Sciences
DUNS #
804355790
City
La Jolla
State
CA
Country
United States
Zip Code
92093
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Basan, Markus; Zhu, Manlu; Dai, Xiongfeng et al. (2015) Inflating bacterial cells by increased protein synthesis. Mol Syst Biol 11:836
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Klumpp, Stefan; Hwa, Terence (2014) Bacterial growth: global effects on gene expression, growth feedback and proteome partition. Curr Opin Biotechnol 28:96-102
Klumpp, Stefan; Scott, Matthew; Pedersen, Steen et al. (2013) Molecular crowding limits translation and cell growth. Proc Natl Acad Sci U S A 110:16754-9
Deris, J Barrett; Kim, Minsu; Zhang, Zhongge et al. (2013) The innate growth bistability and fitness landscapes of antibiotic-resistant bacteria. Science 342:1237435
You, Conghui; Okano, Hiroyuki; Hui, Sheng et al. (2013) Coordination of bacterial proteome with metabolism by cyclic AMP signalling. Nature 500:301-6
Hermsen, Rutger; Deris, J Barrett; Hwa, Terence (2012) On the rapidity of antibiotic resistance evolution facilitated by a concentration gradient. Proc Natl Acad Sci U S A 109:10775-80
Kim, Minsu; Zhang, Zhongge; Okano, Hiroyuki et al. (2012) Need-based activation of ammonium uptake in Escherichia coli. Mol Syst Biol 8:616

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