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.
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.
|Dai, Xiongfeng; Zhu, Manlu; Warren, Mya et al. (2018) Slowdown of Translational Elongation in Escherichia coli under Hyperosmotic Stress. MBio 9:|
|Zhu, Manlu; Dai, Xiongfeng (2018) High Salt Cross-Protects Escherichia coli from Antibiotic Treatment through Increasing Efflux Pump Expression. mSphere 3:|
|Erickson, David W; Schink, Severin J; Patsalo, Vadim et al. (2017) A global resource allocation strategy governs growth transition kinetics of Escherichia coli. Nature 551:119-123|
|Mori, Matteo; Schink, Severin; Erickson, David W et al. (2017) Quantifying the benefit of a proteome reserve in fluctuating environments. Nat Commun 8:1225|
|Mori, Matteo; Hwa, Terence; Martin, Olivier C et al. (2016) Constrained Allocation Flux Balance Analysis. PLoS Comput Biol 12:e1004913|
|Dai, Xiongfeng; Zhu, Manlu; Warren, Mya et al. (2016) Reduction of translating ribosomes enables Escherichia coli to maintain elongation rates during slow growth. Nat Microbiol 2:16231|
|Basan, Markus; Zhu, Manlu; Dai, Xiongfeng et al. (2015) Inflating bacterial cells by increased protein synthesis. Mol Syst Biol 11:836|
|Hui, Sheng; Silverman, Josh M; Chen, Stephen S et al. (2015) Quantitative proteomic analysis reveals a simple strategy of global resource allocation in bacteria. Mol Syst Biol 11:784|
|Basan, Markus; Hui, Sheng; Okano, Hiroyuki et al. (2015) Overflow metabolism in Escherichia coli results from efficient proteome allocation. Nature 528:99-104|
|Klumpp, Stefan; Hwa, Terence (2014) Bacterial growth: global effects on gene expression, growth feedback and proteome partition. Curr Opin Biotechnol 28:96-102|
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