Quantitative Studies of Bacterial Growth Physiology

描述（由申请人提供）：本研究涉及抗生素与细菌表达的抗生素耐药性之间相互作用的基本方面。由于最近发现的细菌基因表达的先天生长率依赖性，即使是未调节的基因的表达也会受到亚致死剂量抗生素的影响。如果表达的基因产物赋予某种抗生素抗性，那么就实现了一个以前不被重视的反馈回路。这种反馈效应可以极大地影响细菌对所应用的抗生素的反应，导致诸如生长双稳态的现象，在生长和非生长状态之间突然转变。该研究计划的长期目标是表征对应于不同生长抑制模式的不同类型的反馈，并量化这些反馈对抗性和细胞生长的影响。实验将首先集中在氯霉素（Cm），一种抑制生长的药物，对E。表达氯霉素乙酰转移酶（CAT）的大肠杆菌细胞，该酶修饰Cm使其失活。选择Cm-CAT系统是因为它具有良好的分子特征，因此可以集中精力隔离组分之间的全局反馈效应。实验将通过批量培养的生物化学测定和使用微流体恒化器室辅助的延时显微镜的单细胞分析的组合进行。生长动力学的定量预测模型将通过将CAT表达和细胞生长的瞬时速率在细胞水平上相关联来开发。具体的目标是建立预测的增长双稳态效应，量化参数，确定其发病，并表征增长和非增长状态之间的过渡的动态。此外，将探讨其他机制的厘米耐药性，将定性的影响，一些其他临床相关的药物，以测试的一般性模型开发。 公共卫生相关性：药物-细菌相互作用的定量预测模型将允许更好地表征细菌对各种抗生素的反应和适应，并揭示驱动抗生素耐药性长期演变的力量。新的知识和见解将指导开发更有效、更难被细菌克服的抗菌策略，从而解决多重耐药细菌带来的日益严重的医学威胁。

DESCRIPTION (provided by applicant): 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.