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。由于最近发现细菌基因表达的先天生长速率依赖性，即使是不受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一些抗生素耐药性，那么一个以前未被认识到的反馈循环就实现了。这种反馈效应可以极大地影响细菌对所施用抗生素的反应，导致生长双稳态等现象，在生长和无生长状态之间突然转变。该研究项目的长期目标是描述不同类型的反馈对应于不同的生长抑制模式，并量化这些反馈对耐药性和细胞生长的影响。实验最初将关注翻译抑制药物氯霉素（Cm）对表达氯霉素乙酰转移酶（CAT）的大肠杆菌细胞生长的影响，该酶修饰Cm使其失去活性。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件之间的全局反馈效应。实验将通过大量培养和单细胞分析的生化分析相结合进行，使用微流控恒化室辅助的延时显微复制。通过在细胞水平上将CAT表达与细胞生长的瞬时速率相关联，将开发生长动力学的定量预测模型。具体目标是建立预测的生长双稳态效应，量化决定其开始的参数，并表征生长和无生长状态之间过渡的动力学。此外，还将探索cm耐药的其他机制，以及一些其他临床相关药物的定性效果，以测试所开发模型的普遍性。

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.