Quantitative Studies of Bacterial Growth Physiology

描述(由申请人提供)：这项研究解决了细菌表达的抗生素和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度依赖性，即使是非调控基因的表达也会受到亚致死剂量的抗生素的影响。如果表达的基因产物具有一定的抗生素耐药性，则实现了以前未被认识到的反馈循环。这种反馈效应可能会极大地影响细菌对应用的抗生素的反应，导致生长双稳态等现象，并在生长和不生长状态之间突然转换。这项研究计划的长期目标是描述与不同的生长抑制模式相对应的不同类型的反馈，并量化这些反馈对抗性和细胞生长的影响。实验最初将集中在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，氯霉素乙酰转移酶可以修饰Cm使其失去活性。选择CM-CAT系统是因为它具有很好的分子特性，因此可以将精力集中在分离组件之间的全局反馈效应上。这些实验将通过大量培养的生化分析和使用延时显微镜的单细胞分析相结合的方式进行，并辅以微流控恒化室。通过在逐个细胞水平上将CAT的表达与细胞的瞬时生长速率相关联，将开发出生长动态的定量预测模型。其具体目标是建立预测的生长双稳效应，量化决定其开始的参数，并表征生长和非生长状态之间的转变的动力学。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影响，以检验所开发模型的通用性。 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描述细菌对各种抗生素的反应和适应，并阐明推动抗生素耐药性长期演变的力量。新的知识和见解将指导制定更有效、更难被细菌克服的抗菌策略，从而解决耐多药细菌带来的日益严重的医疗威胁。

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.