Revealing Stochastic Switches in Bacteria

项目摘要 随机开关是一种广泛的遗传机制，它使单细胞能够切换某些基因 随机开关，不对环境做出反应。这种开关在致病性 细菌，其中它们通常参与产生跨细菌的不同表面蛋白库。 群体，这使得一个子集的细胞，以避免检测由免疫系统。一般来说，随机 开关提供了一种在波动环境中生存的策略，通过维持细胞的亚群， 预先适应的状态，为未来可能不可预测的环境压力做好准备。特别是， 已知这些策略在抗生素持久性中是重要的，抗生素持久性是一种细菌表型状态， 生长缓慢，对抗生素的耐受性增强。 这项资助适用于高灵敏度的单细胞测量结合数学模型来研究 随机切换的三个主要方面。我们使用合成的随机开关来驱动抗生素耐药性 基因，并通过在多日实验中测量抗生素脉冲下的种群动态，我们 对耐药性的出现进行量化和建模，这是一个具有重大临床意义的过程。我们使用随机 开关作为一个模型来研究人口的进化压力时， 通过人口瓶颈从一个环境到另一个环境，这是疾病传播的一个关键组成部分。而且， 我们在大肠杆菌中研究抗生素的持久性，在大肠杆菌中， 不同的人群可以赋予不同程度的抗生素耐受性。通过使用单细胞分析的新方法， 群体数据与表型信息映射，我们调查的遗传网络， 细菌持久性 这项拟议的研究将大大促进对细菌生长过程中随机性作用的理解。 适应通过强调预测数学建模，该研究将提供以下能力： 预测治疗方案对抗生素耐药性的出现和对 持久性，并确定减缓或逆转这些复杂的，生物医学相关的新方法， 流程.

PROJECT SUMMARY Stochastic switches are a broad class of genetic mechanisms that enable single cells to switch certain genes on and off randomly, without responding to their environment. Such switches are prevalent in pathogenic bacteria, where they are often involved in generating diverse surface protein repertoires across the bacterial population, which enables a subset of cells to avoid detection by the immune system. In general, stochastic switches provide a strategy for survival in fluctuating environments, by maintaining subpopulations of cells in pre-adapted states that are prepared for future, possibly unpredictable, environmental stresses. In particular, these strategies are known to be important in antibiotic persistence, a bacterial phenotypic state consisting of slow growth and enhanced tolerance for antibiotics. This grant applies highly sensitive single-cell measurements combined with mathematical models to study three major facets of stochastic switching. We use synthetic stochastic switches to drive antibiotic resistance genes, and by measuring the population dynamics under antibiotic pulses over multi-day experiments, we quantify and model the emergence of resistance, a process of major clinical importance. We use stochastic switches as a model to study the evolutionary pressures that populations experience when transferred from one environment to another through population bottlenecks, a key component of disease transmission. And, we study antibiotic persistence in Escherichia coli, where a continuum of growth states across a bacterial population can confer varying degrees of antibiotic tolerance. By using novel methods for analysis of single cell population data mapped with phenotypic information, we investigate the genetic network that underlies bacterial persistence. The proposed research will substantially advance understanding of the role of stochasticity in bacterial adaptation. Through its emphasis on predictive mathematical modeling, the research will provide the ability to predict the impact of treatment protocols on the emergence of antibiotic resistance and on levels of persistence, and to identify new ways of slowing down or reversing these complex, biomedically relevant processes.