Revealing Stochastic Switches in Bacteria

揭示细菌中的随机开关

• 批准号: 9406188
• 负责人: EDO L KUSSELL
• 金额: $ 37.06万
• 依托单位: NEW YORK UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-15 至 2020-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9406188
• 关键词: Address; Affect; Antibiotic Resistance; Antibiotics; Antitoxins; Bacteria; Behavior; Biological Models; Cell Cycle; Cell physiology; Cells; Clinical; Complex; Data; Detection; Environment; Escherichia coli; Event; Evolution; Frequencies; Future; Genes; Genetic; Grant; Growth; Head; Immune; Immune system; Knowledge; Maps; Measurement; Measures; Membrane Proteins; Metabolic; Methods; Microfluidic Microchips; Microfluidics; Microscopy; Modeling; Molecular; Molecular Probes; Nature; Pathway interactions; Periodicity; Phenotype; Physiologic pulse; Plasmids; Population; Population Dynamics; Population Heterogeneity; Population Pressures; Process; Proliferating; Public Health Applications Research; Research; Resistance; Role; Specific qualifier value; Stress; Testing; Toxin; Treatment Protocols; Trees; antibiotic tolerance; base; biological adaptation to stress; disease transmission; experience; experimental study; mathematical model; novel; pathogen; pathogenic bacteria; predictive modeling; pressure; resistance gene; sample fixation; single cell analysis; synthetic biology; trait; transmission process

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.

项目摘要 随机开关是一类广泛的遗传机制，使单个细胞能够切换某些基因 随机打开和关闭，无需响应其环境。这种开关在病原上很普遍 细菌，它们通常参与在细菌上产生多种表面蛋白质库 种群，使一部分细胞避免通过免疫系统检测。通常，随机 开关通过维持细胞的亚群来在波动环境中生存的策略 为未来（可能是不可预测的环境压力）准备的预先适应的国家。尤其， 这些策略在抗生素持久性中很重要，抗生素持久性是一种细菌表型状态，由 生长缓慢并增强抗生素的耐受性。 该赠款应用高度敏感的单细胞测量结果与数学模型相结合来研究 随机切换的三个主要方面。我们使用合成随机开关来驱动抗生素耐药性 基因，以及通过在多天实验中测量抗生素脉冲下的种群动态，我们 量化和模拟抗性的出现，这是一个主要临床重要性的过程。我们使用随机 开关作为模型，以研究人群从转移时所经历的进化压力 一个环境通过种群瓶颈到另一个环境，这是疾病传播的关键组成部分。和， 我们研究大肠杆菌中的抗生素持久性，在细菌范围内生长状态的连续性 人口可以赋予不同程度的抗生素耐受性。通过使用新颖的方法分析单细胞 用表型信息映射的人口数据，我们研究了基础的遗传网络 细菌持久性。 拟议的研究将大大提高人们对随机性在细菌中的作用的理解 适应。通过强调预测性数学建模，该研究将提供能力 预测治疗方案对抗生素耐药性出现的影响以及 持久性，并确定放慢或逆转这些复杂的生物益处相关的新方法 过程。