Revealing Stochastic Switches in Bacteria: Theory, Modeling, and Experiments

揭示细菌中的随机开关：理论、建模和实验

• 批准号: 8727053
• 负责人: EDO L KUSSELL
• 金额: $ 20.22万
• 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 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8727053
• 关键词: Antibiotics; Bacteria; Bacteriophages; Behavior; Candidate Disease Gene; Carbon; Cell Extracts; Cell Lineage; Cells; Chromosome Mapping; Control Locus; Data; Detection; Devices; Drug Targeting; Drug resistance; Engineering; Environment; Epigenetic Process; Escherichia coli; Frequencies; Future; Genes; Genetic; Genetic Engineering; Goals; Grant; Image Analysis; Immunoglobulin Class Switching; Individual; Kinetics; Label; Liquid substance; Mediating; Methodology; Methods; Methylation; Microfluidic Microchips; Microfluidics; Microscope; Microscopy; Modality; Modeling; Mycobacterium smegmatis; Nature; Operon; Organism; Pathway interactions; Population; Population Dynamics; Proliferating; Proteins; Pseudomonas aeruginosa; Research; Resistance; Running; Short Tandem Repeat; Source; Stress; Temperature; Testing; Tetracycline Resistance; Tetracyclines; Theoretical model; Time; Trees; Variant; Work; antimicrobial; base; biological adaptation to stress; clinically relevant; combat; density; environmental change; genetic manipulation; interest; models and simulation; movie; pathogenic bacteria; research study; response; simulation; sugar; theories; tool

DESCRIPTION (provided by applicant): Specific response pathways, or responsive switches, in bacteria constitute a prevalent survival strategy that involves sensing environmental fluctuations and up-regulating appropriate response genes. Such pathways are implicated in many stress responses, including classical drug resistance such as the tetracycline- resistance operon. Bacteria also possess a diverse class of alternative survival mechanisms, known as stochastic switches, which allow single cells to spontaneously alter their phenotypic state, without sensing and responding to changes in the environment. Stochastic switching mechanisms are prevalent in pathogenic bacteria, maintaining subpopulations of cells in pre-adapted states that are prepared for future environmental stresses, including transfer between different hosts. Experiments at the single-cell level have recently demonstrated that antibiotic persistence is mediated by stochastic switching in several bacteria, including Escherichia coli and Mycobacterium smegmatis. This grant will develop a method to detect stochastic switching behavior of bacteria in many different types of fluctuation conditions. The method relies on a coordinated combination of theory, simulation, and experiments, and is applicable to a large range of bacterial species, including species for which genetic tools do not exist. The experimental approach involves creating a fluctuating condition of interest in a microfluidic device that allows single-cell lineage tracking to be observed continuously over several days. The theoretical approach takes this lineage data, and using simulations and modeling deduces the switching rates that characterize the bacterium's behavior in the given fluctuation. The approach is able to cleanly distinguish between stochastic and responsive switching under diverse fluctuation regimes. The approach will be applied to clinically relevant strains of Escherichia coli, Pseudomonas aeruginosa, and Mycobacterium smegmatis, to reveal unknown stochastic switching modalities. In particular cases, the methodology will be applied to reveal the underlying genetic loci that control the rates of stochastic switching. The goal of the research is to provide a comprehensive picture of the stochastic switching repertoire of these three species, and to develop a general approach for their detection in any species of interest. This will provide a significant advance in ability to detect this important class of bacterial survival mechanisms in diverse species, and to identify genetic loci that constitute key drug targets for combating bacterial persistence.

描述（由申请人提供）：细菌中的特定反应途径或反应开关构成了一种普遍的生存策略，涉及感知环境波动和上调适当的反应基因。这些途径涉及许多应激反应，包括经典的耐药性，如四环素抗性操纵子。细菌还具有多种多样的替代生存机制，称为随机开关，允许单细胞自发改变其表型状态，而不感知和响应环境的变化。随机转换机制在病原菌中普遍存在，使细胞亚群保持在预适应状态，为未来的环境压力做好准备，包括在不同宿主之间转移。最近在单细胞水平上的实验表明，抗生素的持久性是由几种细菌，包括大肠杆菌和耻垢分枝杆菌的随机开关介导的。 这项资助将开发一种方法来检测细菌在许多不同类型的波动条件下的随机切换行为。该方法依赖于理论，模拟和实验的协调结合，适用于大范围的细菌物种，包括不存在遗传工具的物种。该实验方法涉及在微流体装置中创建感兴趣的波动条件，允许在几天内连续观察单细胞谱系跟踪。理论方法采用这种谱系数据，并使用模拟和建模推导出表征细菌在给定波动中的行为的切换率。该方法是能够干净区分随机和响应切换不同的波动制度。 该方法将应用于临床相关菌株的大肠杆菌，铜绿假单胞菌，和耻垢分枝杆菌，揭示未知的随机开关模式。在特定情况下，该方法将被应用于揭示控制随机转换率的潜在遗传位点。这项研究的目标是提供一个全面的图片的随机开关剧目，这三个物种，并制定一个通用的方法，他们在任何物种的利益检测。这将在检测不同物种中这类重要细菌存活机制的能力方面取得重大进展，并确定构成对抗细菌持久性的关键药物靶点的遗传位点。