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: 8538463
• 负责人: EDO L KUSSELL
• 金额: $ 22.15万
• 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/8538463
• 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）：细菌中的特定响应途径或反应性开关构成了一种普遍的生存策略，涉及传感环境波动和上调适当的响应基因。这种途径与许多压力反应有关，包括经典的耐药性，例如四环素抵抗操纵子。细菌还具有多种类别的替代生存机制，称为随机开关，可让单个细胞自发改变其表型状态，而无需感应和响应环境变化。随机切换机制在致病细菌中普遍存在，在预先适应的状态中维持细胞的亚群，这些状态为未来的环境应力做好了准备，包括在不同宿主之间的转移。单细胞水平的实验最近证明，抗生素持久性是通过在包括大肠杆菌和菌分枝杆菌在内的多种细菌中随机切换来介导的。 该赠款将开发一种在许多不同类型的波动条件下检测细菌随机切换行为的方法。该方法依赖于理论，模拟和实验的协调组合，并且适用于各种细菌物种，包括不存在遗传工具的物种。实验方法涉及在微流体设备中创建感兴趣的波动条件，该条件允许在几天内连续观察单细胞谱系跟踪。理论方法采用了这种谱系数据，并使用模拟和建模来推论在给定波动中细菌行为表征的切换速率。该方法能够在各种波动方案下清晰区分随机和响应式切换。 该方法将应用于大肠杆菌，铜绿假单胞菌和分枝杆菌的临床相关菌株，以揭示未知的随机切换方式。在特定情况下，该方法将应用于揭示控制随机切换速率的潜在遗传基因座。这项研究的目的是提供这三种随机切换曲目的全面图片，并开发出一种在任何感兴趣的物种中检测到的一般方法。这将在检测到不同物种中这种重要的细菌存活机制的能力上，并确定构成对抗细菌持久性的关键药物靶标的遗传基因座的能力。