Gene Regulation and Memory in Bacterial Metabolism and Antibiotic Resistance

细菌代谢和抗生素耐药性中的基因调控和记忆

• 批准号: 10566736
• 负责人: EDO L KUSSELL
• 金额: $ 30.87万
• 依托单位: NEW YORK UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2026-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10566736
• 关键词: Address; Algorithmic Analysis; Antibiotic Resistance; Antibiotics; Bacteria; Bar Codes; Biochemical Pathway; Biological; Biophysics; Carbon; Cells; Clinical; Complex; Costs and Benefits; Cues; Culture Media; Custom; Data; Drug resistance; Environment; Enzymes; Escherichia coli; Exposure to; Gene Combinations; Gene Expression Regulation; Genes; Growth; Human; Image Analysis; Inherited; Knowledge; Laboratories; Lac Operon; LacZ Genes; Libraries; Maps; Measurement; Measures; Memory; Metabolic; Metabolic stress; Microbiology; Microfluidics; Microscope; Microscopy; Modeling; Mothers; Mutation; Nutrient; Periodicals; Phenotype; Physiologic pulse; Physiological; Population; Population Dynamics; Process; Production; Property; Proteins; Regulation; Regulator Genes; Repression; Research; Resistance; Resolution; Site-Directed Mutagenesis; Source; Starvation; Stress; Structure; System; Testing; Time; Treatment outcome; Work; antimicrobial; bacterial community; bacterial metabolism; bacterial resistance; biophysical model; cell growth; cost; daughter cell; design; drug modification; efflux pump; environmental stressor; experimental study; fitness; gene induction; gut microbiome; host-associated microbial communities; improved; innovation; insight; interest; microorganism; molecular pump; monolayer; mutant; novel; outcome prediction; predictive modeling; resistance gene; resistance mechanism; resistant strain; response

Project Summary: Bacterial cells have a repertoire of responses that can be used to survive under different types of environmental stress. Changes in carbon sources cause cells to turn on specific metabolic genes, which are later repressed when those sources are depleted. Antibiotic exposure can trigger the expression of molecular pumps that remove the antibiotic from the cell, or the production of enzymes that specifically inactivate or degrade it. In a continually fluctuating environment, the process of turning genes on and off can be inefficient and cause growth lags. Our work shows that bacteria combine their responses with phenotypic memory – the passage of stable proteins from mother to daughter cells – which allows cells to avoid growth lags in fluctuating environments. Using a combination of quantitative microbiology, microfluidics, microscopy, sequencing, and modeling, we will study the costs and benefits of gene regulation in fluctuating environments. We will measure and model the fitness landscape of phenotypic memory using a library of strains with perturbed memory levels. Competition experiments in fluctuating environments will be used to test biophysical and population dynamics models. We present an innovative modular system that enables direct comparison of different gene regulatory strategies – including responsive, bistable, and constitutive regulation – for any gene of interest. We apply the system to study different classes of antibiotic resistance mechanisms. The proposed experiments make use of a custom-built microfluidic ‘chemoflux’ system that we developed, in which bacterial populations grow in monolayers, tracked at single cell resolution under the microscope, while the growth media can be arbitrarily fluctuated in time. Using the chemoflux and our image analysis algorithms, we are able to simultaneously track hundreds of independent bacterial populations, and thereby measure population dynamics in fluctuating environments. We combine experiments with biophysical modeling to gain insights into the costs and benefits of gene regulation and memory. Models are parameterized using experimental data in a wide range of conditions, and rigorously tested by their predictions on competition experiments in fluctuating environments. The range of experiments and modeling employed address different aspects of gene regulation and memory, and allows us to bridge from detailed laboratory measurements to the general biological principles that underlie bacterial survival.

项目概要： 细菌细胞具有一系列反应，可用于在不同类型的环境下生存。 环境压力碳源的变化会导致细胞开启特定的代谢基因， 当这些资源耗尽后，它们会被压抑。抗生素暴露会引发 从细胞中清除抗生素的泵，或者特异性地抑制或抑制抗生素的酶的产生。 在一个持续波动的环境中，开启和关闭基因的过程可能效率低下。 并导致生长滞后。我们的工作表明，细菌联合收割机将它们的反应与表型记忆结合起来， 稳定的蛋白质从母细胞到子细胞的传递-这使得细胞在波动中避免生长滞后 环境. 使用定量微生物学，微流体，显微镜，测序和建模的组合，我们将 研究波动环境中基因调控的成本和收益。我们将测量和建模 表型记忆的适应度景观使用具有扰动记忆水平的菌株库。竞争 将利用波动环境中的实验来检验生物物理和种群动态模型。 我们提出了一个创新的模块化系统，使不同的基因调控的直接比较， 策略-包括响应性、非线性和组成性调节-用于任何感兴趣的基因。我们应用 系统来研究不同类别的抗生素耐药性机制。拟议的实验利用 我们开发了一种定制的微流体“化学通量”系统，其中细菌种群在 单层，在显微镜下以单细胞分辨率跟踪，而生长培养基可以是任意的。 在时间上波动。使用化学通量和我们的图像分析算法，我们能够同时跟踪 数百个独立的细菌种群，从而测量波动的种群动态， 环境. 我们将联合收割机实验与生物物理建模相结合，以深入了解基因治疗的成本和收益。 调节和记忆。使用各种条件下的实验数据对模型进行参数化， 他们的预测在波动环境中的竞争实验中得到了严格的检验。的范围 所采用的实验和建模解决了基因调控和记忆的不同方面， 从详细的实验室测量到细菌的一般生物学原理， 生存