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

基本信息

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
项目类别：
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

项目摘要

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.

项目摘要：细菌细胞具有多种反应的曲目，可用于在不同类型的情况下生存环境压力。碳源的变化导致细胞打开特定的代谢基因，这是后来在这些来源耗尽时被压抑。抗生素暴露会触发分子的表达从细胞中去除抗生素的泵，或者特别失活或降解它。在不断波动的环境中，打开和关闭基因的过程效率低下并导致生长滞后。我们的工作表明，批处理将其反应与表型记忆结合在一起 - 稳定蛋白从母亲到子细胞的通过 - 允许细胞避免生长滞后环境。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节的成本和收益。我们将测量和建模使用具有干扰记忆水平的应变库的表型记忆的健身景观。竞赛波动环境中的实验将用于测试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，该系统可以直接比较不同的基因调节策略（包括响应迅速，双重和本构调节），以供任何感兴趣的基因。我们应用研究不同类别的抗生素抗性机制的系统。提出的实验利用我们开发的定制的微流体“ Chemoflux”系统，其中细菌种群在其中生长单层，在显微镜下以单细胞分辨率进行跟踪，而生长培养基可以任意地进行及时波动。使用ChemoFlux和我们的图像分析算法，我们能够轻松跟踪数百个独立的细菌种群，从而测量人口动态动态环境。我们将实验与生物物理建模相结合，以洞悉基因的成本和收益调节和记忆。使用在各种条件下的实验数据进行参数化，并且通过他们对波动环境中的竞争实验的预测进行了严格的测试。范围实验和建模进行了解决基因调控和记忆的不同方面，并允许我们从详细的实验室测量到基于细菌的一般生物学原理桥接生存。