None-genetic metabolic heterogeneity and its influence on drug tolerance

项目摘要 细胞异质性在生命的所有领域都是普遍存在的。基因相同的细胞可以 即使在相同的环境中生长，也显示出异质的代谢活动。在 细菌，代谢异质性可以塑造整体生长速率，并影响抗生素 宽容在高等生物中，代谢活动在功能上与肿瘤活性和药物活性相关。 宽容虽然mRNA和蛋白质丰度的细胞异质性已经被广泛研究， 尽管已经进行了研究，但关于代谢异质性仍存在许多问题。比如说， 决定了代谢波动的大小和频率代谢异质性 监管？我们能否控制代谢异质性，从而消除耐药细胞？ 对这一重要问题缺乏基本了解，严重限制了 为多种疾病开发有效的治疗方法，其中少数短暂性 耐受性细胞通常导致疾病复发。在过去的几年里，Zhang实验室 开发了几种研究代谢物异质性的方法，包括代谢物生物传感器， 辅助单细胞成像和通过细胞分选的代谢物定量。我们的工作确定了大， 脂肪酸生物合成的非遗传异质性，我们利用代谢异质性 生物技术应用（即化学品生产过剩）。在这个MIRA提案中，我们的目标是 为了系统地了解细菌代谢的异质性，我们使用 用于单细胞代谢分析的现有和新方法。使用大肠杆菌作为 模型，我们将构建一个可调的代谢系统，产生独特的荧光 代谢物，具有受控的通量和代谢物浓度。微流体辅助延时摄影 荧光显微镜将用于同时定量这种浓度， 代谢产物及其生物合成酶、细胞生长速率和单细胞年龄。数据 分析与建模相结合将用于确定起源、动力学和传播 代谢的异质性。此外，使用天然脂肪酸分解代谢途径作为模型， 我们将研究转录和变构调节如何影响代谢异质性。在 此外，我们将探讨代谢异质性对药物耐受性的影响，并寻求 减少代谢异质性和多模态。该项目将揭示新的原则， 管理代谢异质性，并提供一个定量框架来解释各种单一的， 细胞现象了解代谢异质性的调节及其对 药物耐受性将为消除对药物不敏感的异质性变异的策略提供信息。 从而为复发性疾病提供新的治疗方法。

PROJECT SUMMARY Cellular heterogeneity is ubiquitous across all domains of life. Genetically identical cells can display heterogeneous metabolic activities, even when grown in identical environments. In bacteria, metabolic heterogeneity can shape the ensemble growth rate and affect antibiotic tolerance. In higher organisms, metabolic activity is functionally related to tumor activity and drug tolerance. While cellular heterogeneities in mRNA and protein abundance have been extensively studied, many questions remain regarding metabolic heterogeneity. For instance, what determines the size and frequency of metabolic fluctuation? How is metabolic heterogeneity regulated? Can we control metabolic heterogeneity and therefore eliminate drug-tolerant cells? The lack of fundamental understanding about this important topic has severely limited the development of effective treatments for multiple diseases in which a small number of transiently tolerant cells often cause disease recurrence. Over the past few years, the Zhang lab has developed several methods to study metabolite heterogeneity, including metabolite-biosensor- assisted single-cell imaging and metabolite quantification by cell sorting. Our work identified large, non-genetic heterogeneity in fatty acid biosynthesis, and we exploited metabolic heterogeneity for biotechnology applications (i.e. overproduction of chemicals). In this MIRA proposal, we aim to obtain a systematic understanding of bacteria metabolic heterogeneity by using our existing and novel methods for single-cell metabolic analysis. Using Escherichia coli as a model, we will construct a tunable metabolic system that produces a unique fluorescent metabolite with controlled flux and metabolite concentration. Microfluidics-assisted time-lapse fluorescent microscopy will be used to simultaneously quantify the concentrations of this metabolite and its biosynthetic enzyme, the cell growth rate, and the age of single cells. Data analysis combined with modeling will be used to determine the origin, dynamics, and propagation of metabolic heterogeneity. Furthermore, using the native fatty acid catabolic pathway as a model, we will study how transcriptional and allosteric regulations affect metabolic heterogeneity. In addition, we will explore the influence of metabolic heterogeneity on drug tolerance and seek to reduce metabolic heterogeneity and multimodality. This project will reveal novel principles that govern metabolic heterogeneity and provide a quantitative framework to explain various single- cell phenomena. Understanding the regulation of metabolic heterogeneity and its influence on drug tolerance will inform strategies to eliminate heterogeneous variants that are insensitive to drugs, thus providing new treatments for recurrent diseases.