Single-Molecule and Single-Cell Analysis of Transcription Factor-DNA Complexes

转录因子-DNA 复合物的单分子和单细胞分析

• 批准号: 8843459
• 负责人: ROB PHILLIPS
• 金额: $ 33.94万
• 依托单位: CALIFORNIA INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-02-17 至 2017-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8843459
• 关键词: Antibiotic Resistance; Architecture; Bacteria; Bacterial Genes; Bacterial Genome; Base Pairing; Binding; Binding Sites; Biological; Biology; Case Study; Cells; Code; Complex; Coupling; DNA; DNA Sequence; Data; Databases; Decision Making; Development; Dissection; Escherichia coli; Eukaryota; Exhibits; Gene Dosage; Gene Expression; Generic Drugs; Genes; Genetic Transcription; Goals; Hand; Health; Heart; Human; In Vitro; Information Theory; Iron; Length; Life Cycle Stages; Link; Maps; Measurement; Measures; Medicine; Metabolic; Methods; Microbe; Microbial Biofilms; Mining; Modeling; Molecular; Mutagenesis; Organism; Outcome; Output; Positioning Attribute; Process; Prokaryotic Cells; Proteins; Pseudomonas; Regulation; Regulatory Element; Research; Resolution; Role; Site; Sorting - Cell Movement; Staging; Techniques; Testing; Time; Trans-Activators; Transcriptional Regulation; Work; base; design; falls; improved; in vivo; interest; pathogen; preference; promoter; research study; resistance gene; response; single cell analysis; single molecule; success; synthetic biology; tool; transcription factor

DESCRIPTION (provided by applicant): Cells make decisions all the time as part of their normal life cycles. These decisions range from the expression of metabolic preferences to choices critical to human health such as whether cells will enter a state of unchecked proliferation or express antibiotic resistance genes. The overarching goal of the work proposed here is to decipher the rules that manage cellular decisions and to subject models of the regulatory process to stringent experimental tests with the aim of developing a predictive understanding of the regulatory code. Though the kinds of models we develop can be applied to both prokaryotic and eukaryotic regulatory circuits, we are focusing first on bacteria in order to build up an entire suite of tools taking us from regulatory architecture discovery at basepair resolution all the way to the systematic quantitative determination of the input-output functions for these circuits. E. coli is one of the best understood of organisms. And yet, out of its more than four thousand genes, we know almost nothing about how half of them are regulated. The work proposed here is built around three main aims that together will result in a predictive picture of transcriptional regulation. The work in the first aim uses a method known as Sort-Seq that makes it possible to identify the constellation of binding sites for the transcription factors that control a given gene of interest. We will use this method as a tool both to design synthetic networks and as the basis of discovering the regulatory architectures for a number of important bacterial genes for which essentially nothing is known. With this regulatory information in hand, in the second aim, we will characterize the input-output response of regulatory elements whose architectures have been characterized using the Sort-Seq approach. Specifically, systematic experiments will be performed to test how variability in gene expression depends upon key parameters, such as the number of transcription factors and the strength of transcription factor binding sites. One of the most mysterious classes of regulatory network involves the binding of transcription factors at a distance from the genes they control. The third aim develops a mechanistic understanding of how these trans-acting factors control the promoter of interest. This analysis will be made using a combination of in vivo experiments and single-molecule measurements. The outcome of this work will be a robust framework permitting us to go all the way from regulatory architecture discovery with base pair resolution to the systematic quantitative determination of the input- output functions of generic regulatory architectures.

描述（由申请人提供）：作为正常生命周期的一部分，细胞始终做出决定。这些决定的范围从代谢偏好的表达到对人类健康至关重要的选择，例如细胞是否会进入不受控制的增殖状态或表达抗生素抗性基因。这里提出的工作的总体目标是破译管理细胞决策的规则，并对监管过程的模型进行严格的实验测试，目的是发展对监管代码的预测性理解。虽然我们开发的模型可以应用于原核和真核调节回路，但我们首先关注细菌，以便建立一整套工具，使我们从碱基对分辨率的调节架构发现一直到系统定量确定这些回路的输入输出函数。大肠杆菌是最了解的生物体之一。然而，在其 4000 多个基因中，我们几乎不知道其中一半是如何受到调控的。这里提出的工作围绕三个主要目标构建，这三个目标共同将产生转录调控的预测图。第一个目标的工作使用了一种称为 Sort-Seq 的方法，该方法可以识别转录因子的结合位点群 控制特定的感兴趣基因。我们将使用这种方法作为设计合成网络的工具，并作为发现许多基本上未知的重要细菌基因的调控架构的基础。有了这些监管信息，在第二个目标中，我们将描述监管元件的输入输出响应，这些监管元件的架构已使用 Sort-Seq 方法进行了表征。具体来说，将进行系统实验来测试基因表达的变异性如何取决于关键参数，例如转录因子的数量和转录因子结合位点的强度。最神秘的一类调控网络涉及转录因子与它们控制的基因相距一定距离的结合。第三个目标是对这些反式作用因子如何控制感兴趣的启动子形成机械理解。该分析将结合体内实验和单分子测量进行。这项工作的成果将是一个强大的框架，使我们能够从碱基对解析的监管架构发现一直到通用监管架构输入输出功能的系统定量确定。