Probing the real-time kinetics and steady-state dynamics of gene expression

探索基因表达的实时动力学和稳态动力学

• 批准号: 8337328
• 负责人: Narendra Maheshri
• 金额: $ 23.68万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-26 至 2015-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8337328
• 关键词: Affect; Affinity; Architecture; Behavior; Binding Sites; Biochemical; Cells; Cereals; Chromatin; Coin; Complex; Computer Simulation; DNA; DNA Binding; DNA Binding Domain; DNA-Protein Interaction; Decision Making; Delayed Memory; Disease; Engineering; Exhibits; Fluorescence; Fluorescent in Situ Hybridization; Frequencies; Functional RNA; Gene Activation; Gene Expression; Gene Expression Regulation; Gene Proteins; Genes; Genetic Transcription; Genomics; Goals; In Situ; Kinetics; Knowledge; Lead; Measurement; Measures; Mediating; Messenger RNA; Microfluidic Microchips; Mission; Modeling; Molecular; Molecular Genetics; Nature; Noise; Nuclear; Organism; Output; Pathway interactions; Process; Production; Prokaryotic Cells; Property; Proteins; RNA Polymerase II; Recruitment Activity; Regulator Genes; Reporter; Repression; Research Personnel; Resolution; Saccharomyces cerevisiae; Saccharomycetales; Signal Transduction; Step Tests; Structure-Activity Relationship; System; Systems Biology; TATA Box; Techniques; Thermodynamics; Time; United States National Institutes of Health; Variant; Yeasts; base; chromatin remodeling; gene repression; improved; in vivo; infancy; inorganic phosphate; insight; interest; mathematical model; movie; mutant; neglect; novel; promoter; response; single molecule; statistics; therapeutic target; tool; transcription factor

DESCRIPTION (provided by applicant): Gene regulatory networks are decision-making and control systems of cells whose dynamical properties underlie their proper function. Large scale in silico models can aid in understanding how changes in network components lead to qualitatively different dynamics, aberrant function, and disease states. These models often use a bottom-up engineering approach; therefore, the accuracy of models is contingent on appropriate descriptions for component interactions, with just enough detail to capture the essential dynamics. Promoters dictate how gene expression (output) depends on inputs (gene-specific regulators). This relationship has traditionally been described using semi-empirical models based on the thermodynamics of protein-DNA interactions. However, even a single eukaryotic promoter likely exhibits complex dynamics because of many non-equilibrium interactions. Two dynamical properties of single genes that can qualitatively change the dynamics of the regulatory networks in which they are embedded are variability (stochastic noise) in expression of single cells and delays and memory in gene activation or repression. The project goal is to describe these dynamics using simple lumped kinetic models and connect model parameters to promoter architecture. This would be invaluable to improving larger scale efforts aimed at understanding network malfunction in disease or identifying therapeutic targets. Using fluorescence in situ hybridizaition (FISH) to detect single mRNA's in single budding yeast cells, we can extract mRNA distributions to infer steady-state dynamics and noise in gene expression. We have also developed a novel tool, the 'gene oscilloscope', to probe the kinetics of gene activation and the presence of delays and memory in yeast. The Pho4p activator, whose activity is controlled at the level of nuclear localization, is the observable, dynamically controllable input, and a fluorescent reporter is the observable output. Input/output behavior is extracted from movies of single cells grown in microfluidic devices. The current oscilloscope is limited to studies of phosphate-responsive (PHO) genes regulated by Pho4p. In Aim 1, we will identify a minimal shuttling domain in Pho4p to which we can fuse an arbitrary activation and DNA binding domain, creating a modular tool capable of probing an arbitrary promoter. In Aim 2, we will measure mRNA statistics in a panel of synthetic TET promoter variants to assess the suitability of a simple kinetic model in describing those statistics across a range of perturbations and establish how promoter and activator properties influence mRNA statistics. In Aim 3, we will use the gene oscilloscope to study how PHO promoter chromatin architecture confers delays in gene activation and measure accurate delay distributions. We will also study the frequency response of these promoters, to establish whether delayed activation filters high frequency signals. Together, these aims will help establish simple ways of incorporating crucial dynamical properties in models and yield mechanistic insights by comparing how different features of promoters and activators affect kinetics.

描述(申请人提供)：基因调控网络是细胞的决策和控制系统，其动态特性是其正常功能的基础。大规模的计算机模型有助于理解网络组件的变化如何导致本质上不同的动态、异常功能和疾病状态。这些模型通常使用自下而上的工程方法；因此，模型的准确性取决于对组件交互的适当描述，这些描述恰好足以捕捉基本的动态。启动子决定了基因表达(输出)如何依赖于输入(基因特定的调节器)。这种关系传统上是用基于蛋白质-DNA相互作用热力学的半经验模型来描述的。然而，即使是一个单独的真核启动子也可能因为许多非平衡相互作用而表现出复杂的动力学。单基因的两个动力学特性可以定性地改变它们所嵌入的调控网络的动态，这两个特性是单细胞表达的可变性(随机噪声)和基因激活或抑制中的延迟和记忆。该项目的目标是使用简单的集总动力学模型来描述这些动力学，并将模型参数与启动子结构联系起来。这对于改进旨在了解疾病中的网络故障或确定治疗目标的更大规模的努力将是非常宝贵的。利用荧光原位杂交法(FISH)检测单个发芽酵母细胞中的单个mRNA，可以提取mRNA的分布来推断基因表达的稳态动态和噪声。我们还开发了一种新的工具，‘基因示波器’，来探索酵母中基因激活的动力学以及延迟和记忆的存在。Pho4p激活剂的活性被控制在核定域水平，是可观测的、动态可控的输入，而荧光报告器是可观测的输出。输入/输出行为是从微流控设备中生长的单个细胞的电影中提取的。目前的示波器仅限于对由Pho4p调控的磷酸反应(Pho)基因的研究。在目标1中，我们将在Pho4p中确定一个最小的穿梭结构域，我们可以将任意的激活结构域和DNA结合结构域融合到该结构域上，从而创建一个能够探测任意启动子的模块化工具。在目标2中，我们将测量一组合成的Tet启动子变体中的mRNA统计数据，以评估一个简单的动力学模型在描述一系列扰动下的统计数据的适用性，并建立启动子和激活子属性如何影响mRNA统计数据。在目标3中，我们将使用基因示波器研究pho启动子染色质结构如何导致基因激活的延迟，并测量准确的延迟分布。我们还将研究这些启动子的频率响应，以确定延迟激活是否过滤高频信号。总而言之，这些目标将有助于建立简单的方法，将关键的动力学属性纳入模型，并通过比较促进剂和激活剂的不同特征如何影响动力学来产生机械洞察。