Information Integration and Energy Expenditure in Eukaryotic Gene Regulation

真核基因调控中的信息整合和能量消耗

• 批准号: 10676836
• 负责人: Angela H DePace
• 金额: $ 47.03万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-10 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10676836
• 关键词: Address; Area; Attention; Bacteria; Binding; Binding Sites; Biological Models; Biology; Blastoderm; Chromatin; Complex; DNA; DNA Methylation; DNA Sequence; DNA-Directed RNA Polymerase; Data; Development; Differential Equation; Disease; Drosophila genus; Embryo; Energy Metabolism; Energy-Generating Resources; Enhancers; Equilibrium; Eukaryota; Evolution; Funding; Gene Expression; Gene Expression Regulation; Genes; Genetic Transcription; Genome; Goals; Grain; Graph; Laboratories; Link; Markov Chains; Mathematics; Measures; Mediator; Medicine; Messenger RNA; Methods; Modeling; Molecular; Nucleosomes; Organism; Output; Pattern; Phenotype; Physics; Physiology; Positioning Attribute; Post-Translational Protein Processing; Process; Production; Property; Proteins; Regulation; Research; Role; Study models; System; Thermodynamics; Time; Transcriptional Regulation; Work; biological systems; chromatin remodeling; equilibrium model; experimental study; genetic regulatory protein; interest; mRNA Expression; mathematical methods; mathematical model; mathematical theory; neglect; optogenetics; real-time images; recruit; response; theories; transcription factor

Project Abstract Gene regulation – how genes are turned on in the right place, at the right time and in the right amount – is a problem central to most areas of biology and medicine. Our understanding of gene regulation arose from classical studies in bacteria: proteins called “transcription factors” (TFs) bind to regulatory DNA sequences and recruit RNA polymerase (RNAP). The situation in eukaryotes is far more complicated. For example, eukaryotic DNA is packaged around nucleosomes into chromatin and external sources of energy, such as ATP, are used to reorganise chromatin, remodel nucleosomes and post-translationally modify regulatory proteins. Pioneering studies from several laboratories have identified many of the molecular components involved in this regulatory complexity. However, the quantitative concepts used to reason about eukaryotic gene regulation are still largely based on the bacterial paradigm. Our work focuses on addressing this alarming gap. Previously, we developed a strategy of “following the energy” by integrating mathematical models rooted in physics with quantitative and synthetic experiments in the early Drosophila embryo. The fruit fly offers an unrivaled model system for measuring and perturbing gene regulation in a living organism. The mathematics exploits a graph- based approach to Markov processes that permits algebraic calculation of required quantities. This allowed us to identify the functional limits to energy expenditure, while avoiding fitting models to data or numerically simulating differential equations. We have provided strong evidence that energy expenditure away from thermodynamic equilibrium is essential for the functional properties of eukaryotic genes. In the present proposal, we build on this previous strategy. We hypothesize that data from the Drosophila hunchback gene cannot be accounted for by any thermodynamic equilibrium model of regulated recruitment of RNAP, no matter how complicated the molecular details. We believe we can exploit a method of “coarse graining” within the linear framework to establish this remarkably powerful result. We will then extend our experimental methods and modeling beyond regulated recruitment, to analyze the dynamics of RNAP itself and the stochastic production of mRNA. We will introduce real-time imaging of mRNA and optogenetic perturbations of TFs to measure quantitative aspects of gene expression, and will extend our algebraic methods to accommodate such data. We hypothesize that energy expenditure in gene regulation is essential to modulate RNAP dynamics and generate the observed stochastic patterns of hunchback mRNA expression. Our efforts will formulate a new model of hunchback that integrates regulation, energy expenditure, RNAP dynamics and mRNA stochasticity.

项目摘要 基因调控-基因如何在正确的地方，在正确的时间和正确的数量打开-是一个 生物学和医学领域的核心问题。我们对基因调控的理解来自于 细菌的经典研究：称为“转录因子”（TF）的蛋白质与调节DNA序列结合， 募集RNA聚合酶（RNAP）。真核生物的情况要复杂得多。例如，真核生物 DNA被包装在核小体周围进入染色质，并使用外部能源，如ATP 重组染色质、重塑核小体和后修饰调节蛋白。开创性 来自几个实验室的研究已经确定了许多参与这种调节的分子组分， 复杂性然而，用于推理真核生物基因调控的定量概念仍然存在。 很大程度上是基于细菌模式。我们的工作重点是解决这一令人震惊的差距。此前我们 通过整合植根于物理学的数学模型， 在早期果蝇胚胎中进行定量和合成实验。果蝇提供了一个无与伦比的模型 用于测量和干扰活生物体中的基因调节的系统。数学利用了一个图- 基于马尔可夫过程的方法，允许代数计算所需的数量。这让我们 确定能量消耗的功能限制，同时避免将模型拟合到数据或数字上 模拟微分方程我们已经提供了强有力的证据表明，能源消耗远离 热力学平衡对于真核基因的功能特性是必不可少的。本 我们在这个战略的基础上，继续推进这一战略。我们假设果蝇驼背基因的数据 不能用RNAP调节募集的任何热力学平衡模型来解释， 分子细节有多复杂我们相信，我们可以利用“粗粒化”的方法， 线性框架来建立这个非常强大的结果。然后我们将扩展实验方法 并对监管招聘之外的建模，以分析RNAP本身的动态和随机性 mRNA的产生。我们将介绍mRNA的实时成像和TF的光遗传学扰动， 测量基因表达的定量方面，并将扩展我们的代数方法，以适应 这样的数据。我们假设基因调控中的能量消耗对RNAP的调节是必不可少的 动态并产生观察到的hunchback mRNA表达的随机模式。我们的努力将 制定一个新的驼背模型，整合了调节，能量消耗，RNAP动态和 mRNA随机性

项目成果

Transcriptional kinetic synergy: A complex landscape revealed by integrating modeling and synthetic biology.

• DOI: 10.1016/j.cels.2023.02.003
• 发表时间: 2023-04-19
• 期刊: Cell systems
• 影响因子: 9.3

Allosteric conformational ensembles have unlimited capacity for integrating information.

• DOI: 10.7554/elife.65498
• 发表时间: 2021-06-09
• 期刊: eLife
• 影响因子: 7.7
• 作者: Biddle JW;Martinez-Corral R;Wong F;Gunawardena J
• 通讯作者: Gunawardena J

Transcriptional precision and accuracy in development: from measurements to models and mechanisms.

• DOI: 10.1242/dev.146563
• 发表时间: 2017-11-01
• 期刊: Development (Cambridge, England)
• 作者: Bentovim L;Harden TT;DePace AH
• 通讯作者: DePace AH

The linear framework II: using graph theory to analyse the transient regime of Markov processes.

• DOI: 10.3389/fcell.2023.1233808
• 发表时间: 2023
• 期刊: Frontiers in cell and developmental biology
• 影响因子: 5.5

Dissecting the sharp response of a canonical developmental enhancer reveals multiple sources of cooperativity.

剖析典型发育增强剂的敏锐反应揭示了合作性的多种来源。

• DOI: 10.7554/elife.41266
• 发表时间: 2019
• 期刊: eLife
• 影响因子: 7.7
• 作者: Park,Jeehae;Estrada,Javier;Johnson,Gemma;Vincent,BenJ;Ricci-Tam,Chiara;Bragdon,MeghanDj;Shulgina,Yekaterina;Cha,Anna;Wunderlich,Zeba;Gunawardena,Jeremy;DePace,AngelaH
• 通讯作者: DePace,AngelaH