Exploring the Role of Dynamic Chromatin Occupancy in Transcriptional Regulation

探索动态染色质占据在转录调控中的作用

基本信息

批准号：
9082781
负责人：
Alexander J Hartemink
金额：
$ 39.25万
依托单位：
DUKE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-04-01 至 2020-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9082781
关键词：
Binding Proteins Biological Assay Biological Process Cell Cycle Cell Cycle Progression Cell Proliferation Cell division Cell physiology Cells Chromatin Collecting Cell Complex Computer Simulation Computing Methodologies DNA Binding DNase I hypersensitive sites sequencing Data Deoxyribonucleases Development Diabetes Mellitus Elements Etiology Eukaryota Event Exhibits Experimental Models Foundations Genes Genetic Genetic Transcription Genome Genomics Goals High-Throughput Nucleotide Sequencing Human Knowledge Lead Learning Link Location Malignant Neoplasms Measurement Measures Methods Modeling Monitor Non-linear Models Nucleosomes Nucleotides Organism Positioning Attribute Process Production Proteins Regulatory Element Research Resolution Role Saccharomyces cerevisiae Saccharomycetales Sampling Series Signal Transduction Statistical Methods Thermodynamics Time Transcript Transcriptional Regulation Variant Yeasts base cell growth chromatin immunoprecipitation chromatin remodeling clinically relevant cost effective developmental disease fundamental research genome sequencing genome-wide innovation insight learning strategy mutant novel nuclease predictive modeling programs promoter protein complex public health relevance research study response transcription factor whole genome

Binding Proteins Biological Assay Biological Process Cell Cycle Cell Cycle Progression Cell Proliferation Cell division Cell physiology Cells Chromatin Collecting Cell Complex Computer Simulation Computing Methodologies DNA Binding DNase I hypersensitive sites sequencing Data Deoxyribonucleases Development Diabetes Mellitus Elements Etiology Eukaryota Event Exhibits Experimental Models Foundations Genes Genetic Genetic Transcription Genome Genomics Goals High-Throughput Nucleotide Sequencing Human Knowledge Lead Learning Link Location Malignant Neoplasms Measurement Measures Methods Modeling Monitor Non-linear Models Nucleosomes Nucleotides Organism Positioning Attribute Process Production Proteins Regulatory Element Research Resolution Role Saccharomyces cerevisiae Saccharomycetales Sampling Series Signal Transduction Statistical Methods Thermodynamics Time Transcript Transcriptional Regulation Variant Yeasts base cell growth chromatin immunoprecipitation chromatin remodeling clinically relevant cost effective developmental disease fundamental research genome sequencing genome-wide innovation insight learning strategy mutant novel nuclease predictive modeling programs promoter protein complex public health relevance research study response transcription factor whole genome

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项目摘要

DESCRIPTION (provided by applicant): Though the genome's sequence is essentially ﬁxed, its state may be constantly changing inside a cell. Two aspects of this changing state are the speciﬁc arrangement of myriad protein complexes along the genome in the form of chromatin, and the current rate of transcript production for each gene. A fundamental research objective is to understand the relationship between these two, and in particular, how transcript production rates (TxPRs) are inﬂuenced by genome-wide chromatin state. The goal of this proposal is to develop models that are capable of predicting a cell's genome-wide transcription state from knowledge of its genome-wide chromatin state. To build such models requires simultaneously proﬁling a cell's genome-wide chromatin state and transcription state at different times or under different conditions: Observing how the two change together, particularly in the context of directed perturbation, provides the statistical leverage needed to build predictive models that can provide causal insight. In this proposal, models will be developed and validated by monitoring chromatin and transcription in budding yeast as they progress through the cell cycle, a temporal series of highly regulated events controlling cell proliferation, aberrations of which can lead to cancer. Owing to the complexity of this challenge, yeast is used as a starting point because of its compact genome and genetic tractability, but we anticipate our methods will also be applicable in more complex organisms, including human. One major obstacle is that state-of-the-art chromatin immunoprecipitation (ChIP) methods for assaying chromatin state require a separate experiment not only for each time point and experimental condition, but also for each of the 100s-1000s of types of proteins binding along the genome. To overcome this hurdle, this proposal describes a novel method for efﬁciently learning quantitative genome-wide chromatin occupancy proﬁles (GCOPs) using nuclease-digested chromatin at single-base resolution. The proposed method enables the comprehensive determination of quantitative chromatin occupancy of transcription factors, nucleosomes, and other DNA-binding factors across the entire genome without requiring a separate experiment for each. Producing GCOPs in conjunction with high- resolution measurements of TxPRs will allow the development of sophisticated, mechanistically interpretable models that predict transcript production rates as a function of chromatin state. The proposed research will result in (i) efﬁcient new methods for producing quantitative GCOPs that will be applicable in any organism with a sequenced genome; (ii) GCOPs from budding yeast as they progress through the cell cycle, revealing for the ﬁrst time in any organism how genome-wide chromatin occupancy changes over the course of the cell cycle; (iii) characterization of how genome-wide chromatin changes are linked to changes in TxPRs, not only in wild-type yeast, but also under a wide range of genetic and genomic perturbations; and (iv) models learned from all these data that can predict TxPRs on the basis of chromatin occupancy, providing mechanistic insight into how the cell-cycle-regulated transcription program is inﬂuenced by its changing chromatin state.

描述（由适用提供）：尽管基因组的序列基本上是固定的，但其状态可能会在单元格内部不断变化。这种不断变化的状态的两个方面是以染色质形式沿基因组的多种蛋白质复合物的特定排列，以及每个基因的当前转录速率。一个基本的研究目标是了解这两者之间的关系，尤其是转录本生产率（TXPRS）如何受到全基因组染色质状态影响的。该建议的目的是开发能够从了解其全基因组染色质状态的知识中预测细胞全基因组转录状态的模型。要构建这样的模型，只需在不同时间或之下填写细胞的全基因组染色质状态和转录状态不同的条件：观察两者如何一起改变，尤其是在定向扰动的背景下，提供了建立可以提供因果见解的预测模型所需的统计杠杆作用。在此提案中，将通过监测染色质和在萌芽酵母中的转录过程中的转录来开发和验证模型，这些酵母在整个细胞周期中进行，这是一系列临时控制细胞增殖的高度调节事件，其畸变可能导致癌症。由于这一挑战的复杂性，酵母由于其紧凑的基因组和遗传性易干性而被用作起点，但我们预计我们的方法也将适用于包括人类在内的更复杂的组织。一个主要的障碍是，用于测定染色质状态的最先进的染色质免疫沉淀（CHIP）方法不仅需要每个时间点和实验条件，而且还需要一个单独的实验，而且还需要沿基因组结合的100s-1000s类型的蛋白质类型中的每种实验。为了克服这一障碍，该提案描述了一种新的方法，该方法是使用单基碱分辨率下使用核酸酶消化的染色质的核酸酶消化的染色质的，均为全基因组染色质占用率（GCOPS）。提出的方法可以全面确定整个基因组中转录因子，核小体和其他DNA结合因子的定量染色质占用率，而无需为每个基因组进行单独的实验。与TXPR的高分辨率测量结合产生GCOP将允许开发复杂的，机械解释的模型，这些模型可以预测转录生产率是染色质状态的函数。拟议的研究将导致（i）有效的新方法，用于产生定量GCOP，这些方法将适用于任何具有测序基因组的生物体；（ii）从萌芽酵母进行整个细胞周期中进行的GCOP，在任何组织中的第一次揭示了全基因组染色质占用在整个细胞周期过程中如何变化；（iii）表征全基因组染色质的变化与TXPR的变化如何相关，不仅在野生型酵母中，而且在广泛的遗传和基因组扰动下；（iv）从所有这些数据中学到的模型可以根据染色质占用来预测TXPR，从而提供了机械洞察，以了解细胞周期调节的转录程序如何受到其染色质状态的影响。