Exploring the Role of Dynamic Chromatin Occupancy in Transcriptional Regulation

 描述(申请人提供)：虽然基因组的序列本质上是fix的，但它的状态可能在细胞内不断变化。这种变化状态的两个方面是以染色质的形式沿着基因组的无数蛋白质复合体的特殊fic排列，以及每个基因目前的转录本产生速度。一个基本的研究目标是了解这两者之间的关系，特别是转录产生率(TxPR)如何在fl中受到全基因组染色质状态的影响。这项提议的目标是开发能够根据对细胞全基因组染色质状态的了解来预测细胞全基因组转录状态的模型。为了建立这样的模型，需要在不同的时间或在不同的时间或以下同时fi细胞的全基因组染色质状态和转录状态 不同的情况：观察两者如何一起变化，特别是在定向扰动的背景下，可以提供所需的统计杠杆，以建立能够提供因果洞察的预测模型。在这项提议中，将通过监测发芽酵母中染色质和转录在细胞周期中的进展来开发和验证模型，细胞周期是控制细胞增殖的一系列高度调控的事件，其异常可能导致癌症。由于这一挑战的复杂性，酵母因其紧凑的基因组和遗传易操纵性而被用作起点，但我们预计我们的方法也将适用于更复杂的生物，包括人类。一个主要的障碍是，最先进的染色质免疫沉淀(ChIP)方法不仅需要对每个时间点和实验条件进行单独的实验，而且还需要对沿基因组结合的100-1000种类型的蛋白质进行单独的实验。为了克服这一障碍，这项建议描述了一种新的方法，用于EFfi在单碱基分辨率下使用核酸酶消化的染色质来学习定量全基因组染色质占位蛋白fiLES(GCOPS)。该方法能够全面确定转录因子、核小体和其他DNA结合因子在整个基因组中的定量染色质占有率，而不需要对每个因子进行单独的实验。结合TxPR的高分辨率测量生产GCOP将使开发复杂的、机械上可解释的模型成为可能，这些模型预测转录产物的生成速率随染色质状态的变化而变化。这项拟议的研究将导致：(I)产生适用于任何具有已测序基因组的生物的定量fi的新方法；(Ii)来自萌芽酵母的GCOP随着它们在细胞周期中的进展，首次揭示在任何生物体中全基因组染色质的占有率在细胞周期过程中是如何变化的；(Iii)描述全基因组染色质的变化如何与TxPR的变化相关联，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习的模型，可以根据染色质的占有率预测TxPR，提供了对细胞周期调节的转录程序如何在fl中因染色质状态变化而受到影响的机械性见解。

 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.