Exploring the Role of Dynamic Chromatin Occupancy in Transcriptional Regulation

 描述（由申请人提供）：虽然基因组的序列基本上是固定的，但其状态可能在细胞内不断变化。这种变化状态的两个方面是无数蛋白质复合物沿着基因组以染色质形式的特殊排列，以及每个基因的转录产物的当前速率。一个基本的研究目标是了解这两者之间的关系，特别是，转录本生产率（TxPR）是如何在全基因组染色质状态的影响。该提案的目标是开发能够从其全基因组染色质状态的知识预测细胞的全基因组转录状态的模型。为了建立这样的模型，需要同时分析细胞在不同时间或更短时间内的全基因组染色质状态和转录状态。 不同的条件：观察两者如何一起变化，特别是在定向扰动的背景下，提供了建立预测模型所需的统计杠杆，可以提供因果洞察力。在这项提议中，将通过监测芽殖酵母中的染色质和转录来开发和验证模型，因为它们在细胞周期中进展，这是一系列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，酵母因其紧凑的基因组和遗传易处理性而被用作起点，但我们预计我们的方法也将适用于更复杂的生物体，包括人类。一个主要的障碍是，用于测定染色质状态的现有技术的染色质免疫沉淀（ChIP）方法不仅需要针对每个时间点和实验条件进行单独的实验，而且还需要针对沿着基因组沿着结合的100 - 1000种类型的蛋白质中的每一种进行单独的实验。为了克服这一障碍，该提案描述了一种使用单碱基分辨率的核酸酶消化染色质有效学习定量全基因组染色质占用谱（GCOP）的新方法。所提出的方法能够全面测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的高分辨率测量产生GCOP将允许开发复杂的、机械上可解释的模型，该模型预测作为染色质状态的函数的转录物产生速率。拟议的研究将导致（i）有效的新方法，用于生产定量GCOP，适用于任何具有测序基因组的生物体;（ii）来自芽殖酵母的GCOP，因为它们在细胞周期中的进展，首次揭示了任何生物体的全基因组染色质占有率如何在细胞周期过程中变化;（iii）不仅在野生型酵母中，而且在广泛的遗传和基因组扰动下，全基因组染色质变化如何与TxPR的变化相关联的表征;以及（iv）从所有这些数据中学习的模型，其可以基于染色质占有率预测TxPR，从而提供对细胞周期调节的转录程序如何受到其改变的染色质状态的影响的机理性洞察。

 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.