Understanding the mechanisms underlying RNA polymerase II-mediated transcription and exploring their influence on transcriptional dynamics

该项目旨在了解影响RNA聚合酶II （pol II）介导转录的机制，它们之间的相互关系以及它们如何影响转录动力学。主要的过程将是聚合酶暂停，在此过程中，pol II的延伸率在某些位点大大降低。暂停的本质将通过使用各种数据类型从多个角度攻击这个问题来研究。最近开发的时变PRO seq （TV-PRO seq）允许全基因组检测暂停位点和暂停时间的测量。TV-PRO序列数据将与其他数据类型链接，包括组蛋白修饰分布的ChIP序列数据、pol II分布的NET序列数据、染色体可及性的ATAC序列数据、mRNA水平和变异性的单细胞RNA序列数据以及mRNA降解率的SLAM序列数据。对综合数据集进行统计分析，以发现暂停位点和其他基因组特征之间的相关性，将提供机制见解。转录的数学模型将被构建，以解释各种机制和过程，包括pol II暂停，起始，从基因的3'端重新启动和自发分离。这些模型将被设计为提供可以直接与上述数据进行比较的读数，例如聚合酶和沿基因的新生mRNA分布，以及成熟mRNA计数分布。这将允许从理论上探索不同机制之间的相互作用，以及它们如何促进基因表达的随机动力学。模拟/分析结果将适用于数据，使用贝叶斯推理，例如，优化参数值和模型结构。关于转录机制/动力学的结论将通过实验验证来证实。关于转录随机性的见解可能在合成生物学中有应用。

The project aims to understand the mechanisms influencing RNA polymerase II (pol II) -mediated transcription, their relationships with each other and how they affect transcriptional dynamics. The primary process of interest will be polymerase pausing, in which the elongation rate of pol II is greatly reduced at certain sites. The nature of pausing will be investigated by attacking the problem from multiple angles using a variety of data types. The recently developed time-variant PRO seq (TV-PRO seq) allows for genome-wide detection of pausing sites and measurement of pausing times. TV-PRO seq data will be linked with other data types, including ChIP seq data on distributions of histone modifications, NET seq data on pol II distributions, ATAC seq data on chromosome accessibility, single cell RNA seq data on mRNA levels and variability and SLAM seq data on mRNA degradation rates. Statistical analysis of integrated data sets to find correlations between pausing sites and other genomic features will provide mechanistic insights. Mathematical models of transcription will be constructed that account for various mechanisms and processes of interest, including pol II pausing, initiation, reinitation from the gene's 3' terminus and spontaneous disengaging. The models will be designed to provide read-outs that can be directly compared with the aforementioned data, such as polymerase and nascent mRNA distributions along the gene, as well as mature mRNA count distributions. This will allow theoretical exploration of the interplay between different mechanisms and how they contribute to the stochastic dynamics of gene expression. Simulation/analytical results will be fitted to the data, using Bayesian inference to, for example, optimise parameter values and model structures. Conclusions made regarding transcriptional mechanisms/dynamics will be confirmed with experimental verification. Insights regarding stochasticity in transcription may have applications in synthetic biology.