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）介导的转录的机制，它们之间的关系以及它们如何影响转录动力学。感兴趣的主要过程将是聚合酶暂停，其中pol II的延伸速率在某些位点大大降低。暂停的性质将通过使用各种数据类型从多个角度攻击问题来研究。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测量暂停时间。TV-PRO seq数据将与其他数据类型相关联，包括关于组蛋白修饰分布的ChIP seq数据、关于pol II分布的NET seq数据、关于染色体可及性的ATAC seq数据、关于mRNA水平和变异性的单细胞RNA seq数据以及关于mRNA降解速率的SLAM seq数据。综合数据集的统计分析，以找到暂停网站和其他基因组特征之间的相关性将提供机械的见解。将构建转录的数学模型，其解释各种感兴趣的机制和过程，包括pol II暂停、起始、从基因的3'末端的再诱导和自发脱离。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，例如聚合酶和新生mRNA沿着基因的分布，以及成熟mRNA计数分布。这将允许理论探索不同机制之间的相互作用，以及它们如何有助于基因表达的随机动力学。模拟/分析结果将与数据拟合，使用贝叶斯推断，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。关于转录的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.