A kinetic framework to map the genetic determinants of alternative RNA isoform expression

R01：绘制替代RNA异构体遗传决定因素的动力学框架 表达式 项目摘要 中心教条的核心是RNA的转录，这使得来自DNA的指令能够 翻译成蛋白质信息。信使核糖核酸的生物发生是一个复杂且高度调控的过程。 查询转录和RNA处理机器之间的协调，每个机器包括数百个 调控的RNA和蛋白质。这些相互作用经常导致许多可能的替代亚型，例如- 来自单个基因的压力。虽然我们现在可以广泛地分类信使核糖核酸的丰度和变异性 跨细胞状态的异构体，我们预测机械之间的协调能力仍然有限- 导致细胞多样性的反常步骤。大多数信使核糖核酸水平的目录只描述了稳态 M RNA，对调控异构体选择和表达的时空动力学视而不见。然而， 一个信使核糖核酸分子的轨迹和命运可能取决于每一个分子的动力学相互作用的效率。 信使核糖核酸生物发生的步骤。因此，我们必须在与时间尺度相称的时间尺度上直接追踪mrna的通量。 为估计信使核糖核酸生物发生的每一步的速率而做出的监管决定。 虽然量化稳态RNA水平的高通量方法在过去十年中已经成熟， 量化新生RNA并推断核心mRNA生物发生步骤的比率仍然是具有挑战性的，包括 转录起始、延伸、剪接和3‘端切割。技术和机械两方面的特点 来自新生RNA测序数据的混淆的直接动力学测量。现有的对全的方法- 这些步骤的滴定率发现这些比率在不同基因和细胞类型之间存在广泛的差异，但 在估计替代加工决策的速度、确定动力学坐标-- 剪接或3‘端切割事件之间的相互作用，以及作为动力学变异性基础的遗传元件的图谱。 我们建议通过制定一个联合实验和统计框架来应对这些挑战 描述影响RNA处理速度的分子和遗传因素。 首先，我们将建立一个统计模型来估计各个站点的RNA处理速度，并生成 时间分辨的短读新生RNA测序数据来训练这个模型。第二，我们将描述Ki- 单个事件之间的磁性竞争，这是使用受约束的 状态空间模型和生成长读RNA测序数据以识别表达的异构体轨迹。 第三，我们将估计一组基因分型的人类细胞中的RNA加工率，以确定全基因组。 与RNA加工率变异性相关的标题性状基因座(QTL)。对于这三个目标，我们确定 严格的验证指标，并校准我们的速率预测中的不确定性。共同努力，我们的工作将引领 对I)更好地理解分子配位和遗传变异如何调节速率和DECI- Sion在RNA加工中的要点，ii)量化这些速率的通用基因组学框架，以及iii)a 用于解释通过速率扭曲机制起作用的人类疾病相关基因的QTL资源。

R01: A kinetic framework to map the genetic determinants of alternative RNA isoform expression Project Summary At the core of the central dogma is the transcription of RNA, which enables instructions from DNA to be translated into protein messages. The biogenesis of mRNA is a complex and highly regulated process, re- quiring coordination between transcriptional and RNA processing machinery that each comprise hundreds of regulatory RNAs and proteins. These interactions often result in many possible alternative isoforms ex- pressed from a single gene. While we can now extensively catalog the abundance and variability of mRNA isoforms across cellular states, we are still limited in our abilities to predict coordination between the mech- anistic steps that give rise to cellular diversity. Most catalogs of mRNA levels only profile steady-state mRNA and are blind to the spatiotemporal dynamics regulating isoform choice and expression. However, the trajectory and fate of an mRNA molecule likely depends on the efficiency of kinetic interactions at each step of mRNA biogenesis. Thus, we must directly track mRNA fluxes on timescales commensurate with the regulatory decisions being made to estimate the rates of each step of mRNA biogenesis. While high-throughput approaches to quantify steady-state RNA levels have matured in the past decade, it is still challenging to quantify nascent RNA and infer the rates of core mRNA biogenesis steps including transcription initiation, elongation, splicing, and 3’ end cleavage. Both technical and mechanistic features confound direct kinetic measurements from nascent RNA sequencing data. Existing approaches to quan- tify rates of these steps have found extensive variability in these rates across genes and cell types, but have been limited in their ability to estimate rates of alternative processing decisions, identify kinetic coordina- tion between splicing or 3’ end cleavage events, and map genetic elements that underlie kinetic variability. We propose to address these challenges by developing a joint experimental and statistical framework to characterize the molecular and genetic factors affecting RNA processing rates. First, we will build a statistical model to estimate RNA processing rates at individual sites, and generate time-resolved short-read nascent RNA-sequencing data to train this model. Second, we will characterize ki- netic competition between individual events that underlies alternative isoform expression using a constrained state space model and generate long-read RNA sequencing data to identify expressed isoform trajectories. Third, we will estimate RNA processing rates in a population of genotyped human cells to identify quan- titative trait loci (QTL) associated with variability in RNA processing rates. For all three aims, we identify rigorous validation metrics and calibrate the uncertainty in our rate predictions. Together, our work will lead to i) a better understanding of how molecular coordination and genetic variants regulate the rates and deci- sion points in RNA processing, ii) a generalizable genomics framework for quantifying these rates, and iii) a QTL resource to interpret human disease-associated genotypes acting through rate-distorting mechanisms.