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

绘制替代 RNA 亚型表达遗传决定因素的动力学框架

基本信息

批准号：
10638072
负责人：
Barbara Engelhardt
金额：
$ 77.06万
依托单位：
UNIV OF MASSACHUSETTS MED SCH WORCESTER
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-08-01 至 2027-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10638072
关键词：
Address Affect Alleles Alternative Splicing Biogenesis Calibration Catalogs Cells Chromosome Mapping Complex Computer software DNA Data Dependence Disease Disparate Event Exons Experimental Designs Genes Genetic Genetic Determinism Genetic Transcription Genomics Genotype Goals Human Human Cell Line Individual Instruction Joints Kinetics Measurement Messenger RNA Methods Modeling Molecular Outcome Poly A Polyadenylation Population Probability Process Protein Isoforms Proteins Protocols documentation Quantitative Trait Loci RNA RNA Polymerase II RNA Processing RNA Splicing Regulation Resources Series Site Space Models Specific qualifier value Statistical Methods Statistical Models Time Training Transcription Initiation Translating Uncertainty Validation Variant Work blind cell type disease mechanisms study genetic element genetic variant genome wide association study human disease insight kinetic model spatiotemporal trait transcriptome sequencing user-friendly

项目摘要

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.

R01：绘制替代 RNA 亚型遗传决定因素的动力学框架表达项目概要中心法则的核心是 RNA 的转录，它使得 DNA 的指令能够被 mRNA 的生物发生是一个复杂且高度调控的过程。探究转录和 RNA 加工机器之间的协调，每个机器都包含数百个这些相互作用通常会产生许多可能的替代异构体，例如虽然我们现在可以对 mRNA 的丰度和变异性进行初步分类。尽管跨细胞状态的异构体，我们预测机械之间协调的能力仍然有限。大多数 mRNA 水平目录仅描述稳态。 mRNA 对异构体选择和表达的时空动态调节是盲目的。 mRNA 分子的轨迹和命运可能取决于每个分子的动力学相互作用的效率因此，我们必须在与 mRNA 生物合成相对应的时间尺度上直接追踪 mRNA 通量。制定监管决策是为了估计 mRNA 生物合成每个步骤的速率。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 生物发生步骤的速率仍然具有挑战性，包括转录起始、延伸、剪接和 3' 末端切割的技术和机制特征。混淆了现有 RNA 测序数据的直接动力学测量方法。这些步骤的调整率发现，不同基因和细胞类型的这些比率存在广泛的变异性，但他们估计替代处理决策的速率、识别动力学协调的能力受到限制剪接或 3' 末端切割事件之间的相互作用，并绘制动态变异背后的遗传元件图谱。我们建议通过开发联合实验和统计框架来应对这些挑战描述影响 RNA 加工速率的分子和遗传因素。首先，我们将建立一个统计模型来估计各个位点的 RNA 处理速率，并生成时间分辨短读长新生 RNA 测序数据来训练该模型其次，我们将表征 ki-。使用受约束的替代亚型表达基础的各个事件之间的网络竞争状态空间模型并生成长读长 RNA 测序数据以识别表达的亚型轨迹。第三，我们将估计基因分型人类细胞群体中的 RNA 加工速率，以鉴定定量对于所有三个目标，我们确定了与 RNA 加工速率变异相关的滴定性状位点 (QTL)。严格的验证指标和校准我们的速率预测的不确定性，我们的工作将共同引领。 i）更好地了解分子协调和遗传变异如何调节速率和决策 RNA 处理中的关键点，ii) 用于量化这些速率的通用基因组学框架，以及 iii) 用于解释通过速率扭曲机制起作用的人类疾病相关基因型的 QTL 资源。