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

• 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
• 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

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同工型遗传决定剂的动力学框架 表达 项目摘要 中央教条的核心是RNA的转录，这使DNA的说明能够为 翻译成蛋白质信息。 mRNA的生物发生是一个复杂且高度调节的过程， 转录和RNA处理机械之间的协调 调节性RNA和蛋白质。这些相互作用通常会导致许多可能的替代同工型 从单个基因中压制。虽然我们现在可以广泛分类mRNA的抽象和变异性 各个细胞状态的同工型，我们的能力仍然有限，可以预测机甲之间的协调 产生细胞多样性的繁殖步骤。大多数mRNA级别的目录仅剖面稳态 mRNA并视而不见时空动力学预防同工型选择和表达。然而， mRNA分子的轨迹和命运可能取决于每个动力学相互作用的效率 mRNA生物发生的步骤。那就是我们必须直接跟踪与时间标准的mRNA通量 为了估计mRNA生物发生的每个步骤的速率，正在做出调节决策。 尽管在过去的十年中，量化稳态RNA水平的高通量方法已经成熟，但 量化新生的RNA并推断核心mRNA生物发生步骤的速率仍然是挑战 转录启动，伸长，剪接和3'端裂解。技术和机械特征 来自新生RNA测序数据的直接动力学测量。现有的方法 这些步骤的率发现了基因和细胞类型的这些速率的广泛差异，但具有 他们估计替代处理决策率的能力有限，确定动力学坐标 - 剪接或3'结束裂解事件之间的影响，以及构图基于动力学变异性的遗传元素。 我们建议通过开发共同的实验和统计框架来应对这些挑战 表征影响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，以估计各个站点的RNA处理率，并生成 时间分辨的短阅读新生RNA序列数据以训练该模型。其次，我们将描述ki- 使用受约束的单个事件之间的替代同工型表达的单个事件之间的网络竞争 状态空间模型并生成长阅读的RNA测序数据，以识别表达的同工型轨迹。 第三，我们将估算一个基因分型人类细胞中的RNA处理速率，以鉴定 与RNA处理速率变异性相关的代数性状位置（QTL）。对于所有三个目标，我们都会确定 严格的验证指标并校准我们的速率预测的不确定性。在一起，我们的工作将领导 i）更好地理解分子协调和遗传变异的调节率和决定性 RNA处理中的sion点，ii）量化这些速率的可推广基因组学框架，iii）a QTL资源解释通过延伸率延伸机制作用的与人类疾病相关的基因型。