Measuring allele and isoform-specific RBP binding to improve predictive models of RNA splicing

测量等位基因和亚型特异性 RBP 结合以改进 RNA 剪接的预测模型

基本信息

  • 批准号:
    10377311
  • 负责人:
  • 金额:
    $ 6.76万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2021
  • 资助国家:
    美国
  • 起止时间:
    2021-04-01 至 2023-03-31
  • 项目状态:
    已结题

项目摘要

PROJECT SUMMARY Alternative splicing (AS) is a fundamental cellular process that regulates 95% of multi-exon genes to diversify protein output and define cell-type specific functions. Both constitutive splicing and AS are controlled by combinations of cis-acting pre-messenger RNA sequences (pre-mRNA) and trans-acting RNA-binding proteins (RBPs). Therefore, defects in splicing regulatory RNA sequence or RBPs can be highly disruptive to basic cellular activities and often lead to disease, especially neurological and muscular disorders and cancer. While the constitutive splicing code is well established, the AS code is more complicated and, thus, poorly understood. This proposal integrates multiple cutting-edge approaches to take an RBP-centric view of AS to study both cis genetic variants and trans RBP expression effects on RBP binding and splicing outcome. While thousands of non-coding genetic variants are associated with splicing variation, and are thus termed putative splicing quantitative trait loci (sQTLs), the causal variants and their molecular effects, such as RBP binding, are largely unclear. Aim 1 will address this gap by integrating RBP-focused experiments, allele-specific genomics, and state- of-the-art machine learning predictive models to characterize an important category of functional, cis non-coding variants that alter RBP binding. Importantly, I will take a unique approach to include these allele-specific RBP binding data as additional training data for our Convolutional Neural Net model. Model output is expected to much more accurately predict functional RBP binding effects of even a single nucleotide change in sequence, enabling improved interpretation of sQTLs. In addition to genetic variant effects, changes in RBP expression can have amplified downstream effects on RNA splicing. Interestingly, ~86% of RBP genes can be expressed as more than one splice isoform, but most studies to date have ignored RBP isoform-specific abundance and function. Aim 2 will provide foundational experiments to understand differential RBP isoform effects by using a novel approach to knockdown RBP isoforms by targeting Cas13 to unique exon junctions. Data from downstream assays that assess changes in RBP binding, splicing, and RNA localization will be integrated to construct the most comprehensive RBP regulatory networks to date. Results from both aims are essential to mechanistically link RNA sequence and RBP binding to splicing outcome and, ultimately, to phenotype and disease. My long-term goal is to become a principal investigator, where I will continue to leverage molecular biology, machine learning, and statistical genetics to answer unique questions about RNA-mediated associations between non-coding sequence and cellular and disease phenotype. The research and training plans proposed here are strategically tailored to provide ample opportunities to learn and apply machine learning and statistical genetics methods that complement my former PhD training in molecular biology and genomics. My sponsor, co- sponsor, and collaborators at the NYGC are committed to providing the scientific expertise, computational training, and career development mentoring to ensure the successful achievement of my goals.
项目概要 选择性剪接 (AS) 是一种基本的细胞过程,可调节 95% 的多外显子基因以使其多样化 蛋白质输出并定义细胞类型的特定功能。本构剪接和 AS 均由 顺式作用前信使 RNA 序列 (pre-mRNA) 和反式作用 RNA 结合蛋白的组合 (RBP)。因此,调控RNA序列或RBP的剪接缺陷可能对基本细胞功能造成高度破坏。 活动并常常导致疾病,特别是神经和肌肉疾病以及癌症。虽然 本构剪接代码已经很成熟,但 AS 代码更复杂,因此理解甚少。 该提案整合了多种前沿方法,以 RBP 为中心的 AS 观点来研究顺式和顺式 遗传变异和反式 RBP 表达对 RBP 结合和剪接结果的影响。虽然成千上万 非编码遗传变异与剪接变异相关,因此被称为推定剪接 数量性状位点 (sQTL)、因果变异及其分子效应(例如 RBP 结合)很大程度上取决于 不清楚。目标 1 将通过整合以 RBP 为中心的实验、等位基因特异性基因组学和状态来解决这一差距。 最先进的机器学习预测模型来表征功能性、顺式非编码的重要类别 改变 RBP 结合的变体。重要的是,我将采取独特的方法来包含这些等位基因特异性 RBP 绑定数据作为我们的卷积神经网络模型的附加训练数据。模型输出预计为 更准确地预测即使是序列中单个核苷酸变化的功能性 RBP 结合效应, 改进对 sQTL 的解释。除了遗传变异效应外,RBP 表达的变化还可以 对 RNA 剪接具有放大的下游效应。有趣的是,约 86% 的 RBP 基因可以表达为 不止一种剪接异构体,但迄今为止大多数研究都忽略了 RBP 异构体特异性丰度和 功能。目标 2 将提供基础实验,通过使用 通过将 Cas13 靶向独特的外显子连接来敲低 RBP 同工型的新方法。来自下游的数据 评估 RBP 结合、剪接和 RNA 定位变化的测定将被整合以构建 迄今为止最全面的 RBP 监管网络。这两个目标的结果对于机械地来说都是至关重要的 将 RNA 序列和 RBP 结合与剪接结果联系起来,并最终与表型和疾病联系起来。 我的长期目标是成为一名首席研究员,我将继续利用分子生物学, 机器学习和统计遗传学来回答有关 RNA 介导的关联的独特问题 非编码序列与细胞和疾病表型之间的关系。拟议的研究和培训计划 这里经过战略性定制,为学习和应用机器学习和统计提供充足的机会 遗传学方法补充了我之前在分子生物学和基因组学方面的博士学位训练。我的赞助商、合作者 NYGC 的赞助商和合作者致力于提供科学专业知识、计算 培训和职业发展指导,以确保我成功实现目标。

项目成果

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Megan Schertzer的其他文献

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