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

• 批准号: 10377311
• 负责人: Megan Schertzer
• 金额: $ 6.76万
• 依托单位: NEW YORK GENOME CENTER
• 依托单位国家: 美国
• 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
• 来源: https://reporter.nih.gov/project-details/10377311
• 关键词: Achievement; Address; Affect; Alleles; Alternative Splicing; Automobile Driving; Binding; Binding Proteins; Bioinformatics; Biological Assay; Biology; Categories; Cell Line; Cell physiology; Cells; Code; Complement; Complex; Computer Models; DNA sequencing; Data; Defect; Development; Disease; Doctor of Philosophy; Ensure; Event; Exons; Fellowship; Fibroblasts; Foundations; Fractionation; Future; Gene Expression Regulation; Gene Proteins; Genes; Genetic; Genetic Transcription; Genetic study; Genomic Segment; Genomics; Genotype-Tissue Expression Project; Goals; High-Throughput Nucleotide Sequencing; Human; Introns; Knowledge; Lead; Learning; Link; Machine Learning; Malignant Neoplasms; Measures; Mediating; Mentors; Messenger RNA; Methods; Modeling; Molecular; Molecular Biology; Myopathy; Nucleotides; Outcome; Output; Pattern; Phenotype; Play; Principal Investigator; Process; Protein Isoforms; Protein Splicing; Proteins; Quantitative Trait Loci; RNA; RNA Binding; RNA Processing; RNA Sequences; RNA Splicing; RNA metabolism; RNA-Binding Proteins; Regulation; Research Training; Ribonucleic Acid Regulatory Sequences; Role; Site; System; Tissues; Training; Transcript; Translations; Untranslated RNA; Variant; Work; career development; causal variant; cell type; convolutional neural network; differential expression; disease phenotype; experimental study; genetic regulatory protein; genetic variant; improved; knock-down; mRNA Precursor; machine learning prediction; molecular phenotype; nervous system disorder; novel strategies; nuclease; predictive modeling; protein expression; protein function; recruit; skills; statistical and machine learning; trait; transcriptome sequencing

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.

项目摘要 选择性剪接（alternative splicing，AS）是一个基本的细胞过程，它调控着95%的多外显子基因的多样性 蛋白质输出并定义细胞类型特异性功能。组成性剪接和AS都由 顺式作用前信使RNA序列（前mRNA）和反式作用RNA结合蛋白的组合 （限制性商业惯例）。因此，剪接调节RNA序列或RBP的缺陷可能对基本细胞具有高度破坏性， 这些活动往往导致疾病，特别是神经和肌肉疾病和癌症。而 虽然组成型剪接密码已被很好地建立，但AS密码更复杂，因此了解得很少。 该建议集成了多种前沿方法，以基于RBP的AS为中心，研究了 遗传变异和反式RBP表达对RBP结合和剪接结果的影响。而成千上万的 非编码遗传变异与剪接变异相关，因此称为推定剪接 数量性状基因座（sQTL），因果变异及其分子效应，如RBP结合，主要是 不清楚Aim 1将通过整合以RBP为重点的实验，等位基因特异性基因组学和状态- 最先进的机器学习预测模型来表征功能性的重要类别，CIS非编码 改变RBP结合的变体。重要的是，我将采取一种独特的方法，包括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额外训练数据。预计模型输出将 更准确地预测功能性RBP结合效应，即使是序列中的单个核苷酸变化， 使得能够改进sQTL的解释。除了遗传变异的影响，RBP表达的变化可以 对RNA剪接有放大的下游效应。有趣的是，约86%的RBP基因可以表达为 一个以上的剪接异构体，但迄今为止的大多数研究都忽略了RBP异构体特异性丰度， 功能目的2将提供基础实验，以了解不同的RBP亚型的影响，通过使用 通过将Cas 13靶向至独特的外显子连接来敲低RBP同种型的新方法。下游数据 评估RBP结合、剪接和RNA定位变化的测定将被整合以构建 迄今为止最全面的限制性商业惯例监管网络。从这两个目标的结果是必不可少的， 将RNA序列和RBP结合与剪接结果，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，在那里我将继续利用分子生物学， 机器学习和统计遗传学来回答关于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 这里是战略性定制，以提供充足的机会，学习和应用机器学习和统计 遗传学方法，补充了我以前在分子生物学和基因组学方面的博士培训。我的担保人，共同- 赞助商和合作者在NYGC致力于提供科学的专业知识，计算 培训和职业发展指导，以确保成功实现我的目标。