Engineering cell type-specific splicing regulation

工程细胞类型特异性剪接调控

• 批准号: 10633765
• 负责人: Georg Seelig
• 金额: $ 39.57万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-25 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10633765
• 关键词: Algorithm Design; Alternative Splicing; Animals; Biological Assay; Biology; Brain; Cell Line; Cells; Code; Data; Development; Disease; Engineering; Future; Gene Expression; Genes; Genetic; High-Throughput Nucleotide Sequencing; Human Cell Line; Introns; Learning; Libraries; Machine Learning; Maps; Measures; Modeling; Molecular; Mouse Cell Line; Mutation; Neurobiology; Primary Cell Cultures; Production; Protein Isoforms; Proteins; RNA Splicing; RNA-Binding Proteins; Rat Cell Line; Rattus; Reading; Regulation; Regulatory Element; Reporter; Reporter Genes; Research; Role; Slice; Specificity; System; Testing; Tissues; Training; Variant; Work; biological systems; cancer cell; cell type; convolutional neural network; design; disease diagnosis; exon skipping; gene therapy; improved; insight; interest; machine learning model; network architecture; novel; novel strategies; predictive modeling; protein expression; recurrent neural network; side effect; single cell analysis; single-cell RNA sequencing; synthetic biology; therapeutic gene; tool; transcriptome

PROJECT SUMMARY Alternative splicing (AS) is a major driver of protein isoform diversity and is regulated in a highly cell type-specific manner. A better understanding of the cell type-specific splicing code will not only provide novel insights into the role of alternative splicing in disease and development but will also result in novel genetic tools for perturbing and interrogating cell types of interest. Synthetic splicing constructs have been successfully used to target activation of reporter and therapeutic genes to cancer cells carrying mutations in splice factors or to make gene therapies conditional on a small molecular trigger. Existing examples highlight the potential of AS as a programmable control mechanism but do not provide a clear path towards engineering splice regulatory sequences that can be used to target gene expression to any desired cell type or state. Here, we propose to combine synthetic biology with machine learning to generate highly cell type-specific splicing constructs. Building on our earlier work, we will first quantify cell type-specific AS using splicing massively parallel reporter assays (MPRAs). We will focus on exon skipping and intron retention because they are among the most common forms of AS and can be highly cell type-specific. For each type of AS, we will create libraries with hundreds of thousands or even millions of reporters with variation targeted to regions of interest. We will then measure AS for these libraries in a panel of cell lines and cultured primary cells (Specific Aim 1). Next, we will use these data to train machine learning models that can accurately predict AS isoform abundance from reporter gene sequence. We will systematically compare different network architectures and approaches including convolutional and recurrent neural networks. We will then combine models with sequence design approaches previously developed in the lab to generate synthetic sequences with enhanced target cell specificity. We aim to show that we can generate reporter constructs that are specific to any cell type in our panel. We will validate predictions experimentally and use resulting data to iteratively improve model predictions (Specific Aim 2). Finally, we will generalize our approach to an experimental setting that more accurately reflects the diversity and complexity of cell types encountered in multi-cellular biological systems. Specifically, we will perform splicing MPRAs in organotypic developing rat brain slice culture. We will optimize conditions for library delivery to slice culture and we will similarly optimize approaches for reading out splicing MPRAs at the single cell level. We will combine the resulting data with the generative models from Specific Aim 1 to design reporter constructs that precisely target protein expression to cell types of interest (Specific Aim 3). We believe that this work will result in novel genetic tools for biology research and provide a path towards gene therapies with increased specificity and reduced side effects.

项目摘要 选择性剪接（AS）是蛋白质异构体多样性的主要驱动力，并在高度细胞内受到调控。 特定类型的方式。更好地理解细胞类型特异性剪接密码不仅将提供新颖的 深入了解选择性剪接在疾病和发育中的作用，但也将导致新的遗传工具 用于干扰和询问感兴趣的细胞类型。合成剪接构建体已经成功地用于 将报告基因和治疗基因的激活靶向携带剪接因子突变的癌细胞，或 使基因治疗以小分子触发物为条件。现有的例子突出了AS的潜力 但是没有提供通向工程化剪接调节的明确途径 可以用于将基因表达靶向任何所需的细胞类型或状态的序列。在此，我们建议 联合收割机结合合成生物学和机器学习来产生高度细胞类型特异性的剪接构建体。 在我们早期工作的基础上，我们将首先使用剪接大规模平行报告基因量化细胞类型特异性AS 分析（MPRAs）。我们将重点放在外显子跳跃和内含子保留，因为它们是最重要的 AS的常见形式，并且可以是高度细胞类型特异性的。对于每种类型的AS，我们将使用 数十万甚至数百万的报告子，其具有针对感兴趣区域的变化。然后我们将 在一组细胞系和培养的原代细胞中测量这些文库的AS（特异性目的1）。接下来我们就 使用这些数据来训练机器学习模型，这些模型可以准确地预测AS亚型的丰度， 报告基因序列。我们将系统地比较不同的网络架构和方法 包括卷积和递归神经网络。然后，我们将联合收割机模型与序列设计相结合 先前在实验室中开发的用于产生具有增强的靶细胞的合成序列的方法 的特异性我们的目标是表明我们可以在我们的研究中产生对任何细胞类型特异的报告基因构建体。 面板我们将通过实验验证预测，并使用结果数据迭代改进模型 具体目标2（Specific Aim 2）最后，我们将把我们的方法推广到一个实验环境， 准确地反映了在多细胞生物系统中遇到的细胞类型的多样性和复杂性。 具体来说，我们将在器官型发育大鼠脑切片培养中进行剪接MPRA。我们将优化 我们将同样优化用于阅读剪接的方法 单细胞水平的MPRA。我们将联合收割机与Specific Aim的生成模型相结合 1来设计精确地将蛋白质表达靶向至感兴趣的细胞类型的报告构建体（Specific Aim （3）第三章。我们相信，这项工作将为生物学研究带来新的遗传工具，并为人类提供一条通往 基因疗法具有增加的特异性和减少的副作用。