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 替代剪接（AS）是蛋白质同工型多样性的主要驱动力，并在高度细胞中进行调节 特定于类型的方式。更好地理解特定于细胞类型的剪接代码不仅会提供新颖的 深入了解替代剪接在疾病和发育中的作用，但也将导致新颖的遗传工具 用于扰动和询问感兴趣的细胞类型。合成剪接结构已成功使用 将报告基因和治疗基因的激活靶向在剪接因子中携带突变的癌细胞或 使基因疗法在小分子触发器上有条件。现有示例突出了AS的潜力 作为一种可编程的控制机制，但不能为工程剪接调节提供清晰的途径 可用于将基因表达靶向任何所需的细胞类型或状态的序列。在这里，我们建议 将合成生物学与机器学习结合在一起，以生成高细胞类型特异性的剪接构建体。 在我们较早的工作的基础上，我们将首先将特定于细胞类型的特定于拼接定量，以大规模平行的记者 测定（MPRA）。我们将专注于外显子跳过和内含子保留，因为它们是最大的 AS和可以是高度细胞类型的常见形式。对于每种类型的AS，我们将使用 数十万甚至数百万的记者针对感兴趣的地区。然后我们会 测量这些文库在一组细胞系和培养的原代细胞中（特定目标1）。接下来，我们会的 使用这些数据来训练可以准确预测同工型丰度的机器学习模型 报告基因序列。我们将系统地比较不同的网络架构和方法 包括卷积和复发性神经网络。然后，我们将模型与序列设计相结合 以前在实验室开发的方法以生成具有增强目标细胞的合成序列 特异性。我们的目的是表明我们可以生成针对我们的任何单元格类型的记者构造 控制板。我们将通过实验验证预测，并将结果数据用于迭代改进模型 预测（特定目标2）。最后，我们将概括我们的方法，以实验环境更多 准确地反映了多细胞生物系统中遇到的细胞类型的多样性和复杂性。 具体而言，我们将在发育中的大鼠脑切片培养物中执行剪接MPRA。我们将优化 图书馆传递到切片文化的条件，我们将类似地优化读取剪接的方法 MPRA在单细胞水平上。我们将将结果数据与特定目的的生成模型相结合 1设计记者构造，将蛋白质表达靶向细胞类型（特定目的） 3）。我们认为，这项工作将为生物学研究提供新颖的遗传工具，并为通往 具有增加特异性和副作用降低的基因疗法。