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中文摘要
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摘要 研究转录异构体在健康和疾病中的细胞功能的主要挑战之一是缺乏 有多种方法可以特异而有效地下调它们的表达。RNA靶向RNA引导的VI型 CRISPR/Cas13系统是最近开发的一种在哺乳动物细胞中敲除转录本的工具。他们的 核糖核酸酶活性是通过结合与单链RNA互补的CRISPR RNA指南(GRNA)来激活的 目标。到目前为止测试的CRISPR/Cas13系统已被证明实现了高度特异性的击倒 内源转录本,具有最小的非靶标影响,表现优于目前的方法,如RNAi。 虽然CRISPR/Cas13被证明在针对前-mRNA时有效,但在转录组范围内 通过靶向成熟mRNA中的独特连接来敲除转录异构体的策略 分子还没有被探索过。选择最佳的gRNA序列是成功和特异RNA的关键 由Cas13介导的击倒。我们实验室的初步数据显示,使用gRNA靶向序列 CRISPR/Cas13d(最小的Cas13效应子)在成熟mRNA分子中跨越外显子-外显子连接 有效地将转录表达减少高达80%。此外,靶向异构体特定的连接允许它们的 在不影响非靶向亚型的情况下进行个体击倒。这些结果表明,不存在立体结构。 在成熟的信使核糖核酸分子中靶向连接的限制。肿瘤和癌细胞的转录组分析 LINES全面描述了可供选择的转录体亚型的表达水平和特性。在……里面 与此同时,多项研究表明,剪接失调是癌症的一个标志。因果关系 转录异构体表达与肿瘤相关表型之间的关系尚未得到深入研究。这里, 我们将把CRISPR/Cas13系统的适用性扩大到对转录异构体的系统研究 通过1)将大规模实验数据与机器学习方法相结合来定义规则 靶向特定转录连接时的gRNA设计2)优化管道及计算分析 在正向转录池筛选中使用CRISPR/Cas13系统询问细胞所需 转录异构体的功能。通过扩大CRISPR/CAS13系统的应用,我们的管道将 提供询问转录异构体功能所需的分子工具和计算分析 稳健、不偏不倚、高度可扩展的态度。我们期望我们的CRISPR/Cas13方法能够 克服当前方法在鉴定细胞特异性异构体表达和/或比率方面的局限性 潜在的肿瘤发生和耐药性。最后,可以进一步调整CRISPR/CAS13办法,以 作为一种基于RNA的治疗方法,在体内进行转录异构体的靶向治疗。
英文摘要
Abstract One of the major challenges in characterizing transcript isoform cellular function in health and disease is the lack of methods to specifically and efficiently downregulate their expression. RNA-targeting RNA-guided type VI CRISPR/Cas13 systems constitute a recently developed tool to knockdown transcripts in mammalian cells. Their RNase activity is activated by binding of a CRISPR RNA guide (gRNA) complementary to a single-stranded RNA target. The CRISPR/Cas13 systems tested to date have been shown to achieve highly specific knockdown of endogenous transcripts, with minimal off-target effects, outperforming current methodologies such as RNAi. While CRISPR/Cas13 has been shown to work efficiently when targeted to the pre-mRNA, transcriptome-wide strategies that would allow knockdown of transcript isoforms by targeting unique junctions in the mature mRNA molecule have not been explored. Selecting the best gRNA sequences is crucial for successful and specific RNA knockdown mediated by Cas13. Preliminary data from our lab shows that using gRNAs targeting sequences spanning exon-exon junctions in mature mRNA molecules with CRISPR/Cas13d (the smallest Cas13 effector) efficiently reduces transcript expression up to 80%. Moreover, targeting isoform-specific junctions allows for their individual knockdown without affecting non-targeted isoforms. These results suggest that there is no steric constraint in targeting junctions in mature mRNA molecules. Transcriptome analyses of tumors and cancer cell lines have comprehensively described the expression levels and identities of alternative transcript isoforms. In parallel, a number of studies have shown that splicing dysregulation is a hallmark of cancer. The causal link between transcript isoform expression and cancer related phenotypes has not been thoroughly studied. Here, we will expand the applicability of CRISPR/Cas13 systems to the systematic study of transcript isoforms in cancer by 1) combining large-scale experimental data with machine learning approaches to define rules for gRNA design when targeting specific transcript junctions 2) optimizing a pipeline and the computational analysis required for using CRISPR/Cas13 systems in forward transcriptomic pooled screens to interrogate the cellular function of transcript isoforms. By broadening the application of the CRISPR/Cas13 system, our pipeline will provide the molecular tools and computational analysis required for interrogating transcript isoform function in a robust, unbiased and highly expandable manner. We expect that our CRISPR/Cas13 approach will be able to overcome the limitations of current methods in identifying cell-specific isoform expression and/or ratios underlying tumorigenesis and drug resistance. Lastly, the CRISPR/Cas13 approach could be further adapted to perform targeting of transcript isoforms in vivo as an RNA-based therapeutic.
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Delineating the network effects of mental disorder-associated variants using convex optimization methods
  • 批准号:
    10674871
  • 项目类别:
  • 资助金额:
    $76.92万
  • 财政年份:
    2022
  • 负责人:
    David Arthur Knowles
  • 依托单位:
Delineating the network effects of mental disorder-associated variants using convex optimization methods
  • 批准号:
    10504516
  • 项目类别:
  • 资助金额:
    $79.91万
  • 财政年份:
    2022
  • 负责人:
    David Arthur Knowles
  • 依托单位:
Learning the Regulatory Code of Alzheimer's Disease Genomes
Learning the Regulatory Code of Alzheimer's Disease Genomes
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