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中文摘要
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摘要 在健康和疾病中表征转录物同种型细胞功能的主要挑战之一是缺乏 特异性和有效地下调其表达的方法。RNA导向的VI型 CRISPR/Cas 13系统构成了最近开发的在哺乳动物细胞中敲低转录物的工具。他们的 RNA酶活性通过与单链RNA互补的CRISPR RNA向导(gRNA)的结合而被激活 目标迄今为止测试的CRISPR/Cas 13系统已经显示出实现高度特异性敲除。 内源性转录物,具有最小的脱靶效应,优于目前的方法,如RNAi。 虽然CRISPR/Cas 13已被证明在靶向前体mRNA时有效地起作用,但转录组范围内的CRISPR/Cas 13在靶向前体mRNA时有效地起作用。 通过靶向成熟mRNA中的独特接头来敲低转录物亚型的策略 分子尚未被探索。选择最佳gRNA序列对于成功和特异性RNA至关重要 通过Cas 13介导的敲低。我们实验室的初步数据显示,使用gRNA靶向序列, CRISPR/Cas 13 d(最小的Cas 13效应子)在成熟mRNA分子中跨越外显子-外显子连接 有效地减少转录表达高达80%。此外,靶向同种型特异性连接允许它们的 单独敲低而不影响非靶向同种型。这些结果表明,没有空间位阻 限制成熟mRNA分子中的靶向连接。肿瘤和癌细胞的转录组分析 Lines已经全面描述了可选转录物同种型的表达水平和特性。在 同时,许多研究表明剪接失调是癌症的标志。的因果关系 转录物同种型表达与癌症相关表型之间的关系尚未得到彻底研究。在这里, 我们将扩大CRISPR/Cas 13系统的适用性,以系统地研究转录异构体, 1)将大规模实验数据与机器学习方法相结合,以定义用于治疗癌症的规则。 2)优化流水线和计算分析 在正向转录组学合并筛选中使用CRISPR/Cas 13系统来询问细胞 转录异构体的功能。通过扩大CRISPR/Cas 13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、公正和高度可扩展的方式。我们希望我们的CRISPR/Cas 13方法能够 克服了目前方法在鉴定细胞特异性同种型表达和/或比率方面的局限性 潜在的肿瘤发生和耐药性。最后,CRISPR/Cas 13方法可以进一步调整, 作为基于RNA的治疗剂在体内靶向转录物同种型。
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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