Random shRNA Selection

描述（由申请人提供）：在开发小RNA疗法和生物工具中使用RNA干扰（RNAi）的现有范例是使用短发夹环RNA （shRNA）或短干扰RNA （siRNA）干扰单个基因的表达。我们试图挑战的这种现有的“单基因干扰”范式源于使用shrna或sirna作为研究工具来深入了解感兴趣的蛋白质的可能功能。然而，最有效的内源性microRNAs (miRNAs)， shRNAs和sirna的模型，通过7-8个核苷酸的“种子序列”匹配，同时靶向数百个mrna。因此，毫不奇怪，基于干扰单基因表达的小rna疗法和生物工具的开发受到“脱靶”效应的困扰，这可能导致治疗指标不佳。此外，mirna最近被证明可以直接激活和干扰基因表达。控制这种“RNA激活”（RNAa）的规则是未知的。事实上，小rna靶点和作用的完整范围才刚刚开始得到重视。为了充分利用小RNA疗法和生物工具的发展潜力，包括多基因靶向和RNA激活，需要一种完全不同的方法。考虑到这一点，我们设计并合成了第一个在核苷酸水平上完全随机的shrna表达文库。使用我们的文库进行的基于细胞的筛选试验在作用机制方面是无偏的——实际上，我们让细胞告诉我们哪些小rna最有效，毒性最小。成千上万的随机shrna可以在单个组织培养皿中使用选择试验和汇集方法进行筛选。因为只有大约20,000个可能的种子序列（对于规范的RNAi），并且因为shrna是生物活性分子，所以命中序列总是存在的。通过随机突变和重新筛选来优化初始命中序列是很简单的。我们的方法使我们能够识别出最有效、毒性最小的小rna，用作治疗药物或生物工具。我们建议使用我们的文库来鉴定和优化用于干细胞诱导和细胞分化的shRNA序列。我们鉴定和优化的文库序列可以作为shrna在载体上表达，也可以作为sirna转染到细胞中，这些sirna在没有基因组整合的情况下发挥作用。Nanog和Oct4的报告基因构建体将用于鉴定干细胞诱导的shrna， NKX2-5和Ngn3的报告基因构建体将分别用于鉴定心脏和β细胞分化的shrna。微阵列分析和/或蛋白质组学分析将用于识别独特的靶基因集。

DESCRIPTION (provided by applicant): The existing paradigm for the use of RNA interference (RNAi) in the development of small-RNA therapeutics and biologic tools is to interfere with the expression of a single gene using a short-hairpin-loop RNA (shRNA) or short-interfering RNA (siRNA). This existing "single-gene-interference" paradigm, which we seek to challenge, derives from the use of shRNAs or siRNAs as research tools to gain insights into the possible functions of proteins of interest. However, the most potent endogenous microRNAs (miRNAs), on which shRNAs and siRNAs are modeled, target hundreds of mRNAs simultaneously through "seed-sequence" matches of 7-8 nucleotides. Not surprisingly, therefore, efforts to develop small-RNA therapeutics and biologic tools based on interfering with the expression of single genes are plagued by "off-target" effects, which are likely to result in poor therapeutic indices. In addition, miRNAs have recently been shown to directly activate, as well as interfere with, gene expression. The rules governing this "RNA activation" (RNAa) are unknown. Indeed, the complete range of small-RNA targets and effects is only beginning to be appreciated. To harness the full potential for the development of small-RNA therapeutics and biologic tools, including multi-gene targeting and RNA activation, a fundamentally different approach is needed. With this in mind, we designed and synthesized the first shRNA-expressing library that is completely random at the nucleotide level. Cell-based screening assays using our library are unbiased with respect to mechanism(s) of action - in effect, we let the cells tell us which small RNAs are the most effective and least toxic. Hundreds of thousands of random shRNAs can be screened in a single tissue- culture dish using selection assays and a pooled approach. Because there are only approximately 20,000 possible seed sequences (for canonical RNAi), and because shRNAs are bio-active molecules, hit sequences are invariably present. Optimization of initial hit sequences, by random mutagenesis and re-screening, is straightforward. Our approach allows us to identify the most effective, and least toxic, small RNAs to be used as therapeutics or biologic tools. We propose to use our library to identify and optimize shRNA sequences for stem-cell induction and for cell differentiation. Library sequences that we identify and optimize could be expressed from vectors as shRNAs, or transfected into cells as siRNAs, which exert their effects without genomic integration. Reporter constructs for Nanog and Oct4 will be used to identify shRNAs for stem-cell induction, and reporter constructs for NKX2-5 and Ngn3 will be used to identify shRNAs for cardiac and beta-cell differentiation, respectively. Profiling by microarray and/or proteomic analysis will be used to identify unique target gene sets. PUBLIC HEALTH RELEVANCE: This proposal describes an approach to develop novel therapeutics and biologic tools using an shRNA- expressing library that is completely random at the nucleotide level. This approach has implications for the development of stem-cell-based and infectious-disease therapeutics, and is highly relevant to public health.