Defining the Rules for Designing Fully Chemically Modified siRNAs to Treat Genetically Linked Central Nervous System Disorders

项目摘要 小干扰RNA（siRNA）治疗剂特异性地且有效地阻断疾病相关基因的表达。 基因. siRNA的临床应用目前仅限于肝脏中的疾病靶点，但Khvorova实验室已经 开发了一种新的，完全化学修饰的siRNA平台，能够递送到中枢神经系统， 系统（CNS），并导致小鼠和猴脑中基因表达的有效调节6个月。 这项技术提供了一个机会，以治疗遗传定义的神经系统疾病，包括 亨廷顿氏病、肌萎缩侧索硬化和阿尔茨海默氏病（AD）。 广泛的化学修饰保护siRNA免于降解，并且对于体内递送是必不可少的，但是 降低了许多siRNA序列的基因沉默效力。生物信息学算法已经被开发出来 来预测未修饰的siRNA的活性，但是这些算法不能预测化学修饰的siRNA是否具有活性。 修饰的siRNA将具有功能。鉴定高功能siRNA化学修饰模式， 开发一种用于修饰的siRNA序列的预测算法对于广泛应用将是至关重要的 该平台在体内用于治疗AD和其他遗传相关的神经系统疾病。这项提案的目的是 是鉴定影响化学修饰的siRNA的沉默功效的参数。 目的1将确定限制化学修饰的siRNA活性的步骤。使用经过验证的AGO 2- 免疫沉淀技术和小RNA高通量测序方案，加载32个化学标记， 修饰的siRNA序列，每个具有3种不同的化学修饰模式，转化为RNA诱导的沉默 将定量检测RISC复合物的表达，并将这些结果与其体外沉默活性进行比较。这些 这些努力将提供一个数据集，以确定化学和顺序对不同步骤的影响， RISC功能。目标2将设计和筛选6种不同化学修饰模式的192种siRNA（即，用于 总共1152个siRNA），以系统地评估siRNA序列和化学物质是否以及如何改变 修饰模式影响siRNA功效。这1152种siRNA将靶向4种不同的mRNA， AD的治疗靶点。完成这一目标将鉴定出有效降低AD表达的siRNA， 目标的采用多种生物信息学分析方法，包括多参数线性回归，支持 向量机和随机森林，目标3将模型算法，具体预测功能，化学 修饰的siRNA。将通过独立和交叉验证确定性能最佳的算法。 本项目的完成将减少鉴定功能性化学物质所需的体外筛选的程度。 能够靶向CNS中的疾病基因并简化siRNA疗法设计的修饰siRNA 来治疗遗传性神经系统疾病

PROJECT SUMMARY Small interfering RNA (siRNA) therapeutics specifically and potently block the expression of disease-related genes. siRNA clinical utility is currently limited to disease targets in the liver, but the Khvorova lab has developed a novel, fully chemically modified siRNA platform that enables delivery to the central nervous system (CNS) and results in potent modulation of gene expression in mouse and monkey brain for 6 months. This technology provides an opportunity to treat genetically-defined neurological disorders, including Huntington’s disease, amyotrophic lateral sclerosis, and Alzheimer’s disease (AD). Extensive chemical modification protects siRNAs from degradation and is essential for in vivo delivery, but lowers the gene silencing efficacy of many siRNA sequences. Bioinformatics algorithms have been developed to predict the activity of non-modified siRNAs, but these algorithms cannot predict whether a chemically modified siRNA will be functional. Identifying a hyperfunctional siRNA chemical modification pattern and developing a predictive algorithm for modified siRNA sequences will be critical for the widespread application of this platform in vivo to treat AD and other genetically-linked neurological disorders. The goal of this proposal is to identify parameters that impact the silencing efficacy of chemically modified siRNAs. Aim 1 will identify the step(s) that limit the activity of chemically modified siRNAs. Using a validated AGO2- immunoprecipitation technique and a small RNA high-throughput sequencing protocol, loading of 32 chemically modified siRNA sequences, each with 3 different chemical modification patterns, into RNA-induced silencing complex (RISC) will be quantified, and these results will be compared to their in vitro silencing activity. These efforts will provide a data set to determine the impacts of chemistry and sequence on the different steps of RISC function. Aim 2 will design and screen 192 siRNAs in 6 different chemical modification patterns (i.e., for a total of 1152 siRNAs) to systematically assess if and how changes to siRNA sequence and chemical modification patterns impact siRNA efficacy. These 1152 siRNAs will target 4 different mRNAs identified as therapeutic targets for AD. Completing this aim will identify siRNAs that effectively reduce the expression of AD targets. Using multiple bioinformatics analysis methods, including multi-parameter linear regression, support vector machine, and random forest, Aim 3 will model algorithms that specifically predict functional, chemically modified siRNAs. The best performing algorithm will be determined by independent and cross-validations. Completion of this project will decrease the extent of in vitro screening needed to identify functional chemically modified siRNAs capable of targeting disease genes in the CNS and streamline the design of siRNA therapies to treat genetically-defined neurological disorders.