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

项目总结 小干扰RNA(SiRNA)疗法特异性并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，但赫沃洛娃实验室已经 开发了一种新的、完全化学修饰的siRNA平台，使其能够传递到中枢神经系统 系统(CNS)，并导致6个月内对小鼠和猴子大脑中基因表达的有效调节。 这项技术提供了治疗遗传定义的神经疾病的机会，包括 亨廷顿病、肌萎缩侧索硬化症和阿尔茨海默病(AD)。 广泛的化学修饰可以保护siRNA免受降解，对于体内传递是必不可少的，但 降低许多siRNA序列的基因沉默效率。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，但这些算法不能预测化学上的 修饰后的siRNA将发挥作用。鉴定超功能siRNA化学修饰模式并 开发一种针对修饰的siRNA序列的预测算法将是广泛应用的关键 这一平台在体内用于治疗阿尔茨海默病和其他与遗传相关的神经疾病。这项提案的目标是 确定影响化学修饰siRNAs沉默效果的参数。 目标1将确定限制化学修饰的siRNA活性的步骤(S)。使用经过验证的AGO2- 免疫沉淀技术和一种小RNA高通量测序方案，化学负载32 将每个具有3种不同化学修饰模式的siRNA序列修饰为RNA诱导的沉默 复合体(RISC)将被量化，这些结果将与它们的体外沉默活性进行比较。这些 努力将提供一个数据集，以确定化学和顺序对以下不同步骤的影响 RISC功能。AIM 2将设计和筛选6种不同化学修饰模式的192个siRNA(即 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变化 修饰模式影响siRNA的效力。这1152个siRNAs将针对4个不同的mRNAs，它们被确定为 AD的治疗靶点。完成这一目标将识别有效降低AD表达的siRNA 目标。使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，AIM 3将对特定预测函数、化学的算法进行建模 修饰的siRNA。性能最佳的算法将通过独立和交叉验证来确定。 该项目的完成将减少化学鉴定功能所需的体外筛选的程度。 修饰的siRNA能够靶向CNS中的疾病基因并简化siRNA疗法的设计 用来治疗基因定义的神经疾病。

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.