Designing highly active siRNAs for therapeutic applications

The discovery of RNA interference (RNAi) generated considerable interest in developing short interfering RNAs (siRNAs) for understanding basic biology and as the active agents in a new variety of therapeutics. Early studies showed that selecting an active siRNA was not as straightforward as simply picking a sequence on the target mRNA and synthesizing the siRNA complementary to that sequence. As interest in applying RNAi has increased, the methods for identifying active siRNA sequences have evolved from focusing on the simplicity of synthesis and purification, to identifying preferred target sequences and secondary structures, to predicting the thermodynamic stability of the siRNA. As more specific details of the RNAi mechanism have been defined, these have been incorporated into more complex siRNA selection algorithms, increasing the reliability of selecting active siRNAs against a single target. Ultimately, design of the best siRNA therapeutics will require design of the siRNA itself, in addition to design of the vehicle and other components necessary for it to function in vivo. In this minireview, we summarize the evolution of siRNA selection techniques with a particular focus on one issue of current importance to the field, how best to identify those siRNA sequences likely to have high activity. Approaches to designing active siRNAs through chemical and structural modifications will also be highlighted. As the understanding of how to control the activity and specificity of siRNAs improves, the potential utility of siRNAs as human therapeutics will concomitantly grow.     

siSPOTR: a tool for designing highly specific and potent siRNAs for human and mouse

RNA interference (RNAi) serves as a powerful and widely used gene silencing tool for basic biological research and is being developed as a therapeutic avenue to suppress disease-causing genes. However, the specificity and safety of RNAi strategies remains under scrutiny because small inhibitory RNAs (siRNAs) induce off-target silencing. Currently, the tools available for designing siRNAs are biased toward efficacy as opposed to specificity. Prior work from our laboratory and others’ supports the potential to design highly specific siRNAs by limiting the promiscuity of their seed sequences (positions 2–8 of the small RNA), the primary determinant of off-targeting. Here, a bioinformatic approach to predict off-targeting potentials was established using publically available siRNA data from more than 50 microarray experiments. With this, we developed a specificity-focused siRNA design algorithm and accompanying online tool which, upon validation, identifies candidate sequences with minimal off-targeting potentials and potent silencing capacities. This tool offers researchers unique functionality and output compared with currently available siRNA design programs. Furthermore, this approach can greatly improve genome-wide RNAi libraries and, most notably, provides the only broadly applicable means to limit off-targeting from RNAi expression vectors.     

Genome-wide application of RNAi to the discovery of potential drug targets

Progress is being made in the development of RNA interference-based (RNAi-based) strategies for the control of gene expression. It has been demonstrated that small interfering RNAs (siRNAs) can silence the expression of target genes in a sequence-specific manner in mammalian cells. Various groups, including our own, have developed systems for vector-mediated specific RNAi. Vector-based siRNA- (or shRNA) expression libraries directed against the entire human genome and siRNA libraries based on chemically synthesized oligonucleotides now allow the rapid identification of functional genes and potential drug targets. Use of such libraries will enhance our understanding of numerous biological phenomena and contribute to the rational design of drugs against heritable, infectious and malignant diseases.     

Gene silencing through RNA interference (RNAi) in vivo: strategies based on the direct application of siRNAs

RNA interference (RNAi) offers great potential not only for in vitro target validation, but also as a novel therapeutic strategy based on the highly specific and efficient silencing of a target gene, e.g. in tumor therapy. Since it relies on small interfering RNAs (siRNAs), which are the mediators of RNAi-induced specific mRNA degradation, a major issue is the delivery of therapeutically active siRNAs into the target tissue/target cells in vivo. For safety reasons, strategies based on (viral) vector delivery may be of only limited clinical use. The more desirable approach is to directly apply catalytically active siRNAs. This review highlights the recent knowledge on the guidelines for the selection of siRNAs which show high activity in the absence of non-specific siRNA effects. It then focuses on approaches to directly use siRNA molecules in vivo and gives a comprehensive overview of in vivo studies based on the direct application of siRNAs to induce RNAi. One promising approach is the in vivo siRNA delivery through complexation of chemically unmodified siRNAs with polyethylenimine (PEI). The anti-tumoral effects of PEI/siRNA-based targeting of tumor-relevant genes in vivo are described.     

高效siRNA设计的研究进展

RNA干扰(RNA interference, RNAi) 是生物界普遍存在的一种抵御外来基因和病毒感染的保守进化机制, 其本质是siRNA与靶向mRNA特异结合、并由RISC介导其降解, 从而阻止mRNA的翻译, 导致基因沉默。因此, RNAi可以作为基因功能研究、基因治疗等的新工具。但是, 随机设计的siRNA之间沉默效应差别很大。如何针对靶基因设计特异、高效的siRNA就成了一个关键的问题。文章对siRNA设计原则的研究进展进行了总结论述。     

In Silico Target-Specific siRNA Design Based on Domain Transfer in Heterogeneous Data

RNA interference via exogenous small interference RNAs (siRNA) is a powerful tool in gene function study and disease treatment. Designing efficient and specific siRNA on target gene remains the key issue in RNAi. Although various in silico models have been proposed for rational siRNA design, most of them focus on the efficiencies of selected siRNAs, while limited effort has been made to improve their specificities targeted on specific mRNAs, which is related to reducing off-target effects (OTEs) in RNAi. In our study, we propose for the first time that the enhancement of target specificity of siRNA design can be achieved computationally by domain transfer in heterogeneous data sources from different siRNA targets. A transfer learning based method i.e., heterogeneous regression (HEGS) is presented for target-specific siRNA efficacy modeling and feature selection. Based on the model, (1) the target regression model can be built by extracting information from related data in other targets/experiments, thus increasing the target specificity in siRNA design with the help of information from siRNAs binding to other homologous genes, and (2) the potential features correlated to the current siRNA design can be identified even when there is lack of experimental validated siRNA affinity data on this target. In summary, our findings present useful instructions for a better target-specific siRNA design, with potential applications in genome-wide high-throughput screening of effective siRNA, and will provide further insights on the mechanism of RNAi.     

Efficient prediction of siRNAs with siRNArules 1.0: an open-source JAVA approach to siRNA algorithms

RNAi interference and siRNA have become useful tools for investigation of gene function. However, the discovery that not all siRNA are equally efficient made necessary screens or design algorithms to obtain high activity siRNA candidates. Several algorithms have been published, but they remain inefficient, obscure, or commercially restricted. This article describes an open-source JAVA program that is surprisingly efficient at predicting active siRNAs (Pearson correlation coefficient r = 0.52, n = 526 siRNAs). Furthermore, this version 1.0 sets the stage for further improvement of the free code by the open-source community (http://sourceforge.net/).     

Multi-task learning for cross-platform siRNA efficacy prediction: an in-silico study

Background  

Gene silencing using exogenous small interfering RNAs (siRNAs) is now a widespread molecular tool for gene functional study and new-drug target identification. The key mechanism in this technique is to design efficient siRNAs that incorporated into the RNA-induced silencing complexes (RISC) to bind and interact with the mRNA targets to repress their translations to proteins. Although considerable progress has been made in the computational analysis of siRNA binding efficacy, few joint analysis of different RNAi experiments conducted under different experimental scenarios has been done in research so far, while the joint analysis is an important issue in cross-platform siRNA efficacy prediction. A collective analysis of RNAi mechanisms for different datasets and experimental conditions can often provide new clues on the design of potent siRNAs.     

The impact of target site accessibility on the design of effective siRNAs

Small-interfering RNAs (siRNAs) assemble into RISC, the RNA-induced silencing complex, which cleaves complementary mRNAs. Despite their fluctuating efficacy, siRNAs are widely used to assess gene function. Although this limitation could be ascribed, in part, to variations in the assembly and activation of RISC, downstream events in the RNA interference (RNAi) pathway, such as target site accessibility, have so far not been investigated extensively. In this study we present a comprehensive analysis of target RNA structure effects on RNAi by computing the accessibility of the target site for interaction with the siRNA. Based on our observations, we developed a novel siRNA design tool, RNAxs, by combining known siRNA functionality criteria with target site accessibility. We calibrated our method on two data sets comprising 573 siRNAs for 38 genes, and tested it on an independent set of 360 siRNAs targeting four additional genes. Overall, RNAxs proves to be a robust siRNA selection tool that substantially improves the prediction of highly efficient siRNAs.     

Identification of sequence features that predict competition potency of siRNAs

Small interfering RNAs (siRNAs) specifically knock-down target mRNAs via RNA interference (RNAi) mechanism. During this process, introduction of excess amount of exogenous siRNAs could lead to the saturation of cellular RNAi machinery. One consequence of RNAi machinery saturation is the competition between two simultaneously introduced siRNAs, during which one siRNA loses gene silencing activity. Although competition phenomena have been well characterized, the molecular and sequence features of siRNAs that specify the competition potency remain poorly understood. Here, for the first time, we performed a large-scale siRNA competition potency analysis by measuring the competition potency of 56 different siRNAs and ranking them based on their competition potency. We have also established an algorithm to predict the competition potency of siRNAs based upon the conserved sequence features of strong and weak competitor siRNAs. The present study supports our hypothesis that the competition potency of siRNAs is specified by the 5′-half antisense sequence and provides a useful guideline to design siRNAs with minimal RNAi machinery saturation.     

RNAi-induced off-target effects in Drosophila melanogaster: frequencies and solutions

Genes can be silenced with short-interfering RNA molecules (siRNA). siRNAs are widely used to identify gene functions and have high potential for therapeutic treatments. It is critical that the siRNA specifically targets the expression of the gene of interest but has no off-target effects on other genes. Although siRNAs were initially considered to be exclusively active on mature mRNAs in the cytoplasm, additional studies have shown that siRNAs are present in the nucleus as well, suggesting that pre-mRNA sequences containing introns and other untranslated regions can also be targeted. In this study, we investigated the extent to which off-targets may occur in Drosophila melanogaster by looking at mature mRNA sequences and pre-mature RNA sequences separately. First, an in silico approach revealed that, based on sequence similarity, numerous off-targets are predicted to occur in RNAi experiments. Second, existing microarray data were used to investigate a possible effect of the predicted off-targets based on analysis of in vitro data. We found that the occurrence of off-targets in both mature and pre-mature RNA sequences in RNAi experiments can be extensive and significant. Possibilities are discussed how to minimize off-target effects.     

Commercial potential of RNAi

The commercial potential of RNAi is assessed on the basis of successful translation of technology into applications in three areas: (1) drug discovery and research-currently the biggest segment; (2) potential therapeutic applications; and (3) the role of microRNA in molecular diagnostics. RNAi is an important method for analyzing gene function and identifying new drug targets that use dsRNA to knock down or silence specific genes. Sets of siRNAs focused on a specific gene class (siRNA libraries) have the capacity to greatly increase the pace of pathway analysis and functional genomics. RNAi plays an important role in drug discovery by facilitating target validation. The discovery of the role of microRNA (miRNAs) in various pathological processes opens up possible applications in molecular diagnostics, particularly that of cancer. The advantages of RNAi-based therapeutics over traditional pharmaceuticals include the capability for more specific therapies with small molecule siRNA. Drawbacks include the development of resistance in cancer and viral infections as well as the interferon effect. RNAi is closely related to gene therapy and the vectors developed for gene therapy are also being used for delivery of siRNAs. RNAi, along with other related technologies, will contribute to the development of personalised medicine. Although none of the RNAi-based drugs is in the market yet, some are in clinical trials. By the year 2010 the market for RNAi-based drugs is expected to be worth 3.5 billion dollars and is expected to expand to 10.5 billion dollars by the year 2015.