Efficient prediction methods for selecting effective siRNA sequences

VIRsiRNApred: a web server for predicting inhibition efficacy of siRNAs targeting human viruses

BACKGROUND

Selection of effective viral siRNA is an indispensable step in the development of siRNA based antiviral therapeutics. Despite immense potential, a viral siRNA efficacy prediction algorithm is still not available. Moreover, performances of the existing general mammalian siRNA efficacy predictors are not satisfactory for viral siRNAs. Therefore, we have developed "VIRsiRNApred" a support vector machine (SVM) based method for predicting the efficacy of viral siRNA.

METHODS

In the present study, we have employed a new dataset of 1725 viral siRNAs with experimentally verified quantitative efficacies tested under heterogeneous experimental conditions and targeting as many as 37 important human viruses including HIV, Influenza, HCV, HBV, SARS etc. These siRNAs were divided into training (T1380) and validation (V345) datasets. Important siRNA sequence features including mono to penta nucleotide frequencies, binary pattern, thermodynamic properties and secondary structure were employed for model development.

RESULTS

During 10-fold cross validation on T1380 using hybrid approach, we achieved a maximum Pearson Correlation Coefficient (PCC) of 0.55 between predicted and actual efficacy of viral siRNAs. On V345 independent dataset, our best model achieved a maximum correlation of 0.50 while existing general siRNA prediction methods showed PCC from 0.05 to 0.18. However, using leave one out cross validation PCC was improved to 0.58 and 0.55 on training and validation datasets respectively. SVM performed better than other machine learning techniques used like ANN, KNN and REP Tree.

CONCLUSION

VIRsiRNApred is the first algorithm for predicting inhibition efficacy of viral siRNAs which is developed using experimentally verified viral siRNAs. We hope this algorithm would be useful in predicting highly potent viral siRNA to aid siRNA based antiviral therapeutics development. The web server is freely available at http://crdd.osdd.net/servers/virsirnapred/.

Influence of mRNA features on siRNA interference efficacy

Design of small interference RNA (siRNA) is one of the most important steps in effectively applying the RNA interference (RNAi) technology. The current siRNA design often produces inconsistent design results, which often fail to reliably select siRNA with clear silencing effects. We propose that when designing siRNA, one should consider mRNA global features and near siRNA-binding site local features. By a linear regression study, we discovered strong correlations between inhibitory efficacy and both mRNA global features and neighboring local features. This paper shows that, on average, less GC content, fewer stem secondary structures, and more loop secondary structures of mRNA at both global and local flanking regions of the siRNA binding sites lead to stronger inhibitory efficacy. Thus, the use of mRNA global features and near siRNA-binding site local features are essential to successful gene silencing and hence, a better siRNA design. We use a random forest model to predict siRNA efficacy using siRNA features, mRNA features, and near siRNA binding site features. Our prediction method achieved a correlation coefficient of 0.7 in 10-fold cross validation in contrast to 0.63 when using siRNA features only. Our study demonstrates that considering mRNA and near siRNA binding site features helps improve siRNA design accuracy. The findings may also be helpful in understanding binding efficacy between microRNA and mRNA.

Measuring RNAi knockdown using qPCR

We describe a scalable method for measuring changes in gene expression following RNAi in mammalian cells. This protocol outlines methods to transfect cells, isolate total RNA, perform cDNA synthesis, run qPCR reactions with multiplexed TaqMan dual hydrolysis probes, and analyze the results from qPCR using relative quantification (Fleige et al., 2006). These methods can be used to assess the relative knockdown of a set of siRNAs against a target in order to identify the most effective molecule and they can be used to assess the potency of a siRNA or group of siRNAs when performed over a range of doses. When screening panels of siRNAs targeting a given gene, knockdown can be assessed in two phases. In the first phase, the most active molecules are identified in a single dose screen. In the second phase a dose response screen is used to identify the subset of efficacious molecules that are the most potent.

Small interfering ribonucleic acid design strategies for effective targeting and gene silencing

INTRODUCTION

Gene silencing mediated by siRNAs is becoming a promising therapeutic approach. Although many strategies and technologies have been applied to siRNA design, a key issue lies in the selection of efficient design predictors. Furthermore, the development of systemic siRNA delivery strategies, which would enhance the therapeutic effect, remains a central issue.

AREAS COVERED

The review discusses the basic principles of the sequence-specific design criteria of functional siRNAs and possible chemical modifications. Some of the most recent advances in the development of siRNA design algorithms and delivery strategies are also presented. Emphasis is given to the important design rule sets and predictors which determine the functionality of an efficient siRNA.

EXPERT OPINION

The potential and limitations of efficient design predictors obtained from computational algorithms play a crucial role in the development of target-specific siRNAs. Furthermore, the future success of RNA interference therapeutics will depend on their ability to efficiently cross the physiological barriers, selectively target cells-of-interest and finally silence the gene-of-interest without any side effects.

RNA interference in Cryptococcus neoformans

RNA interference (RNAi) is an experimental technique used to suppress individual gene expression in eukaryotic cells in a sequence-dependent manner. The process relies on double-stranded RNA (dsRNA) to target complementary messenger RNA for degradation. Here, we describe two plasmid-based strategies we have developed for RNAi in Cryptococcus neoformans. The pFrame vector utilizes the ACT1 promoter to enable the constitutive synthesis of hairpin RNA (hpRNA), the stem of which constitutes the dsRNA trigger. The pIBB103 vector relies on convergent, inducible GAL7 promoters to independently drive the synthesis of the sense and antisense strands of the interfering sequence; these strands anneal to form the initiating dsRNA molecule. Both vectors are designed to co-silence a "sentinel" gene with an easily scored phenotype to help identify clones in which RNAi is most effective. We provide guidelines for selecting a suitable interfering sequence to trigger RNAi in C. neoformans and describe the steps for subcloning into either vector, transforming C. neoformans by electroporation, screening clones for RNAi-related phenotypes, and evaluating the efficacy and specificity of gene silencing by RNAi.