DSIR: Assessing the Design of Highly Potent siRNA by Testing a Set of Cancer-Relevant Target Genes

Chemically synthesized small interfering RNA (siRNA) is a widespread molecular tool used to knock down genes in mammalian cells. However, designing potent siRNA remains challenging. Among tools predicting siRNA efficacy, very few have been validated on endogenous targets in realistic experimental conditions. We previously described a tool to assist efficient siRNA design (DSIR, Designer of siRNA), which focuses on intrinsic features of the siRNA sequence. Here, we evaluated DSIR’s performance by systematically investigating the potency of the siRNA it designs to target ten cancer-related genes. mRNA knockdown was measured by quantitative RT-PCR in cell-based assays, revealing that over 60% of siRNA sequences designed by DSIR silenced their target genes by at least 70%. Silencing efficacy was sustained even when low siRNA concentrations were used. This systematic analysis revealed in particular that, for a subset of genes, the efficiency of siRNA constructs significantly increases when the sequence is located closer to the 5′-end of the target gene coding sequence, suggesting the distance to the 5′-end as a new feature for siRNA potency prediction. A new version of DSIR incorporating these new findings, as well as the list of validated siRNA against the tested cancer genes, has been made available on the web (http://biodev.extra.cea.fr/DSIR).     

Local RNA target structure influences siRNA efficacy: systematic analysis of intentionally designed binding regions

Contradictory reports in the literature have emphasised either the sequence of small interfering RNAs (siRNA) or the structure of their target molecules to be the major determinant of the efficiency of RNA interference (RNAi) approaches. In the present study, we analyse systematically the contributions of these parameters to siRNA activity by using deliberately designed mRNA constructs. The siRNA target sites were included in well-defined structural elements rendering them either highly accessible or completely involved in stable base-pairing. Furthermore, complementary sequence elements and various hairpins with different stem lengths and designs were used as target sites. Only one of the strands of the siRNA duplex was found to be capable of silencing via its respective target site, indicating that thermodynamic characteristics intrinsic to the siRNA strands are a basic determinant of siRNA activity. A significant obstruction of gene silencing by the same siRNA, however, was observed to be caused by structural features of the substrate RNA. Bioinformatic analysis of the mRNA structures suggests a direct correlation between the extent of gene-knockdown and the local free energy in the target region. Our findings indicate that, although a favourable siRNA sequence is a necessary prerequisite for efficient RNAi, complex target structures may limit the applicability even of carefully chosen siRNAs.     

siPRED: predicting siRNA efficacy using various characteristic methods

Small interfering RNA (siRNA) has been used widely to induce gene silencing in cells. To predict the efficacy of an siRNA with respect to inhibition of its target mRNA, we developed a two layer system, siPRED, which is based on various characteristic methods in the first layer and fusion mechanisms in the second layer. Characteristic methods were constructed by support vector regression from three categories of characteristics, namely sequence, features, and rules. Fusion mechanisms considered combinations of characteristic methods in different categories and were implemented by support vector regression and neural networks to yield integrated methods. In siPRED, the prediction of siRNA efficacy through integrated methods was better than through any method that utilized only a single method. Moreover, the weighting of each characteristic method in the context of integrated methods was established by genetic algorithms so that the effect of each characteristic method could be revealed. Using a validation dataset, siPRED performed better than other predictive systems that used the scoring method, neural networks, or linear regression. Finally, siPRED can be improved to achieve a correlation coefficient of 0.777 when the threshold of the whole stacking energy is ≥-34.6 kcal/mol. siPRED is freely available on the web at http://predictor.nchu.edu.tw/siPRED.     

An Effective Method for Selecting siRNA Target Sequences in Mammalian Cells

RNA interference is a gene-silencing phenomenon triggered by dsRNA (double-stranded RNA) and has been widely used for studying gene functions. The short interfering RNA (siRNA) responsible for RNA interference, however, varies markedly in its gene-silencing efficacy. Because this efficacy depends on the selected target sequences, we developed an effective selection method based on the gene degradation measure (priority score) defined by positional features of individual nucleotides. We tested this method experimentally by using it to select new siRNA target sequences in the homo sapiens cyclin B1 gene (CCNB1) and confirmed that it selected highly effective gene-silencing sequences. The proposed method will therefore be useful for selecting new siRNA target sequences in mammalian cells.     

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.     

Efficient siRNA selection using hybridization thermodynamics

Small interfering RNA (siRNA) are widely used to infer gene function. Here, insights in the equilibrium of siRNA-target hybridization are used for selection of efficient siRNA. The accessibilities of siRNA and target mRNA for hybridization, as measured by folding free energy change, are shown to be significantly correlated with efficacy. For this study, a partition function calculation that considers all possible secondary structures is used to predict target site accessibility; a significant improvement over calculations that consider only the predicted lowest free energy structure or a set of low free energy structures. The predicted thermodynamic features, in addition to siRNA sequence features, are used as input for a support vector machine that selects functional siRNA. The method works well for predicting efficient siRNA (efficacy >70%) in a large siRNA data set from Novartis. The positive predictive value (percentage of sites predicted to be efficient for silencing that are) is as high as 87.6%. The sensitivity and specificity are 22.7 and 96.5%, respectively. When tested on data from different sources, the positive predictive value increased 8.1% by adding equilibrium terms to 25 local sequence features. Prediction of hybridization affinity using partition functions is now available in the RNAstructure software package.     

Accurate transcriptome-wide prediction of microRNA targets and small interfering RNA off-targets with MIRZA-G

Small interfering RNA (siRNA)-mediated knock-down is a widely used experimental approach to characterizing gene function. Although siRNAs are designed to guide the cleavage of perfectly complementary mRNA targets, acting similarly to microRNAs (miRNAs), siRNAs down-regulate the expression of hundreds of genes to which they have only partial complementarity. Prediction of these siRNA ‘off-targets’ remains difficult, due to the incomplete understanding of siRNA/miRNA–target interactions. Combining a biophysical model of miRNA–target interaction with structure and sequence features of putative target sites we developed a suite of algorithms, MIRZA-G, for the prediction of miRNA targets and siRNA off-targets on a genome-wide scale. The MIRZA-G variant that uses evolutionary conservation performs better than currently available methods in predicting canonical miRNA target sites and in addition, it predicts non-canonical miRNA target sites with similarly high accuracy. Furthermore, MIRZA-G variants predict siRNA off-target sites with an accuracy unmatched by currently available programs. Thus, MIRZA-G may prove instrumental in the analysis of data resulting from large-scale siRNA screens.     

Designing of highly effective complementary and mismatch siRNAs for silencing a gene

In past, numerous methods have been developed for predicting efficacy of short interfering RNA (siRNA). However these methods have been developed for predicting efficacy of fully complementary siRNA against a gene. Best of author's knowledge no method has been developed for predicting efficacy of mismatch siRNA against a gene. In this study, a systematic attempt has been made to identify highly effective complementary as well as mismatch siRNAs for silencing a gene.Support vector machine (SVM) based models have been developed for predicting efficacy of siRNAs using composition, binary and hybrid pattern siRNAs. We achieved maximum correlation 0.67 between predicted and actual efficacy of siRNAs using hybrid model. All models were trained and tested on a dataset of 2182 siRNAs and performance was evaluated using five-fold cross validation techniques. The performance of our method desiRm is comparable to other well-known methods. In this study, first time attempt has been made to design mutant siRNAs (mismatch siRNAs). In this approach we mutated a given siRNA on all possible sites/positions with all possible nucleotides. Efficacy of each mutated siRNA is predicted using our method desiRm. It is well known from literature that mismatches between siRNA and target affects the silencing efficacy. Thus we have incorporated the rules derived from base mismatches experimental data to find out over all efficacy of mutated or mismatch siRNAs. Finally we developed a webserver, desiRm (http://www.imtech.res.in/raghava/desirm/) for designing highly effective siRNA for silencing a gene. This tool will be helpful to design siRNA to degrade disease isoform of heterozygous single nucleotide polymorphism gene without depleting the wild type protein.     

Screening of siRNA target sequences by using fragmentized DNA

BACKGROUND: RNA interference (RNAi) has become a powerful tool in silencing target genes in various organisms. In mammals, RNAi can be induced by using short interfering RNA (siRNA). The efficacy of inducing RNAi in mammalian cells by using siRNA depends very much on the selection of the target sequences. METHODS: We developed an siRNA target sequence selection system by first constructing parallel-type siRNA expression vector libraries carrying siRNA expression fragments originating from fragmentized target genes, and then using a group selection system. For a model system, we constructed parallel-type siRNA expression vector libraries against DsRed and GFP reporter genes. RESULTS: We carried out the first screening of groups containing more than 100 random siRNA expression plasmids in total for each target gene, and successfully obtained target sequences with very strong efficacy. Furthermore, we also obtained some clones that express dsRNAs of various lengths that might induce cytotoxicity. CONCLUSIONS: This system should allow us to perform screening for powerful target sequences, by including all possible target sequences for any gene, even without knowing the whole sequence of the target gene in advance. At the same time, target sequences that should be avoided due to cytotoxicity can be identified.     

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.     

Effects on RNAi of the tight structure, sequence and position of the targeted region

RNA interference (RNAi) is a gene-silencing phenomenon that involves the double-stranded RNA-mediated cleavage of mRNA, and small interfering RNAs (siRNAs) can cause RNAi in mammalian cells. There have been many attempts to clarify the mechanism of RNAi, but information about the relationship between the sequence and structure, in particular, a tight structure, of the target RNA and the activities of siRNAs are limited. In the present study, we examined this relationship by introducing the TAR element, which adopts a very stable secondary structure, at different positions within target RNAs. Our results suggested that the activities of siRNAs were affected by the tight stem–loop structure of TAR. In contrast, the position of the target within the mRNA, the binding of the Tat protein to the TAR, and the location of the target within a translated or a noncoding region had only marginal effects on RNAi. When the target sequence was placed in two different orientations, only one orientation had a significant effect on the activities of siRNA, demonstrating that the presence of certain nucleotides at some specific positions was favorable for RNAi. Systematic analysis of 47 different sites within 47 plasmids under identical conditions indicated that it is the target sequence itself, rather than its location, that is the major determinant of siRNA activity.     

Molecular basis for target RNA recognition and cleavage by human RISC

The RNA-Induced Silencing Complex (RISC) is a ribonucleoprotein particle composed of a single-stranded short interfering RNA (siRNA) and an endonucleolytically active Argonaute protein, capable of cleaving mRNAs complementary to the siRNA. The mechanism by which RISC cleaves a target RNA is well understood, however it remains enigmatic how RISC finds its target RNA. Here, we show, both in vitro and in vivo, that the accessibility of the target site correlates directly with the efficiency of cleavage, demonstrating that RISC is unable to unfold structured RNA. In the course of target recognition, RISC transiently contacts single-stranded RNA nonspecifically and promotes siRNA-target RNA annealing. Furthermore, the 5' part of the siRNA within RISC creates a thermodynamic threshold that determines the stable association of RISC and the target RNA. We therefore provide mechanistic insights by revealing features of RISC and target RNAs that are crucial to achieve efficiency and specificity in RNA interference.     

Poly-2'-DNP-RNAs with enhanced efficacy for inhibiting cancer cell growth

It is often believed that small interfering RNA (siRNA) is at least 10-fold more effective than the single-stranded antisense oligonucleotide for silencing the same target gene in the same cells. In view of the recent discovery that the RNA-induced silencing complex (RISC) contains only a single-stranded RNA (ssRNA) molecule and can be reconstituted using single-stranded antisense RNA, such a large difference in efficacy seems puzzling. One possible reason is that hybridization protects siRNA from hydrolysis by endogenous RNase activity until it is incorporated in the RISC, whereas ssRNA is rapidly hydrolyzed. Because the single-stranded poly-2'-O-(2,4-dinitrophenyl)-RNA (DNP-ssRNA) is both RNase resistant and membrane permeable, we synthesized homologous native siRNAs, DNP-siRNAs, native ssRNAs, and DNP-ssRNAs and made a comparative study of their efficacies for inhibiting the growth of two cancer cell lines with different overexpressed target genes under equivalent experimental conditions. It was found that the efficacy of antisense DNP-ssRNA is higher than that of the corresponding siRNA and that the efficacy of native siRNA for inhibiting cell growth can also be enhanced from 2-fold to 6-fold by replacing the native strands of RNA in siRNA with homologous DNP-RNA. Thermal denaturation data show that the hybridization affinity of the DNP-RNA/RNA duplex is higher than that of the native RNA/RNA duplex. Western blotting analysis of A549 cells treated with antisense DNP-ssRNAs containing single mismatching bases shows that the gene silencing by antisense DNP-ssRNA is as sequence specific as that by siRNA. The observed large enhancement of inhibition efficacy of native RNAs by DNP derivatization should be advantageous for both gene silencing studies and therapeutic applications.