OligoWalk: an online siRNA design tool utilizing hybridization thermodynamics

Given an mRNA sequence as input, the OligoWalk web server generates a list of small interfering RNA (siRNA) candidate sequences, ranked by the probability of being efficient siRNA (silencing efficacy greater than 70%). To accomplish this, the server predicts the free energy changes of the hybridization of an siRNA to a target mRNA, considering both siRNA and mRNA self-structure. The free energy changes of the structures are rigorously calculated using a partition function calculation. By changing advanced options, the free energy changes can also be calculated using less rigorous lowest free energy structure or suboptimal structure prediction methods for the purpose of comparison. Considering the predicted free energy changes and local siRNA sequence features, the server selects efficient siRNA with high accuracy using a support vector machine. On average, the fraction of efficient siRNAs selected by the server that will be efficient at silencing is 78.6%. The OligoWalk web server is freely accessible through internet at http://rna.urmc.rochester.edu/servers/oligowalk

RNAi revised - Target mRNA-dependent enhancement of gene silencing

The discovery of RNA interference (RNAi) gave rise to the development of new nucleic acid-based technologies as powerful investigational tools and potential therapeutics. Mechanistic key details of RNAi in humans need to be deciphered yet, before such approaches take root in biomedicine and molecular therapy. We developed and validated an in silico-based model of siRNA-mediated RNAi in human cells in order to link in vitro-derived pre-steady state kinetic data with a quantitative and time-resolved understanding of RNAi on the cellular level. The observation that product release by Argonaute 2 is accelerated in the presence of an excess of target RNA in vitro inspired us to suggest an associative mechanism for the RNA slicer reaction where incoming target mRNAs actively promote dissociation of cleaved mRNA fragments. This novel associative model is compatible with highmultiple turnover rates of RNAibased gene silencing in living cells and accounts for target mRNA concentration-dependent enhancement of the RNAi machinery

Fundamental differences in the equilibrium considerations for siRNA and antisense oligodeoxynucleotide design

Both siRNA and antisense oligodeoxynucleotides (ODNs) inhibit the expression of a complementary gene. In this study, fundamental differences in the considerations for RNA interference and antisense ODNs are reported. In siRNA and antisense ODN databases, positive correlations are observed between the cost to open the mRNA target self-structure and the stability of the duplex to be formed, meaning the sites along the mRNA target with highest potential to form strong duplexes with antisense strands also have the greatest tendency to be involved in pre-existing structure. Efficient siRNA have less stable siRNA–target duplex stability than inefficient siRNA, but the opposite is true for antisense ODNs. It is, therefore, more difficult to avoid target self-structure in antisense ODN design. Self-structure stabilities of oligonucleotide and target correlate to the silencing efficacy of siRNA. Oligonucleotide self-structure correlations to efficacy of antisense ODNs, conversely, are insignificant. Furthermore, self-structure in the target appears to correlate with antisense ODN efficacy, but such that more effective antisense ODNs appear to target mRNA regions with greater self-structure. Therefore, different criteria are suggested for the design of efficient siRNA and antisense ODNs and the design of antisense ODNs is more challenging

HIV-1 RT-dependent DNAzyme expression inhibits HIV-1 replication without the emergence of escape viruses

DNAzymes are easier to prepare and less sensitive to chemical and enzymatic degradation than ribozymes; however, a DNA enzyme expression system has not yet been developed. In this study, we exploited the mechanism of HIV-1 reverse transcription (RT) in a DNA enzyme expression system. We constructed HIV-1 RT-dependent lentiviral DNAzyme expression vectors including the HIV-1 primer binding site, the DNA enzyme, and either a native tRNA (Lys-3), tRMDtRL, or one of two truncated tRNAs (Lys-3), tRMDΔARMtRL or tRMD3′-endtRL. Lentiviral vector-mediated DNAzyme expression showed high levels of inhibition of HIV-1 replication in SupT1 cells. We also demonstrated the usefulness of this approach in a long-term assay, in which we found that the DNAzymes prevented escape from inhibition of HIV. These results suggest that HIV-1 RT-dependent lentiviral vector-derived DNAzymes prevent the emergence of escape mutations

IntaRNA: efficient prediction of bacterial sRNA targets incorporating target site accessibility and seed regions

Motivation: During the last few years, several new small regulatory RNAs (sRNAs) have been discovered in bacteria. Most of them act as post-transcriptional regulators by base pairing to a target mRNA, causing translational repression or activation, or mRNA degradation. Numerous sRNAs have already been identified, but the number of experimentally verified targets is considerably lower. Consequently, computational target prediction is in great demand. Many existing target prediction programs neglect the accessibility of target sites and the existence of a seed, while other approaches are either specialized to certain types of RNAs or too slow for genome-wide searches

Comparing Artificial Neural Networks, General Linear Models and Support Vector Machines in Building Predictive Models for Small Interfering RNAs

Exogenous short interfering RNAs (siRNAs) induce a gene knockdown effect in cells by interacting with naturally occurring RNA processing machinery. However not all siRNAs induce this effect equally. Several heterogeneous kinds of machine learning techniques and feature sets have been applied to modeling siRNAs and their abilities to induce knockdown. There is some growing agreement to which techniques produce maximally predictive models and yet there is little consensus for methods to compare among predictive models. Also, there are few comparative studies that address what the effect of choosing learning technique, feature set or cross validation approach has on finding and discriminating among predictive models.Three learning techniques were used to develop predictive models for effective siRNA sequences including Artificial Neural Networks (ANNs), General Linear Models (GLMs) and Support Vector Machines (SVMs). Five feature mapping methods were also used to generate models of siRNA activities. The 2 factors of learning technique and feature mapping were evaluated by complete 3x5 factorial ANOVA. Overall, both learning techniques and feature mapping contributed significantly to the observed variance in predictive models, but to differing degrees for precision and accuracy as well as across different kinds and levels of model cross-validation.The methods presented here provide a robust statistical framework to compare among models developed under distinct learning techniques and feature sets for siRNAs. Further comparisons among current or future modeling approaches should apply these or other suitable statistically equivalent methods to critically evaluate the performance of proposed models. ANN and GLM techniques tend to be more sensitive to the inclusion of noisy features, but the SVM technique is more robust under large numbers of features for measures of model precision and accuracy. Features found to result in maximally predictive models are not consistent across learning techniques, suggesting care should be taken in the interpretation of feature relevance. In the models developed here, there are statistically differentiable combinations of learning techniques and feature mapping methods where the SVM technique under a specific combination of features significantly outperforms all the best combinations of features within the ANN and GLM techniques

Small RNA interference-mediated gene silencing of heparanase abolishes the invasion, metastasis and angiogenesis of gastric cancer cells

<p>Abstract</p> <p>Background</p> <p>Heparanase facilitates the invasion and metastasis of cancer cells, and is over-expressed in many kinds of malignancies. Our studies indicated that heparanase was frequently expressed in advanced gastric cancers. The aim of this study is to determine whether silencing of heparanase expression can abolish the malignant characteristics of gastric cancer cells.</p> <p>Methods</p> <p>Three heparanase-specific small interfering RNA (siRNAs) were designed, synthesized, and transfected into cultured gastric cancer cell line SGC-7901. Heparanase expression was measured by RT-PCR, real-time quantitative PCR and Western blot. Cell proliferation was detected by MTT colorimetry and colony formation assay. The <it>in vitro </it>invasion and metastasis of cancer cells were measured by cell adhesion assay, scratch assay and matrigel invasion assay. The angiogenesis capabilities of cancer cells were measured by tube formation of endothelial cells.</p> <p>Results</p> <p>Transfection of siRNA against 1496-1514 bp of encoding regions resulted in reduced expression of heparanase, which started at 24 hrs and lasted for 120 hrs post-transfection. The siRNA-mediated silencing of heparanase suppressed the cellular proliferation of SGC-7901 cells. In addition, the <it>in vitro </it>invasion and metastasis of cancer cells were attenuated after knock-down of heparanase. Moreover, transfection of heparanase-specific siRNA attenuated the <it>in vitro </it>angiogenesis of cancer cells in a dose-dependent manner.</p> <p>Conclusions</p> <p>These results demonstrated that gene silencing of heparanase can efficiently abolish the proliferation, invasion, metastasis and angiogenesis of human gastric cancer cells <it>in vitro</it>, suggesting that heparanase-specific siRNA is of potential values as a novel therapeutic agent for human gastric cancer.</p

Crystal structure, stability and in vitro RNAi activity of oligoribonucleotides containing the ribo-difluorotoluyl nucleotide: insights into substrate requirements by the human RISC Ago2 enzyme

Short interfering RNA (siRNA) duplexes are currently being evaluated as antisense agents for gene silencing. Chemical modification of siRNAs is widely expected to be required for therapeutic applications in order to improve delivery, biostability and pharmacokinetic properties. Beyond potential improvements in the efficacy of oligoribonucleotides, chemical modification may also provide insight into the mechanism of mRNA downregulation mediated by the RNA–protein effector complexes (RNA-induced silencing complex or RISC). We have studied the in vitro activity in HeLa cells of siRNA duplexes against firefly luciferase with substitutions in the guide strand of U for the apolar ribo-2,4-difluorotoluyl nucleotide (rF) [Xia, J. et al. (2006) ACS Chem. Biol., 1, 176–183] as well as of C for rF. Whereas an internal rF:A pair adjacent to the Ago2 (‘slicer’ enzyme) cleavage site did not affect silencing relative to the native siRNA duplex, the rF:G pair and other mismatches such as A:G or A:A were not tolerated. The crystal structure at atomic resolution determined for an RNA dodecamer duplex with rF opposite G manifests only minor deviations between the geometries of rF:G and the native U:G wobble pair. This is in contrast to the previously found, significant deviations between the geometries of rF:A and U:A pairs. Comparison between the structures of the RNA duplex containing rF:G and a new structure of an RNA with A:G mismatches with the structures of standard Watson–Crick pairs in canonical duplex RNA leads to the conclusion that local widening of the duplex formed by the siRNA guide strand and the targeted region of mRNA is the most likely reason for the intolerance of human Ago2 (hAgo2), the RISC endonuclease, toward internal mismatch pairs involving native or chemically modified RNA. Contrary to the influence of shape, the thermodynamic stabilities of siRNA duplexes with single rF:A, A:A, G:A or C:A (instead of U:A) or rF:G pairs (instead of C:G) show no obvious correlation with their activities. However, incorporation of three rF:A pairs into an siRNA duplex leads to loss of activity. Our structural and stability data also shed light on the role of organic fluorine as a hydrogen bond acceptor. Accordingly, UV melting (TM) data, osmotic stress measurements, X-ray crystallography at atomic resolution and the results of semi-empirical calculations are all consistent with the existence of weak hydrogen bonds between fluorine and the H-N1(G) amino group in rF:G pairs of the investigated RNA dodecamers

Improving model predictions for RNA interference activities that use support vector machine regression by combining and filtering features

<p>Abstract</p> <p>Background</p> <p>RNA interference (RNAi) is a naturally occurring phenomenon that results in the suppression of a target RNA sequence utilizing a variety of possible methods and pathways. To dissect the factors that result in effective siRNA sequences a regression kernel Support Vector Machine (SVM) approach was used to quantitatively model RNA interference activities.</p> <p>Results</p> <p>Eight overall feature mapping methods were compared in their abilities to build SVM regression models that predict published siRNA activities. The primary factors in predictive SVM models are position specific nucleotide compositions. The secondary factors are position independent sequence motifs (<it>N</it>-grams) and guide strand to passenger strand sequence thermodynamics. Finally, the factors that are least contributory but are still predictive of efficacy are measures of intramolecular guide strand secondary structure and target strand secondary structure. Of these, the site of the 5' most base of the guide strand is the most informative.</p> <p>Conclusion</p> <p>The capacity of specific feature mapping methods and their ability to build predictive models of RNAi activity suggests a relative biological importance of these features. Some feature mapping methods are more informative in building predictive models and overall <it>t</it>-test filtering provides a method to remove some noisy features or make comparisons among datasets. Together, these features can yield predictive SVM regression models with increased predictive accuracy between predicted and observed activities both within datasets by cross validation, and between independently collected RNAi activity datasets. Feature filtering to remove features should be approached carefully in that it is possible to reduce feature set size without substantially reducing predictive models, but the features retained in the candidate models become increasingly distinct. Software to perform feature prediction and SVM training and testing on nucleic acid sequences can be found at the following site: <url>ftp://scitoolsftp.idtdna.com/SEQ2SVM/</url>.</p