Predicting siRNA efficacy based on multiple selective siRNA representations and their combination at score level

Novel Golden Lipid Nanoparticles with Small Interference Ribonucleic Acid for Substrate Reduction Therapy in Fabry Disease

Substrate reduction therapy (SRT) has been proposed as a new gene therapy for Fabry disease (FD) to prevent the formation of globotriaosylceramide (Gb3). Nanomedicines containing different siRNA targeted to Gb3 synthase (Gb3S) were designed. Formulation factors, such as the composition, solid lipid nanoparticles (SLNs) preparation method and the incorporation of different ligands, such as gold nanoparticles (GNs), protamine (P) and polysaccharides, were evaluated. The new siRNA-golden LNPs were efficiently internalized in an FD cell model (IMFE-1), with GNs detected in the cytoplasm and in the nucleus. Silencing efficacy (measured by RT-qPCR) depended on the final composition and method of preparation, with silencing rates up to 90% (expressed as the reduction in Gb3S-mRNA). GNs conferred a higher system efficacy and stability without compromising cell viability and hemocompatibility. Immunocytochemistry assays confirmed Gb3S silencing for at least 15 days with the most effective formulations. Overall, these results highlight the potential of the new siRNA-golden LNP system as a promising nanomedicine to address FD by specific SRT.

A Graph Neural Network Approach for the Analysis of siRNA-Target Biological Networks

Many biological systems are characterised by biological entities, as well as their relationships. These interaction networks can be modelled as graphs, with nodes representing bio-entities, such as molecules, and edges representing relations among them, such as interactions. Due to the current availability of a huge amount of biological data, it is very important to consider in silico analysis methods based on, for example, machine learning, that could take advantage of the inner graph structure of the data in order to improve the quality of the results. In this scenario, graph neural networks (GNNs) are recent computational approaches that directly deal with graph-structured data. In this paper, we present a GNN network for the analysis of siRNA-mRNA interaction networks. siRNAs, in fact, are small RNA molecules that are able to bind to target genes and silence them. These events make siRNAs key molecules as RNA interference agents in many biological interaction networks related to severe diseases such as cancer. In particular, our GNN approach allows for the prediction of the siRNA efficacy, which measures the siRNA's ability to bind and silence a gene target. Tested on benchmark datasets, our proposed method overcomes other machine learning algorithms, including the state-of-the-art predictor based on the convolutional neural network, reaching a Pearson correlation coefficient of approximately 73.6%. Finally, we proposed a case study where the efficacy of a set of siRNAs is predicted for a gene of interest. To the best of our knowledge, GNNs were used for the first time in this scenario.

Cheminformatics Modeling of Gene Silencing for Both Natural and Chemically Modified siRNAs

In designing effective siRNAs for a specific mRNA target, it is critically important to have predictive models for the potency of siRNAs. None of the published methods characterized the chemical structures of individual nucleotides constituting a siRNA molecule; therefore, they cannot predict the potency of gene silencing by chemically modified siRNAs (cm-siRNA). We propose a new approach that can predict the potency of gene silencing by cm-siRNAs, which characterizes each nucleotide (NT) using 12 BCUT cheminformatics descriptors describing its charge distribution, hydrophobic and polar properties. Thus, a 21-NT siRNA molecule is described by 252 descriptors resulting from concatenating all the BCUT values of its composing nucleotides. Partial Least Square is employed to develop statistical models. The Huesken data (2431 natural siRNA molecules) were used to perform model building and evaluation for natural siRNAs. Our results were comparable with or superior to those from Huesken’s algorithm. The Bramsen dataset (48 cm-siRNAs) was used to build and test the models for cm-siRNAs. The predictive r² of the resulting models reached 0.65 (or Pearson r values of 0.82). Thus, this new method can be used to successfully model gene silencing potency by both natural and chemically modified siRNA molecules.

Constructing the boundary between potent and ineffective siRNAs by MG-algorithm with C-features

BACKGROUND

In siRNA based antiviral therapeutics, selection of potent siRNAs is an indispensable step, but these commonly used features are unable to construct the boundary between potent and ineffective siRNAs.

RESULTS

Here, we select potent siRNAs by removing ineffective ones, where these conditions for removals are constructed by C-features of siRNAs, C-features are generated by MG-algorithm, Icc-cluster and the different combinations of some commonly used features, MG-algorithm and Icc-cluster are two different algorithms to search the nearest siRNA neighbors. For the ineffective siRNAs in test data, they are removed from test data by I-iteration, where I-iteration continually updates training data by adding these successively removed siRNAs. Furthermore, the efficacy of siRNAs of test data is predicted by their nearest neighbors of training data.

CONCLUSIONS

By siRNAs of Hencken dataset, results show that our algorithm removes almost ineffective siRNAs from test data, gives the clear boundary between potent and ineffective siRNAs, and accurately predicts the efficacy of siRNAs also. We suggest that our algorithm can provide new insights for selecting the potent siRNAs.

Novel approaches in cancer treatment: preclinical and clinical development of small non-coding RNA therapeutics

Short or small interfering RNAs (siRNAs) and microRNA (miRNAs) are molecules similar in size and function able to inhibit gene expression based on their complementarity with mRNA sequences, inducing the degradation of the transcript or the inhibition of their translation.

siRNAs bind specifically to a single gene location by sequence complementarity and regulate gene expression by specifically targeting transcription units via posttranscriptional gene silencing. miRNAs can regulate the expression of different gene targets through their imperfect base pairing.

This process - known as RNA interference (RNAi) - modulates transcription in order to maintain a correct physiological environment, playing a role in almost the totality of the cellular pathways.

siRNAs have been evolutionary evolved for the protection of genome integrity in response to exogenous and invasive nucleic acids such as transgenes or transposons. Artificial siRNAs are widely used in molecular biology for transient silencing of genes of interest. This strategy allows to inhibit the expression of any target protein of known sequence and is currently used for the treatment of different human diseases including cancer.

Modifications and rearrangements in gene regions encoding for miRNAs have been found in cancer cells, and specific miRNA expression profiles characterize the developmental lineage and the differentiation state of the tumor. miRNAs with different expression patterns in tumors have been reported as oncogenes (oncomirs) or tumor-suppressors (anti-oncomirs). RNA modulation has become important in cancer research not only for development of early and easy diagnosis tools but also as a promising novel therapeutic approach.

Despite the emerging discoveries supporting the role of miRNAs in carcinogenesis and their and siRNAs possible use in therapy, a series of concerns regarding their development, delivery and side effects have arisen.

In this review we report the biology of miRNAs and siRNAs in relation to cancer summarizing the recent methods described to use them as novel therapeutic drugs and methods to specifically deliver them to cancer cells and overcome the limitations in the use of these molecules.

Multiple gene targeting siRNAs for down regulation of Immediate Early-2 (Ie2) and DNA polymerase genes mediated inhibition of novel rat Cytomegalovirus (strain All-03)

Background

Cytomegalovirus (CMV) is an opportunistic pathogen that causes severe complications in congenitally infected newborns and non-immunocompetent individuals. Developing an effective vaccine is a major public health priority and current drugs are fronting resistance and side effects on recipients. In the present study, with the aim of exploring new strategies to counteract CMV replication, several anti-CMV siRNAs targeting IE2 and DNA polymerase gene regions were characterized and used as in combinations for antiviral therapy.

Methods

The rat embryo fibroblast (REF) cells were transfected with multi siRNA before infecting with CMV strain ALL-03. Viral growth inhibition was measured by tissue culture infectious dose (TCID50), cytopathic effect (CPE) and droplet digital PCR (ddPCR) while IE2 and DNA polymerase gene knockdown was determined by real-time PCR. Ganciclovir was deployed as a control to benchmark the efficacy of antiviral activities of respective individual siRNAs.

Results

There was no significant cytotoxicity encountered for all the combinations of siRNAs on REF cells analyzed by MTT colorimetric assay (P > 0.05). Cytopathic effects (CPE) in cells infected by RCMV ALL-03 had developed significantly less and at much slower rate compared to control group. The expression of targeted genes was downregulated successfully resulted in significant reduction (P < 0.05) of viral mRNA and DNA copies (dpb + dpc: 79%, 68%; dpb + ie2b: 68%, 60%; dpb + dpc + ie2b: 48%, 42%). Flow cytometry analysis showed a greater percentage of viable and early apoptosis of combined siRNAs-treated cells compared to control group. Notably, the siRNAs targeting gene regions were sequenced and mutations were not encountered, thereby avoiding the formation of mutant with potential resistant viruses.

Conclusions

In conclusion. The study demonstrated a tremendous promise of innovative approach with the deployment of combined siRNAs targeting at several genes simultaneously with the aim to control CMV replication in host cells.

Systematics for types and effects of RNA variations

Systematics is described for annotation of variations in RNA molecules. The conceptual framework is part of Variation Ontology (VariO) and facilitates depiction of types of variations, their functional and structural effects and other consequences in any RNA molecule in any organism. There are more than 150 RNA related VariO terms in seven levels, which can be further combined to generate even more complicated and detailed annotations. The terms are described together with examples, usually for variations and effects in human and in diseases. RNA variation type has two subcategories: variation classification and origin with subterms. Altogether six terms are available for function description. Several terms are available for affected RNA properties. The ontology contains also terms for structural description for affected RNA type, post-transcriptional RNA modifications, secondary and tertiary structure effects and RNA sugar variations. Together with the DNA and protein concepts and annotations, RNA terms allow comprehensive description of variations of genetic and non-genetic origin at all possible levels. The VariO annotations are readable both for humans and computer programs for advanced data integration and mining.

PVsiRNAPred: Prediction of plant exclusive virus-derived small interfering RNAs by deep convolutional neural network

Plant exclusive virus-derived small interfering RNAs (vsiRNAs) regulate various biological processes, especially important in antiviral immunity. The identification of plant vsiRNAs is important for understanding the biogenesis and function mechanisms of vsiRNAs and further developing anti-viral plants. In this study, we extracted plant vsiRNA sequences from the PVsiRNAdb database. We then utilized deep convolutional neural network (CNN) to develop a deep learning algorithm for predicting plant vsiRNAs based on vsiRNA sequence composition, known as PVsiRNAPred. The key part of PVsiRNAPred is the CNN module, which automatically learns hierarchical representations of vsiRNA sequences related to vsiRNA profiles in plants. When evaluated using an independent testing dataset, the accuracy of the model was 65.70%, which was higher than those of five conventional machine learning method-based classifiers. In addition, PVsiRNAPred obtained a sensitivity of 67.11%, specificity of 64.26% and Matthews correlation coefficient (MCC) of 0.31, and the area under the receiver operating characteristic (ROC) curve (AUC) of PVsiRNAPred was 0.71 in the independent test. The permutation test with 1000 shuffles resulted in a p value of<0.001. The above results reveal that PVsiRNAPred has favorable generalization capabilities. We hope PVsiRNAPred, the first bioinformatics algorithm for predicting plant vsiRNAs, will allow efficient discovery of new vsiRNAs.

Functional features defining the efficacy of cholesterol-conjugated, self-deliverable, chemically modified siRNAs

Progress in oligonucleotide chemistry has produced a shift in the nature of siRNA used, from formulated, minimally modified siRNAs, to unformulated, heavily modified siRNA conjugates. The introduction of extensive chemical modifications is essential for conjugate-mediated delivery. Modifications have a significant impact on siRNA efficacy through interference with recognition and processing by RNAi enzymatic machinery, severely restricting the sequence space available for siRNA design. Many algorithms available publicly can successfully predict the activity of non-modified siRNAs, but the efficiency of the algorithms for designing heavily modified siRNAs has never been systematically evaluated experimentally. Here we screened 356 cholesterol-conjugated siRNAs with extensive modifications and developed a linear regression-based algorithm that effectively predicts siRNA activity using two independent datasets. We further demonstrate that predictive determinants for modified and non-modified siRNAs differ substantially. The algorithm developed from the non-modified siRNAs dataset has no predictive power for modified siRNAs and vice versa. In the context of heavily modified siRNAs, the introduction of chemical asymmetry fully eliminates the requirement for thermodynamic bias, the major determinant for non-modified siRNA efficacy. Finally, we demonstrate that in addition to the sequence of the target site, the accessibility of the neighboring 3′ region significantly contributes to siRNA efficacy.

SiRNA silencing efficacy prediction based on a deep architecture

Background

Small interfering RNA (siRNA) can be used to post-transcriptional gene regulation by knocking down targeted genes. In functional genomics, biomedical research and cancer therapeutics, siRNA design is a critical research topic. Various computational algorithms have been developed to select the most effective siRNA, whereas the efficacy prediction accuracy is not so satisfactory. Many existing computational methods are based on feature engineering, which may lead to biased and incomplete features. Deep learning utilizes non-linear mapping operations to detect potential feature pattern and has been considered perform better than existing machine learning method.

Results

In this paper, to further improve the prediction accuracy and facilitate gene functional studies, we developed a new powerful siRNA efficacy predictor based on a deep architecture. First, we extracted hidden feature patterns from two modalities, including sequence context features and thermodynamic property. Then, we constructed a deep architecture to implement the prediction. On the available largest siRNA database, the performance of our proposed method was measured with 0.725 PCC and 0.903 AUC value. The comparative experiment showed that our proposed architecture outperformed several siRNA prediction methods.

Conclusions

The results demonstrate that our deep architecture is stable and efficient to predict siRNA silencing efficacy. The method could help select candidate siRNA for targeted mRNA, and further promote the development of RNA interference.