Filtering of Ineffective siRNAs and Improved siRNA Design Tool

Delivery Aspects for Implementing siRNA Therapeutics for Blood Diseases

Hematological disorders result in significant health consequences, and traditional therapies frequently entail adverse reactions without addressing the root cause. A potential solution for hematological disorders characterized by gain-of-function mutations lies in the emergence of small interfering RNA (siRNA) molecules as a therapeutic option. siRNAs are a class of RNA molecules composed of double-stranded RNAs that can degrade specific mRNAs, thereby inhibiting the synthesis of underlying disease proteins. Therapeutic interventions utilizing siRNA can be tailored to selectively target genes implicated in diverse hematological disorders, including sickle cell anemia, β-thalassemia, and malignancies such as lymphoma, myeloma, and leukemia. The development of efficient siRNA silencers necessitates meticulous contemplation of variables such as the RNA backbone, stability, and specificity. Transportation of siRNA to specific cells poses a significant hurdle, prompting investigations of diverse delivery approaches, including chemically modified forms of siRNA and nanoparticle formulations with various biocompatible carriers. This review delves into the crucial role of siRNA technology in targeting and treating hematological malignancies and disorders. It sheds light on the latest research, development, and clinical trials, detailing how various pharmaceutical approaches leverage siRNA against blood disorders, mainly concentrating on cancers. It outlines the preferred molecular targets and physiological barriers to delivery while emphasizing the growing potential of various therapeutic delivery methods. The need for further research is articulated in the context of overcoming the shortcomings of siRNA in order to enrich discussions around siRNA's role in managing blood disorders and aiding the scientific community in advancing more targeted and effective treatments.

From sequences to therapeutics: Using machine learning to predict chemically modified siRNA activity

Small interfering RNAs (siRNAs) exemplify the promise of genetic medicine in the discovery of novel therapeutic modalities. Their ability to selectively suppress gene expression makes them ideal candidates for the development of oligonucleotide pharmaceuticals. Recent advancements in machine learning (ML) have facilitated the design of unmodified siRNA and efficacy prediction. However, a model trained to predict the silencing activity of siRNAs with diverse chemical modification patterns is yet to be published despite the importance of such modifications in designing siRNAs with the potential to reach the level of clinical use. This study presents the first application of ML to efficiently classify chemically modified siRNAs on the basis of sequence and chemical modification patterns alone. Three algorithms were evaluated at three classification thresholds and compared according to sensitivity, specificity, consistency of feature weights with empirical knowledge, and performance using an external validation dataset. Finally, possible directions for future research were proposed.

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.

SMRI: A New Method for siRNA Design for COVID-19 Therapy

First discovered in Wuhan, China, SARS-CoV-2 is a highly pathogenic novel coronavirus, which rapidly spread globally and became a pandemic with no vaccine and limited distinctive clinical drugs available till March 13th, 2020. Ribonucleic Acid interference (RNAi) technology, a gene-silencing technology that targets mRNA, can cause damage to RNA viruses effectively. Here, we report a new efficient small interfering RNA (siRNA) design method named Simple Multiple Rules Intelligent Method (SMRI) to propose a new solution of the treatment of COVID-19. To be specific, this study proposes a new model named Base Preference and Thermodynamic Characteristic model (BPTC model) indicating the siRNA silencing efficiency and a new index named siRNA Extended Rules index (SER index) based on the BPTC model to screen high-efficiency siRNAs and filter out the siRNAs that are difficult to take effect or synthesize as a part of the SMRI method, which is more robust and efficient than the traditional statistical indicators under the same circumstances. Besides, to silence the spike protein of SARS-CoV-2 to invade cells, this study further puts forward the SMRI method to search candidate high-efficiency siRNAs on SARS-CoV-2's S gene. This study is one of the early studies applying RNAi therapy to the COVID-19 treatment. According to the analysis, the average value of predicted interference efficiency of the candidate siRNAs designed by the SMRI method is comparable to that of the mainstream siRNA design algorithms. Moreover, the SMRI method ensures that the designed siRNAs have more than three base mismatches with human genes, thus avoiding silencing normal human genes. This is not considered by other mainstream methods, thereby the five candidate high-efficiency siRNAs which are easy to take effect or synthesize and much safer for human body are obtained by our SMRI method, which provide a new safer, small dosage and long efficacy solution for the treatment of COVID-19.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11390-021-0826-x.

RNA Interference and Small RNA Analysis

Small silencing RNAs have provided powerful reverse genetics tools and have opened new areas of research. This introduction describes the use of RNAi to suppress expression of individual genes for loss-of-function analysis. It also summarizes methods for measuring specific and global changes in small RNA expression, as well as methods to inhibit the function of individual endogenous small RNA species such as miRNAs.

RNA Interference Technology

RNA interference (RNAi) is the biological process of mRNA degradation induced by complementary sequences double-stranded (ds) small interfering RNAs (siRNA) and suppression of target gene expression. Exogenous siRNAs (perfectly paired dsRNAs of ∼21–25 nt in length) play an important role in host defense against RNA viruses and in transcriptional and post-transcriptional gene regulation in plants and other eukaryotes. Using RNAi technology by transfecting synthetic siRNAs into eukaryotic cells to silence genes has become an indispensable tool to investigate gene functions, and siRNA-based therapy is being developed to knockdown genes implicated in diseases. Other examples of RNAi technology include method of producing highly potent and purified siRNAs directly from Escherichiacoli cells, based on an unexpected discovery that ectopic expression of p19, a plant viral siRNA-binding protein, stabilizes a cryptic siRNA-like RNA species in bacteria. Those siRNAs, named as pro-siRNA for “prokaryotic siRNA”, are bacterial RNase III products that have chemical and functional properties that like eukaryotic siRNAs.

Computational Tools: RNA Interference in Fungal Therapeutics

There is steady rise in the number of immunocompromised population due to increased use of potent immunosuppression therapies. This is associated with increased risk of acquiring fungal opportunistic infections in immunocompromised patients which account for high morbidity and mortality rates, if left untreated. The conventional antifungal drugs to treat fungal diseases (mycoses) are increasingly becoming inadequate due to observed varied susceptibility of fungi and their recurrent resistance. RNA interference (RNAi), sequence-specific gene silencing, is emerging as a promising new therapeutic approach. This chapter discusses various aspects of RNAi, viz., the fundamental RNAi machinery present in fungi, in silico siRNA features, designing guidelines and tools, siRNA delivery, and validation of gene knockdown for therapeutics against mycoses. Target gene identification is a crucial step in designing of gene-specific siRNA in addition to efficient delivery strategies to bring about effective inhibition of fungi. Subsequently, designed siRNA can be delivered effectively in vitro either by soaking fungi with siRNA or by transforming inverted repeat transgene containing plasmid into fungi, which ultimately generates siRNA(s). Finally, fungal inhibition can be verified at the RNA and protein levels by blotting techniques, fluorescence imaging, and biochemical assays. Despite challenges, several such in vitro studies have spawned optimism around RNAi as a revolutionary new class of therapeutics against mycoses. But, pharmacokinetic parameters need to be evaluated from in vivo studies and clinical trials to recognize RNAi as a novel treatment approach for mycoses.

Evolution of Proteins and Proteomes: A Phylogenetics Approach

The study of evolutionary relationships among protein sequences was one of the first applications of bioinformatics. Since then, and accompanying the wealth of biological data produced by genome sequencing and other high-throughput techniques, the use of bioinformatics in general and phylogenetics in particular has been gaining ground in the study of protein and proteome evolution. Nowadays, the use of phylogenetics is instrumental not only to infer the evolutionary relationships among species and their genome sequences, but also to reconstruct ancestral states of proteins and proteomes and hence trace the paths followed by evolution. Here I survey recent progress in the elucidation of mechanisms of protein and proteome evolution in which phylogenetics has played a determinant role.

Imaging CXCR4 Expression with (99m)Tc-Radiolabeled Small-Interference RNA in Experimental Human Breast Cancer Xenografts

PURPOSE

Noninvasive quantification of chemokine receptor 4 (CXCR4) expression could serve as a prognostic indicator and may be of value for the design of personalized therapies and posttreatment monitoring. The objective of the present study was to assess the use of (99m)Tc-radiolabeled small-interference RNA (siRNA) targeting CXCR4 to detect CXCR4 expression in vivo.

PROCEDURES

CXCR4 siRNAs were radiolabeled with (99m)Tc using the bifunctional chelator hydrazinonicotinamide (HYNIC), and the labeling efficiency, specific activity and radiochemical purity were determined. The stability of the probe in serum was assessed by measuring its radiochemical purity and inhibitory activity by RT-PCR and western blotting. Biodistribution studies and static imaging were performed in MDA-MB-231 tumor-bearing mice.

RESULTS

Radiochemical purity remained highly stable in PBS and fresh human serum at room temperature and at 37 °C. Radiolabeled siRNA1 showed strong inhibitory effects similar to those of unlabeled siRNA1 on both CXCR4 messenger RNA (mRNA) and protein in vitro. The excretion of the probe occurred mainly through the liver and kidneys. Tumors were clearly visualized at 1-10 h after injection of the probe, but not after injection of the control probe.

CONCLUSIONS

(99m)Tc-labeled CXCR4 siRNA1 shows tumor-specific accumulation and could be a promising strategy for the visualization of CXCR4 expression in human breast cancer.

Characterization and RNAi-mediated knockdown of Chitin Synthase A in the potato tuber moth, Phthorimaea operculella

Chitin is a major component of insect exoskeleton, tracheal system and gut where it is synthesized by chitin synthase (CHS) enzymes. In this paper, we report the isolation and RNAi of chitin synthase A (PhoCHSA) from the potato tuber moth Phthorimaea operculella. The full-length cDNA of PhoCHSA is 5,627 bp with 4,689 bp open reading frame coding for 1,563 amino acids. Structural analysis of conceptual amino acid translation showed three distinct regions found in all known insect CHS proteins; N-terminus region having 9 transmembrane helices, middle catalytic region containing several conserved domains identified in insect CHS enzymes, and C-terminus region containing seven transmembrane spans. Phylogenetic analysis showed that PhoCHSA protein clustered with CHSA enzymes identified from insects from different insect orders. RNAi targeting three different regions of the gene showed different efficacy against potato tuber moth larvae and dsRNA targeting the 5′ region has the highest efficacy. Results were verified by qRT-PCR which showed that dsRNA targeting the 5′ region caused the highest reduction in PhoCHSA mRNA level. Our results show the importance of selecting the RNAi target region and that chitin synthase A can be a suitable RNAi target for the potato tuber moth control.

Utilizing Selected Di- and Trinucleotides of siRNA to Predict RNAi Activity

Small interfering RNAs (siRNAs) induce posttranscriptional gene silencing in various organisms. siRNAs targeted to different positions of the same gene show different effectiveness; hence, predicting siRNA activity is a crucial step. In this paper, we developed and evaluated a powerful tool named “siRNApred” with a new mixed feature set to predict siRNA activity. To improve the prediction accuracy, we proposed 2-3NTs as our new features. A Random Forest siRNA activity prediction model was constructed using the feature set selected by our proposed Binary Search Feature Selection (BSFS) algorithm. Experimental data demonstrated that the binding site of the Argonaute protein correlates with siRNA activity. “siRNApred” is effective for selecting active siRNAs, and the prediction results demonstrate that our method can outperform other current siRNA activity prediction methods in terms of prediction accuracy.

Preclinical and clinical development of siRNA-based therapeutics

The discovery of RNA interference, first in plants and Caenorhabditis elegans and later in mammalian cells, led to the emergence of a transformative view in biomedical research. Knowledge of the multiple actions of non-coding RNAs has truly allowed viewing DNA, RNA and proteins in novel ways. Small interfering RNAs (siRNAs) can be used as tools to study single gene function both in vitro and in vivo and are an attractive new class of therapeutics, especially against undruggable targets for the treatment of cancer and other diseases. Despite the potential of siRNAs in cancer therapy, many challenges remain, including rapid degradation, poor cellular uptake and off-target effects. Rational design strategies, selection algorithms, chemical modifications and nanocarriers offer significant opportunities to overcome these challenges. Here, we review the development of siRNAs as therapeutic agents from early design to clinical trial, with special emphasis on the development of EphA2-targeting siRNAs for ovarian cancer treatment.

p27Kip1 knockdown induces proliferation in the organ of Corti in culture after efficient shRNA lentiviral transduction

The cells in the organ of Corti do not exhibit spontaneous cell regeneration; hair cells that die after damage are not replaced. Supporting cells can be induced to transdifferentiate into hair cells, but that would deplete their numbers, therefore impairing epithelium physiology. The loss of p27Kip1 function induces proliferation in the organ of Corti, which raises the possibility to integrate it to the strategies to achieve regeneration. Nevertheless, it is not known if the extent of this proliferative potential, as well as its maintenance in postnatal stages, is compatible with providing a basis for eventual therapeutic manipulation. This is due in part to the limited success of approaches to deliver tools to modify gene expression in the auditory epithelium. We tested the hypothesis that the organ of Corti can undergo significant proliferation when efficient manipulation of the expression of regulators of the cell cycle is achieved. Lentiviral vectors were used to transduce all cochlear cell types, with efficiencies around 4 % for hair cells, 43 % in the overall supporting cell population, and 74 % within lesser epithelial ridge (LER) cells. Expression of short hairpin RNA targeting p27Kip1 encoded by the lentiviral vectors led to measurable proliferation in the organ of Corti and increase in LER cells number but not hair cell regeneration. Our results revalidate the use of lentiviral vectors in the study and in the potential therapeutic approaches for inner ear diseases, as well as demonstrate that efficient manipulation of p27Kip1 is sufficient to induce significant proliferation in the postnatal cochlea.

Designing functional siRNA with reduced off-target effects

RNA interference (RNAi) mediated by small interfering RNA (siRNA) is now widely used to knock down gene expression in a sequence-specific manner, making it a powerful tool not only for studying gene functions but also for therapeutic applications. siRNA decreases the expression level of the intended target gene with complete complementarity by cleaving its mRNA. However, the efficacy of each siRNA widely varies depending on its sequence in mammalian cells; only a limited fraction of randomly designed siRNAs is functional. Moreover, off-target silencing effects arise when the siRNA has partial complementarity in the seed region with unintended genes. Here, we describe the rational designing of functional, off-target effect-reduced siRNAs using siDirect 2.0 Web server (http://siDirect2.RNAi.jp/). By using the default parameters, siDirect 2.0 can design at least one qualified siRNA for >94% of human mRNA sequences in the RefSeq database.

Reconsideration of in silico siRNA design from a perspective of heterogeneous data integration: problems and solutions

The success of RNA interference (RNAi) depends on the interaction between short interference RNAs (siRNAs) and mRNAs. Design of highly efficient and specific siRNAs has become a challenging issue in applications of RNAi. Here, we present a detailed survey on the state-of-the-art siRNAs design, focusing on several key issues with the current in silico RNAi studies, including: (i) inconsistencies among the proposed guidelines for siRNAs design and the incomplete list of siRNAs features, (ii) improper integration of the heterogeneous cross-platform siRNAs data, (iii) inadequate consideration of the binding specificity of the target mRNAs and (iv) reduction in the 'off-target' effect in siRNAs design. With these considerations, the popular in silico siRNAs design rules are reexamined and several inconsistent viewpoints toward siRNAs feature identifications are clarified. In addition, novel computational models for siRNAs design using state-of-art machine learning techniques are discussed, which focus on heterogeneous data integration, joint feature selection and customized siRNAs screening toward highly specific targets. We believe that addressing such issues in siRNA study will provide new clues for further improved design of more efficient and specific siRNAs in RNAi.

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.

Optimized models for design of efficient miR30-based shRNAs

Small hairpin RNAs (shRNAs) became an important research tool in cell biology. Reliable design of these molecules is essential for the needs of large functional genomics projects. To optimize the design of efficient shRNAs, we performed comparative, thermodynamic, and correlation analyses of ~18,000 miR30-based shRNAs with known functional efficiencies, derived from the Sensor Assay project (Fellmann et al., 2011). We identified features of the shRNA guide strand that significantly correlate with the silencing efficiency and performed multiple regression analysis, using 4/5 of the data for training purposes and 1/5 for cross validation. A model that included the position-dependent nucleotide preferences was predictive in the cross-validation data subset (R = 0.39). However, a model, which in addition to the nucleotide preferences included thermodynamic shRNA features such as a thermodynamic duplex stability and position-dependent thermodynamic profile (dinucleotide free energy) was performing better (R = 0.53). Software "miR_Scan" was developed based upon the optimized models. Calculated mRNA target secondary structure stability showed correlation with shRNA silencing efficiency but failed to improve the model. Correlation analysis demonstrates that our algorithm for identification of efficient miR30-based shRNA molecules performs better than approaches that were developed for design of chemically synthesized siRNAs (R(max) = 0.36).

Reconsideration of in-silico siRNA design based on feature selection: a cross-platform data integration perspective

RNA interference via exogenous short interference RNAs (siRNA) is increasingly more widely employed as a tool in gene function studies, drug target discovery and disease treatment. Currently there is a strong need for rational siRNA design to achieve more reliable and specific gene silencing; and to keep up with the increasing needs for a wider range of applications. While progress has been made in the ability to design siRNAs with specific targets, we are clearly at an infancy stage towards achieving rational design of siRNAs with high efficacy. Among the many obstacles to overcome, lack of general understanding of what sequence features of siRNAs may affect their silencing efficacy and of large-scale homogeneous data needed to carry out such association analyses represents two challenges. To address these issues, we investigated a feature-selection based in-silico siRNA design from a novel cross-platform data integration perspective. An integration analysis of 4,482 siRNAs from ten meta-datasets was conducted for ranking siRNA features, according to their possible importance to the silencing efficacy of siRNAs across heterogeneous data sources. Our ranking analysis revealed for the first time the most relevant features based on cross-platform experiments, which compares favorably with the traditional in-silico siRNA feature screening based on the small samples of individual platform data. We believe that our feature ranking analysis can offer more creditable suggestions to help improving the design of siRNA with specific silencing targets. Data and scripts are available at http://csbl.bmb.uga.edu/publications/materials/qiliu/siRNA.html.

Transient receptor potential channel 1 (TRPC1) reduces calcium permeability in heteromeric channel complexes

Specific biological roles of the classical transient receptor potential channel 1 (TRPC1) are still largely elusive. To investigate the function of TRPC1 proteins in cell physiology, we studied heterologously expressed TRPC1 channels and found that recombinant TRPC1 subunits do not form functional homomeric channels. Instead, by electrophysiological analysis TRPC1 was shown to form functional heteromeric, receptor-operated channel complexes with TRPC3, -4, -5, -6, and -7 indicating that TRPC1 proteins can co-assemble with all members of the TRPC subfamily. In all TRPC1-containing heteromers, TRPC1 subunits significantly decreased calcium permeation. The exchange of select amino acids in the putative pore-forming region of TRPC1 further reduced calcium permeability, suggesting that TRPC1 subunits contribute to the channel pore. In immortalized immature gonadotropin-releasing hormone neurons endogenously expressing TRPC1, -2, -5, and -6, down-regulation of TRPC1 resulted in increased calcium permeability and elevated basal cytosolic calcium concentrations. We did not observe any involvement of TRPC1 in store-operated cation influx. Notably, TRPC1 suppressed the migration of gonadotropin-releasing hormone neurons without affecting cell proliferation. Conversely, in TRPC1 knockdown neurons, specific migratory properties like distance covered, locomotion speed, and directionality were increased. These findings suggest a novel regulatory mechanism relying on the expression of TRPC1 and the subsequent formation of heteromeric TRPC channel complexes with reduced calcium permeability, thereby fine-tuning neuronal migration.

Silencing of gene expression in cultured cells using small interfering RNAs

The discovery of RNA interference (RNAi) and related small RNA-mediated regulatory pathways has significantly altered the understanding of gene regulation in eukaryotic cells. In the RNAi pathway, small interfering RNAs (siRNAs) approximately 21 to 23 nucleotides in length serve as the regulatory molecules that guide and induce sequence-specific gene silencing. The use of siRNA-mediated silencing as a tool for investigating gene function is well established in cultured mammalian cells. This unit provides basic approaches to explore the field of RNAi, and hopes to address the importance of optimizing transfection conditions after empirical determinations in order to understand various degrees of silencing efficiency.

Essential notes regarding the design of functional siRNAs for efficient mammalian RNAi

Short interfering RNAs (siRNAs) are widely used to bring about RNA interference (RNAi) in mammalian cells. Numerous siRNAs may be designed for any target gene though most of which would be incapable of efficiently inducing mammalian RNAi. Certain highly functional siRNAs designed for knockout of a particular gene may render unrelated endogenous genes nonfunctional. These major bottlenecks should be properly eliminated when RNAi technologies are employed for any experiment in mammalian functional genomics. This paper thus presents essential notes and findings regarding the proper choice of siRNA-sequence selection algorithms and web-based online software systems.

Optimization of duplex stability and terminal asymmetry for shRNA design

Prediction of efficient oligonucleotides for RNA interference presents a serious challenge, especially for the development of genome-wide RNAi libraries which encounter difficulties and limitations due to ambiguities in the results and the requirement for significant computational resources. Here we present a fast and practical algorithm for shRNA design based on the thermodynamic parameters. In order to identify shRNA and siRNA features universally associated with high silencing efficiency, we analyzed structure-activity relationships in thousands of individual RNAi experiments from publicly available databases (ftp://ftp.ncbi.nlm.nih.gov/pub/shabalin/siRNA/si_shRNA_selector/). Using this statistical analysis, we found free energy ranges for the terminal duplex asymmetry and for fully paired duplex stability, such that shRNAs or siRNAs falling in both ranges have a high probability of being efficient. When combined, these two parameters yield a approximately 72% success rate on shRNAs from the siRecords database, with the target RNA levels reduced to below 20% of the control. Two other parameters correlate well with silencing efficiency: the stability of target RNA and the antisense strand secondary structure. Both parameters also correlate with the short RNA duplex stability; as a consequence, adding these parameters to our prediction scheme did not substantially improve classification accuracy. To test the validity of our predictions, we designed 83 shRNAs with optimal terminal asymmetry, and experimentally verified that small shifts in duplex stability strongly affected silencing efficiency. We showed that shRNAs with short fully paired stems could be successfully selected by optimizing only two parameters: terminal duplex asymmetry and duplex stability of the hypothetical cleavage product, which also relates to the specificity of mRNA target recognition. Our approach performs at the level of the best currently utilized algorithms that take into account prediction of the secondary structure of the target and antisense RNAs, but at significantly lower computational costs. Based on this study, we created the si-shRNA Selector program that predicts both highly efficient shRNAs and functional siRNAs (ftp://ftp.ncbi.nlm.nih.gov/pub/shabalin/siRNA/si_shRNA_selector/).

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.

RESULTS

An elegant multi-task learning paradigm for cross-platform siRNA efficacy prediction is proposed. Experimental studies were performed on a large dataset of siRNA sequences which encompass several RNAi experiments recently conducted by different research groups. By using our multi-task learning method, the synergy among different experiments is exploited and an efficient multi-task predictor for siRNA efficacy prediction is obtained. The 19 most popular biological features for siRNA according to their jointly importance in multi-task learning were ranked. Furthermore, the hypothesis is validated out that the siRNA binding efficacy on different messenger RNAs(mRNAs) have different conditional distribution, thus the multi-task learning can be conducted by viewing tasks at an "mRNA"-level rather than at the "experiment"-level. Such distribution diversity derived from siRNAs bound to different mRNAs help indicate that the properties of target mRNA have important implications on the siRNA binding efficacy.

CONCLUSIONS

The knowledge gained from our study provides useful insights on how to analyze various cross-platform RNAi data for uncovering of their complex mechanism.

De Novo metabolic engineering and the promise of synthetic DNA

The uncertain price and tight supply of crude oil and the ever-increasing demand for clean energy have prompted heightened attention to the development of sustainable fuel technologies that ensure continued economic development while maintaining stewardship of the environment. In the face of these enormous challenges, biomass has emerged as a viable alternative to petroleum for the production of energy, chemicals, and materials owing to its abundance, inexpensiveness, and carbon-neutrality. Moreover, the immense ease and efficiency of biological systems at converting biomass-derived feedstocks into fuels, chemicals, and materials has generated renewed interest in biotechnology as a replacement for traditional chemical processes. Aided by the ever-expanding repertoire of microbial genetics and plant biotechnology, improved understanding of gene regulation and cellular metabolism, and incessantly accumulating gene and protein data, scientists are now contemplating engineering microbial cell factories to produce fuels, chemical feedstocks, polymers and pharmaceuticals in an economically and environmentally sustainable way. This goal resonates with that of metabolic engineering - the improvement of cellular properties through the intelligent design, rational modification, or directed evolution of biochemical pathways, and arguably, metabolic engineering seems best positioned to achieve the concomittant goals of environmental stewardship and economic prolificity.Improving a host organism's cellular traits and the potential design of new phenotypes is strongly dependent on the ability to effectively control the organism's genetic machinery. In fact, finely-tuned gene expression is imperative for achieving an optimal balance between pathway expression and cell viability, while avoiding cytotoxicity due to accumulation of certain gene products or metabolites. Early attempts to engineer a cell's metabolism almost exclusively relied on merely deleting or over-expressing single or multiple genes using recombinant DNA, and intervention targets were predominantly selected based on knowledge of the stoichiometry, kinetics, and regulation of the pathway of interest. However, the distributive nature of metabolic control, as opposed to the existence of a single rate-limiting step, predicates the controlled expression of multiple enzymes in several coordinated pathways to achieve the desired flux, and, as such, simple strategies involving either deleting or over-expressing genes are greatly limited in this context. On the other hand, the use of synthetic or modified promoters, riboswitches, tunable intergenic regions, and translation modulators such as internal ribosome entry sequences, upstream open reading frames, optimized mRNA secondary structures, and RNA silencing have been shown to be enormously conducive to achieving the fine-tuning of gene expression. These modifications to the genetic machinery of the host organism can be best achieved via the use of synthetic DNA technology, and the constant improvement in the affordability and quality of oligonucleotide synthesis suggests that these might well become the mainstay of the metabolic engineering toolbox in the years to come. The possibilities that arise with the use of synthetic oligonucleotides will be delineated herein.

Comparing artificial neural networks, general linear models and support vector machines in building predictive models for small interfering RNAs

Background

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.

Principal Findings

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 3×5 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.

Conclusions

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.

Alternative splicing as a therapeutic target for human diseases

The majority of eukaryotic genes undergo alternative splicing, an evolutionarily conserved phenomenon, to generate functionally diverse protein isoforms from a single transcript. The fact that defective pre-mRNA splicing can generate non-functional and often toxic proteins with catastrophic effects, accurate removal of introns and joining of exons is vital for cell homeostasis. Thus, molecular tools that could either silence a disease-causing gene or regulate its expression in trans will find many therapeutic applications. Here we present two RNA-based approaches, namely RNAi and theophylline-responsive riboswitch that can regulate gene expression by loss-of-function and modulation of splicing, respectively. These strategies are likely to continue to play an integral role in studying gene function and drug discovery.

[Positional and time effects of specific siRNA inhibit the expression of hTERT]

To explore the inhibitory effect in different positions siRNA on hTERT mRNA expression in MCF-7 cells at the different time points, we Designed and chemically synthesized four pairs of siRNA which aimed at human telomerase reverse transcriptase (hTERT) gene specifically, which were transfected into the MCF-7 cells by liposome method, and then the expression of hTERT mRNA in MCF-7 cells was tested respectively by half-quantity RT-PCR at 12 h, 24 h, 48 h, 72 h, and 5 d after transfection. Compared with the control groups, there were three pieces of siRNA that inhibit the expression of hTERT mRNA at 12 h after transfection among the four pieces. The highest inhibition ratio occurred at 48 h after transfection, and after 72 h the ratio descended, when the siRNA sequence was located at the relative simple structure site in the secondary structure of hTERT mRNA had the highest inhibition ratio, which was 75%(P<0.01), which indicated that the specific siRNA had obvious inhibition effect on hTERT gene, and the effect was dependant positionally and timely dependence.

AsiDesigner: exon-based siRNA design server considering alternative splicing

RNA interference (RNAi) with small interfering RNA (siRNA) has become a powerful tool in functional and medical genomic research through directed post-transcriptional gene silencing. In order to apply RNAi technique for eukaryotic organisms, where frequent alternative splicing results in diversification of mRNAs and finally of proteins, we need spliced mRNA isoform silencing to study the function of individual proteins. AsiDesigner is a web-based siRNA design software system, which provides siRNA design capability to account for alternative splicing for mRNA level gene silencing. It provides numerous novel functions including the designing of common siRNAs for the silencing of more than two mRNAs simultaneously, a scoring scheme to evaluate the performance of designed siRNAs by adopting currently known key design factors, a stepwise off-target searching with BLAST and FASTA algorithms and checking the folding secondary structure energy of siRNAs. To do this, we developed a novel algorithm to evaluate the common target region, where siRNAs can be designed to knockdown a specific mRNA isoform or more than two mRNA isoforms from a target gene simultaneously. The developed algorithm and the AsiDesigner were tested and validated as very effective throughout widely performed gene silencing experiments. It is expected that AsiDesigner will play an important role in functional genomics, drug discovery and other molecular biological research. AsiDesigner is freely accessible at http://sysbio.kribb.re.kr/AsiDesigner/.

Structure and sequence motifs of siRNA linked with in vitro down-regulation of morbillivirus gene expression

The most challenging task in RNA interference is the design of active small interfering RNA (siRNA) sequences. Numerous strategies have been published to select siRNA. They have proved effective in some applications but have failed in many others. Nonetheless, all existing guidelines have been devised to select effective siRNAs targeting human or murine genes. They may not be appropriate to select functional sequences that target genes from other organisms like viruses. In this study, we have analyzed 62 siRNA duplexes of 19 bases targeting three genes of three morbilliviruses. In those duplexes, we have checked which features are associated with siRNA functionality. Our results suggest that the intramolecular secondary structure of the targeted mRNA contributes to siRNA efficiency. We also confirm that the presence of at least the sequence motifs U13, A or U19, as well as the absence of G13, cooperate to increase siRNA knockdown rates. Additionally, we observe that G11 is linked with siRNA efficacy. We believe that an algorithm based on these findings may help in the selection of functional siRNA sequences directed against viral genes.

Therapeutic application of RNA interference for hepatitis C virus

RNA interference (RNAi) is a sequence-specific post-transcriptional gene silencing by double-stranded RNA. Because the phenomenon is conserved and ubiquitous in mammalian cells, RNAi has considerable therapeutic potential for human pathogenic gene products. Recent studies have demonstrated the clinical potential of logically designed small interfering RNA (siRNA). However, there are still obstacles in using RNAi as an antiviral therapy, particularly for hepatitis C virus (HCV) that displays a high rate of mutation. Furthermore, delivery is also an important obstacle for siRNA based gene therapy. This paper presents the potential applications and the hurdles facing anti-HCV siRNA drugs. The present review provides insight into the feasible therapeutic strategies of siRNA technology, and its potential for silencing genes associated with HCV disease.

Selective degradation of transcripts in mammalian oocytes and embryos

During the last decade several gene expression analysis studies have been carried out to investigate the transcriptional profile of bovine embryos in response to various culture and treatments conditions. Despite this fact, the function of a large number of genes in mammalian embryogenesis has not yet been investigated or is not known. The conventional gene-knockout experiments have been used extensively to study the function of genes in mammalian embryogenesis. However, these studies are relatively slow and cannot keep pace with the rapid accumulation of new sequence information produced by various genome projects. For this, the posttranscriptional gene silencing (PTGS) by double-stranded RNA (dsRNA), or RNA interference (RNAi), has emerged as a new tool for studying gene function in an increasing number of organisms. The present review will focus on recent developments in the use of RNAi for selective degradation of transcripts in mammalian embryos to elucidate their function in early development.

Comparison of approaches for rational siRNA design leading to a new efficient and transparent method

Current literature describes several methods for the design of efficient siRNAs with 19 perfectly matched base pairs and 2 nt overhangs. Using four independent databases totaling 3336 experimentally verified siRNAs, we compared how well several of these methods predict siRNA cleavage efficiency. According to receiver operating characteristics (ROC) and correlation analyses, the best programs were BioPredsi, ThermoComposition and DSIR. We also studied individual parameters that significantly and consistently correlated with siRNA efficacy in different databases. As a result of this work we developed a new method which utilizes linear regression fitting with local duplex stability, nucleotide position-dependent preferences and total G/C content of siRNA duplexes as input parameters. The new method's discrimination ability of efficient and inefficient siRNAs is comparable with that of the best methods identified, but its parameters are more obviously related to the mechanisms of siRNA action in comparison with BioPredsi. This permits insight to the underlying physical features and relative importance of the parameters. The new method of predicting siRNA efficiency is faster than that of ThermoComposition because it does not employ time-consuming RNA secondary structure calculations and has much less parameters than DSIR. It is available as a web tool called ‘siRNA scales’.

RNA interference as a novel and powerful tool in immunopharmacological research

RNA interference (RNAi), as an evolutionarily conserved mechanism for silencing gene expression, is realized through the actions of both small interference RNA (siRNA) and microRNA. Since its discovery, siRNA has been rapidly deployed not only for the elucidation of gene function, but also for identification of drug targets and as a powerful therapeutic approach for a variety of diseases. In this review, we briefly introduce the mechanisms of RNAi, methods of siRNA design and delivery, and summarized recent researches on the therapeutic potential of RNAi for immune diseases.

Gene manipulation through the use of small interfering RNA (siRNA): from in vitro to in vivo applications

The conventional approach to investigate genotype-phenotype relationships has been the generation of gene targeted murine strains. However, the emergence of RNAi technologies has opened the possibility of much more rapid (and indeed more cost effective) genetic manipulation in vivo at the level of the transcriptome. Successful application of RNAi in vivo depends on intracellular targeted delivery of siRNA/shRNA molecules for efficient knockdown of the desired gene. In this review, we discuss the rationale and different strategies of using siRNA/shRNA for accomplishing the silencing of targeted genes in a spatial and /or temporally regulated manner. We also summarise the steps involved in extending these approaches to in vivo applications, with a specific focus upon the development of silencing in the mouse.

Illuminating the silence: understanding the structure and function of small RNAs

RNA interference (RNAi) is triggered by double-stranded RNA helices that have been introduced exogenously into cells as small interfering (si)RNAs or that have been produced endogenously from small non-coding RNAs known as microRNAs (miRNAs). RNAi has become a standard experimental tool and its therapeutic potential is being aggressively harnessed. Understanding the structure and function of small RNAs, such as siRNAs and miRNAs, that trigger RNAi has shed light on the RNAi machinery. In particular, it has highlighted the assembly and function of the RNA-induced silencing complex (RISC), and has provided guidelines to efficiently silence genes for biological research and therapeutic applications of RNAi.

An accurate and interpretable model for siRNA efficacy prediction

Background

The use of exogenous small interfering RNAs (siRNAs) for gene silencing has quickly become a widespread molecular tool providing a powerful means for gene functional study and new drug target identification. Although considerable progress has been made recently in understanding how the RNAi pathway mediates gene silencing, the design of potent siRNAs remains challenging.

Results

We propose a simple linear model combining basic features of siRNA sequences for siRNA efficacy prediction. Trained and tested on a large dataset of siRNA sequences made recently available, it performs as well as more complex state-of-the-art models in terms of potency prediction accuracy, with the advantage of being directly interpretable. The analysis of this linear model allows us to detect and quantify the effect of nucleotide preferences at particular positions, including previously known and new observations. We also detect and quantify a strong propensity of potent siRNAs to contain short asymmetric motifs in their sequence, and show that, surprisingly, these motifs alone contain at least as much relevant information for potency prediction as the nucleotide preferences for particular positions.

Conclusion

The model proposed for prediction of siRNA potency is as accurate as a state-of-the-art nonlinear model and is easily interpretable in terms of biological features. It is freely available on the web at

Integrated siRNA design based on surveying of features associated with high RNAi effectiveness

Background

Short interfering RNAs have allowed the development of clean and easily regulated methods for disruption of gene expression. However, while these methods continue to grow in popularity, designing effective siRNA experiments can be challenging. The various existing siRNA design guidelines suffer from two problems: they differ considerably from each other, and they produce high levels of false-positive predictions when tested on data of independent origins.

Results

Using a distinctly large set of siRNA efficacy data assembled from a vast diversity of origins (the siRecords data, containing records of 3,277 siRNA experiments targeting 1,518 genes, derived from 1,417 independent studies), we conducted extensive analyses of all known features that have been implicated in increasing RNAi effectiveness. A number of features having positive impacts on siRNA efficacy were identified. By performing quantitative analyses on cooperative effects among these features, then applying a disjunctive rule merging (DRM) algorithm, we developed a bundle of siRNA design rule sets with the false positive problem well curbed. A comparison with 15 online siRNA design tools indicated that some of the rule sets we developed surpassed all of these design tools commonly used in siRNA design practice in positive predictive values (PPVs).

Conclusion

The availability of the large and diverse siRNA dataset from siRecords and the approach we describe in this report have allowed the development of highly effective and generally applicable siRNA design rule sets. Together with ever improving RNAi lab techniques, these design rule sets are expected to make siRNAs a more useful tool for molecular genetics, functional genomics, and drug discovery studies.

On the art of identifying effective and specific siRNAs

Small interfering RNAs (siRNAs) have been widely exploited for sequence-specific gene knockdown, predominantly to investigate gene function in cultured vertebrate cells, and also hold promise as therapeutic agents. Because not all siRNAs that are cognate to a given target mRNA are equally effective, computational tools have been developed based on experimental data to increase the likelihood of selecting effective siRNAs. Furthermore, because target-complementary siRNAs can also target other mRNAs containing sequence segments that are partially complementary to the siRNA, most computational tools include ways to reduce potential off-target effects in the siRNA selection process. Though these methods facilitate selection of functional siRNAs, they do not yet alleviate the need for experimental validation. This perspective provides a practical guide based on current wisdom for selecting siRNAs.

DSTHO: database of siRNAs targeted at human oncogenes: a statistical analysis

Existing treatments of human cancer, which is characterized by abnormal proliferation of cells often lead to fatal outcomes. Sequence selective silencing of oncogene expression using siRNA technology is emerging as a potential solution for cancer treatment. The exclusive selectivity and easy application to virtually any therapeutic target including intracellular factors and transcription factors renders siRNA oligonucleotide applications very promising. However, synthesis of siRNA having sufficient knockdown efficiency is laborious and cost intensive. The database is designed in order to aid the synthesis of siRNAs, which target human oncogenes (OsiRNAs). It provides OsiRNAs of known efficacy from previous experiments with links to published literature and theoretically pre-generated putative target sequences. In addition, links to available theoretical tools, databases and literature corresponding to siRNAs in general are also provided. The links to literature provide information about role of siRNA in therapeutics, chemical properties and transfection methods. Statistical analysis of mono-, di- and tri- mers located in OsiRNAs of known efficacies is performed to identify positional preferences and screen specific motifs. This analysis aids the design and synthesis of effective siRNAs, which particularly target human oncogenes. The database can be accessed at .

siRecords: an extensive database of mammalian siRNAs with efficacy ratings

Short interfering RNAs (siRNAs) have been gaining popularity as the gene knock-down tool of choice by many researchers because of the clean nature of their workings as well as the technical simplicity and cost efficiency in their applications. We have constructed siRecords, a database of siRNAs experimentally tested by researchers with consistent efficacy ratings. This database will help siRNA researchers develop more reliable siRNA design rules; in the mean time, siRecords will benefit experimental researchers directly by providing them with information about the siRNAs that have been experimentally tested against the genes of their interest. Currently, more than 4100 carefully annotated siRNA sequences obtained from more than 1200 published siRNA studies are hosted in siRecords. This database will continue to expand as more experimentally tested siRNAs are published.

AVAILABILITY

The siRecords database can be accessed at http://siRecords.umn.edu/siRecords/

Criteria for effective design, construction, and gene knockdown by shRNA vectors

Background

RNA interference (RNAi) technology is a powerful methodology recently developed for the specific knockdown of targeted genes. RNAi is most commonly achieved either transiently by transfection of small interfering (si) RNA oligonucleotides, or stably using short hairpin (sh) RNA expressed from a DNA vector or virus. Much controversy has surrounded the development of rules for the design of effective siRNA oligonucleotides; and whether these rules apply to shRNA is not well characterized.

Results

To determine whether published algorithms for siRNA oligonucleotide design apply to shRNA, we constructed 27 shRNAs from 11 human genes expressed stably using retroviral vectors. We demonstrate an efficient method for preparing wild-type and mutant control shRNA vectors simultaneously using oligonucleotide hybrids. We show that sequencing through shRNA vectors can be problematic due to the intrinsic secondary structure of the hairpin, and we determine a strategy for effective sequencing by using a combination of modified BigDye chemistries and DNA relaxing agents. The efficacy of knockdown for the 27 shRNA vectors was evaluated against six published algorithms for siRNA oligonucleotide design. Our results show that none of the scoring algorithms can explain a significant percentage of variance in shRNA knockdown efficacy as assessed by linear regression analysis or ROC curve analysis. Application of a modification based on the stability of the 6 central bases of each shRNA provides fair-to-good predictions of knockdown efficacy for three of the algorithms. Analysis of an independent set of data from 38 shRNAs pooled from previous publications confirms these findings.

Conclusion

The use of mixed oligonucleotide pairs provides a time and cost efficient method of producing wild type and mutant control shRNA vectors. The addition to sequencing reactions of a combination of mixed dITP/dGTP chemistries and DNA relaxing agents enables read through the intrinsic secondary structure of problematic shRNA vectors. Six published algorithms for siRNA oligonucleotide design that were tested in this study show little or no efficacy at predicting shRNA knockdown outcome. However, application of a modification based on the central shRNA stability should provide a useful improvement to the design of effective shRNA vectors.

Stable inhibition of hepatitis B virus expression and replication by expressed siRNA

RNA interference might be an efficient antiviral therapy for some obstinate illness. Here, we studied the effects of hepatitis B virus (HBV)-specific 21-nt small interfering RNAs (siRNA) on HBV gene expression and replication in 2.2.15 cells. Seven vectors expressing specific hairpin siRNA driven by the RNA polymerase II-promoter were constructed and transfected into 2.2.15 cells. In the cell strain that can stably express functional siRNA, the HBV surface antigen (HBsAg) and the HBV e antigen (HBeAg) secretion into culture media was inhibited by 86% and 91%, respectively, as shown by an enzyme-linked immunosorbent assay. Immunofluorescence and Western blot indicated similar results. HBV DNA was markedly restrained by 3.28-fold, as assessed by the fluorescent quantitation PCR. Moreover, the HBV mRNA was significantly reduced by 80% based on semiquantitative RT-PCR. In conclusion, the specific siRNA can knock down the HBV gene expression and replication in vitro, and the silence effects have no relationship with interferon response.

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.

Perspective: machines for RNAi

RNA silencing pathways convert the sequence information in long RNA, typically double-stranded RNA, into approximately 21-nt RNA signaling molecules such as small interfering RNAs (siRNAs) and microRNAs (miRNAs). siRNAs and miRNAs provide specificity to protein effector complexes that repress mRNA transcription or translation, or catalyze mRNA destruction. Here, we review our current understanding of how small RNAs are produced, how they are loaded into protein complexes, and how they repress gene expression.