Improved and automated prediction of effective siRNA

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.

Osteomyelitis and non-coding RNAS: A new dimension in disease understanding

Osteomyelitis, a debilitating bone infection, presents considerable clinical challenges due to its intricate etiology and limited treatment options. Despite strides in surgical and chemotherapeutic interventions, the treatment landscape for osteomyelitis remains unsatisfactory. Recent attention has focused on the role of non-coding RNAs (ncRNAs) in the pathogenesis and progression of osteomyelitis. This review consolidates current knowledge on the involvement of distinct classes of ncRNAs, including microRNAs, long ncRNAs, and circular RNAs, in the context of osteomyelitis. Emerging evidence from various studies underscores the potential of ncRNAs in orchestrating gene expression and influencing the differentiation of osteoblasts and osteoclasts, pivotal processes in bone formation. The review initiates by elucidating the regulatory functions of ncRNAs in fundamental cellular processes such as inflammation, immune response, and bone remodeling, pivotal in osteomyelitis pathology. It delves into the intricate network of interactions between ncRNAs and their target genes, illuminating how dysregulation contributes to the establishment and persistence of osteomyelitic infections. Understanding their regulatory roles may pave the way for targeted diagnostic tools and innovative therapeutic interventions, promising a paradigm shift in the clinical approach to this challenging condition. Additionally, we delve into the promising therapeutic applications of these molecules, envisioning novel diagnostic and treatment approaches to enhance the management of this challenging bone infection.

Nano-scale delivery systems for siRNA delivery in cancer therapy: New era of gene therapy empowered by nanotechnology

RNA interference (RNAi) is a unique treatment approach used to decrease a disease's excessive gene expression, including cancer. SiRNAs may find and destroy homologous mRNA sequences within the cell thanks to RNAi processes. However, difficulties such poor cellular uptake, off-target effects, and susceptibility to destruction by serum nucleases in the bloodstream restrict the therapeutic potential of siRNAs. Since some years ago, siRNA-based therapies have been in the process of being translated into the clinic. Therefore, the primary emphasis of this work is on sophisticated nanocarriers that aid in the transport of siRNA payloads, their administration in combination with anticancer medications, and their use in the treatment of cancer. The research looks into molecular manifestations, difficulties with siRNA transport, the design and development of siRNA-based delivery methods, and the benefits and drawbacks of various nanocarriers. The trapping of siRNA in endosomes is a challenge for the majority of delivery methods, which affects the therapeutic effectiveness. Numerous techniques for siRNA release, including as pH-responsive release, membrane fusion, the proton sponge effect, and photochemical disruption, have been studied to overcome this problem. The present state of siRNA treatments in clinical trials is also looked at in order to give a thorough and systematic evaluation of siRNA-based medicines for efficient cancer therapy.

Differential Expression of Non-Coding RNAs in Stem Cell Development and Therapeutics of Bone Disorders

Stem cells' self-renewal and multi-lineage differentiation are regulated by a complex network consisting of signaling factors, chromatin regulators, transcription factors, and non-coding RNAs (ncRNAs). Diverse role of ncRNAs in stem cell development and maintenance of bone homeostasis have been discovered recently. The ncRNAs, such as long non-coding RNAs, micro RNAs, circular RNAs, small interfering RNA, Piwi-interacting RNAs, etc., are not translated into proteins but act as essential epigenetic regulators in stem cells' self-renewal and differentiation. Different signaling pathways are monitored efficiently by the differential expression of ncRNAs, which function as regulatory elements in determining the fate of stem cells. In addition, several species of ncRNAs could serve as potential molecular biomarkers in early diagnosis of bone diseases, including osteoporosis, osteoarthritis, and bone cancers, ultimately leading to the development of new therapeutic strategies. This review aims to explore the specific roles of ncRNAs and their effective molecular mechanisms in the growth and development of stem cells, and in the regulation of osteoblast and osteoclast activities. Furthermore, we focus on and explore the association of altered ncRNA expression with stem cells and bone turnover.

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.

Characterizing genes associated with cancer using the CRISPR/Cas9 system: A systematic review of genes and methodological approaches

The CRISPR/Cas9 system enables a versatile set of genomes editing and genetic-based disease modeling tools due to its high specificity, efficiency, and accessible design and implementation. In cancer, the CRISPR/Cas9 system has been used to characterize genes and explore different mechanisms implicated in tumorigenesis. Different experimental strategies have been proposed in recent years, showing dependency on various intrinsic factors such as cancer type, gene function, mutation type, and technical approaches such as cell line, Cas9 expression, and transfection options. However, the successful methodological approaches, genes, and other experimental factors have not been analyzed. We, therefore, initially considered more than 1,300 research articles related to CRISPR/Cas9 in cancer to finally examine more than 400 full-text research publications. We summarize findings regarding target genes, RNA guide designs, cloning, Cas9 delivery systems, cell enrichment, and experimental validations. This analysis provides valuable information and guidance for future cancer gene validation experiments.

Design of siRNA molecules for silencing of membrane glycoprotein, nucleocapsid phosphoprotein, and surface glycoprotein genes of SARS-CoV2

The global COVID-19 pandemic caused by SARS-CoV2 infected millions of people and resulted in more than 4 million deaths worldwide. Apart from vaccines and drugs, RNA silencing is a novel approach for treating COVID-19. In the present study, siRNAs were designed for the conserved regions targeting three structural genes, M, N, and S, from forty whole-genome sequences of SARS-CoV2 using four different software, RNAxs, siDirect, i-Score Designer, and OligoWalk. Only siRNAs which were predicted in common by all the four servers were considered for further shortlisting. A multistep filtering approach has been adopted in the present study for the final selection of siRNAs by the usage of different online tools, viz., siRNA scales, MaxExpect, DuplexFold, and SMEpred. All these web-based tools consider several important parameters for designing functional siRNAs, e.g., target-site accessibility, duplex stability, position-specific nucleotide preference, inhibitory score, thermodynamic parameters, GC content, and efficacy in cleaving the target. In addition, a few parameters like GC content and dG value of the entire siRNA were also considered for shortlisting of the siRNAs. Antisense strands were subjected to check for any off-target similarities using BLAST. Molecular docking was carried out to study the interactions of guide strands with AGO2 protein. A total of six functional siRNAs (two for each gene) have been finally selected for targeting M, N, and S genes of SARS-CoV2. The siRNAs have not shown any off-target effects, interacted with the domain(s) of AGO2 protein, and were efficacious in cleaving the target mRNA. However, the siRNAs designed in the present study need to be tested in vitro and in vivo in the future.

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.

Nanomaterials-Based siRNA Delivery: Routes of Administration, Hurdles and Role of Nanocarriers

Ribonucleic acid interference (RNAi) is a potential alternative therapeutic approach to knock down the overexpression of genes in several disorders especially cancers with underlying genetic dysfunctions. For silencing of specific genes involved in cell cycle, small/short interfering ribonucleic acids (siRNAs) are being used clinically. The siRNA-based RNAi is more efficient, specific and safe antisense technology than other RNAi approaches. The route of siRNA administration for siRNA therapy depends on the targeted site. However, certain hurdles like poor stability of siRNA, saturation, off-target effect, immunogenicity, anatomical barriers and non-targeted delivery restrict the successful siRNA therapy. Thus, advancement of an effective, secure, and long-term delivery system is prerequisite to the medical utilization of siRNA. Polycationic nanocarriers mediated targeted delivery system is an ideal system to remove these hurdles and to increase the blood retention time and rate of intracellular permeability. In this chapter, we will mainly discuss the different biocompatible, biodegradable, non-toxic (organic, inorganic and hybrid) nanocarriers that encapsulate and shield the siRNA from the different harsh environment and provides the increased systemic siRNA delivery.

A systematic study on the influence of thermodynamic asymmetry of 5'-ends of siRNA duplexes in relation to their silencing potency

siRNA molecules possess high potential as molecular tools and can be used as effective therapeutics in humans. One of the key steps in the action of these molecules is the choice of antisense strand by the RNA-induced silencing complex (RISC). To explain this process, we verified the theory which states that antisense strand selection is based on the thermodynamically less stable 5′ end of siRNA. Based on the studies presented herein, we observed that for the tested siRNA duplexes, the difference in the thermodynamic stability of the terminal, penultimate and pre-penultimate pairs in the duplex siRNA is not the dominant factor in antisense strand selection. We found that both strands in each tested siRNA molecule are used as an antisense strand. The introduction of modified nucleotides, whose impact on the thermodynamic stability of siRNA duplexes was studied, results in changes in antisense strand selection by the RISC complex. The presence of a modified residue often caused predominant selection of only one antisense strand which is at variance with the theory of siRNA strand bias.

Characterization and use of Equine Bone Marrow Mesenchymal Stem Cells in Equine Cartilage Engineering. Study of their Hyaline Cartilage Forming Potential when Cultured under Hypoxia within a Biomaterial in the Presence of BMP-2 and TGF-ß1

Articular cartilage presents a poor capacity for self-repair. Its structure-function are frequently disrupted or damaged upon physical trauma or osteoarthritis in humans. Similar musculoskeletal disorders also affect horses and are the leading cause of poor performance or early retirement of sport- and racehorses. To develop a therapeutic solution for horses, we tested the autologous chondrocyte implantation technique developed on human bone marrow (BM) mesenchymal stem cells (MSCs) on horse BM-MSCs. This technique involves BM-MSC chondrogenesis using a combinatory approach based on the association of 3D-culture in collagen sponges, under hypoxia in the presence of chondrogenic factors (BMP-2 + TGF-β₁) and siRNA to knockdown collagen I and HtrA1. Horse BM-MSCs were characterized before being cultured in chondrogenic conditions to find the best combination to enhance, stabilize, the chondrocyte phenotype. Our results show a very high proliferation of MSCs and these cells satisfy the criteria defining stem cells (pluripotency-surface markers expression). The combination of BMP-2 + TGF-β₁ strongly induces the chondrogenic differentiation of MSCs and prevents HtrA1 expression. siRNAs targeting Col1a1 and Htra1 were functionally validated. Ultimately, the combined use of specific culture conditions defined here with specific growth factors and a Col1a1 siRNAs (50 nM) association leads to the in vitro synthesis of a hyaline-type neocartilage whose chondrocytes present an optimal phenotypic index similar to that of healthy, differentiated chondrocytes. Our results lead the way to setting up pre-clinical trials in horses to better understand the reaction of neocartilage substitute and to carry out a proof-of-concept of this therapeutic strategy on a large animal model.

Strategies for improving the specificity of siRNAs for enhanced therapeutic potential

INTRODUCTION

RNA interference has become a tool of choice in the development of drugs in various therapeutic areas of Post Transcriptional Gene Silencing (PTGS). The critical element in developing successful RNAi therapeutics lies in designing small interfering RNA (siRNA) using an efficient algorithm satisfying the designing criteria. Further, translation of siRNA from bench-side to bedside needs an efficient delivery system and/or chemical modification. Areas covered: This review emphasizes the importance of dicer, the criteria for efficient siRNA design, the currently available algorithms and strategies to overcome off-target effects, immune stimulatory effects and endosomal trap. Expert opinion: Specificity and stability are the primary concerns for siRNA therapeutics. The design criteria and algorithms should be chosen rationally to have a siRNA sequence that binds to the corresponding mRNA as it happens in the Watson and Crick base pairing. However, it must evade a few more hurdles (Endocytosis, Serum stability etc.) to be functional in the cytosol.

Can we increase the speed and efficacy of antidepressant treatments? Part II. Glutamatergic and RNA interference strategies

In the second part we focus on two treatment strategies that may overcome the main limitations of current antidepressant drugs. First, we review the experimental and clinical evidence supporting the use of glutamatergic drugs as fast-acting antidepressants. Secondly, we review the involvement of microRNAs (miRNAs) in the pathophysiology of major depressive disorder (MDD) and the use of small RNAs (e.g.., small interfering RNAs or siRNAs) to knockdown genes in monoaminergic and non-monoaminergic neurons and induce antidepressant-like responses in experimental animals. The development of glutamatergic agents is a promising venue for antidepressant drug development, given the antidepressant properties of the non-competitive NMDA receptor antagonist ketamine. Its unique properties appear to result from the activation of AMPA receptors by a metabolite [(2S,6S;2R,6R)-hydroxynorketamine (HNK)] and mTOR signaling. These effects increase synaptogenesis in prefrontal cortical pyramidal neurons and enhance serotonergic neurotransmission via descending inputs to the raphe nuclei. This view is supported by the cancellation of ketamine's antidepressant-like effects by inhibition of serotonin synthesis. We also review existing evidence supporting the involvement of miRNAs in MDD and the preclinical use of RNA interference (RNAi) strategies to target genes involved in antidepressant response. Many miRNAs have been associated to MDD, some of which e.g., miR-135 targets genes involved in antidepressant actions. Likewise, SSRI-conjugated siRNA evokes faster and/or more effective antidepressant-like responses. Intranasal application of sertraline-conjugated siRNAs directed to 5-HT_1A receptors and SERT evoked much faster changes of pre- and postsynaptic antidepressant markers than those produced by fluoxetine.

Nanomedicine-based combination anticancer therapy between nucleic acids and small-molecular drugs

Anticancer therapy has always been a vital challenge for the development of nanomedicine. Repeated single therapeutic agent may lead to undesirable and severe side effects, unbearable toxicity and multidrug resistance due to complex nature of tumor. Nanomedicine-based combination anticancer therapy can synergistically improve antitumor outcomes through multiple-target therapy, decreasing the dose of each therapeutic agent and reducing side effects. There are versatile combinational anticancer strategies such as chemotherapeutic combination, nucleic acid-based co-delivery, intrinsic sensitive and extrinsic stimulus combinational patterns. Based on these combination strategies, various nanocarriers and drug delivery systems were engineered to carry out the efficient co-delivery of combined therapeutic agents for combination anticancer therapy. This review focused on illustrating nanomedicine-based combination anticancer therapy between nucleic acids and small-molecular drugs for synergistically improving anticancer efficacy.

Improved knockdown from artificial microRNAs in an enhanced miR-155 backbone: a designer's guide to potent multi-target RNAi

Artificial microRNA (amiRNA) sequences embedded in natural microRNA (miRNA) backbones have proven to be useful tools for RNA interference (RNAi). amiRNAs have reduced off-target and toxic effects compared to other RNAi-based methods such as short-hairpin RNAs (shRNA). amiRNAs are often less effective for knockdown, however, compared to their shRNA counterparts. We screened a large empirically-designed amiRNA set in the synthetic inhibitory BIC/miR-155 RNA (SIBR) scaffold and show common structural and sequence-specific features associated with effective amiRNAs. We then introduced exogenous motifs into the basal stem region which increase amiRNA biogenesis and knockdown potency. We call this modified backbone the enhanced SIBR (eSIBR) scaffold. Using chained amiRNAs for multi-gene knockdown, we show that concatenation of miRNAs targeting different genes is itself sufficient for increased knockdown efficacy. Further, we show that eSIBR outperforms wild-type SIBR (wtSIBR) when amiRNAs are chained. Finally, we use a lentiviral expression system in cultured neurons, where we again find that eSIBR amiRNAs are more potent for multi-target knockdown of endogenous genes. eSIBR will be a valuable tool for RNAi approaches, especially for studies where knockdown of multiple targets is desired.

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.

Silencing efficacy prediction: a retrospective study on target mRNA features

Post-transcriptional gene silencing is a widely used method to suppress gene expression. Unfortunately only a portion of siRNAs do successfully reduce gene expression. Target mRNA secondary structures and siRNA-mRNA thermodynamic features are believed to contribute to the silencing activity. However, there is still an open discussion as to what determines siRNA efficacy. In this retrospective study, we analysed the target accessibility comparing very high (VH) compared with low (L) efficacy siRNA sequences obtained from the siRecords Database. We determined the contribution of mRNA target local secondary structures on silencing efficacy. Both the univariable and the multivariable logistic regression evidenced no relationship between siRNA efficacy and mRNA target secondary structures. Moreover, none of the thermodynamic and sequence-base parameters taken into consideration (H-b index, ΔG°_overall, ΔG°_duplex, ΔG°_break-target and GC%) was associated with siRNA efficacy. We found that features believed to be predictive of silencing efficacy are not confirmed to be so when externally evaluated in a large heterogeneous sample. Although it was proposed that silencing efficacy could be influenced by local target accessibility we show that this could be not generalizable because of the diversity of experimental setting that may not be representative of biological systems especially in view of the many local protein factors, usually not taken into consideration, which could hamper the silencing process.

We analysed several siRNA-mRNA target features involved in silencing efficacy. We found out that features believed to be predictive of silencing efficacy are not such when transferred to a larger dataset of experiments and different experimental settings.

A semi-supervised tensor regression model for siRNA efficacy prediction

BACKGROUND

Short interfering RNAs (siRNAs) can knockdown target genes and thus have an immense impact on biology and pharmacy research. The key question of which siRNAs have high knockdown ability in siRNA research remains challenging as current known results are still far from expectation.

RESULTS

This work aims to develop a generic framework to enhance siRNA knockdown efficacy prediction. The key idea is first to enrich siRNA sequences by incorporating them with rules found for designing effective siRNAs and representing them as enriched matrices, then to employ the bilinear tensor regression to predict knockdown efficacy of those matrices. Experiments show that the proposed method achieves better results than existing models in most cases.

CONCLUSIONS

Our model not only provides a suitable siRNA representation but also can predict siRNA efficacy more accurate and stable than most of state-of-the-art models. Source codes are freely available on the web at: http://www.jaist.ac.jp/\~bao/BiLTR/ .

Synthetic RNAs for Gene Regulation: Design Principles and Computational Tools

The use of synthetic non-coding RNAs for post-transcriptional regulation of gene expression has not only become a standard laboratory tool for gene functional studies but it has also opened up new perspectives in the design of new and potentially promising therapeutic strategies. Bioinformatics has provided researchers with a variety of tools for the design, the analysis, and the evaluation of RNAi agents such as small-interfering RNA (siRNA), short-hairpin RNA (shRNA), artificial microRNA (a-miR), and microRNA sponges. More recently, a new system for genome engineering based on the bacterial CRISPR-Cas9 system (Clustered Regularly Interspaced Short Palindromic Repeats), was shown to have the potential to also regulate gene expression at both transcriptional and post-transcriptional level in a more specific way. In this mini review, we present RNAi and CRISPRi design principles and discuss the advantages and limitations of the current design approaches.

Computational design of artificial RNA molecules for gene regulation

RNA interference (RNAi) is a powerful tool for the regulation of gene expression. Small exogenous noncoding RNAs (ncRNAs) such as siRNA and shRNA are the active silencing agents, intended to target and cleave complementary mRNAs in a specific way. They are widely and successfully employed in functional studies, and several ongoing and already completed siRNA-based clinical trials suggest encouraging results in the regulation of overexpressed genes in disease. siRNAs share many aspects of their biogenesis and function with miRNAs, small ncRNA molecules transcribed from endogenous genes which are able to repress the expression of target mRNAs by either inhibiting their translation or promoting their degradation. Although siRNA and artificial miRNA molecules can significantly reduce the expression of overexpressed target genes, cancer and other diseases can also be triggered or sustained by upregulated miRNAs. Thus, in the past recent years, molecular tools for miRNA silencing, such as antagomiRs and miRNA sponges, have been developed. These molecules have shown their efficacy in the derepression of genes downregulated by overexpressed miRNAs. In particular, while a single antagomiR is able to inhibit a single complementary miRNA, an artificial sponge construct usually contains one or more binding sites for one or more miRNAs and functions by competing with the natural targets of these miRNAs. As a consequence, natural miRNA targets are reexpressed at their physiological level. In this chapter we review the most successful methods for the computational design of siRNAs, antagomiRs, and miRNA sponges and describe the most popular tools that implement them.

Lentiviral vector-mediated RNA silencing in the central nervous system

RNA silencing is an established method for investigating gene function and has attracted particular interest because of the potential for generating RNA-based therapeutics. Using lentiviral vectors as an efficient delivery system that offers stable, long-term expression in postmitotic cells further enhances the applicability of an RNA-based gene therapy for the CNS. In this review we provide an overview of both lentiviral vectors and RNA silencing along with design considerations for generating lentiviral vectors capable of RNA silencing. We go on to describe the current preclinical data regarding lentiviral vector-mediated RNA silencing for CNS disorders and discuss the concerns of side effects associated with lentiviral vectors and small interfering RNAs and how these might be mitigated.

Long-range transcriptome sequencing reveals cancer cell growth regulatory chimeric mRNA

mRNA chimeras from chromosomal translocations often play a role as transforming oncogenes. However, cancer transcriptomes also contain mRNA chimeras that may play a role in tumor development, which arise as transcriptional or post-transcriptional events. To identify such chimeras, we developed a deterministic screening strategy for long-range sequence analysis. High-throughput, long-read sequencing was then performed on cDNA libraries from major tumor histotypes and corresponding normal tissues. These analyses led to the identification of 378 chimeras, with an unexpectedly high frequency of expression (≈2 x 10(-5) of all mRNA). Functional assays in breast and ovarian cancer cell lines showed that a large fraction of mRNA chimeras regulates cell replication. Strikingly, chimeras were shown to include both positive and negative regulators of cell growth, which functioned as such in a cell-type-specific manner. Replication-controlling chimeras were found to be expressed by most cancers from breast, ovary, colon, uterus, kidney, lung, and stomach, suggesting a widespread role in tumor development.

What parameters to consider and which software tools to use for target selection and molecular design of small interfering RNAs

The design of small gene silencing RNAs with a high probability of being efficient still has some elements of an art, especially when the lowest concentration of small molecules needs to be utilized. The design of highly target-specific small interfering RNAs or short hairpin RNAs is even a greater challenging task. Some logical schemes and software tools that can be used for simplifying both tasks are presented here. In addition, sequence motifs and sequence composition biases of small interfering RNAs that have to be avoided because of specificity concerns are also detailed.

Methods for selecting effective siRNA target sequences using a variety of statistical and analytical techniques

Short interfering RNA (siRNA) has been widely used for studying gene function in mammalian cells but varies markedly in its gene silencing efficacy. Although many design rules/guidelines for effective siRNAs based on various criteria have been reported recently, there are only a few consistencies among them. This makes it difficult to select effective siRNA sequences in mammalian genes. This chapter first reviews the recently reported siRNA design guidelines and then proposes new methods for selecting effective siRNA sequences from many possible candidates by using decision tree learning, Bayes' theorem, and average silencing probability on the basis of a large number of known effective siRNAs. These methods differ from the previous score-based siRNA design techniques and can predict the probability that a candidate siRNA sequence will be effective. Evaluation of these methods by applying them to recently reported effective and ineffective siRNA sequences for a number of genes indicates that they would be useful for many other genes. They should, therefore, be of general utility for selecting effective siRNA sequences for mammalian genes. The chapter also describes another method using a hidden Markov model to select the optimal functional siRNAs and discusses the frequencies of combinations of two successive nucleotides as an important characteristic of effective siRNA sequences.

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.

SiRNA sequence model: redesign algorithm based on available genome-wide libraries

The evolution of RNA interference (RNAi) and the development of technologies exploiting its biology have enabled scientists to rapidly examine the consequences of depleting a particular gene product in cells. Design tools have been developed based on experimental data to increase the knockdown efficiency of siRNAs. Not all siRNAs that are developed to a given target mRNA are equally effective. Currently available design algorithms take an accession, identify conserved regions among their transcript space, find accessible regions within the mRNA, design all possible siRNAs for these regions, filter them based on multi-scores thresholds, and then perform off-target filtration. These different criteria are used by commercial suppliers to produce siRNA genome-wide libraries for different organisms. In this article, we analyze existing siRNA design algorithms and evaluate weight of design parameters for libraries produced in the last decade. We proved that not all essential parameters are currently applied by siRNA vendors. Based on our evaluation results, we were able to suggest an siRNA sequence pattern. The findings in our study can be useful for commercial vendors improving the design of RNAi constructs, by addressing both the issue of potency and the issue of specificity.

An siRNA designing tool with a unique functional off-target filtering approach

Investigations have revealed that silencing unwanted transcripts or off-targeting can induce false positive phenotype during RNA interference (RNAi)-based gene function study. But still the standard computational approaches towards small interfering RNA (siRNA) off-target minimization fall short in terms of addressing this false positive phenotype issue. Some of these off-targets may interfere with the biochemical pathway being investigated. It may also inadvertently target cell's metabolic pathways with unquantifiable consequences on the processes of user's interest. Here, we report the development of a siRNA selection tool that, for the first time, implements a functional off-target filtering that aims to minimize false positive phenotypes arising from inadvertent targets that are functionally similar or related to the direct target gene, along with a multi-parametric classifier (support vector machine) for optimized selection of potent siRNAs. The functional off-target filtering minimizes the number of off-target genes which are functionally related to the direct target gene, i.e. involved in a common biological process and may have similar phenotype. A text-mining algorithm is used to find related biological processes associated with the direct target and each off-target transcript by comparison of the biological processes associated with these genes. It also gives the user a choice to select one or more off-targets that may be potentially more harmful, from a predicted off-target gene list to be filtered out. Testing with huge set of biologically validated siRNAs from three different sources showed consistent good performance of our tool in terms of effective siRNA selection. It outperformed four potent siRNA selection algorithms of present day in terms of specificity in the selection of highly efficient siRNAs when compared on a common test set. A genome wide testing with potent siRNAs used in high-content screening confirmed validation of 2767 designed siRNAs in terms of phenotypic output. This tool presently supports siRNA designs for human genes and is freely available at http://gyanxet-beta.com .

siRNA-mediated knockdown of two tyrosinase genes from Schistosoma japonicum cultured in vitro

The cross-linking process of eggshell proteins in helminths is dependent on the activities of tyrosinases (TYRs), which can be inhibited by phenol oxidase inhibitors. Two genes encoding TYRs, SjTYR1 and SjTYR2, have been identified in Schistosoma japonicum. In this study, siRNA-mediated RNA interference (RNAi) was performed to silence these two SjTYR genes to evaluate their roles in eggshell formation. The effects of individual or double knockdown of the SjTYR genes were compared by determining SjTYR1/SjTYR2 transcript levels, enzyme activities, and by observing the morphology and amounts of intrauterine eggs. Results showed that SjTYR transcript levels were significantly reduced on the 3rd day post-RNAi. Significant reductions in TYR enzyme activities, as well as obvious changes in morphology and the number of intrauterine eggs followed the reductions in SjTYR transcript levels. On the 8th day after simultaneous knockdown of both SjTYR genes, which effected a 40% reduction in SjTYR1 transcript level and a 59% reduction in SjTYR2 transcript level, we observed an 80% reduction in diphenol oxidase (DPO) activity of TYRs, and a 74% reduction in the number of normal eggs in female uteri. Knockdown of both SjTYR genes has a greater effect than single knockdown of the SjTYR genes. These results demonstrate that both SjTYRs play an important role in eggshell sclerotization of S. japonicum, and that their enzyme activities depend on the transcript levels of two SjTYR genes.

Development of novel cell lines of diabetic dysfunction model fit for cell-based screening tests of medicinal materials

Pdx-1 and Irs-1, genes highly associated with diabetes onset, were knocked down in mouse embryonic stem (ES) cells in order to develop cell line models for diabetes. ES cells with different gene knockdown levels were induced to differentiate to the stage of insulin production. Among the cell lines that differentiated, we identified two in which the levels of expression of both genes were 20-40 % of that of control cells. These cell lines showed appreciable deficiencies in three characteristic malfunctions associated with diabetes, namely, insulin production, insulin reception signaling, and glucose-stimulated insulin secretion. These dysfunctions were consistent with results reported elsewhere from in vivo and in vitro studies. Both cell lines did not show any abnormal morphology such as size, shape, color, and surface roughness. No abnormal expression profiles for 17 genes relevant to diabetes were observed. Therefore, these cell lines fulfilled the criteria for a validated cell model for diabetes. The model cell lines developed here are promising biomaterials for cell-based screening tests of new medicines that may be effective in treating diabetes.

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.

Databases and resources for human small non-coding RNAs

Recent advances in high-throughput sequencing have facilitated the genome-wide studies of small non-coding RNAs (sRNAs). Numerous studies have highlighted the role of various classes of sRNAs at different levels of gene regulation and disease. The fast growth of sequence data and the diversity of sRNA species have prompted the need to organise them in annotation databases. There are currently several databases that collect sRNA data. Various tools are provided for access, with special emphasis on the well-characterised family of micro-RNAs. The striking heterogeneity of the new classes of sRNAs and the lack of sufficient functional annotation, however, make integration of these datasets a difficult task. This review describes the currently available databases for human sRNAs that are accessible via the internet, and some of the large datasets for human sRNAs from high-throughput sequencing experiments that are so far only available as supplementary data in publications. Some of the main issues related to the integration and annotation of sRNA datasets are also discussed.

Silencing activity of 2'-O-methyl modified anti-MDR1 siRNAs with mismatches in the central part of the duplexes

The thermodynamic properties of siRNA duplexes are important for their silencing activity. siRNAs with high thermodynamic stability of both the central part of the duplex and in the whole, usually display low silencing activity. Destabilization of the central part of the siRNA duplex could increase its silencing activity. However, mismatches located in the central part of the duplex could substantially decrease the amount of RNAi efficacy, hindering active RISC formation and function. In this study, we examined the impact of duplex destabilization by nucleotide substitutions in the central part (7-10 nt counting from the 5'-end of the antisense strand) of the nuclease-resistant siRNA on its silencing activity.

Informatics approach to the rational design of siRNA libraries

This chapter surveys the literature for state-of-the-art methods for the rational design of siRNA libraries. It identifies and presents major milestones in the field of computational modeling of siRNA's gene silencing efficacy. Commonly used features of siRNAs are summarized along with major machine learning techniques employed to build the predictive models. It has also outlined several web-enabled siRNA design tools. To face the challenge of modeling and rational design of chemically modified siRNAs, it also proposes a new cheminformatics approach for the representation and characterization of siRNA molecules. Some preliminary results with this new approach are presented to demonstrate the promising potential of this method for the modeling of siRNA's efficacy. Together with novel delivery technologies and chemical modification techniques, rational siRNA design algorithms will ultimately contribute to chemical biology research and the efficient development of siRNA therapeutics.

Toward a complete in silico, multi-layered embryonic stem cell regulatory network

Recent efforts in systematically profiling embryonic stem (ES) cells have yielded a wealth of high-throughput data. Complementarily, emerging databases and computational tools facilitate ES cell studies and further pave the way toward the in silico reconstruction of regulatory networks encompassing multiple molecular layers. Here, we briefly survey databases, algorithms, and software tools used to organize and analyze high-throughput experimental data collected to study mammalian cellular systems with a focus on ES cells. The vision of using heterogeneous data to reconstruct a complete multi-layered ES cell regulatory network is discussed. This review also provides an accompanying manually extracted dataset of different types of regulatory interactions from low-throughput experimental ES cell studies available at http://amp.pharm.mssm.edu/iscmid/literature.

RNAi screen indicates widespread biological function for human natural antisense transcripts

Natural antisense transcripts represent a class of regulatory RNA molecules, which are characterized by their complementary sequence to another RNA transcript. Extensive sequencing efforts suggest that natural antisense transcripts are prevalent throughout the mammalian genome; however, their biological significance has not been well defined. We performed a loss-of-function RNA interference (RNAi) screen, which targeted 797 evolutionary conserved antisense transcripts, and found evidence for a regulatory role for a number of natural antisense transcripts. Specifically, we found that natural antisense transcripts for CCPG1 and RAPGEF3 may functionally disrupt signaling pathways and corresponding biological phenotypes, such as cell viability, either independently or in parallel with the corresponding sense transcript. Our results show that the large-scale siRNA screen can be applied to evaluate natural antisense transcript modulation of fundamental cellular events.

Efficient prediction methods for selecting effective siRNA sequences

Although short interfering RNA (siRNA) has been widely used for studying gene functions in mammalian cells, its gene silencing efficacy varies markedly and there are only a few consistencies among the recently reported design rules/guidelines for selecting siRNA sequences effective for mammalian genes. Another shortcoming of the previously reported methods is that they cannot estimate the probability that a candidate sequence will silence the target gene. This paper first reviewed the recently reported siRNA design guidelines and clarified the problems concerning the guidelines. It then proposed two prediction methods-Radial Basis Function (RBF) network and decision tree learning-and their combined method for selecting effective siRNA target sequences from many possible candidate sequences. They are quite different from the previous score-based siRNA design techniques and can predict the probability that a candidate siRNA sequence will be effective. The methods imply high estimation accuracy for selecting candidate siRNA sequences.

siRNA efficiency: structure or sequence-that is the question

The triumphant success of RNA interference (RNAi) in life sciences is based on its high potency to silence genes in a sequence-specific manner. Nevertheless, the first task for successful RNAi approaches is the identification of highly active small interfering RNAs (siRNAs). Early on, it has been found that the potency of siRNAs can vary drastically. Great progress was made when thermodynamic properties that influence siRNA activity were discovered. Design algorithms based on these parameters enhance the chance to generate potent siRNAs. Still, many siRNAs designed accordingly fail to silence their targeted gene, whereas others are highly efficient despite the fact that they do not fulfil the recommended criteria. Therefore, the accessibility of the siRNA-binding site on the target RNA has been investigated as an additional parameter which is important for RNAi-mediated silencing. These and other factors which are crucial for successful RNAi approaches will be discussed in the present review.

Computational siRNA design 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 to 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 in mRNA level gene silencing. It provides numerous novel functions, including designing common siRNAs for the silencing of more than two mRNAs simultaneously, a scoring scheme to evaluate the performance of designed siRNAs by adopting state-of-the-art design factors, stepwise off-target searching with BLAST and FASTA algorithms, as well as 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 being 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 .

siDirect 2.0: updated software for designing functional siRNA with reduced seed-dependent off-target effect

BACKGROUND

RNA interference (RNAi), mediated by 21-nucleotide (nt)-length small interfering RNAs (siRNAs), is a powerful tool not only for studying gene function but also for therapeutic applications. RNAi, requiring perfect complementarity between the siRNA guide strand and the target mRNA, was believed to be extremely specific. However, a recent growing body of evidence has suggested that siRNA could down-regulate unintended genes whose transcripts possess complementarity to the 7-nt siRNA seed region. This off-target gene silencing may often provide incongruous results obtained from knockdown experiments, leading to misinterpretation. Thus, an efficient algorithm for designing functional siRNAs with minimal off-target effect based on the mechanistic features is considered of value.

RESULTS

We present siDirect 2.0, an update of our web-based software siDirect, which provides functional and off-target minimized siRNA design for mammalian RNAi. The previous version of our software designed functional siRNAs by considering the relationship between siRNA sequence and RNAi activity, and provided them along with the enumeration of potential off-target gene candidates by using a fast and sensitive homology search algorithm. In the new version, the siRNA design algorithm is extensively updated to eliminate off-target effects by reflecting our recent finding that the capability of siRNA to induce off-target effect is highly correlated to the thermodynamic stability, or the melting temperature (Tm), of the seed-target duplex, which is formed between the nucleotides positioned at 2-8 from the 5' end of the siRNA guide strand and its target mRNA. Selection of siRNAs with lower seed-target duplex stabilities (benchmark Tm < 21.5 degrees C) followed by the elimination of unrelated transcripts with nearly perfect match should minimize the off-target effects.

CONCLUSION

siDirect 2.0 provides functional, target-specific siRNA design with the updated algorithm which significantly reduces off-target silencing. When the candidate functional siRNAs could form seed-target duplexes with Tm values below 21.5 degrees C, and their 19-nt regions spanning positions 2-20 of both strands have at least two mismatches to any other non-targeted transcripts, siDirect 2.0 can design at least one qualified siRNA for >94% of human mRNA sequences in RefSeq. siDirect 2.0 is available at http://siDirect2.RNAi.jp/.

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.

Selecting effective siRNA target sequences by using Bayes' theorem

Short interfering RNA (siRNA) has been widely used for studying gene functions in mammalian cells but varies markedly in its gene silencing efficacy. Although many design rules/guidelines for effective siRNAs based on various criteria have been reported recently, there are few consistencies among them. This makes it difficult to select effective siRNA sequences in mammalian genes. Another shortcoming of most previously reported methods is that they cannot estimate the probability that a candidate sequence will silence the target gene. The analytical prediction method proposed in the present study uses Bayes' theorem to select effective siRNA target sequences from many possible candidate sequences. It is quite different from the previous score-based siRNA design techniques and can predict the probability that a candidate siRNA sequence will be effective. The results of evaluating it by applying it to recently reported effective and ineffective siRNA sequences for various genes indicate that it would be useful for many other genes. It should therefore be useful for selecting siRNA sequences effective for mammalian genes.

Phospholipase D family interactions with the cytoskeleton: isoform delta promotes plasma membrane anchoring of cortical microtubules

Phospholipase D (PLD) is a key enzyme in signal transduction - mediating plant responses to various environmental stresses including drought and salinity. Isotype PLDδ interacts with the microtubule cytoskeleton, although it is unclear if, or how, each of the 12 PLD isotypes in Arabidopsis may be involved mechanistically. We employed RNA interference in epidermal cells of Allium porrum L. (leek) leaves, in which the developmental reorientation of cortical microtubule arrays to a longitudinal direction is highly sensitive to experimental manipulation. Using particle bombardment and transient transformation with synthetic siRNAs targeting AtPLDα, β, γ, δ, ॉ and ζ, we examined the effect of 'cross-target' silencing orthologous A. porrum genes on microtubule reorientation dynamics during cell elongation. Co-transformation of individual siRNAs together with a GFP-MBD microtubule-reporter gene revealed that siRNAs targeting AtPLDδ promoted, whereas siRNAs targeting AtPLDβ and γ reduced, longitudinal microtubule orientation in A. porrum. These PLD isotypes, therefore, interact, directly or indirectly, with the cytoskeleton and the microtubule-plasma membrane interface. The unique response of PLDδ to silencing, along with its exclusive localisation to the plasma membrane, indicates that this isotype is specifically involved in promoting microtubule-membrane anchorage.

Using functional genomics to study PINK1 and metabolic physiology

Genome sequencing projects have provided the substrate for an unimaginable number of biological experiments. Further, genomic technologies such as microarrays and quantitative and exquisitely sensitive techniques such as real-time quantitative polymerase chain reaction have made it possible to reliably generate millions of data points per experiment. The data can be high quality and yield entirely new insights into how gene expression is coordinated under complex physiological situations. It can also be that the data and interpretation are meaningless because of a lack of physiological context or experimental control. Thus, functional genomics is now being applied to study metabolic physiology with varying degrees of success. From the genome sequencing projects we also have the information needed to design chemical tools that can knock down a gene transcript, even distinguishing between splice variants in mammalian cells. Use of such technologies, inspired by nature's endogenous RNAi mechanism-microRNA targeting, comes with significant caveats. While the discipline of Pharmacology taught us last century that inhibitor action specificity is dependent on the concentration used, these experiences have been ignored by users of siRNA technologies. What we provide in this chapter is some considerations and observations from functional genomic studies. We are largely concerned with the phase that follows a microarray study, where a candidate gene is selected for manipulation in a system that is considered to be simpler than the in vivo mammalian tissue and thus the methods discussed largely apply to this cell biology phase. We apologize for not referring to all relevant publications and for any technical considerations we have also failed to factor into our discussion.