Detecting microRNA binding and siRNA off-target effects from expression data.
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ABSTRACT:
SUBMITTER: van Dongen S
PROVIDER: S-EPMC2635553 | biostudies-literature | 2008 Dec
REPOSITORIES: biostudies-literature
Publications
Detecting microRNA binding and siRNA off-target effects from expression data.
van Dongen Stijn S Abreu-Goodger Cei C Enright Anton J AJ
Nature methods 20081102 12
Sylamer is a method for detecting microRNA target and small interfering RNA off-target signals in 3' untranslated regions from a ranked gene list, sorted from upregulated to downregulated, after a microRNA perturbation or RNA interference experiment. The output is a landscape plot that tracks occurrence biases using hypergeometric P-values for all words across the gene ranking. We demonstrated the utility, speed and accuracy of this approach on several datasets. ...[more]
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Project description:We perform a large-scale RNA sequencing study to experimentally identify genes that are downregulated by 25 miRNAs. This RNA-seq dataset is combined with public miRNA target binding data to systematically identify miRNA targeting features that are characteristic of both miRNA binding and target downregulation. By integrating these common features in a machine learning framework, we develop and validate an improved computational model for genome-wide miRNA target prediction. All prediction data can be accessed at miRDB ( http://mirdb.org ).
| S-EPMC6341724 | biostudies-literature
Project description:Post-transcriptional regulation by microRNAs and siRNAs depends not only on characteristics of individual binding sites in target mRNA molecules, but also on system-level properties such as overall molecular concentrations. We hypothesize that an intracellular pool of microRNAs/siRNAs faced with a larger number of available predicted target transcripts will downregulate each individual target gene to a lesser extent. To test this hypothesis, we analyzed mRNA expression change from 178 microRNA and siRNA transfection experiments in two cell lines. We find that downregulation of particular genes mediated by microRNAs and siRNAs indeed varies with the total concentration of available target transcripts. We conclude that to interpret and design experiments involving gene regulation by small RNAs, global properties, such as target mRNA abundance, need to be considered in addition to local determinants. We propose that analysis of microRNA/siRNA targeting would benefit from a more quantitative definition, rather than simple categorization of genes as 'target' or 'not a target.' Our results are important for understanding microRNA regulation and may also have implications for siRNA design and small RNA therapeutics.
| S-EPMC2872614 | biostudies-literature
Project description:MicroRNAs (miRNAs) are involved in a variety of human diseases by simultaneously suppressing many gene targets. Thus, the therapeutic value of miRNAs has been intensely studied. However, there are potential limitations with miRNA-based therapeutics such as a relatively moderate impact on gene target regulation and cellular phenotypic control. To address these issues, we proposed to design new chimeric small RNAs (aiRNAs) by incorporating sequences from both miRNAs and siRNAs. These aiRNAs not only inherited functions from natural miRNAs, but also gained new functions of gene knockdown in an siRNA-like fashion. The improved efficacy of multifunctional aiRNAs was demonstrated in our study by design and testing of an aiRNA that inherited the functions of both miR-200a and an AKT1-targeting siRNA for simultaneous suppression of cancer cell motility and proliferation. The general principles of aiRNA design were further validated by engineering new aiRNAs mimicking another miRNA, miR-9. By regulating multiple cellular functions, aiRNAs could be used as an improved tool over miRNAs to target disease-related genes, thus alleviating our dependency on a limited number of miRNAs for the development of RNAi-based therapeutics.
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Project description:RNA interference (RNAi) is widely used to determine the function of genes. We chose this approach to assess the collective function of the highly related reproductive homeobox 3 (Rhox3) gene paralogs. Using a Rhox3 short hairpin (sh) RNA with 100% complementarity to all 8 Rhox3 paralogs, expressed from a CRE-regulated transgene, we successfully knocked down Rhox3 expression in male germ cells in vivo. These Rhox3-shRNA transgenic mice had dramatic defects in spermatogenesis, primarily in spermatocytes and round spermatids. To determine whether this phenotype was caused by reduced Rhox3 expression, we generated mice expressing the Rhox3-shRNA but lacking the intended target of the shRNA-Rhox3. These double-mutant mice had a phenotype indistinguishable from Rhox3-shRNA-expressing mice that was different from mice lacking the Rhox3 paralogs, indicating that the Rhox3 shRNA disrupts spermatogenesis independently of Rhox3. Rhox3-shRNA transgenic mice displayed few alterations in the expression of protein-coding genes, but instead exhibited reduced levels of all endogenous siRNAs we tested. This supported a model in which the Rhox3 shRNA causes spermatogenic defects by sequestering one or more components of the endogenous small RNA biogenesis machinery. Our study serves as a warning for those using shRNA approaches to investigate gene functions in vivo.
| S-EPMC4366048 | biostudies-literature
Project description:BACKGROUND: Gene knock down by RNAi is a highly effective approach to silence gene expression in experimental as well as therapeutic settings. However, this widely used methodology entails serious pitfalls, especially concerning specificity of the RNAi molecules. RESULTS: We tested the most widely used control siRNA directed against GFP for off-target effects and found that it deregulates in addition to GFP a set of endogenous target genes. The off-target effects were dependent on the amount of GFP siRNA transfected and were detected in a variety of cell lines. Since the respective siRNA molecule specific for GFP is widely used as negative control for RNAi experiments, we studied the complete set of off-target genes of this molecule by genome-wide expression profiling. The detected modulated mRNAs had target sequences homologous to the siRNA as small as 8 basepairs in size. However, we found no restriction of sequence homology to 3'UTR of target genes. CONCLUSION: We can show that even siRNAs without a physiological target have sequence-specific off-target effects in mammalian cells. Furthermore, our analysis defines the off-target genes affected by the siRNA that is commonly used as negative control and directed against GFP. Since off-target effects can hardly be avoided, the best strategy is to identify false positives and exclude them from the results. To this end, we provide the set of false positive genes deregulated by the commonly used GFP siRNA as a reference resource for future siRNA experiments.
| S-EPMC2443166 | biostudies-literature
Project description:Short interfering RNAs (siRNAs) are promising therapeutics that make use of the RNA interference (RNAi) pathway, but liabilities arising from the native RNA structure necessitate chemical modification for drug development. Advances in the structural characterization of components of the human RNAi pathway have enabled structure-guided optimization of siRNA properties. Here we report the 2.3 Å resolution crystal structure of human Argonaute 2 (hAgo2), a key nuclease in the RNAi pathway, bound to an siRNA guide strand bearing an unnatural triazolyl nucleotide at position 1 (g1). Unlike natural nucleotides, this analogue inserts deeply into hAgo2's central RNA binding cleft and thus is able to modulate pairing between guide and target RNAs. The affinity of the hAgo2-siRNA complex for a seed-only matched target was significantly reduced by the triazolyl modification, while the affinity for a fully matched target was unchanged. In addition, siRNA potency for off-target repression was reduced (4-fold increase in IC50) by the modification, while on-target knockdown was improved (2-fold reduction in IC50). Controlling siRNA on-target versus microRNA (miRNA)-like off-target potency by projection of substituent groups into the hAgo2 central cleft from g1 is a new approach to enhance siRNA selectivity with a strong structural rationale.
| S-EPMC4962782 | biostudies-literature
Project description:Small interfering RNAs (siRNAs) are important tools for knocking down targeted genes, and have been widely applied to biological and biomedical research. To design siRNAs, two important aspects must be considered: the potency in knocking down target genes and the off-target effect on any nontarget genes. Although many studies have produced useful tools to design potent siRNAs, off-target prevention has mostly been delegated to sequence-level alignment tools such as BLAST. We hypothesize that whole-genome thermodynamic analysis can identify potential off-targets with higher precision and help us avoid siRNAs that may have strong off-target effects. To validate this hypothesis, two siRNA sets were designed to target three human genes IDH1, ITPR2 and TRIM28. They were selected from the output of two popular siRNA design tools, siDirect and siDesign. Both siRNA design tools have incorporated sequence-level screening to avoid off-targets, thus their output is believed to be optimal. However, one of the sets we tested has off-target genes predicted by Picky, a whole-genome thermodynamic analysis tool. Picky can identify off-target genes that may hybridize to a siRNA within a user-specified melting temperature range. Our experiments validated that some off-target genes predicted by Picky can indeed be inhibited by siRNAs. Similar experiments were performed using commercially available siRNAs and a few off-target genes were also found to be inhibited as predicted by Picky. In summary, we demonstrate that whole-genome thermodynamic analysis can identify off-target genes that are missed in sequence-level screening. Because Picky prediction is deterministic according to thermodynamics, if a siRNA candidate has no Picky predicted off-targets, it is unlikely to cause off-target effects. Therefore, we recommend including Picky as an additional screening step in siRNA design.
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Project description:MOTIVATION:miRNA isoforms (isomiRs) are produced from the same arm as the archetype miRNA with a few nucleotides different at 5 and/or 3 termini. These well-conserved isomiRs are functionally important and have contributed to the evolution of miRNA genes. Accurate detection of differential expression of miRNAs can bring new insights into the cellular function of miRNA and a further improvement in miRNA-based diagnostic and prognostic applications. However, very few methods take isomiR variations into account in the analysis of miRNA differential expression. RESULTS:To overcome this challenge, we developed a novel approach to take advantage of the multidimensional structure of isomiR data from the same miRNAs, termed as a multivariate differential expression by Hotelling's T2 test (MDEHT). The utilization of the information hidden in isomiRs enables MDEHT to increase the power of identifying differentially expressed miRNAs that are not marginally detectable in univariate testing methods. We conducted rigorous and unbiased comparisons of MDEHT with seven commonly used tools in simulated and real datasets from The Cancer Genome Atlas. Our comprehensive evaluations demonstrated that the MDEHT method was robust among various datasets and outperformed other commonly used tools in terms of Type I error rate, true positive rate and reproducibility. AVAILABILITY AND IMPLEMENTATION:The source code for identifying and quantifying isomiRs and performing miRNA differential expression analysis is available at https://github.com/amanzju/MDEHT. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.
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Project description:BACKGROUND:MicroRNAs (miRNAs) are approximately 22-nucleotide long regulatory RNA that mediate RNA interference by binding to cognate mRNA target regions. Here, we present a distributed kernel SVM-based binary classification scheme to predict miRNA targets. It captures the spatial profile of miRNA-mRNA interactions via smooth B-spline curves. This is accomplished separately for various input features, such as thermodynamic and sequence-based features. Further, we use a principled approach to uniformly model both canonical and non-canonical seed matches, using a novel seed enrichment metric. Finally, we verify our miRNA-mRNA pairings using an Elastic Net-based regression model on TCGA expression data for four cancer types to estimate the miRNAs that together regulate any given mRNA. RESULTS:We present a suite of algorithms for miRNA target prediction, under the banner Avishkar, with superior prediction performance over the competition. Specifically, our final kernel SVM model, with an Apache Spark backend, achieves an average true positive rate (TPR) of more than 75 percent, when keeping the false positive rate of 20 percent, for non-canonical human miRNA target sites. This is an improvement of over 150 percent in the TPR for non-canonical sites, over the best-in-class algorithm. We are able to achieve such superior performance by representing the thermodynamic and sequence profiles of miRNA-mRNA interaction as curves, devising a novel seed enrichment metric, and learning an ensemble of miRNA family-specific kernel SVM classifiers. We provide an easy-to-use system for large-scale interactive analysis and prediction of miRNA targets. All operations in our system, namely candidate set generation, feature generation and transformation, training, prediction, and computing performance metrics are fully distributed and are scalable. CONCLUSIONS:We have developed an efficient SVM-based model for miRNA target prediction using recent CLIP-seq data, demonstrating superior performance, evaluated using ROC curves for different species (human or mouse), or different target types (canonical or non-canonical). We analyzed the agreement between the target pairings using CLIP-seq data and using expression data from four cancer types. To the best of our knowledge, we provide the first distributed framework for miRNA target prediction based on Apache Hadoop and Spark. AVAILABILITY:All source code and sample data are publicly available at https://bitbucket.org/cellsandmachines/avishkar. Our scalable implementation of kernel SVM using Apache Spark, which can be used to solve large-scale non-linear binary classification problems, is available at https://bitbucket.org/cellsandmachines/kernelsvmspark.
| S-EPMC6175706 | biostudies-literature