miRNA Target Prediction Algorithms Research Articles

Overview of microRNA Target Analysis Tools

microRNA (miRNA) target prediction plays an important role in studying the post-transcriptional regulation by miRNAs. Numerous target prediction tools have been utilized in several research approaches for in silico analysis.This type of analysis provides the initial step for further experimental validation in biological systems in order to complete the target validation.In this review, we summarize the computational tools based on a single method for miRNA target prediction (single algorithm tools) and also the comparative target prediction tools, utilized in several research approaches for in silico analysis. Comparative target prediction tools have provided a novel methodology for miRNA target prediction by reducing the number of false positives. Such tools are combining results from multiple single algorithm tools, facilitating, in this way, the reduction of the false positive results and providing a more accurate prediction. The main goal of this review is to summarize the available literature on the miRNA target prediction algorithms and tools in an extensive manner.

MiR-96 regulates FOXO1-mediated cell apoptosis in bladder cancer

Transitional cell carcinoma (TCC) is one of the most common types of malignancies and a leading cause of genitourinary system cancer mortality worldwide. The tumor suppressor gene FOXO1, a member of the forkhead box O (FOXO) subfamily of transcription factors, is downregulated in a number of cancers, including TCC; however, the underlying mechanisms are poorly understood. In the present study, we used microRNA (miRNA) target prediction algorithms to identify a conserved potential miR-96 binding site in the 3'-untranslated region (3'-UTR) of FOXO1. Using quantitative real-time PCR (qRT-PCR) and northern blot analysis, we identified that miR-96 was downregulated in TCC tissues compared to normal bladder tissues (NB), suggesting that the loss of FOXO1 expression in TCC may be mediated by miR-96. To confirm this, we transfected pre-miR-96/anti-miR-96 into the T24 TCC cell line and revealed that miR-96 expression was sufficient to significantly reduce FOXO1 expression. Conversely, FOXO1 expression was not completely restored by the inhibition of miR-96 in T24 cells. Moreover, RNA silencing of FOXO1 significantly reduced miR-96 inhibitor-mediated T24 cell apoptosis. In conclusion, our study demonstrates that the miR-96 targeting of FOXO1 is upregulated in TCC; in addition, TCC tumorigenesis may be partly due to the ability of miR-96 to promote FOXO1 repression, thereby bypassing cell apoptosis controls.

Abstract 2290: MiR-335 suppresses neuroblastoma cell invasiveness by direct targeting of multiple genes from the non-canonical TGF-beta signalling pathway

Abstract MicroRNAs are post-transcriptional gene regulators that play a ubiquitous role in cellular functioning. Aberrant expression of miRNAs has been correlated with many cancer types, including neuroblastoma. Neuroblastoma is an often fatal pediatric cancer derived from precursor cells of the sympathetic nervous system. The single most important indicator of unfavourable metastatic disease is amplification of the MYCN oncogenic transcription factor. MiR-335 is significantly down-regulated in MYCN amplified tumors (Bray et al Plos One 2009; 4:e7850) suggesting a possible tumor suppressive function. This study aims to determine if miR-335 functions as a tumor suppressor in neuroblastoma and to elucidate the mechanism by which it elicits this effect. Ectopic up-regulation of miR-335 significantly reduced the motile and invasive capacity of neuroblastoma cell lines. Inversely, down-regulation of miR-335 significantly increased their migratory and invasive potential. mRNA expression profiling arrays in conjunction with the miRNA target prediction algorithm TargetScan identified potential targets of miR-335. Three of these candidate genes, ROCK1, MAPK1 and LRG1, were selected for further investigation because of their known or predicted involvement in cell migration and invasion. Down-regulation of these genes by miR-335 was validated at mRNA and protein levels and direct targeting by miR-335 was confirmed by luciferase reporter assays. siRNA-mediated inhibition of each of these three genes recapitulated the diminished cell migration and invasion associated with miR-335 up-regulation. ROCK1 and MAPK1 are members of two of the major branches of the oncogenic non-canonical TGF-beta signalling pathway, whose signals converge to tightly regulate the phosphorylation or activation status of the motor protein myosin light chain (MLC). LRG1 is a putative TGF-beta pathway member that has been proposed to bind to TGF-beta receptor II. We demonstrate that siRNA mediated inhibition of ROCK1, MAPK1 and LRG1 leads to a significant reduction in phosphorylated levels of MLC protein. Furthermore, we demonstrate that up-regulation of miR-335 inhibits phosphorylation or activation of MLC through targeting of these three genes. Finally, we demonstrate using a MYCN repressible cell line and MYCN ChIP-chip experiments that miR-335 is directly repressed by the oncogenic transcription factor MYCN. Therefore, we conclude that miR-335 is directly repressed by MYCN in MYCN amplified neuroblastoma tumors leading to the increased expression of the oncogenic non-canonical TGF-beta pathway members ROCK1, MAPK1 and LRG1 which function to enhance the metastatic propensity of the tumor cells. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 2290. doi:1538-7445.AM2012-2290

Abstract 2289: miR-497 targets cell cycle regulator WEE1 in neuroblastoma

Abstract Neuroblastoma (NB) is a pediatric cancer that originates from precursor cells of the sympathetic nervous system. Despite advances in treatment and disease management, the overall 5-year survival rates remain poor, depending on patient age, histopathology and genetic subtype. Low expression of miR-497 was identified as being significantly (p<0.015) associated with poor event free and overall NB patient survival, indicating a potential tumor suppressor role (Bray et al., 2009). Furthermore, down-regulation of miR-497 has been demonstrated in a number of multi-drug resistant cancer cell lines. Here we present our findings on the functional effects of exogenous miR-497 expression in NB cell lines, and we identify WEE1 as a direct target of miR-497. Ectopic over-expression of miR-497 resulted in significantly decreased cell viability for MYCN amplified (MNA) Kelly cells (p<0.04) and NB1691 cells (p<0.03) (MTS assay), indicating that this miRNA must be targeting mRNA sequences from gene(s) with significant anti-proliferative effects. We examined the list of putative target genes generated by the TargetScan miRNA Target prediction algorithm and determined that, WEE1, a tyrosine kinase with a key role as an inhibitory regulator of the G2/M checkpoint, is a predicted target of miR-497. Expression analysis from our cohort of NB patients (n=47) identified that higher expression of WEE1 was significantly associated with poor overall survival and event free survival (p<0.03). In silico analysis (web based R2 microarray analysis platform from the Academic Medical Center (www.r2.humangenetics-amc.nl)) from a large independent data set (n=283) supports this association of WEE1 with less favourable outcome in NB (p<0.0001). miR-497 expression was found to be inversely correlated with WEE1 in the tumor cohort, consistent with direct targeting. Ectopic over-expression of miR-497 did not affect WEE1 mRNA levels but resulted in reduced WEE1 protein levels in both Kelly and NB1691 cells. Direct targeting of the 3′UTR of WEE1 by miR-497 was validated using luciferase reporter assays (p<0.002). In order to elucidate the mechanism of miR-497 down-regulation in poor prognosis tumors, we examined methylation DNA immunoprecipitation (MeDIP) data from 15 primary NB tumors and identified a hypermethylated site 166 bp up-stream of the miR-497. There was a significant inverse correlation between methylation at this site and levels of miR-497 expression. The miRNA methylation status was validated using bisulphite sequencing, and we also determined that miR-497 expression could be significantly increased following treatment of NB cell lines with the de-methylating agent 5-aza-cytidine. We conclude that miR-497 is down-regulated in neuroblastoma by hypermethylation and that functional studies indicate a tumor suppressive function for this miRNA, potentially through direct targeting of the G2/M checkpoint tyrosine kinase WEE1. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 2289. doi:1538-7445.AM2012-2289

Abstract 1013: Enriched miR-126 bioactivity marks the primitive compartment in human AML and regulates leukemia stem cell numbers

Abstract Previous work has shown miRNAs are dysregulated in acute myeloid leukemia (AML), however, there is little known regarding miRNA expression and function in human leukemia stem cells (LSC). In order to elucidate the role of miRNA in LSC, we performed miRNA profiling on fractionated subpopulations of primary AML patient samples. Supervised analysis guided by the in vivo SCID leukemia initiating cell (SL-IC) capacity of each sub-population generated a unique miRNA signature associated with LSC enriched fractions. The biological activity of our top candidate, miR-126, was confirmed at single cell resolution by using a novel bidirectional lentivirus miRNA reporter system in vitro and within primary AML patient samples xenografted into immune-deficient NSG mice. These data suggest that primitive AML cells may express high levels of bioactive miR-126 relative to more “differentiated” blast populations. To test this hypothesis, we FACS sorted miR-126 genetic reporter vector transduced primary AML patient samples and transplanted these populations into immune-compromised secondary mouse recipients. The results of these proof-of-concept experiments demonstrates our ability to prospectively isolate LSC enriched fractions in all 4 AML patient samples tested using only a single biomarker, miR-126. Finally, to understand the functional relevance of miR-126 expression within primitive human AML cells, stable enforced expression and knockdown of miR-126 was achieved using lentiviral vectors. Enforced expression in four primary AML xenografts resulted in a several fold increase of CD34+CD117+ lentivirus marked leukemia cells after 12 weeks. In addition, the miR-126/OE cells showed reduced differentiation marker expression (CD14, CD15) with no significant differences in AML graft size. To determine if the expanded population had SL-IC activity or was a downstream leukemic progenitor, limiting dilution assays were performed by transplantation of FACS sorted lentivirus marked cells into secondary recipient mice for 12 weeks. A 3-20 fold increase in LSC activity was observed with miR-126 forced expression compared to control cells. These data suggest that high levels of miR-126 bioactivity support self-renewal/maintenance of primitive AML cells at the cost of aberrant differentiation. We performed microarray analysis of a primitive human AML cells after miR-126/OE and miR-126/KD. The principal signalling pathway(s) under direct control of miR-126 in primitive AML cells were revealed by subjecting our array data to Gene Set Enrichment Analysis (GSEA) in combination with several published miRNA target prediction algorithms. In summary, this work demonstrates that miR-126 is more abundant and biologically active within the leukemia stem/progenitor cell compartment of the AML functional hierarchy and serves to regulate AML stem cell numbers. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 1013. doi:1538-7445.AM2012-1013

Impact of miR-7 over-expression on the proteome of Chinese hamster ovary cells

MicroRNAs play critical roles in the regulation of biological processes such as growth, apoptosis, productivity and secretion thus representing a potential route toward enhancing desirable characteristics of mammalian cells for biopharmaceutical production. We have previously found that miR-7 over-expression significantly inhibits the growth of CHO-SEAP cells without impacting cellular viability, with an associated increase in normalised productivity. Understanding the biological basis of this effect might open the way to new strategies for bioprocess-relevant growth regulation. In this study we have carried out a quantitative label-free LC-MS profiling study of proteins exhibiting altered levels following over-expression of miR-7 to gain insights into potential mechanisms involved in the observed phenotype. From the analysis we found 93 proteins showing decreased levels and 74 proteins with increased levels following over-expression of miR-7. Pathway analysis suggests that proteins involved in protein translation (e.g. ribosomal proteins), RNA and DNA processing (including histones) are enriched in the list of proteins showing decreased expression. Proteins involved in protein folding and secretion were found to be up-regulated following miR-7 over-expression. In silico bioinformatic analysis using miRWalk, which combined the output from 6 selected miRNA target prediction algorithms, was used to evaluate if any of the down-regulated proteins were potential direct targets of miR-7. Two genes, stathmin and catalase, which both have known roles in the regulation of cellular growth, were found to overlap a number of the predictive target database searches in both mouse and rat, and are likely to be possible direct targets of miR-7 in CHO cells. This is the first report investigating the impact of a miRNA on the proteome of CHO cells.

Predicting kissing interactions in microRNA-target complex and assessment of microRNA activity.

MicroRNAs (miRNAs) are a class of short RNA molecules that play an important role in post-transcriptional gene regulation. Computational prediction of the miRNA target sites in mRNA is crucial for understanding the mechanism of miRNA-mRNA interactions. We here develop a new computational model that allows us to treat a variety of miRNA-mRNA kissing interactions, which have been ignored in the currently existing miRNA target prediction algorithms. By including all the different inter- and intra-molecular base pairs, this new model can predict both the structural accessibility of the target sites and the binding affinity (free energy). Applications of the model to a test set of 105 miRNA-gene systems show a notably improved success rate of 83/105. We found that although the binding affinity alone predicts the miRNA repression efficiency with a high success rate of 73/105, the structure in the seed region can significantly influence the miRNA activity. The method also allows us to efficiently search for the potent miRNA from a pool of miRNA candidates for any given gene target. Furthermore, extension of the method may enable predictions of the three-dimensional (3D) structures of miRNA/mRNA complexes.

MultiMiTar: A Novel Multi Objective Optimization based miRNA-Target Prediction Method

BackgroundMachine learning based miRNA-target prediction algorithms often fail to obtain a balanced prediction accuracy in terms of both sensitivity and specificity due to lack of the gold standard of negative examples, miRNA-targeting site context specific relevant features and efficient feature selection process. Moreover, all the sequence, structure and machine learning based algorithms are unable to distribute the true positive predictions preferentially at the top of the ranked list; hence the algorithms become unreliable to the biologists. In addition, these algorithms fail to obtain considerable combination of precision and recall for the target transcripts that are translationally repressed at protein level.Methodology/Principal FindingIn the proposed article, we introduce an efficient miRNA-target prediction system MultiMiTar, a Support Vector Machine (SVM) based classifier integrated with a multiobjective metaheuristic based feature selection technique. The robust performance of the proposed method is mainly the result of using high quality negative examples and selection of biologically relevant miRNA-targeting site context specific features. The features are selected by using a novel feature selection technique AMOSA-SVM, that integrates the multi objective optimization technique Archived Multi-Objective Simulated Annealing (AMOSA) and SVM.Conclusions/SignificanceMultiMiTar is found to achieve much higher Matthew’s correlation coefficient (MCC) of 0.583 and average class-wise accuracy (ACA) of 0.8 compared to the others target prediction methods for a completely independent test data set. The obtained MCC and ACA values of these algorithms range from −0.269 to 0.155 and 0.321 to 0.582, respectively. Moreover, it shows a more balanced result in terms of precision and sensitivity (recall) for the translationally repressed data set as compared to all the other existing methods. An important aspect is that the true positive predictions are distributed preferentially at the top of the ranked list that makes MultiMiTar reliable for the biologists. MultiMiTar is now available as an online tool at www.isical.ac.in/~bioinfo_miu/multimitar.htm. MultiMiTar software can be downloaded from www.isical.ac.in/~bioinfo_miu/multimitar-download.htm.

Age-associated changes in expression of small, noncoding RNAs, including microRNAs, in C. elegans

Small, noncoding RNAs (sncRNAs), including microRNAs (miRNAs), impact diverse biological events through the control of gene expression and genome stability. However, the role of these sncRNAs in aging remains largely unknown. To understand the contribution of sncRNAs to the aging process, we performed small RNA profiling by deep-sequencing over the course of Caenorhabditis elegans (C. elegans) aging. Many small RNAs, including a significant number of miRNAs, change their expression during aging in C. elegans. Further studies of miRNA expression changes under conditions that modify lifespan demonstrate the tight control of their expression during aging. Adult-specific loss of argonaute-like gene-1 (alg-1) activity, which is necessary for miRNA maturation and function, resulted in an abnormal lifespan, suggesting that miRNAs are, indeed, required in adulthood for normal aging. miRNA target prediction algorithms combined with transcriptome data and pathway enrichment analysis revealed likely targets of these age-associated miRNAs with known roles in aging, such as mitochondrial metabolism. Furthermore, a computational analysis of our deep-sequencing data identified additional age-associated sncRNAs, including miRNA star strands, novel miRNA candidates, and endo-siRNA sequences. We also show an increase of specific transfer RNA (tRNA) fragments during aging, which are known to be induced in response to stress in several organisms. This study suggests that sncRNAs including miRNAs contribute to lifespan regulation in C. elegans, and indicates new connections between aging, stress responses, and the small RNA world.

PsRNATarget: a plant small RNA target analysis server

Plant endogenous non-coding short small RNAs (20–24 nt), including microRNAs (miRNAs) and a subset of small interfering RNAs (ta-siRNAs), play important role in gene expression regulatory networks (GRNs). For example, many transcription factors and development-related genes have been reported as targets of these regulatory small RNAs. Although a number of miRNA target prediction algorithms and programs have been developed, most of them were designed for animal miRNAs which are significantly different from plant miRNAs in the target recognition process. These differences demand the development of separate plant miRNA (and ta-siRNA) target analysis tool(s). We present psRNATarget, a plant small RNA target analysis server, which features two important analysis functions: (i) reverse complementary matching between small RNA and target transcript using a proven scoring schema, and (ii) target-site accessibility evaluation by calculating unpaired energy (UPE) required to ‘open’ secondary structure around small RNA’s target site on mRNA. The psRNATarget incorporates recent discoveries in plant miRNA target recognition, e.g. it distinguishes translational and post-transcriptional inhibition, and it reports the number of small RNA/target site pairs that may affect small RNA binding activity to target transcript. The psRNATarget server is designed for high-throughput analysis of next-generation data with an efficient distributed computing back-end pipeline that runs on a Linux cluster. The server front-end integrates three simplified user-friendly interfaces to accept user-submitted or preloaded small RNAs and transcript sequences; and outputs a comprehensive list of small RNA/target pairs along with the online tools for batch downloading, key word searching and results sorting. The psRNATarget server is freely available at http://plantgrn.noble.org/psRNATarget/.

Practical Aspects of microRNA Target Prediction

microRNAs (miRNAs) are endogenous non-coding RNAs that control gene expression at the posttranscriptional level. These small regulatory molecules play a key role in the majority of biological processes and their expression is also tightly regulated. Both the deregulation of genes controlled by miRNAs and the altered miRNA expression have been linked to many disorders, including cancer, cardiovascular, metabolic and neurodegenerative diseases. Therefore, it is of particular interest to reliably predict potential miRNA targets which might be involved in these diseases. However, interactions between miRNAs and their targets are complex and very often there are numerous putative miRNA recognition sites in mRNAs. Many miRNA targets have been computationally predicted but only a limited number of these were experimentally validated. Although a variety of miRNA target prediction algorithms are available, results of their application are often inconsistent. Hence, finding a functional miRNA target is still a challenging task. In this review, currently available and frequently used computational tools for miRNA target prediction, i.e., PicTar, TargetScan, DIANA-microT, miRanda, rna22 and PITA are outlined and various practical aspects of miRNA target analysis are extensively discussed. Moreover, the performance of three algorithms (PicTar, TargetScan and DIANA-microT) is both demonstrated and evaluated by performing an in-depth analysis of miRNA interactions with mRNAs derived from genes triggering hereditary neurological disorders known as trinucleotide repeat expansion diseases (TREDs), such as Huntington’s disease (HD), a number of spinocerebellar ataxias (SCAs), and myotonic dystrophy type 1 (DM1).

RepTar: a database of predicted cellular targets of host and viral miRNAs

Computational identification of putative microRNA (miRNA) targets is an important step towards elucidating miRNA functions. Several miRNA target-prediction algorithms have been developed followed by publicly available databases of these predictions. Here we present a new database offering miRNA target predictions of several binding types, identified by our recently developed modular algorithm RepTar. RepTar is based on identification of repetitive elements in 3′-UTRs and is independent of both evolutionary conservation and conventional binding patterns (i.e. Watson–Crick pairing of ‘seed’ regions). The modularity of RepTar enables the prediction of targets with conventional seed sites as well as rarer targets with non-conventional sites, such as sites with seed wobbles (G-U pairing in the seed region), 3′-compensatory sites and the newly discovered centered sites. Furthermore, RepTar’s independence of conservation enables the prediction of cellular targets of the less evolutionarily conserved viral miRNAs. Thus, the RepTar database contains genome-wide predictions of human and mouse miRNAs as well as predictions of cellular targets of human and mouse viral miRNAs. These predictions are presented in a user-friendly database, which allows browsing through the putative sites as well as conducting simple and advanced queries including data intersections of various types. The RepTar database is available at http://reptar.ekmd.huji.ac.il.

A two-step site and mRNA-level model for predicting microRNA targets

BackgroundDespite experiments showing that the number of microRNA (miRNA) target sites is critical for miRNA targeting, most existing methods focus on identifying individual miRNA target sites and do not model contributions of multiple target sites to miRNA regulation. To address this possible fault, we developed a miRNA target prediction model that recognizes the individual characteristics of functional binding sites and the global characteristics of miRNA-targeted mRNAs.ResultsBenchmark experiments showed that this two-step model generally had a higher overall performance than other established miRNA target prediction algorithms and that the model was especially suited to identify true miRNA targets among genes that all contain conserved target sites.ConclusionsThis improved performance could partly be explained by the model not relying on conservation when predicting targets. The critical factors for the model's performance, however, were mRNA-level features that characterized the number and strength of individual target sites within the mRNA. The model is available for online predictions or as pre-computed predictions on the human genome http://tare.medisin.ntnu.no/mirna_target.

Improving performance of mammalian microRNA target prediction

BackgroundMicroRNAs (miRNAs) are single-stranded non-coding RNAs known to regulate a wide range of cellular processes by silencing the gene expression at the protein and/or mRNA levels. Computational prediction of miRNA targets is essential for elucidating the detailed functions of miRNA. However, the prediction specificity and sensitivity of the existing algorithms are still poor to generate meaningful, workable hypotheses for subsequent experimental testing. Constructing a richer and more reliable training data set and developing an algorithm that properly exploits this data set would be the key to improve the performance current prediction algorithms.ResultsA comprehensive training data set is constructed for mammalian miRNAs with its positive targets obtained from the most up-to-date miRNA target depository called miRecords and its negative targets derived from 20 microarray data. A new algorithm SVMicrO is developed, which assumes a 2-stage structure including a site support vector machine (SVM) followed by a UTR-SVM. SVMicrO makes prediction based on 21 optimal site features and 18 optimal UTR features, selected by training from a comprehensive collection of 113 site and 30 UTR features. Comprehensive evaluation of SVMicrO performance has been carried out on the training data, proteomics data, and immunoprecipitation (IP) pull-down data. Comparisons with some popular algorithms demonstrate consistent improvements in prediction specificity, sensitivity and precision in all tested cases. All the related materials including source code and genome-wide prediction of human targets are available at http://compgenomics.utsa.edu/svmicro.html.ConclusionsA 2-stage SVM based new miRNA target prediction algorithm called SVMicrO is developed. SVMicrO is shown to be able to achieve robust performance. It holds the promise to achieve continuing improvement whenever better training data that contain additional verified or high confidence positive targets and properly selected negative targets are available.

Role of microRNA-199a-5p and discoidin domain receptor 1 in human hepatocellular carcinoma invasion

BackgroundMicro-ribonucleic acid (miRNA)-199a-5p has been reported to be decreased in hepatocellular carcinoma (HCC) compared to normal tissue. Discoidin domain receptor-1 (DDR1) tyrosine kinase, involved in cell invasion-related signaling pathway, was predicted to be a potential target of miR-199a-5p by the use of miRNA target prediction algorithms. The aim of this study was to investigate the role of miR-199a-5p and DDR1 in HCC invasion.MethodsMature miR-199a-5p and DDR1 expression were evaluated in tumor and adjacent non-tumor liver tissues from 23 patients with HCC undergoing liver resection and five hepatoma cell lines by the use of real-time quantitative RT-PCR (qRT-PCR) analysis. The effect of aberrant miR-199a-5p expression on cell invasion was assessed in vitro using HepG2 and SNU-182 hepatoma cell lines. Luciferase reporter assay was employed to validate DDR1 as a putative miR-199a-5p target gene. Regulation of DDR1 expression by miR-199a-5p was assessed by the use qRT-PCR and western blotting analysis.ResultsA significant down-regulation of miR-199a-5p was observed in 65.2% of HCC tissues and in four of five cell lines. In contrast, DDR1 expression was significantly increased in 52.2% of HCC samples and in two of five cell lines. Increased DDR1 expression in HCC was associated with advanced tumor stage. DDR1 was shown to be a direct target of miR-199a-5p by luciferase reporter assay. Transfection of miR-199a-5p inhibited invasion of HepG2 but not SNU-182 hepatoma cells.ConclusionsDecreased expression of miR-199a-5p contributes to increased cell invasion by functional deregulation of DDR1 activity in HCC. However, the effect of miR-199a-5p on DDR1 varies among individuals and hepatoma cell lines. These findings may have significant translational relevance for development of new targeted therapies as well as prognostic prediction for patients with HCC.

ANIMAL MICRORNA TARGET PREDICTION USING DIVERSE SEQUENCE-SPECIFIC DETERMINANTS

Many recent studies have shown that access of animal microRNAs (miRNAs) to their complementary sites in target mRNAs is determined by several sequence-specific determinants beyond the seed regions in the 5′ end of miRNAs. These factors have been related to the repressive power of miRNAs and used in some programs to predict the efficacy of miRNA complementary sites. However, these factors have not been systematically examined regarding their capacities for improving miRNA target prediction. We develop a new miRNA target prediction algorithm, called Hitsensor, by incorporating many sequence-specific features that determine complementarities between miRNAs and their targets, in addition to the canonical seed regions in the 5′ ends of miRNAs. We evaluate the performance of our algorithm on 720 known animal miRNA:target pairs in four species, Homo sapiens, Mus musculus, Drosophila melanogaster and Caenorhabditis elegans. Our experimental results show that Hitsensor outperforms five popular existing algorithms, indicating that our unique scheme for quantifying the determinants of complementary sites is effective in improving the performance of a miRNA target prediction algorithm. We also examine the effectiveness of miRNA-mediated repression for the predicted targets by using a published quantitative protein expression dataset of miR-223 knockout in mouse neutrophils. Hitsensor identifies more targets than the existing algorithms, and the predicted targets of Hitsensor show comparable protein level changes to those of the existing algorithms.

Survey of Computational Algorithms for MicroRNA Target Prediction

MicroRNAs (miRNAs) are 19 to 25 nucleotides non-coding RNAs known to possess important post-transcriptional regulatory functions. Identifying targeting genes that miRNAs regulate are important for understanding their specific biological functions. Usually, miRNAs down-regulate target genes through binding to the complementary sites in the 3' untranslated region (UTR) of the targets. In part, due to the large number of miRNAs and potential targets, an experimental based prediction design would be extremely laborious and economically unfavorable. However, since the bindings of the animal miRNAs are not a perfect one-to-one match with the complementary sites of their targets, it is difficult to predict targets of animal miRNAs by accessing their alignment to the 3' UTRs of potential targets. Consequently, sophisticated computational approaches for miRNA target prediction are being considered as essential methods in miRNA research.We surveyed most of the current computational miRNA target prediction algorithms in this paper. Particularly, we provided a mathematical definition and formulated the problem of target prediction under the framework of statistical classification. Moreover, we summarized the features of miRNA-target pairs in target prediction approaches and discussed these approaches according to two categories, which are the rule-based and the data-driven approaches. The rule-based approach derives the classifier mainly on biological prior knowledge and important observations from biological experiments, whereas the data driven approach builds statistic models using the training data and makes predictions based on the models. Finally, we tested a few different algorithms on a set of experimentally validated true miRNA-target pairs [1] and a set of false miRNA-target pairs, derived from miRNA overexpression experiment [2]. Receiver Operating Characteristic (ROC) curves were drawn to show the performances of these algorithms.

Rhythmic Expression of MicroRNA-26a Regulates the L-type Voltage-gated Calcium Channel α1C Subunit in Chicken Cone Photoreceptors

MicroRNAs (miRNAs) modulate gene expression by degrading or inhibiting translation of messenger RNAs (mRNAs). Here, we demonstrated that chicken microRNA-26a (gga-mir-26a) is a key posttranscriptional regulator of photoreceptor L-type voltage-gated calcium channel alpha1C subunit (L-VGCCalpha1C) expression, and its own expression has a diurnal rhythm, thereby explaining the rhythmic nature of L-VGCCalpha1Cs. Circadian oscillators in retinal photoreceptors provide a mechanism that allows photoreceptors to anticipate daily illumination changes. In photoreceptors, L-VGCC activities are under circadian control, which are higher at night and lower during the day. Interestingly, the mRNA level of VGCCalpha1D oscillates, but those for VGCCalpha1C do not. However, the protein expression of both VGCCalpha1C and alpha1D are higher at night in cone photoreceptors. The underlying mechanism regulating L-VGCCalpha1C protein expression was not clear until now. In vitro targeting reporter assays verified that gga-mir-26a specifically targeted the L-VGCCalpha1C 3'-untranslated region, and gga-mir-26a expression in the retina peaked during the day. After transfection with gga-mir-26a, L-VGCCalpha1C protein expression and L-VGCC current density decreased. Therefore, the rhythmic expression of gga-mir-26a regulated the protein expression of the L-VGCCalpha1C subunit. Additionally, both CLOCK (circadian locomoter output cycles kaput) and CREB (cAMP-response element-binding protein-1) activated gga-mir-26a expression in vitro. This result implies that gga-mir-26a might be a downstream target of circadian oscillators. Our work has uncovered new functional roles for miRNAs in the regulation of circadian rhythms in cone photoreceptors. Circadian regulated miRNAs could serve as the link between the core oscillator and output signaling that further govern biological functions.

MicroRNA-133 regulates the expression of GLUT4 by targeting KLF15 and is involved in metabolic control in cardiac myocytes

GLUT4 shows decreased levels in failing human adult hearts. We speculated that GLUT4 expression in cardiac muscle may be fine-tuned by microRNAs. Forced expression of miR-133 decreased GLUT4 expression and reduced insulin-mediated glucose uptake in cardiomyocytes. A computational miRNA target prediction algorithm showed that KLF15 is one of the targets of miR-133. It was confirmed that over-expression of miR-133 reduced the protein level of KLF15, which reduced the level of the downstream target GLUT4. Cardiac myocytes infected with lenti-decoy, in which the 3′UTR with tandem sequences complementary to miR-133 was linked to the luciferase reporter gene, had decreased miR-133 levels and increased levels of GLUT4. The expression levels of KLF15 and GLUT4 were decreased at the left ventricular hypertrophy and congestive heart failure stage in a rat model. The present results indicated that miR-133 regulates the expression of GLUT4 by targeting KLF15 and is involved in metabolic control in cardiomyocytes.

Reexamining microRNA Site Accessibility in Drosophila: A Population Genomics Study

Kertesz et al. (Nature Genetics 2008) described PITA, a miRNA target prediction algorithm based on hybridization energy and site accessibility. In this note, we used a population genomics approach to reexamine their data and found that the PITA algorithm had lower specificity than methods based on evolutionary conservation at comparable levels of sensitivity.We also showed that deeply conserved miRNAs tend to have stronger hybridization energies to their targets than do other miRNAs. Although PITA had higher specificity in predicting targets than a naïve seed-match method, this signal was primarily due to the use of a single cutoff score for all miRNAs and to the observed correlation between conservation and hybridization energy. Overall, our results clarify the accuracy of different miRNA target prediction algorithms in Drosophila and the role of site accessibility in miRNA target prediction.