Progress in miRNA target prediction and identification

Recently, the identification of miRNA targets has received much attention. The strategies to determine miRNA targets include bioinformatic prediction and experimental assays. The bioinformatic prediction methods are mainly based on the confirmed rules of interaction between miRNAs and their targets, and are carried out by programs, such as miRanda, TargetScan, TargetScanS, RNAhybrid, DIANA-microT, PicTar, RNA22 and FindTar, which follow well-known principles. The experimental assays to find miRNA targets employ immunoprecipitation of AGO proteins to identify interacting mRNAs, or the analysis of mRNA or protein levels to identify genes which can be regulated by miRNAs. The improvement of current bioinformatic and experimental assays and the development of novel assays will enable greater efficiency in the identification of miRNA targets and thus facilitate miRNA research. This paper describes progress in the prediction and identification of miRNA targets.     

Bio-informatic trends for the determination of miRNA-target interactions in mammals

MicroRNAs (miRNAs) are small non-coding RNA molecules that regulate mRNAs through a sequence-specific mechanism. By virtue of their structure and mechanism of action, computational methods have been devised to investigate the encoding of miRNA genes and the targets of miRNA action. A variety of assumptions have predicated the implementation of these various computational solutions. Evolutionary sequence conservation, secondary structure, and folding energetics are some of the assumptions that have been used. The success of these different computational solutions has been evaluated for both elucidation of new miRNAs and deducing targets of miRNA action. While the focus is on search techniques for new miRNAs, we have compared the programs miRseeker, miRScan, PalGrade, ProMiR, and miRAlign as examples of implementation of these techniques. For these programs, a benchmark comparison between theoretical estimation and actual identification is possible. We have also compared the target prediction programs TargetScanS, PicTar, DIANA-microT, miRanda, and RNAhybrid. However, it is difficult to rigorously assess the benchmark performance of these programs due to the difficulty in confirming their theoretical predictions.     

Impact of miRNA Sequence on miRNA Expression and Correlation between miRNA Expression and Cell Cycle Regulation in Breast Cancer Cells

The miRNAs regulate cell functions by inhibiting expression of proteins. Research on miRNAs had usually focused on identifying targets by base pairing between miRNAs and their targets. Instead of identifying targets, this paper proposed an innovative approach, namely impact significance analysis, to study the correlation between mature sequence, expression across patient samples or time and global function on cell cycle signaling of miRNAs. With three distinct types of data: The Cancer Genome Atlas miRNA expression data for 354 human breast cancer specimens, microarray of 266 miRNAs in mouse Embryonic Stem cells (ESCs), and Reverse Phase Protein Array (RPPA) transfected by 776 miRNAs in MDA-MB-231 cell line, we linked the expression and function of miRNAs by their mature sequence and discovered systematically that the similarity of miRNA expression enhances the similarity of miRNA function, which indicates the miRNA expression can be used as a supplementary factor to predict miRNA function. The results also show that both seed region and 3'' portion are associated with miRNA expression levels across human breast cancer specimens and in ESCs; miRNAs with similar seed tend to have similar 3'' portion. And we discussed that the impact of 3'' portion, including nucleotides , is not significant for miRNA function. These results provide novel insights to understand the correlation between miRNA sequence, expression and function. They can be applied to improve the prediction algorithm and the impact significance analysis can also be implemented to similar analysis for other small RNAs such as siRNAs.     

Assessing the utility of thermodynamic features for microRNA target prediction under relaxed seed and no conservation requirements

Background

Many computational microRNA target prediction tools are focused on several key features, including complementarity to 5′seed of miRNAs and evolutionary conservation. While these features allow for successful target identification, not all miRNA target sites are conserved and adhere to canonical seed complementarity. Several studies have propagated the use of energy features of mRNA:miRNA duplexes as an alternative feature. However, different independent evaluations reported conflicting results on the reliability of energy-based predictions. Here, we reassess the usefulness of energy features for mammalian target prediction, aiming to relax or eliminate the need for perfect seed matches and conservation requirement.

Methodology/Principal Findings

We detect significant differences of energy features at experimentally supported human miRNA target sites and at genome-wide sites of AGO protein interaction. This trend is confirmed on datasets that assay the effect of miRNAs on mRNA and protein expression changes, and a simple linear regression model leads to significant correlation of predicted versus observed expression change. Compared to 6-mer seed matches as baseline, application of our energy-based model leads to ∼3–5-fold enrichment on highly down-regulated targets, and allows for prediction of strictly imperfect targets with enrichment above baseline.

Conclusions/Significance

In conclusion, our results indicate significant promise for energy-based miRNA target prediction that includes a broader range of targets without having to use conservation or impose stringent seed match rules.     

Complementary miRNA pairs suggest a regulatory role for miRNA:miRNA duplexes

microRNAs (miRNAs) are 21-22-nucleotide noncoding RNAs that are widely believed to regulate complementary mRNA targets. However, due to the modest amount of pairing involved, only a few out of the hundreds of known animal miRNAs have thus far been connected to mRNA targets. Here, we considered the possibility that miRNAs might regulate non-mRNA targets, namely other miRNAs. To do so, we conducted a systematic assessment of the nearly complete catalogs of animal miRNAs for potential miRNA:miRNA complements. Our analysis uncovered several compelling examples that strongly suggest a function for miRNA duplexes, thus adding a potential layer of regulatory sophistication to the small RNA world. Interestingly, the most striking examples involve miRNAs complementary to members of the K-box family and Brd-box family, two classes of miRNAs previously implicated in regulation of Notch target genes. We emphasize that patterns of nucleotide constraint indicate that miRNA complementarity is not a simple consequence of miRNA:miRNA* complementarity; however, our findings do suggest that the potential regulatory consequences of the latter also deserve investigation.     

Prediction of both conserved and nonconserved microRNA targets in animals

MOTIVATION: MicroRNAs (miRNAs) are involved in many diverse biological processes and they may potentially regulate the functions of thousands of genes. However, one major issue in miRNA studies is the lack of bioinformatics programs to accurately predict miRNA targets. Animal miRNAs have limited sequence complementarity to their gene targets, which makes it challenging to build target prediction models with high specificity. RESULTS: Here we present a new miRNA target prediction program based on support vector machines (SVMs) and a large microarray training dataset. By systematically analyzing public microarray data, we have identified statistically significant features that are important to target downregulation. Heterogeneous prediction features have been non-linearly integrated in an SVM machine learning framework for the training of our target prediction model, MirTarget2. About half of the predicted miRNA target sites in human are not conserved in other organisms. Our prediction algorithm has been validated with independent experimental data for its improved performance on predicting a large number of miRNA down-regulated gene targets. AVAILABILITY: All the predicted targets were imported into an online database miRDB, which is freely accessible at http://mirdb.org.     

A GAL4/UAS luciferase system to identify the miRNAs target mRNAs in <Emphasis Type="Italic">Drosophila</Emphasis> S2 cells

MicroRNAs (miRNAs) have been shown to regulate gene expression through the sequence-specific base pairing with their target mRNAs. However, our understanding of the biological roles of miRNAs is still quite limited, and only a handful of miRNAs have been assigned by genetic analysis in part owing to the difficulty in the identification of their targets. Although computational methods have shown to be helpful in the prediction of miRNA targets, a major obstacle has been the lack of quick and efficient experimental procedures to verify these targets. In this report, we describe a UAS/GAL4-based reporter system for this purpose. Our data indicate it an assay of miRNA–target gene interaction, with greater sensitivity over the previously reported methods, and may be useful for more efficient identification/validation the miRNA targets in Drosophila cell lines.     

In vitro quantification of specific microRNA using molecular beacons

MicroRNAs (miRNAs), a class of non-coding RNAs, have become a major focus of molecular biology research because of their diverse genomic origin and ability to regulate an array of cellular processes. Although the biological functions of miRNA are yet to be fully understood, tissue levels of specific miRNAs have been shown to correlate with pathological development of disease. Here, we demonstrate that molecular beacons can readily distinguish mature- and pre-miRNAs, and reliably quantify miRNA expression. We found that molecular beacons with DNA, RNA and combined locked nucleic acid (LNA)–DNA backbones can all detect miRNAs of low (<1 nM) concentrations in vitro, with RNA beacons having the highest detection sensitivity. Furthermore, we found that molecular beacons have the potential to distinguish miRNAs that have slight variations in their nucleotide sequence. These results suggest that the molecular beacon-based approach to assess miRNA expression and distinguish mature and precursor miRNA species is quite robust, and has the promise for assessing miRNA levels in biological samples.     

microRNA计算发现方法的研究进展

microRNA (miRNA)是近几年发现的一类长度为～21 nt的内源非编码小RNA, 在植物和动物中发挥着重要而广泛的调控功能。它的发现主要有cDNA克隆测序和计算发现两条途径。由于cDNA克隆测序方法受miRNA表达的时间和组织特异性以及表达水平的影响, 而计算发现可以弥补其不足, 因此miRNA的计算发现方法研究受到了广泛的重视。文章对近几年计算发现miRNA的研究进展进行了综述, 根据计算发现方法的本质, 将计算发现方法归纳为5类, 分别是同源片段搜索方法、基于比较基因组学的预测方法、基于序列和结构特征打分的预测方法、结合作用靶标的预测方法和基于机器学习的预测方法, 并对各类方法的原理、核心思想、优点和局限性进行了分析, 最后探讨了进一步的发展方向。