MicroRNAs and their targets: recognition, regulation and an emerging reciprocal relationship

MicroRNAs (miRNAs) have emerged as key gene regulators in diverse biological pathways. These small non-coding RNAs bind to target sequences in mRNAs, typically resulting in repressed gene expression. Several methods are now available for identifying miRNA target sites, but the mere presence of an miRNA-binding site is insufficient for predicting target regulation. Regulation of targets by miRNAs is subject to various levels of control, and recent developments have presented a new twist; targets can reciprocally control the level and function of miRNAs. This mutual regulation of miRNAs and target genes is challenging our understanding of the gene-regulatory role of miRNAs in vivo and has important implications for the use of these RNAs in therapeutic settings.     

The evolving role of microRNAs in animal gene expression

MicroRNAs (miRNAs) constitute an abundant family of 22-nucleotide RNAs that base-pair to target mRNAs and typically inhibit their expression. To assess the global impact of animal miRNAs on gene regulation, the expression of predicted targets and their cognate miRNAs was extensively analyzed in mammals and Drosophila. In general, targets are co-expressed at relatively low or undetectable levels in the same tissues as the miRNAs predicted to regulate them. Additionally, genes that are highly co-expressed with miRNAs usually lack target sites. The authors conclude that many animal genes are under evolutionary pressure to maintain or avoid complementary sites to miRNAs. Thus, the miRNA pathway broadly contributes to the complex gene regulatory networks that shape animal tissue development and identity.     

HomoTarget: A new algorithm for prediction of microRNA targets in Homo sapiens

MiRNAs play an essential role in the networks of gene regulation by inhibiting the translation of target mRNAs. Several computational approaches have been proposed for the prediction of miRNA target-genes. Reports reveal a large fraction of under-predicted or falsely predicted target genes. Thus, there is an imperative need to develop a computational method by which the target mRNAs of existing miRNAs can be correctly identified. In this study, combined pattern recognition neural network (PRNN) and principle component analysis (PCA) architecture has been proposed in order to model the complicated relationship between miRNAs and their target mRNAs in humans. The results of several types of intelligent classifiers and our proposed model were compared, showing that our algorithm outperformed them with higher sensitivity and specificity. Using the recent release of the mirBase database to find potential targets of miRNAs, this model incorporated twelve structural, thermodynamic and positional features of miRNA:mRNA binding sites to select target candidates.     

Inferring the perturbed microRNA regulatory networks from gene expression data using a network propagation based method

Background

MicroRNAs (miRNAs) are a class of endogenous small regulatory RNAs. Identifications of the dys-regulated or perturbed miRNAs and their key target genes are important for understanding the regulatory networks associated with the studied cellular processes. Several computational methods have been developed to infer the perturbed miRNA regulatory networks by integrating genome-wide gene expression data and sequence-based miRNA-target predictions. However, most of them only use the expression information of the miRNA direct targets, rarely considering the secondary effects of miRNA perturbation on the global gene regulatory networks.

Results

We proposed a network propagation based method to infer the perturbed miRNAs and their key target genes by integrating gene expressions and global gene regulatory network information. The method used random walk with restart in gene regulatory networks to model the network effects of the miRNA perturbation. Then, it evaluated the significance of the correlation between the network effects of the miRNA perturbation and the gene differential expression levels with a forward searching strategy. Results show that our method outperformed several compared methods in rediscovering the experimentally perturbed miRNAs in cancer cell lines. Then, we applied it on a gene expression dataset of colorectal cancer clinical patient samples and inferred the perturbed miRNA regulatory networks of colorectal cancer, including several known oncogenic or tumor-suppressive miRNAs, such as miR-17, miR-26 and miR-145.

Conclusions

Our network propagation based method takes advantage of the network effect of the miRNA perturbation on its target genes. It is a useful approach to infer the perturbed miRNAs and their key target genes associated with the studied biological processes using gene expression data.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-255) contains supplementary material, which is available to authorized users.     

MicroRNAs target gene and signaling pathway by bioinformatics analysis in the cardiac hypertrophy

Cardiac hypertrophy is a physiological adaptive response of the heart to diverse pathophysiological stimuli. Initially, it may be adaptive to normalize wall stress and to preserve contractile performance. This adaptive process may gradually progress to dilated cardiomyopathy, fibrotic diseases, arrhythmia, heart failure and even sudden death. Although various molecular pathways responsible for the coordinated control of the hypertrophic program, little is known about their underlying molecular mechanisms. Very recently, increasing evidence showed that miRNAs are key modulators of both cardiovascular development and function, which govern the process of cardiac hypertrophy and heart failure. MicroRNAs (miRNAs) act in a complex functional network in which each single miRNAs might control thousands of distinct target genes, and each single protein-coding gene can be regulated by many different miRNAs. Identifying the roles of miRNAs, their target genes and signaling pathways in cardiac hypertrophy by bioinformatic analysis will provide more insight into the molecular mechanisms underlying this disease process. Currently, bioinformatics resource such as GO and KEGG was applied to describe the miRNAs target genes function and identify the mRNA interaction networks that are responsible for various cellular processes. It provides a useful approach to observe the function of microRNA in physiological and pathological conditions. In this review, we will give a discussion on the dysregulation of specific miRNAs in cardiac hypertrophy and signaling pathways linking the hypertrophy-regulating miRNAs to the pathological process of cardiac hypertrophy. Finally, we place special emphasis on the essential role of bioinformatics analysis to predict the target genes and miRNAs gene networks.     

计算识别microRNA及其靶基因

小RNA的发现为基因调控系统研究提供了新的方向。在多数物种中已经发现了大量的小RNA。这一领域已经成为了近来研究的热点,在研究起始阶段,计算学方法已经成为实验研究中不可或缺的工具,许多发现是由生物学实验与计算学方法共同合作来完成的。在这篇综述中,我们总结了前人关于小RNA及其靶基因识别的理论知识。最后,讨论了关于预测小RNA及其靶基因的计算学方法和相关软件。