MicroRNA target prediction: theory and practice

Abstract

The present study is one of the few that includes tissue samples in the evaluation of target prediction algorithms designed to detect microRNA (miRNA) sequences that might interact with particular messenger RNA (mRNA) sequences. Twelve different target prediction tools were used to find miRNA sequences that might interact with CCL20 gene expression. Different algorithms predicted controversial miRNA sequences for CCL20 regulation due to a different weighting of parameters. Hsa-miR-21 and hsa-miR-145 suggested by four or more programs were chosen for further investigation. Possible real interaction of these miRNA sequences with CCL20 gene expression was monitored using luciferase assays and expression analyses of tissue samples of colorectal adenocarcinoma by either qRT-PCR or ELISA. Folding status of seed-binding sites in complete mRNA and 3'UTR of CCL20 was predicted. Prediction of miRNA expression was attempted based on CCL20 expression data. Eight of the target prediction tools forecasted a role for hsa-miR-21 and four mentioned hsa-miR-145 in CCL20 gene regulation. Laboratory experimentation showed that CCL20 may serve as a target of hsa-miR-21 but not hsa-miR-145. Expression of the molecules resulted in no clear assertion. Folding of seed-binding sites was predicted to be relatively constant for the complete mRNA and 3'UTR. Predicting miRNA expression based on target gene expression was impossible. This might be attributable to the fact that effects of miRNA activity may oscillate between gene product repression and activation. Additional systematic studies are needed to address this issue.

Unveiling ncRNA regulatory axes in atherosclerosis progression

“…Recent years have seen the proliferation of algorithms and web servers designed to predict miRNA targets [233], of which a few have established themselves as reference tools ( [234] and see Table 4). Although it is not the aim of this review to make a comparative analysis of these algorithms (see [235] for a recent review on this topic), it is widely accepted that their predictions are frequently inconsistent, inaccurate and plenty of false positives [236,237]. One answer to this problem has been the development of secondary algorithms that perform a more comprehensive analysis by combining the outputs of a number of primary target predictions (e.g.…”

miR-206 inhibits FN1 expression and proliferation and promotes apoptosis of rat type II alveolar epithelial cells

“…Although much remains to be fully studied, previous investigations suggest that miRNAs serve an important role by controlling the function of various genes involved in cell functions (13–15). miRNAs regulate gene expression post-transcriptionally and inhibit the expression of the target genes (13,25).…”

Integrated miRNA/mRNA Counter-Expression Analysis Highlights Oxidative Stress-Related Genes CCR7 and FOXO1 as Blood Markers of Coronary Arterial Disease

“…The identification of miRNAs and downstream mRNA-target interactions is an important topic to study regulatory miRNA/mRNA networks and their alterations in human disease. This has been approached with computer tools [10], that frequently originate a high number of false positive results [11,12], or with experimental techniques, that are complex and cumbersome to perform (see [13] and references therein). For these reasons, a number of groups have developed different protocols to integrate miRNA target predictions with expression data from microarray experiments in an attempt to facilitate the identification of miRNA/mRNA pairs without the difficulties of experimental models or the unacceptable high false positive rates of computational methods (see [14] and references therein).…”