HuMiTar: A sequence-based method for prediction of human microRNA targets

Abstract

BackgroundMicroRNAs (miRs) are small noncoding RNAs that bind to complementary/partially complementary sites in the 3' untranslated regions of target genes to regulate protein production of the target transcript and to induce mRNA degradation or mRNA cleavage. The ability to perform accurate, high-throughput identification of physiologically active miR targets would enable functional characterization of individual miRs. Current target prediction methods include traditional approaches that are based on specific base-pairing rules in the miR's seed region and implementation of cross-species conservation of the target site, and machine learning (ML) methods that explore patterns that contrast true and false miR-mRNA duplexes. However, in the case of the traditional methods research shows that some seed region matches that are conserved are false positives and that some of the experimentally validated target sites are not conserved.ResultsWe present HuMiTar, a computational method for identifying common targets of miRs, which is based on a scoring function that considers base-pairing for both seed and non-seed positions for human miR-mRNA duplexes. Our design shows that certain non-seed miR nucleotides, such as 14, 18, 13, 11, and 17, are characterized by a strong bias towards formation of Watson-Crick pairing. We contrasted HuMiTar with several representative competing methods on two sets of human miR targets and a set of ten glioblastoma oncogenes. Comparison with the two best performing traditional methods, PicTar and TargetScanS, and a representative ML method that considers the non-seed positions, NBmiRTar, shows that HuMiTar predictions include majority of the predictions of the other three methods. At the same time, the proposed method is also capable of finding more true positive targets as a trade-off for an increased number of predictions. Genome-wide predictions show that the proposed method is characterized by 1.99 signal-to-noise ratio and linear, with respect to the length of the mRNA sequence, computational complexity. The ROC analysis shows that HuMiTar obtains results comparable with PicTar, which are characterized by high true positive rates that are coupled with moderate values of false positive rates.ConclusionThe proposed HuMiTar method constitutes a step towards providing an efficient model for studying translational gene regulation by miRs.

Insight into the role of microRNAs in brain tumors (Review)

“…Consistent with these findings RECK and TIMP3 levels drop, whereas miR-21 expression increases during glioma progression to higher grades (40). Although PTEN tumor suppressor gene has been validated as a miR-21 target by computational analysis (47,48), down-regulation of mir-21 by specific antisense oligonucleotide leads to an inhibition of tumor growth in glioblastoma cell lines and xenograft tumors independently of PTEN mutational status. This is particularly interesting as up to 80% of GBMs have a PTEN deficiency, and the prognosis of GBM can be predicted based on the levels of expression of PTEN (49,50).…”

MiR-30a-5p Antisense Oligonucleotide Suppresses Glioma Cell Growth by Targeting SEPT7

“…Bioinformatics analyses with HuMiTar, a sequence-based method for prediction of human microRNA targets [9], have predicted that several miRNAs, including miR-30a-5p, are involved in the post-transcriptional regulation of SEPT7.…”

Downregulation of miR-21 inhibits EGFR pathway and suppresses the growth of human glioblastoma cells independent of PTEN status

“…Computational analysis predicts that PTEN is a target gene for miR-21 24,31 and the Akt pathway is regulated by PTEN. 29 PTEN inactivation and EGFR and Akt activation frequently occur in glioblastomas.…”

“…To further test this hypothesis, we generated a reporter gene construct pGL3-PTEN-3-UTR that contains the putative miR-21 binding site. 24,31,33 The pGL3-PTEN-3 0 -UTR construct and pGL-3 control plasmid were separately co-transfected with AS-miR-21 or control ODN into U251, A172 (PTEN deleted), and H4 (PTEN status was not clear) glioma cells followed by measurement of luciferase activity in the transfected cells at 3 days after transfection. The results showed that the AS-miR-21 caused a four-to sixfold decrease in luciferase activity in pGL3-PTEN-3 0 -UTR-transfected cells (Figure 4a and b).…”