Computational identification of microRNA targets

Recent experiments have shown that the genomes of organisms such as worm, fly, human, and mouse encode hundreds of microRNA genes. Many of these microRNAs are thought to regulate the translational expression of other genes by binding to partially complementary sites in messenger RNAs. Phenotypic and expression analysis suggests an important role of microRNAs during development. Therefore, it is of fundamental importance to identify microRNA targets. However, no experimental or computational high-throughput method for target site identification in animals has been published yet. Our main result is a new computational method that is designed to identify microRNA target sites. This method recovers with high specificity known microRNA target sites that have previously been defined experimentally. Based on these results, we present a simple model for the mechanism of microRNA target site recognition. Our model incorporates both kinetic and thermodynamic components of target recognition. When we applied our method to a set of 74 Drosophila melanogaster microRNAs, searching 3'UTR sequences of a predefined set of fly mRNAs for target sites which were evolutionary conserved between D. melanogaster and Drosophila pseudoobscura, we found that many key developmental body patterning genes such as hairy and fushi-tarazu are likely to be translationally regulated by microRNAs.     

Differential MicroRNA Regulation Correlates with Alternative Polyadenylation Pattern between Breast Cancer and Normal Cells

Alternative polyadenylation (APA) could result in mRNA isoforms with variable lengths of 3′ UTRs. Gain of microRNA target sites in the 3′ UTR of a long mRNA isoform may cause different regulation from the corresponding short isoform. It has been known that cancer cells globally exhibit a lower ratio of long and short isoforms (LSR); that is, they tend to express larger amounts of short isoforms. The objective of this study is to illustrate the relationship between microRNA differential regulation and LSR. We retrieved public APA annotations and isoform expression profiles of breast cancer and normal cells from a high-throughput sequencing method study specific for the mRNA 3′ end. Combining microRNA expression profiles, we performed statistical analysis to reveal and estimate microRNA regulation on APA patterns in a global scale. First, we found that the amount of microRNA target sites in the alternative UTR (aUTR), the region only present in long isoforms, could affect the LSR of the target genes. Second, we observed that the genes whose aUTRs were targeted by up-regulated microRNAs in cancer cells had an overall lower LSR. Furthermore, the target sites of up-regulated microRNAs tended to appear in aUTRs. Finally, we demonstrated that the amount of target sites for up-regulated microRNAs in aUTRs correlated with the LSR change between cancer and normal cells. The results indicate that up-regulation of microRNAs might cause lower LSRs of target genes in cancer cells through degradation of their long isoforms. Our findings provide evidence of how microRNAs might play a crucial role in APA pattern shifts from normal to cancerous or proliferative states.     

microRNA target predictions across seven Drosophila species and comparison to mammalian targets

microRNAs are small noncoding genes that regulate the protein production of genes by binding to partially complementary sites in the mRNAs of targeted genes. Here, using our algorithm PicTar, we exploit cross-species comparisons to predict, on average, 54 targeted genes per microRNA above noise in Drosophila melanogaster. Analysis of the functional annotation of target genes furthermore suggests specific biological functions for many microRNAs. We also predict combinatorial targets for clustered microRNAs and find that some clustered microRNAs are likely to coordinately regulate target genes. Furthermore, we compare microRNA regulation between insects and vertebrates. We find that the widespread extent of gene regulation by microRNAs is comparable between flies and mammals but that certain microRNAs may function in clade-specific modes of gene regulation. One of these microRNAs (miR-210) is predicted to contribute to the regulation of fly oogenesis. We also list specific regulatory relationships that appear to be conserved between flies and mammals. Our findings provide the most extensive microRNA target predictions in Drosophila to date, suggest specific functional roles for most microRNAs, indicate the existence of coordinate gene regulation executed by clustered microRNAs, and shed light on the evolution of microRNA function across large evolutionary distances. All predictions are freely accessible at our searchable Web site http://pictar.bio.nyu.edu.     

Regulation of p27Kip1 by miRNA 221/222 in Glioblastoma

Levels of p27Kip1, a key negative regulator of the cell cycle, are often decreased in cancer. In most cancers, levels of p27Kip1 mRNA are unchanged and increased proteolysis of the p27Kip1 protein is thought to be the primary mechanism for its down-regulation. Here we show that p27Kip1 protein levels are also down-regulated by microRNAs in cancer cells. We used RNA interference to reduce Dicer levels in human glioblastoma cell lines and found that this caused an increase in p27Kip1 levels and a decrease in cell proliferation. When the coding sequence for the 3'UTR of the p27Kip1 mRNA was inserted downstream of a luciferase reporter gene, Dicer depletion also enhanced expression of the reporter gene product. The microRNA target site software TargetScan predicts that the 3'UTR of p27Kip1 mRNA contains multiple sites for microRNAs. These include two sites for microRNA 221 and 222, which have been shown to be upregulated in glioblastoma relative to adjacent normal brain tissue. The genes for microRNA 221 and microRNA 222 occupy adjacent sites on the X chromosome; their expression appears to be coregulated and they also appear to have the same target specificity. Antagonism of either microRNA 221 or 222 in glioblastoma cells also caused an increase in p27Kip1 levels and enhanced expression of the luciferase reporter gene fused to the p27Kip1 3'UTR. These data show that p27Kip1 is a direct target for microRNAs 221 and 222, and suggest a role for these microRNAs in promoting the aggressive growth of human glioblastoma.     

Refining microRNA target predictions: Sorting the wheat from the chaff

microRNAs are short RNAs that reduce gene expression by binding to their targets. The accurate prediction of microRNA targets is essential to understanding the function of microRNAs. Computational predictions indicate that all human genes may be regulated by microRNAs, with each microRNA possibly targeting thousands of genes. Here we discuss computational methods for identifying mammalian microRNA targets and refining them for further experimental validation. We describe microRNA target prediction resources and procedures and how they integrate with various types of experimental techniques that aim to validate them or further explore their function. We also provide a list of target prediction databases and explain how these are curated.     

Prediction of personalized microRNA activity

MicroRNAs have been known to regulate almost all physiological and pathological processes by suppressing their target genes. In humans, more than 1000 microRNAs have been identified, each of which targets dozens or even hundreds of genes. Facing this huge repertoire of microRNA targeting, it is important to identify which microRNAs are active, i.e., down-regulating their targets, in specific physiological or pathological conditions. Predicting active microRNAs is different from predicting microRNA targets because the authentic target genes of a microRNA are often not directly and solely regulated by that microRNA, leading to inconsistent expression changes between the microRNA and its true targets. Several computational programs have been proposed to predict the activity of a microRNA from the expressions of its target genes. These programs performed well when being applied on the expression data obtained from distinct tissue types or from experiments that transfect a microRNA into cells (i.e., non-physiological). But the performance of microRNA activity prediction is not clear on the expression data from the same tissue type in two physiological conditions, e.g., liver tissues from cancer patients and healthy people. In this work, we evaluate the performance of two microRNA activity prediction programs using seven expression data sets, all of which compare samples in two physiological conditions, as well as propose a new approach that predicts microRNA activity with an accuracy of over 80%. Unlike current methods, which predict active microRNAs by comparing two groups of samples, e.g., tumor versus normal, our new approach compares each diseased sample with all the samples in the control group. In other words, it can predict the microRNA activity of a person. In this work, this new application is named to predict “personalized microRNA activity”.     

Genome-wide prioritization of disease genes and identification of disease-disease associations from an integrated human functional linkage network

Background

Recent studies have shown that the regulatory effect of microRNAs can be investigated by examining expression changes of their target genes. Given this, it is useful to define an overall metric of regulatory effect for a specific microRNA and see how this changes across different conditions.

Results

Here, we define a regulatory effect score (RE-score) to measure the inhibitory effect of a microRNA in a sample, essentially the average difference in expression of its targets versus non-targets. Then we compare the RE-scores of various microRNAs between two breast cancer subtypes: estrogen receptor positive (ER⁺) and negative (ER^-). We applied this approach to five microarray breast cancer datasets and found that the expression of target genes of most microRNAs was more repressed in ER^- than ER⁺; that is, microRNAs appear to have higher RE-scores in ER^- breast cancer. These results are robust to the microRNA target prediction method. To interpret these findings, we analyzed the level of microRNA expression in previous studies and found that higher microRNA expression was not always accompanied by higher inhibitory effects. However, several key microRNA processing genes, especially Ago2 and Dicer, were differentially expressed between ER^- and ER⁺ breast cancer, which may explain the different regulatory effects of microRNAs in these two breast cancer subtypes.

Conclusions

The RE-score is a promising indicator to measure microRNAs' inhibitory effects. Most microRNAs exhibit higher RE-scores in ER^- than in ER⁺ samples, suggesting that they have stronger inhibitory effects in ER^- breast cancers.     

Cepred: Predicting the Co-Expression Patterns of the Human Intronic microRNAs with Their Host Genes

Identifying the tissues in which a microRNA is expressed could enhance the understanding of the functions, the biological processes, and the diseases associated with that microRNA. However, the mechanisms of microRNA biogenesis and expression remain largely unclear and the identification of the tissues in which a microRNA is expressed is limited. Here, we present a machine learning based approach to predict whether an intronic microRNA show high co-expression with its host gene, by doing so, we could infer the tissues in which a microRNA is high expressed through the expression profile of its host gene. Our approach is able to achieve an accuracy of 79% in the leave-one-out cross validation and 95% on an independent testing dataset. We further estimated our method through comparing the predicted tissue specific microRNAs and the tissue specific microRNAs identified by biological experiments. This study presented a valuable tool to predict the co-expression patterns between human intronic microRNAs and their host genes, which would also help to understand the microRNA expression and regulation mechanisms. Finally, this framework can be easily extended to other species.