Identification Of microRNA Targets Research Articles

A Novel Cellular Microrna Target Selection System

microRNAs are a class of small, noncoding RNAs that control gene expression by translational repression or messenger RNA (mRNA) degradation through binding to target sites within 3′ UTRs. More than 900 microRNAs are expressed in humans, and up to 30% of human genes may be regulated by microRNAs. Although a number of tools have been developed for identifying and profiling microRNAs, cataloging microRNAs and their expression patterns does not provide a complete picture of their cellular roles. In order to gain such an understanding, it is important to identify which mRNAs are regulated by specific microRNAs. Imperfect base pairing of microRNAs to target RNAs complicates the prediction of microRNA targets based on sequence alignment alone (1). Current experimental approaches to verifying predicted interactions are laborious and time-consuming. We have developed a unique technology for identifying the microRNAs that bind to 3′ UTRs using a cellular selection system. Our miR-Selection lentivector features a dual reporter system with firefly luciferase (Fire) and a cytotoxic sensor (Ctx). The miR-Selection platform captures the 3′ UTR to microRNA binding event using a survival screen by modulating the reduction of the cytotoxic sensor. Quantitative validation is made simple using the built-in luciferase reporter. This powerful and elegant technology enables the accurate identification of microRNA targets (Figure 1).

Computational methods for the identification of microRNA targets

MicroRNAs are pivotal regulators of development and cellular homeostasis. They act as post-transcriptional regulators, which control the stability and translation efficiency of their target mRNAs. The prediction of microRNA targets and detection of microRNA-mRNA regulatory modules (MRMs) are crucial components for understanding of microRNA functions. Numerous computational methods for microRNA target prediction have been developed. Computationally-predicted targets have been recently used in the integrative analysis of microRNA and mRNA expression analysis to identify microRNA targets and MRMs. In this article we review these recent developments in the integrative analysis methods. We also discuss the remaining challenges and our insights on future directions.

Experimental identification of microRNA targets on the 3′ untranslated region of human FMR1 gene

FMR1 gene plays an important role in the development of central nervous system. Down-regulation of the FMR1 expression leads to fragile X syndrome. MicroRNAs (miRNAs) can repress gene expression by base pairing with their mRNA targets. By computer programs analysis, five miRNAs: miR-19a, miR-19b, miR-142, miR-302b* and miR-323-3p potentially target to different sites on the FMR1 3′ untranslated region (3′ UTR), except that miR-19a and miR-19b share the same targeting site. To test whether these miRNAs repress reporter gene expression by interacting with the miRNA targets on the FMR1 3′ UTR, we developed two chimeric constructs: one construct expressing a firefly luciferase with the FMR1 3′ UTR or its miRNA target mutations and the other construct expressing a pre-miRNA fusing with GFP. Luciferase assay co-transfecting with the two constructs showed that the miRNAs, miR-19b, miR-302b* and miR-323-3p could repress gene expression in HEK-293 cells, suggesting a role of these miRNAs in the regulation of the FMR1 expression. The constructs used in this study can be widely used to identify the miRNA targets in any interested genes, which will greatly promote the current progress in understanding the biological function of miRNAs.

Abstract 1953: Functional identification of microRNA targets by integrated proteomics and microarray profiling: miR-193b in breast cancer

Abstract MicroRNAs (miRNAs) regulate protein expression by targeting messenger RNAs (mRNAs) for cleavage or by inducing translational repression. Despite advances in bioinformatic predictions, it is a significant challenge to identify protein targets that the miRNAs regulate. Here, we applied a novel strategy combining functional data from quantitative proteomics using iTRAQ (isobaric tag for relative and absolute quantitation), gene expression microarray profiling and bioinformatic target predictions in order to characterize targets regulated by miRNAs. We focused on exploring the anti-cancer effects of miR-193b. According to our high-throughput functional studies (Leivonen et al., Oncogene, 2009), miR-193b induces cell cycle arrest and inhibition of estrogen-depedent growth when overexpressed in breast cancer cell lines. Pre-miR-193b was over-expressed in MCF-7 breast cancer cells for 48 h, and the cell lysates were subjected to iTRAQ isobaric labelling followed by mass-spectrometric analyses. We identified over 600 proteins, of which 11 were repeatedly and significantly (p<0.05) down-regulated, and 6 up-regulated by miR-193b as compared to controls. Three of the down-regulated proteins were predicted to be targeted by miR-193b by the TargetScan algorithm. Of the predicted miR-193b targets, two were also among the most prominently down-regulated genes at the mRNA level according to microarray profiling of miR-193b-transfected MCF-7 cells. These data were validated by western blotting and quantitative RT-PCR. The results indicated that miR-193b targets included proteins which have previously been linked to breast cancer progression and poor prognosis, such as 14-3-3ζ (YWHAZ), as well as mini-chromosome maintenance protein-7 (MCM7), which is associated with DNA replication and cell cycle control. In conclusion, we demonstrate here that the combination of proteomic expression profiling and gene expression microarray profiling is a powerful strategy to functionally identify protein targets and mechanisms of action for miRNAs that have anti-cancer effects when transfected into breast cancer cells. Note: This abstract was not presented at the AACR 101st Annual Meeting 2010 because the presenter was unable to attend. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 1953.

Transcriptome-wide identification of microRNA targets in rice

MicroRNA (miRNA)-guided target RNA expression is vital for a wide variety of biological processes in eukaryotes. Currently, miRBase (version 13) lists 142 and 353 miRNAs from Arabidopsis and rice (Oryza sativa), respectively. The integration of miRNAs in diverse biological networks relies upon the confirmation of their RNA targets. In contrast with the well-characterized miRNA targets that are cleaved in Arabidopsis, only a few such targets have been confirmed in rice. To identify small RNA targets in rice, we applied the 'degradome sequencing' approach, which globally identifies the remnants of small RNA-directed target cleavage by sequencing the 5' ends of uncapped RNAs. One hundred and sixty targets of 53 miRNA families (24 conserved and 29 rice-specific) and five targets of TAS3-small interfering RNAs (siRNAs) were identified. Surprisingly, an additional conserved target for miR398, which has not been reported so far, has been validated. Besides conserved homologous transcripts, 23 non-conserved genes for nine conserved miRNAs and 56 genes for 29 rice-specific miRNAs were also identified as targets. Besides miRNA targets, the rice degradome contained fragments derived from MIRNA precursors. A closer inspection of these fragments revealed a unique pattern distinct from siRNA-producing loci. This attribute can serve as one of the ancillary criteria for separating miRNAs from siRNAs in plants.

MicroRNAs and their targets from Arabidopsis to rice: half conserved and half diverged

MicroRNAs are 21to 24-nucleotide long, endogenous non-coding RNAs in eukaryotes (Hannon, 2002). Mature microRNAs generated by Dicer are incorporated into an RNA-induced silencing complex (RISC), resulting in gene silencing via the cleavage of a target mRNA or the repression of target mRNA translation (Qi et al, 2005). Thus, microRNAs play a key role in post-transcriptional gene silencing, and microRNA-based gene silencing pathways are critical in developmental regulation, biotic and abiotic adaptations, and hormone responses in plants (Baulcombe, 2004; Jones-Rhoades et al., 2006). In microRNA-based gene silencing, the microRNAtarget mRNA pair determines the specificity of the biological effect. Hundreds of microRNAs have been identified in plants by cloning and deep genomic sequencing (Griffiths-Jones et al., 2008). Thus, a key step in determining the biological function of a microRNA is to identify its target. However, our knowledge of microRNA targets is incomplete due to limitations imposed by the methods used to identify microRNA targets. Traditionally, microRNA targets have been identified from computational predictions based on sequence matching between a microRNA and its target mRNA (Rhoades et al., 2002). Thus, a new approach is needed to facilitate the identification of microRNA targets on a global scale. Degradome sequencing is a new approach that integrates high-throughput sequencing, experimental approaches, and bioinformatic analyses (German et al., 2009). More than 100 microRNA targets in Arabidopsis have been isolated using this method (Addo-Quaye et al., 2008; German et al., 2008), suggesting that degradome sequencing is a powerful tool for microRNA-target pair identification. The isolation and experimental validation of microRNAtarget pairs from the monocot model plant rice using a degradome sequencing approach was reported in this issue by Dr. Xiaofeng Cao’s group (Zhou et al., 2010). They identified 177 mRNAs that were targeted by 87 unique microRNAs (Zhou et al., 2010). Among them, microRNAs known to be conserved in the plant kingdom targeted about half of the identified target mRNAs, and, interestingly, about 70% of the target mRNAs encoded transcription factors (Zhou et al., 2010). One example is the mi156SBP-box gene mRNA pair (Fig. 1A). The Arabidopsis genome encodes thirteen SBP-box genes (SPLs), ten of which are mi156 targets (Schwab et al., 2005; Wu et al., 2009; Wang et al., 2009). The mi156-SPL pathway regulates the vegetative-phase transition, flowering, leaf initiation, and apical dominance in Arabidopsis (Schwab et al., 2005; Wu et al., 2009; Wang et al., 2009). Consistent with the microRNA-target of the mi156-SPL mRNA pairs in Arabidopsis, eleven SPL mRNAs are mi156 targets in rice (Zhou et al., 2010). These conserved microRNA-target pairs may represent common regulatory nodes for the regulation of fundamental developmental processes and responses to environmental signals in the plant kingdom. The second half of the identified target genes were found to be targeted by non-conserved microRNAs (Zhou et al., 2010). These non-conserved microRNA-target pairs may be involved in riceor grass-specific processes or responses, such as osa-miR528/Os0738290 interaction (Fig. 1B). Interestingly, a large proportion of the nonconserved microRNAs targeted the 5’or 3’-untranslated region of their target mRNAs (Zhou et al., 2010). Thus, these non-conserved microRNAs may regulate gene expression via manners other than mRNA cleavage, and translational repression by microRNAs is more common in rice than in Arabidopsis. Therefore, rice is a better model than Arabidopsis to use in determining the mechanism of microRNA-mediated translational repression in plants. What will be the next step after this useful and much needed atlas of microRNA-target interactions is completed? First, it will be possible to address the biological functions of microRNA-target transcript pairs in rice. The genetic manipulation of microRNA-mRNA pairs in planta Received December 19, 2009; accepted December 23, 2009

Lost in translation: an assessment and perspective for computational microRNA target identification

MicroRNAs (miRNAs) are a class of short endogenously expressed RNA molecules that regulate gene expression by binding directly to the messenger RNA of protein coding genes. They have been found to confer a novel layer of genetic regulation in a wide range of biological processes. Computational miRNA target prediction remains one of the key means used to decipher the role of miRNAs in development and disease. Here we introduce the basic idea behind the experimental identification of miRNA targets and present some of the most widely used computational miRNA target identification programs. The review includes an assessment of the prediction quality of these programs and their combinations. Supplementary data are available at Bioinformatics online.

Statistical Use of Argonaute Expression and RISC Assembly in microRNA Target Identification

MicroRNAs (miRNAs) posttranscriptionally regulate targeted messenger RNAs (mRNAs) by inducing cleavage or otherwise repressing their translation. We address the problem of detecting m/miRNA targeting relationships in homo sapiens from microarray data by developing statistical models that are motivated by the biological mechanisms used by miRNAs. The focus of our modeling is the construction, activity, and mediation of RNA-induced silencing complexes (RISCs) competent for targeted mRNA cleavage. We demonstrate that regression models accommodating RISC abundance and controlling for other mediating factors fit the expression profiles of known target pairs substantially better than models based on m/miRNA expressions alone, and lead to verifications of computational target pair predictions that are more sensitive than those based on marginal expression levels. Because our models are fully independent of exogenous results from sequence-based computational methods, they are appropriate for use as either a primary or secondary source of information regarding m/miRNA target pair relationships, especially in conjunction with high-throughput expression studies.

Identification of novel microRNA targets based on microRNA signatures in bladder cancer

MicroRNAs (miRNAs) are small noncoding RNAs that negatively regulate protein-coding genes. To identify miRNAs that have a tumor suppressive function in bladder cancer (BC), 156 miRNAs were screened in 14 BCs, 5 normal bladder epithelium (NBE) samples and 3 BC cell lines. We identified a subset of 7 miRNAs (miR-145, miR-30a-3p, miR-133a, miR-133b, miR-195, miR-125b and miR-199a*) that were significantly downregulated in BCs. To confirm these results, 104 BCs and 31 NBEs were subjected to real-time RT-PCR-based experiments, and the expression levels of each miRNA were significantly downregulated in BCs (p < 0.0001 in all). Receiver-operating characteristic curve analysis revealed that the expression levels of these miRNAs had good sensitivity (>70%) and specificity (>75%) to distinguish BC from NBE. Our target search algorithm and gene-expression profiling in BCs (Kawakami et al., Oncol Rep 2006;16:521-31) revealed that Keratin7 (KRT7) mRNA was a common target of the downregulated miRNAs, and the mRNA expression levels of KRT7 were significantly higher in BCs than in NBEs (p = 0.0004). Spearman rank correlation analysis revealed significant inverse correlations between KRT7 mRNA expression and each downregulated miRNA (p < 0.0001 in all). Gain-of-function analysis revealed that KRT7 mRNA was significantly reduced by transfection of 3 miRNAs (miR-30-3p, miR-133a and miR-199a*) in the BC cell line (KK47). In addition, significant decreases in cell growth were observed after transfection of 3 miRNAs and si-KRT7 in KK47, suggesting that miR-30-3p, miR-133a and miR-199a* may have a tumor suppressive function through the mechanism underlying transcriptional repression of KRT7.

Searching for targets of viral microRNAs

The identification of targets of virally encoded microRNAs is essential to understanding their role in the viral life cycle. A new study reports the successful use of a microarray-based approach to identify a key target of a microRNA encoded by Kaposi's sarcoma–associated herpesvirus, with implications for microRNA target identification in other contexts as well.

MiRTif: a support vector machine-based microRNA target interaction filter

BackgroundMicroRNAs (miRNAs) are a set of small non-coding RNAs serving as important negative gene regulators. In animals, miRNAs turn down protein translation by binding to the 3' UTR regions of target genes with imperfect complementary pairing. The identification of microRNA targets has become one of the major challenges of miRNA research. Bioinformatics investigations on miRNA target have resulted in a number of target prediction tools. Although these tools are capable of predicting hundreds of targets for a given miRNA, many of them suffer from high false positive rates, indicating the need for a post-processing filter for the predicted targets. Once trained with experimentally validated true and false targets, machine learning methods appear to be ideal approaches to distinguish the true targets from the false ones.ResultsWe present a miRNA target filtering system named MiRTif (miRNA:target interaction filter). The system is a support vector machine (SVM) classifier trained with 195 positive and 38 negative miRNA:target interaction pairs, all experimentally validated. Each miRNA:target interaction pair is divided into a seed and a non-seed region. The encoded feature vector contains various k-gram frequencies in the seed, the non-seed and the entire regions. Informative features are selected based on their discriminating abilities. Prediction accuracies are assessed using 10-fold cross-validation experiments. Our system achieves AUC (area under the ROC curve) of 0.86, sensitivity of 83.59%, and specificity of 73.68%. More importantly, the system correctly predicts majority of the false positive miRNA:target interactions (28 out of 38). The possibility of over-fitting due to the relatively small negative sample set has also been investigated using a set of non-validated and randomly selected targets (from miRBase).ConclusionMiRTif is designed as a post-processing filter that takes miRNA:target interactions predicted by other target prediction softwares such as TargetScanS, PicTar and miRanda as inputs, and determines how likely the given interaction is a real or a pseudo one. MiRTif can be accessed from .

3′ Terminal Nucleotides Determine Thermodynamic Stabilities of Mismatches at the Ends of RNA Helices

The thermodynamic stabilities of consecutive mismatches at the ends of RNA helices are determined by the 3' terminal nucleotides. More than 40 RNA duplexes containing terminal motifs of 3 or more nucleotides were studied by optical melting experiments. Up to three noncanonical pairs of nucleotides at the end of RNA helices provide additional thermodynamic stability. 3' nucleotides contribute more stability than 5' nucleotides, and purines contribute more stability than pyrimidines. The additional stability of a second or third 3' nucleotide stacking on a purine is the same for both dangling ends and consecutive terminal mismatches. Current predictions underestimate RNA duplex stabilities with terminal motifs by 1.4 kcal/mol on average, which is an order of magnitude in a binding constant at 37 degrees C. Accurate thermodynamic parameters for these terminal motifs will contribute to improvements in RNA secondary structure predictions, identification of microRNA targets, and design of siRNA therapeutics with fewer off-target effects.

MP-2.02: Identification of Novel MicroRNA Targets Based on MicroRNA Signature in Bladder Cancer

MP-2.02: Identification of Novel MicroRNA Targets Based on MicroRNA Signature in Bladder Cancer

A scoring matrix approach to detecting miRNA target sites

BackgroundExperimental identification of microRNA (miRNA) targets is a difficult and time consuming process. As a consequence several computational prediction methods have been devised in order to predict targets for follow up experimental validation. Current computational target prediction methods use only the miRNA sequence as input. With an increasing number of experimentally validated targets becoming available, utilising this additional information in the search for further targets may help to improve the specificity of computational methods for target site prediction.ResultsWe introduce a generic target prediction method, the Stacking Binding Matrix (SBM) that uses both information about the miRNA as well as experimentally validated target sequences in the search for candidate target sequences. We demonstrate the utility of our method by applying it to both animal and plant data sets and compare it with miRanda, a commonly used target prediction method.ConclusionWe show that SBM can be applied to target prediction in both plants and animals and performs well in terms of sensitivity and specificity. Open source code implementing the SBM method, together with documentation and examples are freely available for download from the address in the Availability and Requirements section.

Isolation of microRNA targets using biotinylated synthetic microRNAs

MicroRNAs are small regulatory RNAs found in multicellular organisms where they post-transcriptionally regulate gene expression. In animals, microRNAs bind mRNAs via incomplete base pairings making the identification of microRNA targets inherently difficult. Here, we present a detailed method for experimental identification of microRNA targets based on affinity purification of tagged microRNAs associated with their targets.

Identification of Human microRNA Targets From Isolated Argonaute Protein Complexes

MicroRNAs (miRNAs) constitute a class of small non-coding RNAs that regulate gene expression on the level of translation and/or mRNA stability. Mammalian miRNAs associate with members of the Argonaute (Ago) protein family and bind to partially complementary sequences in the 3’ untranslated region (UTR) of specific target mRNAs. Computer algorithms based on factors such as free binding energy or sequence conservation have been used to predict miRNA target mRNAs. Based on such predictions, up to one third of all mammalian mRNAs seem to be under miRNA regulation. However, due to the low degree of complementarity between the miRNA and its target, such computer programs are often imprecise and therefore not very reliable. Here we report the first biochemical identification approach of miRNA targets from human cells. Using highly specific monoclonal antibodies against members of the Ago protein family, we co-immunoprecipitate Ago-bound mRNAs and identify them by cloning. Interestingly, most of the identified targets are also predicted by different computer programs. Moreover, we randomly analyzed six different target candidates and were able to experimentally validate five as miRNA targets. Our data clearly indicate that miRNA targets can be experimentally identified from Ago complexes and therefore provide a new tool to directly analyze miRNA function.

A guide through present computational approaches for the identification of mammalian microRNA targets

Computational microRNA (miRNA) target prediction is a field in flux. Here we present a guide through five widely used mammalian target prediction programs. We include an analysis of the performance of these individual programs and of various combinations of these programs. For this analysis we compiled several benchmark data sets of experimentally supported miRNA-target gene interactions. Based on the results, we provide a discussion on the status of target prediction and also suggest a stepwise approach toward predicting and selecting miRNA targets for experimental testing.

Serum response factor regulates a muscle-specific microRNA that targets Hand2 during cardiogenesis

Gradients of signalling and transcription factors govern many aspects of embryogenesis, highlighting the need for spatiotemporal control of regulatory protein levels. MicroRNAs are phylogenetically conserved small RNAs that regulate the translation of target messenger RNAs, providing a mechanism for protein dose regulation. Here we show that microRNA-1-1 (miR-1-1) and miR-1-2 are specifically expressed in cardiac and skeletal muscle precursor cells. We found that the miR-1 genes are direct transcriptional targets of muscle differentiation regulators including serum response factor, MyoD and Mef2. Correspondingly, excess miR-1 in the developing heart leads to a decreased pool of proliferating ventricular cardiomyocytes. Using a new algorithm for microRNA target identification that incorporates features of RNA structure and target accessibility, we show that Hand2, a transcription factor that promotes ventricular cardiomyocyte expansion, is a target of miR-1. This work suggests that miR-1 genes titrate the effects of critical cardiac regulatory proteins to control the balance between differentiation and proliferation during cardiogenesis.

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.

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 suggest 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 which is designed to identify microRNA target sites. This method recovers with high specificity known microRNA target sites which previously have 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 Drosophila melanogaster and Drosophila pseudoobscura, we found that a number of key developmental body patterning genes such as hairy and fushi-tarazu are likely to be translationally regulated by microRNAs.