Clarifying mammalian RISC assembly in vitro

<p>Abstract</p> <p>Background</p> <p>Argonaute, the core component of the RNA induced silencing complex (RISC), binds to mature miRNAs and regulates gene expression at transcriptional or post-transcriptional level. We recently reported that Argonaute 2 (Ago2) also assembles into complexes with miRNA precursors (pre-miRNAs). These Ago2:pre-miRNA complexes are catalytically active <it>in vitro </it>and constitute non-canonical RISCs.</p> <p>Results</p> <p>The use of pre-miRNAs as guides by Ago2 bypasses Dicer activity and complicates <it>in vitro </it>RISC reconstitution. In this work, we characterized Ago2:pre-miRNA complexes and identified RNAs that are targeted by miRNAs but not their corresponding pre-miRNAs. Using these target RNAs we were able to recapitulate <it>in vitro </it>pre-miRNA processing and canonical RISC loading, and define the minimal factors required for these processes.</p> <p>Conclusions</p> <p>Our results indicate that Ago2 and Dicer are sufficient for processing and loading of miRNAs into RISC. Furthermore, our studies suggest that Ago2 binds primarily to the 5'- and alternatively, to the 3'-end of select pre-miRNAs.</p

MicroTar: predicting microRNA targets from RNA duplexes

BACKGROUND: The accurate prediction of a comprehensive set of messenger RNAs (targets) regulated by animal microRNAs (miRNAs) remains an open problem. In particular, the prediction of targets that do not possess evolutionarily conserved complementarity to their miRNA regulators is not adequately addressed by current tools. RESULTS: We have developed MicroTar, an animal miRNA target prediction tool based on miRNA-target complementarity and thermodynamic data. The algorithm uses predicted free energies of unbound mRNA and putative mRNA-miRNA heterodimers, implicitly addressing the accessibility of the mRNA 3' untranslated region. MicroTar does not rely on evolutionary conservation to discern functional targets, and is able to predict both conserved and non-conserved targets. MicroTar source code and predictions are accessible at , where both serial and parallel versions of the program can be downloaded under an open-source licence. CONCLUSION: MicroTar achieves better sensitivity than previously reported predictions when tested on three distinct datasets of experimentally-verified miRNA-target interactions in C. elegans, Drosophila, and mouse

A genetic screen for components of the mammalian RNA interference pathway in Bloom-deficient mouse embryonic stem cells

Genetic screens performed in model organisms have helped identify key components of the RNA interference (RNAi) pathway. Recessive genetic screens have recently become feasible through the use of mouse embryonic stem (ES) cells that are Bloom's syndrome protein (Blm) deficient. Here, we developed and performed a recessive genetic screen to identify components of the mammalian RNAi pathway in Blm-deficient ES cells. Genome-wide mutagenesis using a retroviral gene trap strategy resulted in the isolation of putative homozygous RNAi mutant cells. Candidate clones were confirmed by an independent RNAi-based reporter assay and the causative gene trap integration site was identified using molecular techniques. Our screen identified multiple mutant cell lines of Argonaute 2 (Ago2), a known essential component of the RNAi pathway. This result demonstrates that true RNAi components can be isolated by this screening strategy. Furthermore, Ago2 homozygous mutant ES cells provide a null genetic background to perform mutational analyses of the Ago2 protein. Using genetic rescue, we resolve an important controversy regarding the role of two phenylalanine residues in Ago2 activity

Accurate microRNA target prediction correlates with protein repression levels

MicroRNAs are small endogenously expressed non-coding RNA molecules that regulate target gene expression through translation repression or messenger RNA degradation. MicroRNA regulation is performed through pairing of the microRNA to sites in the messenger RNA of protein coding genes. Since experimental identification of miRNA target genes poses difficulties, computational microRNA target prediction is one of the key means in deciphering the role of microRNAs in development and diseas

Inferring MicroRNA Activities by Combining Gene Expression with MicroRNA Target Prediction

MicroRNAs (miRNAs) play crucial roles in a variety of biological processes via regulating expression of their target genes at the mRNA level. A number of computational approaches regarding miRNAs have been proposed, but most of them focus on miRNA gene finding or target predictions. Little computational work has been done to investigate the effective regulation of miRNAs.We propose a method to infer the effective regulatory activities of miRNAs by integrating microarray expression data with miRNA target predictions. The method is based on the idea that regulatory activity changes of miRNAs could be reflected by the expression changes of their target transcripts measured by microarray. To validate this method, we apply it to the microarray data sets that measure gene expression changes in cell lines after transfection or inhibition of several specific miRNAs. The results indicate that our method can detect activity enhancement of the transfected miRNAs as well as activity reduction of the inhibited miRNAs with high sensitivity and specificity. Furthermore, we show that our inference is robust with respect to false positives of target prediction.A huge amount of gene expression data sets are available in the literature, but miRNA regulation underlying these data sets is largely unknown. The method is easy to be implemented and can be used to investigate the miRNA effective regulation underlying the expression change profiles obtained from microarray experiments

A high throughput experimental approach to identify miRNA targets in human cells

The study of human microRNAs is seriously hampered by the lack of proper tools allowing genome-wide identification of miRNA targets. We performed Ribonucleoprotein ImmunoPrecipitation—gene Chip (RIP-Chip) using antibodies against wild-type human Ago2 in untreated Hodgkin lymphoma (HL) cell lines. Ten to thirty percent of the gene transcripts from the genome were enriched in the Ago2-IP fraction of untreated cells, representing the HL miRNA-targetome. In silico analysis indicated that ∼40% of these gene transcripts represent targets of the abundantly co-expressed miRNAs. To identify targets of miR-17/20/93/106, RIP-Chip with anti-miR-17/20/93/106 treated cells was performed and 1189 gene transcripts were identified. These genes were analyzed for miR-17/20/93/106 target sites in the 5′-UTRs, coding regions and 3′-UTRs. Fifty-one percent of them had miR-17/20/93/106 target sites in the 3′-UTR while 19% of them were predicted miR-17/20/93/106 targets by TargetScan. Luciferase reporter assay confirmed targeting of miR-17/20/93/106 to the 3′-UTRs of 8 out of 10 genes. In conclusion, we report a method which can establish the miRNA-targetome in untreated human cells and identify miRNA specific targets in a high throughput manner. This approach is applicable to identify miRNA targets in any human tissue sample or purified cell population in an unbiased and physiologically relevant manner

Correlation of microRNA levels during hypoxia with predicted target mRNAs through genome-wide microarray analysis

<p>Abstract</p> <p>Background</p> <p>Low levels of oxygen in tissues, seen in situations such as chronic lung disease, necrotic tumors, and high altitude exposures, initiate a signaling pathway that results in active transcription of genes possessing a hypoxia response element (HRE). The aim of this study was to investigate whether a change in miRNA expression following hypoxia could account for changes in the cellular transcriptome based on currently available miRNA target prediction tools.</p> <p>Methods</p> <p>To identify changes induced by hypoxia, we conducted mRNA- and miRNA-array-based experiments in HT29 cells, and performed comparative analysis of the resulting data sets based on multiple target prediction algorithms. To date, few studies have investigated an environmental perturbation for effects on genome-wide miRNA levels, or their consequent influence on mRNA output.</p> <p>Results</p> <p>Comparison of miRNAs with predicted mRNA targets indicated a lower level of concordance than expected. We did, however, find preliminary evidence of combinatorial regulation of mRNA expression by miRNA.</p> <p>Conclusion</p> <p>Target prediction programs and expression profiling techniques do not yet adequately represent the complexity of miRNA-mediated gene repression, and new methods may be required to better elucidate these pathways. Our data suggest the physiologic impact of miRNAs on cellular transcription results from a multifaceted network of miRNA and mRNA relationships, working together in an interconnected system and in context of hundreds of RNA species. The methods described here for comparative analysis of cellular miRNA and mRNA will be useful for understanding genome wide regulatory responsiveness and refining miRNA predictive algorithms.</p

MTar: a computational microRNA target prediction architecture for human transcriptome

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) play an essential task in gene regulatory networks by inhibiting the expression of target mRNAs. As their mRNA targets are genes involved in important cell functions, there is a growing interest in identifying the relationship between miRNAs and their target mRNAs. So, there is now a imperative need to develop a computational method by which we can identify the target mRNAs of existing miRNAs. Here, we proposed an efficient machine learning model to unravel the relationship between miRNAs and their target mRNAs.</p> <p>Results</p> <p>We present a novel computational architecture MTar for miRNA target prediction which reports 94.5% sensitivity and 90.5% specificity. We identified 16 positional, thermodynamic and structural parameters from the wet lab proven miRNA:mRNA pairs and MTar makes use of these parameters for miRNA target identification. It incorporates an Artificial Neural Network (ANN) verifier which is trained by wet lab proven microRNA targets. A number of hitherto unknown targets of many miRNA families were located using MTar. The method identifies all three potential miRNA targets (5' seed-only, 5' dominant, and 3' canonical) whereas the existing solutions focus on 5' complementarities alone.</p> <p>Conclusion</p> <p>MTar, an ANN based architecture for identifying functional regulatory miRNA-mRNA interaction using predicted miRNA targets. The area of target prediction has received a new momentum with the function of a thermodynamic model incorporating target accessibility. This model incorporates sixteen structural, thermodynamic and positional features of residues in miRNA: mRNA pairs were employed to select target candidates. So our novel machine learning architecture, MTar is found to be more comprehensive than the existing methods in predicting miRNA targets, especially human transcritome.</p

Receptor for Activated Protein Kinase C: Requirement for Efficient MicroRNA Function and Reduced Expression in Hepatocellular Carcinoma

MicroRNAs (miRNAs) are important regulators of gene expression that control physiological and pathological processes. A global reduction in miRNA abundance and function is a general trait of human cancers, playing a causal role in the transformed phenotype. Here, we sought to newly identify genes involved in the regulation of miRNA function by performing a genetic screen using reporter constructs that measure miRNA function and retrovirus-based random gene disruption. Of the six genes identified, RACK1, which encodes “receptor for activated protein kinase C” (RACK1), was confirmed to be necessary for full miRNA function. RACK1 binds to KH-type splicing regulatory protein (KSRP), a member of the Dicer complex, and is required for the recruitment of mature miRNAs to the RNA-induced silencing complex (RISC). In addition, RACK1 expression was frequently found to be reduced in hepatocellular carcinoma. These findings suggest the involvement of RACK1 in miRNA function and indicate that reduced miRNA function, due to decreased expression of RACK1, may have pathologically relevant roles in liver cancers

Finding microRNA regulatory modules in human genome using rule induction

Background: MicroRNAs (miRNAs) are a class of small non-coding RNA molecules (20-24 nt), which are believed to participate in repression of gene expression. They play important roles in several biological processes (e.g. cell death and cell growth). Both experimental and computational approaches have been used to determine the function of miRNAs in cellular processes. Most efforts have concentrated on identification of miRNAs and their target genes. However, understanding the regulatory mechanism of miRNAs in the gene regulatory network is also essential to the discovery of functions of miRNAs in complex cellular systems. To understand the regulatory mechanism of miRNAs in complex cellular systems, we need to identify the functional modules involved in complex interactions between miRNAs and their target genes. Results: We propose a rule-based learning method to identify groups of miRNAs and target genes that are believed to participate cooperatively in the post-transcriptional gene regulation, so-called miRNA regulatory modules (MRMs). Applying our method to human genes and miRNAs, we found 79 MRMs. The MRMs are produced from multiple information sources, including miRNA-target binding information, gene expression and miRNA expression profiles. Analysis of two first MRMs shows that these MRMs consist of highly-related miRNAs and their target genes with respect to biological processes. Conclusion: The MRMs found by our method have high correlation in expression patterns of miRNAs as well as mRNAs. The mRNAs included in the same module shared similar biological functions, indicating the ability of our method to detect functionality-related genes. Moreover, review of the literature reveals that miRNAs in a module are involved in several types of human cancer