A combined computational-experimental approach predicts human microRNA targets

Folding and finding RNA secondary structure

Optimal exploitation of the expanding database of sequences requires rapid finding and folding of RNAs. Methods are reviewed that automate folding and discovery of RNAs with algorithms that couple thermodynamics with chemical mapping, NMR, and/or sequence comparison. New functional noncoding RNAs in genome sequences can be found by combining sequence comparison with the assumption that functional noncoding RNAs will have more favorable folding free energies than other RNAs. When a new RNA is discovered, experiments and sequence comparison can restrict folding space so that secondary structure can be rapidly determined with the help of predicted free energies. In turn, secondary structure restricts folding in three dimensions, which allows modeling of three-dimensional structure. An example from a domain of a retrotransposon is described. Discovery of new RNAs and their structures will provide insights into evolution, biology, and design of therapeutics. Applications to studies of evolution are also reviewed.

The miR-15/107 group of microRNA genes: evolutionary biology, cellular functions, and roles in human diseases

The miR-15/107 group of microRNA (miRNA) gene is increasingly appreciated to serve key functions in humans. These miRNAs regulate gene expression involved in cell division, metabolism, stress response, and angiogenesis in vertebrate species. The miR-15/107 group has also been implicated in human cancers, cardiovascular disease and neurodegenerative disease, including Alzheimer's disease. Here we provide an overview of the following: (1) the evolution of miR-15/107 group member genes; (2) the expression levels of miRNAs in mammalian tissues; (3) evidence for overlapping gene-regulatory functions by different miRNAs; (4) the normal biochemical pathways regulated by miR-15/107 group miRNAs; and (5) the roles played by these miRNAs in human diseases. Membership in this group is defined based on sequence similarity near the mature miRNAs' 5' end: all include the sequence AGCAGC. Phylogeny of this group of miRNAs is incomplete; thus, a definitive taxonomic classification (e.g., designation as a "superfamily") is currently not possible. While all vertebrates studied to date express miR-15a, miR-15b, miR-16, miR-103, and miR-107, mammals alone are known to express miR-195, miR-424, miR-497, miR-503, and miR-646. Multiple different miRNAs in the miR-15/107 group are expressed at moderate to high levels in human tissues. We present data on the expression of all known miR-15/107 group members in human cerebral cortical gray matter and white matter using new miRNA profiling microarrays. There is extensive overlap in the mRNAs targeted by miR-15/107 group members. We show new data from cultured H4 cancer cells that demonstrate similarities in mRNAs targeted by miR-16 and miR-103 and also support the importance of the mature miRNAs' 5' seed region in mRNA target recognition. In conclusion, the miR-15/107 group of miRNA genes is a fascinating topic of study for evolutionary biologists, miRNA biochemists, and clinically oriented translational researchers alike.

MicroRNAs induced during ischemic preconditioning

BACKGROUND AND PURPOSE

MicroRNAs (miRNA) are single-stranded short RNA molecules that regulate gene expression by either degradation or translational repression of mRNA. Although miRNAs control a number of conditions and diseases, few neuroprotective miRNAs have been described. In this study, we investigated neuroprotective miRNAs induced early in ischemic preconditioning.

METHODS

Ischemic preconditioning or focal cerebral ischemia was induced in mice by transient occlusion of the middle cerebral artery for 15 or 120 minutes. We prepared RNA samples from the ischemic cortex at 3 or 24 hours after the onset of ischemia. Selective miRNAs then were synthesized and transfected into Neuro-2a cells before oxygen-glucose deprivation.

RESULTS

We detected a total of 360 miRNAs. Two miRNA families, miR-200 and miR-182, were selectively upregulated at 3 hours after ischemic preconditioning. Transfections of some of these were neuroprotective in in vitro ischemia. Among them, miR-200b, miR-200c, and miR-429 targeted prolyl hydroxylase 2 and had the best neuroprotective effect.

CONCLUSIONS

Two miRNA families, miR-200 and miR-182, were upregulated early after ischemic preconditioning and the miR-200 family was neuroprotective mainly by downregulating prolyl hydroxylase 2 levels. These miRNAs may be useful in future research and therapeutic applications.

Identifying co-regulating microRNA groups

BACKGROUND

Current miRNA target prediction tools have the common problem that their false positive rate is high. This renders identification of co-regulating groups of miRNAs and target genes unreliable. In this study, we describe a procedure to identify highly probable co-regulating miRNAs and the corresponding co-regulated gene groups. Our procedure involves a sequence of statistical tests: (1) identify genes that are highly probable miRNA targets; (2) determine for each such gene, the minimum number of miRNAs that co-regulate it with high probability; (3) find, for each such gene, the combination of the determined minimum size of miRNAs that co-regulate it with the lowest p-value; and (4) discover for each such combination of miRNAs, the group of genes that are co-regulated by these miRNAs with the lowest p-value computed based on GO term annotations of the genes.

RESULTS

Our method identifies 4, 3 and 2-term miRNA groups that co-regulate gene groups of size at least 3 in human. Our result suggests some interesting hypothesis on the functional role of several miRNAs through a "guilt by association" reasoning. For example, miR-130, miR-19 and miR-101 are known neurodegenerative diseases associated miRNAs. Our 3-term miRNA table shows that miR-130/19/101 form a co-regulating group of rank 22 (p-value =1.16 x 10(-2)). Since miR-144 is co-regulating with miR-130, miR-19 and miR-101 of rank 4 (p-value = 1.16 x 10(-2)) in our 4-term miRNA table, this suggests hsa-miR-144 may be neurodegenerative diseases related miRNA.

CONCLUSIONS

This work identifies highly probable co-regulating miRNAs, which are refined from the prediction by computational tools using (1) signal-to-noise ratio to get high accurate regulating miRNAs for every gene, and (2) Gene Ontology to obtain functional related co-regulating miRNA groups. Our result has partly been supported by biological experiments. Based on prediction by TargetScanS, we found highly probable target gene groups in the Supplementary Information. This result might help biologists to find small set of miRNAs for genes of interest rather than huge amount of miRNA set.

SUPPLEMENTARY INFORMATION

http://www.deakin.edu.au/~phoebe/JBCBAnChen/JBCB.htm.

Computational prediction of microRNAs encoded in viral and other genomes

We present an overview of selected computational methods for microRNA prediction. It is especially aimed at viral miRNA detection. As the number of microRNAs increases and the range of genomes encoding miRNAs expands, it seems that these small regulators have a more important role than has been previously thought. Most microRNAs have been detected by cloning and Northern blotting, but experimental methods are biased towards abundant microRNAs as well as being time-consuming. Computational detection methods must therefore be refined to serve as a faster, better, and more affordable method for microRNA detection. We also present data from a small study investigating the problems of computational miRNA prediction. Our findings suggest that the prediction of microRNA precursor candidates is fairly easy, while excluding false positives as well as exact prediction of the mature microRNA is hard. Finally, we discuss possible improvements to computational microRNA detection.

MicroRNAs and target site screening reveals a pre-microRNA-30e variant associated with schizophrenia

MicroRNAs (miRNAs) are short, non-coding RNAs that regulate the stability and translation of mRNA targets. Increasing evidence suggests that miRNAs could be involved in the initiation and progression of neuropsychiatric disorders. Prior to this study, six miRNAs had been reported to show a significantly abnormal expression level in schizophrenic brains. Also, common single nucleotide polymorphisms within two miRNA transcripts have shown genetic associations with schizophrenia. However, it remains largely unknown whether variants in these miRNA genes and/or in their target sites are associated with schizophrenia. Here, we selected the above eight miRNAs, plus 15 of their experimentally validated target sites, as candidate susceptibility factors for schizophrenia, for mutation screening and further association studies in Chinese case-control samples. We identified a new potentially functional variant ss178077483 located in the pre-mir-30e, which was strongly associated with schizophrenia (allelic P=0.00017; genotypic P=0.00015), with an odds ratio of 4.952 (95% confidence interval: 1.887-12.998). We also demonstrated that this new variant ss178077483, combined with mir-30e rs7556088 and mir-24-MAPK14 rs3804452, showed a weak gene-gene interaction for schizophrenia risk (P=0.001). In addition, analysis of gene expression demonstrated that expression of the mature mir-30e in the peripheral leukocytes was significantly higher in patients' group than in the control group (P=6.79e-7).This is the first study to indicate that mir-30e ss178077483 plays a role in schizophrenia susceptibility. It suggests that the contribution of mir-30e to the processes that lead to schizophrenia should be further investigated.

Detecting microRNA activity from gene expression data

Background

MicroRNAs (miRNAs) are non-coding RNAs that regulate gene expression by binding to the messenger RNA (mRNA) of protein coding genes. They control gene expression by either inhibiting translation or inducing mRNA degradation. A number of computational techniques have been developed to identify the targets of miRNAs. In this study we used predicted miRNA-gene interactions to analyse mRNA gene expression microarray data to predict miRNAs associated with particular diseases or conditions.

Results

Here we combine correspondence analysis, between group analysis and co-inertia analysis (CIA) to determine which miRNAs are associated with differences in gene expression levels in microarray data sets. Using a database of miRNA target predictions from TargetScan, TargetScanS, PicTar4way PicTar5way, and miRanda and combining these data with gene expression levels from sets of microarrays, this method produces a ranked list of miRNAs associated with a specified split in samples. We applied this to three different microarray datasets, a papillary thyroid carcinoma dataset, an in-house dataset of lipopolysaccharide treated mouse macrophages, and a multi-tissue dataset. In each case we were able to identified miRNAs of biological importance.

Conclusions

We describe a technique to integrate gene expression data and miRNA target predictions from multiple sources.

miR-31 functions as a negative regulator of lymphatic vascular lineage-specific differentiation in vitro and vascular development in vivo

The lymphatic vascular system maintains tissue fluid homeostasis, helps mediate afferent immune responses, and promotes cancer metastasis. To address the role microRNAs (miRNAs) play in the development and function of the lymphatic vascular system, we defined the in vitro miRNA expression profiles of primary human lymphatic endothelial cells (LECs) and blood vascular endothelial cells (BVECs) and identified four BVEC signature and two LEC signature miRNAs. Their vascular lineage-specific expression patterns were confirmed in vivo by quantitative real-time PCR and in situ hybridization. Functional characterization of the BVEC signature miRNA miR-31 identified a novel BVEC-specific posttranscriptional regulatory mechanism that inhibits the expression of lymphatic lineage-specific transcripts in vitro. We demonstrate that suppression of lymphatic differentiation is partially mediated via direct repression of PROX1, a transcription factor that functions as a master regulator of lymphatic lineage-specific differentiation. Finally, in vivo studies of Xenopus and zebrafish demonstrated that gain of miR-31 function impaired venous sprouting and lymphatic vascular development, thus highlighting the importance of miR-31 as a negative regulator of lymphatic development. Collectively, our findings identify miR-31 is a potent regulator of vascular lineage-specific differentiation and development in vertebrates.

Gene bi-targeting by viral and human miRNAs

BACKGROUND

MicroRNAs (miRNAs) are an abundant class of small noncoding RNAs (20-24 nts) that can affect gene expression by post-transcriptional regulation of mRNAs. They play important roles in several biological processes (e.g., development and cell cycle regulation). Numerous bioinformatics methods have been developed to identify the function of miRNAs by predicting their target mRNAs. Some viral organisms also encode miRNAs, a fact that contributes to the complex interactions between viruses and their hosts. A need arises to understand the functional relationship between viral and host miRNAs and their effect on viral and host genes. Our approach to meet this challenge is to identify modules where viral and host miRNAs cooperatively regulate host gene expression.

RESULTS

We present a method to identify groups of viral and host miRNAs that cooperate in post-transcriptional gene regulation, and their target genes that are involved in similar biological processes. We call these groups (genes and miRNAs of human and viral origin) - modules. The modules are found in a new two-stage procedure, which we call bi-targeting, and is presented in this paper. The stages are (i) a new and efficient target prediction, and (ii) a new method for clustering objects of three different data types. In this work we integrate multiple information sources, including miRNA-target binding information, miRNA expression profiles, and GO annotations. Our hypotheses and the methods have been tested on human and Epstein Barr virus (EBV) miRNAs and human genes, for which we found 34 modules. We provide supporting evidence from biological and medical literature for two of our modules. Our code and data are available at http://www.cs.bgu.ac.il/~vaksler/BiTargeting.htm

CONCLUSIONS

The presented algorithm, which makes use of diverse biological data, is demonstrated to be an efficient approach for finding bi-targeting modules of viral and human miRNAs. These modules can contribute to a better understanding of viral-host interactions and the role that miRNAs play in them.

Pharmacological potential of RNAi--focus on miRNA

RNA interference (RNAi) is a cellular process that is widely used as a research tool to control the expression of specific genes and has the potential as a therapeutic strategy for many diseases. MicroRNAs (miRNAs) and short interfering RNAs (siRNAs) are the two principal categories of small RNAs that induce RNAi in a broad spectrum of eukaryotic organisms including human cells. miRNAs have an enormous capacity to regulate multiple genes and the expression of approximately 30% of the human genes is affected by these non-coding RNAs. Because many miRNAs are specifically expressed during disease, miRNAs are interesting tools for pharmacology and understanding the function of specific miRNAs will help to identify novel drug targets. Furthermore, miRNA-based diagnostics as well as therapeutic interventions are being developed for clinical applications.

High-throughput experimental studies to identify miRNA targets directly, with special focus on the mammalian brain

We review the pertinent literature on methods used in high-throughput experimental identification of microRNA (miRNA) "targets" with emphasis on neurochemical studies. miRNAs are short regulatory noncoding RNAs that play important roles in the mammalian brain. The functions of miRNAs are related to their binding of RNAs including mRNAs. Since mammalian miRNAs tend to bind to target mRNAs via imperfect complementarity, understanding exactly which target mRNAs are recognized by which specific miRNAs is a challenge. Based on early experimental evidence, a set of "binding rules" for miRNAs has been described. These have focused on the 5' "seed" region of miRNAs binding to the 3' untranslated region of targeted mRNAs. Bioinformaticians have applied these algorithms for theoretical miRNA target prediction. To date, the different computational methods are not in agreement with each other and do not explain all miRNA targets as defined using high-throughput experimental methods. We consider these latter techniques which identify putative miRNA targets directly. Each experimental approach involves specific assumptions and potential technical pitfalls. Some of these direct experimental methods for miRNA target identification have used co-immunoprecipitation (RIP-Chip and others) and transfection-based experimental design. Topics related to experimentally identified miRNA targets are discussed, with special emphasis on studies pertinent to the mammalian brain.

Dysregulation of kallikrein-related peptidases in renal cell carcinoma: potential targets of miRNAs

Renal cell carcinoma (RCC) accounts for 3% of all adult malignancies and currently no diagnostic marker exists. Kallikrein-related peptidases (KLKs) have been implicated in numerous cancers including ovarian, prostate, and breast carcinoma. KLKs 5, 6, 10, and 11 have decreased expression in RCC when compared to normal kidney tissue. Our bioinformatic analysis indicated that theKLK 1,6, and7genes have decreased expression in RCC. We experimentally verified these results and found that decreased expression ofKLKs 1and3were significantly associated with the clear cell RCC subtype (p<0.001). An analysis of miRNAs differentially expressed in RCC showed that 61 of the 117 miRNAs that were reported to be dysregulated in RCC were predicted to target KLKs. We experimentally validated two targets using two independent approaches. Transfection of miR-224 into HEK-293 cells resulted in decreased KLK1 protein levels. A luciferase assay demonstrated that hsa-let-7f can target KLK10 in the RCC cell line ACHN. Our results, showing differential expression of KLKs in RCC, suggest that KLKs could be novel diagnostic markers for RCC and that their dysregulation could be under miRNA control. The observation that KLKs could represent targets for miRNAs suggests a post-transcriptional regulatory mechanism with possible future therapeutic applications.

A dictionary on microRNAs and their putative target pathways

While in the last decade mRNA expression profiling was among the most popular research areas, over the past years the study of non-coding RNAs, especially microRNAs (miRNAs), has gained increasing interest. For almost 900 known human miRNAs hundreds of pretended targets are known. However, there is only limited knowledge about putative systemic effects of changes in the expression of miRNAs and their regulatory influence. We determined for each known miRNA the biochemical pathways in the KEGG and TRANSPATH database and the Gene Ontology categories that are enriched with respect to its target genes. We refer to these pathways and categories as target pathways of the corresponding miRNA. Investigating target pathways of miRNAs we found a strong relation to disease-related regulatory pathways, including mitogen-activated protein kinase (MAPK) signaling cascade, Transforming growth factor (TGF)-beta signaling pathway or the p53 network. Performing a sophisticated analysis of differentially expressed genes of 13 cancer data sets extracted from gene expression omnibus (GEO) showed that targets of specific miRNAs were significantly deregulated in these sets. The respective miRNA target analysis is also a novel part of our gene set analysis pipeline GeneTrail. Our study represents a comprehensive theoretical analysis of the relationship between miRNAs and their predicted target pathways. Our target pathways analysis provides a ‘miRNA-target pathway’ dictionary, which enables researchers to identify target pathways of differentially regulated miRNAs.

miRSel: automated extraction of associations between microRNAs and genes from the biomedical literature

Background

MicroRNAs have been discovered as important regulators of gene expression. To identify the target genes of microRNAs, several databases and prediction algorithms have been developed. Only few experimentally confirmed microRNA targets are available in databases. Many of the microRNA targets stored in databases were derived from large-scale experiments that are considered not very reliable. We propose to use text mining of publication abstracts for extracting microRNA-gene associations including microRNA-target relations to complement current repositories.

Results

The microRNA-gene association database miRSel combines text-mining results with existing databases and computational predictions. Text mining enables the reliable extraction of microRNA, gene and protein occurrences as well as their relationships from texts. Thereby, we increased the number of human, mouse and rat miRNA-gene associations by at least three-fold as compared to e.g. TarBase, a resource for miRNA-gene associations.

Conclusions

Our database miRSel offers the currently largest collection of literature derived miRNA-gene associations. Comprehensive collections of miRNA-gene associations are important for the development of miRNA target prediction tools and the analysis of regulatory networks. miRSel is updated daily and can be queried using a web-based interface via microRNA identifiers, gene and protein names, PubMed queries as well as gene ontology (GO) terms. miRSel is freely available online at http://services.bio.ifi.lmu.de/mirsel.

Identification of a New Pathway for Tumor Progression: MicroRNA-181b Up-Regulation and CBX7 Down-Regulation by HMGA1 Protein

High mobility group A (HMGA) overexpression plays a critical role in neoplastic transformation. To investigate whether HMGA acts by regulating the expression of microRNAs, we analyzed the microRNA expression profile of human breast adenocarcinoma cells (MCF7) transfected with the HMGA1 gene, which results in a highly malignant phenotype. Among the microRNAs induced by HMGA1, we focused on miR-181b, which was overexpressed in several malignant neoplasias including breast carcinomas. We show that miR-181b regulates CBX7 protein levels, which are down-regulated in cancer, and promotes cell cycle progression. We also demonstrate that CBX7, being negatively regulated by HMGA, is able to negatively regulate miR-181b expression. Finally, there was a direct correlation between HMGA1 and miR-181b expression and an inverse correlation between HMGA1 and CBX7 expression in human breast carcinomas. These data indicate the presence of a novel pathway involving HMGA1, miR-181b, and CBX7, which leads to breast cancer progression.

MicroRNAs--targeting and target prediction

MicroRNAs (miRNAs) are a class of small noncoding RNAs that can regulate many genes by base pairing to sites in mRNAs. The functionality of miRNAs overlaps that of short interfering RNAs (siRNAs), and many features of miRNA targeting have been revealed experimentally by studying miRNA-mimicking siRNAs. This review outlines the features associated with animal miRNA targeting and describes currently available prediction tools.

A probabilistic framework to improve microrna target prediction by incorporating proteomics data

Due to the difficulties in identifying microRNA (miRNA) targets experimentally in a high-throughput manner, several computational approaches have been proposed. To this date, most leading algorithms are based on sequence information alone. However, there has been limited overlap between these predictions, implying high false-positive rates, which underlines the limitation of sequence-based approaches. Considering the repressive nature of miRNAs at the mRNA translational level, here we describe a probabilistic model to make predictions by combining sequence complementarity, miRNA expression level, and protein abundance. Our underlying assumption is that, given sequence complementarity between a miRNA and its putative mRNA targets, the miRNA expression level should be high and the protein abundance of the mRNA should be low. Having identified a set of confident predictions, we then built a second probabilistic model to trace back to the mRNA expression of the confident targets to investigate the mechanisms of the miRNA-mediated post-transcriptional regulation. Our results suggest that translational repression (which has no effect on mRNA level), instead of mRNA degradation, is the dominant mechanism in miRNA regulation. This observation explained the previously observed discordant correlation between mRNA expression and protein abundance.

The DIANA-mirExTra web server: from gene expression data to microRNA function

Background

High-throughput gene expression experiments are widely used to identify the role of genes involved in biological conditions of interest. MicroRNAs (miRNA) are regulatory molecules that have been functionally associated with several developmental programs and their deregulation with diverse diseases including cancer.

Methodology/Principal Findings

Although miRNA expression levels may not be routinely measured in high-throughput experiments, a possible involvement of miRNAs in the deregulation of gene expression can be computationally predicted and quantified through analysis of overrepresented motifs in the deregulated genes 3′ untranslated region (3′UTR) sequences. Here, we introduce a user-friendly web-server, DIANA-mirExTra (www.microrna.gr/mirextra) that allows the comparison of frequencies of miRNA associated motifs between sets of genes that can lead to the identification of miRNAs responsible for the deregulation of large numbers of genes. To this end, we have investigated different approaches and measures, and have practically implemented them on experimental data.

Conclusions/Significance

On several datasets of miRNA overexpression and repression experiments, our proposed approaches have successfully identified the deregulated miRNA. Beyond the prediction of miRNAs responsible for the deregulation of transcripts, the web-server provides extensive links to DIANA-mirPath, a functional analysis tool incorporating miRNA targets in biological pathways. Additionally, in case information about miRNA expression changes is provided, the results can be filtered to display the analysis for miRNAs of interest only.

Computational prediction and experimental validation of evolutionarily conserved microRNA target genes in bilaterian animals

BACKGROUND

In many eukaryotes, microRNAs (miRNAs) bind to complementary sites in the 3'-untranslated regions (3'-UTRs) of target messenger RNAs (mRNAs) and regulate their expression at the stage of translation. Recent studies have revealed that many miRNAs are evolutionarily conserved; however, the evolution of their target genes has yet to be systematically characterized. We sought to elucidate a set of conserved miRNA/target-gene pairs and to analyse the mechanism underlying miRNA-mediated gene regulation in the early stage of bilaterian evolution.

RESULTS

Initially, we extracted five evolutionarily conserved miRNAs (let-7, miR-1, miR-124, miR-125/lin-4, and miR-34) among five diverse bilaterian animals. Subsequently, we designed a procedure to predict evolutionarily conserved miRNA/target-gene pairs by introducing orthologous gene information. As a result, we extracted 31 orthologous miRNA/target-gene pairs that were conserved among at least four diverse bilaterian animals; the prediction set showed prominent enrichment of orthologous miRNA/target-gene pairs that were verified experimentally. Approximately 84% of the target genes were regulated by three miRNAs (let-7, miR-1, and miR-124) and their function was classified mainly into the following categories: development, muscle formation, cell adhesion, and gene regulation. We used a reporter gene assay to experimentally verify the downregulation of six candidate pairs (out of six tested pairs) in HeLa cells.

CONCLUSIONS

The application of our new method enables the identification of 31 miRNA/target-gene pairs that were expected to have been regulated from the era of the common bilaterian ancestor. The downregulation of all six candidate pairs suggests that orthologous information contributed to the elucidation of the primordial set of genes that has been regulated by miRNAs; it was also an efficient tool for the elimination of false positives from the predicted candidates. In conclusion, our study identified potentially important miRNA-target pairs that were evolutionarily conserved throughout diverse bilaterian animals and that may provide new insights into early-stage miRNA functions.

Anti-Argonaute RIP-Chip shows that miRNA transfections alter global patterns of mRNA recruitment to microribonucleoprotein complexes

MicroRNAs (miRNAs) play key roles in gene expression regulation by guiding Argonaute (AGO)-containing microribonucleoprotein (miRNP) effector complexes to target polynucleotides. There are still uncertainties about how miRNAs interact with mRNAs. Here we employed a biochemical approach to isolate AGO-containing miRNPs from human H4 tumor cells by co-immunoprecipitation (co-IP) with a previously described anti-AGO antibody. Co-immunoprecipitated (co-IPed) RNAs were subjected to downstream Affymetrix Human Gene 1.0 ST microarray analysis. During rigorous validation, the "RIP-Chip" assay identified target mRNAs specifically associated with AGO complexes. RIP-Chip was performed after transfecting brain-enriched miRNAs (miR-107, miR-124, miR-128, and miR-320) and nonphysiologic control miRNA to identify miRNA targets. As expected, the miRNA transfections altered the mRNA content of the miRNPs. Specific mRNA species recruited to miRNPs after miRNA transfections were moderately in agreement with computational target predictions. In addition to recruiting mRNA targets into miRNPs, miR-107 and to a lesser extent miR-128, but not miR-124 or miR-320, caused apparent exclusion of some mRNAs that are normally associated with miRNPs. MiR-107 and miR-128 transfections also result in decreased AGO mRNA and protein levels. However, AGO mRNAs were not recruited to miRNPs after either miR-107 or miR-128 transfection, confirming that miRNAs may alter gene expression without stable association between particular mRNAs and miRNPs. In summary, RIP-Chip assays constitute an optimized, validated, direct, and high-throughput biochemical assay that provides data about specific miRNA:mRNA interactions, as well as global patterns of regulation by miRNAs.

Comprehensive analysis of microRNA-mRNA co-expression in circadian rhythm

To investigate the potential role of microRNA (miRNA) in the regulation of circadian rhythm, we performed microarray-based expression profiling study of both miRNA and mRNA in mouse liver for 48 h at 4-hour intervals. Circadian miRNA-mRNA target pair is defined as the pair both elements of which show circadian expression patterns and the sequence-based target relationship of which can be predicted. Circadian initiators, Clock and Bmal1, showed inversely correlated circadian expression patterns against their corresponding miRNAs, miR-181d and miR-191, targeting them. In contrast, circadian suppressors, Per, Cry, CKIe and Rev-erba, exhibited positively correlated circadian expression patterns to their corresponding miRNAs. Genomic location analysis revealed that intronic region showed higher abundance of cyclic than non-cyclic miRNAs targeting circadian genes while other (i.e., 3-UTR, exon and intergenic) regions showed no difference. It is suggested that miRNAs are involved in the regulation of peripheral circadian rhythm in mouse liver by modulating Clock:Bmal1 complex. Identifying specific miRNAs and their targets that are critically involved in circadian rhythm will provide a better understanding of the regulation of circadian- clock system.

MTar: a computational microRNA target prediction architecture for human transcriptome

BACKGROUND

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.

RESULTS

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.

CONCLUSION

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.

Computational methods to identify miRNA targets

MicroRNAs (miRNAs) are short RNA molecules that regulate the post-transcriptional expression of their target genes. This regulation may take the form of stable translational or degradation of the target transcript, although the mechanisms governing the outcome of miRNA-mediated regulation remain largely unknown. While it is becoming clear that miRNAs are core components of gene regulatory networks, elucidating precise roles for each miRNA within these networks will require an accurate means of identifying target genes and assessing the impact of miRNAs on individual targets. Numerous computational methods for predicting targets are currently available. These methods vary widely in their emphasis, accuracy, and ease of use for researchers. This review will focus on a comparison of the available computational methods in animals, with an emphasis on approaches that are informed by experimental analysis of microRNA:target complexes.

Conserved MicroRNA miR-8/miR-200 and its target USH/FOG2 control growth by regulating PI3K

How body size is determined is a long-standing question in biology, yet its regulatory mechanisms remain largely unknown. Here, we find that a conserved microRNA miR-8 and its target, USH, regulate body size in Drosophila. miR-8 null flies are smaller in size and defective in insulin signaling in fat body that is the fly counterpart of liver and adipose tissue. Fat body-specific expression and clonal analyses reveal that miR-8 activates PI3K, thereby promoting fat cell growth cell-autonomously and enhancing organismal growth non-cell-autonomously. Comparative analyses identify USH and its human homolog, FOG2, as the targets of fly miR-8 and human miR-200, respectively. USH/FOG2 inhibits PI3K activity, suppressing cell growth in both flies and humans. FOG2 directly binds to p85alpha, the regulatory subunit of PI3K, and interferes with the formation of a PI3K complex. Our study identifies two novel regulators of insulin signaling, miR-8/miR-200 and USH/FOG2, and suggests their roles in adolescent growth, aging, and cancer.

Sequence and structure analysis of noncoding RNAs

Noncoding RNAs (ncRNAs) are increasingly recognized as important functional molecules in the cell. Here we give a short overview of fundamental computational techniques to analyze ncRNAs that can help us better understand their function. Topics covered include prediction of secondary structure from the primary sequence, prediction of consensus structures for homologous sequences, search for homologous sequences in databases using sequence and structure comparisons, annotation of tRNAs, rRNAs, snoRNAs, and microRNAs, de novo prediction of novel ncRNAs, and prediction of RNA/RNA interactions including miRNA target prediction.

Profiling of miRNA expression and prediction of target genes

MicroRNAs (miRNAs) represent a new class of small noncoding RNAs expressed in plants and animals. They are responsible for the regulation of up to 30% of human genes, thus underlying their influence on almost all cellular pathways. Recent studies have shown miRNAs to be differentially expressed in cancers. Furthermore, several miRNAs are associated with fragile sites, preferential sites of translation, deletion and amplification that are often altered in cancers. However, despite the progress in identifying miRNA genes, knowledge about their functions and specific target genes is still limited. This chapter highlights the technical advances in miRNA gene profiling and discusses the limitations of target prediction programs and the necessity of defining the roles that govern miRNA specificity and target discrimination in vivo.

Progress in miRNA target prediction and identification

Recently, the identification of miRNA targets has received much attention. The strategies to determine miRNA targets include bioinformatic prediction and experimental assays. The bioinformatic prediction methods are mainly based on the confirmed rules of interaction between miRNAs and their targets, and are carried out by programs, such as miRanda, TargetScan, TargetScanS, RNAhybrid, DIANA-microT, PicTar, RNA22 and FindTar, which follow well-known principles. The experimental assays to find miRNA targets employ immunoprecipitation of AGO proteins to identify interacting mRNAs, or the analysis of mRNA or protein levels to identify genes which can be regulated by miRNAs. The improvement of current bioinformatic and experimental assays and the development of novel assays will enable greater efficiency in the identification of miRNA targets and thus facilitate miRNA research. This paper describes progress in the prediction and identification of miRNA targets.

In silico method for systematic analysis of feature importance in microRNA-mRNA interactions

Background

MicroRNA (miRNA), which is short non-coding RNA, plays a pivotal role in the regulation of many biological processes and affects the stability and/or translation of mRNA. Recently, machine learning algorithms were developed to predict potential miRNA targets. Most of these methods are robust but are not sensitive to redundant or irrelevant features. Despite their good performance, the relative importance of each feature is still unclear. With increasing experimental data becoming available, research interest has shifted from higher prediction performance to uncovering the mechanism of microRNA-mRNA interactions.

Results

Systematic analysis of sequence, structural and positional features was carried out for two different data sets. The dominant functional features were distinguished from uninformative features in single and hybrid feature sets. Models were developed using only statistically significant sequence, structural and positional features, resulting in area under the receiver operating curves (AUC) values of 0.919, 0.927 and 0.969 for one data set and of 0.926, 0.874 and 0.954 for another data set, respectively. Hybrid models were developed by combining various features and achieved AUC of 0.978 and 0.970 for two different data sets. Functional miRNA information is well reflected in these features, which are expected to be valuable in understanding the mechanism of microRNA-mRNA interactions and in designing experiments.

Conclusions

Differing from previous approaches, this study focused on systematic analysis of all types of features. Statistically significant features were identified and used to construct models that yield similar accuracy to previous studies in a shorter computation time.

Computational approaches for microRNA studies: a review

MicroRNAs (miRNAs) are one class of tiny, endogenous RNAs that can regulate messenger RNA (mRNA) expression by targeting homologous sequences in mRNAs. Their aberrant expressions have been observed in many cancers and several miRNAs have been convincingly shown to play important roles in carcinogenesis. Since the discovery of this small regulator, computational methods have been indispensable tools in miRNA gene finding and functional studies. In this review we first briefly outline the biological findings of miRNA genes, such as genomic feature, biogenesis, gene structure, and functional mechanism. We then discuss in detail the three main aspects of miRNA computational studies: miRNA gene finding, miRNA target prediction, and regulation of miRNA genes. Finally, we provide perspectives on some emerging issues, including combinatorial regulation by miRNAs and functional binding sites beyond the 3'-untranslated region (3'UTR) of target mRNAs. Available online resources for miRNA computational studies are also provided.

Pharmacology of epigenetics in brain disorders

Epigenetics is a rapidly growing field and holds great promise for a range of human diseases, including brain disorders such as Rett syndrome, anxiety and depressive disorders, schizophrenia, Alzheimer disease and Huntington disease. This review is concerned with the pharmacology of epigenetics to treat disorders of the epigenome whether induced developmentally or manifested/acquired later in life. In particular, we will focus on brain disorders and their treatment by drugs that modify the epigenome. While the use of DNA methyl transferase inhibitors and histone deacetylase inhibitors in in vitro and in vivo models have demonstrated improvements in disease-related deficits, clinical trials in humans have been less promising. We will address recent advances in our understanding of the complexity of the epigenome with its many molecular players, and discuss evidence for a compromised epigenome in the context of an ageing or diseased brain. We will also draw on examples of species differences that may exist between humans and model systems, emphasizing the need for more robust pre-clinical testing. Finally, we will discuss fundamental issues to be considered in study design when targeting the epigenome.

MicroRNA in pancreatic cancer: pathological, diagnostic and therapeutic implications

MicroRNAs (miRNAs) are a group of small non-coding RNA molecules of 17-25 nucleotides (nt) in length, predicted to control the activity of about 30% of all protein-coding genes in mammals. Altered expressions of miRNAs are reported in various cancers and may associate with cancer pathogenesis, apoptosis, and cell growth, thereby functioning as either tumor suppressors or oncogenes. Recent reports showed that deregulation of miRNA contribute to tumor development and progression and hence, have diagnostic and prognostic value in several human malignancies. This review discusses the current status of miRNA in pancreatic cancer development, progression, diagnosis, and therapy.

MicroRNAs and epigenetic regulation in the mammalian inner ear: implications for deafness

Sensorineural hearing loss is the most common sensory disorder in humans and derives, in most cases, from inner-ear defects or degeneration of the cochlear sensory neuroepithelial hair cells. Genetic factors make a significant contribution to hearing impairment. While mutations in 51 genes have been associated with hereditary sensorineural nonsyndromic hearing loss (NSHL) in humans, the responsible mutations in many other chromosomal loci linked with NSHL have not been identified yet. Recently, mutations in a noncoding microRNA (miRNA) gene, MIR96, which is expressed specifically in the inner-ear hair cells, were linked with progressive hearing loss in humans and mice. Furthermore, additional miRNAs were found to have essential roles in the development and survival of inner-ear hair cells. Epigenetic mechanisms, in particular, DNA methylation and histone modifications, have also been implicated in human deafness, suggesting that several layers of noncoding genes that have never been studied systematically in the inner-ear sensory epithelia are required for normal hearing. This review aims to summarize the current knowledge about the roles of miRNAs and epigenetic regulatory mechanisms in the development, survival, and function of the inner ear, specifically in the sensory epithelia, tectorial membrane, and innervation, and their contribution to hearing.

miR-145 directs intestinal maturation in zebrafish

The rapid specification and differentiation of the embryonic zebrafish gut is essential to provide contractility for the digestion of food. The role of microRNAs in modulating gut epithelial or smooth muscle differentiation is currently not known. Here we show that the microRNA miR-145 is strongly expressed in zebrafish gut smooth muscle and regulates its development. Modulation of miR-145 levels results in gut smooth muscle and epithelium maturation defects. Loss of miR-145 results in defects of smooth muscle function as measured by decreased nitric oxide production but also leads to increased expression of the embryonic smooth muscle markers sm22alpha-b, nm-mhc-b, and smoothelin. Defects in gut epithelial maturation are also present as observed by immature morphology and a complete loss of alkaline phosphatase expression. Loss or gain of miR-145 function phenocopies defects observed with altered gata6 expression and accordingly, we show that miR-145 directly represses gata6, and that gata6 is a major miR-145 target in vitro and in vivo. miR-145 therefore plays a critical role in promoting the maturation of both layers of the gut during development through regulation of gata6.

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 INFORMATION

Supplementary data are available at Bioinformatics online.

Accurate microRNA target prediction correlates with protein repression levels

Background

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 disease.

Results

DIANA-microT 3.0 is an algorithm for microRNA target prediction which is based on several parameters calculated individually for each microRNA and combines conserved and non-conserved microRNA recognition elements into a final prediction score, which correlates with protein production fold change. Specifically, for each predicted interaction the program reports a signal to noise ratio and a precision score which can be used as an indication of the false positive rate of the prediction.

Conclusion

Recently, several computational target prediction programs were benchmarked based on a set of microRNA target genes identified by the pSILAC method. In this assessment DIANA-microT 3.0 was found to achieve the highest precision among the most widely used microRNA target prediction programs reaching approximately 66%. The DIANA-microT 3.0 prediction results are available online in a user friendly web server at

miR-145 participates with TP53 in a death-promoting regulatory loop and targets estrogen receptor-alpha in human breast cancer cells

Understanding the consequences of miR-145 reintroduction in human breast cancer (BC) could reveal its tumor-suppressive functions and may disclose new aspects of BC biology. Therefore, we characterized the effects of miR-145 re-expression in BC cell lines by using proliferation and apoptosis assays. As a result, we found that miR-145 exhibited a pro-apoptotic effect, which is dependent on TP53 activation, and that TP53 activation can, in turn, stimulate miR-145 expression, thus establishing a death-promoting loop between miR-145 and TP53. We also found that miR-145 can downregulate estrogen receptor-alpha (ER-alpha) protein expression through direct interaction with two complementary sites within its coding sequence. In conclusion, we described a tumor suppression function of miR-145 in BC cell lines, and we linked miR-145 to TP53 and ER-alpha. Moreover, our findings support a view that miR-145 re-expression therapy could be mainly envisioned in the specific group of patients with ER-alpha-positive and/or TP53 wild-type tumors.

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.

Minireview: epigenetic changes in ovarian cancer

Epigenetic aberrations, including DNA methylation, histone modifications, and micro-RNA dysregulation, are now well established in the development and progression of ovarian cancer, and their gradual accumulation is associated with advancing disease stage and grade. Epigenetic aberrations are relatively stable, associated with distinct disease subtypes, and present in circulating serum, representing promising diagnostic, prognostic, and pharmacodynamic biomarkers. In contrast to DNA mutations and deletions, aberrant gene-repressive epigenetic modifications are potentially reversible by epigenetic therapies, including inhibitors of DNA methylation or histone-modifying enzymes. Although epigenetic monotherapies have not shown activity against solid tumors, including ovarian cancer, preclinical studies suggest they will be effective when used in combination with one another or with conventional chemotherapeutics, and combinatorial epigenetic therapy regiments are being examined in cancer clinical trials. A greater understanding of the role of epigenetics in ovarian neoplasia will provide for improved interventions against this devastating malignancy.

MicroRNA-9 inhibits ovarian cancer cell growth through regulation of NF-kappaB1

MicroRNAs are emerging as important regulators of cancer-related processes. Our studies show that microRNA-9 (miR-9) is downregulated in human ovarian cancer relative to normal ovary, and overexpression of miR-9 suppresses cell growth in vitro. Furthermore, the 3'-UTR of NF-kappaB1 mRNA is found to be regulated directly by miR-9, demonstrating that NF-kappaB1 is a functionally important target of miR-9 in ovarian cancer cells. When miR-9 is overexpressed in ovarian cancer cells, the mRNA and protein levels of NF-kappaB1 are both suppressed, whereas inhibition of miR-9 results in an increase in the NF-kappaB1 expression level. Ovarian cancer tissues display significantly low expression of miR-9 and a high level of NF-kappaB1 compared with normal tissues, indicating that regulation of NF-kappaB1 by miR-9 is an important mechanism for miR-9 to inhibit ovarian cancer proliferation.

TargetMiner: microRNA target prediction with systematic identification of tissue-specific negative examples

MOTIVATION

Prediction of microRNA (miRNA) target mRNAs using machine learning approaches is an important area of research. However, most of the methods suffer from either high false positive or false negative rates. One reason for this is the marked deficiency of negative examples or miRNA non-target pairs. Systematic identification of non-target mRNAs is still not addressed properly, and therefore, current machine learning approaches are compelled to rely on artificially generated negative examples for training.

RESULTS

In this article, we have identified approximately 300 tissue-specific negative examples using a novel approach that involves expression profiling of both miRNAs and mRNAs, miRNA-mRNA structural interactions and seed-site conservation. The newly generated negative examples are validated with pSILAC dataset, which elucidate the fact that the identified non-targets are indeed non-targets.These high-throughput tissue-specific negative examples and a set of experimentally verified positive examples are then used to build a system called TargetMiner, a support vector machine (SVM)-based classifier. In addition to assessing the prediction accuracy on cross-validation experiments, TargetMiner has been validated with a completely independent experimental test dataset. Our method outperforms 10 existing target prediction algorithms and provides a good balance between sensitivity and specificity that is not reflected in the existing methods. We achieve a significantly higher sensitivity and specificity of 69% and 67.8% based on a pool of 90 feature set and 76.5% and 66.1% using a set of 30 selected feature set on the completely independent test dataset. In order to establish the effectiveness of the systematically generated negative examples, the SVM is trained using a different set of negative data generated using the method in Yousef et al. A significantly higher false positive rate (70.6%) is observed when tested on the independent set, while all other factors are kept the same. Again, when an existing method (NBmiRTar) is executed with the our proposed negative data, we observe an improvement in its performance. These clearly establish the effectiveness of the proposed approach of selecting the negative examples systematically.

AVAILABILITY

TargetMiner is now available as an online tool at www.isical.ac.in/ approximately bioinfo_miu

MicroRNAs in Cardiovascular Biology and Heart Disease

MicroRNAs play important roles in many cellular and biological functions via the regulation of mRNA target translation. In the cardiovascular field, microRNAs are now acknowledged as fundamental in regulating the expression of genes that governs physiological and pathological myocardial adaptation to stress. Here, we review recent progress in the understanding of microRNA functions and their involvement in heart disease.

Abundant conserved microRNA target sites in the 5'-untranslated region and coding sequence

Recent studies have shown that miRNAs can target the promoter and CDS region. Thus, we predicted miRNA target sites in the 5'-UTR, CDS and 3'-UTR of Homo sapiens, Mus musculus and Drosophila melanogaster using miRanda and TargetScan. Target-site densities normalized with the average region length were higher in the 5'-UTR than 3'-UTR in all three organisms but were lower in the negative data set. Interestingly, the putative target sites were more conserved than non-target regions in both the 5'-UTR and 3'-UTR, implying that target sites in the 5'-UTR are subject to high selective pressure and might be functional. In Drosophila, 48 of 78 (61.5%) miRNAs showed high similarities with predicted siRNAs. Based on the results of previous experimental studies and a large-scale statistical analysis, we conclude that miRNA-mediated regulation is not limited to the 3'-UTR. However, the functionality of target sites in the 5'-UTR and CDS requires thorough investigation.

Microarray based analysis of microRNA expression in rat cerebral cortex after traumatic brain injury

MicroRNAs (miRNAs) are very important regulators of biological processes such as development, cellular differentiation, and tumor generation. MiRNA microarray has been found to be a high throughput global analysis tool for detecting miRNA expression profiling, and miRNA expression profiling will facilitate the study of the biological function of miRNAs. In this report, we describe the miRNA expression level in rat cerebral cortex after traumatic brain injury using microarray method. We choose several time points post brain injury: 6 h, 24 h, 48 h and 72 h, respectively, to reveal differential expression of miRNAs in rat brain cortex compared with control groups. Our research revealed that 136 miRNAs were expressing at 6 h post injury, in which 13 miRNAs were more than 2-fold up-regulated, and 14 miRNAs were more than 2-fold down-regulated; 118 miRNAs were expressing at 24 h post injury, in which 4 miRNAs were more than 2-fold up-regulated, and 23 miRNAs were more than 2-fold down-regulated; 149 miRNAs were expressing at 48 h post injury, in which 16 miRNAs were more than 2-fold up-regulated, and 11 miRNAs were more than 2-fold down-regulated; and 203 miRNAs were expressing at 72 h post injury, in which 19 miRNAs were more than 2-fold up-regulated, and 5 miRNAs were more than 2-fold down-regulated. Furthermore, we revealed global up-regulation of miR-21 expression within all the four time points post injury. Finally, we utilized qRT-PCR methods to verify the microarray results. The qRT-PCR results indicated good consistency with the results of the microarray method. Our microarray based analysis of microRNA expression in rat cerebral cortex after traumatic brain injury has shown that some microRNA such as miR-21 could be involved in the intricate process of TBI course.

MicroRNA signature of malignant mesothelioma with potential diagnostic and prognostic implications

MicroRNAs (miRNAs) post-transcriptionally regulate the expression of target genes, and may behave as oncogenes or tumor suppressors. Human malignant mesothelioma is an asbestos-related cancer, with poor prognosis and low median survival. Here we report, for the first time, a cross-evaluation of miRNA expression in mesothelioma (MPP-89, REN) and human mesothelial cells (HMC-telomerase reverse transcriptase). Microarray profiling, confirmed by real-time quantitative RT-PCR, revealed a differential expression of miRNAs between mesothelioma and mesothelial cells. In addition, a computational analysis combining miRNA and gene expression profiles allowed the accurate prediction of genes potentially targeted by dysregulated miRNAs. Several predicted genes belong to terms of Gene Ontology (GO) that are associated with the development and progression of mesothelioma. This suggests that miRNAs may be key players in mesothelioma oncogenesis. We further investigated miRNA expression on a panel of 24 mesothelioma specimens, representative of the three histotypes (epithelioid, biphasic, and sarcomatoid), by quantitative RT-PCR. The expression of miR-17-5p, miR-21, miR-29a, miR-30c, miR-30e-5p, miR-106a, and miR-143 was significantly associated with the histopathological subtypes. Notably, the reduced expression of two miRNAs (miR-17-5p and miR-30c) correlated with better survival of patients with sarcomatoid subtype. Our preliminary analysis points at miRNAs as potential diagnostic and prognostic markers of mesothelioma, and suggests novel tools for the therapy of this malignancy.

MicroRNA expression profiling in diabetic GK rat model

MicroRNAs (miRNAs), which are a newly identified class of small single-stranded non-coding RNAs, regulate their target genes via post-transcriptional pathway. It has been proved that miRNAs play important roles in many biological processes. To better understand miRNA function on type 2 diabetes, we used an oligonucleotide microarray to monitor miRNA expression profiles of Goto-Kakizaki (GK) and Wistar rats' skeletal muscle. It was found that seven miRNAs were downexpressed and two miRNAs were over-expressed in the muscle of GK rats. Among them, miR-24 showed the most prominent change. p38 MAPK, which is a direct target of miR-24, also showed expression difference. All the data give a clue that miR-24 might be associated with diabetes through down-regulation of p38 MAPK.

RNA-interference-based Gene Therapy Approaches to HIV Type-1 Treatment: Tackling the Hurdles from Bench to Bedside

RNA interference (RNAi) is a cellular mechanism that can be induced by small interfering RNAs (siRNAs) to mediate sequence-specific gene silencing by cleavage of the targeted messenger RNA. RNAi can be used as an antiviral approach to silence HIV type-1 (HIV-1) through stable expression of precursors, such as short hairpin RNAs (shRNAs), which are processed into siRNAs that can elicit degradation of HIV-1 RNAs. At the beginning of 2008, the first clinical trial using a lentivirus with an RNA-based gene therapy against HIV-1 was initiated. The antiviral molecules in this gene therapy consist of three RNA effectors, one of which triggers the RNAi pathway. This review article focuses on the basic principles of an RNAi-based gene therapy against HIV-1, including delivery methods, target selection, viral escape possibilities, systems for multiplexing siRNAs to achieve a durable therapy and the in vitro and in vivo test systems to evaluate the efficacy and safety of such a therapy.