DIANA-microT Web server upgrade supports Fly and Worm miRNA target prediction and bibliographic miRNA to disease association

microRNAs (miRNAs) are small endogenous RNA molecules that are implicated in many biological processes through post-transcriptional regulation of gene expression. The DIANA-microT Web server provides a user-friendly interface for comprehensive computational analysis of miRNA targets in human and mouse. The server has now been extended to support predictions for two widely studied species: Drosophila melanogaster and Caenorhabditis elegans. In the updated version, the Web server enables the association of miRNAs to diseases through bibliographic analysis and provides insights for the potential involvement of miRNAs in biological processes. The nomenclature used to describe mature miRNAs along different miRBase versions has been extensively analyzed, and the naming history of each miRNA has been extracted. This enables the identification of miRNA publications regardless of possible nomenclature changes. User interaction has been further refined allowing users to save results that they wish to analyze further. A connection to the UCSC genome browser is now provided, enabling users to easily preview predicted binding sites in comparison to a wide array of genomic tracks, such as single nucleotide polymorphisms. The Web server is publicly accessible in www.microrna.gr/microT-v4

The role of MicroRNAs in diseases

Σημείωση: διατίθεται συμπληρωματικό υλικό σε ξεχωριστό αρχείο

TarBase 6.0: capturing the exponential growth of miRNA targets with experimental support

As the relevant literature and the number of experiments increase at a super linear rate, databases that curate and collect experimentally verified microRNA (miRNA) targets have gradually emerged. These databases attempt to provide efficient access to this wealth of experimental data, which is scattered in thousands of manuscripts. Aim of TarBase 6.0 (http://www.microrna.gr/tarbase) is to face this challenge by providing a significant increase of available miRNA targets derived from all contemporary experimental techniques (gene specific and high-throughput), while incorporating a powerful set of tools in a user-friendly interface. TarBase 6.0 hosts detailed information for each miRNA–gene interaction, ranging from miRNA- and gene-related facts to information specific to their interaction, the experimental validation methodologies and their outcomes. All database entries are enriched with function-related data, as well as general information derived from external databases such as UniProt, Ensembl and RefSeq. DIANA microT miRNA target prediction scores and the relevant prediction details are available for each interaction. TarBase 6.0 hosts the largest collection of manually curated experimentally validated miRNA–gene interactions (more than 65 000 targets), presenting a 16.5–175-fold increase over other available manually curated databases

An APOA5 3′ UTR Variant Associated with Plasma Triglycerides Triggers APOA5 Downregulation by Creating a Functional miR-485-5p Binding Site

APOA5 c.∗158C>T (rs2266788), located in the 3′ UTR, belongs to APOA5 haplotype 2 (APOA5∗2), which is strongly associated with plasma triglyceride levels and modulates the occurrence of both moderate and severe hypertriglyceridemia. Individuals with APOA5∗2 display reduced APOA5 expression at the posttranscriptional level. However, the functionality of this haplotype remains unclear. We hypothesized that the hypertriglyceridemic effects of APOA5∗2 could involve miRNA regulation in the APOA5 3′ UTR. Bioinformatic studies have identified the creation of a potential miRNA binding site for liver-expressed miR-485-5p (MIRN485-5p) in the mutant APOA5 3′ UTR with the c.∗158C allele. In human embryonic kidney 293T (HEK293T) cells cotransfected with an APOA5 3′ UTR luciferase reporter vector and a miR485-5p precursor, c.∗158C allele expression was significantly decreased. Moreover, in HuH-7 cells endogenously expressing miR-485-5p, we observed that luciferase activity was significantly lower in the presence of the c.∗158C allele than in the presence of the c.∗158T allele, which was completely reversed by a miR-485-5p inhibitor. We demonstrated that the rare c.∗158C APOA5 allele creates a functional target site for liver-expressed miR-485-5p. Therefore, we propose that the well-documented hypertriglyceridemic effect of APOA5∗2 involves an APOA5 posttranscriptional downregulation mediated by miR-485-5p

ProteoMirExpress: inferring microRNA-centered regulatory networks from high-throughput proteomic and transcriptome data

MicroRNAs (miRNAs) regulate gene expression through translational repression and RNA degradation. Recently developed high-throughput proteomic methods measure gene expression changes at protein levels, and therefore can reveal the direct effects of miRNAs’ translational repression. Here, we present a web server, ProteoMirExpress that integrates proteomic and mRNA expression data together to infer miRNA-centered regulatory networks. With both high throughput data from the users, ProteoMirExpress is able to discover not only miRNA targets that have mRNA decreased, but also subgroups of targets whose proteins are suppressed but mRNAs are not significantly changed or whose mRNAs are decreased but proteins are not significantly changed, which were usually ignored by most current methods. Furthermore, both direct and indirect targets of miRNAs can be detected. Therefore ProteoMirExpress provides more comprehensive miRNA-centered regulatory networks. We use several published data to assess the quality of our inferred networks and prove the value of our server. ProteoMirExpress is available at http://jjwanglab.org/ProteoMirExpress, with free access to academic users.postprin

miRNA data analysis workflow

Micro RNAs (miRNA) have been shown to regulate many biological processes by silencing the expression of their target genes. They are small non-coding RNAs that have been found in all types of organisms from eukaryotes to viruses. It has been shown that one miRNA can have several target genes and on the other hand, one gene can be targeted by several different miRNAs. Thus, the analysis of miRNA data is complicated. The aim of this project was to develop a workflow for miRNA functional analysis and test its functionality with some published datasets. The workflow for the functional analysis of miRNAs includes miRNA differential expression analysis, target gene identification and functional enrichment analysis. The first aim of this project was to find a suitable database or a set of databases to retrieve miRNA target predictions. By literature search, information was gathered about different miRNA target prediction databases that are currently available. mirDIP4.1, which collects predictions from 30 different resources and is updated frequently, was selected as the source of miRNA target predictions. For the functional analysis, two different tools were tested. First one of these, R/Bioconductor package mdgsa is based on gene set enrichment analysis. The other one, BUFET is a python script that performs overrepresentation analysis with empirical correction for bias that is often observed in miRNA functional analysis. For the testing of these algorithms, datasets from three different publications were used with miRNA target predictions from various sources. As expected, the results from different approaches differed both from the original publications and from each other. One reason for the differences observed in results compared to those of the original method publications was the different target prediction database that was used here

IDENTIFICATION OF CELL SIGNALING PATHWAY REGULATED BY MICRORNAS IN CANCER CELLS USING A SYSTEMS BIOLOGICAL APPROACH

MicroRNAs (miRNAs) are single-stranded, non-coding RNA molecules that regulate gene expression via imperfect binding of the miRNA to specific sites in the 3\u27 untranslated region of the mRNAs. Because prediction of miRNA targets is an essential step for understanding the functional roles of miRNAs, many computational approaches have been developed to identify miRNA targets. However, identifying targets remains challenging due to the inherent limitation of current prediction approaches based on imperfect complementarity between miRNA and its target mRNAs. To overcome these current limitations, we developed a novel correlation-based approach that is sequence independence to predict functional targets of miRNAs by step-wise integration of the expression data of miRNAs, mRNAs, and proteins from NCI-60 cell lines. A correlation matrix between expression of miRNAs and mRNAs was first generated and later integrated with the correlation matrix between expression of mRNAs and signaling proteins. Because these integrated matrices reflect the association of miRNAs and signaling pathways, they were used to predict potential signaling pathways regulated by certain miRNAs. We implemented a web-based tool, miRPP, based on our approach. As validation of our approach, we also demonstrated that miR-500 regulates the MAPK pathway in melanoma and breast cancer cells as predicted by our algorithms. In additional experiments, we further identified PPFIA1 as a direct target of miR-500 that regulates MAP2K1 in the MAPK pathway. In conclusion, we developed a systematic analysis approach that can predict signaling pathways regulated by particular miRNAs. Our approach can be used to investigate the unknown regulatory role of miRNAs in signaling pathways and gene regulatory networks