Relative contribution of sequence and structure features to the mRNA binding of Argonaute/EIF2C-miRNA complexes and the degradation of miRNA targets

Selective inhibition of rabies virus gene expression by human miRNAs: a therapeutic approach using the 7mer-m8 model

MicroRNAs, abbreviated as miRNAs, have a substantial impact on viral infections through their ability to control gene expression and influence the interactions between the host and the virus. This work investigates the capacity of miRNAs to selectively inhibit the expression of rabies virus genes, specifically Nucleoprotein N, Phosphoprotein M1 and M2, Transmembrane Glycoprotein G, and L protein. The 7mer-m8 model was utilized to identify human miRNAs that target these viral genes. The interactions between the miRNAs and the genes were then assessed based on binding energy, GC content, and stability. The findings indicated that miRNAs, including miR-1279, miR-4251, miR-4288, and miR-12117, successfully target the N gene. In addition, other miRNAs target the remaining viral genes, indicating their capacity to bind and potentially inhibit viral replication. In addition, docking experiments have verified the stability of miRNA-mRNA duplexes, as evidenced by the high free energy values that indicate strong and reliable contacts between miRNA and gene. These findings indicate that certain human miRNAs have the potential to be effective therapeutic agents against the rabies virus by suppressing gene expression. This offers a new and innovative strategy to fight against this deadly infection.

Advanced computational predictive models of miRNA-mRNA interaction efficiency

The modeling of miRNA-mRNA interactions holds significant implications for synthetic biology and human health. However, this research area presents specific challenges due to the multifaceted nature of mRNA downregulation by miRNAs, influenced by numerous factors including competition or synergism among miRNAs and mRNAs. In this study, we present an improved computational model for predicting miRNA-mRNA interactions, addressing aspects not previously modeled. Firstly, we integrated a novel set of features that significantly enhanced the predictor's performance. Secondly, we demonstrated the cell-specific nature of certain aspects of miRNA-mRNA interactions, highlighting the importance of designing models tailored to specific cell types for improved accuracy. Moreover, we introduce a miRNA binding site interaction model (miBSIM) that, for the first time, accounts for both the distribution of miRNA binding sites along the mRNA and their respective strengths in regulating mRNA stability. Our analysis suggests that distant miRNA sites often compete with each other, revealing the intricate interplay of binding site interactions. Overall, our new predictive model shows a significant improvement of up to 6.43% over previous models in the field. The code of our model is available at https://www.cs.tau.ac.il/~tamirtul/miBSIM.

The SARS-CoV-2 protein NSP2 enhances microRNA-mediated translational repression

Viruses use microRNAs (miRNAs) to impair the host antiviral response and facilitate viral infection by expressing their own miRNAs or co-opting cellular miRNAs. miRNAs inhibit translation initiation of their target mRNAs by recruiting the GIGYF2-4EHP (or EIF4E2) translation repressor complex to the mRNA 5'-cap structure. We recently reported that the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-encoded non-structural protein 2 (NSP2) interacts with GIGYF2. This interaction is critical for blocking translation of the Ifnb1 mRNA that encodes the cytokine interferon β, and thereby impairs the host antiviral response. However, it is not known whether NSP2 also affects miRNA-mediated silencing. Here, we demonstrate the pervasive augmentation of miRNA-mediated translational repression of cellular mRNAs by NSP2. We show that NSP2 interacts with argonaute 2 (AGO2), the core component of the miRNA-induced silencing complex (miRISC), via GIGYF2 and enhances the translational repression mediated by natural miRNA-binding sites in the 3' untranslated region of cellular mRNAs. Our data reveal an additional layer of the complex mechanism by which SARS-CoV-2 and likely other coronaviruses manipulate the host gene expression program by co-opting the host miRNA-mediated silencing machinery.

Determinants of Functional MicroRNA Targeting

MicroRNAs (miRNAs) play cardinal roles in regulating biological pathways and processes, resulting in significant physiological effects. To understand the complex regulatory network of miRNAs, previous studies have utilized massivescale datasets of miRNA targeting and attempted to computationally predict the functional targets of miRNAs. Many miRNA target prediction tools have been developed and are widely used by scientists from various fields of biology and medicine. Most of these tools consider seed pairing between miRNAs and their mRNA targets and additionally consider other determinants to improve prediction accuracy. However, these tools exhibit limited prediction accuracy and high false positive rates. The utilization of additional determinants, such as RNA modifications and RNA-binding protein binding sites, may further improve miRNA target prediction. In this review, we discuss the determinants of functional miRNA targeting that are currently used in miRNA target prediction and the potentially predictive but unappreciated determinants that may improve prediction accuracy.

Silencing lung cancer genes using miRNAs identified by 7mer-seed matching

Lung cancer (LC) is the main cause of cancer-associated deaths in both men and women globally with a very high mortality rate. The microRNAs (miRNAs) are a class of noncoding RNAs consisting of 18-25 nucleotides. They inhibit translation of protein through binding to complementary target mRNAs. The non-coding miRNAs are recognized as potent biomarkers for detection, development and treatment of malignancy. In this study, we screened a set of 12 genes over expressed in small cell lung cancer, non small cell lung cancer and the genes involved in both categories and their binding sites for human miRNAs as no work was reported yet. Screening of human miRNAs revealed that a few genes showed numerous miRNA binding sites. Free energy values of mRNA sequences revealed that they might acquire compact folded structure causing complexity for miRNAs to interact. GC content in the target site was relatively higher than that of their flanks. It was observed through analysis of cosine similarity metric and compAI parameters that the genes related to lung cancer were encoded with non optimal codons and thus might be translationally less efficient for producing polypeptides. Gene ontology analysis was carried out to understand the diverse functions of these 12 genes.

Human antigen R regulates hypoxia-induced mitophagy in renal tubular cells through PARKIN/BNIP3L expressions

Mitochondrial dysfunction contributes to the pathophysiology of acute kidney injury (AKI). Mitophagy selectively degrades damaged mitochondria and thereby regulates cellular homeostasis. RNA-binding proteins (RBPs) regulate RNA processing at multiple levels and thereby control cellular function. In this study, we aimed to understand the role of human antigen R (HuR) in hypoxia-induced mitophagy process in the renal tubular cells. Mitophagy marker expressions (PARKIN, p-PARKIN, PINK1, BNIP3L, BNIP3, LC3) were determined by western blot analysis. Immunofluorescence studies were performed to analyze mitophagosome, mitolysosome, co-localization of p-PARKIN/TOMM20 and BNIP3L/TOMM20. HuR-mediated regulation of PARKIN/BNIP3L expressions was determined by RNA-immunoprecipitation analysis and RNA stability experiments. Hypoxia induced mitochondrial dysfunction by increased ROS, decline in membrane potential and activated mitophagy through up-regulated PARKIN, PINK1, BNIP3 and BNIP3L expressions. HuR knockdown studies revealed that HuR regulates hypoxia-induced mitophagosome and mitolysosome formation. HuR was significantly bound to PARKIN and BNIP3L mRNA under hypoxia and thereby up-regulated their expressions through mRNA stability. Altogether, our data highlight the importance of HuR in mitophagy regulation through up-regulating PARKIN/BNIP3L expressions in renal tubular cells.

Plasma miR-1273g-3p acts as a potential biomarker for early Breast Ductal Cancer diagnosis

Circulating miRNAs presenting in plasma in a stable manner have been demonstrated their potential role as a promising biomarkers in many human diseases, such as Alzheimer's disease, melanoma and ovarian carcinoma. However, few circulating miRNAs could be used for breast ductal cancer diagnosis. Here, we identified miR-1273g-3p as a biomarker for detecting breast ductal cancer. We detected miR-1273g-3p levels in the plasma of 39 sporadic breast ductal cancer patients and 40 healthy donors by Stem-loop Quantitative Real-time PCR (qRT-PCR). The results showed the plasma miR-1273g-3p level were significantly up-regulated in breast ductal cancer patients compared with healthy donors (p=0.0139). Receiver operating characteristic (ROC) curve also revealed the significantly diagnostic ability of miR-1273g-3p in patients (p=0.0414). In addition, the plasma level of miR-1273g-3p was closely related to IIIB-IIIC TNM stage. We also confirmed the higher expression level of miR-1273g-3p in breast cancer cell lines MCF-7 (4.872±0.537) than normal breast cells (Hs 578Bst). Taken together, miR-1273g-3p could represent as a potential biomarker for early breast ductal cancer diagnosis.

Prediction of the miRNA interactome - Established methods and upcoming perspectives

MicroRNAs (miRNAs) are well-studied small noncoding RNAs involved in post-transcriptional gene regulation in a wide range of organisms, including mammals. Their function is mediated by base pairing with their target RNAs. Although many features required for miRNA-mediated repression have been described, the identification of functional interactions is still challenging. In the last two decades, numerous Machine Learning (ML) models have been developed to predict their putative targets. In this review, we summarize the biological knowledge and the experimental data used to develop these ML models. Recently, Deep Neural Network-based models have also emerged in miRNA interaction modeling. We thus outline established and emerging models to give a perspective on the future developments needed to improve the identification of genes directly regulated by miRNAs.

The biochemical basis of microRNA targeting efficacy

MicroRNAs (miRNAs) act within Argonaute proteins to guide repression of messenger RNA targets. Although various approaches have provided insight into target recognition, the sparsity of miRNA-target affinity measurements has limited understanding and prediction of targeting efficacy. Here, we adapted RNA bind-n-seq to enable measurement of relative binding affinities between Argonaute-miRNA complexes and all sequences ≤12 nucleotides in length. This approach revealed noncanonical target sites specific to each miRNA, miRNA-specific differences in canonical target-site affinities, and a 100-fold impact of dinucleotides flanking each site. These data enabled construction of a biochemical model of miRNA-mediated repression, which was extended to all miRNA sequences using a convolutional neural network. This model substantially improved prediction of cellular repression, thereby providing a biochemical basis for quantitatively integrating miRNAs into gene-regulatory networks.

MiR-192, miR-200c and miR-17 are fibroblast-mediated inhibitors of colorectal cancer invasion

Colorectal cancer remains a leading cause of cancer-related death worldwide. A previous transcriptomics based study characterized molecular subgroups of which the stromal subgroup was associated with the worst clinical outcome. Micro-RNAs (miRNAs) are well-known regulators of gene expression and can follow a non-linear repression mechanism. We set up a model combining piecewise linear and linear regression and applied this combined regression model to a comprehensive colon adenocarcinoma dataset. We identified miRNAs involved in regulating characteristic gene sets, particularly extracellular matrix remodeling in the stromal subgroup. Comparison of expression data from separated (epithelial) cancer cells and stroma cells or fibroblasts associate these regulatory interactions with infiltrating stromal or tumor-associated fibroblasts. MiR-200c, miR-17 and miR-192 were identified as the most promising candidates regulating genes crucial for extracellular matrix remodeling. We validated our computational findings by in vitro assays. Enforced expression of either miR-200c, miR-17 or miR-192 in untransformed human colon fibroblasts down-regulated 85% of all predicted target genes. Expressing these miRNAs singly or in combination in human colon fibroblasts co-cultured with colon cancer cells considerably reduced cancer cell invasion validating these miRNAs as cancer cell infiltration suppressors in tumor associated fibroblasts.

Predicting microRNA targeting efficacy in Drosophila

Background

MicroRNAs (miRNAs) are short regulatory RNAs that derive from hairpin precursors. Important for understanding the functional roles of miRNAs is the ability to predict the messenger RNA (mRNA) targets most responsive to each miRNA. Progress towards developing quantitative models of miRNA targeting in Drosophila and other invertebrate species has lagged behind that of mammals due to the paucity of datasets measuring the effects of miRNAs on mRNA levels.

Results

We acquired datasets suitable for the quantitative study of miRNA targeting in Drosophila. Analyses of these data expanded the types of regulatory sites known to be effective in flies, expanded the mRNA regions with detectable targeting to include 5′ untranslated regions, and identified features of site context that correlate with targeting efficacy in fly cells. Updated evolutionary analyses evaluated the probability of conserved targeting for each predicted site and indicated that more than a third of the Drosophila genes are preferentially conserved targets of miRNAs. Based on these results, a quantitative model was developed to predict targeting efficacy in insects. This model performed better than existing models, and it drives the most recent version, v7, of TargetScanFly.

Conclusions

Our evolutionary and functional analyses expand the known scope of miRNA targeting in flies and other insects. The existence of a quantitative model that has been developed and trained using Drosophila data will provide a valuable resource for placing miRNAs into gene regulatory networks of this important experimental organism.

Electronic supplementary material

The online version of this article (10.1186/s13059-018-1504-3) contains supplementary material, which is available to authorized users.

Single-cell mRNA profiling reveals the hierarchical response of miRNA targets to miRNA induction

miRNAs are small RNAs that regulate gene expression post-transcriptionally. By repressing the translation and promoting the degradation of target mRNAs, miRNAs may reduce the cell-to-cell variability in protein expression, induce correlations between target expression levels, and provide a layer through which targets can influence each other's expression as "competing RNAs" (ceRNAs). However, experimental evidence for these behaviors is limited. Combining mathematical modeling with RNA sequencing of individual human embryonic kidney cells in which the expression of two distinct miRNAs was induced over a wide range, we have inferred parameters describing the response of hundreds of miRNA targets to miRNA induction. Individual targets have widely different response dynamics, and only a small proportion of predicted targets exhibit high sensitivity to miRNA induction. Our data reveal for the first time the response parameters of the entire network of endogenous miRNA targets to miRNA induction, demonstrating that miRNAs correlate target expression and at the same time increase the variability in expression of individual targets across cells. The approach is generalizable to other miRNAs and post-transcriptional regulators to improve the understanding of gene expression dynamics in individual cell types.

Translation and Translational Control in Dinoflagellates

Dinoflagellates are unicellular protists that feature a multitude of unusual nuclear features, including large genomes, packaging of DNA without histones, and multiple gene copies organized as tandem gene arrays. Furthermore, all dinoflagellate mRNAs experience trans-splicing with a common 22-nucleotide splice leader (SL) sequence. These features challenge some of the concepts and assumptions about the regulation of gene expression derived from work on model eukaryotes such as yeasts and mammals. Translational control in the dinoflagellates, based on extensive study of circadian bioluminescence and by more recent microarray and transcriptome analyses, is now understood to be a crucial element in regulating gene expression. A picture of the translation machinery of dinoflagellates is emerging from the recent availability of transcriptomes of multiple dinoflagellate species and the first complete genome sequences. The components comprising the translational control toolkit of dinoflagellates are beginning to take shape and are outlined here.

Circulating microRNAs in breast cancer: novel diagnostic and prognostic biomarkers

Effective management of breast cancer depends on early diagnosis and proper monitoring of patients' response to therapy. However, these goals are difficult to achieve because of the lack of sensitive and specific biomarkers for early detection and for disease monitoring. Accumulating evidence in the past several years has highlighted the potential use of peripheral blood circulating nucleic acids such as DNA, mRNA and micro (mi)RNA in breast cancer diagnosis, prognosis and for monitoring response to anticancer therapy. Among these, circulating miRNA is increasingly recognized as a promising biomarker, given the ease with which miRNAs can be isolated and their structural stability under different conditions of sample processing and isolation. In this review, we provide current state-of-the-art of miRNA biogenesis, function and discuss the advantages, limitations, as well as pitfalls of using circulating miRNAs as diagnostic, prognostic or predictive biomarkers in breast cancer management.

MicroRNA-495 Inhibits Gastric Cancer Cell Migration and Invasion Possibly via Targeting High Mobility Group AT-Hook 2 (HMGA2)

Background

Gastric cancer is one of the most common malignancies, and has a high mortality rate. miR-495 acts as a suppressor in some cancers and HMGA2 (high mobility group AT-hook 2) is a facilitator for cell growth and epithelial-mesenchymal transition (EMT), but little is known about their effect in gastric cancer. This study aimed to investigate the role and mechanism of miR-495 in gastric cancer.

Material/Methods

miR-495 levels were quantitatively analyzed in gastric cancer tissue and GES-1, SGC-7901, BGC-823, and HGC-27 cell lines by qRT-PCR. Levels of miR-495 and HMGA2 were altered by cell transfection, after which cell migration and invasion were examined by Transwell and E-cadherin (CDH1); vimentin (VIM), and alpha smooth muscle actin (ACTA2) were detected by qRT-PCR and Western blotting. The interaction between miR-495 and HMGA2 was verified by dual-luciferase reporter assay.

Results

miR-495 was significantly downregulated in cancer tissue and cell lines (p<0.05). Its overexpression inhibited cell migration and invasion, elevated CDH1, and inhibited VIM and ACTA2 levels in BGC-823 and HGC-27 cells. miR-495 directly inhibited HMGA2, which was upregulated in gastric cancer tissue, and promoted cell migration and invasion, inhibited CDH1, and elevated VIM and ACTA2.

Conclusions

miR-495 acts as a tumor suppressor in gastric cancer by inhibiting cell migration and invasion, which may be associated with its direct inhibition on HMGA2. These results suggest a promising therapeutic strategy for gastric cancer treatment.

Predicting effective microRNA target sites in mammalian mRNAs

MicroRNA targets are often recognized through pairing between the miRNA seed region and complementary sites within target mRNAs, but not all of these canonical sites are equally effective, and both computational and in vivo UV-crosslinking approaches suggest that many mRNAs are targeted through non-canonical interactions. Here, we show that recently reported non-canonical sites do not mediate repression despite binding the miRNA, which indicates that the vast majority of functional sites are canonical. Accordingly, we developed an improved quantitative model of canonical targeting, using a compendium of experimental datasets that we pre-processed to minimize confounding biases. This model, which considers site type and another 14 features to predict the most effectively targeted mRNAs, performed significantly better than existing models and was as informative as the best high-throughput in vivo crosslinking approaches. It drives the latest version of TargetScan (v7.0; targetscan.org), thereby providing a valuable resource for placing miRNAs into gene-regulatory networks.

Expression of microRNA‑26b and identification of its target gene EphA2 in pituitary tissues in Yanbian cattle

microRNAs (miRNAs/miRs) are a class of single-stranded non-coding RNA molecules of 19–24 nucleotides (nt) in length. They are widely expressed in animals, plants, bacteria and viruses. Via specific mRNA complementary pairing of target genes, miRNAs are able to regulate the expression of mRNA levels or inhibit protein translation following transcription. miRNA expression has a time- and space specificity, and it is involved in cell proliferation and differentiation, apoptosis, development, tumor metastasis occurrence and other biological processes. miR-26b is an miRNA of 22 nt and is important in the regulation of cellular processes. With the advancement of molecular biology techniques in recent years, there have been extensive investigations into miR-26b. Numerous studies have observed that miR-26b is involved in early embryonic development, cell proliferation regulation, pituitary hormone secretion and other physiological activities. miRNAs are associated with the function of propagation. The present study used reverse transcription quantitative polymerase chain reaction to detect the relative expression levels of miR-26b in the pituitary tissue of Yanbian cattle at different developmental stages. The 2^−ΔΔCt method was used to calculate the relative gene expression levels. The miRNA target gene database TargetScan and RNA22 were used for prediction of the miR-26b target gene and selective recognition was also performed. The results demonstrated that miR-26b is expressed in the pituitary tissues of Yanbian cattle at 6 and 24 months of age. The relative expression levels of miR-26b in the pituitary tissues of 24-month-old Yanbian cattle were 2.41 times that of those in the six-month-old Yanbian cattle, demonstrating significant differences in the relative expression (P<0.01). The relative expression of the candidate target genes, EphA2 and miR-26b, exhibited the opposite expression pattern. The relative expression levels in the pituitary tissues of six-month-old Yanbian cattle were 3.34 times that of those in 24-month-old Yanbian cattle (P<0.01). There are miR-26b binding sites in the 3′-untranslated region (3′-UTR) of EphA2 in bovine, human, murine and other mammalian mRNAs, suggesting that the EphA2 gene may be a target gene of miR-26b. The results of a Luciferase reporter system assay revealed that miR-26b is able to suppress EphA2 expression at the transcription level. Following the site-directed mutagenesis of plasmid EphA2 3′-UTR pmirGLO-MUT- and miR-26b mimic-transfected HeLa cells, the dual-luciferase reporter gene assay revealed that there were three consecutive nucleotide mutations in the 3′-UTR, binding with the predicted seed region. This may have caused the miR-26b inhibition of luciferase activity to decrease from 60% in the wild-type to 26%, suggesting that miR-26b achieved its function via binding with the TACTTGAA sequence of the 3′-UTR in EphA2. In conclusion, the present study successfully assessed the expression pattern of miR-26b in the pituitary tissue of Yanbian cattle, and also confirmed that EphA2 was a target gene of miR-26b in Yanbian cattle in vitro. The present study provided the theoretical basis to further investigate the role of miR-26b in early embryonic development, pituitary hormone secretion and other reproductive functions.

Accurate transcriptome-wide prediction of microRNA targets and small interfering RNA off-targets with MIRZA-G

Small interfering RNA (siRNA)-mediated knock-down is a widely used experimental approach to characterizing gene function. Although siRNAs are designed to guide the cleavage of perfectly complementary mRNA targets, acting similarly to microRNAs (miRNAs), siRNAs down-regulate the expression of hundreds of genes to which they have only partial complementarity. Prediction of these siRNA ‘off-targets’ remains difficult, due to the incomplete understanding of siRNA/miRNA–target interactions. Combining a biophysical model of miRNA–target interaction with structure and sequence features of putative target sites we developed a suite of algorithms, MIRZA-G, for the prediction of miRNA targets and siRNA off-targets on a genome-wide scale. The MIRZA-G variant that uses evolutionary conservation performs better than currently available methods in predicting canonical miRNA target sites and in addition, it predicts non-canonical miRNA target sites with similarly high accuracy. Furthermore, MIRZA-G variants predict siRNA off-target sites with an accuracy unmatched by currently available programs. Thus, MIRZA-G may prove instrumental in the analysis of data resulting from large-scale siRNA screens.

Bioinformatics tools for achieving better gene silencing in plants

RNA interference (RNAi) is one of the most popular and effective molecular technologies for knocking down the expression of an individual gene of interest in living organisms. Yet the technology still faces the major issue of nonspecific gene silencing, which can compromise gene functional characterization and the interpretation of phenotypes associated with individual gene knockdown. Designing an effective and target-specific small interfering RNA (siRNA) for induction of RNAi is therefore the major challenge in RNAi-based gene silencing. A 'good' siRNA molecule must possess three key features: (a) the ability to specifically silence an individual gene of interest, (b) little or no effect on the expressions of unintended siRNA gene targets (off-target genes), and (c) no cell toxicity. Although several siRNA design and analysis algorithms have been developed, only a few of them are specifically focused on gene silencing in plants. Furthermore, current algorithms lack a comprehensive consideration of siRNA specificity, efficacy, and nontoxicity in siRNA design, mainly due to lack of integration of all known rules that govern different steps in the RNAi pathway. In this review, we first describe popular RNAi methods that have been used for gene silencing in plants and their serious limitations regarding gene-silencing potency and specificity. We then present novel, rationale-based strategies in combination with computational and experimental approaches to induce potent, specific, and nontoxic gene silencing in plants.

Plant microRNA-target interaction identification model based on the integration of prediction tools and support vector machine

Background

Confident identification of microRNA-target interactions is significant for studying the function of microRNA (miRNA). Although some computational miRNA target prediction methods have been proposed for plants, results of various methods tend to be inconsistent and usually lead to more false positive. To address these issues, we developed an integrated model for identifying plant miRNA–target interactions.

Results

Three online miRNA target prediction toolkits and machine learning algorithms were integrated to identify and analyze Arabidopsis thaliana miRNA-target interactions. Principle component analysis (PCA) feature extraction and self-training technology were introduced to improve the performance. Results showed that the proposed model outperformed the previously existing methods. The results were validated by using degradome sequencing supported Arabidopsis thaliana miRNA-target interactions. The proposed model constructed on Arabidopsis thaliana was run over Oryza sativa and Vitis vinifera to demonstrate that our model is effective for other plant species.

Conclusions

The integrated model of online predictors and local PCA-SVM classifier gained credible and high quality miRNA-target interactions. The supervised learning algorithm of PCA-SVM classifier was employed in plant miRNA target identification for the first time. Its performance can be substantially improved if more experimentally proved training samples are provided.

Identification and consequences of miRNA-target interactions--beyond repression of gene expression

Comparative genomics analyses and high-throughput experimental studies indicate that a microRNA (miRNA) binds to hundreds of sites across the transcriptome. Although the knockout of components of the miRNA biogenesis pathway has profound phenotypic consequences, most predicted miRNA targets undergo small changes at the mRNA and protein levels when the expression of the miRNA is perturbed. Alternatively, miRNAs can establish thresholds in and increase the coherence of the expression of their target genes, as well as reduce the cell-to-cell variability in target gene expression. Here, we review the recent progress in identifying miRNA targets and the emerging paradigms of how miRNAs shape the dynamics of target gene expression.

Systematic design and functional analysis of artificial microRNAs

Unlike short interfering RNAs (siRNAs), which are commonly designed to repress a single messenger RNA (mRNA) target through perfect base pairing, microRNAs (miRNAs) are endogenous small RNAs that have evolved to concurrently repress multiple mRNA targets through imperfect complementarity. MicroRNA target recognition is primarily determined by pairing of the miRNA seed sequence (nucleotides 2-8) to complementary match sites in each mRNA target. Whereas siRNA technology is well established for single target knockdown, the design of artificial miRNAs for multi-target repression is largely unexplored. We designed and functionally analysed over 200 artificial miRNAs for simultaneous repression of pyruvate carboxylase and glutaminase by selecting all seed matches shared by their 3' untranslated regions. Although we identified multiple miRNAs that repressed endogenous protein expression of both genes, seed-based artificial miRNA design was highly inefficient, as the majority of miRNAs with even perfect seed matches did not repress either target. Moreover, commonly used target prediction programs did not substantially discriminate effective artificial miRNAs from ineffective ones, indicating that current algorithms do not fully capture the features important for artificial miRNA targeting and are not yet sufficient for designing artificial miRNAs. Our analysis suggests that additional factors are strong determinants of the efficacy of miRNA-mediated target repression and remain to be discovered.

MicroRNA target finding by comparative genomics

MicroRNAs (miRNAs) have been implicated in virtually every metazoan biological process, exerting a widespread impact on gene expression. MicroRNA repression is conferred by relatively short "seed match" sequences, although the degree of repression varies widely for individual target sites. The factors controlling whether, and to what extent, a target site is repressed are not fully understood. As an alternative to target prediction based on sequence alone, comparative genomics has emerged as an invaluable tool for identifying miRNA targets that are conserved by natural selection, and hence likely effective and important. Here we present a general method for quantifying conservation of miRNA seed match sites, separating it from background conservation, controlling for various biases, and predicting miRNA targets. This method is useful not only for generating predictions but also as a tool for empirically evaluating the importance of various target prediction criteria.

Human RNAi pathway: crosstalk with organelles and cells

Understanding gene regulation mechanisms has been a serious challenge in biology. As a novel mechanism, small non-coding RNAs are an alternative means of gene regulation in a specific and efficient manner. There are growing reports on regulatory roles of these RNAs including transcriptional gene silencing/activation and post-transcriptional gene silencing events. Also, there are several known small non-coding RNAs which all work through RNA interference pathway. Interestingly, these small RNAs are secreted from cells toward targeted cells presenting new communication approach in cell-cell or cell-organ signal transduction. In fact, understanding cellular and molecular basis of these pathways will strongly improve developing targeted therapies and potent and specific regulatory tools. This study will review some of the most recent findings in this subject and will introduce a super-pathway RNA interference-based small RNA silencing network.

Modeling the binding specificity of the RNA-binding protein GLD-1 suggests a function of coding region-located sites in translational repression

To understand the function of the hundreds of RNA-binding proteins (RBPs) that are encoded in animal genomes it is important to identify their target RNAs. Although it is generally accepted that the binding specificity of an RBP is well described in terms of the nucleotide sequence of its binding sites, other factors such as the structural accessibility of binding sites or their clustering, to enable binding of RBP multimers, are also believed to play a role. Here we focus on GLD-1, a translational regulator of Caenorhabditis elegans, whose binding specificity and targets have been studied with a variety of methods such as CLIP (cross-linking and immunoprecipitation), RIP-Chip (microarray measurement of RNAs associated with an immunoprecipitated protein), profiling of polysome-associated mRNAs and biophysical determination of binding affinities of GLD-1 for short nucleotide sequences. We show that a simple biophysical model explains the binding of GLD-1 to mRNA targets to a large extent, and that taking into account the accessibility of putative target sites significantly improves the prediction of GLD-1 binding, particularly due to a more accurate prediction of binding in transcript coding regions. Relating GLD-1 binding to translational repression and stabilization of its target transcripts we find that binding sites along the entire transcripts contribute to functional responses, and that CDS-located sites contribute most to translational repression. Finally, biophysical measurements of GLD-1 affinity for a small number of oligonucleotides appear to allow an accurate reconstruction of the sequence specificity of the protein. This approach can be applied to uncover the specificity and function of other RBPs.

miRmap web: Comprehensive microRNA target prediction online

MicroRNAs (miRNAs) posttranscriptionally repress the expression of protein-coding genes. Based on the partial complementarity between miRNA and messenger RNA pairs with a mandatory so-called ‘seed’ sequence, many thousands of potential targets can be identified. Our open-source software library, miRmap, ranks these potential targets with a biologically meaningful criterion, the repression strength. MiRmap combines thermodynamic, evolutionary, probabilistic and sequence-based features, which cover features from TargetScan, PITA, PACMIT and miRanda. Our miRmap web application offers a user-friendly and feature-rich resource for browsing precomputed miRNA target predictions for model organisms, as well as for predicting and ranking targets for user-submitted sequences. MiRmap web integrates sorting, filtering and exporting of results from multiple queries, as well as providing programmatic access, and is available at http://mirmap.ezlab.org.

CLIP-based prediction of mammalian microRNA binding sites

Prediction and validation of microRNA (miRNA) targets are essential for understanding functions of miRNAs in gene regulation. Crosslinking immunoprecipitation (CLIP) allows direct identification of a huge number of Argonaute-bound target sequences that contain miRNA binding sites. By analysing data from CLIP studies, we identified a comprehensive list of sequence, thermodynamic and target structure features that are essential for target binding by miRNAs in the 3′ untranslated region (3′ UTR), coding sequence (CDS) region and 5′ untranslated region (5′ UTR) of target messenger RNA (mRNA). The total energy of miRNA:target hybridization, a measure of target structural accessibility, is the only essential feature common for both seed and seedless sites in all three target regions. Furthermore, evolutionary conservation is an important discriminating feature for both seed and seedless sites. These features enabled us to develop novel statistical models for the predictions of both seed sites and broad classes of seedless sites. Through both intra-dataset validation and inter-dataset validation, our approach showed major improvements over established algorithms for predicting seed sites and a class of seedless sites. Furthermore, we observed good performance from cross-species validation, suggesting that our prediction framework can be valuable for broad application to other mammalian species and beyond. Transcriptome-wide binding site predictions enabled by our approach will greatly complement the available CLIP data, which only cover small fractions of transcriptomes and known miRNAs due to non-detectable levels of expression. Software and database tools based on the prediction models have been developed and are available through Sfold web server at http://sfold.wadsworth.org.

Biological basis of miRNA action when their targets are located in human protein coding region

Recent analyses have revealed many functional microRNA (miRNA) targets in mammalian protein coding regions. But, the mechanisms that ensure miRNA function when their target sites are located in protein coding regions of mammalian mRNA transcripts are largely unknown. In this paper, we investigate some potential biological factors, such as target site accessibility and local translation efficiency. We computationally analyze these two factors using experimentally identified miRNA targets in human protein coding region. We find site accessibility is significantly increased in miRNA target region to facilitate miRNA binding. At the mean time, local translation efficiency is also selectively decreased near miRNA target region. GC-poor codons are preferred in the flank region of miRNA target sites to ease the access of miRNA targets. Within-genome analysis shows substantial variations of site accessibility and local translation efficiency among different miRNA targets in the genome. Further analyses suggest target gene’s GC content and conservation level could explain some of the differences in site accessibility. On the other hand, target gene’s functional importance and conservation level can affect local translation efficiency near miRNA target region. We hence propose both site accessibility and local translation efficiency are important in miRNA action when miRNA target sites are located in mammalian protein coding regions.

Analysis of CDS-located miRNA target sites suggests that they can effectively inhibit translation

Most of what is presently known about how miRNAs regulate gene expression comes from studies that characterized the regulatory effect of miRNA binding sites located in the 3′ untranslated regions (UTR) of mRNAs. In recent years, there has been increasing evidence that miRNAs also bind in the coding region (CDS), but the implication of these interactions remains obscure because they have a smaller impact on mRNA stability compared with miRNA-target interactions that involve 3′ UTRs. Here we show that miRNA-complementary sites that are located in both CDS and 3′-UTRs are under selection pressure and share the same sequence and structure properties. Analyzing recently published data of ribosome-protected fragment profiles upon miRNA transfection from the perspective of the location of miRNA-complementary sites, we find that sites located in the CDS are most potent in inhibiting translation, while sites located in the 3′ UTR are more efficient at triggering mRNA degradation. Our study suggests that miRNAs may combine targeting of CDS and 3′ UTR to flexibly tune the time scale and magnitude of their post-transcriptional regulatory effects.

MicroRNAs in human cancer

Mature microRNAs (miRNAs) are single-stranded RNA molecules of 20-23-nucleotide (nt) length that control gene expression in many cellular processes. These molecules typically reduce the translation and stability of mRNAs, including those of genes that mediate processes in tumorigenesis, such as inflammation, cell cycle regulation, stress response, differentiation, apoptosis, and invasion. miRNA targeting is initiated through specific base-pairing interactions between the 5' end ("seed" region) of the miRNA and sites within coding and untranslated regions (UTRs) of mRNAs; target sites in the 3' UTR lead to more effective mRNA destabilization. Since miRNAs frequently target hundreds of mRNAs, miRNA regulatory pathways are complex. To provide a critical overview of miRNA dysregulation in cancer, we first discuss the methods currently available for studying the role of miRNAs in cancer and then review miRNA genomic organization, biogenesis, and mechanism of target recognition, examining how these processes are altered in tumorigenesis. Given the critical role miRNAs play in tumorigenesis processes and their disease specific expression, they hold potential as therapeutic targets and novel biomarkers.

In silico identification and characterization of conserved miRNAs and their target genes in sweet potato (Ipomoea batatas L.) expressed sequence tags (ESTs)

The endogenous small non-coding micro RNAs (miRNAs), which are typically ~21-24 nt nucleotides, play a crucial role in regulating the intrinsic normal growth of cells and development of the plants as well as in maintaining the integrity of genomes. These small non-coding RNAs function as the universal specificity factors in post-transcriptional gene silencing. Discovering miRNAs, identifying their targets, and further inferring miRNA functions is a routine process to understand normal biological processes of miRNAs and their roles in the development of plants. Comparative genomics based approach using expressed sequence tags (EST) and genome survey sequences (GSS) offer a cost-effective platform for identification and characterization of miRNAs and their target genes in plants. Despite the fact that sweet potato (Ipomoea batatas L.) is an important staple food source for poor small farmers throughout the world, the role of miRNA in various developmental processes remains largely unknown. In this paper, we report the computational identification of miRNAs and their target genes in sweet potato from their ESTs. Using comparative genomics-based approach, 8 potential miRNA candidates belonging to miR168, miR2911, and miR156 families were identified from 23 406 ESTs in sweet potato. A total of 42 target genes were predicted and their probable functions were illustrated. Most of the newly identified miRNAs target transcription factors as well as genes involved in plant growth and development, signal transduction, metabolism, defense, and stress response. The identification of miRNAs and their targets is expected to accelerate the pace of miRNA discovery, leading to an improved understanding of the role of miRNA in development and physiology of sweet potato, as well as stress response.

Finding microRNA targets in plants: current status and perspectives

MicroRNAs (miRNAs), a class of ∼20–24 nt long non-coding RNAs, have critical roles in diverse biological processes including development, proliferation, stress response, etc. With the development and availability of experimental technologies and computational approaches, the field of miRNA biology has advanced tremendously over the last decade. By sequence complementarity, miRNAs have been estimated to regulate certain mRNA transcripts. Although it was once thought to be simple and straightforward to find plant miRNA targets, this viewpoint is being challenged by genetic and biochemical studies. In this review, we summarize recent progress in plant miRNA target recognition mechanisms, principles of target prediction, and introduce current experimental and computational tools for plant miRNA target prediction. At the end, we also present our thinking on the outlook for future directions in the development of plant miRNA target finding methods.

MiRmap: comprehensive prediction of microRNA target repression strength

MicroRNAs, or miRNAs, post-transcriptionally repress the expression of protein-coding genes. The human genome encodes over 1000 miRNA genes that collectively target the majority of messenger RNAs (mRNAs). Base pairing of the so-called miRNA ‘seed’ region with mRNAs identifies many thousands of putative targets. Evaluating the strength of the resulting mRNA repression remains challenging, but is essential for a biologically informative ranking of potential miRNA targets. To address these challenges, predictors may use thermodynamic, evolutionary, probabilistic or sequence-based features. We developed an open-source software library, miRmap, which for the first time comprehensively covers all four approaches using 11 predictor features, 3 of which are novel. This allowed us to examine feature correlations and to compare their predictive power in an unbiased way using high-throughput experimental data from immunopurification, transcriptomics, proteomics and polysome fractionation experiments. Overall, target site accessibility appears to be the most predictive feature. Our novel feature based on PhyloP, which evaluates the significance of negative selection, is the best performing predictor in the evolutionary category. We combined all the features into an integrated model that almost doubles the predictive power of TargetScan. miRmap is freely available from http://cegg.unige.ch/mirmap.

Analysis of the accessibility of CLIP bound sites reveals that nucleation of the miRNA:mRNA pairing occurs preferentially at the 3'-end of the seed match

To find out whether the AGO-miRNA complex is more sensitive to the accessibility of a particular region inside the seed match, we analyze in detail the accessibility of a wide set of miRNA binding sites validated by PAR-CLIP and HITS-CLIP experiments. Our analysis reveals that nucleotides at the 3'-end of bound seed matches are significantly more accessible than nucleotides at the 5'-end as well as nucleotides at any positions in the unbound seed matches. We show that the accessibility of a single nucleotide at the 3'-end is more effective than the accessibility of several nucleotides at the 5'-end in discriminating between functional and nonfunctional binding sites. Analysis of mRNA and protein fold changes induced by miRNA overexpression demonstrates that genes with accessible nucleation regions at the 3'-end are down-regulated more strongly than genes whose accessible nucleation regions are located elsewhere within the seed match. We also observed an increase in the precision of the miRNA target prediction algorithm PACMIT when accessibility toward the 3'-end of the seed match was required. The pronounced sensitivity of the AGO-miRNA complex to the accessibility of the 3'-end of the seed match suggests that, in most cases, nucleation occurs in this region. We show that this conclusion is consistent with previous experimental studies.

MicroRNAs, diet, and cancer: new mechanistic insights on the epigenetic actions of phytochemicals

There is growing interest in the epigenetic mechanisms that impact human health and disease, including the role of microRNAs (miRNAs). These small (18-25 nucleotide), evolutionarily conserved, non-coding RNA molecules regulate gene expression in a post-transcriptional manner. Several well-orchestered regulatory mechanisms involving miRNAs have been identified, with the potential to target multiple signaling pathways dysregulated in cancer. Since the initial discovery of miRNAs, there has been progress towards therapeutic applications, and several natural and synthetic chemopreventive agents also have been evaluated as modulators of miRNA expression in different cancer types. This review summarizes the most up-to-date information related to miRNA biogenesis, and critically evaluates proposed miRNA regulatory mechanisms in relation to cancer signaling pathways, as well as other epigenetic modifications (DNA methylation patterns, histone marks) and their involvement in drug resistance. We also discuss the mechanisms by which dietary factors regulate miRNA expression, in the context of chemoprevention versus therapy.

Global or local? Predicting secondary structure and accessibility in mRNAs

Determining the structural properties of mRNA is key to understanding vital post-transcriptional processes. As experimental data on mRNA structure are scarce, accurate structure prediction is required to characterize RNA regulatory mechanisms. Although various structure prediction approaches are available, it is often unclear which to choose and how to set their parameters. Furthermore, no standard measure to compare predictions of local structure exists. We assessed the performance of different methods using two types of data: transcriptome-wide enzymatic probing information and a large, curated set of cis-regulatory elements. To compare the approaches, we introduced structure accuracy, a measure that is applicable to both global and local methods. Our results showed that local folding was more accurate than the classic global approach. We investigated how the locality parameters, maximum base pair span and window size, influenced the prediction performance. A span of 150 provided a reasonable balance between maximizing the number of accurately predicted base pairs, while minimizing effects of incorrect long-range predictions. We characterized the error at artificial sequence ends, which we reduced by setting the window size sufficiently greater than the maximum span. Our method, LocalFold, diminished all border effects and produced the most robust performance.

Optimal use of conservation and accessibility filters in microRNA target prediction

It is generally accepted that filtering microRNA (miRNA) target predictions by conservation or by accessibility can reduce the false discovery rate. However, these two strategies are usually not exploited in a combined and flexible manner. Here, we introduce PACCMIT, a flexible method that filters miRNA binding sites by their conservation, accessibility, or both. The improvement in performance obtained with each of these three filters is demonstrated on the prediction of targets for both i) highly and ii) weakly conserved miRNAs, i.e., in two scenarios in which the miRNA-target interactions are subjected to different evolutionary pressures. We show that in the first scenario conservation is a better filter than accessibility (as both sensitivity and precision are higher among the top predictions) and that the combined filter improves performance of PACCMIT even further. In the second scenario, on the other hand, the accessibility filter performs better than both the conservation and combined filters, suggesting that the site conservation is not equally effective in rejecting false positive predictions for all miRNAs. Regarding the quality of the ranking criterion proposed by Robins and Press and used in PACCMIT, it is shown that top ranking interactions correspond to more downregulated proteins than do the lower ranking interactions. Comparison with several other target prediction algorithms shows that the ranking of predictions provided by PACCMIT is at least as good as the ranking generated by other conservation-based methods and considerably better than the energy-based ranking used in other accessibility-based methods.

Target gene expression levels and competition between transfected and endogenous microRNAs are strong confounding factors in microRNA high-throughput experiments

Background

MicroRNA (miRNA) target genes tend to have relatively long and conserved 3' untranslated regions (UTRs), but to what degree these characteristics contribute to miRNA targeting is poorly understood. Different high-throughput experiments have, for example, shown that miRNAs preferentially regulate genes with both short and long 3' UTRs and that target site conservation is both important and irrelevant for miRNA targeting.

Results

We have analyzed several gene context-dependent features, including 3' UTR length, 3' UTR conservation, and messenger RNA (mRNA) expression levels, reported to have conflicting influence on miRNA regulation. By taking into account confounding factors such as technology-dependent experimental bias and competition between transfected and endogenous miRNAs, we show that two factors - target gene expression and competition - could explain most of the previously reported experimental differences. Moreover, we find that these and other target site-independent features explain about the same amount of variation in target gene expression as the target site-dependent features included in the TargetScan model.

Conclusions

Our results show that it is important to consider confounding factors when interpreting miRNA high throughput experiments and urge special caution when using microarray data to compare average regulatory effects between groups of genes that have different average gene expression levels.

Advances in microRNA experimental approaches to study physiological regulation of gene products implicated in CNS disorders

The central nervous system (CNS) is a remarkably complex organ system, requiring an equally complex network of molecular pathways controlling the multitude of diverse, cellular activities. Gene expression is a critical node at which regulatory control of molecular networks is implemented. As such, elucidating the various mechanisms employed in the physiological regulation of gene expression in the CNS is important both for establishing a reference for comparison to the diseased state and for expanding the set of validated drug targets available for disease intervention. MicroRNAs (miRNAs) are an abundant class of small RNA that mediates potent inhibitory effects on global gene expression. Recent advances have been made in methods employed to study the contribution of these miRNAs to gene expression. Here we review these latest advances and present a methodological workflow from the perspective of an investigator studying the physiological regulation of a gene of interest. We discuss methods for identifying putative miRNA target sites in a transcript of interest, strategies for validating predicted target sites, assays for detecting miRNA expression, and approaches for disrupting endogenous miRNA function. We consider both advantages and limitations, highlighting certain caveats that inform the suitability of a given method for a specific application. Through careful implementation of the appropriate methodologies discussed herein, we are optimistic that important discoveries related to miRNA participation in CNS physiology and dysfunction are on the horizon.

ANIMAL MICRORNA TARGET PREDICTION USING DIVERSE SEQUENCE-SPECIFIC DETERMINANTS

Many recent studies have shown that access of animal microRNAs (miRNAs) to their complementary sites in target mRNAs is determined by several sequence-specific determinants beyond the seed regions in the 5′ end of miRNAs. These factors have been related to the repressive power of miRNAs and used in some programs to predict the efficacy of miRNA complementary sites. However, these factors have not been systematically examined regarding their capacities for improving miRNA target prediction. We develop a new miRNA target prediction algorithm, called Hitsensor, by incorporating many sequence-specific features that determine complementarities between miRNAs and their targets, in addition to the canonical seed regions in the 5′ ends of miRNAs. We evaluate the performance of our algorithm on 720 known animal miRNA:target pairs in four species, Homo sapiens, Mus musculus, Drosophila melanogaster and Caenorhabditis elegans. Our experimental results show that Hitsensor outperforms five popular existing algorithms, indicating that our unique scheme for quantifying the determinants of complementary sites is effective in improving the performance of a miRNA target prediction algorithm. We also examine the effectiveness of miRNA-mediated repression for the predicted targets by using a published quantitative protein expression dataset of miR-223 knockout in mouse neutrophils. Hitsensor identifies more targets than the existing algorithms, and the predicted targets of Hitsensor show comparable protein level changes to those of the existing algorithms.

Genome-wide approaches in the study of microRNA biology

MicroRNAs (miRNAs), a class of ∼21-23 nucleotide long non-coding RNAs (ncRNAs), have critical roles in diverse biological processes that encompass development, proliferation, apoptosis, stress response, and fat metabolism. miRNAs recognize their target mRNA transcripts by partial sequence complementarity and collectively have been estimated to regulate the majority of human genes. Consequently, misregulation of miRNAs or disruption of their target sites in genes has been implicated in a variety of human diseases ranging from cancer metastasis to neurological disorders. With the development and availability of genomic technologies and computational approaches, the field of miRNA biology has advanced tremendously over the last decade. Here we review the genome-wide approaches that have allowed for the discovery of new miRNAs, the characterization of their targets, and a systems-level view of their impact.