Energizing miRNA research: a review of the role of miRNAs in lipid metabolism, with a prediction that miR-103/107 regulates human metabolic pathways

MicroRNAs (miRNAs) are powerful regulators of gene expression. Although first discovered in worm larvae, miRNAs play fundamental biological roles-including in humans-well beyond development. MiRNAs participate in the regulation of metabolism (including lipid metabolism) for all animal species studied. A review of the fascinating and fast-growing literature on miRNA regulation of metabolism can be parsed into three main categories: (1) adipocyte biochemistry and cell fate determination; (2) regulation of metabolic biochemistry in invertebrates; and (3) regulation of metabolic biochemistry in mammals. Most research into the 'function' of a given miRNA in metabolic pathways has concentrated on a given miRNA acting upon a particular 'target' mRNA. Whereas in some biological contexts the effects of a given miRNA:mRNA pair may predominate, this might not be the case generally. In order to provide an example of how a single miRNA could regulate multiple 'target' mRNAs or even entire human metabolic pathways, we include a discussion of metabolic pathways that are predicted to be regulated by the miRNA paralogs, miR-103 and miR-107. These miRNAs, which exist in vertebrate genomes within introns of the pantothenate kinase (PANK) genes, are predicted by bioinformatics to affect multiple mRNA targets in pathways that involve cellular Acetyl-CoA and lipid levels. Significantly, PANK enzymes also affect these pathways, so the miRNA and 'host' gene may act synergistically. These predictions require experimental verification. In conclusion, a review of the literature on miRNA regulation of metabolism leads us believe that the future will provide researchers with many additional energizing revelations.     

Integrative analysis of transcriptome and miRNome unveils the key regulatory connections involved in different stages of hepatocellular carcinoma

Dysregulated molecular processes are the major factors that drive and feed the signaling processes involved in carcinogenesis. In recent years, regulation of mRNAs by microRNAs (miRNAs) has been found to play a vital role in many cancers including hepatocellular carcinoma (HCC). However, genomewide studies defining molecular regulatory circuits at both mRNA and miRNA levels are just emerging. To uncover the molecular and functional processes involved in liver tumorigenesis at mRNA and miRNA level, a co‐expression‐based network of miRNAs was constructed from multiple miRNA profiles. The applicability of the network approach to microRNA expression profiles was assessed. Although the clustering consistency of miRNAs across the profiles was found moderate, miRNA networking has been found informative. Furthermore, microRNA network modules were integrated with the functionally defined mRNA modules derived from an mRNA co‐expression network of an earlier study. Three highly clustered regulatory circuits of mRNA–miRNA modules have been identified as involved in hepatocyte, inflammatory–stress and proliferative process activated subcategories of HCC. A subset of the proliferative miRNA module was found clustered in the 14q32.31 chromosomal region. The current integrative network analysis of mRNA–miRNA modules shows the intricate miRNA–mRNA functional circuits and signaling interactions involved in liver tumorigenesis.     

Construction of integrated microRNA and mRNA immune cell signatures to predict survival of patients with breast and ovarian cancer

In the tumor microenvironment, immune cells have emerged as key regulators of cancer progression. While much work has focused on characterizing tumor‐related immune cells through gene expression profiling, microRNAs (miRNAs) have also been reported to regulate immune cells in the tumor microenvironment. Using regression‐based computational methods, we have constructed for the first time, immune cell signatures based on miRNA expression from The Cancer Genome Atlas breast and ovarian cancer datasets. Combined with existing mRNA immune cell signatures, the integrated mRNA‐miRNA leukocyte signatures are better able to delineate prognostic immune cell subsets within both cancers compared to the mRNA or miRNA signatures alone. Moreover, using the miRNA signatures, the anti‐inflammatory M2 macrophages emerged as the most significantly prognostic cell type in the breast cancer data (HR [hazard ratio]: 12.9; CI [confidence interval]: 3.09‐52.9; P = 4.22E?4), whereas the pro‐inflammatory M1 macrophages emerged as the most prognostic immune cell type in the ovarian cancer data (HR: 0.2; CI: 0.04‐0.56, P = 5.02E?3). These results suggest that our integrated miRNA and mRNA leukocyte signatures could be used to better delineate prognostic leukocyte subsets within cancers, whereas continued investigation may further support the regulatory relationships predicted between the miRNAs and immune cells found within our signature matrices.     

Regulation of the Drosophila lin-41 homologue dappled by let-7 reveals conservation of a regulatory mechanism within the LIN-41 subclade.

Drosophila Dappled (DPLD) is a member of the RBCC/TRIM superfamily, a protein family involved in numerous diverse processes such as developmental timing and asymmetric cell divisions. DPLD belongs to the LIN-41 subclade, several members of which are micro RNA (miRNA) regulated. We re-examined the LIN-41 subclade members and their relation to other RBCC/TRIMs and dpld paralogs, and identified a new Drosophila muscle specific RBCC/TRIM: Another B-Box Affiliate, ABBA. In silico predictions of candidate miRNA regulators of dpld identified let-7 as the strongest candidate. Overexpression of dpld led to abnormal eye development, indicating that strict regulation of dpld mRNA levels is crucial for normal eye development. This phenotype was sensitive to let-7 dosage, suggesting let-7 regulation of dpld in the eye disc. A cell-based assay verified let-7 miRNA down-regulation of dpld expression by means of its 3'-untranslated region. Thus, dpld seems also to be miRNA regulated, suggesting that miRNAs represent an ancient mechanism of LIN-41 regulation.     

Localization‐ and mutation‐dependent microRNA (miRNA) expression signatures in gastrointestinal stromal tumours (GISTs), with a cluster of co‐expressed miRNAs located at 14q32.31

The molecular biology and clinical behaviour of gastrointestinal stromal tumours (GISTs) are associated with their anatomical localization (stomach or intestine), and also with the mutation status of the receptor tyrosine kinases KIT and PDGFRA. Twelve GISTs were evaluated for differential miRNA expression signatures by use of microarrays representing 734 human miRNAs. Thirty‐two miRNAs were found to be differentially expressed according to localization and mutation status. Differential expression was further analysed and confirmed for four miRNAs (miR‐132, miR‐221, miR‐222, and miR‐504) by qRT‐PCR in 49 additional GISTs. Differentially expressed miRNAs were functionally mapped to KIT/PDGFRA signalling and G1/S‐phase transition of the cell cycle, revealing 22 predicted miRNA/mRNA interactions for ten gene targets from KIT/PDGFRA signalling, and 12 interactions for 12 gene targets of G1/S‐phase transition. Moreover, the expression of 44 miRNAs clustered in a genetically imprinted region at 14q32.31 was found to be strongly correlated in the microarray analysis. This was confirmed for two selected miRNAs (miR‐134 and miR‐370) from the 14q32.31 cluster by qRT‐PCR in 49 additional GISTs, and the expression of these two miRNAs was significantly lower in GISTs with 14q loss, and also in GISTs with tumour progress. miRNA profiling may prove to be a key determinant of the biology and clinical features of GISTs Copyright © 2009 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.     

A large imprinted microRNA gene cluster at the mouse Dlk1-Gtl2 domain

microRNAs (or miRNAs) are small noncoding RNAs (21 to 25 nucleotides) that are processed from longer hairpin RNA precursors and are believed to be involved in a wide range of developmental and cellular processes, by either repressing translation or triggering mRNA degradation (RNA interference). By using a computer-assisted approach, we have identified 46 potential miRNA genes located in the human imprinted 14q32 domain, 40 of which are organized as a large cluster. Although some of these clustered miRNA genes appear to be encoded by a single-copy DNA sequence, most of them are arranged in tandem arrays of closely related sequences. In the mouse, this miRNA gene cluster is conserved at the homologous distal 12 region. In vivo all the miRNAs that we have detected are expressed in the developing embryo (both in the head and in the trunk) and in the placenta, whereas in the adult their expression is mainly restricted to the brain. We also show that the miRNA genes are only expressed from the maternally inherited chromosome and that their imprinted expression is regulated by an intergenic germline-derived differentially methylated region (IG-DMR) located approximately 200 kb upstream from the miRNA cluster. The functions of these miRNAs, which seem only conserved in mammals, are discussed both in terms of epigenetic control and gene regulation during development.     

miRNAs 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 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 mostly achieved 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.     

Predicting miRNA-mediated gene silencing mode based on miRNA-target duplex features

There are two main mechanisms of miRNA-mediated gene silencing: either mRNA degradation or translational repression. However, the precise mechanism of target mRNAs regulated by miRNA remains unclear. As a complementary approach to experiment, a computational method was proposed to recognize the mechanism of miRNA-mediated gene silencing in human. We have analyzed extensive features correlated with miRNA-mediated silencing mechanism of mRNA. It is found that, the duplex structure, the number of binding sites and the structural accessibility of target site region are effective factors in determining whether a target mRNA is cleaved or only translationally inhibited. An SVM-based classifier was constructed to predict the regulation mode of miRNA based on these informative features. The results indicated that the approach proposed is effective in distinguishing whether a target mRNA is cleaved or translationally inhibited in human. Furthermore, the web server microDoR (http://reprod.njmu.edu.cn/microdor) has been developed and is freely available for users.     

Identifying direct miRNA–mRNA causal regulatory relationships in heterogeneous data

Discovering the regulatory relationships between microRNAs (miRNAs) and mRNAs is an important problem that interests many biologists and medical researchers. A number of computational methods have been proposed to infer miRNA–mRNA regulatory relationships, and are mostly based on the statistical associations between miRNAs and mRNAs discovered in observational data. The miRNA–mRNA regulatory relationships identified by these methods can be both direct and indirect regulations. However, differentiating direct regulatory relationships from indirect ones is important for biologists in experimental designs. In this paper, we present a causal discovery based framework (called DirectTarget) to infer direct miRNA–mRNA causal regulatory relationships in heterogeneous data, including expression profiles of miRNAs and mRNAs, and miRNA target information. DirectTarget is applied to the Epithelial to Mesenchymal Transition (EMT) datasets. The validation by experimentally confirmed target databases suggests that the proposed method can effectively identify direct miRNA–mRNA regulatory relationships. To explore the upstream regulators of miRNA regulation, we further identify the causal feedforward patterns (CFFPs) of TF–miRNA–mRNA to provide insights into the miRNA regulation in EMT. DirectTarget has the potential to be applied to other datasets to elucidate the direct miRNA–mRNA causal regulatory relationships and to explore the regulatory patterns.     

In silico mapping of polymorphic miRNA–mRNA interactions in autoimmune thyroid diseases

Abstract

MicroRNAs (miRNAs) are important regulators of gene expression and translation. The genetic variants altering miRNA targets have been associated with many diseases. Here we systematically mapped the human genetic polymorphisms that may affect miRNA–mRNA interactions in the autoimmune thyroid disease (AITD) pathway. We also mapped the polymorphic miRNA target sites in the genes that have been linked to AITDs or other thyroid-related diseases/phenotypes in genome-wide association studies (GWAS). These genetic polymorphisms may potentially contribute to the pathogenesis of AITDs and other thyroid diseases. The polymorphic miRNA–mRNA interactions we mapped in the AITD pathway and the GWAS-informed thyroid disease loci may provide insights into the possible miRNA-mediated molecular mechanisms through which genetic variants assert their influences on thyroid diseases and phenotypes.     

Cupid: simultaneous reconstruction of microRNA-target and ceRNA networks

We introduce a method for simultaneous prediction of microRNA–target interactions and their mediated competitive endogenous RNA (ceRNA) interactions. Using high-throughput validation assays in breast cancer cell lines, we show that our integrative approach significantly improves on microRNA–target prediction accuracy as assessed by both mRNA and protein level measurements. Our biochemical assays support nearly 500 microRNA–target interactions with evidence for regulation in breast cancer tumors. Moreover, these assays constitute the most extensive validation platform for computationally inferred networks of microRNA–target interactions in breast cancer tumors, providing a useful benchmark to ascertain future improvements.MicroRNAs (miRNAs) regulate RNA stability and mRNA translation (Filipowicz et al. 2008) and their dysregulation has been implicated in a wide range of human diseases including cancer (Garzon et al. 2009). Consequently, establishing accurate and comprehensive repertoires of miRNA–target interactions is a necessary step toward elucidating their mechanistic role in pathophysiology. Dissecting miRNA regulation, however, has proven challenging because candidate miRNA binding sites are ubiquitous and their regulatory effects are context specific (Liu et al. 2005; Lu et al. 2005; Mukherji et al. 2011). As a result, and despite their relatively low accuracy, computational prediction methods that incorporate context-specific data are preferred for screening for miRNA–target interactions in tumor contexts (Carroll et al. 2013; Erhard et al. 2014).To address these challenges, we introduce Cupid, an integrative framework for the context-specific inference of miRNA targets. Cupid integrates sequence-based evidence and functional clues derived from RNA and miRNA expression analysis, predicting candidate miRNA binding sites and associated target genes using ensemble machine learning classifiers that are trained on validated interactions. Candidate interactions emerging from this step are then refined based on independent, context-specific clues, including their predicted ability to mediate competitive endogenous RNA (ceRNA) interactions, where mRNA compete for shared miRNA regulators (Fig. 1A; Tay et al. 2014). Thus, Cupid simultaneously infers both interaction types (ceRNA and miRNA–target interactions). In addition, we considered evidence for combinatorial regulation by multiple miRNA species (Fig. 1B; Boissonneault et al. 2009; Xu et al. 2011) and for indirect miRNA regulation through effector proteins (Fig. 1C). Taken individually, these clues are predictive of bona fide miRNA–target interactions and can significantly improve the tradeoff between precision and recall.Open in a separate windowFigure 1.Methodology. (A) Cupid first reevaluates sites predicted by TargetScan, miRanda, and PITA, selecting and rescoring each candidate site (Step I). Sites are used to select and score miRNA-target interactions (Step II), which are then examined for evidence for mediating ceRNA interactions (Step III). In addition, to support interaction prediction we considered (B) evidence for combinatorial regulation between miRNAs and (C) evidence for indirect regulation by miRNAs through effectors. (D) The majority of site predictions by TargetScan, miRanda, and PITA are exclusive to a single algorithm; for example, <10% of sites predicted by miRanda are also predicted by another method. (E) Cupid predicted 529K miRNA-target interactions in Step II, excluding 60% of Step I candidate interactions. As a result, it makes considerably fewer predictions than TargetScan, miRanda, and PITA. (F) Cupid Step III predictions include less than a quarter of Step I candidate interactions.We show that Cupid predictions outperform other leading algorithms, based on multiple experimental assays, including PAR-CLIP data, miRNA perturbation followed by mRNA and protein expression profiles, and 3′ luciferase activity assays. Critically, while Cupid predicts fewer interactions than other methods (Fig. 1D–F), its predictions are much more likely to be consistent with experimental evidence. This is critical since high false-positive prediction rates are a key limitation of current miRNA–target prediction methods.     

Eukaryote-specific domains in translation initiation factors: implications for translation regulation and evolution of the translation system

Computational analysis of sequences of proteins involved in translation initiation in eukaryotes reveals a number of specific domains that are not represented in bacteria or archaea. Most of these eukaryote-specific domains are known or predicted to possess an alpha-helical structure, which suggests that such domains are easier to invent in the course of evolution than are domains of other structural classes. A previously undetected, conserved region predicted to form an alpha-helical domain is delineated in the initiation factor eIF4G, in Nonsense-mediated mRNA decay 2 protein (NMD2/UPF2), in the nuclear cap-binding CBP80, and in other, poorly characterized proteins, which is named the NIC (NMD2, eIF4G, CBP80) domain. Biochemical and mutagenesis data on NIC-containing proteins indicate that this predicted domain is one of the central adapters in the regulation of mRNA processing, translation, and degradation. It is demonstrated that, in the course of eukaryotic evolution, initiation factor eIF4G, of which NIC is the core, conserved portion, has accreted several additional, distinct predicted domains such as MI (MA-3 and eIF4G ) and W2, which probably was accompanied by acquisition of new regulatory interactions.