Inference of miRNA targets using evolutionary conservation and pathway analysis. BMC Bioinformatics. 2007;8:69. [PMID: 17331257 PMCID: PMC1838429 DOI: 10.1186/1471-2105-8-69]

Hsa-miRNA-765 as a key mediator for inhibiting growth, migration and invasion in fulvestrant-treated prostate cancer

Fulvestrant (ICI-182,780) has recently been shown to effectively suppress prostate cancer cell growth in vitro and in vivo. But it is unclear whether microRNAs play a role in regulating oncogene expression in fulvestrant-treated prostate cancer. Here, this study reports hsa-miR-765 as the first fulvestrant-driven, ERβ-regulated miRNA exhibiting significant tumor suppressor activities like fulvestrant, against prostate cancer cell growth via blockage of cell-cycle progression at the G2/M transition, and cell migration and invasion possibly via reduction of filopodia/intense stress-fiber formation. Fulvestrant was shown to upregulate hsa-miR-765 expression through recruitment of ERβ to the 5′-regulatory-region of hsa-miR-765. HMGA1, an oncogenic protein in prostate cancer, was identified as a downstream target of hsa-miR-765 and fulvestrant in cell-based experiments and a clinical study. Both the antiestrogen and the hsa-miR-765 mimic suppressed HMGA1 protein expression. In a neo-adjuvant study, levels of hsa-miR-765 were increased and HMGA1 expression was almost completely lost in prostate cancer specimens from patients treated with a single dose (250 mg) of fulvestrant 28 days before prostatectomy. These findings reveal a novel fulvestrant signaling cascade involving ERβ-mediated transcriptional upregulation of hsa-miR-765 that suppresses HMGA1 protein expression as part of the mechanism underlying the tumor suppressor action of fulvestrant in prostate cancer.

Cross-platform toxicogenomics for the prediction of non-genotoxic hepatocarcinogenesis in rat

In the area of omics profiling in toxicology, i.e. toxicogenomics, characteristic molecular profiles have previously been incorporated into prediction models for early assessment of a carcinogenic potential and mechanism-based classification of compounds. Traditionally, the biomarker signatures used for model construction were derived from individual high-throughput techniques, such as microarrays designed for monitoring global mRNA expression. In this study, we built predictive models by integrating omics data across complementary microarray platforms and introduced new concepts for modeling of pathway alterations and molecular interactions between multiple biological layers. We trained and evaluated diverse machine learning-based models, differing in the incorporated features and learning algorithms on a cross-omics dataset encompassing mRNA, miRNA, and protein expression profiles obtained from rat liver samples treated with a heterogeneous set of substances. Most of these compounds could be unambiguously classified as genotoxic carcinogens, non-genotoxic carcinogens, or non-hepatocarcinogens based on evidence from published studies. Since mixed characteristics were reported for the compounds Cyproterone acetate, Thioacetamide, and Wy-14643, we reclassified these compounds as either genotoxic or non-genotoxic carcinogens based on their molecular profiles. Evaluating our toxicogenomics models in a repeated external cross-validation procedure, we demonstrated that the prediction accuracy of our models could be increased by joining the biomarker signatures across multiple biological layers and by adding complex features derived from cross-platform integration of the omics data. Furthermore, we found that adding these features resulted in a better separation of the compound classes and a more confident reclassification of the three undefined compounds as non-genotoxic carcinogens.

Fifteen years SIB Swiss Institute of Bioinformatics: life science databases, tools and support

The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) was created in 1998 as an institution to foster excellence in bioinformatics. It is renowned worldwide for its databases and software tools, such as UniProtKB/Swiss-Prot, PROSITE, SWISS-MODEL, STRING, etc, that are all accessible on ExPASy.org, SIB's Bioinformatics Resource Portal. This article provides an overview of the scientific and training resources SIB has consistently been offering to the life science community for more than 15 years.

MicroRNA binding sites in the coding region of mRNAs: extending the repertoire of post-transcriptional gene regulation

It is well established that microRNAs (miRNAs) induce mRNA degradation by binding to 3' untranslated regions (UTRs). The functionality of sites in the coding domain sequence (CDS), on the other hand, remains under discussion. Such sites have limited impact on target mRNA abundance and recent work suggests that miRNAs bind in the CDS to inhibit translation. What then could be the regulatory benefits of translation inhibition through CDS targeting compared to mRNA degradation following 3' UTR binding? We propose that these domain-dependent effects serve to diversify the functional repertoire of post-transcriptional gene expression control. Possible regulatory benefits may include tuning the time-scale and magnitude of post-transcriptional regulation, regulating protein abundance depending on or independently of the cellular state, and regulation of the protein abundance of alternative splice variants. Finally, we review emerging evidence that these ideas may generalize to RNA-binding proteins beyond miRNAs and Argonaute proteins.

Macros in microRNA target identification: a comparative analysis of in silico, in vitro, and in vivo approaches to microRNA target identification

MicroRNAs (miRNAs) are short RNA molecules that modulate post-transcriptional gene expression by partial or incomplete base-pairing to the complementary sequences on their target genes. Sequence-based miRNA target gene recognition enables the utilization of computational methods, which are highly informative in identifying a subset of putative miRNA targets from the genome. Subsequently, single miRNA-target gene binding is evaluated experimentally by in vitro assays to validate and quantify the transcriptional or post-transcriptional effects of miRNA-target gene interaction. Although ex vivo approaches are instructive in providing a basis for further analyses, in vivo genetic studies are critical to determine the occurrence and biological relevance of miRNA targets under physiological conditions. In the present review, we summarize the important features of each of the experimental approaches, their technical and biological limitations, and future challenges in light of the complexity of miRNA target gene recognition.

Global coevolution of human microRNAs and their target genes

MicroRNAs (miRNAs) have presumably contributed to the emergence of the novel expression patterns, higher brain functions, and skills underlying human evolution. However, it is incompletely understood how new miRNAs have evolved in the human lineage because their initial emergence predictably entailed deleterious consequences due to their powerful multitarget effects. Here, we report genetic variation and conservation parameters for miRNAs and their predicted targets in the genomes of 1,092 humans and 58 additional organisms. We show that miRNAs were evolutionarily more conserved than their predicted binding sites, which were inversely subject to the accumulation of single-nucleotide variations over short evolutionary timescales. Moreover, the predictably "younger" human-specific miRNAs presented lower genetic variation than other miRNAs; their targets displayed higher genetic variation compared with other miRNA targets in diverse human populations; and neuronal miRNAs showed yet lower levels of genetic variation and were found to target more protein-coding genes than nonneuronal miRNAs. Furthermore, enrichment analysis indicated that targets of human-specific miRNAs primarily perform neuronal functions. Specifically, the genomic regions harboring the vertebrate-conserved neuronal miRNA-132 presented considerably higher conservation scores than those of its target genes throughout evolution, whereas both the recently evolved human miRNA-941 and its acquired targets showed relatively low conservation. Our findings demonstrate inversely correlated genetic variation around miRNAs and their targets, consistent with theories of coevolution of these elements and the predicted role attributed to miRNAs in recent human evolution.

Ha-ras and β-catenin oncoproteins orchestrate metabolic programs in mouse liver tumors

The process of hepatocarcinogenesis in the diethylnitrosamine (DEN) initiation/phenobarbital (PB) promotion mouse model involves the selective clonal outgrowth of cells harboring oncogene mutations in Ctnnb1, while spontaneous or DEN-only-induced tumors are often Ha-ras- or B-raf-mutated. The molecular mechanisms and pathways underlying these different tumor sub-types are not well characterized. Their identification may help identify markers for xenobiotic promoted versus spontaneously occurring liver tumors. Here, we have characterized mouse liver tumors harboring either Ctnnb1 or Ha-ras mutations via integrated molecular profiling at the transcriptional, translational and post-translational levels. In addition, metabolites of the intermediary metabolism were quantified by high resolution (1)H magic angle nuclear magnetic resonance. We have identified tumor genotype-specific differences in mRNA and miRNA expression, protein levels, post-translational modifications, and metabolite levels that facilitate the molecular and biochemical stratification of tumor phenotypes. Bioinformatic integration of these data at the pathway level led to novel insights into tumor genotype-specific aberrant cell signaling and in particular to a better understanding of alterations in pathways of the cell intermediary metabolism, which are driven by the constitutive activation of the β-Catenin and Ha-ras oncoproteins in tumors of the two genotypes.

Seq and CLIP through the miRNA world

High-throughput sequencing of RNAs crosslinked to Argonaute proteins reveals not only a multitude of atypical miRNA binding sites but also of miRNA targets with atypical functions, and can be used to infer quantitative models of miRNA-target interaction strength.

Understanding principles of miRNA target recognition and function through integrated biological and bioinformatics approaches

In recent times, microRNA (miRNA) have emerged as primary regulators of fundamental biological processes including cellular differentiation, proliferation, apoptosis, as well as synaptic plasticity. However, miRNAs bind their targets with only partial complementarity, making it very challenging to determine exactly how a miRNA is functioning in specific biological environments. This review discusses key principles of miRNA target recognition and function which have emerged through the progressive advancement of biological and bioinformatics approaches. Ultimately, the integration of gene expression and biochemical methods with sequence- and systems-based bioinformatics approaches will reveal profound insights regarding the importance of target contextual features in determining miRNA target recognition and regulatory outcome, as well as the importance of RNA interaction networks in enabling miRNA to regulate different target genes and functions in specific biological contexts. There is therefore a demand for the elegant design of future experiments such that principles of context-specific miRNA target recognition and regulatory outcome can be accurately modeled in normal developmental and disease states.

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.

Computational methods for microRNA target prediction

MicroRNAs (miRNAs) are important players in gene regulation. The final and maybe the most important step in their regulatory pathway is the targeting. Targeting is the binding of the miRNA to the mature RNA via the RNA-induced silencing complex. Expression patterns of miRNAs are highly specific in respect to external stimuli, developmental stage, or tissue. This is used to diagnose diseases such as cancer in which the expression levels of miRNAs are known to change considerably. Newly identified miRNAs are increasing in number with every new release of miRBase which is the main online database providing miRNA sequences and annotation. Many of these newly identified miRNAs do not yet have identified targets. This is especially the case in animals where the miRNA does not bind to its target as perfectly as it does in plants. Valid targets need to be identified for miRNAs in order to properly understand their role in cellular pathways. Experimental methods for target validations are difficult, expensive, and time consuming. Having considered all these facts it is of crucial importance to have accurate computational miRNA target predictions. There are many proposed methods and algorithms available for predicting targets for miRNAs, but only a few have been developed to become available as independent tools and software. There are also databases which collect and store information regarding predicted miRNA targets. Current approaches to miRNA target prediction produce a huge amount of false positive and an unknown amount of false negative results, and thus the need for better approaches is evermore evident. This chapter aims to give some detail about the current tools and approaches used for miRNA target prediction, provides some grounds for their comparison, and outlines a possible future.

Timescales and bottlenecks in miRNA-dependent gene regulation

Application of a kinetic model of miRNA-mediated gene regulation to experimental data sets shows that the timescale of regulation is slower than previously assumed, due to bottlenecks imposed by miRNA turnover in the RNA-induced silencing complex and by slow protein decay.

A mathematical model links the dynamics of miRNA expression and loading into the Argonaute protein to the dynamics of miRNA targets.

Loading of miRNAs into Argonaute and the slow decay of proteins impose two bottlenecks on the speed of miRNA-mediated regulation.

Accelerated miRNA turnover is necessary for regulating target expression on the timescale of a day.

MiRNAs are post-transcriptional regulators that contribute to the establishment and maintenance of gene expression patterns. Although their biogenesis and decay appear to be under complex control, the implications of miRNA expression dynamics for the processes that they regulate are not well understood. We derived a mathematical model of miRNA-mediated gene regulation, inferred its parameters from experimental data sets, and found that the model describes well time-dependent changes in mRNA, protein and ribosome density levels measured upon miRNA transfection and induction. The inferred parameters indicate that the timescale of miRNA-dependent regulation is slower than initially thought. Delays in miRNA loading into Argonaute proteins and the slow decay of proteins relative to mRNAs can explain the typically small changes in protein levels observed upon miRNA transfection. For miRNAs to regulate protein expression on the timescale of a day, as miRNAs involved in cell-cycle regulation do, accelerated miRNA turnover is necessary.

A useful method of identifying of miRNAs which can down-regulate Zeb-2

Background

Although identification of the target mRNAs of micro RNAs (miRNAs) is essential to understanding their function, the low complementarity between miRNAs and their target mRNAs has complicated this process. In this study, we sought to identify miRNAs which reduce the expression of the transcription factor Zeb-2, a transcriptional repressor of E-cadherin which is known to be down regulated by members of the miR-200 family (miR-200a,b,c miR-429, and miR-141).

Findings

We first used a computational target predicting system to identify 82 candidate miRNAs which bound the 3′UTR region of the Zeb-2 mRNA. Of these 82 miRNAs, precursors for 51 were available in our miRNA precursor library. Pre-miR™ Precursor Molecules for these 51 miRNAs were co-transfected into NIH3T3 cells with a luciferase reporter vector containing the 3′UTR region of the Zeb-2 mRNA. Seven miRNAs (miR-141, mi-183, miR-200a, miR-200b, miR-200c, miR-429 and miR-666-5p) were shown to down-regulate luciferase activity and Western blotting analysis confirmed that Pre-miR™ Precursor Molecules for these seven miRNAs induced expression of E-cadherin and miScript target protector against miR-183 and miR-666-5p abrogated this effect. Moreover, an Anti-miR™ miRNA Inhibitor targeting miR-183 and miR-666-5p repressed expression of E-cadherin.

Conclusions

We have established a method to identify miRNAs that bind the 3′UTR region of the Zeb-2 mRNA and that induce expression of E-cadherin, possibly by down-regulating the expression of Zeb-2. Our method may be more widely applicable for identifying miRNAs that bind target mRNA 3′UTR regions and down-regulate the expression of proteins encoded by these mRNAs.

Splicing variants of the porcine betaine-homocysteine S-methyltransferase gene: implications for mammalian metabolism

Betaine-homocysteine S-methyltransferase (BHMT) activity is only detected in the liver of rodents, but in both the liver and kidney cortex of humans and pigs; therefore, the pig was chosen as a model to define the spatial and temporal expression of BHMT during development. During fetal development, a total of ten splice variants of bhmt were expressed at varying levels across a wide range of porcine tissues. Two variants contained an identical ORF that encoded a C-terminal truncated form of BHMT (tBHMT). The bhmt transcripts were expressed at significant levels in the liver and kidney from day 45 of gestation (G45) onward. The transcripts encoding tBHMT represented 5-13% of the total bhmt transcripts in G30 fetus, G45 liver, and adult liver and kidney cortex. The dominant structural feature of wild type BHMT is an (βα)8 barrel, however, a modeled structure of tBHMT suggests that this protein would assume a horseshoe fold and lack methyltransferase activity. Low BHMT activity was detected in the G30 fetus, and slightly increased levels of activity were observed in the liver from G45 and G90 fetuses. The bhmt promoter contained three key CpG sites, and methylation of these sites was significantly higher in adult lung compared to adult liver. The data reported herein suggest that genomic DNA methylation and variation of the 5' and 3' UTRs of bhmt transcripts are key regulators for the level of BHMT transcription and translation.

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.

Target Gene and Function Prediction of Differentially Expressed MicroRNAs in Lactating Mammary Glands of Dairy Goats

MicroRNAs are small noncoding RNAs that can regulate gene expression, and they can be involved in the regulation of mammary gland development. The differential expression of miRNAs during mammary gland development is expected to provide insight into their roles in regulating the homeostasis of mammary gland tissues. To screen out miRNAs that should have important regulatory function in the development of mammary gland from miRNA expression profiles and to predict their function, in this study, the target genes of differentially expressed miRNAs in the lactating mammary glands of Laoshan dairy goats are predicted, and then the functions of these miRNAs are analyzed via bioinformatics. First, we screen the expression patterns of 25 miRNAs that had shown significant differences during the different lactation stages in the mammary gland. Then, these miRNAs are clustered according to their expression patterns. Computational methods were used to obtain 215 target genes for 22 of these miRNAs. Combining gene ontology annotation, Fisher's exact test, and KEGG analysis with the target prediction for these miRNAs, the regulatory functions of miRNAs belonging to different clusters are predicted.

Acute stress alters amygdala microRNA miR-135a and miR-124 expression: inferences for corticosteroid dependent stress response

The amygdala is a brain structure considered a key node for the regulation of neuroendocrine stress response. Stress-induced response in amygdala is accomplished through neurotransmitter activation and an alteration of gene expression. MicroRNAs (miRNAs) are important regulators of gene expression in the nervous system and are very well suited effectors of stress response for their ability to reversibly silence specific mRNAs. In order to study how acute stress affects miRNAs expression in amygdala we analyzed the miRNA profile after two hours of mouse restraint, by microarray analysis and reverse transcription real time PCR. We found that miR-135a and miR-124 were negatively regulated. Among in silico predicted targets we identified the mineralocorticoid receptor (MR) as a target of both miR-135a and miR-124. Luciferase experiments and endogenous protein expression analysis upon miRNA upregulation and inhibition allowed us to demonstrate that mir-135a and mir-124 are able to negatively affect the expression of the MR. The increased levels of the amygdala MR protein after two hours of restraint, that we analyzed by western blot, negatively correlate with miR-135a and miR-124 expression. These findings point to a role of miR-135a and miR-124 in acute stress as regulators of the MR, an important effector of early stress response.

BayMiR: inferring evidence for endogenous miRNA-induced gene repression from mRNA expression profiles

Background

Popular miRNA target prediction techniques use sequence features to determine the functional miRNA target sites. These techniques commonly ignore the cellular conditions in which miRNAs interact with their targets in vivo. Gene expression data are rich resources that can complement sequence features to take into account the context dependency of miRNAs.

Results

We introduce BayMiR, a new computational method, that predicts the functionality of potential miRNA target sites using the activity level of the miRNAs inferred from genome-wide mRNA expression profiles. We also found that mRNA expression variation can be used as another predictor of functional miRNA targets. We benchmarked BayMiR, the expression variation, Cometa, and the TargetScan “context scores” on two tasks: predicting independently validated miRNA targets and predicting the decrease in mRNA abundance in miRNA overexpression assays. BayMiR performed better than all other methods in both benchmarks and, surprisingly, the variation index performed better than Cometa and some individual determinants of the TargetScan context scores. Furthermore, BayMiR predicted miRNA target sets are more consistently annotated with GO and KEGG terms than similar sized random subsets of genes with conserved miRNA seed regions. BayMiR gives higher scores to target sites residing near the poly(A) tail which strongly favors mRNA degradation using poly(A) shortening. Our work also suggests that modeling multiplicative interactions among miRNAs is important to predict endogenous mRNA targets.

Conclusions

We develop a new computational method for predicting the target mRNAs of miRNAs. BayMiR applies a large number of mRNA expression profiles and successfully identifies the mRNA targets and miRNA activities without using miRNA expression data. The BayMiR package is publicly available and can be readily applied to any mRNA expression data sets.

Molecular responses to toxicological stressors: profiling microRNAs in wild Atlantic salmon (Salmo salar) exposed to acidic aluminum-rich water

Atlantic salmon (Salmo salar) is among the most sensitive organisms toward acidic, aluminum exposure. Main documented responses to this type of stress are a combination of hypoxia and loss of blood plasma ions. Physiological responses to stress in fish are often grouped into primary, secondary and tertiary responses, where the above mentioned effects are secondary responses, while primary responses include endocrine changes as measurable levels of catecholamines and corticosteroids. In this study we have exposed young (14 months) Atlantic salmon to acid/Al water (pH ≈ 5.6, Al(i) ≈ 80 μg L⁻¹) for 3 days, and obtained clear and consistent decrease of Na⁺ and Cl⁻ ions, and increases of glucose in blood plasma, hematocrit and P(CO₂) in blood. We did not measure plasma cortisol (primary response compound), but analyzed effects on microRNA level (miRNA) in muscle tissue, as this may represent initial markers of primary stress responses. miRNAs regulate diverse biological processes, many are evolutionarily conserved, and hundreds have been identified in various animals, although only in a few fish species. We used a novel high-throughput sequencing (RNA-Seq) method to identify miRNAs in Atlantic salmon and specific miRNAs as potential early markers for stress. A total of 18 miRNAs were significantly differentially expressed (FDR<0.1) in exposed compared to control fish, four down-regulated and 14 up-regulated. An unsupervised hierarchical clustering of significant miRNAs revealed two clusters representing exposed and non-exposed individuals. Utilizing the genome of the zebrafish and bioinformatic tools, we identified 224 unique miRNAs in the Atlantic salmon samples sequenced. Additional laboratory studies focusing on function, stress dose-responses and temporal expression of the identified miRNAs will facilitate their use as initial markers for stress responses.

Large-scale screens of miRNA-mRNA interactions unveiled that the 3'UTR of a gene is targeted by multiple miRNAs

Animal microRNA (miRNA) target prediction is still a challenge, although many prediction programs have been exploited. MiRNAs exert their function through partially binding the messenger RNAs (mRNAs; likely at 3′ untranslated regions [3′UTRs]), which makes it possible to detect the miRNA-mRNA interactions in vitro by co-transfection of miRNA and a luciferase reporter gene containing the target mRNA fragment into mammalian cells under a dual-luciferase assay system. Here, we constructed a human miRNA expression library and used a dual-luciferase assay system to perform large-scale screens of interactions between miRNAs and the 3′UTRs of seven genes, which included more than 3,000 interactions with triplicate experiments for each interaction. The screening results showed that the 3′UTR of one gene can be targeted by multiple miRNAs. Among the prediction algorithms, a Bayesian phylogenetic miRNA target identification algorithm and a support vector machine (SVM) presented a relatively better performance (27% for EIMMo and 24.7% for miRDB) against the average precision (17.3%) of the nine prediction programs used here. Additionally, we noticed that a relatively high conservation level was shown at the miRNA 3′ end targeted regions, as well as the 5′ end (seed region) binding sites.

Computational assessment of the cooperativity between RNA binding proteins and MicroRNAs in Transcript Decay

Transcript degradation is a widespread and important mechanism for regulating protein abundance. Two major regulators of transcript degradation are RNA Binding Proteins (RBPs) and microRNAs (miRNAs). We computationally explored whether RBPs and miRNAs cooperate to promote transcript decay. We defined five RBP motifs based on the evolutionary conservation of their recognition sites in 3′UTRs as the binding motifs for Pumilio (PUM), U1A, Fox-1, Nova, and UAUUUAU. Recognition sites for some of these RBPs tended to localize at the end of long 3′UTRs. A specific group of miRNA recognition sites were enriched within 50 nts from the RBP recognition sites for PUM and UAUUUAU. The presence of both a PUM recognition site and a recognition site for preferentially co-occurring miRNAs was associated with faster decay of the associated transcripts. For PUM and its co-occurring miRNAs, binding of the RBP to its recognition sites was predicted to release nearby miRNA recognition sites from RNA secondary structures. The mammalian miRNAs that preferentially co-occur with PUM binding sites have recognition seeds that are reverse complements to the PUM recognition motif. Their binding sites have the potential to form hairpin secondary structures with proximal PUM binding sites that would normally limit RISC accessibility, but would be more accessible to miRNAs in response to the binding of PUM. In sum, our computational analyses suggest that a specific set of RBPs and miRNAs work together to affect transcript decay, with the rescue of miRNA recognition sites via RBP binding as one possible mechanism of cooperativity.

Transcript degradation represents an important mechanism of regulation used in diverse biological processes, including during development to eliminate maternally inherited transcripts, in adult tissues to define cell lineages, and as part of signaling pathways to down-regulate unneeded transcripts. RNA binding proteins (RBPs) and microRNAs are two major classes of molecules utilized to degrade transcripts. Using computational methods, we analyzed the genomewide cooperativity between microRNA and RBP recognition sites. We observed cooperativity between Pumilio (PUM) and specific microRNAs that impacts transcript decay. Our analysis suggests that approximately seven mammalian microRNAs preferentially co-localize with PUM binding sites, and these microRNAs have recognition motifs that are reverse complements to the PUM recognition motif. Their binding sites are more likely to form RNA hairpin structures with proximal PUM recognition sites that would limit microRNA efficiency, but would be more accessible to microRNAs in response to the binding of PUM. These results indicate that rescuing microRNA recognition sites from hairpin structures may be an important role for PUM.

MicroRNA-276a functions in ellipsoid body and mushroom body neurons for naive and conditioned olfactory avoidance in Drosophila

MicroRNA (miRNA)-mediated gene regulation plays a key role in brain development and function. But there are few cases in which the roles of individual miRNAs have been elucidated in behaving animals. We report a miR-276a::DopR regulatory module in Drosophila that functions in distinct circuits for naive odor responses and conditioned odor memory. Drosophila olfactory aversive memory involves convergence of the odors (conditioned stimulus) and the electric shock (unconditioned stimulus) in mushroom body (MB) neurons. Dopamine receptor DopR mediates the unconditioned stimulus inputs onto MB. Distinct dopaminergic neurons also innervate ellipsoid body (EB), where DopR function modulates arousal to external stimuli. We demonstrate that miR-276a is required in MB neurons for memory formation and in EB for naive responses to odors. Both roles of miR-276a are mediated by tuning DopR expression. The dual role of this miR-276a::DopR genetic module in these two neural circuits highlights the importance of miRNA-mediated gene regulation within distinct circuits underlying both naive behavioral responses and memory.

Advances in the techniques for the prediction of microRNA targets

MicroRNAs (miRNAs) are small, non-coding, endogenous RNA molecules that play important roles in a variety of normal and diseased biological processes by post-transcriptionally regulating the expression of target genes. They can bind to target messenger RNA (mRNA) transcripts of protein-coding genes and negatively control their translation or cause mRNA degradation. miRNAs have been found to actively regulate a variety of cellular processes, including cell proliferation, death, and metabolism. Therefore, their study is crucial for the better understanding of cellular functions in eukaryotes. To better understand the mechanisms of miRNA: mRNA interaction and their cellular functions, it is important to identify the miRNA targets accurately. In this paper, we provide a brief review for the advances in the animal miRNA target prediction methods and available resources to facilitate further study of miRNAs and their functions.

Identification of microRNA-mRNA functional interactions in UVB-induced senescence of human diploid fibroblasts

BACKGROUND

Cellular senescence can be induced by a variety of extrinsic stimuli, and sustained exposure to sunlight is a key factor in photoaging of the skin. Accordingly, irradiation of skin fibroblasts by UVB light triggers cellular senescence, which is thought to contribute to extrinsic skin aging, although molecular mechanisms are incompletely understood. Here, we addressed molecular mechanisms underlying UVB induced senescence of human diploid fibroblasts.

RESULTS

We observed a parallel activation of the p53/p21(WAF1) and p16(INK4a)/pRb pathways. Using genome-wide transcriptome analysis, we identified a transcriptional signature of UVB-induced senescence that was conserved in three independent strains of human diploid fibroblasts (HDF) from skin. In parallel, a comprehensive screen for microRNAs regulated during UVB-induced senescence was performed which identified five microRNAs that are significantly regulated during the process. Bioinformatic analysis of miRNA-mRNA networks was performed to identify new functional mRNA targets with high confidence for miR-15a, miR-20a, miR-20b, miR-93, and miR-101. Already known targets of these miRNAs were identified in each case, validating the approach. Several new targets were identified for all of these miRNAs, with the potential to provide new insight in the process of UVB-induced senescence at a genome-wide level. Subsequent analysis was focused on miR-101 and its putative target gene Ezh2. We confirmed that Ezh2 is regulated by miR-101 in human fibroblasts, and found that both overexpression of miR-101 and downregulation of Ezh2 independently induce senescence in the absence of UVB irradiation. However, the downregulation of miR-101 was not sufficient to block the phenotype of UVB-induced senescence, suggesting that other UVB-induced processes induce the senescence response in a pathway redundant with upregulation of miR-101.

CONCLUSION

We performed a comprehensive screen for UVB-regulated microRNAs in human diploid fibroblasts, and identified a network of miRNA-mRNA interactions mediating UVB-induced senescence. In addition, miR-101 and Ezh2 were identified as key players in UVB-induced senescence of HDF.

Minireview: miRomics in endocrinology: a novel approach for modeling endocrine diseases

MicroRNAs (miRNAs) are small (16-24 nucleotides) noncoding RNAs that negatively regulate gene expression. Growing evidence demonstrates that miRNAs participate in the regulation of numerous physiological and pathological processes. The clinical utility of the cell-type-specific miRNA expression profile (miRomics) has been directly demonstrated in molecular classification of tumor samples and in prediction of prognosis or therapeutic responsiveness. Identification of the relevant miRNAs and their targets requires both in silico and molecular biological methods. In this review, we summarize the methodological arsenal used in miRNA-related research, and through our own data on adrenal tumors, we present how miRNA could be integrated into omics-based networks. The expanding knowledge obtained from miRNA research may lead to the development of novel diagnostic and treatment modalities in future.

Identifying dysfunctional miRNA-mRNA regulatory modules by inverse activation, cofunction, and high interconnection of target genes: a case study of glioblastoma

BACKGROUND

Accumulating evidence demonstrates that complex diseases may arise from cooperative effects of multiple dysfunctional miRNAs. Thus, identifying abnormal functions cooperatively regulated by multiple miRNAs is useful for understanding the pathogenesis of complex diseases.

METHODS

In this study, we proposed a multistep method to identify dysfunctional miRNA-mRNA regulatory modules (dMiMRMs) in a specific disease, in which a group of miRNAs cooperatively regulate a group of target genes involved in a specific function. We identified dysfunctional miRNAs, which were differentially expressed and inversely regulated most of their target genes, by integrating paired miRNA and mRNA expression profiles and miRNA target information. Then, we identified cooperative functional units, in each of which a pair of miRNAs cooperatively repressed function-enriched and highly interconnected target genes. Finally, the cooperative functional units were assembled into dMiMRMs.

RESULTS

We applied our method to glioblastoma (GBM) and identified GBM-associated dMiMRMs at the population, subtype, and individual levels. We identified 5 common dMiMRMs using all GBM samples, 3 of which were associated with the prognosis in patients with GBM and were better predictors of prognosis than were miRNAs or mRNAs alone. By applying our approach to GBM subtypes, we found consistent dMiMRMs across GBM subtypes, and some subtype-specific dMiMRMs were observed. Furthermore, personalized dMiMRMs were identified, suggesting significant individual differences in different patients with GBM.

CONCLUSIONS

Our method provides the capability to identify miRNA-mediated dysfunctional mechanisms underlying complex diseases.

Where we stand, where we are moving: Surveying computational techniques for identifying miRNA genes and uncovering their regulatory role

Traditional biology was forced to restate some of its principles when the microRNA (miRNA) genes and their regulatory role were firstly discovered. Typically, miRNAs are small non-coding RNA molecules which have the ability to bind to the 3'untraslated region (UTR) of their mRNA target genes for cleavage or translational repression. Existing experimental techniques for their identification and the prediction of the target genes share some important limitations such as low coverage, time consuming experiments and high cost reagents. Hence, many computational methods have been proposed for these tasks to overcome these limitations. Recently, many researchers emphasized on the development of computational approaches to predict the participation of miRNA genes in regulatory networks and to analyze their transcription mechanisms. All these approaches have certain advantages and disadvantages which are going to be described in the present survey. Our work is differentiated from existing review papers by updating the methodologies list and emphasizing on the computational issues that arise from the miRNA data analysis. Furthermore, in the present survey, the various miRNA data analysis steps are treated as an integrated procedure whose aims and scope is to uncover the regulatory role and mechanisms of the miRNA genes. This integrated view of the miRNA data analysis steps may be extremely useful for all researchers even if they work on just a single step.

A computational approach for identifying microRNA-target interactions using high-throughput CLIP and PAR-CLIP sequencing

Background

MicroRNAs (miRNAs) play a critical role in down-regulating gene expression. By coupling with Argonaute family proteins, miRNAs bind to target sites on mRNAs and employ translational repression. A large amount of miRNA-target interactions (MTIs) have been identified by the crosslinking and immunoprecipitation (CLIP) and the photoactivatable-ribonucleoside-enhanced CLIP (PAR-CLIP) along with the next-generation sequencing (NGS). PAR-CLIP shows high efficiency of RNA co-immunoprecipitation, but it also lead to T to C conversion in miRNA-RNA-protein crosslinking regions. This artificial error obviously reduces the mappability of reads. However, a specific tool to analyze CLIP and PAR-CLIP data that takes T to C conversion into account is still in need.

Results

We herein propose the first CLIP and PAR-CLIP sequencing analysis platform specifically for miRNA target analysis, namely miRTarCLIP. From scratch, it automatically removes adaptor sequences from raw reads, filters low quality reads, reverts C to T, aligns reads to 3'UTRs, scans for read clusters, identifies high confidence miRNA target sites, and provides annotations from external databases. With multi-threading techniques and our novel C to T reversion procedure, miRTarCLIP greatly reduces the running time comparing to conventional approaches. In addition, miRTarCLIP serves with a web-based interface to provide better user experiences in browsing and searching targets of interested miRNAs. To demonstrate the superior functionality of miRTarCLIP, we applied miRTarCLIP to two public available CLIP and PAR-CLIP sequencing datasets. miRTarCLIP not only shows comparable results to that of other existing tools in a much faster speed, but also reveals interesting features among these putative target sites. Specifically, we used miRTarCLIP to disclose that T to C conversion within position 1-7 and that within position 8-14 of miRNA target sites are significantly different (p value = 0.02), and even more significant when focusing on sites targeted by top 102 highly expressed miRNAs only (p value = 0.01). These results comply with previous findings and further suggest that combining miRNA expression and PAR-CLIP data can improve accuracy of the miRNA target prediction.

Conclusion

To sum up, we devised a systematic approach for mining miRNA-target sites from CLIP-seq and PAR-CLIP sequencing data, and integrated the workflow with a graphical web-based browser, which provides a user friendly interface and detailed annotations of MTIs. We also showed through real-life examples that miRTarCLIP is a powerful tool for understanding miRNAs. Our integrated tool can be accessed online freely at http://miRTarCLIP.mbc.nctu.edu.tw.

A biophysical miRNA-mRNA interaction model infers canonical and noncanonical targets

We introduce a biophysical model of miRNA-target interaction and infer its parameters from Argonaute 2 cross-linking and immunoprecipitation data. We show that a substantial fraction of human miRNA target sites are noncanonical and that predicted target-site affinity correlates well with the extent of target destabilization. Our model provides a rigorous biophysical approach to miRNA target identification beyond ad hoc miRNA seed-based methods.

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.

Reprogramming of the microRNA transcriptome mediates resistance to rapamycin

The mammalian target of rapamycin (mTOR) is a central regulator of cell proliferation that is often deregulated in cancer. Inhibitors of mTOR, including rapamycin and its analogues, are being evaluated as antitumor agents. For their promise to be fulfilled, it is of paramount importance to identify the mechanisms of resistance and develop novel therapies to overcome it. Given the emerging role of microRNAs (miRNAs) in tumorigenesis, we hypothesized that miRNAs could play important roles in the response of tumors to mTOR inhibitors. Long-term rapamycin treatment showed extensive reprogramming of miRNA expression, characterized by up-regulation of miR-17-92 and related clusters and down-regulation of tumor suppressor miRNAs. Inhibition of members of the miR-17-92 clusters or delivery of tumor suppressor miRNAs restored sensitivity to rapamycin. This study identifies miRNAs as new downstream components of the mTOR-signaling pathway, which may determine the response of tumors to mTOR inhibitors. It also identifies potential markers to assess the efficacy of treatment and provides novel therapeutic targets to treat rapamycin-resistant tumors.

The gene vitellogenin affects microRNA regulation in honey bee (Apis mellifera) fat body and brain

In honey bees, Vitellogenin (Vg) is hypothesized to be a major factor affecting hormone signaling, food-related behavior, immunity, stress resistance and lifespan. Likewise microRNAs play important roles in posttranscriptional gene regulation and affect many biological processes. The action of microRNAs and Vg are known to intersect in the context of reproduction; however, the role of these associations on social behavior is unknown. The phenotypic effects of Vg knockdown are best established and studied in the forager stage of workers. Thus, we exploited the well-established RNA interference (RNAi) protocol for Vg knockdown to investigate its downstream effects on microRNA population in honey bee foragers' brain and fat body tissue. To identify microRNAs that are differentially expressed between tissues in control and knockdown foragers, we used µParaflo® microfluidic oligonucleotide microRNA microarrays. Our results show 76 and 74 microRNAs were expressed in the brain of control and knockdown foragers whereas 66 and 69 microRNAs were expressed in the fat body of control and knockdown foragers respectively. Target prediction identified potential seed matches for a differentially expressed subset of microRNAs affected by Vg knockdown. These candidate genes are involved in a broad range of biological processes including insulin signaling, juvenile hormone (JH) and ecdysteroid signaling previously shown to affect foraging behavior. Thus, here we demonstrate a causal link between the Vg knockdown forager phenotype and variation in the abundance of microRNAs in different tissues with possible consequences for regulation of foraging behavior.

Discovering functional microRNA-mRNA regulatory modules in heterogeneous data

microRNAs (miRNAs) are small non-coding RNAs that cause mRNA degradation and translation inhibition. They are pivotal regulators of development and cellular homeostasis through their control of diverse processes. Recently, great efforts have been made to elucidate many targets that are affected by miRNAs, but the functions of most miRNAs and their precise regulatory mechanisms remain elusive. With more and more matched expression profiles of miRNAs and mRNAs having been made available, it is of great interest to utilize both expression profiles and sequence information to discover the functional regulatory networks of miRNAs and their target mRNAs for potential biological processes that they may participate in. In this chapter, we first briefly review the computational methods for discovering miRNA targets and miRNA-mRNA regulatory modules, and then focus on a method of identifying functional miRNA-mRNA regulatory modules by integrating multiple data sets from different sources.

Web resources for microRNA research

Over the last decade thousands of microRNAs (miRNAs) have been discovered in all kinds of taxa. The ever growing number of identified miRNA genes required ordered cataloging and annotation. This has led to the development of miRNA web resources.MiRNA web resources can be referred to either as web accessible databases (repositories) or web applications that provide a defined computational task upon user request. Today, more than three dozen web accessible resources exist that gather, organize and annotate all kinds of miRNA related data. According to the type of data or data processing method, these miRNA web resources can be classified as miRNA sequence and annotation databases, resources and tools for predicted as well as experimentally validated targets, databases of miRNA regulation and expression, functional annotation and mapping databases and a number of other tools and resources that are species-specific or focus on particular phenotypes.This chapter provides an overview of the different types of miRNA web resources and their purpose and gives some examples for each category. Furthermore, some valuable miRNA web applications will be introduced. Finally, strategies for miRNA data retrieval and associated risks and pitfalls will be discussed.

Widespread inference of weighted microRNA-mediated gene regulation in cancer transcriptome analysis

MicroRNAs (miRNAs) comprise a gene-regulatory network through sequence complementarity with target mRNAs. Previous studies have shown that mammalian miRNAs decrease many target mRNA levels and reduce protein production predominantly by target mRNA destabilization. However, it has not yet been fully assessed whether this scheme is widely applicable to more realistic conditions with multiple miRNA fluctuations. By combining two analytical frameworks for detecting the enrichment of gene sets, Gene Set Enrichment Analysis (GSEA) and Functional Assignment of miRNAs via Enrichment (FAME), we developed GSEA–FAME analysis (GFA), which enables the prediction of miRNA activities from mRNA expression data using rank-based enrichment analysis and weighted evaluation of miRNA–mRNA interactions. This cooperative approach delineated a better widespread correlation between miRNA expression levels and predicted miRNA activities in cancer transcriptomes, thereby providing proof-of-concept of the mRNA-destabilization scenario. In an integrative analysis of The Cancer Genome Atlas (TCGA) multidimensional data including profiles of both mRNA and miRNA, we also showed that GFA-based inference of miRNA activity could be used for the selection of prognostic miRNAs in the development of cancer survival prediction models. This approach proposes a next-generation strategy for the interpretation of miRNA function and identification of target miRNAs as biomarkers and therapeutic targets.

MicroRNA-centric measurement improves functional enrichment analysis of co-expressed and differentially expressed microRNA clusters

Background

Functional annotations are available only for a very small fraction of microRNAs (miRNAs) and very few miRNA target genes are experimentally validated. Therefore, functional analysis of miRNA clusters has typically relied on computational target gene prediction followed by Gene Ontology and/or pathway analysis. These previous methods share the limitation that they do not consider the many-to-many-to-many tri-partite network topology between miRNAs, target genes, and functional annotations. Moreover, the highly false-positive nature of sequence-based target prediction algorithms causes propagation of annotation errors throughout the tri-partite network.

Results

A new conceptual framework is proposed for functional analysis of miRNA clusters, which extends the conventional target gene-centric approaches to a more generalized tri-partite space. Under this framework, we construct miRNA-, target link-, and target gene-centric computational measures incorporating the whole tri-partite network topology. Each of these methods and all their possible combinations are evaluated on publicly available miRNA clusters and with a wide range of variations for miRNA-target gene relations. We find that the miRNA-centric measures outperform others in terms of the average specificity and functional homogeneity of the GO terms significantly enriched for each miRNA cluster.

Conclusions

We propose novel miRNA-centric functional enrichment measures in a conceptual framework that connects the spaces of miRNAs, genes, and GO terms in a unified way. Our comprehensive evaluation result demonstrates that functional enrichment analysis of co-expressed and differentially expressed miRNA clusters can substantially benefit from the proposed miRNA-centric approaches.

miRNA target enrichment analysis reveals directly active miRNAs in health and disease

microRNAs (miRNAs) are short non-coding regulatory RNA molecules. The activity of a miRNA in a biological process can often be reflected in the expression program that characterizes the outcome of the activity. We introduce a computational approach that infers such activity from high-throughput data using a novel statistical methodology, called minimum-mHG (mmHG), that examines mutual enrichment in two ranked lists. Based on this methodology, we provide a user-friendly web application that supports the statistical assessment of miRNA target enrichment analysis (miTEA) in the top of a ranked list of genes or proteins. Using miTEA, we analyze several target prediction tools by examining performance on public miRNA constitutive expression data. We also apply miTEA to analyze several integrative biology data sets, including a novel matched miRNA/mRNA data set covering nine human tissue types. Our novel findings include proposed direct activity of miR-519 in placenta, a direct activity of the oncogenic miR-15 in different healthy tissue types and a direct activity of the poorly characterized miR-768 in both healthy tissue types and cancer cell lines. The miTEA web application is available at http://cbl-gorilla.cs.technion.ac.il/miTEA/.

Dicer partners expand the repertoire of miRNA targets

Processing of pre-miRNAs by Dicer is regulated by its dsRNA-binding protein partner, and leads to the generation of alternative miRNA forms with distinct target sets.

Identifying miRNAs, targets and functions

microRNAs (miRNAs) are small endogenous non-coding RNAs that function as the universal specificity factors in post-transcriptional gene silencing. Discovering miRNAs, identifying their targets and further inferring miRNA functions have been a critical strategy for understanding normal biological processes of miRNAs and their roles in the development of disease. In this review, we focus on computational methods of inferring miRNA functions, including miRNA functional annotation and inferring miRNA regulatory modules, by integrating heterogeneous data sources. We also briefly introduce the research in miRNA discovery and miRNA-target identification with an emphasis on the challenges to computational biology.

HomoTarget: a new algorithm for prediction of microRNA targets in Homo sapiens

MiRNAs play an essential role in the networks of gene regulation by inhibiting the translation of target mRNAs. Several computational approaches have been proposed for the prediction of miRNA target-genes. Reports reveal a large fraction of under-predicted or falsely predicted target genes. Thus, there is an imperative need to develop a computational method by which the target mRNAs of existing miRNAs can be correctly identified. In this study, combined pattern recognition neural network (PRNN) and principle component analysis (PCA) architecture has been proposed in order to model the complicated relationship between miRNAs and their target mRNAs in humans. The results of several types of intelligent classifiers and our proposed model were compared, showing that our algorithm outperformed them with higher sensitivity and specificity. Using the recent release of the mirBase database to find potential targets of miRNAs, this model incorporated twelve structural, thermodynamic and positional features of miRNA:mRNA binding sites to select target candidates.

The expression levels of microRNA-361-5p and its target VEGFA are inversely correlated in human cutaneous squamous cell carcinoma

Vascular endothelial growth factor A (VEGFA) plays a key role in the angiogenesis of human skin. Elevated levels of VEGFA are associated with several pathological conditions, including chronic inflammatory skin diseases and several types of skin cancer. In particular, squamous cell carcinoma (SCC) of the skin, the second most common skin cancer in the general population, is characterized by invasive growth, pronounced angiogenesis and elevated levels of VEGFA. The processing, turnover and production of VEGFA are extensively regulated at the post-transcriptional level, both by RNA-binding proteins and microRNAs (miRNAs). In the present study, we identified a new miRNA recognition element in a downstream conserved region of the VEGFA 3'-UTR. We confirmed the repressive effect of miR-361-5p on this element in vitro, identifying the first target for this miRNA. Importantly, we found that miR-361-5p levels are inversely correlated with VEGFA expression in SCC and in healthy skin, indicating that miR-361-5p could play a role in cancers.

Prediction of altered 3'- UTR miRNA-binding sites from RNA-Seq data: the swine leukocyte antigen complex (SLA) as a model region

THE SLA (swine leukocyte antigen, MHC: SLA) genes are the most important determinants of immune, infectious disease and vaccine response in pigs; several genetic associations with immunity and swine production traits have been reported. However, most of the current knowledge on SLA is limited to gene coding regions. MicroRNAs (miRNAs) are small molecules that post-transcriptionally regulate the expression of a large number of protein-coding genes in metazoans, and are suggested to play important roles in fine-tuning immune mechanisms and disease responses. Polymorphisms in either miRNAs or their gene targets may have a significant impact on gene expression by abolishing, weakening or creating miRNA target sites, possibly leading to phenotypic variation. We explored the impact of variants in the 3'-UTR miRNA target sites of genes within the whole SLA region. The combined predictions by TargetScan, PACMIT and TargetSpy, based on different biological parameters, empowered the identification of miRNA target sites and the discovery of polymorphic miRNA target sites (poly-miRTSs). Predictions for three SLA genes characterized by a different range of sequence variation provided proof of principle for the analysis of poly-miRTSs from a total of 144 M RNA-Seq reads collected from different porcine tissues. Twenty-four novel SNPs were predicted to affect miRNA-binding sites in 19 genes of the SLA region. Seven of these genes (SLA-1, SLA-6, SLA-DQA, SLA-DQB1, SLA-DOA, SLA-DOB and TAP1) are linked to antigen processing and presentation functions, which is reminiscent of associations with disease traits reported for altered miRNA binding to MHC genes in humans. An inverse correlation in expression levels was demonstrated between miRNAs and co-expressed SLA targets by exploiting a published dataset (RNA-Seq and small RNA-Seq) of three porcine tissues. Our results support the resource value of RNA-Seq collections to identify SNPs that may lead to altered miRNA regulation patterns.

One decade of development and evolution of microRNA target prediction algorithms

Nearly two decades have passed since the publication of the first study reporting the discovery of microRNAs (miRNAs). The key role of miRNAs in post-transcriptional gene regulation led to the performance of an increasing number of studies focusing on origins, mechanisms of action and functionality of miRNAs. In order to associate each miRNA to a specific functionality it is essential to unveil the rules that govern miRNA action. Despite the fact that there has been significant improvement exposing structural characteristics of the miRNA–mRNA interaction, the entire physical mechanism is not yet fully understood. In this respect, the development of computational algorithms for miRNA target prediction becomes increasingly important. This manuscript summarizes the research done on miRNA target prediction. It describes the experimental data currently available and used in the field and presents three lines of computational approaches for target prediction. Finally, the authors put forward a number of considerations regarding current challenges and future directions.

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.

"Seed-Milarity" confers to hsa-miR-210 and hsa-miR-147b similar functional activity

Specificity of interaction between a microRNA (miRNA) and its targets crucially depends on the seed region located in its 5′-end. It is often implicitly considered that two miRNAs sharing the same biological activity should display similarity beyond the strict six nucleotide region that forms the seed, in order to form specific complexes with the same mRNA targets. We have found that expression of hsa-miR-147b and hsa-miR-210, though triggered by different stimuli (i.e. lipopolysaccharides and hypoxia, respectively), induce very similar cellular effects in term of proliferation, migration and apoptosis. Hsa-miR-147b only shares a “minimal” 6-nucleotides seed sequence with hsa-miR-210, but is identical with hsa-miR-147a over 20 nucleotides, except for one base located in the seed region. Phenotypic changes induced after heterologous expression of miR-147a strikingly differ from those induced by miR-147b or miR-210. In particular, miR-147a behaves as a potent inhibitor of cell proliferation and migration. These data fit well with the gene expression profiles observed for miR-147b and miR-210, which are very similar, and the gene expression profile of miR-147a, which is distinct from the two others. Bioinformatics analysis of all human miRNA sequences indicates multiple cases of miRNAs from distinct families exhibiting the same kind of similarity that would need to be further characterized in terms of putative functional redundancy. Besides, it implies that functional impact of some miRNAs can be masked by robust expression of miRNAs belonging to distinct families.

Chromatin measurements reveal contributions of synthesis and decay to steady-state mRNA levels

Histone modification, polymerase binding, mRNA half-life, and miRNA abundance measurements in mouse cells are used to dissect the relative contribution of each to mRNA levels, revealing control primarily at the level of transcription, with minor contributions from post-transcriptional processes.

A linear model of three histone modifications and RNAP II occupancy can predict >80% of the variance in mRNA levels.

mRNA half-life explains an additional 1.4% variance in mRNA levels.

miRNA-mediated silencing does not explain any variance on a genome-wide scale.

H3K36me3 has different predictive power in dividing and non-dividingcells.

Messenger RNA levels in eukaryotes are controlled by multiple consecutive regulatory processes, which can be classified into two layers: primary transcriptional regulation at the chromosomal level and secondary, co- and post-transcriptional regulation of the mRNA. To identify the individual contribution of these layers to steady-state RNA levels requires separate quantification. Using mouse as a model organism, we show that chromatin features are sufficient to model RNA levels but with different sensitivities in dividing versus postmitotic cells. In both cases, chromatin-derived transcription rates explain over 80% of the observed variance in measured RNA levels. Further inclusion of measurements of mRNA half-life and microRNA expression data enabled the identification of a low quantitative contribution of RNA decay by either microRNA or general differential turnover to final mRNA levels. Together, this establishes a chromatin-based quantitative model for the contribution of transcriptional and post-transcriptional processes to steady-state levels of messenger RNA.

MicroRNAs: molecular features and role in cancer

microRNAs (miRNAs) are small noncoding endogenously produced RNAs that play key roles in controlling the expression of many cellular proteins. Once they are recruited and incorporated into a ribonucleoprotein complex miRISC, they can target specific mRNAs in a miRNA sequence-dependent process and interfere in the translation into proteins of the targeted mRNAs via several mechanisms. Consequently, miRNAs can regulate many cellular pathways and processes. Dysregulation of their physiological roles may largely contribute to disease. In particular, in cancer, miRNAs can be involved in the deregulation of the expression of important genes that play key roles in tumorigenesis, tumor development, and angiogenesis and have oncogenic or tumor suppressor roles. This review focuses on the biogenesis and maturation of miRNAs, their mechanisms of gene regulation, and the way their expression is deregulated in cancer. The involvement of miRNAs in several oncogenic pathways such as angiogenesis and apoptosis, and in the inter-cellular dialog mediated by miRNA-loaded exosomes as well as the development of new therapeutical strategies based on miRNAs will be discussed.