Structural aspects of microRNA biogenesis

One of the biggest surprises at the beginning of the 'post-genome era' was the discovery of numerous genes encoding microRNAs. They were found in genomes of such diverse organisms as Caenorhabditis elegans, Drosophila melanogaster, Arabidopsis thaliana, and Homo sapiens which implies their important role in multicellular life evolution. The number of microRNA genes is estimated to be nearly 1% of that of protein-coding genes. Their products, tiny RNAs, are thought to regulate gene expression during development, organogenesis, and very likely during many other processes, by hybridizing to their target mRNAs. The cellular functions of mRNAs that are regulated by microRNAs are only beginning to be revealed, and details of this regulation mechanism are still poorly understood. In this article we discuss the possible mechanisms of microRNA biogenesis with special emphasis on their structural aspects. We have focused on the factors and effects that may be responsible for the existing length differences between different microRNAs, and for the observed length heterogeneity within some individual microRNA species.     

Intrinsic Noise of microRNA-Regulated Genes and the ceRNA Hypothesis

MicroRNAs are small noncoding RNAs that regulate genes post-transciptionally by binding and degrading target eukaryotic mRNAs. We use a quantitative model to study gene regulation by inhibitory microRNAs and compare it to gene regulation by prokaryotic small non-coding RNAs (sRNAs). Our model uses a combination of analytic techniques as well as computational simulations to calculate the mean-expression and noise profiles of genes regulated by both microRNAs and sRNAs. We find that despite very different molecular machinery and modes of action (catalytic vs stoichiometric), the mean expression levels and noise profiles of microRNA-regulated genes are almost identical to genes regulated by prokaryotic sRNAs. This behavior is extremely robust and persists across a wide range of biologically relevant parameters. We extend our model to study crosstalk between multiple mRNAs that are regulated by a single microRNA and show that noise is a sensitive measure of microRNA-mediated interaction between mRNAs. We conclude by discussing possible experimental strategies for uncovering the microRNA-mRNA interactions and testing the competing endogenous RNA (ceRNA) hypothesis.     

Target Repression Induced by Endogenous microRNAs: Large Differences,Small Effects

MicroRNAs are small RNAs that regulate protein levels. It is commonly assumed that the expression level of a microRNA is directly correlated with its repressive activity – that is, highly expressed microRNAs will repress their target mRNAs more. Here we investigate the quantitative relationship between endogenous microRNA expression and repression for 32 mature microRNAs in Drosophila melanogaster S2 cells. In general, we find that more abundant microRNAs repress their targets to a greater degree. However, the relationship between expression and repression is nonlinear, such that a 10-fold greater microRNA concentration produces only a 10% increase in target repression. The expression/repression relationship is the same for both dominant guide microRNAs and minor mature products (so-called passenger strands/microRNA* sequences). However, we find examples of microRNAs whose cellular concentrations differ by several orders of magnitude, yet induce similar repression of target mRNAs. Likewise, microRNAs with similar expression can have very different repressive abilities. We show that the association of microRNAs with Argonaute proteins does not explain this variation in repression. The observed relationship is consistent with the limiting step in target repression being the association of the microRNA/RISC complex with the target site. These findings argue that modest changes in cellular microRNA concentration will have minor effects on repression of targets.     

Repression of protein synthesis by miRNAs: how many mechanisms?

MicroRNAs are approximately 21-nucleotide-long regulators of gene expression that gain access to their target mRNAs by complementary base pairing. Recent studies have revealed that animal microRNAs might take diverse routes to repress gene expression, affecting both target mRNA levels and translation. Mechanistic details of microRNA-mediated repression are starting to emerge but a comprehensive picture of the inhibition, and particularly the effects on mRNA translation, is still lacking. Recent data support different microRNA mechanisms and a role for cytoplasmic processing bodies in the degradation and storage of mRNAs targeted by microRNA regulators.     

Computational identification of microRNA targets

Recent experiments have shown that the genomes of organisms such as worm, fly, human, and mouse encode hundreds of microRNA genes. Many of these microRNAs are thought to regulate the translational expression of other genes by binding to partially complementary sites in messenger RNAs. Phenotypic and expression analysis suggests an important role of microRNAs during development. Therefore, it is of fundamental importance to identify microRNA targets. However, no experimental or computational high-throughput method for target site identification in animals has been published yet. Our main result is a new computational method that is designed to identify microRNA target sites. This method recovers with high specificity known microRNA target sites that have previously been defined experimentally. Based on these results, we present a simple model for the mechanism of microRNA target site recognition. Our model incorporates both kinetic and thermodynamic components of target recognition. When we applied our method to a set of 74 Drosophila melanogaster microRNAs, searching 3'UTR sequences of a predefined set of fly mRNAs for target sites which were evolutionary conserved between D. melanogaster and Drosophila pseudoobscura, we found that many key developmental body patterning genes such as hairy and fushi-tarazu are likely to be translationally regulated by microRNAs.     

microRNA target predictions across seven Drosophila species and comparison to mammalian targets

microRNAs are small noncoding genes that regulate the protein production of genes by binding to partially complementary sites in the mRNAs of targeted genes. Here, using our algorithm PicTar, we exploit cross-species comparisons to predict, on average, 54 targeted genes per microRNA above noise in Drosophila melanogaster. Analysis of the functional annotation of target genes furthermore suggests specific biological functions for many microRNAs. We also predict combinatorial targets for clustered microRNAs and find that some clustered microRNAs are likely to coordinately regulate target genes. Furthermore, we compare microRNA regulation between insects and vertebrates. We find that the widespread extent of gene regulation by microRNAs is comparable between flies and mammals but that certain microRNAs may function in clade-specific modes of gene regulation. One of these microRNAs (miR-210) is predicted to contribute to the regulation of fly oogenesis. We also list specific regulatory relationships that appear to be conserved between flies and mammals. Our findings provide the most extensive microRNA target predictions in Drosophila to date, suggest specific functional roles for most microRNAs, indicate the existence of coordinate gene regulation executed by clustered microRNAs, and shed light on the evolution of microRNA function across large evolutionary distances. All predictions are freely accessible at our searchable Web site http://pictar.bio.nyu.edu.     

Integration of mRNA Expression Profile,Copy Number Alterations,and microRNA Expression Levels in Breast Cancer to Improve Grade Definition

Defining the aggressiveness and growth rate of a malignant cell population is a key step in the clinical approach to treating tumor disease. The correct grading of breast cancer (BC) is a fundamental part in determining the appropriate treatment. Biological variables can make it difficult to elucidate the mechanisms underlying BC development. To identify potential markers that can be used for BC classification, we analyzed mRNAs expression profiles, gene copy numbers, microRNAs expression and their association with tumor grade in BC microarray-derived datasets. From mRNA expression results, we found that grade 2 BC is most likely a mixture of grade 1 and grade 3 that have been misclassified, being described by the gene signature of either grade 1 or grade 3. We assessed the potential of the new approach of integrating mRNA expression profile, copy number alterations, and microRNA expression levels to select a limited number of genomic BC biomarkers. The combination of mRNA profile analysis and copy number data with microRNA expression levels led to the identification of two gene signatures of 42 and 4 altered genes (FOXM1, KPNA4, H2AFV and DDX19A) respectively, the latter obtained through a meta-analytical procedure. The 42-based gene signature identifies 4 classes of up- or down-regulated microRNAs (17 microRNAs) and of their 17 target mRNA, and the 4-based genes signature identified 4 microRNAs (Hsa-miR-320d, Hsa-miR-139-5p, Hsa-miR-567 and Hsa-let-7c). These results are discussed from a biological point of view with respect to pathological features of BC. Our identified mRNAs and microRNAs were validated as prognostic factors of BC disease progression, and could potentially facilitate the implementation of assays for laboratory validation, due to their reduced number.     

Diverse correlation patterns between microRNAs and their targets during tomato fruit development indicates different modes of microRNA actions

MicroRNAs negatively regulate the accumulation of mRNAs therefore when they are expressed in the same cells their expression profiles show an inverse correlation. We previously described one positively correlated miRNA/target pair, but it is not known how widespread this phenomenon is. Here, we investigated the correlation between the expression profiles of differentially expressed miRNAs and their targets during tomato fruit development using deep sequencing, Northern blot and RT-qPCR. We found an equal number of positively and negatively correlated miRNA/target pairs indicating that positive correlation is more frequent than previously thought. We also found that the correlation between microRNA and target expression profiles can vary between mRNAs belonging to the same gene family and even for the same target mRNA at different developmental stages. Since microRNAs always negatively regulate their targets, the high number of positively correlated microRNA/target pairs suggests that mutual exclusion could be as widespread as temporal regulation. The change of correlation during development suggests that the type of regulatory circuit directed by a microRNA can change over time and can be different for individual gene family members. Our results also highlight potential problems for expression profiling-based microRNA target identification/validation.     

MicroRNA expression profiling in neurogenesis of adipose tissue-derived stem cells

Adipose tissue-derived stem cells (ADSCs) are one population of adult stem cells that can self renew and differentiate into multiple lineages. Because of advantages in method and quantity of acquisition, ADSCs are gaining attention as an alternative source of bone marrow mesenchymal stem cells. In this study, we performed microRNA profiling of undifferentiated and of neurally-differentiated ADSCs to identify the responsible microRNAs in neurogenesis using this type of stem cell. MicroRNAs from four different donors were analysed by microarray. Compared to the undifferentiation control, we identified 39–101 microRNAs with more than two-fold higher expression and 3–9 microRNAs with two-fold lower expression. The identified microRNAs were further analysed in terms of gene ontology (GO) in relation with neurogenesis, based on their target mRNAs predicted by computational analysis. This study revealed the specific microRNAs involved in neurogenesis via microRNA microarray, and may provide the basic information for genetic induction of adult stem cell differentiation using microRNAs.     

A role for the P-body component GW182 in microRNA function

In animals, the majority of microRNAs regulate gene expression through the RNA interference (RNAi) machinery without inducing small-interfering RNA (siRNA)-directed mRNA cleavage. Thus, the mechanisms by which microRNAs repress their targets have remained elusive. Recently, Argonaute proteins, which are key RNAi effector components, and their target mRNAs were shown to localize to cytoplasmic foci known as P-bodies or GW-bodies. Here, we show that the Argonaute proteins physically interact with a key P-/GW-body subunit, GW182. Silencing of GW182 delocalizes resident P-/GW-body proteins and impairs the silencing of microRNA reporters. Moreover, mutations that prevent Argonaute proteins from localizing in P-/GW-bodies prevent translational repression of mRNAs even when Argonaute is tethered to its target in a siRNA-independent fashion. Thus, our results support a functional link between cytoplasmic P-bodies and the ability of a microRNA to repress expression of a target mRNA.     

Altered microRNA expression in human heart disease.

MicroRNAs are recently discovered regulators of gene expression and are becoming increasingly recognized as important regulators of heart function. Genome-wide profiling of microRNAs in human heart failure has not been reported previously. We measured expression of 428 microRNAs in 67 human left ventricular samples belonging to control (n = 10), ischemic cardiomyopathy (ICM, n = 19), dilated cardiomyopathy (DCM, n = 25), or aortic stenosis (AS, n = 13) diagnostic groups. miRNA expression between disease and control groups was compared by ANOVA with Dunnett's post hoc test. We controlled for multiple testing by estimating the false discovery rate. Out of 428 microRNAs measured, 87 were confidently detected; 43 were differentially expressed in at least one disease group. In supervised clustering, microRNA expression profiles correctly grouped samples by their clinical diagnosis, indicating that microRNA expression profiles are distinct between diagnostic groups. This was further supported by class prediction approaches, in which the class (control, ICM, DCM, AS) predicted by a microRNA-based classifier matched the clinical diagnosis 69% of the time (P < 0.001). These data show that expression of many microRNAs is altered in heart disease and that different types of heart disease are associated with distinct changes in microRNA expression. These data will guide further studies of the contribution of microRNAs to heart disease pathogenesis.     

Paradoxical microRNAs

In mammalian cells, microRNAs regulate the expression of target mRNAs generally by reducing their stability and/or translation, and thereby control diverse cellular processes such as senescence. We recently reported the differential abundance of microRNAs in young (early-passage, proliferating) relative to senescent (late-passage, non-proliferating) WI-38 human diploid fibroblasts. Here we report that the levels of the vast majority of mRNAs were unaltered in senescent compared to young WI-38 cells, while overall mRNA translation was potently reduced in senescent cells. Downregulation of Dicer or Drosha, two major enzymes in microRNA biogenesis, lowered microRNA levels, but, unexpectedly, it also reduced global translation. While a reduction in Dicer levels markedly enhanced cellular senescence, reduction of Drosha levels did not, suggesting that the Drosha/Dicer effects on translation may be independent of senescence, and further suggesting that microRNAs may directly or indirectly enhance mRNA translation in WI-38 cells. We discuss possible scenarios through which Dicer/Drosha/microRNAs could enhance translation.