Autism-associated SNPs in the clock genes _npas2_, _per1_ and the homeobox gene _en2_ alter DNA sequences that show characteristics of microRNA genes.

Intronic single nucleotide polymorphisms (SNPs) in the clock genes _npas2_ and _per1_ and the homeobox gene _en2_ are reported to be associated with autism. This bioinformatics analysis of the intronic regions which contain the autism-associated SNPs rs1861972 and rs1861973 in _en2_, rs1811399 in _npas2_, and rs885747 in _per1_, shows that these regions encode RNA transcripts with predicted structural characteristics of microRNAs. These microRNA-like structures are disrupted _in silico_ by the presence of the autism enriched alleles of rs1861972, rs1861973, rs1811399 and rs885747 specifically, as compared with the minor alleles of these SNPs. The predicted gene targets of these microRNA-like structures include genes reported to be implicated in autism (_gabrb3_, _shank3_) and genes causative of diseases co-morbid with autism (_mecp2_ and _rai1_). The inheritance of the AC haplotype of rs1861972 - rs1861973 in _en2_, the C allele of rs1811399 in _npas2_, and the C allele of rs1234747 in _per1_ may contribute to the causes of autism by affecting microRNA genes that are co-expressed along with the homeobox gene _en2_ and the circadian genes _npas2_ and _per1_

microRNA expression in peripheral blood cells following acute ischemic stroke and their predicted gene targets.

BackgroundmicroRNA (miRNA) are important regulators of gene expression. In patients with ischemic stroke we have previously shown that differences in immune cell gene expression are present. In this study we sought to determine the miRNA that are differentially expressed in peripheral blood cells of patients with acute ischemic stroke and thus may regulate immune cell gene expression.MethodsmiRNA from peripheral blood cells of forty-eight patients with ischemic stroke and vascular risk factor controls were compared. Differentially expressed miRNA in patients with ischemic stroke were determined by microarray with qRT-PCR confirmation. The gene targets and pathways associated with ischemic stroke that may be regulated by the identified miRNA were characterized.ResultsIn patients with acute ischemic stroke, miR-122, miR-148a, let-7i, miR-19a, miR-320d, miR-4429 were decreased and miR-363, miR-487b were increased compared to vascular risk factor controls. These miRNA are predicted to regulate several genes in pathways previously identified by gene expression analyses, including toll-like receptor signaling, NF-κβ signaling, leukocyte extravasation signaling, and the prothrombin activation pathway.ConclusionsSeveral miRNA are differentially expressed in blood cells of patients with acute ischemic stroke. These miRNA may regulate leukocyte gene expression in ischemic stroke including pathways involved in immune activation, leukocyte extravasation and thrombosis

Elevating microRNA-122 in blood improves outcomes after temporary middle cerebral artery occlusion in rats.

Because our recent studies have demonstrated that miR-122 decreased in whole blood of patients and in whole blood of rats following ischemic stroke, we tested whether elevating blood miR-122 would improve stroke outcomes in rats. Young adult rats were subjected to a temporary middle cerebral artery occlusion (MCAO) or sham operation. A polyethylene glycol-liposome-based transfection system was used to administer a miR-122 mimic after MCAO. Neurological deficits, brain infarction, brain vessel integrity, adhesion molecule expression and expression of miR-122 target and indirect-target genes were examined in blood at 24 h after MCAO with or without miR-122 treatment. miR-122 decreased in blood after MCAO, whereas miR-122 mimic elevated miR-122 in blood 24 h after MCAO. Intravenous but not intracerebroventricular injection of miR-122 mimic decreased neurological deficits and brain infarction, attenuated ICAM-1 expression, and maintained vessel integrity after MCAO. The miR-122 mimic also down-regulated direct target genes (e.g. Vcam1, Nos2, Pla2g2a) and indirect target genes (e.g. Alox5, Itga2b, Timp3, Il1b, Il2, Mmp8) in blood after MCAO which are predicted to affect cell adhesion, diapedesis, leukocyte extravasation, eicosanoid and atherosclerosis signaling. The data show that elevating miR-122 improves stroke outcomes and we postulate this occurs via downregulating miR-122 target genes in blood leukocytes

MMpred: functional miRNA – mRNA interaction analyses by miRNA expression prediction

Background: MicroRNA (miRNA) directed gene repression is an important mechanism of posttranscriptional regulation. Comprehensive analyses of how microRNA influence biological processes requires paired miRNA-mRNA expression datasets. However, a review of both GEO and ArrayExpress repositories revealed few such datasets, which was in stark contrast to the large number of messenger RNA (mRNA) only datasets. It is of interest that numerous primary miRNAs (precursors of microRNA) are known to be co-expressed with coding genes (host genes). Results: We developed a miRNA-mRNA interaction analyses pipeline. The proposed solution is based on two miRNA expression prediction methods – a scaling function and a linear model. Additionally, miRNA-mRNA anticorrelation analyses are used to determine the most probable miRNA gene targets (i.e. the differentially expressed genes under the influence of up- or down-regulated microRNA). Both the consistency and accuracy of the prediction method is ensured by the application of stringent statistical methods. Finally, the predicted targets are subjected to functional enrichment analyses including GO, KEGG and DO, to better understand the predicted interactions. Conclusions: The MMpred pipeline requires only mRNA expression data as input and is independent of third party miRNA target prediction methods. The method passed extensive numerical validation based on the binding energy between the mature miRNA and 3’ UTR region of the target gene. We report that MMpred is capable of generating results similar to that obtained using paired datasets. For the reported test cases we generated consistent output and predicted biological relationships that will help formulate further testable hypotheses

Random walks on mutual microRNA-target gene interaction network improve the prediction of disease-associated microRNAs

Background: MicroRNAs (miRNAs) have been shown to play an important role in pathological initiation, progression and maintenance. Because identification in the laboratory of disease-related miRNAs is not straightforward, numerous network-based methods have been developed to predict novel miRNAs in silico. Homogeneous networks (in which every node is a miRNA) based on the targets shared between miRNAs have been widely used to predict their role in disease phenotypes. Although such homogeneous networks can predict potential disease-associated miRNAs, they do not consider the roles of the target genes of the miRNAs. Here, we introduce a novel method based on a heterogeneous network that not only considers miRNAs but also the corresponding target genes in the network model. Results: Instead of constructing homogeneous miRNA networks, we built heterogeneous miRNA networks consisting of both miRNAs and their target genes, using databases of known miRNA-target gene interactions. In addition, as recent studies demonstrated reciprocal regulatory relations between miRNAs and their target genes, we considered these heterogeneous miRNA networks to be undirected, assuming mutual miRNA-target interactions. Next, we introduced a novel method (RWRMTN) operating on these mutual heterogeneous miRNA networks to rank candidate disease-related miRNAs using a random walk with restart (RWR) based algorithm. Using both known disease-associated miRNAs and their target genes as seed nodes, the method can identify additional miRNAs involved in the disease phenotype. Experiments indicated that RWRMTN outperformed two existing state-of-the-art methods: RWRMDA, a network-based method that also uses a RWR on homogeneous (rather than heterogeneous) miRNA networks, and RLSMDA, a machine learning-based method. Interestingly, we could relate this performance gain to the emergence of "disease modules" in the heterogeneous miRNA networks used as input for the algorithm. Moreover, we could demonstrate that RWRMTN is stable, performing well when using both experimentally validated and predicted miRNA-target gene interaction data for network construction. Finally, using RWRMTN, we identified 76 novel miRNAs associated with 23 disease phenotypes which were present in a recent database of known disease-miRNA associations. Conclusions: Summarizing, using random walks on mutual miRNA-target networks improves the prediction of novel disease-associated miRNAs because of the existence of "disease modules" in these networks

MicroR159 regulation of most conserved targets in Arabidopsis has negligible phenotypic effects

BACKGROUND A current challenge of microRNA (miRNA) research is the identification of biologically relevant miRNA:target gene relationships. In plants, high miRNA:target gene complementarity has enabled accurate target predictions, and slicing of target mRNAs has facilitated target validation through rapid amplification of 5' cDNA ends (5'-RACE) analysis. Together, these approaches have identified more than 20 targets potentially regulated by the deeply conserved miR159 family in Arabidopsis, including eight MYB genes with highly conserved miR159 target sites. However, genetic analysis has revealed the functional specificity of the major family members, miR159a and miR159b is limited to only two targets, MYB33 and MYB65. Here, we examine the functional role of miR159 regulation for the other potential MYB target genes. RESULTS For these target genes, functional analysis failed to identify miR159 regulation that resulted in any major phenotypic impact, either at the morphological or molecular level. This appears to be mainly due to the quiescent nature of the remaining family member, MIR159c. Although its expression overlaps in a temporal and spatial cell-specific manner with a subset of these targets in anthers, the abundance of miR159c is extremely low and concomitantly a mir159c mutant displays no anther defects. Examination of potential miR159c targets with conserved miR159 binding sites found neither their spatial or temporal expression domains appeared miR159 regulated, despite the detection of miR159-guided cleavage products by 5'-RACE. Moreover, expression of a miR159-resistant target (mMYB101) resulted predominantly in plants that are indistinguishable from wild type. Plants that displayed altered morphological phenotypes were found to be ectopically expressing the mMYB101 transgene, and hence were misrepresentative of the in vivo functional role of miR159. CONCLUSIONS This study presents a novel explanation for a paradox common to plant and animal miRNA systems, where among many potential miRNA-target relationships usually only a few appear physiologically relevant. The identification of a quiescent miR159c:target gene regulatory module in anthers provides a likely rationale for the presence of conserved miR159 binding sites in many targets for which miR159 regulation has no obvious functional role. Remnants from the demise of such modules may lead to an overestimation of miRNA regulatory complexity when investigated using bioinformatic, 5'-RACE or transgenic approaches.RSA was funded by an ANU postgraduate scholarship and by a CSIRO Emerging Science Initiative. JL is the recipient of an ANU international student postgraduate scholarship. This research was supported by an Australian Research Council grant DP0773270

miRDB: An online database for prediction of functional microRNA targets

MicroRNAs (miRNAs) are small noncoding RNAs that act as master regulators in many biological processes. miRNAs function mainly by downregulating the expression of their gene targets. Thus, accurate prediction of miRNA targets is critical for characterization of miRNA functions. To this end, we have developed an online database, miRDB, for miRNA target prediction and functional annotations. Recently, we have performed major updates for miRDB. Specifically, by employing an improved algorithm for miRNA target prediction, we now present updated transcriptome-wide target prediction data in miRDB, including 3.5 million predicted targets regulated by 7000 miRNAs in five species. Further, we have implemented the new prediction algorithm into a web server, allowing custom target prediction with user-provided sequences. Another new database feature is the prediction of cell-specific miRNA targets. miRDB now hosts the expression profiles of over 1000 cell lines and presents target prediction data that are tailored for specific cell models. At last, a new web query interface has been added to miRDB for prediction of miRNA functions by integrative analysis of target prediction and Gene Ontology data. All data in miRDB are freely accessible at http://mirdb.org

Genetic variation in the 3′-UTR of CYP1A2, CYP2B6, CYP2D6, CYP3A4, NR1I2, and UGT2B7: potential effects on regulation by microRNA and pharmacogenomics relevance

Introduction: Pharmacogenomics research has concentrated on variation in genes coding for drug metabolising enzymes, transporters and nuclear receptors. However, variation affecting microRNA could also play a role in drug response. This project set out to investigate potential microRNA target sites in 11 genes and the extent of variation in the 3'-UTR of six selected genes; CYP1A2, CYP2B6, CYP2D6, CYP3A4, NR1I2 and UGT2B7. Methods: Fifteen microRNA target prediction algorithms were used to identify microRNAs predicted to regulate 11 genes. The 3'-UTR of the 6 genes which topped the list of potential microRNA targets was sequenced in 30 black South Africans. In addition, genetic variants within these genes were investigated for interference with mRNA-microRNA interactions. Potential effects of observed variants were determined using in silico prediction tools. Results: The 11 genes coding for DMEs, transporters and nuclear receptors were predicted to be targets of microRNAs with CYP2B6, NR1I2 (PXR), CYP3A4 and CYP1A2, interacting with the most microRNAs. The majority of identified genetic variants were predicted to interfere with microRNA regulation. For example, the variant, rs1054190C in NR1I2 was predicted to result in the presence of a binding site for the microRNA miR-1250-5p, while the variant rs1054191G was predicted to result in the absence of a recognition site for miR-371b-3p, miR-4258 and miR-4707-3p. Fifteen of the seventeen, novel variants occurred within microRNA target sequences.Conclusion: The 3'-UTR harbours variation that is likely to influence regulation of specific genes by microRNA. In silico prediction followed by functional validation could aid in decoding the contribution of variation in the 3'-UTR, to some unexplained heritability that affects drug response. Understanding the specific role of each microRNA may lead to identification of markers for targeted therapy and therefore improve personalized drug treatment