Computational identification of microRNAs and their targets

MicroRNA and Hemophilia-A Disease: Bioinformatics Prediction and Experimental Analysis

Objective

Hemophilia-A is a common genetic abnormality resulted from decreased or lack of factor VIII (FVIII) pro-coagulant protein function caused by mutations in the F8 gene. Majority of molecular studies consider screening of mutations and their relevant impacts on the quality and expression levels of FVIII. Interestingly, some of the functions in FVIII suggest a probable involvement of small non-coding RNAs embedded within the sequence of F8 gene. Therefore, microRNAs which are encoded within the F8 gene might have a role in hemophilia development. In this study, miRNAs production in the F8 gene was investigated by bioinformatics prediction and experimental validation.

Materials and Methods

In this experimental study, bioinformatics tools have been utilized to seek the novel microRNAs inserted within human F8 gene. The ability to express new microRNAs in F8 locus was studied through reliable bioinformatics databases such as SSCProfiler, RNA fold, miREval, miR-FIND, UCSC genome browser and miRBase. Then, expression and processing of the predicted microRNAs were examined based on bioinformatics methods, in the HEK293 cell lines.

Results

We are unable to confirm existence of the considered mature microRNAs in the transfected cells.

Conclusion

We hope that through changing experimental conditions, designing new primers or altering cell lines as well as the expression of vectors, exogenous and endogenous expressions of the predicted miRNA will be confirmed.

Analysis of microRNA profile of Anopheles sinensis by deep sequencing and bioinformatic approaches

BACKGROUND

microRNAs (miRNAs) are small non-coding RNAs widely identified in many mosquitoes. They are reported to play important roles in development, differentiation and innate immunity. However, miRNAs in Anopheles sinensis, one of the Chinese malaria mosquitoes, remain largely unknown.

METHODS

We investigated the global miRNA expression profile of An. sinensis using Illumina Hiseq 2000 sequencing. Meanwhile, we applied a bioinformatic approach to identify potential miRNAs in An. sinensis. The identified miRNA profiles were compared and analyzed by two approaches. The selected miRNAs from the sequencing result and the bioinformatic approach were confirmed with qRT-PCR. Moreover, target prediction, GO annotation and pathway analysis were carried out to understand the role of miRNAs in An. sinensis.

RESULTS

We identified 49 conserved miRNAs and 12 novel miRNAs by next-generation high-throughput sequencing technology. In contrast, 43 miRNAs were predicted by the bioinformatic approach, of which two were assigned as novel. Comparative analysis of miRNA profiles by two approaches showed that 21 miRNAs were shared between them. Twelve novel miRNAs did not match any known miRNAs of any organism, indicating that they are possibly species-specific. Forty miRNAs were found in many mosquito species, indicating that these miRNAs are evolutionally conserved and may have critical roles in the process of life. Both the selected known and novel miRNAs (asi-miR-281, asi-miR-184, asi-miR-14, asi-miR-nov5, asi-miR-nov4, asi-miR-9383, and asi-miR-2a) could be detected by quantitative real-time PCR (qRT-PCR) in the sequenced sample, and the expression patterns of these miRNAs measured by qRT-PCR were in concordance with the original miRNA sequencing data. The predicted targets for the known and the novel miRNAs covered many important biological roles and pathways indicating the diversity of miRNA functions. We also found 21 conserved miRNAs and eight counterparts of target immune pathway genes in An. sinensis based on the analysis of An. gambiae.

CONCLUSIONS

Our results provide the first lead to the elucidation of the miRNA profile in An. sinensis. Unveiling the roles of mosquito miRNAs will undoubtedly lead to a better understanding of mosquito biology and mosquito-pathogen interactions. This work lays the foundation for the further functional study of An. sinensis miRNAs and will facilitate their application in vector control.

Targeting miRNA for Therapeutics Using a Micronome Based Method for Identification of miRNA-mRNA Pairs and Validation of Key Regulator miRNA

MicroRNAs are 18-22 bp long non-coding sequences and play a critical role in diverse biological processes, through modulation of gene expression at the post-transcriptional level by binding at the 3'-untranslated region of target mRNA. Consequent upon the discovery of structural and functional features of miRNA targeting, several molecular methods have been developed to identify miRNA targets. However, these methods suffer several drawbacks, including technical challenges, requirement of high cell volumes, inability to differentiate between direct and indirect targets, cell/tissue as well as experimental-specificity and imprecise binding site information. Alternatively in silico approach enables the exploration of the potential miRNA-mRNA pairs to investigate signature miRNA and proteins involved in the signaling of various diseases. Here, we describe micronome-based standard method for identification of miRNA-mRNA pairs as well as validation of key regulator miRNA.

The highly pathogenic H5N1 influenza A virus down-regulated several cellular MicroRNAs which target viral genome

Higher and prolonged viral replication is critical for the increased pathogenesis of the highly pathogenic avian influenza (HPAI) subtype of H5N1 influenza A virus (IAV) over the lowly pathogenic H1N1 IAV strain. Recent studies highlighted the considerable roles of cellular miRNAs in host defence against viral infection. In this report, using a 3'UTR reporter system, we identified several putative miRNA target sites buried in the H5N1 virus genome. We found two miRNAs, miR-584-5p and miR-1249, that matched with the PB2 binding sequence. Moreover, we showed that these miRNAs dramatically down-regulated PB2 expression, and inhibited replication of H5N1 and H1N1 IAVs in A549 cells. Intriguingly, these miRNAs expression was differently regulated in A549 cells infected with the H5N1 and H1N1 viruses. Furthermore, transfection of miR-1249 inhibitor enhanced the PB2 expression and promoted the replication of H5N1 and H1N1 IAVs. These results suggest that H5N1 virus may have evolved a mechanism to escape host-mediated inhibition of viral replication through down-regulation of cellular miRNAs, which target its viral genome.

Micronome revealed miR-19a/b as key regulator of SOCS3 during cancer related inflammation of oral squamous cell carcinoma

Although significant advances have been established in molecular biology of Oral squamous cell carcinoma (OSCC), innovative strategies are still required to further understand detailed molecular mechanisms. Using bioinformatic approach, we aim to explore the potential miRNA-mRNA pairs in cancer related inflammatory response and investigate their potential roles as signature miRNA and proteins in the signaling pathway. Firstly, the differentially expressed genes of OSCC were selected which then underwent gene ontology to identify genes engaged in inflammatory response and its regulation. Validated miRNAs were retrieved and miRNAs with complete complementarily with their targets were visualized for miRNA-mRNA regulatory network. Protein-protein interactions of inflammatory and its regulatory genes were analyzed for interacting genes involved in signaling pathway. Eight universal miRNAs were obtained for inflammation and its regulation. miRNA-19a/b showed significant influence in controlling inflammatory response in OSCC. Therefore, micronome on deregulated genes in inflammation identifies miRNA-mRNA pairs which have high potential to be targeted for diagnostic and treatment applications in OSCC.

Identification of conserved and novel microRNAs in Porphyridium purpureum via deep sequencing and bioinformatics

Background

Porphyridium purpureum has been utilized in important industrial and pharmaceutical fields. The identification of microRNAs (miRNAs) in this unique species is of great importance: such identification can help fill gaps in the small RNA (sRNA) studies of this organism and help to elucidate essential biological processes and their regulation mechanisms in this special micro alga.

Results

In this study, 254 high-confidence miRNAs (203 conserved miRNAs and 51 novel miRNAs) were identified by sRNA deep sequencing (sRNA-seq) combined with bioinformatics. A total of 235 putative miRNA families were predicted, including 192 conserved families and 43 species-specific families. The conservation and diversity of predicted miRNA families were analysed in different plant species. Both the 100 % northern blot validation rate (VR) of four randomly selected miRNAs and the results of stem-loop quantitative real time RT-PCR (qRT-PCR) assays of 25 randomly selected miRNAs demonstrated that the majority of the miRNAs identified in this study are credible. A total of 14,958 and 2184 genes were predicted to be targeted by the 186 conserved and 41 novel miRNAs. Gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that some target genes likely provide valuable references for further understanding of vital functions in P. purpureum. In addition, a cytoscape network will provide some clues for research into the complex biological processes that occur in this unique alga.

Conclusions

We first identified a large set of conserved and novel miRNAs in P. purpureum. The characteristic and validation analysis on miRNAs demonstrated authenticity of identification data. Functional annotation of target genes and metabolic pathways they involved in illuminated the direction for further utilization and development this micro alga based on its unique properties.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2985-7) contains supplementary material, which is available to authorized users.

Delivery and targeting of miRNAs for treating liver fibrosis

Liver fibrosis is a pathological condition originating from liver damage that leads to excess accumulation of extracellular matrix (ECM) proteins in the liver. Viral infection, chronic injury, local inflammatory responses and oxidative stress are the major factors contributing to the onset and progression of liver fibrosis. Multiple cell types and various growth factors and inflammatory cytokines are involved in the induction and progression of this disease. Various strategies currently being tried to attenuate liver fibrosis include the inhibition of HSC activation or induction of their apoptosis, reduction of collagen production and deposition, decrease in inflammation, and liver transplantation. Liver fibrosis treatment approaches are mainly based on small drug molecules, antibodies, oligonucleotides (ODNs), siRNA and miRNAs. MicroRNAs (miRNA or miR) are endogenous noncoding RNA of ~22 nucleotides that regulate gene expression at post transcription level. There are several miRNAs having aberrant expressions and play a key role in the pathogenesis of liver fibrosis. Single miRNA can target multiple mRNAs, and we can predict its targets based on seed region pairing, thermodynamic stability of pairing and species conservation. For in vivo delivery, we need some additional chemical modification in their structure, and suitable delivery systems like micelles, liposomes and conjugation with targeting or stabilizing the moiety. Here, we discuss the role of miRNAs in fibrogenesis and current approaches of utilizing these miRNAs for treating liver fibrosis.

Ensemble learning can significantly improve human microRNA target prediction

MicroRNAs (miRNAs) regulate the function of their target genes by down-regulating gene expression, participating in various biological processes. Since the discovery of the first miRNA, computational tools have been essential to predict targets of given miRNAs that can be biologically verified. The precise mechanism underlying miRNA-mRNA interaction has not yet been elucidated completely, and it is still difficult to predict miRNA targets computationally in a robust fashion, despite the large number of in silico prediction methodologies in existence. Because of this limitation, different target prediction tools often report different and (occasionally conflicting) sets of targets. Therefore, we propose a novel target prediction methodology called stacking-based miRNA interaction learner ensemble (SMILE) that employs the concept of stacked generalization (stacking), which is a type of ensemble learning that integrates the outcomes of individual prediction tools with the aim of surpassing the performance of the individual tools. We tested the proposed SMILE method on human miRNA-mRNA interaction data derived from public databases. In our experiments, SMILE improved the accuracy of the target prediction significantly in terms of the area under the receiver operating characteristic curve. Any new target prediction tool can easily be incorporated into the proposed methodology as a component learner, and we anticipate that SMILE will provide a flexible and effective framework for elucidating in vivo miRNA-mRNA interaction.

Overexpression of microRNA gga-miR-1650 decreases the replication of avian leukosis virus subgroup J in infected cells

MicroRNAs (miRNAs) are a class of small regulatory non-coding RNAs that modulate gene expression at the post-transcriptional level, playing a crucial role in cell differentiation and development. Recently, some reports have demonstrated that a number of cellular miRNAs play a role during viral infection. In this study, a luciferase-reporter system carrying the 5' untranslated region (5' UTR) and 3' UTR of avian leukosis virus subgroup J (ALV-J) was used to determine whether cellular miRNAs are involved in ALV-J infection. The miRNA gga-miR-1650 was screened for its potential interaction with the 5' UTR of ALV-J and the ability to suppress luciferase-reporter activity. A mutational analysis of predicted gga-miR-1650-binding sites showed that the 5' and 3' ends of gga-miR-1650 contributed to the interaction between gga-miR-1650 and its target located at the 5' UTR. Overexpression of miRNA gga-miR-1650 was shown to downregulate the expression of the Gag protein and influence the replication of ALV-J through binding to the 5' UTR. Overall, this report provides the basis for the development of new strategies for anti-ALV-J intervention.

The admiR-able advances in cardiovascular biology through the zebrafish model system

MicroRNAs are small non-coding RNAs endogenously expressed by all tissues during development and adulthood. They regulate gene expression by controlling the stability of targeted messenger RNA. In cardiovascular tissues microRNAs play a role by modulating essential genes involved in heart and blood vessel development and homeostasis. The zebrafish (Danio rerio) system is a recognized vertebrate model system useful to study cardiovascular biology; recently, it has been used to investigate microRNA functions during natural and pathological states. In this review, we will illustrate the advantages of the zebrafish model in the study of microRNAs in heart and vascular cells, providing an update on recent discoveries using the zebrafish to identify new microRNAs and their targeted genes in cardiovascular tissues. Lastly, we will provide evidence that the zebrafish is an optimal model system to undercover new microRNA functions in vertebrates and to improve microRNA-based therapeutic approaches.

MicroRNA-185 oscillation controls circadian amplitude of mouse Cryptochrome 1 via translational regulation

The role of the 3′-untranslated region of the mouse Cryptochrome 1 (mCry1) gene is studied at the posttranscriptional level. The results suggest that miR-185 plays a role in the fine-tuned regulation of mCRY1 protein expression by controlling the rhythmicity of mCry1 mRNA translation.

Mammalian circadian rhythm is observed not only at the suprachiasmatic nucleus, a master pacemaker, but also throughout the peripheral tissues. Investigation of the regulation of clock gene expression has mainly focused on transcriptional and posttranslational modifications, and little is known about the posttranscriptional regulation of these genes. In the present study, we investigate the role of microRNAs (miRNAs) in the posttranscriptional regulation of the 3′-untranslated region (UTR) of the mouse Cryptochrome 1 (mCry1) gene. Knockdown of Drosha, Dicer, or Argonaute2 increased mCry1-3′UTR reporter activity. The presence of the miRNA recognition element of mCry1 that is important for miR-185 binding decreased mCRY1 protein, but not mRNA, level. Cytoplasmic miR-185 levels were nearly antiphase to mCRY1 protein levels. Furthermore, miR-185 knockdown elevated the amplitude of mCRY1 protein oscillation. Our results suggest that miR-185 plays a role in the fine-tuned regulation of mCRY1 protein expression by controlling the rhythmicity of mCry1 mRNA translation.

Predicted MicroRNAs for Mammalian Circadian Rhythms

There is little evidence for the involvement of microRNAs (miRs) in the regulation of circadian rhythms, despite the potential relevance of these elements in the posttranscriptional regulation of the clock machinery. The present work aimed to identify miRs targeting circadian genes through a predictive analysis of conserved miRs in mammals. Besides 23 miRs previously associated with circadian rhythms, we found a number of interesting candidate genes, equally predicted by the 3 software programs used, including miR-9, miR-24, miR25, miR-26, miR-27, miR-29, miR-93, miR-211, miR-302, and miR-346. Moreover, several miRs are predicted to be regulated by circadian transcription factors, such as CLOCK/BMAL, DEC2, and REV-ERBalpha. Using real-time PCR we demonstrated that the selected candidate miR-27b showed a daily variation in human leukocytes. This study presents predicted feedback loops for mammalian molecular clock and the first description of an miR with in vivo daily variation in humans.

Germline hereditary, somatic mutations and microRNAs targeting-SNPs in congenital heart defects

Somatic mutations and dysregulation by microRNAs (miRNAs) may have a pivotal role in the Congenital Heart Defects (CHDs). The purpose of the study was to assess both somatic and germline mutations in the GATA4 and NKX2.5 genes as well as to identify 3'UTR single nucleotide polymorphisms (SNPs) in the miRNA target sites. We enrolled 30 patients (13 males; 13.4±8.3 years) with non-syndromic CHD. GATA4 and NKX2.5 genes were screened in cardiac tissue of sporadic and in blood samples of familial cases. Computational methods were used to detect putative miRNAs in the 3'UTR region and to assess the Minimum Free Energy of hybridization (MFE, kcal/mol). Difference of MFEs (ΔMFE) ≥4 kcal/mol between alleles was considered biologically relevant on miRNA binding. The sum of all ΔMFEs (|ΔMFEtot|=∑|ΔMFE|) was calculated in order to predict the biological importance of SNPs binding more miRNAs. No evidence of novel GATA4 and NKX2.5 mutations was found both in sporadic and familial patients. Bioinformatic analysis revealed 27 putative miRNAs binding to identified SNPs in the 3'UTR of GATA4. ΔMFE ≥4 kcal/mol between alleles was obtained for the +354A>C (miR-4299), +587A>G (miR-604), +1355G>A (miR-548v, miR-139-5p) and +1521C>G (miR-583, miR-3125, miR-3928) SNPs. The +1521C>G SNP showed the highest ΔMFEtot (21.66 kcal/mol). Luciferase reporter assays indicated that miR-583 was dose-dependently effective in regulating +1521 C allele compared with +1521 G allele. Based on the analysis of 100 CHD cases and 204 healthy newborns, the +1521 G allele was also associated with a lower risk of CHD (OR=0.5, 95% CI 0.3-0.9, p=0.03), likely due to the relatively low binding of the miRNA and high levels of protein. These results suggest that common SNPs in the 3'UTR of GATA4 alter miRNA gene regulation contributing to the pathogenesis of CHDs.

Computational analysis and predictive modeling of small molecule modulators of microRNA

Background

MicroRNAs (miRNA) are small endogenously transcribed regulatory RNA which modulates gene expression at a post transcriptional level. These small RNAs have now been shown to be critical regulators in a number of biological processes in the cell including pathophysiology of diseases like cancers. The increasingly evident roles of microRNA in disease processes have also motivated attempts to target them therapeutically. Recently there has been immense interest in understanding small molecule mediated regulation of RNA, including microRNA.

Results

We have used publicly available datasets of high throughput screens on small molecules with potential to inhibit microRNA. We employed computational methods based on chemical descriptors and machine learning to create predictive computational models for biological activity of small molecules. We further used a substructure based approach to understand common substructures potentially contributing to the activity.

Conclusion

We generated computational models based on Naïve Bayes and Random Forest towards mining small RNA binding molecules from large molecular datasets. We complement this with substructure based approach to identify and understand potentially enriched substructures in the active dataset. We use this approach to identify miRNA binding potential of a set of approved drugs, suggesting a probable novel mechanism of off-target activity of these drugs. To the best of our knowledge, this is the first and most comprehensive computational analysis towards understanding RNA binding activities of small molecules and predictive modeling of these activities.

Identification of miRNAs in sorghum by using bioinformatics approach

MicroRNAs (miRNAs) regulate gene expression mainly by post-transcriptional gene silencing (PTGS) and in some cases by transcriptional genes silencing (TGS). miRNAs play critical roles in developmental processes, nutrient homeostasis, abiotic stress and pathogen responses of plants. In contrast to the large number of miRNAs predicted in cereal model plant rice, only 148 miRNAs were predicted in sorghum till date (miRBase release 17). This suggested that miRNAs identified in sorghum is far from saturation. Hence, we developed a bioinformatics pipeline using an in-house PERL script and publicly available structure prediction tools to identify miRNAs and their target genes from publically available Expressed Sequence Tags (EST) and Genomic Survey Sequence (GSS). About 1379 known and unique plant miRNAs from 33 different crops were used to predict new miRNAs in sorghum. We identified 31 new miRNAs belonging to 10 different miRNA families. We predicted 72 potential target genes for 31 miRNAs, and most of these target genes are predicted to be involved in plant growth and development.These newly identified miRNAs add to the growing database of miRNA and lay the foundation for further understanding of miRNA function in sorghum plant development.

Down-regulated miR-331-5p and miR-27a are associated with chemotherapy resistance and relapse in leukaemia

Multidrug resistance (MDR) and disease relapse are challenging clinical problems in the treatment of leukaemia. Relapsed disease is frequently refractory to chemotherapy and exhibits multiple drug resistance. Therefore, it is important to identify the mechanism by which cancer cells develop resistance. In this study, we used microRNA (miRNA) microarray and qRT-PCR approaches to investigate the expression of miRNAs in three leukaemia cell lines with different degrees of resistance to doxorubicin (DOX) compared with their parent cell line, K562. The expression of miR-331–5p and miR-27a was inversely correlated with the expression of a drug-resistant factor, P-glycoprotein (P-gp), in leukaemia cell lines with gradually increasing resistance. The development of drug resistance is regulated by the expression of the P-gp. Transfection of the K562 and, a human promyelocytic cell line (HL) HL60 DOX-resistant cells with miR-331–5p and miR-27a, separately or in combination, resulted in the increased sensitivity of cells to DOX, suggesting that correction of altered expression of miRNAs may be used for therapeutic strategies to overcome leukaemia cell resistance. Importantly, miR-331–5p and miR-27a were also expressed at lower levels in a panel of relapse patients compared with primary patients at diagnosis, further illustrating that leukaemia relapse might be a consequence of deregulation of miR-331–5p and miR-27a.

MicroRNAs in Drosophila development

Micro-ribonucleic acids (miRNAs) are small (21-24 nucleotide), endogenously expressed, noncoding RNAs that have emerged as important posttranscriptional regulators of gene expression. MiRNAs have been identified and cloned from diverse eukaryotic organisms where they have been shown to control important physiological and developmental processes such as apoptosis, cell division, and differentiation. A high level of conservation of some miRNAs across phyla further emphasizes their importance as posttranscriptional regulators. Research in a variety of model systems has been instrumental in dissecting the biological functions of miRNAs. In this chapter, we discuss the current literature on the role of miRNAs as developmental regulators in Drosophila.

Analysis of microRNA signatures using size-coded ligation-mediated PCR

The expression pattern and regulatory functions of microRNAs (miRNAs) are intensively investigated in various tissues, cell types and disorders. Differential miRNA expression signatures have been revealed in healthy and unhealthy tissues using high-throughput profiling methods. For further analyses of miRNA signatures in biological samples, we describe here a simple and efficient method to detect multiple miRNAs simultaneously in total RNA. The size-coded ligation-mediated polymerase chain reaction (SL-PCR) method is based on size-coded DNA probe hybridization in solution, followed-by ligation, PCR amplification and gel fractionation. The new method shows quantitative and specific detection of miRNAs. We profiled miRNAs of the let-7 family in a number of organisms, tissues and cell types and the results correspond with their incidence in the genome and reported expression levels. Finally, SL-PCR detected let-7 expression changes in human embryonic stem cells as they differentiate to neuron and also in young and aged mice brain and bone marrow. We conclude that the method can efficiently reveal miRNA signatures in a range of biological samples.

Prediction of targets for microRNAs

MicroRNAs (miRNAs) are small 20-22 nt long RNAs which function as post-transcriptional regulators altering the expression of genes either by blocking translation or by destabilizing mRNAs (for recent reviews see, e.g., Zhang et al. (J Cell Physiol, 210:279-289) and Engels and Hutvagner (Oncogene, 25:6163-6169)). A central problem in miRNA biology is to identify the mRNAs regulated by miRNAs - the miRNA targets. A large number (>10) of bioinformatics methods have been developed to address this question, but unfortunately the scarcity of experimentally validated targets makes it hard to objectively judge the performance of the methods (for an attempt see Sethupathy et al. (Nat Methods, 3:881-886). Nevertheless, here I will give some guidelines on how to use the existing tools to find miRNA targets.

A BAYESIAN GRAPHICAL MODELING APPROACH TO MICRORNA REGULATORY NETWORK INFERENCE

It has been estimated that about 30% of the genes in the human genome are regulated by microRNAs (miRNAs). These are short RNA sequences that can down-regulate the levels of mRNAs or proteins in animals and plants. Genes regulated by miRNAs are called targets. Typically, methods for target prediction are based solely on sequence data and on the structure information. In this paper we propose a Bayesian graphical modeling approach that infers the miRNA regulatory network by integrating expression levels of miRNAs with their potential mRNA targets and, via the prior probability model, with their sequence/structure information. We use a directed graphical model with a particular structure adapted to our data based on biological considerations. We then achieve network inference using stochastic search methods for variable selection that allow us to explore the huge model space via MCMC. A time-dependent coefficients model is also implemented. We consider experimental data from a study on a very well-known developmental toxicant causing neural tube defects, hyperthermia. Some of the pairs of target gene and miRNA we identify seem very plausible and warrant future investigation. Our proposed method is general and can be easily applied to other types of network inference by integrating multiple data sources.

Computational Identification of Putative miRNAs from Felis Catus

microRNAs represent a class of noncoding small RNAs of approximately 20–23 nt length, which are evolutionarily conserved and play a vital role in various biological processes by either degrading or repressing mRNA translation. The Felis catus (cat) genome sequence has been published, and just revealed the number of miRNAs in the genome–-without mention of any further details on these miRNAs. This paper discusses an in silico comparative approach using all known sequences of vertebrate pre-miRNA as query sequence, and report 405 putative miRNAs from cat genome. We determine the identity values of pre-miRNAs and mature miRNAs besides statistical sequence characteristics. Interestingly, among 405 miRNAs–-90, 53 and 50 showed 100% identity to cattle, human and dog, respectively. Further, we have validated 6 miRNAs, whose identity are <85% with the query sequence and validated them using MiPred algorithm. We also identify 25 miRNA clusters in cat based on their homologs in other vertebrates. Most importantly, based on identities among pre-miRNA, mature miRNA, miRNA families and clusters, we observe that miRNAs from cat are more identical to cattle, than humans. Our results, therefore may add a new dimension to the studies related to the evolution of cat.

Got target? Computational methods for microRNA target prediction and their extension

MicroRNAs (miRNAs) are a class of small RNAs of 19-23 nucleotides that regulate gene expression through target mRNA degradation or translational gene silencing. The miRNAs are reported to be involved in many biological processes, and the discovery of miRNAs has been provided great impacts on computational biology as well as traditional biology. Most miRNA-associated computational methods comprise the prediction of miRNA genes and their targets, and increasing numbers of computational algorithms and web-based resources are being developed to fulfill the need of scientists performing miRNA research. Here we summarize the rules to predict miRNA targets and introduce some computational algorithms that have been developed for miRNA target prediction and the application of the methods. In addition, the issue of target gene validation in an experimental way will be discussed.

MicroRNA hsa-miR-135b regulates mineralization in osteogenic differentiation of human unrestricted somatic stem cells

Unrestricted somatic stem cells (USSCs) have been recently identified in human umbilical cord blood and have been shown to differentiate into lineages representing all 3 germ layers. To characterize microRNAs that may regulate osteogenic differentiation of USSCs, we carried out expression analysis for 157 microRNAs using quantitative RT-PCR before and after osteogenic induction (t = 0.5, 24, 72, 168, 216 h). Three microRNAs, hsa-miR-135b, hsa-miR-224, and hsa-miR-31, were consistently down-regulated during osteogenesis of USSC line 1. Hsa-miR-135b was shown to be the most profoundly down-regulated in osteogenesis of USSC line 1 and further confirmed to be down-regulated in the osteogenic differentiation of 2 additional USSC lines. Function of hsa-miR-135b in osteogenesis of USSCs was examined by retroviral overexpression, which resulted in an evident decreased mineralization, indicating that hsa-miR-135b down-regulation is functionally important for full osteogenic differentiation of USSCs. MicroRNAs have been shown to regulate negatively expression of their target gene(s). To identify putative targets of hsa-miR-135b, we performed cDNA microarray expression analysis. We selected in total 10 transcripts that were down-regulated (>or=2-fold) in response to hsa-miR-135b overexpression at day 7 and day 9 of osteogenic differentiation. The function of most of these targets in human osteogenesis is unknown and requires further investigation. Markedly, quantitative RT-PCR data showed decreased expression of osteogenic markers IBSP and Osterix, both known to be involved in bone mineralization, in osteogenesis of USSCs that overexpress hsa-miR-135b. This finding suggests that hsa-miR-135b may control osteoblastic differentiation of USSCs by regulating expression of bone-related genes.

Cellular microRNAs inhibit replication of the H1N1 influenza A virus in infected cells

MicroRNAs (miRNAs) are a class of noncoding RNAs of lengths ranging from 18 to 23 nucleotides (nt) that play critical roles in a wide variety of biological processes. There is a growing amount of evidence that miRNAs play critical roles in intricate host-pathogen interaction networks, but the involvement of miRNAs during influenza viral infection is unknown. To determine whether the cellular miRNAs play an important role in H1N1 influenza A viral infections, 3' untranslated region (UTR) reporter analysis was used to identify putative miRNA targets in the influenza virus genome, and virus proliferation analysis was used to detect the effect of the screened miRNAs on the replication of H1N1 influenza A virus (A/WSN/33) in MDCK cells. The results showed that miRNA 323 (miR-323), miR-491, and miR-654 inhibit replication of the H1N1 influenza A virus through binding to the PB1 gene. Moreover mutational analysis of the predicted miRNA binding sites showed that the three miRNAs bind to the same conserved region of the PB1 gene. Intriguingly, despite the fact that the miRNAs and PB1 mRNA binding sequences are not a perfect match, the miRNAs downregulate PB1 expression through mRNA degradation instead of translation repression. This is the first demonstration that cellular miRNAs regulate influenza viral replication by degradation of the viral gene. Our findings support the notion that any miRNA has antiviral potential, independent of its cellular function, and that the cellular miRNAs play an important role in the host, defending against virus infection.

Statistical models for local occurrences of RNA structures

We develop in this article the necessary statistical theory for computing, for instance, E-values when searching long sequences for the occurrences of local RNA-structures. We show in particular how the theory can be used for estimating scoring parameters with the purpose of optimizing the discriminative performance of the algorithm. The results are implemented in the program StemSearch, which can search for stem loop structures that are formed by, for example, micro RNA precursors. We illustrate the use of the estimation method in practice by considering three miRNA target datasets from Human, Arabidopsis, and C. elegans and by optimizing three penalty parameters in StemSearch. We show that the optimization can improve the discriminative performance considerably when using a first order Markov model as null-distribution. Finally, we compare the output from StemSearch with that of RNALfold, and we discuss some notable differences that are primarily due to fundamental differences in the choice of parameters.

Genome-wide mapping of conserved microRNAs and their host transcripts in Tribolium castaneum

MicroRNAs (miRNAs) are endogenous 22-nt RNAs, which play important regulatory roles by post-transcriptional gene silencing. A computational strategy has been developed for the identification of conserved miRNAs based on features of known metazoan miRNAs in red flour beetle (Tribolium castaneum), which is regarded as one of the major laboratory models of arthropods. Among 118 putative miRNAs, 47% and 53% of the predicted miRNAs from the red flour beetle are harbored by known protein-coding genes (intronic) and genes located outside (intergenic miRNA), respectively. There are 31 intronic miRNAs in the same transcriptional orientation as the host genes, which may share RNA polymerase II and spliceosomal machinery with their host genes for their biogenesis. A hypothetical feedback model has been proposed based on the analysis of the relationship between intronic miRNAs and their host genes in the development of red flour beetle.

In silico miRNA prediction in metazoan genomes: balancing between sensitivity and specificity

BACKGROUND

MicroRNAs (miRNAs), short approximately 21-nucleotide RNA molecules, play an important role in post-transcriptional regulation of gene expression. The number of known miRNA hairpins registered in the miRBase database is rapidly increasing, but recent reports suggest that many miRNAs with restricted temporal or tissue-specific expression remain undiscovered. Various strategies for in silico miRNA identification have been proposed to facilitate miRNA discovery. Notably support vector machine (SVM) methods have recently gained popularity. However, a drawback of these methods is that they do not provide insight into the biological properties of miRNA sequences.

RESULTS

We here propose a new strategy for miRNA hairpin prediction in which the likelihood that a genomic hairpin is a true miRNA hairpin is evaluated based on statistical distributions of observed biological variation of properties (descriptors) of known miRNA hairpins. These distributions are transformed into a single and continuous outcome classifier called the L score. Using a dataset of known miRNA hairpins from the miRBase database and an exhaustive set of genomic hairpins identified in the genome of Caenorhabditis elegans, a subset of 18 most informative descriptors was selected after detailed analysis of correlation among and discriminative power of individual descriptors. We show that the majority of previously identified miRNA hairpins have high L scores, that the method outperforms miRNA prediction by threshold filtering and that it is more transparent than SVM classifiers.

CONCLUSION

The L score is applicable as a prediction classifier with high sensitivity for novel miRNA hairpins. The L-score approach can be used to rank and select interesting miRNA hairpin candidates for downstream experimental analysis when coupled to a genome-wide set of in silico-identified hairpins or to facilitate the analysis of large sets of putative miRNA hairpin loci obtained in deep-sequencing efforts of small RNAs. Moreover, the in-depth analyses of miRNA hairpins descriptors preceding and determining the L score outcome could be used as an extension to miRBase entries to help increase the reliability and biological relevance of the miRNA registry.

MicroRNA-125b is a novel negative regulator of p53

The p53 transcription factor is a key tumor suppressor and a central regulator of the stress response. To ensure a robust and precise response to cellular signals, p53 gene expression must be tightly regulated from the transcriptional to the post-translational levels. Computational predictions suggest that several microRNAs are involved in the post-transcriptional regulation of p53. Here we demonstrate that miR-125b, a brain-enriched microRNA, is a bona fide negative regulator of p53 in both zebrafish and humans. miR-125b-mediated down-regulation of p53 is strictly dependent on the binding of miR-125b to a microRNA response element in the 3' untranslated region of p53 mRNA. Overexpression of miR-125b represses the endogenous level of p53 protein and suppresses apoptosis in human neuroblastoma cells and human lung fibroblast cells. In contrast, knockdown of miR-125b elevates the level of p53 protein and induces apoptosis in human lung fibroblasts and in the zebrafish brain. This phenotype can be rescued significantly by either an ablation of endogenous p53 function or ectopic expression of miR-125b in zebrafish. Interestingly, miR-125b is down-regulated when zebrafish embryos are treated with gamma-irradiation or camptothecin, corresponding to the rapid increase in p53 protein in response to DNA damage. Ectopic expression of miR-125b suppresses the increase of p53 and stress-induced apoptosis. Together, our study demonstrates that miR-125b is an important negative regulator of p53 and p53-induced apoptosis during development and during the stress response.

Discovery of functional miRNA-mRNA regulatory modules with computational methods

The identification of miRNAs and their target mRNAs and the construction of their regulatory networks may give new insights into biological procedures. This study proposes a computational method to discover the functional miRNA-mRNA regulatory modules (FMRMs), that is, groups of miRNAs and their target mRNAs that are believed to participate cooperatively in post-transcriptional gene regulation under specific conditions. The proposed method identifies negatively regulated patterns of miRNAs and mRNAs which associate with cancer and normal conditions, respectively, in a prostate cancer data set. GO and the literature also suggest that they may relate with prostate cancer. It can potentially identify the biologically relevant chains of 'miRNA-->target gene --> condition'.

Predicting microRNA targets in time-series microarray experiments via functional data analysis

Background

MicroRNA (miRNA) target prediction is an important component in understanding gene regulation. One approach is computational: searching nucleotide sequences for miRNA complementary base pairing. An alternative approach explored in this paper is the use of gene expression profiles from time-series microarray experiments to aid in miRNA target prediction. This requires distinguishing genuine targets from genes that are secondarily down-regulated as part of the same regulatory module. We use a functional data analytic (FDA) approach, FDA being a subfield of statistics that extends standard multivariate techniques to datasets with predictor and/or response variables that are functional.

Results

In a miR-124 transfection experiment spanning 120 hours, for genes with measurably down-regulated mRNA, exploratory functional data analysis showed differences in expression profiles over time between directly and indirectly down-regulated genes, such as response latency and biphasic response for direct miRNA targets. For prediction, an FDA approach was shown to effectively classify direct miR-124 targets from time-series microarray data (accuracy 88%; AUC 0.96), providing better performance than multivariate approaches.

Conclusion

Exploratory FDA analysis can reveal interesting aspects of dynamic microarray miRNA studies. Predictive FDA models can be applied where computational miRNA target predictors fail or are unreliable, e.g. when there is a lack of evolutionary conservation, and can provide posterior probabilities to provide additional confirmatory evidence to validate candidate miRNA targets computationally predicted using sequence information. This approach would be applicable to the investigation of other miRNAs and suggests that dynamic microarray studies at a higher time resolution could reveal further details on miRNA regulation.

IntaRNA: efficient prediction of bacterial sRNA targets incorporating target site accessibility and seed regions

Motivation: During the last few years, several new small regulatory RNAs (sRNAs) have been discovered in bacteria. Most of them act as post-transcriptional regulators by base pairing to a target mRNA, causing translational repression or activation, or mRNA degradation. Numerous sRNAs have already been identified, but the number of experimentally verified targets is considerably lower. Consequently, computational target prediction is in great demand. Many existing target prediction programs neglect the accessibility of target sites and the existence of a seed, while other approaches are either specialized to certain types of RNAs or too slow for genome-wide searches.

Results: We introduce INTARNA, a new general and fast approach to the prediction of RNA–RNA interactions incorporating accessibility of target sites as well as the existence of a user-definable seed. We successfully applied INTARNA to the prediction of bacterial sRNA targets and determined the exact locations of the interactions with a higher accuracy than competing programs.

Availability:http://www.bioinf.uni-freiburg.de/Software/

Contact:IntaRNA@informatik.uni-freiburg.de

Supplementary information:Supplementary data are available at Bioinformatics online.

Molecular medicine of microRNAs: structure, function and implications for diabetes

MicroRNAs (miRNAs) are a family of endogenous small noncoding RNA molecules, of 19–28 nucleotides in length. In humans, up to 3% of all genes are estimated to encode these evolutionarily conserved sequences. miRNAs are thought to control expression of thousands of target mRNAs. Mammalian miRNAs generally negatively regulate gene expression by repressing translation, possibly through effects on mRNA stability and compartmentalisation, and/or the translation process itself. An extensive range of in silico and experimental techniques have been applied to our understanding of the occurrence and functional relevance of such sequences, and antisense technologies have been successfully used to control miRNA expression in vitro and in vivo. Interestingly, miRNAs have been identified in both normal and pathological conditions, including differentiation and development, metabolism, proliferation, cell death, viral infection and cancer. Of specific relevance and excitement to the area of diabetes research, miRNA regulation has been implicated in insulin secretion from pancreatic β-cells, diabetic heart conditions and nephropathy. Further analyses of miRNAs in vitro and in vivo will, undoubtedly, enable us determine their potential to be exploited as therapeutic targets in diabetes.

Thinking about RNA? MicroRNAs in the brain

MicroRNAs (miRNAs) are a recently discovered class of small RNA molecules implicated in a wide range of diverse gene regulatory mechanisms. Interestingly, numerous miRNAs are expressed in a spatially and temporally controlled manner in the nervous system. This suggests that gene regulation networks based on miRNA activities may be particularly relevant in neurons. Recent studies show the involvement of RNA-mediated gene silencing in neurogenesis, neural differentiation, synaptic plasticity, and neurologic and psychiatric diseases. This review focuses on the roles of miRNAs in the gene regulation of the nervous system.

Computational prediction and experimental validation of Ciona intestinalis microRNA genes

Background

This study reports the first collection of validated microRNA genes in the sea squirt, Ciona intestinalis. MicroRNAs are processed from hairpin precursors to ~22 nucleotide RNAs that base pair to target mRNAs and inhibit expression. As a member of the subphylum Urochordata (Tunicata) whose larval form has a notochord, the sea squirt is situated at the emergence of vertebrates, and therefore may provide information about the evolution of molecular regulators of early development.

Results

In this study, computational methods were used to predict 14 microRNA gene families in Ciona intestinalis. The microRNA prediction algorithm utilizes configurable microRNA sequence conservation and stem-loop specificity parameters, grouping by miRNA family, and phylogenetic conservation to the related species, Ciona savignyi. The expression for 8, out of 9 attempted, of the putative microRNAs in the adult tissue of Ciona intestinalis was validated by Northern blot analyses. Additionally, a target prediction algorithm was implemented, which identified a high confidence list of 240 potential target genes. Over half of the predicted targets can be grouped into the gene ontology categories of metabolism, transport, regulation of transcription, and cell signaling.

Conclusion

The computational techniques implemented in this study can be applied to other organisms and serve to increase the understanding of the origins of non-coding RNAs, embryological and cellular developmental pathways, and the mechanisms for microRNA-controlled gene regulatory networks.

Host-virus genome interactions: macro roles for microRNAs

MicroRNAs are recently discovered small endogenous non-coding RNAs. These small RNAs of approximately 22 nucleotide length are crucial post-transcriptional regulators of gene expression in a wide spectrum of normal and abnormal biological processes including antiviral defence, oncogenesis and development in higher eukaryotes. Of late, a number of viruses have also been shown to encode for microRNAs. The host- and virus-encoded microRNAs and their targets together thus form a novel regulatory layer of genetic interactions between the host and the virus. Recent reports have thrown light on this new regulatory layer. A clear understanding of the cross-talk between the host and virus would not only enable us to understand the molecular basis of viral pathogenesis, but also enable us to develop better therapeutic strategies. This review discusses the intricacies of host-virus cross-talk mediated by microRNAs. Recent trends in this field and the challenges that need to be addressed are also discussed.

SnoReport: computational identification of snoRNAs with unknown targets

Unlike tRNAs and microRNAs, both classes of snoRNAs, which direct two distinct types of chemical modifications of uracil residues, have proved to be surprisingly difficult to find in genomic sequences. Most computational approaches so far have explicitly used the fact that snoRNAs predominantly target ribosomal RNAs and spliceosomal RNAs. The target is specified by a short stretch of sequence complementarity between the snoRNA and its target. This sequence complementarity to known targets crucially contributes to sensitivity and specificity of snoRNA gene finding algorithms. The discovery of 'orphan' snoRNAs, which either have no known target, or which target ordinary protein-coding mRNAs, however, begs the question whether this class of 'housekeeping' non-coding RNAs is much more widespread and might have a diverse set of regulatory functions. In order to approach this question, we present here a combination of RNA secondary structure prediction and machine learning that is designed to recognize the two major classes of snoRNAs, box C/D and box H/ACA snoRNAs, among ncRNA candidate sequences. The snoReport approach deliberately avoids any usage of target information. We find that the combination of the conserved sequence boxes and secondary structure constraints as a pre-filter with SVM classifiers based on a small set of structural descriptors are sufficient for a reliable identification of snoRNAs. Tests of snoReport on data from several recent experimental surveys show that the approach is feasible; the application to a dataset from a large-scale comparative genomics survey for ncRNAs suggests that there are likely hundreds of previously undescribed 'orphan' snoRNAs still hidden in the human genome.

AVAILABILITY

The snoReport software is implemented in ANSI C. The source code is available under the GNU Public License at http://www.bioinf.uni-leipzig.de/Software/snoReport.

Principles and limitations of computational microRNA gene and target finding

In 2001 there were four PubMed entries matching the word "microRNA" (miRNA). Interestingly, this number has now far exceeded 1300 and is still rapidly increasing. This more than anything demonstrates the extreme attention this field has had within a short period of time. With the large amounts of sequence data being generated, the need for analysis by computational approaches is obvious. Here, we review the general principles used in computational gene and target finding, and discuss the strengths and weaknesses of the methods. Several methods rely on detection of evolutionary conserved candidates, but recent methods have challenged this paradigm by simultaneously searching for the gene and the corresponding target(s). Whereas the early methods made predictions based on sets of hand-derived rules from precursor-miRNA structure or observed target-miRNA interactions, recent methods apply machine learning techniques. Even though these methods are already powerful, the amount of data they rely on is still limited. Since it is evident that data are continuously being generated, it must be anticipated that these methods will further improve their performance.

Algorithms for mapping of mRNA targets for microRNA

MicroRNAs (miRNAs) are involved in human health and disease as endogenous suppressors of the translation of coding genes. At this early point of time in miRNA biology, it is important to identify specific cognate mRNA targets for miRNA. Investigation of the significance of miRNAs in disease processes relies on algorithms that hypothetically link specific miRNAs to their putative target genes. The development of such algorithms represents a hot area of research in biomedical informatics. Lack of biological data linking specific miRNAs to their respective mRNA targets represents the most serious limitation at this time. This article presents a concise review addressing the most popular concepts underlying state-of-the-art algorithms and principles aimed at target mapping for specific miRNAs. Strategies for improvement of the current bioinformatics tools and effective approaches for biological validation are discussed.

Computational Methods for MicroRNA Target Prediction

The discovery of microRNAs (miRNAs) has introduced a new paradigm into gene regulatory systems. Large numbers of miRNAs have been identified in a wide range of species, and most of them are known to downregulate translation of messenger RNAs (mRNAs) via imperfect binding of the miRNA to a specific site or sites in the 3' untranslated region (UTR) of the mRNA. Identification of genes targeted by miRNAs is widely believed to be an important step toward understanding the role of miRNAs in gene regulatory networks. As part of the effort to understand interactions between miRNAs and their targets, computational algorithms have been developed based on observed rules for features such as the degree of hybridization between the two RNA molecules. These in silico approaches provide important tools for miRNA target detection, and together with experimental validation, help to reveal regulated targets of miRNAs. Here, we summarize the knowledge that has been accumulated about the principles of target recognition by miRNAs and the currently available computational methodologies for prediction of miRNA target genes.