Human pre-miRNAs Research Articles

Substrate promiscuity of Dicer toward precursors of the let-7 family and their 3′-end modifications

The human let-7 miRNA family consists of thirteen members that play critical roles in many biological processes, including development timing and tumor suppression, and their levels are disrupted in several diseases. Dicer is the endoribonuclease responsible for processing the precursor miRNA (pre-miRNA) to yield the mature miRNA, and thereby plays a crucial role in controlling the cellular levels of let-7 miRNAs. It is well established that the sequence and structural features of pre-miRNA hairpins such as the 5′-phosphate, the apical loop, and the 2-nt 3′-overhang are important for the processing activity of Dicer. Exceptionally, nine precursors of the let-7 family (pre-let-7) contain a 1-nt 3′-overhang and get mono-uridylated in vivo, presumably to allow efficient processing by Dicer. Pre-let-7 are also oligo-uridylated in vivo to promote their degradation and likely prevent their efficient processing by Dicer. In this study, we systematically investigated the impact of sequence and structural features of all human let-7 pre-miRNAs, including their 3′-end modifications, on Dicer binding and processing. Through the combination of SHAPE structural probing, in vitro binding and kinetic studies using purified human Dicer, we show that despite structural discrepancies among pre-let-7 RNAs, Dicer exhibits remarkable promiscuity in binding and cleaving these substrates. Moreover, the 1- or 2-nt 3′-overhang, 3′-mono-uridylation, and 3′-oligo-uridylation of pre-let-7 substrates appear to have little effect on Dicer binding and cleavage rates. Thus, this study extends current knowledge regarding the broad substrate specificity of Dicer and provides novel insight regarding the effect of 3′-modifications on binding and cleavage by Dicer.

The Human Pre-miRNA Distance Distribution for Exploring Disease Association.

MicroRNAs (miRNAs), playing an important role in cell differentiation, development, gene regulation, and apoptosis, have attracted much attention in recent years. miRNAs were shown to be involved in the mechanisms of various diseases, and certainly, they can be employed as useful disease biomarkers. The phylogenetic tree analysis of miRNA biomarkers is a useful tool to investigate the association between various diseases as well as the association between viruses and disease. In addition to the phylogenetic tree analysis, a more advanced study is to use the miRNA distance distribution to evaluate the similarity of the miRNA biomarkers. The mature miRNA distance distribution based on mature miRNA sequences has been derived. The averages of the pairwise distances of miRNA biomarkers for several associated diseases were shown to be smaller than the overall mean of all miRNAs, which indicates the high similarity of miRNA biomarkers for associated diseases. In addition to the mature miRNA, the precursor miRNA (pre-miRNA) may be more useful to explore the similarity of miRNAs because the mature miRNA duplex is released from the pre-miRNA. Therefore, in this study, the distance distributions based on human pre-miRNA stem-loop sequences were derived. The 1917 human miRNA stem-loop sequences in the miRBase dataset were used to derive the pre-miRNA distance distribution, and this is the first study to provide the distance distribution based on the human pre-miRNAs. The similarity of miRNA biomarkers for several associated diseases or vaccines was examined using the derived distribution, and the results show that the similarity of pre-miRNA biomarkers may be a feasible way to help explore the disease association.

Secondary structure RNA elements control the cleavage activity of DICER

The accurate and efficient cleavage of shRNAs and pre-miRNAs by DICER is crucial for their gene-silencing activity. Here, we conduct high-throughput DICER cleavage assays for more than ~20,000 different shRNAs and show the comprehensive cleavage activities of DICER on these sequences. We discover a single-nucleotide bulge (22-bulge), which facilitates the cleavage activity of DICER on shRNAs and human pre-miRNAs. As a result, this 22-bulge enhances the gene-silencing activity of shRNAs and the accuracy of miRNA biogenesis. In addition, various single-nucleotide polymorphism-edited 22-bulges are found to govern the cleavage sites of DICER on pre-miRNAs and thereby control their functions. Finally, we identify the single cleavage of DICER and reveal its molecular mechanism. Our findings improve the understanding of the DICER cleavage mechanism, provide a foundation for the design of accurate and efficient shRNAs for gene-silencing, and indicate the function of bulges in regulating miRNA biogenesis.

Selectivity of Exportin 5 binding to human precursor microRNAs

ABSTRACT Exportin 5 (Exp5, XPO5) is a nuclear export factor that functions in the microRNA (miRNA) biogenesis pathway to transport precursor miRNAs (pre-miRNAs) from the nucleus to the cytoplasm. Most of our current understanding of the Exp5 and pre-miRNA interaction is based on the investigation of how Exp5 binds to human pre-miR-30a (pre-miR-30 for short). As there are hundreds of human miRNA genes, how representative pre-miR-30 is, whether or how Exp5 interacts with distinct cargoes differentially, or whether Exp5 regulates miRNA expression, is unknown. Here we examined and compared the interactions between Exp5 and 157 human pre-miRNAs. We found that Exp5 binds distinct pre-miRNAs with modest variations in efficiencies, with the 3ʹ overhang and the apical loop in pre-miRNAs being the two major discriminating factors. Exp5 binding efficiencies do not significantly correlate with endogenous miRNA expression, suggesting that Exp5 activity does not contribute to differential miRNA expression in vivo. Nonetheless, in human cells with reduced Exp5 levels, preferential Exp5 binding correlated with miRNA expression changes. Thus, our study provides a global picture of how Exp5 interacts with human pre-miRNAs and its role in regulating miRNA expression. Abbreviations: Exp5: Exportin 5; miRNA: microRNA; pri-miRNA: primary microRNA; pre-miRNA: precursor microRNA; nt: nucleotide

Deep neural networks for human microRNA precursor detection

BackgroundMicroRNAs (miRNAs) play important roles in a variety of biological processes by regulating gene expression at the post-transcriptional level. So, the discovery of new miRNAs has become a popular task in biological research. Since the experimental identification of miRNAs is time-consuming, many computational tools have been developed to identify miRNA precursor (pre-miRNA). Most of these computation methods are based on traditional machine learning methods and their performance depends heavily on the selected features which are usually determined by domain experts. To develop easily implemented methods with better performance, we investigated different deep learning architectures for the pre-miRNAs identification.ResultsIn this work, we applied convolution neural networks (CNN) and recurrent neural networks (RNN) to predict human pre-miRNAs. We combined the sequences with the predicted secondary structures of pre-miRNAs as input features of our models, avoiding the feature extraction and selection process by hand. The models were easily trained on the training dataset with low generalization error, and therefore had satisfactory performance on the test dataset. The prediction results on the same benchmark dataset showed that our models outperformed or were highly comparable to other state-of-the-art methods in this area. Furthermore, our CNN model trained on human dataset had high prediction accuracy on data from other species.ConclusionsDeep neural networks (DNN) could be utilized for the human pre-miRNAs detection with high performance. Complex features of RNA sequences could be automatically extracted by CNN and RNN, which were used for the pre-miRNAs prediction. Through proper regularization, our deep learning models, although trained on comparatively small dataset, had strong generalization ability.

MiRNAmotif—A Tool for the Prediction of Pre-miRNA–Protein Interactions

MicroRNAs (miRNAs) are short, non-coding post-transcriptional gene regulators. In mammalian cells, mature miRNAs are produced from primary precursors (pri-miRNAs) using canonical protein machinery, which includes Drosha/DGCR8 and Dicer, or the non-canonical mirtron pathway. In plant cells, mature miRNAs are excised from pri-miRNAs by the DICER-LIKE1 (DCL1) protein complex. The involvement of multiple regulatory proteins that bind directly to distinct miRNA precursors in a sequence- or structure-dependent manner adds to the complexity of the miRNA maturation process. Here, we present a web server that enables searches for miRNA precursors that can be recognized by diverse RNA-binding proteins based on known sequence motifs to facilitate the identification of other proteins involved in miRNA biogenesis. The database used by the web server contains known human, murine, and Arabidopsis thaliana pre-miRNAs. The web server can also be used to predict new RNA-binding protein motifs based on a list of user-provided sequences. We show examples of miRNAmotif applications, presenting precursors that contain motifs recognized by Lin28, MCPIP1, and DGCR8 and predicting motifs within pre-miRNA precursors that are recognized by two DEAD-box helicases—DDX1 and DDX17. miRNAmotif is released as an open-source software under the MIT License. The code is available at GitHub (www.github.com/martynaut/mirnamotif). The webserver is freely available at http://mirnamotif.ibch.poznan.pl.

Targeting of G-Quadruplex Harboring Pre-miRNA 92b by LNA Rescues PTEN Expression in NSCL Cancer Cells

Since the elevated levels of microRNAs (miRNAs) often cause various diseases, selective inhibition of miRNA maturation is an important therapeutic strategy. Commonly used anti-miRNA strategies are limited to targeting of mature miRNAs, as the upstream targeting of miRNA maturation with an oligonucleotide is challenging due to the presence of a stable pre-miRNA stem-loop structure. Previously, we reported that about 16% of known human pre-miRNAs have the potential to adopt G-quadruplex (GQ) structures alternatively to canonical stem-loops. Herein, we showed that a rationally designed locked nucleic acid (LNA) binds specifically the GQ conformation of pre-miRNA 92b and inhibits pre-miRNA maturation. Further, we showed that the LNA treatment rescues PTEN expression in non-small-cell lung cancer (NSCLC) cells, which is suppressed by the elevated level of miRNA 92b. Treatment of LNA significantly decreases the IC50 of doxorubicin for NSCLC cells. This strategy can be developed as a novel anti-miRNA therapeutic approach to target GQ harboring miRNAs. This can potentially be a more powerful approach than targeting of the mature miRNA, as it is an upstream targeting and can reduce both 3' and the 5' mature miRNA levels at once.

BRAda: A Robust Method for Identification of Pre-microRNAs by Combining Adaboost Framework with BP and RF

Background: MicroRNAs (miRNAs) are a set of non-coding, short (approximately 21nt) RNAs that play an important role as a regulator in biological processes in the cells. The identification and discovery of pre-miRNAs are beneficial in understanding the regulatory process, the functions of miRNAs and other genes, and furthermore in biological evolution. Methods: Machine learning method has been a powerful technology in distinguishing the real premiRNAs from other hairpin-like sequences (pseudo pre-miRNAs). However, most of the commonly used classifiers are not promising in predicting performances on independent testing data sets. To overcome this, we proposed a novel BRAda algorithm integrating BP neural network and random forest classifier based on two balanced training sets. By distributing weights to these classifiers and the proposed 98-dimensional features, we obtained a strong classifier with high-accuracy and stability. Furthermore, based on the novel classifier we proposed, two independent testing sets (undated human and non-human pre-miRNAs) were employed to evaluate the prediction performance. Results: The novel method BRAda algorithm is significantly outperformed the other methods in identifying both human and non-human pre-miRNAs. Conclusion: The novel algorithm integrated BP neural network and random forest classifier based on two balanced training sets. Compared with other state-of-art machine-learning methods, the performance of BRAda was perfect (the ACC is over 99%) according to the validation. Besides, though the algorithm was trained by human gene sets, the prediction performance on non-human testing sets was also excellent (the average ACC is over 97%), which means the method not only has high stability but also robustness. By experiments and validation, the authors showed the method is an effective tool for pre-miRNA identification. Keywords: Biological process, BRAda, BP neural network, genes, Pre-miRNA identification, random forest.

A Machine Learning Approach for MicroRNA Precursor Prediction in Retro-transcribing Virus Genomes.

Identification of microRNA (miRNA) precursors has seen increased efforts in recent years. The difficulty in experimental detection of pre-miRNAs increased the usage of computational approaches. Most of these approaches rely on machine learning especially classification. In order to achieve successful classification, many parameters need to be considered such as data quality, choice of classifier settings, and feature selection. For the latter one, we developed a distributed genetic algorithm on HTCondor to perform feature selection. Moreover, we employed two widely used classification algorithms libSVM and random forest with different settings to analyze the influence on the overall classification performance. In this study we analyzed 5 human retro virus genomes; Human endogenous retrovirus K113, Hepatitis B virus (strain ayw), Human T lymphotropic virus 1, Human T lymphotropic virus 2, Human immunodeficiency virus 2, and Human immunodeficiency virus 1. We then predicted pre-miRNAs by using the information from known virus and human pre-miRNAs. Our results indicate that these viruses produce novel unknown miRNA precursors which warrant further experimental validation.

A Machine Learning Approach for MicroRNA Precursor Prediction in Retro-transcribing Virus Genomes

SummaryIdentification of microRNA (miRNA) precursors has seen increased efforts in recent years. The difficulty in experimental detection of pre-miRNAs increased the usage of computational approaches. Most of these approaches rely on machine learning especially classification. In order to achieve successful classification, many parameters need to be considered such as data quality, choice of classifier settings, and feature selection. For the latter one, we developed a distributed genetic algorithm on HTCondor to perform feature selection. Moreover, we employed two widely used classification algorithms libSVM and random forest with different settings to analyze the influence on the overall classification performance. In this study we analyzed 5 human retro virus genomes; Human endogenous retrovirus K113, Hepatitis B virus (strain ayw), Human T lymphotropic virus 1, Human T lymphotropic virus 2, Human immunodeficiency virus 2, and Human immunodeficiency virus 1. We then predicted pre-miRNAs by using the information from known virus and human pre-miRNAs. Our results indicate that these viruses produce novel unknown miRNA precursors which warrant further experimental validation.

LBSizeCleav: improved support vector machine (SVM)-based prediction of Dicer cleavage sites using loop/bulge length.

BackgroundDicer is necessary for the process of mature microRNA (miRNA) formation because the Dicer enzyme cleaves pre-miRNA correctly to generate miRNA with correct seed regions. Nonetheless, the mechanism underlying the selection of a Dicer cleavage site is still not fully understood. To date, several studies have been conducted to solve this problem, for example, a recent discovery indicates that the loop/bulge structure plays a central role in the selection of Dicer cleavage sites. In accordance with this breakthrough, a support vector machine (SVM)-based method called PHDCleav was developed to predict Dicer cleavage sites which outperforms other methods based on random forest and naive Bayes. PHDCleav, however, tests only whether a position in the shift window belongs to a loop/bulge structure.ResultIn this paper, we used the length of loop/bulge structures (in addition to their presence or absence) to develop an improved method, LBSizeCleav, for predicting Dicer cleavage sites. To evaluate our method, we used 810 empirically validated sequences of human pre-miRNAs and performed fivefold cross-validation. In both 5p and 3p arms of pre-miRNAs, LBSizeCleav showed greater prediction accuracy than PHDCleav did. This result suggests that the length of loop/bulge structures is useful for prediction of Dicer cleavage sites.ConclusionWe developed a novel algorithm for feature space mapping based on the length of a loop/bulge for predicting Dicer cleavage sites. The better performance of our method indicates the usefulness of the length of loop/bulge structures for such predictions.

IMiRNA-PseDPC: microRNA precursor identification with a pseudo distance-pair composition approach

A microRNA (miRNA) is a small non-coding RNA molecule, functioning in transcriptional and post-transcriptional regulation of gene expression. The human genome may encode over 1000 miRNAs. Albeit poorly characterized, miRNAs are widely deemed as important regulators of biological processes. Aberrant expression of miRNAs has been observed in many cancers and other disease states, indicating that they are deeply implicated with these diseases, particularly in carcinogenesis. Therefore, it is important for both basic research and miRNA-based therapy to discriminate the real pre-miRNAs from the false ones (such as hairpin sequences with similar stem-loops). Particularly, with the avalanche of RNA sequences generated in the post-genomic age, it is highly desired to develop computational sequence-based methods for effectively identifying the human pre-miRNAs. Here, we propose a predictor called “iMiRNA-PseDPC”, in which the RNA sequences are formulated by a novel feature vector called “pseudo distance-pair composition” (PseDPC) with 10 types of structure statuses. Rigorous cross-validations on a much larger and more stringent newly constructed benchmark data-set showed that our approach has remarkably outperformed the existing ones in either prediction accuracy or efficiency, indicating the new predictor is quite promising or at least may become a complementary tool to the existing predictors in this area. For the convenience of most experimental scientists, a user-friendly web server for the new predictor has been established at http://bioinformatics.hitsz.edu.cn/iMiRNA-PseDPC/, by which users can easily get their desired results without the need to go through the mathematical details. It is anticipated that the new predictor may become a useful high throughput tool for genome analysis particularly in dealing with large-scale data.

Web server for prediction of miRNAs and their precursors and binding sites

A microRNA (miRNA) is a small noncoding RNA molecule about 22 nucleotides in length. The paper describes a web server for predicting miRNAs and their precursors and binding sites. The predictions are based on either sequence similarity to known miRNAs of 223 organisms or context-structural hidden Markov models. It has been shown that the proposed methods of prediction of human miRNAs and pre-miRNAs outperform the existing ones in accuracy. The average deviation of predicted 5'-ends of human miRNAs from actual positions is 3.13 nt in the case of predicting one pair of complementary miRNAs (miRNA-miRNA* duplex). A useful option for our application is the prediction of an additional miRNA pair. In this mode, the pairs closest to actual miRNA deviate by 1.61 nt on average. The proposed method also shows good performance in predicting mouse miRNAs. Binding sites for miRNAs are predicted by two known approaches based on complementarity and thermodynamic stability of the miRNA-mRNA duplex and on a new approach, which takes into account miRNAs competition for the site. The role of the secondary structure in miRNA processing is considered. The web server is available at http://wwwmgs.bionet.nsc.ru/mgs/programs/rnaanalys/.

MatPred: Computational Identification of Mature MicroRNAs within Novel Pre-MicroRNAs.

Background. MicroRNAs (miRNAs) are short noncoding RNAs integral for regulating gene expression at the posttranscriptional level. However, experimental methods often fall short in finding miRNAs expressed at low levels or in specific tissues. While several computational methods have been developed for predicting the localization of mature miRNAs within the precursor transcript, the prediction accuracy requires significant improvement. Methodology/Principal Findings. Here, we present MatPred, which predicts mature miRNA candidates within novel pre-miRNA transcripts. In addition to the relative locus of the mature miRNA within the pre-miRNA hairpin loop and minimum free energy, we innovatively integrated features that describe the nucleotide-specific RNA secondary structure characteristics. In total, 94 features were extracted from the mature miRNA loci and flanking regions. The model was trained based on a radial basis function kernel/support vector machine (RBF/SVM). Our method can predict precise locations of mature miRNAs, as affirmed by experimentally verified human pre-miRNAs or pre-miRNAs candidates, thus achieving a significant advantage over existing methods. Conclusions. MatPred is a highly effective method for identifying mature miRNAs within novel pre-miRNA transcripts. Our model significantly outperformed three other widely used existing methods. Such processing prediction methods may provide important insight into miRNA biogenesis.

MicroRNAs control transcription factor NF-kB (p65) expression in human ovarian cells.

MicroRNAs (miRNAs) are known to influence ovarian cell proliferation, apoptosis and hormone release, but it remains unknown whether miRNAs affect ovarian functions via transcription factors. We examined the effect of miRNAs on nuclear factor-κappaB (NF-kB) (p65) expression in human ovarian luteinized granulosa cells. We transfected cultured primary human ovarian luteinized granulosa cells with 80 different constructs encoding human pre-miRNAs and then evaluated NF-kB (p65) expression (percentage of cells containing p65) by immunocytochemistry. We found that 21 of the constructs stimulated NF-kB (p65) expression and 18 of the constructs inhibited NF-kB (p65) expression. This is the first direct demonstration that miRNAs affect NF-kB (p65) expression and the first genome-scale miRNA screen to identify upregulation and downregulation of NF-kB accumulation by miRNAs in the ovary. Novel miRNAs that affect the NF-kB signalling pathway could be useful for the control of NF-kB-dependent reproductive processes and the treatment of NF-kB-dependent reproductive disorders.

Abstract 1492: Prediction of cancer progression in Barrett's esophagus patients using miRNA profiling

Abstract Background: Barrett's esophagus (BE) is a metaplastic disease with risk to develop esophageal adenocarcinoma (EAC). Current screening method for BE patients (endoscopy and biopsy) is discomfort, expensive and relatively risky for patients. Moreover, the procedure is complicated by subjective histopathological examination. Approximately only 2% of BE patients annually progress to adenocarcinoma. The aim of our study was to identify miRNA profiles in order to predict cancer progression in high-risk BE patients. Methods and patients: FFPE samples were obtained from 68 patients (10 EAC, 4 high-grade and 21 low-grade dysplasia of esophagus, and 33 BE). There were 49 males and 19 females; median age was 58 years, range 27 - 97 years. Total RNA was purified from FFPE biopsy samples using Proteinase K treatment followed by RNeasy kit (Qiagen). Microarray analysis was performed using the GeneChip miRNA 3.0 Array (Affymetrix). The data were analyzed using “R” software and the Bioconductor package. Results: In total, 68 GeneChip miRNA 3.0 Arrays were performed in 68 patients (8 poor quality arrays were excluded). Expression profiles of 1733 human miRNAs, 1658 human pre-miRNAs and 2216 human sno-RNAs and sca-RNAs were analyzed. In supervised clustering analysis we found 29 differentially expressed miRNAs, 7 pre-miRNAs and 9 sno-RNAs (p<0.001 or logFC <-2 or logFC >2) in EAC patients compared to BE patients. One of the dominant findings was decreased expression of miR-22 in EAC samples, which is known to function as a tumor suppressor and histone deacetylase 4 regulator. Conclusion: We have identified miRNA profiles allowing for better diagnostics of cancer progression in BE patients, which, however, will require further validation. Acknowledgment: This work was financially supported by IGA MZ CR NT13585-3/202, IGA UP LF 2013_015, CZ.1.05/2.1.00/01.0030 and CZ.1.07/2.3.00/30.0004. Citation Format: Josef Srovnal, Ondrej Slaby, Jiri Ehrmann, Jan Gregar, Lenka Radova, Michaela Sedlackova, Marian Hajduch. Prediction of cancer progression in Barrett's esophagus patients using miRNA profiling. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 1492. doi:10.1158/1538-7445.AM2014-1492

Computational prediction of disease microRNAs in domestic animals.

BackgroundThe most important means of identifying diseases before symptoms appear is through the discovery of disease-associated biomarkers. Recently, microRNAs (miRNAs) have become highly useful biomarkers of infectious, genetic and metabolic diseases in human but they have not been well studied in domestic animals. It is probable that many of the animal homologs of human disease-associated miRNAs may be involved in domestic animal diseases. Here we describe a computational biology study in which human disease miRNAs were utilized to predict orthologous miRNAs in cow, chicken, pig, horse, and dog.ResultsWe identified 287 human disease-associated miRNAs which had at least one 100% identical animal homolog. The 287 miRNAs were associated with 359 human diseases referenced in 2,863 Pubmed articles. Multiple sequence analysis indicated that over 60% of known horse mature miRNAs found perfect matches in human disease-associated miRNAs, followed by dog (50%). As expected, chicken had the least number of perfect matches (5%). Phylogenetic analysis of miRNA precursors indicated that 85% of human disease pre-miRNAs were highly conserved in animals, showing less than 5% nucleotide substitution rates over evolutionary time. As an example we demonstrated conservation of human hsa-miR-143-3p which is associated with type 2 diabetes and targets AKT1 gene which is highly conserved in pig, horse and dog. Functional analysis of AKT1 gene using Gene Ontology (GO) showed that it is involved in glucose homeostasis, positive regulation of glucose import, positive regulation of glycogen biosynthetic process, glucose transport and response to food.ConclusionsThis data provides the animal and veterinary research community with a resource to assist in generating hypothesis-driven research for discovering animal disease-related miRNA from their datasets and expedite development of prophylactic and disease-treatment strategies and also influence research efforts to identify novel disease models in large animals. Integrated data is available for download at http://agbase.hpc.msstate.edu/cgi-bin/animal_mirna.cgi.

AB INITIO HUMAN miRNA AND PRE-miRNA PREDICTION

MicroRNAs (miRNAs) are small single-stranded noncoding RNAs that play an important role in post-transcriptional regulation of gene expression. In this paper, we present a web server for ab initio prediction of the human miRNAs and their precursors. The prediction methods are based on the hidden Markov Models and the context-structural characteristics. By taking into account the identified patterns of primary and secondary structures of the pre-miRNAs, a new HMM model is proposed and the existing context-structural Markov model is modified. The evaluation of the method performance has shown that it can accurately predict novel human miRNAs. Comparing with the existing methods we demonstrate that our method has a higher prediction quality both for human pre-miRNAs and miRNAs. The models have also showed good results in the prediction of the mouse miRNAs. The web server is available at http://wwwmgs.bionet.nsc.ru/mgs/programs/rnaanalys (mirror http://miRNA.at.nsu.ru ).

Bioinformatics analysis on structural features of microRNA precursors in insects

To date, thousands of microRNAs (miRNAs) and their precursors (pre-miRNAs) have been identified in insects and their nucleotide sequences deposited in the miRBase database. In the present work, we have systematically analyzed, utilizing bioinformatics tools, the featural differences between human and insect pre-miRNAs, as well as differences across 24 insect species. Results showed that the nucleotide composition, sequence length, nucleotides preference and secondary structure features between human and insects were different. Subsequently, with the aid of three available SVM-based prediction programs, pre-miRNA sequences were evaluated and given corresponding scores. Thus it was found that of 2633 sequences from the 24 chosen insect species, 2229 (84.7%) were successfully recognized by the Mirident classifier, higher than Triplet-SVM (72.5%) and PMirP (72.6%). In contrast, four species, including the domesticated silkworm, Bombyx mori L., the fruit fly, Drosophila melanogaster Meigen, the honeybee, Apis mellifera L. and the red flour beetle, Tribolium castaneum (Herbst), were found to be largely responsible for the poor performance of some sequence matching. Compared with other species, B. mori especially showed the worst performance with the lowest average MFE index (0.73). Collectively these results pave the way for understanding specificity and diversity of miRNA precursors in insects, and lay the foundation for the further development of more suitable algorisms for insects.

MiRNASNP: a database of miRNA related SNPs and their effects on miRNA function

Background MicroRNAs (miRNAs) are af amily of endogenous small non-coding RNAs involved in various developmental and physiological processes by negatively regulating gene expression. Single nucleotide polymorphisms (SNPs) are important variations for the diversity among individuals, as well as leading to phenotypes, traits, and diseases. What will happen when SNPs meet miRNAs? To date, a number of studies have demonstrated that SNPs in target sites or miRNA genes were associated with diseases [1-3]. Our aim is to characterize these functional miRNA related SNPs comprehensively. Results Through mapping SNPs onto miRNAs, we totally identified 757 SNPs (including indel polymorphisms) in 440 human pre-miRNAs, 218 SNPs in pre-miRNAs of other 8 species and thousands of SNPs in pre-miRNAs flanking regions. Of them, 50 SNPs are in the seed regions of 41 human miRNAs. Two different methods (TargetScan and miRanda) were used to predict the target sites for the wild type miRNAs and SNP-miRNAs. By comparing the targets of wild type miRNA with that of SNP-miRNA, we obtained the potential miRNA targets loss and gain. We found about half of the targets will be changed by SNP in miRNA seed region. In addition, we experimentally confirmed seven loss of-function SNPs and one gain-of-function SNP by luciferase report assay. We also mapped SNPs onto gene 3’ untranslated region (3’UTR) and identified tens of thousands of SNPs in 3’UTRs which would affect the miRNA/gene interaction, either destroy a real target or create an illegal target. Among these miRNA related SNPs, we think the SNPs with relatively high minor allele frequencies (MAF), high MAF difference between populations and undergoing positive selection pressure will be important candidates for population phonotype research and complex trait studies. All useful data were compiled into miRNASNP, a user-friendly free online database (http://www.bioguo. org/miRNASNP/).