Prediction of viral microRNA precursors based on human microRNA precursor sequence and structural features

Exploring the interplay between miRNAs, apoptosis and viral load, in Dengue virus infection

Dengue virus (DENV) is a major global health concern, causing millions of infections annually. Understanding the cellular response to DENV infection is crucial for developing effective therapies. This study provides an in-depth analysis of the cellular response to Dengue virus (DENV) infection, with a specific focus on the interplay between microRNAs (miRNAs), apoptosis, and viral load across different DENV serotypes. Utilizing a variety of cell lines infected with four DENV serotypes, the research methodically quantifies viral load, and the expression levels of miRNA-15, miRNA-16, and BCL2 protein, alongside measuring apoptosis markers. Methodologically, the study employs quantitative PCR for viral load and miRNA expression analysis, and Western blot for apoptosis and BCL2 detection, with a statistical framework that includes ANOVA and correlation analysis to discern significant differences and relationships. The findings reveal that despite similar viral loads across DENV serotypes, DENV-2 exhibits a marginally higher load. A notable upregulation of miRNA-15 and miRNA-16 correlates positively with increased viral load, suggesting their potential role in modulating viral replication. Concurrently, a marked activation of caspases 3 and 7, along with changes in BCL2 protein levels, underscores the role of apoptosis in the cellular response to DENV infection. Conclusively, the study enhances the understanding of miRNA involvement in DENV pathogenesis, highlighting miRNA-15 and miRNA-16 as potential regulatory agents in viral replication and apoptosis. These findings pave the way for further exploration into miRNA-based therapeutic strategies against DENV infection.

Mirnacle: machine learning with SMOTE and random forest for improving selectivity in pre-miRNA ab initio prediction

Background

MicroRNAs (miRNAs) are key gene expression regulators in plants and animals. Therefore, miRNAs are involved in several biological processes, making the study of these molecules one of the most relevant topics of molecular biology nowadays. However, characterizing miRNAs in vivo is still a complex task. As a consequence, in silico methods have been developed to predict miRNA loci. A common ab initio strategy to find miRNAs in genomic data is to search for sequences that can fold into the typical hairpin structure of miRNA precursors (pre-miRNAs). The current ab initio approaches, however, have selectivity issues, i.e., a high number of false positives is reported, which can lead to laborious and costly attempts to provide biological validation. This study presents an extension of the ab initio method miRNAFold, with the aim of improving selectivity through machine learning techniques, namely, random forest combined with the SMOTE procedure that copes with imbalance datasets.

Results

By comparing our method, termed Mirnacle, with other important approaches in the literature, we demonstrate that Mirnacle substantially improves selectivity without compromising sensitivity. For the three datasets used in our experiments, our method achieved at least 97% of sensitivity and could deliver a two-fold, 20-fold, and 6-fold increase in selectivity, respectively, compared with the best results of current computational tools.

Conclusions

The extension of miRNAFold by the introduction of machine learning techniques, significantly increases selectivity in pre-miRNA ab initio prediction, which optimally contributes to advanced studies on miRNAs, as the need of biological validations is diminished. Hopefully, new research, such as studies of severe diseases caused by miRNA malfunction, will benefit from the proposed computational tool.

Ensemble-based classification approach for micro-RNA mining applied on diverse metagenomic sequences

Background

MicroRNAs (miRNAs) are endogenous ∼22 nt RNAs that are identified in many species as powerful regulators of gene expressions. Experimental identification of miRNAs is still slow since miRNAs are difficult to isolate by cloning due to their low expression, low stability, tissue specificity and the high cost of the cloning procedure. Thus, computational identification of miRNAs from genomic sequences provide a valuable complement to cloning. Different approaches for identification of miRNAs have been proposed based on homology, thermodynamic parameters, and cross-species comparisons.

Results

The present paper focuses on the integration of miRNA classifiers in a meta-classifier and the identification of miRNAs from metagenomic sequences collected from different environments. An ensemble of classifiers is proposed for miRNA hairpin prediction based on four well-known classifiers (Triplet SVM, Mipred, Virgo and EumiR), with non-identical features, and which have been trained on different data. Their decisions are combined using a single hidden layer neural network to increase the accuracy of the predictions. Our ensemble classifier achieved 89.3% accuracy, 82.2% f–measure, 74% sensitivity, 97% specificity, 92.5% precision and 88.2% negative predictive value when tested on real miRNA and pseudo sequence data. The area under the receiver operating characteristic curve of our classifier is 0.9 which represents a high performance index.

The proposed classifier yields a significant performance improvement relative to Triplet-SVM, Virgo and EumiR and a minor refinement over MiPred.

The developed ensemble classifier is used for miRNA prediction in mine drainage, groundwater and marine metagenomic sequences downloaded from the NCBI sequence reed archive. By consulting the miRBase repository, 179 miRNAs have been identified as highly probable miRNAs. Our new approach could thus be used for mining metagenomic sequences and finding new and homologous miRNAs.

Conclusions

The paper investigates a computational tool for miRNA prediction in genomic or metagenomic data. It has been applied on three metagenomic samples from different environments (mine drainage, groundwater and marine metagenomic sequences). The prediction results provide a set of extremely potential miRNA hairpins for cloning prediction methods. Among the ensemble prediction obtained results there are pre-miRNA candidates that have been validated using miRbase while they have not been recognized by some of the base classifiers.

Identification and validation of sugarcane streak mosaic virus-encoded microRNAs and their targets in sugarcane

Plants have developed several defense mechanisms to cope with various pathogens (bacteria, fungi, virus, and phytoplasma). Among these, RNA interference (RNAi)-mediated defense against viral infection was found to be a major innate immune response. As a counter attack strategy against the host defense, viruses produce suppressors of host RNAi pathway. MicroRNAs (miRNAs) are an abundant class of short (~18-22 nucleotide) non-coding single-stranded RNAs involved in RNAi pathway leading to post-transcriptional regulation of gene expression. Sugarcane streak mosaic virus (SCSMV) is a distinct strain of Potyviridae family which has a single-stranded positive-sense RNA genome causing mosaic disease in sugarcane. In this study, we computationally predicted and experimentally validated the miRNA encoded by the SCSMV genome with detection efficiency of 99.9 % in stem-loop RT-qPCR and predicted their potential gene targets in sugarcane. These sugarcane target genes considerably broaden future investigation of the SCSMV-encoded miRNA function during viral pathogenesis and might be applied as a new strategy for controlling mosaic disease in sugarcane.

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 ).

Cross-kingdom sequence similarities between human micro-RNAs and plant viruses

Micro-RNAs regulate the expression of cellular and tissue phenotypes at a post-transcriptional level through a complex process involving complementary interactions between micro-RNAs and messenger-RNAs. Similar nucleotide interactions have been shown to occur as cross-kingdom events; for example, between plant viruses and plant micro-RNAs and also between animal viruses and animal micro-RNAs. In this study, this view is expanded to look for cross-kingdom similarities between plant virus and human micro-RNA sequences. A method to identify significant nucleotoide sequence similarities between plant viruses and hsa micro-RNAs was created. Initial analyses demonstrate that plant viruses contain nucleotide sequences which exactly match the seed sequences of human micro-RNAs in both parallel and anti-parallel directions. For example, the bean common mosaic virus strain NL4 from Colombia contains sequences that match exactly the seed sequence for micro-RNA of the hsa-mir-1226 in the parallel direction, which suggests a cross-kingdom conservation. Similarly, the rice yellow stunt viral cRNA contains a sequence that is an exact match in the anti-parallel direction to the seed sequence of hsa-micro-RNA let-7b. The functional implications of these results need to be explored. The finding of these cross-kingdom sequence similarities is a useful starting point in support of bench level investigations.

Antiviral Stratagems Against HIV-1 Using RNA Interference (RNAi) Technology

The versatility of human immunodeficiency virus (HIV)-1 and its evolutionary potential to elude antiretroviral agents by mutating may be its most invincible weapon. Viruses, including HIV, in order to adapt and survive in their environment evolve at extremely fast rates. Given that conventional approaches which have been applied against HIV have failed, novel and more promising approaches must be employed. Recent studies advocate RNA interference (RNAi) as a promising therapeutic tool against HIV. In this regard, targeting multiple HIV sites in the context of a combinatorial RNAi-based approach may efficiently stop viral propagation at an early stage. Moreover, large high-throughput RNAi screens are widely used in the fields of drug development and reverse genetics. Computer-based algorithms, bioinformatics, and biostatistical approaches have been employed in traditional medicinal chemistry discovery protocols for low molecular weight compounds. However, the diversity and complexity of RNAi screens cannot be efficiently addressed by these outdated approaches. Herein, a series of novel workflows for both wet- and dry-lab strategies are presented in an effort to provide an updated review of state-of-the-art RNAi technologies, which may enable adequate progress in the fight against the HIV-1 virus.

Genome analysis of the new human polyomaviruses

Polyomaviridae is a growing family of naked, double-stranded DNA viruses that infect birds and mammals. The last few years, several new members infecting birds or primates have been discovered, including seven human polyomaviruses: KI, WU, Merkel cell polyomavirus, HPyV6, HPyV7, trichodysplasia spinulosa-associated polyomavirus, and HPyV9. In addition, DNA and antibodies against the monkey lymphotropic polyomavirus have been detected in humans, indicating that this virus can also infect man. However, little is known about the route of infection, transmission, cell tropism, and, with the exception of Merkel cell polyomavirus and trichodysplasia spinulosa-associated polyomavirus, the pathogenicity of these viruses. This review compares the genomes of these emerging human polyomaviruses with previously known polyomaviruses detected in man, reports mutations in different isolates, and predicts structural and functional properties of their viral proteins.

A fast ab-initio method for predicting miRNA precursors in genomes

miRNAs are small non coding RNA structures which play important roles in biological processes. Finding miRNA precursors in genomes is therefore an important task, where computational methods are required. The goal of these methods is to select potential pre-miRNAs which could be validated by experimental methods. With the new generation of sequencing techniques, it is important to have fast algorithms that are able to treat whole genomes in acceptable times. We developed an algorithm based on an original method where an approximation of miRNA hairpins are first searched, before reconstituting the pre-miRNA structure. The approximation step allows a substantial decrease in the number of possibilities and thus the time required for searching. Our method was tested on different genomic sequences, and was compared with CID-miRNA, miRPara and VMir. It gives in almost all cases better sensitivity and selectivity. It is faster than CID-miRNA, miRPara and VMir: it takes ≈ 30 s to process a 1 MB sequence, when VMir takes 30 min, miRPara takes 20 h and CID-miRNA takes 55 h. We present here a fast ab-initio algorithm for searching for pre-miRNA precursors in genomes, called miRNAFold. miRNAFold is available at http://EvryRNA.ibisc.univ-evry.fr/.

VDA, a method of choosing a better algorithm with fewer validations

The multitude of bioinformatics algorithms designed for performing a particular computational task presents end-users with the problem of selecting the most appropriate computational tool for analyzing their biological data. The choice of the best available method is often based on expensive experimental validation of the results. We propose an approach to design validation sets for method comparison and performance assessment that are effective in terms of cost and discrimination power.

Validation Discriminant Analysis (VDA) is a method for designing a minimal validation dataset to allow reliable comparisons between the performances of different algorithms. Implementation of our VDA approach achieves this reduction by selecting predictions that maximize the minimum Hamming distance between algorithmic predictions in the validation set. We show that VDA can be used to correctly rank algorithms according to their performances. These results are further supported by simulations and by realistic algorithmic comparisons in silico.

VDA is a novel, cost-efficient method for minimizing the number of validation experiments necessary for reliable performance estimation and fair comparison between algorithms.

Our VDA software is available at http://sourceforge.net/projects/klugerlab/files/VDA/