Integrative Approaches for microRNA Target Prediction: Combining Sequence Information and the Paired mRNA and miRNA Expression Profiles

miRcorrNet: machine learning-based integration of miRNA and mRNA expression profiles, combined with feature grouping and ranking

A better understanding of disease development and progression mechanisms at the molecular level is critical both for the diagnosis of a disease and for the development of therapeutic approaches. The advancements in high throughput technologies allowed to generate mRNA and microRNA (miRNA) expression profiles; and the integrative analysis of these profiles allowed to uncover the functional effects of RNA expression in complex diseases, such as cancer. Several researches attempt to integrate miRNA and mRNA expression profiles using statistical methods such as Pearson correlation, and then combine it with enrichment analysis. In this study, we developed a novel tool called miRcorrNet, which performs machine learning-based integration to analyze miRNA and mRNA gene expression profiles. miRcorrNet groups mRNAs based on their correlation to miRNA expression levels and hence it generates groups of target genes associated with each miRNA. Then, these groups are subject to a rank function for classification. We have evaluated our tool using miRNA and mRNA expression profiling data downloaded from The Cancer Genome Atlas (TCGA), and performed comparative evaluation with existing tools. In our experiments we show that miRcorrNet performs as good as other tools in terms of accuracy (reaching more than 95% AUC value). Additionally, miRcorrNet includes ranking steps to separate two classes, namely case and control, which is not available in other tools. We have also evaluated the performance of miRcorrNet using a completely independent dataset. Moreover, we conducted a comprehensive literature search to explore the biological functions of the identified miRNAs. We have validated our significantly identified miRNA groups against known databases, which yielded about 90% accuracy. Our results suggest that miRcorrNet is able to accurately prioritize pan-cancer regulating high-confidence miRNAs. miRcorrNet tool and all other supplementary files are available at https://github.com/malikyousef/miRcorrNet.

MicroRNA-29b-3p Inhibits the Migration and Invasion of Gastric Cancer Cells by Regulating the Autophagy-Associated Protein MAZ

Purpose

The purpose of this study was to investigate the relationship between microRNA-29b-3p (miR-29b-3p) and myc-associated zinc finger protein (MAZ) expression and the effects of this interaction on the proliferation, migration, and invasion of gastric cancer cells.

Methods

qPCR and Western blots were used to detect the expression of miR-29b-3p and MAZ. The dual luciferase reporter gene system was used to explore whether MAZ is the target of miR-29b-3p. Cell function experiments and a mouse tumorigenesis model were used to determine the effects of miR-29b-3p overexpression and MAZ depletion on proliferation, migration, and invasion in gastric cancer cell lines and on tumor growth.

Results

The expression level of miR-29b-3p was low and the expression level of MAZ was high in gastric cancer cells compared with normal human gastric mucosal epithelial cells. MAZ was the target gene of miR-29b-3p. The upregulation of miR-29b-3p reduces the expression of MAZ. Overexpression of miR-29b-3p and downregulation of MAZ inhibited the proliferation and migration of cancer cells and induced apoptosis by controlling the expression of autophagy-related proteins. MiR-29b-3p mimics inhibit tumor growth in mice.

Conclusion

MiR-29b-3p inhibits the migration and invasion of gastric cancer cells by regulating the autophagy-related protein MAZ.

Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences

Advances in the technologies and informatics used to generate and process large biological data sets (omics data) are promoting a critical shift in the study of biomedical sciences. While genomics, transcriptomics and proteinomics, coupled with bioinformatics and biostatistics, are gaining momentum, they are still, for the most part, assessed individually with distinct approaches generating monothematic rather than integrated knowledge. As other areas of biomedical sciences, including metabolomics, epigenomics and pharmacogenomics, are moving towards the omics scale, we are witnessing the rise of inter-disciplinary data integration strategies to support a better understanding of biological systems and eventually the development of successful precision medicine. This review cuts across the boundaries between genomics, transcriptomics and proteomics, summarizing how omics data are generated, analysed and shared, and provides an overview of the current strengths and weaknesses of this global approach. This work intends to target students and researchers seeking knowledge outside of their field of expertise and fosters a leap from the reductionist to the global-integrative analytical approach in research.

Single-cell microRNA-mRNA co-sequencing reveals non-genetic heterogeneity and mechanisms of microRNA regulation

Measuring multiple omics profiles from the same single cell opens up the opportunity to decode molecular regulation that underlies intercellular heterogeneity in development and disease. Here, we present co-sequencing of microRNAs and mRNAs in the same single cell using a half-cell genomics approach. This method demonstrates good robustness (~95% success rate) and reproducibility (R2 = 0.93 for both microRNAs and mRNAs), yielding paired half-cell microRNA and mRNA profiles, which we can independently validate. By linking the level of microRNAs to the expression of predicted target mRNAs across 19 single cells that are phenotypically identical, we observe that the predicted targets are significantly anti-correlated with the variation of abundantly expressed microRNAs. This suggests that microRNA expression variability alone may lead to non-genetic cell-to-cell heterogeneity. Genome-scale analysis of paired microRNA-mRNA co-profiles further allows us to derive and validate regulatory relationships of cellular pathways controlling microRNA expression and intercellular variability.

MiR-3470b promotes bovine ephemeral fever virus replication via directly targeting mitochondrial antiviral signaling protein (MAVS) in baby hamster Syrian kidney cells

BACKGROUND

Bovine ephemeral fever virus (BEFV), the causative agent of bovine ephemeral fever, is an economically important pathogen of cattle and water buffalo. MicroRNAs (miRNAs) are endogenous 21-23 nt small non-coding RNA molecules that binding to a multiple of target mRNAs and functioning in the regulation of viral replication including the miRNA-mediated antiviral defense. However, the reciprocal interaction between bovine ephemeral fever virus replication and host miRNAs still remain poorly understood. The aim of our study herein was to investigate the exact function of miR-3470b and its molecular mechanisms during BEFV infection.

RESULTS

In this study, we found a set of microRNAs induced by BEFV infection using small RNA deep sequencing, and further identified BEFV infection could significantly up-regulate the miR-3470b expression in Baby Hamster Syrian Kidney cells (BHK-21) after 24 h and 48 h post-infection (pi) compared to normal BHK-21 cells without BEFV infection. Additionally, the target association between miR-3470b and mitochondrial antiviral signaling protein (MAVS) was predicted by target gene prediction tools and further validated using a dual-luciferase reporter assay, and the expression of MAVS mRNA and protein levels was negatively associated with miR-3470b levels. Furthermore, the miR-3470b mimic transfection significantly contributed to increase the BEFV N mRNA, G protein level and viral titer, respectively, whereas the miR-3470b inhibitor had the opposite effect on BEFV replication. Moreover, the overexpression of MAVS or silencing of miR-3470b by its inhibitors suppressed BEFV replication, and knockdown of MAVS by small interfering RNA also promoted the replication of BEFV.

CONCLUSIONS

Our findings is the first to reveal that miR-3470b as a novel host factor regulates BEFV replication via directly targeting the MAVS gene in BHK-21 cells and may provide a potential strategy for developing effective antiviral therapy.

Improving microRNA target prediction with gene expression profiles

BACKGROUND

Mammalian genomes encode for thousands of microRNAs, which can potentially regulate the majority of protein-coding genes. They have been implicated in development and disease, leading to great interest in understanding their function, with computational methods being widely used to predict their targets. Most computational methods rely on sequence features, thermodynamics, and conservation filters; essentially scanning the whole transcriptome to predict one set of targets for each microRNA. This has the limitation of not considering that the same microRNA could have different sets of targets, and thus different functions, when expressed in different types of cells.

RESULTS

To address this problem, we combine popular target prediction methods with expression profiles, via machine learning, to produce a new predictor: TargetExpress. Using independent data from microarrays and high-throughput sequencing, we show that TargetExpress outperforms existing methods, and that our predictions are enriched in functions that are coherent with the added expression profile and literature reports.

CONCLUSIONS

Our method should be particularly useful for anyone studying the functions and targets of miRNAs in specific tissues or cells. TargetExpress is available at: http://targetexpress.ceiabreulab.org/ .

Tiny giants of gene regulation: experimental strategies for microRNA functional studies

The discovery over two decades ago of short regulatory microRNAs (miRNAs) has led to the inception of a vast biomedical research field dedicated to understanding these powerful orchestrators of gene expression. Here we aim to provide a comprehensive overview of the methods and techniques underpinning the experimental pipeline employed for exploratory miRNA studies in animals. Some of the greatest challenges in this field have been uncovering the identity of miRNA-target interactions and deciphering their significance with regard to particular physiological or pathological processes. These endeavors relied almost exclusively on the development of powerful research tools encompassing novel bioinformatics pipelines, high-throughput target identification platforms, and functional target validation methodologies. Thus, in an unparalleled manner, the biomedical technology revolution unceasingly enhanced and refined our ability to dissect miRNA regulatory networks and understand their roles in vivo in the context of cells and organisms. Recurring motifs of target recognition have led to the creation of a large number of multifactorial bioinformatics analysis platforms, which have proved instrumental in guiding experimental miRNA studies. Subsequently, the need for discovery of miRNA-target binding events in vivo drove the emergence of a slew of high-throughput multiplex strategies, which now provide a viable prospect for elucidating genome-wide miRNA-target binding maps in a variety of cell types and tissues. Finally, deciphering the functional relevance of miRNA post-transcriptional gene silencing under physiological conditions, prompted the evolution of a host of technologies enabling systemic manipulation of miRNA homeostasis as well as high-precision interference with their direct, endogenous targets. For further resources related to this article, please visit the WIREs website.

An update of miRNASNP database for better SNP selection by GWAS data, miRNA expression and online tools

MicroRNAs (miRNAs) are key regulators of gene expression involved in a broad range of biological processes. MiRNASNP aims to provide single nucleotide polymorphisms (SNPs) in miRNAs and genes that may impact miRNA biogenesis and/or miRNA target binding. Advanced miRNA research provided abundant data about miRNA expression, validated targets and related phenotypic variants. In miRNASNP v2.0, we have updated our previous database with several new data and features, including: (i) expression level and expression correlation of miRNAs and target genes in different tissues, (ii) linking SNPs to the results of genome-wide association studies, (iii) integrating experimentally validated miRNA:mRNA interactions, (iv) adding multiple filters to prioritize functional SNPs. In addition, as a supplement of the database, we have set up three flexible online tools to analyse the influence of novel variants on miRNA:mRNA binding. A new nice web interface was designed for miRNASNP v2.0 allowing users to browse, search and download. We aim to maintain the miRNASNP as a solid resource for function, genetics and disease studies of miRNA-related SNPs. Database URL: http://bioinfo.life. hust.edu.cn/miRNASNP2/.

A Bayesian Framework to Improve MicroRNA Target Prediction by Incorporating External Information

MicroRNAs (miRNAs) are small regulatory RNAs that play key gene-regulatory roles in diverse biological processes, particularly in cancer development. Therefore, inferring miRNA targets is an essential step to fully understanding the functional properties of miRNA actions in regulating tumorigenesis. Bayesian linear regression modeling has been proposed for identifying the interactions between miRNAs and mRNAs on the basis of the integrated sequence information and matched miRNA and mRNA expression data; however, this approach does not use the full spectrum of available features of putative miRNA targets. In this study, we integrated four important sequence and structural features of miRNA targeting with paired miRNA and mRNA expression data to improve miRNA-target prediction in a Bayesian framework. We have applied this approach to a gene-expression study of liver cancer patients and examined the posterior probability of each miRNA-mRNA interaction being functional in the development of liver cancer. Our method achieved better performance, in terms of the number of true targets identified, than did other methods.

Scan for Motifs: a webserver for the analysis of post-transcriptional regulatory elements in the 3' untranslated regions (3' UTRs) of mRNAs

Background

Gene expression in vertebrate cells may be controlled post-transcriptionally through regulatory elements in mRNAs. These are usually located in the untranslated regions (UTRs) of mRNA sequences, particularly the 3′UTRs.

Results

Scan for Motifs (SFM) simplifies the process of identifying a wide range of regulatory elements on alignments of vertebrate 3′UTRs. SFM includes identification of both RNA Binding Protein (RBP) sites and targets of miRNAs. In addition to searching pre-computed alignments, the tool provides users the flexibility to search their own sequences or alignments. The regulatory elements may be filtered by expected value cutoffs and are cross-referenced back to their respective sources and literature. The output is an interactive graphical representation, highlighting potential regulatory elements and overlaps between them. The output also provides simple statistics and links to related resources for complementary analyses. The overall process is intuitive and fast. As SFM is a free web-application, the user does not need to install any software or databases.

Conclusions

Visualisation of the binding sites of different classes of effectors that bind to 3′UTRs will facilitate the study of regulatory elements in 3′ UTRs.