Integrative in silico approaches to analyse microRNA-mediated responses in human diseases

Advancements in sequencing technologies have facilitated omics level information generation for various diseases in human. High-throughput technologies have become a powerful tool to understand differential expression studies and transcriptional network analysis. An understanding of complex transcriptional networks in human diseases requires integration of datasets representing different RNA species including microRNA (miRNA) and messenger RNA (mRNA). This review emphasises on conceptual explanation of generalized workflow and methodologies to the miRNA mediated responses in human diseases by using different in silico analysis. Although, there have been many prior explorations in miRNA-mediated responses in human diseases, the advantages, limitations and overcoming the limitation through different statistical techniques have not yet been discussed. This review focuses on miRNAs as important gene regulators in human diseases, methodologies for miRNA-target gene prediction and data driven methods for enrichment and network analysis for miRnome-targetome interactions. Additionally, it proposes an integrative workflow to analyse structural components of networks obtained from high-throughput data. This review explains how to apply the existing methods to analyse miRNA-mediated responses in human diseases. It addresses unique characteristics of different analysis, its limitations and its statistical solutions influencing the choice of methods for the analysis through a workflow. Moreover, it provides an overview of promising common integrative approaches to comprehend miRNA-mediated gene regulatory events in biological processes in humans. The proposed methodologies and workflow shall help in the analysis of multi-source data to identify molecular signatures of various human diseases.

Small RNA Targets: Advances in Prediction Tools and High-Throughput Profiling

MicroRNAs (miRNAs) are an abundant class of small non-coding RNAs that regulate gene expression at the post-transcriptional level. They are suggested to be involved in most biological processes of the cell primarily by targeting messenger RNAs (mRNAs) for cleavage or translational repression. Their binding to their target sites is mediated by the Argonaute (AGO) family of proteins. Thus, miRNA target prediction is pivotal for research and clinical applications. Moreover, transfer-RNA-derived fragments (tRFs) and other types of small RNAs have been found to be potent regulators of Ago-mediated gene expression. Their role in mRNA regulation is still to be fully elucidated, and advancements in the computational prediction of their targets are in their infancy. To shed light on these complex RNA-RNA interactions, the availability of good quality high-throughput data and reliable computational methods is of utmost importance. Even though the arsenal of computational approaches in the field has been enriched in the last decade, there is still a degree of discrepancy between the results they yield. This review offers an overview of the relevant advancements in the field of bioinformatics and machine learning and summarizes the key strategies utilized for small RNA target prediction. Furthermore, we report the recent development of high-throughput sequencing technologies, and explore the role of non-miRNA AGO driver sequences.

miRNAs from Plasma Extracellular Vesicles Are Signatory Noninvasive Prognostic Biomarkers against Atherosclerosis in LDLr-/-Mice

Circular microRNAs (miRNAs) have become central in pathophysiological conditions of atherosclerosis (AS). However, the biomarkers for diagnosis and therapeutics against AS are still unclear. The atherosclerosis models in low-density lipoprotein receptor deficiency (LDLr^−/−) mice were established with a high-fat diet (HFD). The extraction kit isolated extracellular vesicles from plasma. Total RNAs were extracted from LDLr^−/− mice in plasma extracellular vesicles. Significantly varying miRNAs were detected by employing Illumina HiSeq 2000 deep sequencing technology. Target gene predictions of miRNAs were employed by related software that include RNAhybrid, TargetScan, miRanda, and PITA. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) further analyzed the intersection points of predicted results. The results showed that the HFD group gradually formed atherosclerotic plaques in thoracic aorta compared with the control group. Out of 17, 8 upregulated and 9 downregulated miRNAs with a significant difference were found in the plasma extracellular vesicles that were further cross-examined by sequencing and bioinformatics analysis. Focal adhesion and Ras signaling pathway were found to be the most closely related pathways through GO and KEGG pathway analyses. The 8 most differentially expressed up- and downregulated miRNAs were further ascertained by TaqMan-based qRT-PCR. TaqMan-based qRT-PCR and in situ hybridization further validated the most differentially expressed miRNAs (miR-378d, miR-181b-5p, miR-146a-5p, miR-421-3p, miR-350-3p, and miR-184-3p) that were consistent with deep sequencing analysis suggesting a promising potential of utility to serve as diagnostic biomarkers against AS. The study gives a comprehensive profile of circular miRNAs in atherosclerosis and may pave the way for identifying biomarkers and novel targets for atherosclerosis.

Recent development of bioinformatics tools for microRNA target prediction

MicroRNAs (miRNAs) are central players that regulate the post-transcriptional processes of gene expression. Binding of miRNAs to target mRNAs can repress their translation by inducing the degradation or by inhibiting the translation of the target mRNAs. High-throughput experimental approaches for miRNA target identification are costly and time-consuming, depending on various factors. It is vitally important to develop the bioinformatics methods for accurately predicting miRNA targets. With the increase of RNA sequences in the post-genomic era, bioinformatics methods are being developed for miRNA studies specially for miRNA target prediction. This review summarizes the current development of state-of-the-art bioinformatics tools for miRNA target prediction, points out the progress and limitations of the available miRNA databases, and their working principles. Finally, we discuss the caveat and perspectives of the next-generation algorithms for the prediction of miRNA targets.

Overexpression miR-24-3p repressed Bim expression to confer tamoxifen resistance in breast cancer

Endocrine therapy resistance represents a major challenge to the successful treatment of patients with breast cancer. The development of tamoxifen resistance commonly occurrs during the treatment of patients with breast cancer whereas its underlying mechanisms remain elusive. Here, we found that miR-24-3p regulated tamoxifen sensitivity in breast cancer cells. Forced overexpression of miR-24-3p augmented tamoxifen-induced cell viability inhibition in breast cancer cells, while knockdown of miR-24-3p partially attenuated the cytotoxicity effect of tamoxifen. Moreover, we discovered Bim as a target gene of miR-24-3p in breast cancer cells by RNA immunoprecipitation, quantitative reverse transcription polymerase chain reaction, Western blot, and dual luciferase reporter assay. In our established tamoxifen resistant MCF7 cell line (MCF7/TAM), there was a significant elevation of miR-24-3p and decrease of BIM expression compared with parental MCF7 cells. In addition, the inhibition of miR-24-3p could reverse the tamoxifen resistance of MCF7/TAM cells by the induction of cell apoptosis. Silencing of Bim expression blocked miR-24-3p inhibitor-induced elevation of tamoxifen sensitivity of MCF7/TAM cells. Using tumor tissues from patients with breast cancer, we also found that the expression of miR-24-3p was negatively correlated with Bim mRNA expression. Collectively, our study highlighted the pivotal role of miR-24-3p overexpression in mediating the development of tamoxifen resistance in breast cancer and suggested miR-24-3p might be a predictor or target for patients with breast cancer.