DIANA-miRGen v4: indexing promoters and regulators for more than 1500 microRNAs

Validation of Selected MicroRNA Transcriptome Data in the Bovine Corpus Luteum during Early Pregnancy by RT-qPCR

In cattle, the corpus luteum (CL) is pivotal in maintaining early pregnancy by secreting progesterone. To establish pregnancy, the conceptus produces interferon-τ, preventing luteolysis and initiating the transformation of the CL spurium into a CL verum. Although this transformation is tightly regulated, limited data are available on the expression of microRNAs (miRNAs) during and after this process. To address this gap, we re-analyzed previously published RNA-Seq data of CL from pregnant cows and regressed CL from non-pregnant cows. This analysis identified 44 differentially expressed miRNAs. From this pool, three miRNAs-bta-miR-222-3p, bta-miR-29c, and bta-miR-2411-3p-were randomly selected for relative quantification. Using bovine ovaries (n = 14) obtained from an abattoir, total RNA (including miRNAs) was extracted and converted to cDNA for RT-qPCR. The results revealed that bta-miR-222-3p was downregulated (p = 0.016) in pregnant females compared to non-pregnant cows with regressed CL. However, no differences in miRNA expression were observed between CL of pregnant and non-pregnant cows for bta-miR-29c (p > 0.32) or bta-miR-2411-3p (p > 0.60). In silico prediction approaches indicated that these miRNAs are involved in pathways regulating pregnancy maintenance, such as the VEGF- and FoxO-signaling pathways. Additionally, their biogenesis is regulated by GABPA and E2F4 transcription factors. The validation of selected miRNA expression in the CL during pregnancy by RT-qPCR provides novel insights that could potentially lead to the identification of biomarkers related to CL physiology and pregnancy outcome.

Molecular and Therapeutic Roles of Non-Coding RNAs in Oral Cancer-A Review

Oral cancer (OC) is among the most common malignancies in the world. Despite advances in therapy, the worst-case scenario for OC remains metastasis, with a 50% survival rate. Therefore, it is critical to comprehend the pathophysiology of the condition and to create diagnostic and treatment plans for OC. The development of high-throughput genome sequencing has revealed that over 90% of the human genome encodes non-coding transcripts, or transcripts that do not code for any proteins. This paper describes the function of these different kinds of non-coding RNAs (ncRNAs) in OC as well as their intriguing therapeutic potential. The onset and development of OC, as well as treatment resistance, are linked to dysregulated ncRNA expression. These ncRNAs' potentially significant roles in diagnosis and prognosis have been suggested by their differing expression in blood or saliva. We have outlined every promising feature of ncRNAs in the treatment of OC in this study.

Recent progress in miRNA biogenesis and decay

MicroRNAs are a class of small regulatory RNAs that mediate regulation of protein synthesis by recognizing sequence elements in mRNAs. MicroRNAs are processed through a series of steps starting from transcription and primary processing in the nucleus to precursor processing and mature function in the cytoplasm. It is also in the cytoplasm where levels of mature microRNAs can be modulated through decay mechanisms. Here, we review the recent progress in the lifetime of a microRNA at all steps required for maintaining their homoeostasis. The increasing knowledge about microRNA regulation upholds great promise as therapeutic targets.

Transcriptional repression of lncRNA and miRNA subsets mediated by LRF during erythropoiesis

Non-coding RNA (ncRNA) species, mainly long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) have been currently imputed for lesser or greater involvement in human erythropoiesis. These RNA subsets operate within a complex circuit with other epigenetic components and transcription factors (TF) affecting chromatin remodeling during cell differentiation. Lymphoma/leukemia-related (LRF) TF exerts higher occupancy on DNA CpG rich sites and is implicated in several differentiation cell pathways and erythropoiesis among them and also directs the epigenetic regulation of hemoglobin transversion from fetal (HbF) to adult (HbA) form by intervening in the γ-globin gene repression. We intended to investigate LRF activity in the evolving landscape of cells' commitment to the erythroid lineage and specifically during HbF to HbA transversion, to qualify this TF as potential repressor of lncRNAs and miRNAs. Transgenic human erythroleukemia cells, overexpressing LRF and further induced to erythropoiesis, were subjected to expression analysis in high LRF occupancy genetic loci-producing lncRNAs. LRF abundance in genetic loci transcribing for studied lncRNAs was determined by ChIP-Seq data analysis. qPCRs were performed to examine lncRNA expression status. Differentially expressed miRNA pre- and post-erythropoiesis induction were assessed by next-generation sequencing (NGS), and their promoter regions were charted. Expression levels of lncRNAs were correlated with DNA methylation status of flanked CpG islands, and contingent co-regulation of hosted miRNAs was considered. LRF-binding sites were overrepresented in LRF overexpressing cell clones during erythropoiesis induction and exerted a significant suppressive effect towards lncRNAs and miRNA collections. Based on present data interpretation, LRF's multiplied binding capacity across genome is suggested to be transient and associated with higher levels of DNA methylation. KEY MESSAGES: During erythropoiesis, LRF displays extensive occupancy across genetic loci. LRF significantly represses subsets of lncRNAs and miRNAs during erythropoiesis. Promoter region CpG islands' methylation levels affect lncRNA expression. MiRNAs embedded within lncRNA loci show differential regulation of expression.

Mechanisms of circRNA/lncRNA-miRNA interactions and applications in disease and drug research

In recent years, breakthroughs in bioinformatics have been made with the discovery of many functionally significant non-coding RNAs (ncRNAs). The discovery of these ncRNAs has further demonstrated the multi-level characteristics of intracellular gene expression regulation, which plays an important role in assisting diagnosis, guiding clinical drug use and determining prognosis in the treatment process of various diseases. microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs) are the three major types of ncRNAs that interact with each other. Studies have shown that lncRNAs and circRNAs can sponge miRNAs, thereby influencing normal physiological processes and regulating mRNA expression and, thus, the physiological state of cells. This paper summarizes the mechanism of action and research progress of the three ncRNA and seven types of modalities. This summary is intended to provide new ideas for diagnosing and treating diseases and researching and developing new drugs.

Post-transcriptional regulation of polycistronic microRNAs

An important proportion of microRNA (miRNA) genes tend to lie close to each other within animal genomes. Such genomic organization is generally referred to as miRNA clusters. Even though many miRNA clusters have been greatly studied, most attention has been usually focused on functional impacts of clustered miRNA co-expression. However, there is also another compelling aspect about these miRNA clusters, their polycistronic nature. Being transcribed on a single RNA precursor, polycistronic miRNAs benefit from common transcriptional regulation allowing their coordinated expression. And yet, numerous reports have revealed striking discrepancies in the accumulation of mature miRNAs produced from the same cluster. Indeed, the larger polycistronic transcripts can act as platforms providing unforeseen post-transcriptional regulatory mechanisms controlling individual miRNA processing, thus leading to differential miRNA expression, and sometimes even challenging the general assumption that polycistronic miRNAs are co-expressed. In this review, we aim to address the current knowledge about how miRNA polycistrons are post-transcriptionally regulated. In particular, we will focus on the mechanisms occurring at the level of the primary transcript, which are highly relevant for individual miRNA processing and as such have a direct repercussion on miRNA function within the cell. This article is categorized under: RNA Processing > Processing of Small RNAs Regulatory RNAs/RNAi/Riboswitches > Biogenesis of Effector Small RNAs RNA Interactions with Proteins and Other Molecules > RNA-Protein Complexes.

Machine learning in the development of targeting microRNAs in human disease

A microRNA is a small, single-stranded, non-coding ribonucleic acid that plays a crucial role in RNA silencing and can regulate gene expression. With the in-depth study of miRNA in development and disease, miRNA has become an attractive target for novel therapeutic strategies. Exploring miRNA targeting therapy only through experiments is expensive and laborious, so it is essential to develop novel and efficient computational methods to narrow down the search. Recent advances in machine learning applied in biomedical informatics provide opportunities to explore miRNA-targeting drugs, thus promoting miRNA therapeutics. This review provides an overview of recent advancements in miRNA targeting therapeutic using machine learning. First, we mainly describe the basics of predicting miRNA targeting drugs, including pharmacogenomic data resources and data preprocessing. Then we present primary machine learning algorithms and elaborate their application in discovering relationships among miRNAs, drugs, and diseases. Along with the progress of miRNA targeting therapeutics, we finally analyze and discuss the current challenges and opportunities that machine learning confronts.

Overview of Computational and Experimental Methods to Identify Tissue-Specific MicroRNA Targets

As ubiquitous posttranscriptional regulators of gene expression, microRNAs (miRNAs) play key roles in cell physiology and function across taxa. In the last two decades, we have gained a good understanding about miRNA biogenesis pathways, modes of action, and consequences of miRNA-mediated gene regulation. More recently, research has focused on exploring causes for miRNA dysregulation, miRNA-mediated crosstalk between genes and signaling pathways, and the role of miRNAs in disease.This chapter discusses methods for the identification of miRNA-target interactions and causes for tissue-specific miRNA-target regulation. Computational approaches for predicting miRNA target sites and assessing tissue-specific target regulation are discussed. Moreover, there is an emphasis on features that affect miRNA target recognition and how high-throughput sequencing protocols can help in assessing miRNA-mediated gene regulation on a genome-wide scale. In addition, this chapter introduces some experimental approaches for the validation of miRNA targets as well as web-based resources sharing predicted and validated miRNA-target interactions.

DeepTSS: multi-branch convolutional neural network for transcription start site identification from CAGE data

BACKGROUND

The widespread usage of Cap Analysis of Gene Expression (CAGE) has led to numerous breakthroughs in understanding the transcription mechanisms. Recent evidence in the literature, however, suggests that CAGE suffers from transcriptional and technical noise. Regardless of the sample quality, there is a significant number of CAGE peaks that are not associated with transcription initiation events. This type of signal is typically attributed to technical noise and more frequently to random five-prime capping or transcription bioproducts. Thus, the need for computational methods emerges, that can accurately increase the signal-to-noise ratio in CAGE data, resulting in error-free transcription start site (TSS) annotation and quantification of regulatory region usage. In this study, we present DeepTSS, a novel computational method for processing CAGE samples, that combines genomic signal processing (GSP), structural DNA features, evolutionary conservation evidence and raw DNA sequence with Deep Learning (DL) to provide single-nucleotide TSS predictions with unprecedented levels of performance.

RESULTS

To evaluate DeepTSS, we utilized experimental data, protein-coding gene annotations and computationally-derived genome segmentations by chromatin states. DeepTSS was found to outperform existing algorithms on all benchmarks, achieving 98% precision and 96% sensitivity (accuracy 95.4%) on the protein-coding gene strategy, with 96.66% of its positive predictions overlapping active chromatin, 98.27% and 92.04% co-localized with at least one transcription factor and H3K4me3 peak.

CONCLUSIONS

CAGE is a key protocol in deciphering the language of transcription, however, as every experimental protocol, it suffers from biological and technical noise that can severely affect downstream analyses. DeepTSS is a novel DL-based method for effectively removing noisy CAGE signal. In contrast to existing software, DeepTSS does not require feature selection since the embedded convolutional layers can readily identify patterns and only utilize the important ones for the classification task. This study highlights the key role that DL can play in Molecular Biology, by removing the inherent flaws of experimental protocols, that form the backbone of contemporary research. Here, we show how DeepTSS can unleash the full potential of an already popular and mature method such as CAGE, and push the boundaries of coding and non-coding gene expression regulator research even further.

Updated review of advances in microRNAs and complex diseases: experimental results, databases, webservers and data fusion

MicroRNAs (miRNAs) are gene regulators involved in the pathogenesis of complex diseases such as cancers, and thus serve as potential diagnostic markers and therapeutic targets. The prerequisite for designing effective miRNA therapies is accurate discovery of miRNA-disease associations (MDAs), which has attracted substantial research interests during the last 15 years, as reflected by more than 55 000 related entries available on PubMed. Abundant experimental data gathered from the wealth of literature could effectively support the development of computational models for predicting novel associations. In 2017, Chen et al. published the first-ever comprehensive review on MDA prediction, presenting various relevant databases, 20 representative computational models, and suggestions for building more powerful ones. In the current review, as the continuation of the previous study, we revisit miRNA biogenesis, detection techniques and functions; summarize recent experimental findings related to common miRNA-associated diseases; introduce recent updates of miRNA-relevant databases and novel database releases since 2017, present mainstream webservers and new webserver releases since 2017 and finally elaborate on how fusion of diverse data sources has contributed to accurate MDA prediction.

TRmir: A Comprehensive Resource for Human Transcriptional Regulatory Information of MiRNAs

MicroRNAs (miRNAs) are small non-coding RNAs, which play important roles in regulating various biological functions. Many available miRNA databases have provided a large number of valuable resources for miRNA investigation. However, not all existing databases provide comprehensive information regarding the transcriptional regulatory regions of miRNAs, especially typical enhancer, super-enhancer (SE), and chromatin accessibility regions. An increasing number of studies have shown that the transcriptional regulatory regions of miRNAs, as well as related single-nucleotide polymorphisms (SNPs) and transcription factors (TFs) have a strong influence on human diseases and biological processes. Here, we developed a comprehensive database for the human transcriptional regulation of miRNAs (TRmir), which is focused on providing a wealth of available resources regarding the transcriptional regulatory regions of miRNAs and annotating their potential roles in the regulation of miRNAs. TRmir contained a total of 5,754,414 typical enhancers/SEs and 1,733,966 chromatin accessibility regions associated with 1,684 human miRNAs. These regions were identified from over 900 human H3K27ac ChIP-seq, ATAC-seq, and DNase-seq samples. Furthermore, TRmir provided detailed (epi)genetic information about the transcriptional regulatory regions of miRNAs, including TFs, common SNPs, risk SNPs, linkage disequilibrium (LD) SNPs, expression quantitative trait loci (eQTLs), 3D chromatin interactions, and methylation sites, especially supporting the display of TF binding sites in the regulatory regions of over 7,000 TF ChIP-seq samples. In addition, TRmir integrated miRNA expression and related disease information, supporting extensive pathway analysis. TRmir is a powerful platform that offers comprehensive information about the transcriptional regulation of miRNAs for users and provides detailed annotations of regulatory regions. TRmir is free for academic users and can be accessed at http://bio.liclab.net/trmir/index.html.

Bioinformatic Tools for the Analysis and Prediction of ncRNA Interactions

Noncoding RNAs (ncRNAs) play prominent roles in the regulation of gene expression via their interactions with other biological molecules such as proteins and nucleic acids. Although much of our knowledge about how these ncRNAs operate in different biological processes has been obtained from experimental findings, computational biology can also clearly substantially boost this knowledge by suggesting possible novel interactions of these ncRNAs with other molecules. Computational predictions are thus used as an alternative source of new insights through a process of mutual enrichment because the information obtained through experiments continuously feeds through into computational methods. The results of these predictions in turn shed light on possible interactions that are subsequently validated experimentally. This review describes the latest advances in databases, bioinformatic tools, and new in silico strategies that allow the establishment or prediction of biological interactions of ncRNAs, particularly miRNAs and lncRNAs. The ncRNA species described in this work have a special emphasis on those found in humans, but information on ncRNA of other species is also included.

Non-coding RNA crosstalk with nuclear receptors in liver disease

The dysregulation of nuclear receptors (NRs) underlies the pathogenesis of a variety of liver disorders. Non-coding RNAs (ncRNAs) are defined as RNA molecules transcribed from DNA but not translated into proteins. MicroRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are two types of ncRNAs that have been extensively studied for regulating gene expression during diverse cellular processes. NRs as therapeutic targets in liver disease have been exemplified by the successful application of their pharmacological ligands in clinics. MiRNA-based reagents or drugs are emerging as flagship products in clinical trials. Advancing our understanding of the crosstalk between NRs and ncRNAs is critical to the development of diagnostic and therapeutic strategies. This review summarizes recent findings on the reciprocal regulation between NRs and ncRNAs (mainly on miRNAs and lncRNAs) and their implication in liver pathophysiology, which might be informative to the translational medicine of targeting NRs and ncRNAs in liver disease.

Upregulation of Mir342 in Diet-Induced Obesity Mouse and the Hypothalamic Appetite Control

In obesity and type 2 diabetes, numerous genes are differentially expressed, and microRNAs are involved in transcriptional regulation of target mRNAs, but miRNAs critically involved in the appetite control are not known. Here, we identified upregulation of miR-342-3p and its host gene Evl in brain and adipose tissues in C57BL/6 mice fed with high fat-high sucrose (HFHS) chow by RNA sequencing. Mir342 (-/-) mice fed with HFHS chow were protected from obesity and diabetes. The hypothalamic arcuate nucleus neurons co-express Mir342 and EVL. The percentage of activated NPY⁺pSTAT3⁺ neurons were reduced, while POMC⁺pSTAT3⁺ neurons increased in Mir342 (-/-) mice, and they demonstrated the reduction of food intake and amelioration of metabolic phenotypes. Snap25 was identified as a major target gene of miR-342-3p and the reduced expression of Snap25 may link to functional impairment hypothalamic neurons and excess of food intake. The inhibition of miR-342-3p may be a potential candidate for miRNA-based therapy.