Computational Prediction of Functional MicroRNA-mRNA Interactions

microRNA-Mediated Regulation of Oxidative Stress in Cardiovascular Diseases

BACKGROUND

Cardiovascular diseases (CVDs) are the leading cause of mortality globally, often linked to oxidative stress. MicroRNAs (miRNAs) have emerged as significant regulators of oxidative stress within the cardiovascular system.

OBJECTIVE

This review examines the complex relationship between miRNAs and oxidative stress, clarifying their effects on gene expression pathways related to ROS production and detoxification in CVDs.

METHODS

From August to October 2024, we conducted a comprehensive search of PubMed, Scopus, Web of Science, and Google Scholar for studies published between 2014 and 2024 investigating the role of miRNAs in oxidative stress and cardiovascular diseases.

RESULTS

Specific miRNAs have been identified as critical regulators in the pathophysiology of CVDs, with distinct expression patterns correlated with conditions such as hypertension, coronary artery disease, and heart failure. For instance, miR-21 exacerbates oxidative stress by targeting genes essential for redox homeostasis, while miR-210 promotes endothelial cell survival under hypoxic conditions by mitigating ROS levels.

CONCLUSION

The reciprocal relationship between miRNAs and oxidative stress highlights the potential for therapeutic interventions targeting miRNA expression and activity in managing CVDs. Understanding these molecular mechanisms is vital for developing innovative strategies to address oxidative damage in cardiac tissues and improve cardiovascular health outcomes.

MicroRNAs and proteolytic cleavage of receptors in cancers: A comprehensive review of regulatory interactions and therapeutic implications

Cancer remains a challenging disease worldwide, necessitating innovative approaches to better comprehend its underlying molecular mechanisms and devise effective therapeutic strategies. Over the past decade, microRNAs (miRNAs) have emerged as crucial players in cancer progression due to their regulatory roles in various cellular processes. Moreover, the involvement of unwanted soluble receptors has gained increasing attention because they contribute to tumorigenesis or drug resistance by disrupting normal signaling pathways and neutralizing ligands. This comprehensive review explores the intricate interplay between miRNAs and unwanted-soluble receptors in the context of cancer biology. This study provides an analysis of the regulatory interactions between miRNAs and these receptors, elucidating how miRNAs can either suppress or enhance their expression. MiRNAs can directly target receptor transcripts, thereby regulating soluble receptor levels. They also modulate the proteolytic cleavage of membrane-bound receptors into soluble forms by targeting sheddases, such as ADAMs and MMPs. Furthermore, the review delves into the therapeutic potential of manipulating miRNAs to modulate unwanted soluble receptors. Various strategies, including synthetic miRNA mimics or anti-miRNAs, hold promise for restoring or inhibiting miRNA function to counteract aberrant receptor activity. Moreover, exploring miRNA-based delivery systems may provide targeted and precise therapies that minimizing off-target effects. In conclusion, this review sheds light on the intricate regulatory networks involving miRNAs and unwanted soluble receptors in cancer biology thereby uncovering novel therapeutic targets, and paving the way for developing innovative anti-cancer therapies.

Drug Discovery and Development of miRNA-Based Nucleotide Drugs for Gastrointestinal Cancer

Short non-coding RNAs, miRNAs, play roles in the control of cell growth and differentiation in cancer. Reportedly, the introduction of miRNAs could reduce the biologically malignant behavior of cancer cells, suggesting a possible use as therapeutic reagents. Given that the forced expression of several miRNAs, including miR-302, results in the cellular reprograming of human and mouse cells, which is similar to the effects of the transcription factors Oct4, Sox2, Klf4, and c-Myc, this suggests that the selective introduction of several miRNAs will be able to achieve anti-cancer effects at the epigenetic and metabolic levels. In this review article, we bring together the recent advances made in studies of microRNA-based therapeutic approaches to therapy-resistant cancers, especially in gastrointestinal organs.

The role of non-coding RNA in lupus nephritis

Systemic lupus erythematosus (SLE) is a common autoimmune disease with multiple manifestations. The renal implication, also called lupus nephritis (LN) is the most regular type of complication and results in adverse outcomes. Multiple studies revealed the importance of non-coding RNA in diseases, likewise observed in nephropathies, particularly LN. Long-non-coding RNA (lncRNA) is a group of RNA that are more than 200 nucleotides in length. And in circular RNA (circRNA), the head and tail of RNA are connected by a 3' → 5' phosphodiester bond. Both two types of non-coding RNA play important roles in LN pathogenesis through the competitive endogenous RNA (ceRNA) effect. LncRNAs and circRNAs can sponge miRNAs and consequently act on downstream signaling pathways, which are capable to influence various aspects of LN, including cell proliferation, inflammation, and oxidative stress. And lncRNAs and circRNAs have the potential to act as biomarkers to diagnose LN and distinguish whether SLE patients with LN or not. In the future, lncRNAs and circRNAs may be accessible therapeutic targets.

Integrated MicroRNA-mRNA Analyses of Distinct Expression Profiles in Hyperoxia-Induced Bronchopulmonary Dysplasia in Neonatal Mice

OBJECTIVE

Bronchopulmonary dysplasia (BPD) is a common chronic lung disease of preterm neonates; the underlying pathogenesis is not fully understood. Recent studies suggested microRNAs (miRNAs) may be involved in BPD.

STUDY DESIGN

miRNA and mRNA microarrays were performed to analyze the expression profiles of miRNA and mRNA in BPD and control lung tissues after oxygen and air exposure on day 21. Bioinformatics methods, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), were performed to predict the potential functions of differentially expressed genes. Then, miRNA-mRNA regulatory network was constructed by protein-protein interaction (PPI) data and TarBase data.

RESULTS

Our results showed that a total of 192 differentially expressed miRNAs (74 downregulated and 118 upregulated) and 1,225 differentially expressed mRNAs (479 downregulated and 746 upregulated) were identified between BPD mice and normoxia-control mice. GO and KEGG analysis showed that for downregulated genes, the top significant enriched GO terms and KEGG pathways were both mainly related to immune and inflammation processes; for upregulated genes, the top significant enriched GO terms and KEGG pathways were both mainly related to extracellular matrix (ECM) remodeling. PPI network and miRNA-mRNA regulatory network construction revealed that the key genes and pathways associated with inflammation and immune regulation.

CONCLUSION

Our findings revealed the integrated miRNA-mRNA data of distinct expression profiles in hyperoxia-induced BPD mice, and may provide some clues of the potential biomarkers for BPD, and provide novel insights into the development of new promising biomarkers for the treatment of BPD.

KEY POINTS

· Integrated advanced bioinformatics methods may offer a better way to understand the molecular expression profiles involved in BPD.. · ECM remodeling, inflammation, and immune regulation may be essential to BPD.. · The miRNA-mRNA regulatory network construction may contribute to develop new biomarkers for the treatment of BPD..

Detection of features predictive of microRNA targets by integration of network data

Gene activity is controlled by multiple molecular mechanisms, for instance through transcription factors or by microRNAs (miRNAs), among others. Established bioinformatics tools for the prediction of miRNA target genes face the challenge of ensuring accuracy, due to high false positive rates. Further, these tools present poor overlap. However, we demonstrated that it is possible to filter good predictions of miRNA targets from the bulk of all predictions by using information from the gene regulatory network. Here, we take advantage of this strategy that selects a large subset of predicted microRNA binding sites as more likely to possess less false-positives because of their over-representation in RE1 silencing transcription factor (REST)-regulated genes from the background of TargetScanHuman 7.2 predictions to identify useful features for the prediction of microRNA targets. These enriched miRNA families would have silencing activity for neural transcripts overlapping the repressive activity on neural genes of REST. We analyze properties of associated microRNA binding sites and contrast the outcome to the background. We found that the selected subset presents significant differences respect to the background: (i) lower GC-content in the vicinity of the predicted miRNA binding site, (ii) more target genes with multiple identical microRNA binding sites and (iii) a higher density of predicted microRNA binding sites close to the 3’ terminal end of the 3’-UTR. These results suggest that network selection of miRNA-mRNA pairs could provide useful features to improve microRNA target prediction.

Investigating miRNA-mRNA interactions and gene regulatory networks from VTA dopaminergic neurons following perinatal nicotine and alcohol exposure using Bayesian network analysis

MicroRNAs play an important role in gene regulation for many biological systems, including nicotine and alcohol addiction. However, the underlying mechanism behind miRNAs and mRNA interaction is not well characterized. Microarrays are commonly used to quantify the expression levels of mRNAs and/or miRNAs simultaneously. In this study, we performed a Bayesian network analysis to identify mRNA and miRNA interactions following perinatal exposure to nicotine and/or alcohol. We utilized three sets of microarray data to predict the regulation relationship between mRNA and miRNAs. Following perinatal alcohol exposure, we identified two miRNAs: miR-542-5p and miR-874-3p, that exhibited a strong mutual influence on several mRNA in gene regulatory pathways, mainly Axon guidance and Dopaminergic synapses. Finally, we confirmed our predicted addiction pathways based on the Bayesian network analysis with the widely used Kyoto Encyclopedia of Genes and Genomes (KEGG)-based database and identified comparable relevant miRNA-mRNA pairs. We believe the Bayesian network can provide insight into the complexity biological process related to addiction and can potentially be applied to other diseases.

Experimental MicroRNA Targeting Validation

microRNAs (miRNAs) have recently been recognized as a new dimension of posttranscriptional regulation. It is well defined that most human protein-coding genes are regulated by one or more miRNAs. Therefore, it is crucial to identify genes targeted by the miRNAs to better understand their functions. Although bioinformatics tools have the ability to identify target candidates it is still essential to identify physiological targets by experimental approaches. Currently, the majority of miRNA-target experimental validation approaches assess the changes in target expression in mRNA or protein level upon miRNA upregulation or downregulation. Additionally, finding out direct physical interactions between miRNAs and their targets is also among the experimental techniques. In this chapter we reviewed the existing experimental techniques for miRNA target identification by considering their advantages and potential drawbacks.

Computational Detection of Pre-microRNAs

MicroRNA (miRNA) studies have been one of the most popular research areas in recent years. Although thousands of miRNAs have been detected in several species, the majority remains unidentified. Thus, finding novel miRNAs is a vital element for investigating miRNA mediated posttranscriptional gene regulation machineries. Furthermore, experimental methods have challenging inadequacies in their capability to detect rare miRNAs, and are also limited to the state of the organism under examination (e.g., tissue type, developmental stage, stress-disease conditions). These issues have initiated the creation of high-level computational methodologies endeavoring to distinguish potential miRNAs in silico. On the other hand, most of these tools suffer from high numbers of false positives and/or false negatives and as a result they do not provide enough confidence for validating all their predictions experimentally. In this chapter, computational difficulties in detection of pre-miRNAs are discussed and a machine learning based approach that has been designed to address these issues is reviewed.

Regulation of MicroRNAs

MicroRNAs are RNAs of about 18-24 nucleotides in lengths, which are found in the small noncoding RNA class and have a crucial role in the posttranscriptional regulation of gene expression, cellular metabolic pathways, and developmental events. These small but essential molecules are first processed by Drosha and DGCR8 in the nucleus and then released into the cytoplasm, where they cleaved by Dicer to form the miRNA duplex. These duplexes are bound by the Argonaute (AGO) protein to form the RNA-induced silencing complex (RISC) in a process called RISC loading. Transcription of miRNAs, processing with Drosha and DGCR8 in the nucleus, cleavage by Dicer, binding to AGO proteins and forming RISC are the most critical steps in miRNA biogenesis. Additional molecules involved in biogenesis at these stages can enhance or inhibit these processes, which can radically change the fate of the cell. Biogenesis is regulated by many checkpoints at every step, primarily at the transcriptional level, in the nucleus, cytoplasm, with RNA regulation, RISC loading, miRNA strand selection, RNA methylation/uridylation, and turnover rate. Moreover, in recent years, different regulation mechanisms have been discovered in noncanonical Drosha or Dicer-independent pathways. This chapter seeks answers to how miRNA biogenesis and function are regulated through both canonical and non-canonical pathways.

Endogenous miRNA Sponges

MicroRNAs (miRNAs) are a class of noncoding RNAs of 17-22 nucleotides in length with a critical function in posttranscriptional gene regulation. These master regulators are themselves subject to regulation both transcriptionally and posttranscriptionally. Recently, miRNA function has been shown to be modulated by exogenous RNA molecules that function as miRNA sponges. Interestingly, endogenous transcripts such as transcribed pseudogenes, long noncoding RNAs (lncRNAs), circular RNAs (circRNAs) and mRNAs may serve as natural miRNA sponges. These transcripts, which bind to miRNAs and competitively sequester them away from their targets, are naturally existing endogenous miRNA sponges, called competing endogenous RNAs (ceRNAs). Here we present a historical background of miRNAs, exogenous and endogenous miRNA sponges as well as some examples of endogenous miRNA sponges involved in regulatory mechanisms associated with various diseases, developmental stages, and other cellular processes.

44 Current Challenges in miRNomics

Mature microRNAs (miRNAs) are short RNA sequences about 18-24 nucleotide long, which provide the recognition key within RISC for the posttranscriptional regulation of target RNAs. Considering the canonical pathway, mature miRNAs are produced via a multistep process. Their transcription (pri-miRNAs) and first processing step via the microprocessor complex (pre-miRNAs) occur in the nucleus. Then they are exported into the cytosol, processed again by Dicer (dsRNA) and finally a single strand (mature miRNA) is incorporated into RISC (miRISC). The sequence of the incorporated miRNA provides the function of RNA target recognition via hybridization. Following binding of the target, the mRNA is either degraded or translation is inhibited, which ultimately leads to less protein production. Conversely, it has been shown that binding within the 5' UTR of the mRNA can lead to an increase in protein product. Regulation of homeostasis is very important for a cell; therefore, all steps in the miRNA-based regulation pathway, from transcription to the incorporation of the mature miRNA into RISC, are under tight control. While much research effort has been exerted in this area, the knowledgebase is not sufficient for accurately modelling miRNA regulation computationally. The computational prediction of miRNAs is, however, necessary because it is not feasible to investigate all possible pairs of a miRNA and its target, let alone miRNAs and their targets. We here point out open challenges important for computational modelling or for our general understanding of miRNA-based regulation and show how their investigation is beneficial. It is our hope that this collection of challenges will lead to their resolution in the near future.

MicroRNA Targeting

MicroRNAs (miRNAs) are small noncoding elements that play essential roles in the posttranscriptional regulation of biochemical processes. miRNAs recognize and target multiple mRNAs; therefore, investigating miRNA dysregulation is an indispensable strategy to understand pathological conditions and to design innovative drugs. Targeting miRNAs in diseases improve outcomes of several therapeutic strategies thus, this present study highlights miRNA targeting methods through experimental assays and bioinformatics tools. The first part of this review focuses on experimental miRNA targeting approaches for elucidating key biochemical pathways. A growing body of evidence about the miRNA world reveals the fact that it is not possible to uncover these molecules' structural and functional characteristics related to the biological processes with a deterministic approach. Instead, a systemic point of view is needed to truly understand the facts behind the natural complexity of interactions and regulations that miRNA regulations present. This task heavily depends both on computational and experimental capabilities. Fortunately, several miRNA bioinformatics tools catering to nonexperts are available as complementary wet-lab approaches. For this purpose, this work provides recent research and information about computational tools for miRNA targeting research.

MicroRNAs in Genetic Etiology of Human Diseases

Since their first discovery more than 20 years ago, miRNAs have been subject to deliberate research and analysis for revealing their physiological or pathological involvement. Regulatory roles of miRNAs in signal transduction, gene expression, and cellular processes in development, differentiation, proliferation, apoptosis, and homeostasis also imply their critical role in disease pathogenesis. Their roles in cancer, neurodegenerative diseases, and other systemic diseases have been studied broadly. In these regulatory pathways, their mutations and target sequence variations play critical roles to determine their functional repertoire. In this chapter, we summarize studies that investigated the role of mutations, polymorphisms, and other variations of miRNAs in respect to pathological processes.

Systems analysis of miRNA biomarkers to inform drug safety

microRNAs (miRNAs or miRs) are short non-coding RNA molecules which have been shown to be dysregulated and released into the extracellular milieu as a result of many drug and non-drug-induced pathologies in different organ systems. Consequently, circulating miRs have been proposed as useful biomarkers of many disease states, including drug-induced tissue injury. miRs have shown potential to support or even replace the existing traditional biomarkers of drug-induced toxicity in terms of sensitivity and specificity, and there is some evidence for their improved diagnostic and prognostic value. However, several pre-analytical and analytical challenges, mainly associated with assay standardization, require solutions before circulating miRs can be successfully translated into the clinic. This review will consider the value and potential for the use of circulating miRs in drug-safety assessment and describe a systems approach to the analysis of the miRNAome in the discovery setting, as well as highlighting standardization issues that at this stage prevent their clinical use as biomarkers. Highlighting these challenges will hopefully drive future research into finding appropriate solutions, and eventually circulating miRs may be translated to the clinic where their undoubted biomarker potential can be used to benefit patients in rapid, easy to use, point-of-care test systems.

DisCoVering potential candidates of RNAi-based therapy for COVID-19 using computational methods

The ongoing pandemic of a novel coronavirus (SARS-CoV-2) leads to international concern; thus, emergency interventions need to be taken. Due to the time-consuming experimental methods for proposing useful treatments, computational approaches facilitate investigating thousands of alternatives simultaneously and narrow down the cases for experimental validation. Herein, we conducted four independent analyses for RNA interference (RNAi)-based therapy with computational and bioinformatic methods. The aim is to target the evolutionarily conserved regions in the SARS-CoV-2 genome in order to down-regulate or silence its RNA. miRNAs are denoted to play an important role in the resistance of some species to viral infections. A comprehensive analysis of the miRNAs available in the body of humans, as well as the miRNAs in bats and many other species, were done to find efficient candidates with low side effects in the human body. Moreover, the evolutionarily conserved regions in the SARS-CoV-2 genome were considered for designing novel significant siRNA that are target-specific. A small set of miRNAs and five siRNAs were suggested as the possible efficient candidates with a high affinity to the SARS-CoV-2 genome and low side effects. The suggested candidates are promising therapeutics for the experimental evaluations and may speed up the procedure of treatment design. Materials and implementations are available at: https://github.com/nrohani/SARS-CoV-2.

maTE: discovering expressed interactions between microRNAs and their targets

MOTIVATION

Disease is often manifested via changes in transcript and protein abundance. MicroRNAs (miRNAs) are instrumental in regulating protein abundance and may measurably influence transcript levels. miRNAs often target more than one mRNA (for humans, the average is three), and mRNAs are often targeted by more than one miRNA (for the genes considered in this study, the average is also three). Therefore, it is difficult to determine the miRNAs that may cause the observed differential gene expression. We present a novel approach, maTE, which is based on machine learning, that integrates information about miRNA target genes with gene expression data. maTE depends on the availability of a sufficient amount of patient and control samples. The samples are used to train classifiers to accurately classify the samples on a per miRNA basis. Multiple high scoring miRNAs are used to build a final classifier to improve separation.

RESULTS

The aim of the study is to find a set of miRNAs causing the regulation of their target genes that best explains the difference between groups (e.g. cancer versus control). maTE provides a list of significant groups of genes where each group is targeted by a specific miRNA. For the datasets used in this study, maTE generally achieves an accuracy well above 80%. Also, the results show that when the accuracy is much lower (e.g. ∼50%), the set of miRNAs provided is likely not causative of the difference in expression. This new approach of integrating miRNA regulation with expression data yields powerful results and is independent of external labels and training data. Thereby, this approach allows new avenues for exploring miRNA regulation and may enable the development of miRNA-based biomarkers and drugs.

AVAILABILITY AND IMPLEMENTATION

The KNIME workflow, implementing maTE, is available at Bioinformatics online.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Bioinformatical Analysis of miRNA-mRNA Interaction Network Underlying Macrophage Aging and Cholesterol-Responsive Difference between Young and Aged Macrophages

Purpose

Macrophage aging is involved with the occurrence and progression of age-related macular degeneration (AMD). The purpose of this study was to identify the specific microRNAs (miRNA), mRNAs, and their interactions underlying macrophage aging and response to cholesterol through bioinformatical analysis in order to get a better understanding of the mechanism of AMD.

Methods

The microarray data were obtained from Gene Expression Omnibus (accession GSE111304 and GSE111382). The age-related differentially expressed genes in macrophages were identified using R software. Further miRNA-mRNA interactions were analyzed through miRWalk, mirTarBase, starBase, and then produced by Cytoscape. The functional annotations including Gene Ontology and KEGG pathways of the miRNA target genes were performed by the DAVID and the STRING database. In addition, protein-protein interaction network was constructed to identify the key genes in response to exogenous cholesterol.

Results

When comparing aged and young macrophages, a total of 14 miRNAs and 101 mRNAs were detected as differentially expressed. Besides, 19 validated and 544 predicted miRNA-mRNA interactions were detected. Lipid metabolic process was found to be associated with macrophage aging through functional annotations of the miRNA targets. After being treated with oxidized and acetylated low-density lipoprotein, miR-714 and 16 mRNAs differentially expressed in response to both kinds of cholesterol between aged and young macrophages. Among them, 6 miRNA-mRNA predicted pairs were detected. The functional annotations were mainly related to lipid metabolism process and farnesyl diphosphate farnesyl transferase 1 (FDFT1) was identified to be the key gene in the difference of response to cholesterol between aged and young macrophages.

Conclusions

Lipid metabolic process was critical in both macrophage aging and response to cholesterol thus was regarded to be associated with the occurrence and progression of AMD. Moreover, miR-714-FDFT1 may modulate cholesterol homeostasis in aged macrophages and have the potential to be a novel therapeutic target for AMD.

Differential MicroRNA-Signatures in Thyroid Cancer Subtypes

Thyroid cancer is one of the most common endocrine cancers, with an increasing trend in the last few decades. Although papillary thyroid cancer is the most frequent subtype compared with follicular or anaplastic thyroid cancer, it can dedifferentiate to a more aggressive phenotype, and the recurrence rate is high. The cells of follicular adenomas and follicular carcinomas appear identical in cytology, making the preoperative diagnosis difficult. On the other hand, anaplastic thyroid cancer poses a significant clinical challenge due to its aggressive nature with no effective therapeutic options. In the past several years, the roles of genetic alterations of thyroid tumors have been documented, with a remarkable correlation between genotype and phenotype, indicating that distinct molecular changes are associated with a multistep tumorigenic process. Besides mRNA expression profiles, small noncoding microRNA (miRNA) expression also showed critical functions for cell differentiation, proliferation, angiogenesis, and resistance to apoptosis and finally activating invasion and metastasis in cancer. Several high-throughput sequencing studies demonstrate that miRNA expression signatures contribute clinically relevant information including types of thyroid cancer, tumor grade, and prognosis. This review summarizes recent findings on miRNA signatures in thyroid cancer subtypes.

Non-Coding RNAs and their Integrated Networks

Eukaryotic genomes are pervasively transcribed. Besides protein-coding RNAs, there are different types of non-coding RNAs that modulate complex molecular and cellular processes. RNA sequencing technologies and bioinformatics methods greatly promoted the study of ncRNAs, which revealed ncRNAs' essential roles in diverse aspects of biological functions. As important key players in gene regulatory networks, ncRNAs work with other biomolecules, including coding and non-coding RNAs, DNAs and proteins. In this review, we discuss the distinct types of ncRNAs, including housekeeping ncRNAs and regulatory ncRNAs, their versatile functions and interactions, transcription, translation, and modification. Moreover, we summarize the integrated networks of ncRNA interactions, providing a comprehensive landscape of ncRNAs regulatory roles.