miRNA Target Prediction Research Articles

Medical Treatment of Hypercortisolism with Relacorilant: Final Results of the Phase 3 GRACE Study

Diabetic cardiomyopathy (DCM) is a cardiometabolic syndrome, manifested by ventricular diastolic or systolic dysfunction which occurs in approximately 12% of diabetic patients. Diabetes perpetuates the development of microvascular complications that can exacerbate cardiovascular pathologies. Understanding the common molecular targets underlying these two conditions is important for designing effective interventions that can address diabetes and related complications simultaneously. Herein, we aim to investigate the underlying network of key modules associated with diabetic cardiomyopathy. In current study, microarray gene expression profiles of diabetic and cardiomyopathy patients possessing accession number (GSE30122 and GSE20966) and (GSE89714, GSE3586, and GSE1145) were retrieved from Gene Expression Omnibus (GEO) database to explore key modulators. The differentially expressed genes (DEGs) were analyzed via GEO2R tool, based on the cut off criteria (p ≤ 0.05) and log2(FC)>|±1|. 37 overlapped DEGs were identified which then subjected to miRNA target prediction, protein-protein interaction (PPI) network construction and module screening via TargetScan, ShinyGO, STRING, and Cytoscape respectively, to screen hub genes as potential targets. Functional gene enrichment analysis identified enrichment of DEGs in heart contraction, apoptosis, cytokines signaling, beta adrenergic signaling and GPCR neurotransmitter receptor activity. The miRNA-mRNA regulatory network identified 9 hub genes, including ADRB1, TGFB2, RAP2A, CALD1, WIPF1, ATP10a, AKAP1, NR4A3, EXT1 and 4 hub miRNAs including miR-199-5, miR-200a, mir141-3, and miR-19-3p based on their degree of connectivity and centrality within the network. Conclusively, our analysis unveiled hub genes and miRNAs associated with diabetes-linked cardiomyopathy that might considered as biomarkers and offer a reliable tool for developing effective therapeutic strategies in future.

DeepmiRNATar: A deep learning-based model for miRNA targets prediction

MicroRNAs (miRNAs) play a crucial role in regulating fundamental biological processes such as the cell cycle, differentiation, and apoptosis by directly interacting with multiple genes (mRNAs). This regulatory mechanism has a profound impact on cellular function and the overall physiological condition of an organism. However, the prediction of miRNA-mRNA interactions encounters computational challenges in the field of biology due to the diverse sequences and complex data patterns. To overcome these obstacles, this research effort introduced DeepmiRNATar, a tool designed to precisely pinpoint miRNA targets, offering essential assistance in the realm of disease management. DeepmiRNATar leverages the Word2vec-based DeepLncLoc approach for encoding miRNA sequence characteristics and utilizes the DNABERT pre-trained model for in-depth semantic comprehension of target sequences. Through the integration of TextCNN, BiLSTM, and SpatialConv Attention mechanisms, the model scrutinizes structural features, temporal relationships, and overall interactions within the sequences. Following a series of experimental assessments, DeepmiRNATar attained an impressive AUC of 99.15% on the evaluation dataset, on par with the current leading prediction methodologies. Notably, the precision-recall curve, sensitivity, and F-measure values reached 99.18%, 97.43%, and 95.47%, respectively. Compared to existing miRNA target prediction models, DeepmiRNATar demonstrates a notable enhancement in overall predictive accuracy. The successful creation and experimental validation of the DeepmiRNATar model signify a significant advancement in miRNA target identification technology.

Potential Involvement of miR-144 in the Regulation of Hair Follicle Development and Cycle Through Interaction with Lhx2.

Cashmere, known as "soft gold", is a highly prized fiber from Cashmere goats, produced by secondary hair follicles. Dermal papilla cells, located at the base of these follicles, regulate the proliferation and differentiation of hair matrix cells, which are essential for hair growth and cashmere formation. Recent studies emphasize the role of microRNAs (miRNAs) in controlling gene expression within these processes. This study centered on exploring the targeted regulatory interaction between miR-144 and the Lhx2 gene. Utilizing methodologies like miRNA target prediction, luciferase reporter assays, and quantitative PCR, they assessed the interplay between miR-144 and Lhx2. Dermal papilla cells derived from Cashmere goats were cultured and transfected with either miR-144 mimics or inhibitors to observe the subsequent effects on Lhx2 expression. The results demonstrated that miR-144 directly targets the Lhx2 gene by binding to its mRNA, leading to a decrease in Lhx2 expression. This modulation of Lhx2 levels influenced the behavior of dermal papilla cells, affecting their ability to regulate hair matrix cell proliferation and differentiation. Consequently, the manipulation of miR-144 levels had a significant impact on the growth cycle of cashmere wool. The findings suggest miR-144 regulates hair follicle dynamics by targeting Lhx2, offering insights into hair growth mechanisms. This could lead to innovations in enhancing cashmere production, fleece quality, and addressing hair growth disorders. Future research may focus on adjusting miR-144 levels to optimize Lhx2 expression and promote hair follicle activity.

Identification and Analysis of Plant miRNAs: Evolution of In-silico Resources and Future Challenges

Abstract: Endogenous small RNAs (miRNA) are the key regulators of numerous eukaryotic lineages playing an important role in a broad range of plant development. Computational analysis of miRNAs facilitates the understanding of miRNA-based regulations in plants. The discovery of small non-coding RNAs has led to a greater understanding of gene regulation, and the development of bioinformatic tools has enabled the identification of microRNAs (miRNAs) and their targets. The need for comprehensive miRNA analysis is being accomplished by the development of advanced computational tools/algorithms and databases. Each resource has its own specificity and limitations for the analysis. This review provides a comprehensive overview of various algorithms used by computational tools, software, and databases for plant miRNA analysis. However, over a period of about two decades, a lot of knowledge has been added to our understanding of the biogenesis and functioning of miRNAs in other plants. Several parameters were already integrated and others need to be incorporated in order to give more accurate and efficient results. The reassessment of computational recourses (based on old algorithms) is required on the basis of new miRNA research and development. Generally, computational methods, including ab-initio and homology search-based methods, are used for miRNA identification and target prediction. This review presents the new challenges faced by the existing computational methods and the need to develop new tools and advanced algorithms and highlight the limitations of existing computational tools and methods, and emphasizing the need for a comprehensive platform for miRNA gene exploration.

Altered exosomal miRNA profiles in patients with paraneoplastic cerebellar degeneration.

Patients with ovarian cancer (OC) may develop anti-Yo-associated paraneoplastic cerebellar degeneration (PCD)-a cerebellar ataxia associated with tumor-induced autoimmunity against CDR2 and CDR2L proteins. Dysregulation of circulating exosomal microRNAs (miRNAs) occur in OC. Here, we investigated whether PCD is associated with changes in the exosomal miRNA profiles of OC patients. Serum exosomes were isolated from patients with OC (n = 15), patients with OC and anti-Yo-associated PCD (n = 14) and healthy controls (HC, n = 15). Small RNA sequencing was used to identify differentially expressed miRNAs. Receiver operating characteristic curves were used to evaluate biomarker sensitivity and specificity, and miRNA target prediction analysis was employed to elucidate gene targets. OC patients with PCD exhibited a distinct exosomal miRNA expression profile. We detected 103 differentially expressed exosomal miRNAs in PCD patients compared to OC patients without PCD and 139 differentially expressed exosomal miRNAs compared to controls. Particularly miR-486-5p, miR-4732-5p, miR-98-5p and miR-21-5p exhibited notable sensitivity and specificity for discriminating PCD patients from both OC patients without PCD and healthy controls. miRNA target prediction showed that several of the differentially expressed miRNAs in PCD patients targeted the CDR2 and CDR2L genes. Our results demonstrate that OC patients with anti-Yo-associated PCD exhibit a distinct exosomal miRNA profile compared to OC patients without PCD. Several of the differentially expressed exosomal miRNAs in PCD patients showed diagnostic potential and may hold relevance for understanding the pathogenesis of PCD.

Human macrophages are immunnoprofiled by pericardial fluid small extracellular vesicles modulating lipid metabolism mechanisms

Abstract Background Coronary artery disease (CAD) is the main cause of myocardial infarction and consequent ischemic heart disease (IHD). Macrophages are central to CAD. Small extracellular vesicles (sEVs) can shuttle microRNAs and other molecular cargos from cell to cell, mediating response in target cells. Recent evidence supports the sEVs role in modulating macrophage phenotype. The pericardial fluid (PF) contains sEVs and immune cells including cavity-like macrophages. Purpose This study investigates whether PF-sEVs regulate macrophages, contributing to a specific immunophenotype in CAD patients. Methods PF was collected from CAD patients undergoing coronary bypass surgery (CABG) and non-atherosclerotic patients operated for mitral valve repair (control group). sEVs were isolated and characterised for size (Nanosight tracking analysi), tetrasapanin content (Nanoview chips) and microRNA content (RNA seq analysis). Monocytes from healthy donors were isolated from buffy coats and differentiated into macrophages following established protocols. Macrophages were incubated with either CAD-sEVs or non-CAD sEVs for 24h at 37oC. The cells were collected and processed for mRNA analyses (qRT-PCR) and flow cytometry. Further bioinformatics were employed to understand functional pathways targeted by PF-sEVs-miRNA. Results In line with the human-PF macrophages profile, exposure to CAD-sEVs reduces the anti-inflammatory profile of human macrophages. CAD-sEVs treated macrophages showed a CCR2-, CD36+low, CD206+low profile. While non-CAD-sEVs did not statistically differ from PBS nor untouched groups, CAD-sEVs increased the mRNA level of IL1a, IL1b, TNFa and decreased MRC1. Bioinformatic analysis showed that 861 miRNAs were decreased in the PF-sEVs from CAD patients compared to non-CAD. miRNA targets prediction and pathway analyses reported that clusters of PF-miRNAs could regulate CD36, decreased in PF-macrophages. Conclusions We demonstrate, that sEVs isolated from PF-CAD reduce the anti-inflammatory profile of human macrophages, and target crucial lipid metabolism pathways. These clinically relevant results could drive to decipher improved therapeutics to modulate the epicardial/myocardial immune response in CAD patients.

MicroRNA-1307-3p contributes to breast cancer progression through PRM2.

Despite advances in screening and therapy, breast cancer (BC) remains the predominant cancer in women globally. Dysregulation of microRNAs (miRNAs) is pivotal in carcinogenesis across various cancers, including BC. Evidence indicates that miR-1307-3p is upregulated in BC tumors, yet its target genes are not fully elucidated. This study aimed to explore how miR-1307-3p regulates BC proliferation, migration, invasion, and angiogenesis and to identify potential target genes. Basal miR-1307-3p levels were quantified in BC cell lines MDA-MB-231 and MCF-7, as well as MCF-10A using quantitative real-time reverse transcription-PCR (RT-qPCR). The impact of miR-1307-3p inhibition on BC cell proliferation, migration, invasion, and angiogenesis was assessed. Nine miRNA-target prediction databases identified potential miR-1307-3p targets. Target expression was validated using RT-qPCR, Western blot, and dual-luciferase reporter assays. MiR-1307-3p was overexpressed in MDA-MB-231 and MCF-7 compared to MCF-10A. Inhibiting miR-1307-3p significantly reduced BC cell proliferation, migration, invasion, and angiogenesis. Bioinformatics analysis identified 17 potential miR-1307-3p targets, with protamine 2 (PRM2) overexpression confirmed via Western blot and dual-luciferase assays. MiR-1307-3p overexpression in BC promotes proliferation, migration, invasion, and angiogenesis. PRM2 emerges as a novel miR-1307-3p target in BC.

Scanning sample-specific miRNA regulation from bulk and single-cell RNA-sequencing data

BackgroundRNA-sequencing technology provides an effective tool for understanding miRNA regulation in complex human diseases, including cancers. A large number of computational methods have been developed to make use of bulk and single-cell RNA-sequencing data to identify miRNA regulations at the resolution of multiple samples (i.e. group of cells or tissues). However, due to the heterogeneity of individual samples, there is a strong need to infer miRNA regulation specific to individual samples to uncover miRNA regulation at the single-sample resolution level.ResultsHere, we develop a framework, Scan, for scanning sample-specific miRNA regulation. Since a single network inference method or strategy cannot perform well for all types of new data, Scan incorporates 27 network inference methods and two strategies to infer tissue-specific or cell-specific miRNA regulation from bulk or single-cell RNA-sequencing data. Results on bulk and single-cell RNA-sequencing data demonstrate the effectiveness of Scan in inferring sample-specific miRNA regulation. Moreover, we have found that incorporating the prior information of miRNA targets can generally improve the accuracy of miRNA target prediction. In addition, Scan can contribute to construct cell/tissue correlation networks and recover aggregate miRNA regulatory networks. Finally, the comparison results have shown that the performance of network inference methods is likely to be data-specific, and selecting optimal network inference methods is required for more accurate prediction of miRNA targets.ConclusionsScan provides a useful method to help infer sample-specific miRNA regulation for new data, benchmark new network inference methods and deepen the understanding of miRNA regulation at the resolution of individual samples.

Target prediction of potential candidate miRNAs from Oryza sativa to silence the Pyricularia oryzae genome in rice blast

Rice (Oryza sativa) is a staple food for billions of people across the globe, that feeds nearly three-quarters of the human population on Earth, particularly in Asian countries. Rice yield has been drastically reduced and severely affected by various biotic and abiotic stresses, especially pathogens. Controlling the attack of such pathogens is a matter of immediate concern as yield losses in rice crops could deprive millions of lives of nourishment worldwide. Pyricularia oryzae is one such pathogen that has been considered the major disease of rice because of its worldwide geographic distribution. P. oryzae belongs to the kingdom fungi, that causes rice blast ultimately adversely affecting the yield of the rice crop. Keeping in view this alarming scenario, the present study was designed so that the identifications of genome-encoded miRNAs of Oryza sativa were employed to target and silence the genome of P. oryzae. This study accomplished the computational analysis of algorithms related to miRNA target prediction. Four computational target prediction algorithms i.e., psRNATarget, RNA22, miRanda, and RNAhybrid were utilized in this investigation. The consensus among target prediction algorithms was created to discover six miRNAs from the O. sativa genome with the conservation of the target site fully evaluated on the genome of P. oryzae. The discovery of these novel six miRNAs in Oryza sativa paved a strong way toward the control of this disease in rice. It will open doors for further research in the field of gene silencing in rice. These miRNAs can be designed and employed in the future as experimentation to create constructs regarding the silencing of P. oryzae in rice crops. In the future, this research would be surely helpful for the development of P. oryzae resistant rice varieties.

Profiles and interactions of gut microbiome and intestinal microRNAs in pediatric Crohn's disease.

Since previous studies have focused on the change of the fecal gut microbiome and intestinal tissue miRNA in pediatric Crohn's disease (CD), the relationship between them in different stages is still not clear. This is the first study to explore the gut microbiota and miRNA and their correlations with the Pediatric Crohn's Disease Activity Index (PCDAI). Crohn's Disease Endoscopic Index of Severity (CDEIS), and calprotectin, by applying two omics approach in three different groups (active CD, CD in remission with exclusive enteral nutrition or infliximab induction therapy, and the healthy controls). Both gut microbiome structure and the miRNA profiles were significantly changed in the different stage of CD. Seven key gut microbiome at species and eight key miRNAs were found, and their close interactions were further fully investigated by miRNA target prediction.

Analysis of miRNAs in milk of four livestock species

BackgroundMilk is essential for mammalian nutrition because it provides vital nutrients for growth and development. Milk composition, which is influenced by genetic and environmental factors, supports lactation, a complex process crucial for milk production and quality. Recent research has focused on noncoding RNAs, particularly microRNAs (miRNAs), which are present in body fluids and regulate gene expression post-transcriptionally. This study comprehensively characterizes miRNAs in milk of four livestock species, namely Bubalus bubalis, Capra hircus, Equus asinus, and Ovis aries and identifies potential target genes.ResultsHigh-throughput sequencing of milk RNA resulted in distinct read counts across species: B. bubalis (8,790,441 reads), C. hircus (12,976,275 reads), E. asinus (9,385,067 reads), and O. aries (7,295,297 reads). E. asinus had the highest RNA mapping rate (94.6%) and O. aries the lowest (84.8%). A substantially greater proportion of miRNAs over other small RNAs was observed for the donkey milk sample (7.74%) compared to buffalo (0.87%), goat (1.57%), and sheep (1.12%). Shared miRNAs, which included miR-200a, miR-200b, miR-200c, and miR-23a among others, showed varying expression levels across species, confirmed by qPCR analysis. Functional annotation of predicted miRNA target genes highlighted diverse roles, with an enrichment in functions linked to metabolism and immunity. Pathway analysis identified immune response pathways as significant, with several miRNAs targeting specific genes across species, suggesting their regulatory function in milk.ConclusionsBoth conserved and species-specific miRNAs were detected in milk of the investigated species. The identified target genes of these miRNAs have important roles in neonatal development, adaptation, growth, and immune response. Furthermore, they influence milk and meat production traits in livestock.

Advancing microRNA target site prediction with transformer and base-pairing patterns.

MicroRNAs (miRNAs) are short non-coding RNAs involved in various cellular processes, playing a crucial role in gene regulation. Identifying miRNA targets remains a central challenge and is pivotal for elucidating the complex gene regulatory networks. Traditional computational approaches have predominantly focused on identifying miRNA targets through perfect Watson-Crick base pairings within the seed region, referred to as canonical sites. However, emerging evidence suggests that perfect seed matches are not a prerequisite for miRNA-mediated regulation, underscoring the importance of also recognizing imperfect, or non-canonical, sites. To address this challenge, we propose Mimosa, a new computational approach that employs the Transformer framework to enhance the prediction of miRNA targets. Mimosa distinguishes itself by integrating contextual, positional and base-pairing information to capture in-depth attributes, thereby improving its predictive capabilities. Its unique ability to identify non-canonical base-pairing patterns makes Mimosa a standout model, reducing the reliance on pre-selecting candidate targets. Mimosa achieves superior performance in gene-level predictions and also shows impressive performance in site-level predictions across various non-human species through extensive benchmarking tests. To facilitate research efforts in miRNA targeting, we have developed an easy-to-use web server for comprehensive end-to-end predictions, which is publicly available at http://monash.bioweb.cloud.edu.au/Mimosa.

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.

Benchmarking the negatives: Effect of negative data generation on the classification of miRNA-mRNA interactions.

MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression post-transcriptionally. In animals, this regulation is achieved via base-pairing with partially complementary sequences on mainly 3' UTR region of messenger RNAs (mRNAs). Computational approaches that predict miRNA target interactions (MTIs) facilitate the process of narrowing down potential targets for experimental validation. The availability of new datasets of high-throughput, direct MTIs has led to the development of machine learning (ML) based methods for MTI prediction. To train an ML algorithm, it is beneficial to provide entries from all class labels (i.e., positive and negative). Currently, no high-throughput assays exist for capturing negative examples. Therefore, current ML approaches must rely on either artificially generated or inferred negative examples deduced from experimentally identified positive miRNA-target datasets. Moreover, the lack of uniform standards for generating such data leads to biased results and hampers comparisons between studies. In this comprehensive study, we collected methods for generating negative data for animal miRNA-target interactions and investigated their impact on the classification of true human MTIs. Our study relies on training ML models on a fixed positive dataset in combination with different negative datasets and evaluating their intra- and cross-dataset performance. As a result, we were able to examine each method independently and evaluate ML models' sensitivity to the methodologies utilized in negative data generation. To achieve a deep understanding of the performance results, we analyzed unique features that distinguish between datasets. In addition, we examined whether one-class classification models that utilize solely positive interactions for training are suitable for the task of MTI classification. We demonstrate the importance of negative data in MTI classification, analyze specific methodological characteristics that differentiate negative datasets, and highlight the challenge of ML models generalizing interaction rules from training to testing sets derived from different approaches. This study provides valuable insights into the computational prediction of MTIs that can be further used to establish standards in the field.

MiR-185–5p rewires cisplatin resistance by restoring miR-203a-3p expression via downregulation of SOX9

Chemotherapeutic drug resistance is a challenge for the effective treatment of OSCC. There are a couple of studies on the involvement of microRNAs (miRNAs) in chemoresistance of oral squamous cell carcinoma (OSCC), but the exact molecular events in many cases are not clearly understood. In this work, we intend to track down key miRNA(s) and unveil their regulatory molecular mechanisms in imparting chemoresistance in this lethal cancer. We analyzed gene and miRNA array profiles of drug-resistant OSCC cells, predicted miRNA targets, performed enrichment analysis, and validated our findings in cisplatin-sensitive and cisplatin-resistant SCC9 and H357 OSCC cells. We evaluated the anticancer and chemosensitivity roles of selected miRNA by adopting several molecular assays like qRT-PCR, MTT assay, wound healing assay, fluorescence imaging by DCFHDA, AO/EB staining, DAPI, and γ-H2AX accumulation assay. We also validated the miRNA-target binding by qRT-PCR and luciferase reporter assay. Among the enriched miRNAs, we found miR-185–5p downregulated in cisplatin-resistant OSCC cells as a signature miRNA modulating chemoresistance. The upregulation of miR-185–5p by mimic transfection restores cisplatin sensitivity by decreasing cell viability in a dose-dependent manner and increasing ROS-induced DNA damage and apoptosis. miR-185–5p overexpression increases miR-203a-3p expression through negative regulation of SOX9. siRNA-mediated silencing of the SOX9 also shows similar results. Mechanistically, miR-185–5p dependent miR-203a-3p expression decreases cisplatin efflux and cisplatin-induced DNA damage repair by regulating ABCC1, ABCB1, RRM2, and RAN. This study will pave the way for employing this miR-185–5p as a combination therapeutic strategy to combat cisplatin resistance in oral cancer.

MiR-494-3p regulates skin fibroblast activities by mediating fibromodulin production.

Skin wound healing is a well-coordinated process in which various cells and factors participate, during which fibroblast exhibits a critical role by exerting its multiple activities, including proliferation, migration, invasion, and differentiation. Previous studies have identified that fibromodulin (FMOD) could enhance dermal wound healing by promoting skin fibroblast activities, but little is known about its upstream regulator. We occasionally found that FMOD expression was downregulated in skin fibroblast by transforming growth factor-β1 treatment. It was hypothesized that microRNAs (miRNA) in skin fibroblast could downregulate FMOD production and blocking them would increase FMOD expression, as well as promote skin wound healing. Here, by utilizing combined analysis of miRNA microarray from the Gene Expression Omnibus database and miRNA targets prediction, we successfully identified a miRNA, termed miR-494-3p, could regulate FMOD production in human skin fibroblast (BJ fibroblast). The functional analysis revealed that miR-494-3p mimics could inhibit BJ fibroblast migration and invasion but not proliferation and differentiation, while miR-494-3p inhibition markedly promotes migration, invasion, and differentiation of BJ fibroblast. Moreover, we established FMOD overexpression (OE) and knockout BJ fibroblast. We found that FMOD OE could rescue the inhibitory effects of miR-494-3p mimics on the migration and invasion of BJ fibroblast. In contrast, the miR-494-3p inhibitor transfection could not enhance migration, invasion, and differentiation of FMOD KO BJ fibroblast. Together, our results suggest that miR-494-3p may be a potential target for skin wound management via regulating FMOD production.

CRISPR-based dissection of microRNA-23a ~ 27a ~ 24-2 cluster functionality in hepatocellular carcinoma

The miR-23a ~ 27a ~ 24-2 cluster, commonly upregulated in diverse cancers, including hepatocellular carcinoma (HCC), raises questions about the specific functions of its three mature miRNAs and their integrated function. Utilizing CRISPR knockout (KO), CRISPR interference (CRISPRi), and CRISPR activation (CRISPRa) technologies, we established controlled endogenous miR-23a ~ 27 ~ a24-2 cell models to unravel their roles and signaling pathways in HCC. Both miR-23a KO and miR-27a KO displayed reduced cell growth in vitro and in vivo, revealing an integrated oncogenic function. Functional analysis indicated cell cycle arrest, particularly at the G2/M phase, through the downregulation of CDK1/cyclin B activation. High-throughput RNA-seq, combined with miRNA target prediction, unveiled the miR-23a/miR-27a-regulated gene network, validated through diverse technologies. While miR-23a and miR-27a exhibited opposing roles in cell migration and mesenchymal-epithelial transition, an integrated CRISPRi/a analysis suggested an oncogenic role of the miR-23a ~ 27a ~ 24-2 cluster in cell migration. This involvement potentially encompasses two signaling axes: miR-23a-BMPR2 and miR-27a-TMEM170B in HCC cells. In conclusion, our CRISPRi/a study provides a valuable tool for comprehending the integrated roles and underlying mechanisms of endogenous miRNA clusters, paving the way for promising directions in miRNA-targeted therapy interventions.

Transcriptomic Analysis Reveals the Effects of miR-122 Overexpression in the Liver of Qingyuan Partridge Chickens.

The liver of chickens is essential for maintaining physiological activities and homeostasis. This study aims to investigate the specific function and molecular regulatory mechanism of microRNA-122 (miR-122), which is highly expressed in chicken liver. A lentivirus-mediated overexpression vector of miR-122 was constructed and used to infect 12-day-old female Qingyuan Partridge chickens. Transcriptome sequencing analysis was performed to identify differentially expressed genes in the liver. Overexpression of miR-122 resulted in 776 differentially expressed genes (DEGs). Enrichment analyses, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) revealed associations with lipid metabolism, cellular senescence, cell adhesion molecules, and the MAPK signaling pathway. Eight potential target genes of miR-122 (ARHGAP32, CTSD, LBH, PLEKHB2, SEC14L1, SLC2A1, SLC6A14, and SP8) were identified through miRNA target prediction platforms and literature integration. This study provides novel insights into the molecular regulatory mechanisms of miR-122 in chicken liver, highlighting its role in key biological processes and signaling pathways. These discoveries enhance our understanding of miR-122's impact on chicken liver function and offer valuable information for improving chicken production performance and health.

Micro Target: MicroRNA Target Prediction and Validation with Experimentally Positive and Negative Examples

MicroRNAs (miRNAs) usually controls the gene by binding to complementary sites of 3’ untranslated region of its target genes. Numerous criteria-based and machine learning approaches are available in the literature to predict miRNA–mRNA interactions, but most of them struggle with either high false positive or false negative rates and also don’t show good validation with experimentally validated positive and negative examples. Here we present microTarget, a new computational approach for identifying miRNA target genes which are based on complementarity score, thermodynamic duplex stability and also independent of conservation of target sites in related genomes. In this article, we validated our algorithm using positive and negative data from the literature in various human tissues, and our method outperformed existing computational methods such as miRanda, RNA22, and PITA. Receiver operating characteristic curves (ROC) and Matthew's correlation coefficient (MCC) were calculated using experimentally validated data, and they reveal that microTarget greatly improves miRNA target prediction compared to the three algorithms employed individually. Additionally, an F-score analysis demonstrated that microTarget greatly enhances the relevance of the other techniques. Thus, microTarget is a useful tool for biologists looking for miRNA targets and integrating them into biological contexts.

Towards an enhanced understanding of osteoanabolic effects of PTH-induced microRNAs on osteoblasts using a bioinformatic approach.

In this study, we used a bioinformatic approach to construct a miRNA-target gene interaction network potentially involved in the anabolic effect of parathyroid hormone analogue teriparatide [PTH (1-34)] on osteoblasts. We extracted a dataset of 26 microRNAs (miRNAs) from previously published studies and predicted miRNA target interactions (MTIs) using four software tools: DIANA, miRWalk, miRDB, and TargetScan. By constructing an interactome of PTH-regulated miRNAs and their predicted target genes, we elucidated signaling pathways regulating pluripotency of stem cells, the Hippo signaling pathway, and the TGF-beta signaling pathway as the most significant pathways in the effects of PTH on osteoblasts. Furthermore, we constructed intersection of MTI networks for these three pathways and added validated interactions. There are 8 genes present in all three selected pathways and a set of 18 miRNAs are predicted to target these genes, according to literature data. The most important genes in all three pathways were BMPR1A, BMPR2 and SMAD2 having the most interactions with miRNAs. Among these miRNAs, only miR-146a-5p and miR-346 have validated interactions in these pathways and were shown to be important regulators of these pathways. In addition, we also propose miR-551b-5p and miR-338-5p for further experimental validation, as they have been predicted to target important genes in these pathways but none of their target interactions have yet been verified. Our wet-lab experiment on miRNAs differentially expressed between PTH (1-34) treated and untreated mesenchymal stem cells supports miR-186-5p from the literature obtained data as another prominent miRNA. The meticulous selection of miRNAs outlined will significantly support and guide future research aimed at discovering and understanding the crucial pathways of osteoanabolic PTH-epigenetic effects on osteoblasts. Additionally, they hold potential for the discovery of new PTH target genes, innovative biomarkers for the effectiveness and safety of osteoporosis-affected treatment, as well as novel therapeutic targets.