miRNA-target Gene Interactions Research Articles

Experimental capture of miRNA targetomes: disease-specific 3′UTR library-based miRNA targetomics for Parkinson’s disease

The identification of targetomes remains a challenge given the pleiotropic effect of miRNAs, the limited effects of miRNAs on individual targets, and the sheer number of estimated miRNA–target gene interactions (MTIs), which is around 44,571,700. Currently, targetome identification for single miRNAs relies on computational evidence and functional studies covering smaller numbers of targets. To ensure that the targetome analysis could be experimentally verified by functional assays, we employed a systematic approach and explored the targetomes of four miRNAs (miR-129-5p, miR-129-1-3p, miR-133b, and miR-873-5p) by analyzing 410 predicted target genes, both of which were previously associated with Parkinson’s disease (PD). After performing 13,536 transfections, we validated 442 of the 705 putative MTIs (62,7%) through dual luciferase reporter assays. These analyses increased the number of validated MTIs by at least 2.1-fold for miR-133b and by a maximum of 24.3-fold for miR-873-5p. Our study contributes to the experimental capture of miRNA targetomes by addressing i) the ratio of experimentally verified MTIs to predicted MTIs, ii) the sizes of disease-related miRNA targetomes, and iii) the density of MTI networks. A web service to support the analyses on the MTI level is available online (https://ccb-web.cs.uni-saarland.de/utr-seremato), and all the data have been added to the miRATBase database (https://ccb-web.cs.uni-saarland.de/miratbase).

Pinpointing Functionally Relevant miRNAs in Classical Hodgkin Lymphoma Pathogenesis.

Classical Hodgkin lymphoma (cHL) is a hematological malignancy of B-cell origin. The tumor cells in cHL are referred to as Hodgkin and Reed-Sternberg (HRS) cells. This review provides an overview of the currently known miRNA-target gene interactions. In addition, we pinpointed other potential regulatory roles of microRNAs (miRNAs) by focusing on genes related to processes relevant for cHL pathogenesis, i.e., loss of B-cell phenotypes, immune evasion, and growth support. A cHL-specific miRNA signature was generated based on the available profiling studies. The interactions relevant for cHL were extracted by comprehensively reviewing the existing studies on validated miRNA-target gene interactions. The miRNAs with potential critical roles included miR-155-5p, miR-148a-3p, miR-181a-5p, miR-200, miR-23a-3p, miR-125a/b, miR-130a-3p, miR-138, and miR-143-3p, which target, amongst others, PU.1, ETS1, HLA-I, PD-L1, and NF-κB component genes. Overall, we provide a comprehensive perspective on the relevant miRNA-target gene interactions which can also serve as a foundation for future functional studies into the specific roles of the selected miRNAs in cHL pathogenesis.

Expression changes of miRNA-regulated genes associated with the formation of the leafy head in cabbage (Brassica oleracea var. capitata)

The vegetative development of cabbage (B. oleracea var. capitata) passes through seedling, rosette, folding and heading stages. Leaves that form the rosette are large and mostly flat. In the following developmental stages, the plants produce leaves that curve inward to produce the leafy head. Many microRNAs and their target genes have been described participating in leaf development and leaf curvature. The aim of this study is to investigate the role of miRNA-regulated genes in the transition from the rosette to the heading stage. We compared the miRNA and gene abundances between emerging rosette and heading leaves. To remove transcripts (miRNAs and genes) whose regulation was most likely associated with plant age rather than the change from rosette to heading stage, we utilized a non-heading collard green (B. oleracea var. acephala) morphotype as control. This resulted in 33 DEMs and 1 998 DEGs with likely roles in the transition from rosette to heading stage in cabbage. Among these 1 998 DEGs, we found enriched GO terms related to DNA-binding transcription factor activity, transcription regulator activity, iron ion binding, and photosynthesis. We predicted the target genes of these 33 DEMs and focused on the subset that was differentially expressed (1 998 DEGs) between rosette and heading stage leaves to construct miRNA-target gene interaction networks. Our main finding is a role for miR396b-5p targeting two Arabidopsis thaliana orthologues of GROWTH REGULATING FACTORs 3 (GRF3) and -4 (GRF4) in pointed cabbage head formation.

RFEM: A framework for essential microRNA identification in mice based on rotation forest and multiple feature fusion

With the increasing number of microRNAs (miRNAs), identifying essential miRNAs has become an important task that needs to be solved urgently. However, there are few computational methods for essential miRNA identification. Here, we proposed a novel framework called Rotation Forest for Essential MicroRNA identification (RFEM) to predict the essentiality of miRNAs in mice. We first constructed 1,264 miRNA features of all miRNA samples by fusing 38 miRNA features obtained from the PESM paper and 1,226 miRNA functional features calculated based on miRNA-target gene interactions. Then, we employed 182 training samples with 1,264 features to train the rotation forest model, which was applied to compute the essentiality scores of the candidate samples. The main innovations of RFEM were as follows: 1) miRNA functional features were introduced to enrich the diversity of miRNA features; 2) the rotation forest model used decision tree as the base classifier and could increase the difference among base classifiers through feature transformation to achieve better ensemble results. Experimental results show that RFEM significantly outperformed two previous models with the AUC (AUPR) of 0.942 (0.944) in three comparison experiments under 5-fold cross validation, which proved the model's reliable performance. Moreover, ablation study was further conducted to demonstrate the effectiveness of the novel miRNA functional features. Additionally, in the case studies of assessing the essentiality of unlabeled miRNAs, experimental literature confirmed that 7 of the top 10 predicted miRNAs have crucial biological functions in mice. Therefore, RFEM would be a reliable tool for identifying essential miRNAs.

MicroRNA-mRNA Networks Linked to Inflammation and Immune System Regulation in Inflammatory Bowel Disease.

By performing a comprehensive meta-analysis, we integrated miRNA expression data from nine studies in IBD. We obtained detailed information on significantly deregulated miRNAs (fold change, FC ≥ 2 and p < 0.05), sample type and number, and platform applied for analysis in the training and validation sets. Further bioinformatic analyses were performed to identify miRNA target genes, by using the microRNA Data Integration Portal tool. We also sought to identify statistically enriched pathways of genes regulated by miRNAs using ToppGene Suite. Additional analyses were performed to filter for genes expressed in intestinal tissue using the European Bioinformatics Institute (EBI) database. Our findings showed the upregulation of 15 miRNAs in CD and 33 in UC. Conversely, six miRNAs were downregulated in CD, while seven were downregulated in UC. These results indicate a greater deregulation of miRNAs in UC compared to CD. Of note, miRNA target genes were enriched for immune system regulation pathways. Among significantly deregulated miRNAs with a higher number of miRNA-target gene interactions, we identified miR-199a-5p and miR-362-3p altered in CD, while among UC case patients, miRNA-target gene interactions were higher for miR-155-5p. The identified miRNAs play roles in regulating genes associated with immune system regulation and inflammation in IBD. Such miRNAs and their target genes have the potential to serve as clinically relevant biomarkers. These findings hold promise for enhancing the accuracy of diagnoses and facilitating the development of personalized treatment strategies for individuals with various forms of IBD.

MicroRNA Signatures for Pancreatic Cancer and Chronic Pancreatitis: Expression Profiling by NGS.

Pancreatic ductal adenocarcinoma (PDAC) is a deadly disease due to the lack of early detection. Because chronic pancreatitis (CP) patients are a high-risk group for pancreatic cancer, this study aimed to assess the differential miRNA profile in pancreatic tissue of patients with CP and pancreatic cancer. MiRNAs were isolated from formalin-fixed paraffin-embedded pancreatic tissue of 22 PDAC patients, 18 CP patients, and 10 normal pancreatic tissues from autopsy (C) cases and processed for next-generation sequencing. Known and novel miRNAs were identified and analyzed for differential miRNA expression, target prediction, and pathway enrichment between groups. Among the miRNAs identified, 166 known and 17 novel miRNAs were found exclusively in PDAC tissues, while 106 known and 10 novel miRNAs were found specifically in CP tissues. The pathways targeted by PDAC-specific miRNAs and differentially expressed miRNAs between PDAC versus CP tissues and PDAC versus control tissues were the proteoglycans pathway, Hippo signaling pathway, adherens junction, and transforming growth factor-β signaling pathway. This study resulted in a set of exclusive and differentially expressed miRNAs in PDAC and CP can be assessed for their diagnostic value. In addition, studying the role of miRNA-target gene interactions in carcinogenesis may open new therapeutic avenues.

Investigating the Mechanism of Arsenic-induced Ferroptosis in the Skin

Background: Ferroptosis, an oxidative and iron-dependent cell death, is a new type of regulated cell death. There are few studies on the mechanisms of ferroptosis in the skin and related diseases. Arsenic is shown to induce ferroptosis cell death. This study aimed to decipher the relationship between arsenic exposure and ferroptosis cell death in the skin. Methods: Arsenic-gene interactions were obtained. Then, skin-specific arsenic-gene interactions were screened. Ferroptosis-related genes were identified. Analysis of functional and biological interactions was performed to identify possible mechanisms. Results: The arsenic-gene interactions and the ferroptosis-related genes showed an overlap of 59 genes. Functional enrichment, protein-protein interaction, and transcription factor (TF)/miRNA target gene interaction analyses were used to look into the mechanism of arsenic-induced ferroptosis in the skin. ACTB, CTNNB1, HSPA8, SRC, RACK1, CD44, and SQSTM1 were identified as key proteins. Gene ontology analysis of these proteins indicated the mitochondrial morphology and functionality changes following arsenic-induced ferroptosis in the skin. HIF1A and SP1 TFs regulate a large number of genes compared to other TFs. Ten miRNAs with high interaction with ferroptosis-associated genes were identified. Conclusion: This work investigated the mechanism of arsenic-induced ferroptosis in the skin and identified key genes and regulators, and functional analysis highlighted the role of mitochondria in this skin exposure.

Identifying Genetic Signatures from Single-Cell RNA Sequencing Data by Matrix Imputation and Reduced Set Gene Clustering

In this current era, the identification of both known and novel cell types, the representation of cells, predicting cell fates, classifying various tumor types, and studying heterogeneity in various cells are the key areas of interest in the analysis of single-cell RNA sequencing (scRNA-seq) data. Due to the nature of the data, cluster identification in single-cell sequencing data with high dimensions presents several difficulties. In this paper, we introduce a new framework that combines various strategies such as imputed matrix, minimum redundancy maximum relevance (MRMR) feature selection, and shrinkage clustering to discover gene signatures from scRNA-seq data. Firstly, we conducted the pre-filtering of the “drop-out” value in the data focusing solely on imputing the identified “drop-out” values. Next, we applied the MRMR feature selection method to the imputed data and obtained the top 100 features based on the MRMR feature selection optimization scores for further downstream analysis. Thereafter, we employed shrinkage clustering on the selected feature matrix to identify the cell clusters using a global optimization approach. Finally, we applied the Limma-Voom R tool employing voom normalization and an empirical Bayes test to detect differentially expressed features with a false discovery rate (FDR) &lt; 0.001. In addition, we performed the KEGG pathway and gene ontology enrichment analysis of the identified biomarkers using David 6.8 software. Furthermore, we conducted miRNA target detection for the top gene markers and performed miRNA target gene interaction network analysis using the Cytoscape online tool. Subsequently, we compared our detected 100 markers with our previously detected top 100 cluster-specified markers ranked by FDR of the latest published article and discovered three common markers; namely, Cyp2b10, Mt1, Alpi, along with 97 novel markers. In addition, the Gene Set Enrichment Analysis (GSEA) of both marker sets also yields similar outcomes. Apart from this, we performed another comparative study with another published method, demonstrating that our model detects more significant markers than that model. To assess the efficiency of our framework, we apply it to another dataset and identify 20 strongly significant up-regulated markers. Additionally, we perform a comparative study of different imputation methods and include an ablation study to prove that every key phase of our framework is essential and strongly recommended. In summary, our proposed integrated framework efficiently discovers differentially expressed stronger gene signatures as well as up-regulated markers in single-cell RNA sequencing data.

Analysis the mechanism of cadmium-induced cytotoxicity in mouse testicular mesenchymal cells with miRNA transcriptomic

To analyze the mechanism of cadmium-induced cytotoxicity in mouse testicular mesenchymal cells(TM3) with transcriptome sequencing and bioinformatics techniques. TM3 cells were selected as the cell model and divided into control group(no cadmium treatment) and cadmium-treated group(20 μmol/L CdCl_2). After 24 hours of administration, cells were harvested to extract total RNA, and then miRNA expression profiles were obtained by sequencing program after RNA quality detection. The fold change(FC) &gt;2, P&lt;0.05 was used as the standard to screen for differentially expressed miRNAs. The quantitative real-time polymerase chain reaction(qRT-PCR) was used to verify the differentially expressed miRNAs. Then, their target genes were predicted by miRanda software to construct miRNA-target gene interaction network, and their target genes were enriched by gene ontology(GO) and Kyoto encyclopedia of genes and genomes(KEGG) pathway function. A total of 26 differentially expressed miRNAs were identified which may be related to cadmium-induced TM3 cytotoxicity, including 19 up-regulated and 7 down-regulated miRNAs. The result of qRT-PCR were consistent with the miRNA sequencing result. Meanwhile, bioinformatics analysis result showed that the 26 differentially expressed miRNAs predicted 657 target genes. GO enrichment was mainly classified into biological regulation, metabolic process, protein binding and catalytic activity. KEGG pathway analysis showed that target genes were significantly involved in mitogen-activated protein kinase(MAPK) and tumor necrosis factor(TNF) signal pathways closely related to inflammatory response and apoptosis. Cadmium can lead to the differential expression of miRNAs in TM3 cells, and its target genes may be involved in Cd-induced TM3 cytotoxicity through signaling pathways such as MAPK and TNF.

A Complex Heterogeneous Network Model of Disease Regulated by Noncoding RNAs: A Case Study of Unstable Angina Pectoris.

MicroRNAs (miRNAs) are important types of noncoding RNAs, and there is a lack of holistic and systematic understanding of the functions they play in disease. We proposed a research strategy, including two parts network analysis and network modelling, to analyze, model, and predict the regulatory network of miRNAs from a network perspective, using unstable angina pectoris as an example. In the network analysis section, we proposed the WGCNA & SimCluster method using both correlation and similarity to find hub miRNAs, and validation on two datasets showed better results than the methods using correlation or similarity alone. In the network modelling section, we used six knowledge graph or graph neural network models for link prediction of three types of edges and multilabel classification of two types of nodes. Comparative experiments showed that the RotatE model was a good model for link prediction, while the RGCN model was the best model for multilabel classification. Potential target genes were predicted for hub miRNAs and validation of hub miRNA-target gene interactions, target genes as biomarkers and target gene functions were performed using a three-step validation approach. In conclusion, our study provides a new strategy to analyze and model miRNA regulatory networks.

Time-regulated transcripts with the potential to modulate human pluripotent stem cell-derived cardiomyocyte differentiation

BackgroundHuman-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CM) are a promising disease model, even though hiPSC-CMs cultured for extended periods display an undifferentiated transcriptional landscape. MiRNA–target gene interactions contribute to fine-tuning the genetic program governing cardiac maturation and may uncover critical pathways to be targeted.MethodsWe analyzed a hiPSC-CM public dataset to identify time-regulated miRNA–target gene interactions based on three logical steps of filtering. We validated this process in silico using 14 human and mouse public datasets, and further confirmed the findings by sampling seven time points over a 30-day protocol with a hiPSC-CM clone developed in our laboratory. We then added miRNA mimics from the top eight miRNAs candidates in three cell clones in two different moments of cardiac specification and maturation to assess their impact on differentiation characteristics including proliferation, sarcomere structure, contractility, and calcium handling.ResultsWe uncovered 324 interactions among 29 differentially expressed genes and 51 miRNAs from 20,543 transcripts through 120 days of hiPSC-CM differentiation and selected 16 genes and 25 miRNAs based on the inverse pattern of expression (Pearson R-values < − 0.5) and consistency in different datasets. We validated 16 inverse interactions among eight genes and 12 miRNAs (Person R-values < − 0.5) during hiPSC-CMs differentiation and used miRNAs mimics to verify proliferation, structural and functional features related to maturation. We also demonstrated that miR-124 affects Ca2+ handling altering features associated with hiPSC-CMs maturation.ConclusionWe uncovered time-regulated transcripts influencing pathways affecting cardiac differentiation/maturation axis and showed that the top-scoring miRNAs indeed affect primarily structural features highlighting their role in the hiPSC-CM maturation.

Abstract P1109: Investigating The Role Of Human Conserved Microrna-193 In The Aging Drosophila Heart

MicroRNAs (miRNAs) are short, non-coding, RNAs that post-transcriptionally regulate gene expression and are ideal candidates for the regulation of the genes responsible in the heart. Profiling studies have shown circulating miRNAs to be implicated in human cardiovascular disease with the potential to be used as biomarkers for various cardiovascular pathologies. Highly conserved with Drosophila miR-193, human miR-193 is expressed in the heart and has been established as a biomarker to distinguish between patients with different types of heart failure, including HFPEF, hypertrophic cardiomyopathy, ischemia reperfusion injury, right ventricular remodeling, cardiac fibrosis, and congenital heart disease. However, without the mechanism and regulation of its target genes, the use of miR-193 as a biomarker or therapeutic is limited. We have characterized the impact of miR-193 loss on homoeostasis of heart morphology and function in drosophila during aging using a combination of null mutants and heart specific dysregulation of miR-193 expression. Using SOHA (semi-automated optical heartbeat analysis), we observed changes occurring to heart structure and function with a loss of miR-193. With miR-193 dysregulation, significant changes in heart rate, heart size (diastolic and systolic diameters), arrhythmia index, and diastolic/relaxation interval time, began at 10 days post-eclosion and continued as the flies aged. Understanding the role of miRNAs in specific tissues and disease states will ultimately depend on our ability to dissect the fundamental strategies and mechanisms by which miRNAs regulate cardiac biology. Using in silico analysis, we have identified multiple human conserved putative miRNA-target gene interactions to further characterize the molecular mechanisms underlying the observed phenotypes. Our results suggest that the human conserved Drosophila miR-193 is essential for normal cardiac function. In miR-193 loss of function, we see significant changes to heart rate, heart size, and interval time consistent with an early aging phenotype. The conservation of miR-193 and its predicted gene targets enable us to discover the molecular mechanisms that underlie miRNA dysregulation in cardiovascular disease.

Multi-Relation Graph Embedding for Predicting miRNA-Target Gene Interactions by Integrating Gene Sequence Information.

Accumulated studies have found that miRNAs are in charge of many complex diseases such as cancers by modulating gene expression. Predicting miRNA-target interactions is beneficial for uncovering the crucial roles of miRNAs in regulating target genes and the progression of diseases. The emergence of large-scale genomic and biological data as well as the recent development in heterogeneous networks provides new opportunities for miRNA target identification. Compared with conventional methods, computational methods become a decent solution for high efficiency. Thus, designing a method that could excavate valid information from the heterogeneous network and gene sequences is in great demand for improving the prediction accuracy. In this study, we proposed a graph-based model named MRMTI for the prediction of miRNA-target interactions. MRMTI utilized the multi-relation graph convolution module and the Bi-LSTM module to incorporate both network topology and sequential information. The learned embeddings of miRNAs and genes were then used to calculate the prediction scores of miRNA-target pairs. Comparisons with other state-of-the-art graph embedding methods and existing bioinformatic tools illustrated the superiority of MRMTI under multiple criteria metrics. Three variants of MRMTI implied the positive effect of multi-relation. The experimental results of case studies further demonstrated the prominent ability of MRMTI in predicting novel associations.

Comprehensive analysis of microRNAs and their target genes in oral submucous fibrosis.

The objective of the study is to understand the role of experimentally validated microRNAs (miRNAs) contributing to the acquisition of oncogenic phenotype in oral submucous fibrosis (OSF) by computational analysis. A comprehensive review was carried out to corroborate and summarize altered miRNA expression in OSF by retrieving relevant publications querying MEDLINE, Web of Science, Embase, and Scopus. The association between the miRNA-mRNA was performed using miRTarBase 8.0. The visualization of the miRNA-mRNA interaction was plotted using Cytoscape. MIENTURNET was used for the pathway analysis. Enrichment analysis was carried out for elucidating the hierarchical functions of miRNAs related to the acquisition of biological processes involved in the development of cancer. Thirteen miRNAs (hsa-miR-499a, hsa-miR-200b, hsa-miR-200c, hsa-miR-1246, hsa-miR-31, hsa-miR-10b, hsa-miR-21, hsa-miR-203, hsa-miR-455, hsa-miR-760, hsa-miR-623, hsa-miR-610, and hsa-miR-509-3-5p) were found to be deregulated in OSF. A total of 371 experimentally validated genes were shown to be interacting with the OSF-associated miRNAs. The targets of antifibrotic and profibrotic miRNAs were enriched in the cancer-related pathways. Dysregulated miRNAand its target genes illustrate the physiological role of miRNAs in fibrosis. Understanding the miRNA-mediated fibrotic signaling and targetting the specific miRNA-target gene interaction might provide relevant cues to ameliorate the fibrotic disease.

MiRNA profiling of human nasopharyngeal carcinoma cell lines HONE1 and CNE2 after X-ray therapy.

Radiotherapy is the main treatment for nasopharyngeal carcinoma. The radioresistance mechanism of cells is related to miRNAs. To investigate the miRNA profiling of HONE1 and CNE2 after X-ray therapy. The HONE1 and CNE2 cells were treated with X-ray at 4 Gy, 8 Gy, 16 Gy, and 20 Gy doses. The cell lines CNE2 with the best therapy effects and HONE1 with the worst therapy effects were screened out. Apoptosis and cell viability were detected with flow cytometry and Cell Counting Kit-8 (CCK-8). High-throughput sequencing was performed. A miRNA library was constructed. The miRNA annotation expression distribution, family prediction and target gene interaction, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted. The 24-hour 20 Gy dose X-rays were selected as the optimal therapy conditions. The CNE2_C, CNE2_M, HONE1_C and HONE1_M miRNAs accounted for 26.5%, 31.7%, 21.3%, and 22.9% of the Cleandata reads count, respectively, and the contents of rRNAs accounted for 24.9%, 14.7%, 25.1%, and 25.1% of the Cleandata reads count, respectively. The miRNAs with differential expression between the HONE1 and CNE2 cell lines including hsa-miR-21-5p, hsa-let-7a-5p, hsa-miR-125a-5p, hsa-miR-26a-5p, hsa-let-7f-5p, hsa-miR-20a-5p, and hsa-miR-24a-3p. There were also differentially expressed miRNAs in HONE1_C vs. HONE1_M, such as hsa-miR-21-5p and hsa-let-7i-5p. The differentially expressed miRNA in CNE2_C vs. CNE2_M was hsa-miR-148b-3p. The Gene Ontology analysis showed that the differentially expressed miRNA interacting genes in HONE1_M vs. CNE2_M were mainly enriched in biological process such as negative and positive regulation of transcription from RNA polymerase II promoter, cellular component such as cytosol and molecular function such as protein binding factor. The KEGG pathway analysis revealed that the differentially expressed miRNA interacting genes in HONE1_M vs. CNE2_M were enriched in the cancer-related pathways, such as pathways in cancer, MAPK signaling pathway and Wnt signaling pathway. Twelve miRNAs and 9 genes which contribute to X-ray radiation resistance were identified. Among those with differential expression between the HONE1 and CNE2 cell lines, which played a regulatory role in multiple pathways, were hsa-miR-20a-5p, hsa-let-7a-5p, hsa-let-7f5p, hsa-let-7i-5p, hsa-miR-30e-5p, hsa-miR-148b-3p, and hsa-miR-200c-3p. The corresponding genes were MAPK1, SOS1, TGFBR1, TGFBR2, TP53, CASP3, CCNE2, PTEN, and CDK2.

Discovery and characterization of differentially expressed soybean miRNAs and their targets during soybean mosaic virus infection unveils novel insight into Soybean-SMV interaction

BackgroundSoybean mosaic virus (SMV) is one of the most devastating pathogens of soybean. MicroRNAs (miRNAs) are a class of non-coding RNAs (21–24 nucleotides) which are endogenously produced by the plant host as part of a general gene expression regulatory mechanisms, but also play roles in regulating plant defense against pathogens. However, miRNA-mediated plant response to SMV in soybean is not as well documented.ResultIn this study, we analyzed 18 miRNA libraries, including three biological replicates from two soybean lines (Resistant and susceptible lines to SMV strain SC3 selected from the near-isogenic lines of Qihuang No. 1 × Nannong1138-2) after virus infection at three different time intervals (0 dpi, 7 dpi and 14 dpi). A total of 1,092 miRNAs, including 608 known miRNAs and 484 novel miRNAs were detected. Differential expression analyses identified the miRNAs profile changes during soybean-SMV interaction. Then, miRNAs potential target genes were predicted via data mining, and functional annotation was done by Gene Ontology (GO) analysis. The expression patterns of several miRNAs were validated by quantitative real-time PCR. We also validated the miRNA-target gene interaction by agrobacterium-mediated transient expression in Nicotiana benthamiana.ConclusionWe have identified a large number of miRNAs and their target genes and also functional annotations. We found that multiple miRNAs were differentially expressed in the two lines and targeted a series of NBS-LRR resistance genes. It is worth mentioning that many of these genes exist in the previous fine-mapping interval of the resistance gene locus. Our study provides additional information on soybean miRNAs and an insight into the role of miRNAs during SMV-infection in soybean.

Analysing miRNA-Target Gene Networks in Inflammatory Bowel Disease and Other Complex Diseases Using Transcriptomic Data

Patients with inflammatory bowel disease (IBD) are known to have perturbations in microRNA (miRNA) levels as well as altered miRNA regulation. Although experimental methods have provided initial insights into the functional consequences that may arise due to these changes, researchers are increasingly utilising novel bioinformatics approaches to further dissect the role of miRNAs in IBD. The recent exponential increase in transcriptomics datasets provides an excellent opportunity to further explore the role of miRNAs in IBD pathogenesis. To effectively understand miRNA-target gene interactions from gene expression data, multiple database resources are required, which have become available in recent years. In this technical note, we provide a step-by-step protocol for utilising these state-of-the-art resources, as well as systems biology approaches to understand the role of miRNAs in complex disease pathogenesis. We demonstrate through a case study example how to combine the resulting miRNA-target gene networks with transcriptomics data to find potential disease-specific miRNA regulators and miRNA-target genes in Crohn’s disease. This approach could help to identify miRNAs that may have important disease-modifying effects in IBD and other complex disorders, and facilitate the discovery of novel therapeutic targets.

MiRNA-Regulated Pathways for Hypertrophic Cardiomyopathy: Network-Based Approach to Insight into Pathogenesis.

Hypertrophic cardiomyopathy (HCM) is the most common hereditary heart disease. The wide spread of high-throughput sequencing casts doubt on its monogenic nature, suggesting the presence of mechanisms of HCM development independent from mutations in sarcomeric genes. From this point of view, HCM may arise from the interactions of several HCM-associated genes, and from disturbance of regulation of their expression. We developed a bioinformatic workflow to study the involvement of signaling pathways in HCM development through analyzing data on human heart-specific gene expression, miRNA-target gene interactions, and protein–protein interactions, available in open databases. Genes regulated by a pool of miRNAs contributing to human cardiac hypertrophy, namely hsa-miR-1-3p, hsa-miR-19b-3p, hsa-miR-21-5p, hsa-miR-29a-3p, hsa-miR-93-5p, hsa-miR-133a-3p, hsa-miR-155-5p, hsa-miR-199a-3p, hsa-miR-221-3p, hsa-miR-222-3p, hsa-miR-451a, and hsa-miR-497-5p, were considered. As a result, we pinpointed a module of TGFβ-mediated SMAD signaling pathways, enriched by targets of the selected miRNAs, that may contribute to the cardiac remodeling in HCM. We suggest that the developed network-based approach could be useful in providing a more accurate glimpse on pathological processes in the disease pathogenesis.

Discovery of Biomarkers in Hepatocellular Carcinoma Metastasis Using Bioinformatic Analysis

Background: Liver cancer is one of the most deadly malignancies worldwide. Tumor metastasis is the main cause of liver cancer-related death. So far, the mechanism of liver cancer metastasis is far away from fully elucidated. In this study, we aimed to discover key regulators involved in liver cancer metastasis by data mining. Methods: Two different types of data, including mRNA microarray (GSE6222 and GSE6764) and miRNA microarray (GSE67138), were analyzed. A total of 83 intersectant differently expressed genes (DEGs) with the same expression pattern in GSE6222 and GSE6764 were identified. One hundred and thirty-one differently expressed miRNAs (DEMs) were identified in GSE 67138. Furthermore, a total of 26 pairs of miRNA-target, including 18 DEMs and 13 DEGs were identified as critical miRNA-target axis via miRNA-target gene interaction analysis. Result and Conclusion: Among the 18 DEMs and 13 DEGs, 10 miRNAs and 10 target genes are significantly correlated with patients’ survival (p&lt;0.05). Our results and methods might be interesting for data mining and helpful for further experimental functional validation.

Systematic Search for Novel Circulating Biomarkers Associated with Extracellular Vesicles in Alzheimer's Disease: Combining Literature Screening and Database Mining Approaches.

miRNAs play an important role in neurodegenerative diseases. Many miRNA-target gene interactions (MTI) have been experimentally confirmed and associated with Alzheimer’s disease (AD). miRNAs may also be contained within extracellular vesicles (EVs), mediators of cellular communication and a potential source of circulating biomarkers in body fluids. Therefore, EV-associated miRNAs (EV-miRNAs) in peripheral blood could support earlier and less invasive AD diagnostics. We aimed to prioritize EV-related miRNA with AD-related genes and to identify the most promising candidates for novel AD biomarkers. A list of unique EV-miRNAs from the literature was combined with a known set of AD risk genes and enriched for MTI. Additionally, miRNAs associated with the AD phenotype were combined with all known target genes in MTI enrichment. Expression in different sample types was analyzed to identify AD-associated miRNAs with the greatest potential as AD circulating biomarkers. Four common MTI were observed between EV-miRNAs and AD-associated miRNAs: hsa-miR-375–APH1B, hsa-miR-107–CDC42SE2, hsa-miR-375–CELF2, and hsa-miR-107–IL6. An additional 61 out of 169 unique miRNAs (36.1%) and seven out of 84 unique MTI (8.3%), observed in the body fluids of AD patients, were proposed as very strong AD-circulating biomarker candidates. Our analysis summarized several potential novel AD biomarkers, but further studies are needed to evaluate their potential in clinical practice.