miRNA-target Interactions Research Articles

The Identification of New Pharmacological Targets for the Treatment of Glaucoma: A Network Pharmacology Approach

Background: Glaucoma is a progressive optic neuropathy characterized by the neurodegeneration and death of retinal ganglion cells (RGCs), leading to blindness. Current glaucoma interventions reduce intraocular pressure but do not address retinal neurodegeneration. In this effort, to identify new pharmacological targets for glaucoma management, we employed a network pharmacology approach. Methods: We first retrieved transcriptomic data from GEO, an NCBI database, and carried out GEO2R (an interactive web tool aimed at comparing two or more groups of samples in a GEO dataset). The GEO2R statistical analysis aimed at identifying the top differentially expressed genes (DEGs) and used these as input of STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) app within Cytoscape software, which builds networks of proteins starting from input DEGs. Analyses of centrality metrics using Cytoscape were carried out to identify nodes (genes or proteins) involved in network stability. We also employed the web-server software MIRNET 2.0 to build miRNA–target interaction networks for a re-analysis of the GSE105269 dataset, which reports analyses of microRNA expressions. Results: The pharmacological targets, identified in silico through analyses of the centrality metrics carried out with Cytoscape, were rescored based on correlations with entries in the PubMed and clinicaltrials.gov databases. When there was no match (82 out of 135 identified central nodes, in 8 analyzed networks), targets were considered “potential innovative” targets for the treatment of glaucoma, after further validation studies. Conclusions: Several druggable targets, such as GPCRs (e.g., 5-hydroxytryptamine 5A (5-HT5A) and adenosine A2B receptors) and enzymes (e.g., lactate dehydrogenase A or monoamine oxidase B), were found to be rescored as “potential innovative” pharmacological targets for glaucoma treatment.

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.

Muscle-derived microRNAs correlated with thigh lean mass gains during progressive resistance training in older adults.

Resistance training (RT) remains the most effective treatment for age-related declines in muscle mass. However, many older adults experience attenuated muscle hypertrophy in response to RT when compared with younger adults. This may be attributed to underlying molecular processes that are dysregulated by aging and exacerbated by improperly prescribed RT weekly volume, intensity, and/or frequency doses. MicroRNAs (miRNAs) are key epigenetic regulators that impact signaling pathways and protein expression within cells, are dynamic and responsive to exercise stimuli, and are often dysregulated in diseases. In this study, we used untargeted miRNA-seq to examine miRNA in skeletal muscle and serum-derived exosomes of older adults (n = 18, 11 M/7 F, 66 ± 1 yr) who underwent three times per wk RT for 30 wk [e.g., high intensity three times/wk (HHH, n = 9) or alternating high-low-high (HLH) intensity (n = 9)], after a standardized 4-wk washin. Within each tissue, miRNAs were clustered into modules based on pairwise correlation using weighted gene correlation network analysis (WGCNA). Modules were tested for association with the magnitude of RT-induced thigh lean mass (TLM) change [as measured by dual-energy X-ray absorptiometry (DXA)]. Although no modules were unique to training dose, we identified miRNA modules in skeletal muscle associated with TLM gains irrespective of exercise dose. Using miRNA-target interactions, we analyzed key miRNAs in significant modules for their potential regulatory involvement in biological pathways. Findings point toward potential miRNAs that may be informative biomarkers and could also be evaluated as potential therapeutic targets as an adjuvant to RT to maximize skeletal muscle mass accrual in older adults.NEW & NOTEWORTHY In this work, we identified a set of microRNAs correlated with thigh lean mass gains in a group of older adults. To our knowledge, this is the first time these microRNAs have been identified as novel predictive biomarkers correlating with lean mass gains in aging adults. As biomarkers, these may help interventionalists identify older individuals that are positively responding to an exercise intervention.

Global-local aware Heterogeneous Graph Contrastive Learning for multifaceted association prediction in miRNA-gene-disease networks.

Unraveling the intricate network of associations among microRNAs (miRNAs), genes, and diseases is pivotal for deciphering molecular mechanisms, refining disease diagnosis, and crafting targeted therapies. Computational strategies, leveraging link prediction within biological graphs, present a cost-efficient alternative to high-cost empirical assays. However, while plenty of methods excel at predicting specific associations, such as miRNA-disease associations (MDAs), miRNA-target interactions (MTIs), and disease-gene associations (DGAs), a holistic approach harnessing diverse data sources for multifaceted association prediction remains largely unexplored. The limited availability of high-quality data, as vitro experiments to comprehensively confirm associations are often expensive and time-consuming, results in a sparse and noisy heterogeneous graph, hindering an accurate prediction of these complex associations. To address this challenge, we propose a novel framework called Global-local aware Heterogeneous Graph Contrastive Learning (GlaHGCL). GlaHGCL combines global and local contrastive learning to improve node embeddings in the heterogeneous graph. In particular, global contrastive learning enhances the robustness of node embeddings against noise by aligning global representations of the original graph and its augmented counterpart. Local contrastive learning enforces representation consistency between functionally similar or connected nodes across diverse data sources, effectively leveraging data heterogeneity and mitigating the issue of data scarcity. The refined node representations are applied to downstream tasks, such as MDA, MTI, and DGA prediction. Experiments show GlaHGCL outperforming state-of-the-art methods, and case studies further demonstrate its ability to accurately uncover new associations among miRNAs, genes, and diseases. We have made the datasets and source code publicly available at https://github.com/Sue-syx/GlaHGCL.

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.

In silico approach to investigate the potential HKT gene responsive to salt stress in rice

Rice is frequently subjected to various environmental stresses, resulting in significant production losses, with drought and salinity are the leading causes of plant damage globally. This study aims to characterize and understand the function of rice high-affinity potassium transporters (HKTs) genes in response to salinity stress. Initially, the genome-wide analysis was undertaken to reveal the evolutionarily conserved function of the OsHKT in higher plants. To investigate the transcription level of OsHKT during the vegetative and reproductive stages, two microarray datasets (GSE19024 and GSE3053) were analyzed, and salt-treated samples were subsequently evaluated using real-time PCR. Differentially expressed genes (DEGs) were identified from microarray datasets (GSE41650 and GSE14403), followed by constructing a DEG network that highlighted interaction partners of the OsHKTs. Genome mining of rice revealed 9 HKT genes, namely OsHKT1;1–1;5 and OsHKT2;1–2;4. These genes exhibited a well-conserved domain structure called TrkH. Comprehensive phylogenetic and motif analyses clustered genes encoding HKT proteins into seven monophyletic groups, and the motifs were relatively conserved. Ka/Ks ratios indicated a high degree of purifying selection during evolutionary time. Gene ontology findings suggested the involvement of OsHKT in stress response. Besides, several CRE motifs in the promoter regions of OsHKT have demonstrated their potential roles in abiotic stress responses. Furthermore, we analyzed the top 250 significant DEGs from the two datasets (p-value < 0.05; fold two change ≥ 1 or ≤ − 1) to evaluate the relationship among the DEGs and HKTs. Three co-expressed OsHKT genes were discovered to be upregulated in seedlings under salinity treatment, including OsP5CS2, OsHAK1, and OsNHX2, whereas OsP5CS1 and OsHAK27 were downregulated. The transcripts of OsHKT were found to be differentially expressed in a tissue-specific manner. Analysis of microarray datasets validated by real-time PCR shows that OsHKT1;5 had a higher expression level, followed by OsHKT1;1, OsHKT1;3, and OsHKT2;1 after salinity treatment. In addition, several micro-RNA targets in rice HKT genes regulate their expression in response to stress. This study paves the way for future investigation on genes and miRNA-target interaction in plants under environmental stresses, offering potential strategies to enhance stress tolerance in crops via targeted ion transport modification.

Identifying miRNA as biomarker for breast cancer subtyping using association rule

— This paper presents a comprehensive study focused on breast cancer subtyping, utilizing a multifaceted approach that integrates feature selection, machine learning classifiers, and miRNA regulatory networks. The feature selection process begins with the CFS algorithm, followed by the Apriori algorithm for association rule generation, resulting in the identification of significant features tailored to Luminal A, Luminal B, HER-2 enriched, and Basal-like subtypes. The subsequent application of Random Forest (RF) and Support Vector Machine (SVM) classifiers yielded promising results, with the SVM model achieving an overall accuracy of 76.60 % and the RF model demonstrating robust performance at 80.85 %. Detailed accuracy metrics revealed strengths and areas for refinement, emphasizing the potential for optimizing subtype-specific recall. To explore the regulatory landscape in depth, an analysis of selected miRNAs was conducted using MIENTURNET, a tool for visualizing miRNA-target interactions. While FDR analysis raised concerns for HER-2 and Basal-like subtypes, Luminal A and Luminal B subtypes showcased significant miRNA-gene interactions. Functional enrichment analysis for Luminal A highlighted the role of Ovarian steroidogenesis, implicating specific miRNAs such as hsa-let-7c-5p and hsa-miR-125b-5p as potential diagnostic biomarkers and regulators of Luminal A breast cancer. Luminal B analysis uncovered associations with the MAPK signaling pathway, with miRNAs like hsa-miR-203a-3p and hsa-miR-19a-3p exhibiting potential diagnostic and therapeutic significance. In conclusion, this integrative approach combines machine learning techniques with miRNA analysis to provide a holistic understanding of breast cancer subtypes. The identified miRNAs and associated pathways offer insights into potential diagnostic biomarkers and therapeutic targets, contributing to the ongoing efforts to improve breast cancer diagnostics and personalized treatment strategies.

A Brain Anti-Senescence Transcriptional Program Triggered by Hypothalamic-Derived Exosomal microRNAs.

In contrast to the hypothesis that aging results from cell-autonomous deterioration processes, the programmed longevity theory proposes that aging arises from a partial inactivation of a "longevity program" aimed at maintaining youthfulness in organisms. Supporting this hypothesis, age-related changes in organisms can be reversed by factors circulating in young blood. Concordantly, the endocrine secretion of exosomal microRNAs (miRNAs) by hypothalamic neural stem cells (htNSCs) regulates the aging rate by enhancing physiological fitness in young animals. However, the specific molecular mechanisms through which hypothalamic-derived miRNAs exert their anti-aging effects remain unexplored. Using experimentally validated miRNA-target gene interactions and single-cell transcriptomic data of brain cells during aging and heterochronic parabiosis, we identify the main pathways controlled by these miRNAs and the cell-type-specific gene networks that are altered due to age-related loss of htNSCs and the subsequent decline in specific miRNA levels in the cerebrospinal fluid (CSF). Our bioinformatics analysis suggests that these miRNAs modulate pathways associated with senescence and cellular stress response, targeting crucial genes such as Cdkn2a, Rps27, and Txnip. The oligodendrocyte lineage appears to be the most responsive to age-dependent loss of exosomal miRNA, leading to significant derepression of several miRNA target genes. Furthermore, heterochronic parabiosis can reverse age-related upregulation of specific miRNA-targeted genes, predominantly in brain endothelial cells, including senescence promoting genes such as Cdkn1a and Btg2. Our findings support the presence of an anti-senescence mechanism triggered by the endocrine secretion of htNSC-derived exosomal miRNAs, which is associated with a youthful transcriptional signature.

Identification and coregulation pattern analysis of long noncoding RNAs in the mouse brain after Angiostrongylus cantonensis infection

BackgroundAngiostrongyliasis is a highly dangerous infectious disease. Angiostrongylus cantonensis larvae migrate to the mouse brain and cause symptoms, such as brain swelling and bleeding. Noncoding RNAs (ncRNAs) are novel targets for the control of parasitic infections. However, the role of these molecules in A. cantonensis infection has not been fully clarified.MethodsIn total, 32 BALB/c mice were randomly divided into four groups, and the infection groups were inoculated with 40 A. cantonensis larvae by gavage. Hematoxylin and eosin (H&E) staining and RNA library construction were performed on brain tissues from infected mice. Differential expression of long noncoding RNAs (lncRNAs) and mRNAs in brain tissues was identified by high-throughput sequencing. The pathways and functions of the differentially expressed lncRNAs were determined by Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses. The functions of the differentially expressed lncRNAs were further characterized by lncRNA‒microRNA (miRNA) target interactions. The potential host lncRNAs involved in larval infection of the brain were validated by quantitative real-time polymerase chain reaction (qRT‒PCR).ResultsThe pathological results showed that the degree of brain tissue damage increased with the duration of infection. The transcriptome results showed that 859 lncRNAs and 1895 mRNAs were differentially expressed compared with those in the control group, and several lncRNAs were highly expressed in the middle–late stages of mouse infection. GO and KEGG pathway analyses revealed that the differentially expressed target genes were enriched mainly in immune system processes and inflammatory response, among others, and several potential regulatory networks were constructed.ConclusionsThis study revealed the expression profiles of lncRNAs in the brains of mice after infection with A. cantonensis. The lncRNAs H19, F630028O10Rik, Lockd, AI662270, AU020206, and Mexis were shown to play important roles in the infection of mice with A. cantonensis infection.Graphical

PMDAGS: Predicting miRNA-Disease Associations With Graph Nonlinear Diffusion Convolution Network and Similarities.

Many studies have proven that microRNAs (miRNAs) can participate in a wide range of biological processes and can be considered as potential noninvasive biomarkers for disease diagnosis and prognosis. Therefore, many computational methods have been developed to identifying miRNA-disease associations, ultimately enhancing the efficiency of disease diagnosis and treatment. In this study, we also introduced a new computational method called PMDAGS, which predicts miRNA-disease associations by utilizing graph nonlinear diffusion convolution network and similarities. PMDAGS first calculates miRNA similarity and disease similarity based on miRNA-target interactions, disease-gene associations and known miRNA-disease associations, respectively. Next, we construct the initial feature of each miRNA (disease) by concatenating its final similarity vector with its known association vector. Based on the known miRNA-disease association network and the initial feature vector of each node, we further apply nonlinear diffusion graph convolution network model to extract the feature embedding vectors. Finally, we concatenate the feature embedding vectors of miRNA and disease and input them into a multi-layer perceptron to identify potential miRNA-disease associations. We conduct 5-fold cross validation (5CV), 10-fold cross validation (10CV), and global leave-one-out cross validation (GLOOCV) on HMDD v2.0 and HMDD v3.2. PMDAGS achieves AUCs of 0.9222, 0.9228, and 0.9221 under 5CV, 10CV and GLOOCV on HMDD v2.0, respectively. In addition, PMDAGS also achieves AUC values of 0.9366, 0.9377, and 0.9376 under 5CV, 10CV and GLOOCV on HMDD v3.2, respectively. According to the experimental results, we can conclude that PMDAGS outperforms other compared methods and can effectively predict miRNA-disease associations.

Pathway analysis for functional characterization of microRNAs

DIANA-miRPath webserver enables the exploration of combined miRNA effects using predicted or experimentally supported miRNA interactions. Its latest version (DIANA-miRPath v4.0, http://www.microrna.gr/miRPathv4 ), introduces the capacity to tailor its target-based miRNA functional analysis engine towards specific biological/experimental contexts. Via a modular interface with rich interaction, annotation and parameterization options, users can perform enrichment analysis on Gene Ontology (GO) terms, KEGG and REACTOME pathways, gene sets from Molecular Signatures Database (MSigDB) and PFAM. Included miRNA interaction sets are derived from state-of-the-art resources of experimentally supported (DIANA-TarBase v8.0, miRTarBase and microCLIP cell-type-specific interactions) or from in silico miRNA-target interactions (DIANA-microT-2023 and TargetScan predictions). Bulk and single-cell expression datasets from The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression project (GTEx), as well as single-cell expression atlases can be used to assess expression change of targeted genes within terms, across a wide range of states. A discrete module to perform miRNA-tailored CRISPR knock-out screen analyses deems possible the investigation of selected miRNAs within conditions under study. Hellenic Foundation for Research and Innovation (HFRI) under the 1 st Call for HFRI Research Projects to Support Faculty Members &amp; Researchers and Procure High-Value Research Equipment grant (Project Number 2563). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Profiling of Groundnut bud necrosis orthotospovirus–responsive microRNA and their targets in tomato based on deep sequencing

Tomato, an extensively cultivated vegetable crop produces miRNAs in response to infection with Groundnut bud necrosis orthotospovirus, a viral pathogen causing significant economic losses. High-throughput miRNA sequencing was performed on tomato leaves inoculated with GBNV and mock-inoculated leaves as controls. Analysis revealed 73 known miRNAs belonging to 24 miRNA families, with variable expression levels. Interestingly, 39 miRNAs were upregulated, and 34 were downregulated in response to GBNV infection. Stem-loop quantitative reverse transcription PCR validated the differential expression of selected miRNAs. Additionally, 30 miRNA encoded proteins were identified to be involved in disease resistance and susceptibility. The miRNA-target interactions were found to play significant roles in cellular and metabolic activities, as well as modulating signaling pathways during the plant-virus interaction. The findings shed light on the intricate regulatory network of miRNAs in tomato response to viral infection and may contribute to developing strategies for improving crop protection against viral diseases.

Integrated microRNA and proteome analysis of cancer datasets with MoPC.

MicroRNAs (miRNAs) are small molecules that play an essential role in regulating gene expression by post-transcriptional gene silencing. Their study is crucial in revealing the fundamental processes underlying pathologies and, in particular, cancer. To date, most studies on miRNA regulation consider the effect of specific miRNAs on specific target mRNAs, providing wet-lab validation. However, few tools have been developed to explain the miRNA-mediated regulation at the protein level. In this paper, the MoPC computational tool is presented, that relies on the partial correlation between mRNAs and proteins conditioned on the miRNA expression to predict miRNA-target interactions in multi-omic datasets. MoPC returns the list of significant miRNA-target interactions and plot the significant correlations on the heatmap in which the miRNAs and targets are ordered by the chromosomal location. The software was applied on three TCGA/CPTAC datasets (breast, glioblastoma, and lung cancer), returning enriched results in three independent targets databases.

Empowering prediction of miRNA–mRNA interactions in species with limited training data through transfer learning

MicroRNAs (miRNAs) play a crucial role in mRNA regulation. Identifying functionally important mRNA targets of a specific miRNA is essential for uncovering its biological function and assisting miRNA–based drug development. Datasets of high-throughput direct bona fide miRNA–target interactions (MTIs) exist only for a few model organisms, prompting the need for computational prediction. However, the scarcity of data poses a challenge in training accurate machine learning models for MTI prediction. In this study, we explored the potential of transfer learning technique (with ANN and XGB) to address the limited data challenge by leveraging the similarities in interaction rules between species. Furthermore, we introduced a novel approach called TransferSHAP for estimating the feature importance of transfer learning in tabular dataset tasks. We demonstrated that transfer learning improves MTI prediction accuracy for species with limited datasets and identified the specific interaction features the models employed to transfer information across different species.

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

The vegetative development of cabbage (Brassicaoleracea 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 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.

Unraveling the role of miRNAs as biomarkers in Chagas cardiomyopathy: Insights into molecular pathophysiology.

Chagas cardiomyopathy (ChCM) is a severe form of Chagas disease and a major cause of cardiovascular morbidity and mortality. The dysregulation of the immune response leads to cardiac remodeling and functional disruptions, resulting in life-threatening complications. Conventional diagnostic methods have limitations, and therapeutic response evaluation is challenging. MicroRNAs (miRNAs), important regulators of gene expression, show potential as biomarkers for diagnosis and prognosis. This review aims to summarize experimental findings on miRNA expression in ChCM and explore the potential of these miRNAs as biomarkers of Chagas disease. The search was conducted in the US National Library of Medicine MEDLINE/PubMed public database using the terms "Chagas cardiomyopathy" OR "Chagas disease" AND "microRNA" OR "miRNA" OR "miR." Additionally, bioinformatics analysis was performed to investigate miRNA-target interactions and explore enrichment pathways of gene ontology biological processes and molecular functions. The miR-21, miR-146b, miR-146a, and miR-155 consistently exhibited up-regulation, whereas miR-145 was down-regulated in ChCM. These specific miRNAs have been linked to fibrosis, immune response, and inflammatory processes in heart tissue. Moreover, the findings from various studies indicate that these miRNAs have the potential as biomarkers for the disease and could be targeted in therapeutic strategies for ChCM. In this review, we point out miR-21, miR-146b, miR-146a, miR-155, and miR-145-5p role in the complex mechanisms of ChCM. These miRNAs have been shown as potential biomarkers for precise diagnosis, reliable prognostic evaluation, and effective treatment strategies in the ChCM.

Bioinformatics Prediction for Network-Based Integrative Multi-Omics Expression Data Analysis in Hirschsprung Disease.

Hirschsprung's disease (HSCR) is a rare developmental disorder in which enteric ganglia are missing along a portion of the intestine. HSCR has a complex inheritance, with RET as the major disease-causing gene. However, the pathogenesis of HSCR is still not completely understood. Therefore, we applied a computational approach based on multi-omics network characterization and clustering analysis for HSCR-related gene/miRNA identification and biomarker discovery. Protein-protein interaction (PPI) and miRNA-target interaction (MTI) networks were analyzed by DPClusO and BiClusO, respectively, and finally, the biomarker potential of miRNAs was computationally screened by miRNA-BD. In this study, a total of 55 significant gene-disease modules were identified, allowing us to propose 178 new HSCR candidate genes and two biological pathways. Moreover, we identified 12 key miRNAs with biomarker potential among 137 predicted HSCR-associated miRNAs. Functional analysis of new candidates showed that enrichment terms related to gene ontology (GO) and pathways were associated with HSCR. In conclusion, this approach has allowed us to decipher new clues of the etiopathogenesis of HSCR, although molecular experiments are further needed for clinical validations.